Package: a4 Version: 1.58.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo, Rgraphviz, GOstats, hgu95av2.db License: GPL-3 MD5sum: 56d24e58ae66f2b7d9ae48722e4f90c8 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Umbrella Package Description: Umbrella package is available for the entire Automated Affymetrix Array Analysis suite of package. biocViews: Microarray Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4 git_branch: RELEASE_3_22 git_last_commit: a25ed23 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/a4_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4_1.58.0.tgz vignettes: vignettes/a4/inst/doc/a4vignette.pdf vignetteTitles: a4vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4/inst/doc/a4vignette.R dependencyCount: 85 Package: a4Base Version: 1.58.0 Depends: a4Preproc, a4Core Imports: methods, graphics, grid, Biobase, annaffy, mpm, genefilter, limma, multtest, glmnet, gplots Suggests: Cairo, ALL, hgu95av2.db, nlcv Enhances: gridSVG, JavaGD License: GPL-3 MD5sum: 5efa159e954c71e254ed20e25895e3d3 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Base Package Description: Base utility functions are available for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray Author: Willem Talloen [aut], Tine Casneuf [aut], An De Bondt [aut], Steven Osselaer [aut], Hinrich Goehlmann [aut], Willem Ligtenberg [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4Base git_branch: RELEASE_3_22 git_last_commit: b4398e6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/a4Base_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4Base_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4Base_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4Base_1.58.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 suggestsMe: epimutacions dependencyCount: 76 Package: a4Classif Version: 1.58.0 Depends: a4Core, a4Preproc Imports: methods, Biobase, ROCR, pamr, glmnet, varSelRF, utils, graphics, stats Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 20d28cecc9aa57a7cf36e34d0c253104 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Classification Package Description: Functionalities for classification of Affymetrix microarray data, integrating within the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, GeneExpression, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Classif git_branch: RELEASE_3_22 git_last_commit: f42a7db git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/a4Classif_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4Classif_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4Classif_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4Classif_1.58.0.tgz vignettes: vignettes/a4Classif/inst/doc/a4Classif-vignette.html vignetteTitles: a4Classif package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Classif/inst/doc/a4Classif-vignette.R dependsOnMe: a4 dependencyCount: 33 Package: a4Core Version: 1.58.0 Imports: Biobase, glmnet, methods, stats Suggests: knitr, rmarkdown License: GPL-3 MD5sum: bd94b43ae8b1dd53a0959f6693b6305e NeedsCompilation: no Title: Automated Affymetrix Array Analysis Core Package Description: Utility functions for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Core git_branch: RELEASE_3_22 git_last_commit: 3326ae3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/a4Core_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4Core_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4Core_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4Core_1.58.0.tgz vignettes: vignettes/a4Core/inst/doc/a4Core-vignette.html vignetteTitles: a4Core package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Core/inst/doc/a4Core-vignette.R dependsOnMe: a4, a4Base, a4Classif, nlcv dependencyCount: 20 Package: a4Preproc Version: 1.58.0 Imports: BiocGenerics, Biobase Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 2c56d3bd7d3290c917ecb611a3702ce8 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Preprocessing Package Description: Utility functions to pre-process data for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Preprocessing Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Preproc git_branch: RELEASE_3_22 git_last_commit: dad31bc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/a4Preproc_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4Preproc_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4Preproc_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4Preproc_1.58.0.tgz vignettes: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.html vignetteTitles: a4Preproc package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.R dependsOnMe: a4, a4Base, a4Classif suggestsMe: graphite dependencyCount: 7 Package: a4Reporting Version: 1.58.0 Imports: methods, xtable Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 9d31fea8b39d29a5f040b531c1a4d4d3 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Reporting Package Description: Utility functions to facilitate the reporting of the Automated Affymetrix Array Analysis Reporting set of packages. biocViews: Microarray Author: Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Reporting git_branch: RELEASE_3_22 git_last_commit: cfed3da git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/a4Reporting_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4Reporting_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4Reporting_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4Reporting_1.58.0.tgz vignettes: vignettes/a4Reporting/inst/doc/a4reporting-vignette.html vignetteTitles: a4Reporting package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Reporting/inst/doc/a4reporting-vignette.R dependsOnMe: a4 dependencyCount: 4 Package: ABarray Version: 1.78.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: 6ad7c6708ff912ee32a00b355328d3b0 NeedsCompilation: no Title: Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data. Description: Automated pipline to perform gene expression analysis for Applied Biosystems Genome Survey Microarray (AB1700) data format. Functions include data preprocessing, filtering, control probe analysis, statistical analysis in one single function. A GUI interface is also provided. The raw data, processed data, graphics output and statistical results are organized into folders according to the analysis settings used. biocViews: Microarray, OneChannel, Preprocessing Author: Yongming Andrew Sun Maintainer: Yongming Andrew Sun git_url: https://git.bioconductor.org/packages/ABarray git_branch: RELEASE_3_22 git_last_commit: 60b191e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ABarray_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ABarray_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ABarray_1.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ABarray_1.78.0.tgz vignettes: vignettes/ABarray/inst/doc/ABarray.pdf, vignettes/ABarray/inst/doc/ABarrayGUI.pdf vignetteTitles: ABarray gene expression, ABarray gene expression GUI interface hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 17 Package: abseqR Version: 1.28.0 Depends: R (>= 3.5.0) Imports: ggplot2, RColorBrewer, circlize, reshape2, VennDiagram, plyr, flexdashboard, BiocParallel (>= 1.1.25), png, grid, gridExtra, rmarkdown, knitr, vegan, ggcorrplot, ggdendro, plotly, BiocStyle, stringr, utils, methods, grDevices, stats, tools, graphics Suggests: testthat License: GPL-3 | file LICENSE MD5sum: 9ec04b2ef23b8f4349857cf8ce6a8d27 NeedsCompilation: no Title: Reporting and data analysis functionalities for Rep-Seq datasets of antibody libraries Description: AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output. biocViews: Sequencing, Visualization, ReportWriting, QualityControl, MultipleComparison Author: JiaHong Fong [cre, aut], Monther Alhamdoosh [aut] Maintainer: JiaHong Fong URL: https://github.com/malhamdoosh/abseqR SystemRequirements: pandoc (>= 1.19.2.1) VignetteBuilder: knitr BugReports: https://github.com/malhamdoosh/abseqR/issues git_url: https://git.bioconductor.org/packages/abseqR git_branch: RELEASE_3_22 git_last_commit: 79e7852 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/abseqR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/abseqR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/abseqR_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/abseqR_1.28.0.tgz vignettes: vignettes/abseqR/inst/doc/abseqR.pdf vignetteTitles: Introduction to abseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/abseqR/inst/doc/abseqR.R dependencyCount: 109 Package: ABSSeq Version: 1.64.0 Depends: R (>= 2.10), methods Imports: locfit, limma Suggests: edgeR License: GPL (>= 3) MD5sum: b12fd803c7aedb9aacdc391886e63231 NeedsCompilation: no Title: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences Description: Inferring differential expression genes by absolute counts difference between two groups, utilizing Negative binomial distribution and moderating fold-change according to heterogeneity of dispersion across expression level. biocViews: DifferentialExpression Author: Wentao Yang Maintainer: Wentao Yang git_url: https://git.bioconductor.org/packages/ABSSeq git_branch: RELEASE_3_22 git_last_commit: f6fb00c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ABSSeq_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ABSSeq_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ABSSeq_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ABSSeq_1.64.0.tgz vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf vignetteTitles: ABSSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABSSeq/inst/doc/ABSSeq.R importsMe: metaseqR2 dependencyCount: 10 Package: acde Version: 1.40.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: c04facfe97488a7fc1a49a1bfb8a78fa NeedsCompilation: no Title: Artificial Components Detection of Differentially Expressed Genes Description: This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR). The methods on this package are described in the vignette or in the article 'Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine and S. Restrepo (2015, pending publication). biocViews: DifferentialExpression, TimeCourse, PrincipalComponent, GeneExpression, Microarray, mRNAMicroarray Author: Juan Pablo Acosta, Liliana Lopez-Kleine Maintainer: Juan Pablo Acosta git_url: https://git.bioconductor.org/packages/acde git_branch: RELEASE_3_22 git_last_commit: 95c7bd4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/acde_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/acde_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/acde_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/acde_1.40.0.tgz vignettes: vignettes/acde/inst/doc/acde.pdf vignetteTitles: Identification of Differentially Expressed Genes with Artificial Components hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/acde/inst/doc/acde.R dependencyCount: 3 Package: ACE Version: 1.28.0 Depends: R (>= 3.4) Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods, grDevices, GenomicRanges Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: f0b843e4f3f2fad1aae5cd68307ad8a8 NeedsCompilation: no Title: Absolute Copy Number Estimation from Low-coverage Whole Genome Sequencing Description: Uses segmented copy number data to estimate tumor cell percentage and produce copy number plots displaying absolute copy numbers. biocViews: CopyNumberVariation, DNASeq, Coverage, WholeGenome, Visualization, Sequencing Author: Jos B Poell Maintainer: Jos B Poell URL: https://github.com/tgac-vumc/ACE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ACE git_branch: RELEASE_3_22 git_last_commit: 1cb1960 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ACE_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ACE_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ACE_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ACE_1.28.0.tgz vignettes: vignettes/ACE/inst/doc/ACE_vignette.html vignetteTitles: ACE vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACE/inst/doc/ACE_vignette.R dependencyCount: 66 Package: aCGH Version: 1.88.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, grDevices, graphics, methods, stats, splines, utils License: GPL-2 Archs: x64 MD5sum: cbe5bf2eb2110fb72ee343202be2a250 NeedsCompilation: yes Title: Classes and functions for Array Comparative Genomic Hybridization data Description: Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects. biocViews: CopyNumberVariation, DataImport, Genetics Author: Jane Fridlyand , Peter Dimitrov Maintainer: Peter Dimitrov git_url: https://git.bioconductor.org/packages/aCGH git_branch: RELEASE_3_22 git_last_commit: a03dea2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/aCGH_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/aCGH_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/aCGH_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/aCGH_1.88.0.tgz vignettes: vignettes/aCGH/inst/doc/aCGH.pdf vignetteTitles: aCGH Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aCGH/inst/doc/aCGH.R dependsOnMe: CRImage importsMe: ADaCGH2 dependencyCount: 17 Package: ACME Version: 2.66.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) MD5sum: 25c0151143b6187331f05e8c8c5cb027 NeedsCompilation: yes Title: Algorithms for Calculating Microarray Enrichment (ACME) Description: ACME (Algorithms for Calculating Microarray Enrichment) is a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". It does not rely on a specific array technology (although the array should be a "tiling" array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory. biocViews: Technology, Microarray, Normalization Author: Sean Davis Maintainer: Sean Davis URL: http://watson.nci.nih.gov/~sdavis git_url: https://git.bioconductor.org/packages/ACME git_branch: RELEASE_3_22 git_last_commit: 61ddf84 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ACME_2.66.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ACME_2.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ACME_2.66.0.tgz vignettes: vignettes/ACME/inst/doc/ACME.pdf vignetteTitles: ACME hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACME/inst/doc/ACME.R suggestsMe: oligo dependencyCount: 7 Package: ADaCGH2 Version: 2.50.0 Depends: R (>= 3.2.0), parallel, ff Imports: bit, DNAcopy, tilingArray, waveslim, cluster, aCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi, GLAD License: GPL (>= 3) Archs: x64 MD5sum: e201b380fb2ba3218fbb22bb3161bba2 NeedsCompilation: yes Title: Analysis of big data from aCGH experiments using parallel computing and ff objects Description: Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data. biocViews: Microarray, CopyNumberVariants Author: Ramon Diaz-Uriarte and Oscar M. Rueda . Wavelet-based aCGH smoothing code from Li Hsu and Douglas Grove . Imagemap code from Barry Rowlingson . HaarSeg code from Erez Ben-Yaacov; downloaded from . Code from ffbase by Edwin de Jonge , Jan Wijffels, Jan van der Laan. Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/adacgh2 git_url: https://git.bioconductor.org/packages/ADaCGH2 git_branch: RELEASE_3_22 git_last_commit: 62d2794 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ADaCGH2_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ADaCGH2_2.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ADaCGH2_2.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ADaCGH2_2.50.0.tgz vignettes: vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf, vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf, vignettes/ADaCGH2/inst/doc/benchmarks.pdf vignetteTitles: ADaCGH2-long-examples.pdf, ADaCGH2 Overview, benchmarks.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADaCGH2/inst/doc/ADaCGH2.R dependencyCount: 85 Package: ADAM Version: 1.26.0 Depends: R(>= 3.5), stats, utils, methods Imports: Rcpp (>= 0.12.18), GO.db (>= 3.6.0), KEGGREST (>= 1.20.2), knitr, pbapply (>= 1.3-4), dplyr (>= 0.7.6), DT (>= 0.4), stringr (>= 1.3.1), SummarizedExperiment (>= 1.10.1) LinkingTo: Rcpp Suggests: testthat, rmarkdown, BiocStyle License: GPL (>= 2) Archs: x64 MD5sum: fe4bbccd1ff735bc78f18496c50ff11b NeedsCompilation: yes Title: ADAM: Activity and Diversity Analysis Module Description: ADAM is a GSEA R package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions (Gene Ontology and KEGG pathways as default) and show their significance by calculating p-values referring togene diversity and activity. Each group of genes is called GFAG (Group of Functionally Associated Genes). biocViews: GeneSetEnrichment, Pathways, KEGG, GeneExpression, Microarray Author: André Luiz Molan [aut], Giordano Bruno Sanches Seco [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAM git_branch: RELEASE_3_22 git_last_commit: 11eb08c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ADAM_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ADAM_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ADAM_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ADAM_1.26.0.tgz vignettes: vignettes/ADAM/inst/doc/ADAM.html vignetteTitles: "Using ADAM" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAM/inst/doc/ADAM.R dependsOnMe: ADAMgui dependencyCount: 90 Package: ADAMgui Version: 1.26.0 Depends: R(>= 3.6), stats, utils, methods, ADAM Imports: GO.db (>= 3.5.0), dplyr (>= 0.7.6), shiny (>= 1.1.0), stringr (>= 1.3.1), stringi (>= 1.2.4), varhandle (>= 2.0.3), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), ggpubr (>= 0.1.8), ggsignif (>= 0.4.0), reshape2 (>= 1.4.3), RColorBrewer (>= 1.1-2), colorRamps (>= 2.3), DT (>= 0.4), data.table (>= 1.11.4), gridExtra (>= 2.3), shinyjs (>= 1.0), knitr, testthat Suggests: markdown, BiocStyle License: GPL (>= 2) MD5sum: 44c24c1175f6e867997f4c4c6a13496a NeedsCompilation: no Title: Activity and Diversity Analysis Module Graphical User Interface Description: ADAMgui is a Graphical User Interface for the ADAM package. The ADAMgui package provides 2 shiny-based applications that allows the user to study the output of the ADAM package files through different plots. It's possible, for example, to choose a specific GFAG and observe the gene expression behavior with the plots created with the GFAGtargetUi function. Features such as differential expression and foldchange can be easily seen with aid of the plots made with GFAGpathUi function. biocViews: GeneSetEnrichment, Pathways, KEGG Author: Giordano Bruno Sanches Seco [aut], André Luiz Molan [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho URL: TBA VignetteBuilder: knitr BugReports: https://github.com/jrybarczyk/ADAMgui/issues git_url: https://git.bioconductor.org/packages/ADAMgui git_branch: RELEASE_3_22 git_last_commit: 4f77d85 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ADAMgui_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ADAMgui_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ADAMgui_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ADAMgui_1.26.0.tgz vignettes: vignettes/ADAMgui/inst/doc/ADAMgui.html vignetteTitles: "Using ADAMgui" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAMgui/inst/doc/ADAMgui.R dependencyCount: 162 Package: ADAPT Version: 1.4.0 Depends: R (>= 4.1.0) Imports: Rcpp (>= 1.0.8), RcppArmadillo (>= 0.10.8), RcppParallel (>= 5.1.5), phyloseq (>= 1.39.0), methods, stats, ggplot2 (>= 3.4.1), ggrepel (>= 0.9.1) LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: rmarkdown (>= 2.11), knitr (>= 1.37), testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: b8cbe873025618ada3cdd96a0572eebe NeedsCompilation: yes Title: Analysis of Microbiome Differential Abundance by Pooling Tobit Models Description: ADAPT carries out differential abundance analysis for microbiome metagenomics data in phyloseq format. It has two innovations. One is to treat zero counts as left censored and use Tobit models for log count ratios. The other is an innovative way to find non-differentially abundant taxa as reference, then use the reference taxa to find the differentially abundant ones. biocViews: DifferentialExpression, Microbiome, Normalization, Sequencing, Metagenomics, Software, MultipleComparison Author: Mukai Wang [aut, cre] (ORCID: ), Simon Fontaine [ctb], Hui Jiang [ctb], Gen Li [aut, ctb] Maintainer: Mukai Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAPT git_branch: RELEASE_3_22 git_last_commit: f9fd0d9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ADAPT_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ADAPT_1.3.0.zip vignettes: vignettes/ADAPT/inst/doc/ADAPT-manual.html vignetteTitles: ADAPT Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ADAPT/inst/doc/ADAPT-manual.R dependencyCount: 72 Package: adductomicsR Version: 1.26.0 Depends: R (>= 3.6), adductData, ExperimentHub, AnnotationHub Imports: parallel (>= 3.3.2), data.table (>= 1.10.4), OrgMassSpecR (>= 0.4.6), foreach (>= 1.4.3), mzR (>= 2.14.0), ade4 (>= 1.7.6), rvest (>= 0.3.2), pastecs (>= 1.3.18), reshape2 (>= 1.4.2), pracma (>= 2.0.4), DT (>= 0.2), fpc (>= 2.1.10), doSNOW (>= 1.0.14), fastcluster (>= 1.1.22), RcppEigen (>= 0.3.3.3.0), bootstrap (>= 2017.2), smoother (>= 1.1), dplyr (>= 0.7.5), zoo (>= 1.8), stats (>= 3.5.0), utils (>= 3.5.0), graphics (>= 3.5.0), grDevices (>= 3.5.0), methods (>= 3.5.0), datasets (>= 3.5.0) Suggests: knitr (>= 1.15.1), rmarkdown (>= 1.5), Rdisop (>= 1.34.0), testthat License: Artistic-2.0 MD5sum: 77793165d1392b9b0866985313f13e97 NeedsCompilation: no Title: Processing of adductomic mass spectral datasets Description: Processes MS2 data to identify potentially adducted peptides from spectra that has been corrected for mass drift and retention time drift and quantifies MS1 level mass spectral peaks. biocViews: MassSpectrometry,Metabolomics,Software,ThirdPartyClient,DataImport, GUI Author: Josie Hayes Maintainer: Josie Hayes VignetteBuilder: knitr BugReports: https://github.com/JosieLHayes/adductomicsR/issues git_url: https://git.bioconductor.org/packages/adductomicsR git_branch: RELEASE_3_22 git_last_commit: c15f935 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/adductomicsR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/adductomicsR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/adductomicsR_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/adductomicsR_1.26.0.tgz vignettes: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.html vignetteTitles: Adductomics workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.R dependencyCount: 135 Package: ADImpute Version: 1.20.0 Depends: R (>= 4.0) Imports: checkmate, BiocParallel, data.table, DrImpute, kernlab, MASS, Matrix, methods, rsvd, S4Vectors, SAVER, SingleCellExperiment, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 + file LICENSE MD5sum: 16a6855ac91c0136e18ff596797fa902 NeedsCompilation: no Title: Adaptive Dropout Imputer (ADImpute) Description: Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble. biocViews: GeneExpression, Network, Preprocessing, Sequencing, SingleCell, Transcriptomics Author: Ana Carolina Leote [cre, aut] (ORCID: ) Maintainer: Ana Carolina Leote VignetteBuilder: knitr BugReports: https://github.com/anacarolinaleote/ADImpute/issues git_url: https://git.bioconductor.org/packages/ADImpute git_branch: RELEASE_3_22 git_last_commit: 4634280 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ADImpute_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ADImpute_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ADImpute_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ADImpute_1.20.0.tgz vignettes: vignettes/ADImpute/inst/doc/ADImpute_tutorial.html vignetteTitles: ADImpute tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ADImpute/inst/doc/ADImpute_tutorial.R dependencyCount: 54 Package: adSplit Version: 1.80.0 Depends: R (>= 2.1.0), methods (>= 2.1.0) Imports: AnnotationDbi, Biobase (>= 1.5.12), cluster (>= 1.9.1), GO.db (>= 1.8.1), graphics, grDevices, KEGGREST (>= 1.30.1), multtest (>= 1.6.0), stats (>= 2.1.0) Suggests: golubEsets (>= 1.0), vsn (>= 1.5.0), hu6800.db (>= 1.8.1) License: GPL (>= 2) Archs: x64 MD5sum: e89c6bb78b04ec7b44bc64eb9813f609 NeedsCompilation: yes Title: Annotation-Driven Clustering Description: This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined. biocViews: Microarray, Clustering Author: Claudio Lottaz, Joern Toedling Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/adSplit.shtml git_url: https://git.bioconductor.org/packages/adSplit git_branch: RELEASE_3_22 git_last_commit: 0efcd35 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/adSplit_1.80.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/adSplit_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/adSplit_1.80.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/adSplit_1.80.0.tgz vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf vignetteTitles: Annotation-Driven Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adSplit/inst/doc/tr_2005_02.R dependencyCount: 52 Package: adverSCarial Version: 1.8.0 Depends: R (>= 3.5.0) Imports: gtools, S4Vectors, methods, DelayedArray Suggests: knitr, RUnit, BiocGenerics, TENxPBMCData, CHETAH, stringr, LoomExperiment License: MIT + file LICENSE MD5sum: d7b618b030cb44e1b44fb325efde3c4a NeedsCompilation: no Title: adverSCarial, generate and analyze the vulnerability of scRNA-seq classifier to adversarial attacks Description: adverSCarial is an R Package designed for generating and analyzing the vulnerability of scRNA-seq classifiers to adversarial attacks. The package is versatile and provides a format for integrating any type of classifier. It offers functions for studying and generating two types of attacks, single gene attack and max change attack. The single-gene attack involves making a small modification to the input to alter the classification. The max-change attack involves making a large modification to the input without changing its classification. The CGD attack is based on an estimated gradient descent. against adversarial attacks. The package provides a comprehensive solution for evaluating the robustness of scRNA-seq classifiers against adversarial attacks. biocViews: Software, SingleCell, Transcriptomics, Classification Author: Ghislain FIEVET [aut, cre] (ORCID: ), Sébastien HERGALANT [aut] (ORCID: ) Maintainer: Ghislain FIEVET VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/adverSCarial git_branch: RELEASE_3_22 git_last_commit: 4d9ce43 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/adverSCarial_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/adverSCarial_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/adverSCarial_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/adverSCarial_1.8.0.tgz vignettes: vignettes/adverSCarial/inst/doc/vign01_adverSCarial.html, vignettes/adverSCarial/inst/doc/vign02_overView_analysis.html, vignettes/adverSCarial/inst/doc/vign03_adapt_classifier.html, vignettes/adverSCarial/inst/doc/vign04_advRandWalkMinChange.html vignetteTitles: Vign01_adverSCarial, Vign02_overView_analysis, Vign03_adapt_classifiers, Vign04_advRandWalkMinChange hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/adverSCarial/inst/doc/vign01_adverSCarial.R, vignettes/adverSCarial/inst/doc/vign02_overView_analysis.R dependencyCount: 22 Package: AffiXcan Version: 1.28.0 Depends: R (>= 3.6), SummarizedExperiment Imports: MultiAssayExperiment, BiocParallel, crayon Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: e97f5c287170793e36136eac077e5806 NeedsCompilation: no Title: A Functional Approach To Impute Genetically Regulated Expression Description: Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known. biocViews: GeneExpression, Transcription, GeneRegulation, DimensionReduction, Regression, PrincipalComponent Author: Alessandro Lussana [aut, cre] Maintainer: Alessandro Lussana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AffiXcan git_branch: RELEASE_3_22 git_last_commit: b3b1c91 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AffiXcan_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AffiXcan_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AffiXcan_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AffiXcan_1.28.0.tgz vignettes: vignettes/AffiXcan/inst/doc/AffiXcan.html vignetteTitles: AffiXcan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffiXcan/inst/doc/AffiXcan.R dependencyCount: 56 Package: affxparser Version: 1.82.0 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: x64 MD5sum: 9f748ecae1c1ed5c10113e2dbced634d NeedsCompilation: yes Title: Affymetrix File Parsing SDK Description: Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms, OneChannel Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman [ctb], Kasper Daniel Hansen [aut, cre], Jim Hester [ctb], Martin Morgan [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/affxparser BugReports: https://github.com/HenrikBengtsson/affxparser/issues git_url: https://git.bioconductor.org/packages/affxparser git_branch: RELEASE_3_22 git_last_commit: f2f233c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/affxparser_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affxparser_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affxparser_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affxparser_1.82.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder importsMe: affyILM, cn.farms, EventPointer, GeneRegionScan, ITALICS, oligo suggestsMe: TIN, aroma.affymetrix, aroma.apd dependencyCount: 0 Package: affy Version: 1.88.0 Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5) Imports: affyio (>= 1.13.3), BiocManager, graphics, grDevices, methods, preprocessCore, stats, utils LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools, hgu95av2cdf License: LGPL (>= 2.0) Archs: x64 MD5sum: 85a03c51a040597094ab24e8067e5830 NeedsCompilation: yes Title: Methods for Affymetrix Oligonucleotide Arrays Description: The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it. biocViews: Microarray, OneChannel, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Benjamin Milo Bolstad , and Crispin Miller with contributions from Magnus Astrand , Leslie M. Cope , Robert Gentleman, Jeff Gentry, Conrad Halling , Wolfgang Huber, James MacDonald , Benjamin I. P. Rubinstein, Christopher Workman , John Zhang Maintainer: Robert D. Shear URL: https://bioconductor.org/packages/affy BugReports: https://github.com/rafalab/affy/issues git_url: https://git.bioconductor.org/packages/affy git_branch: RELEASE_3_22 git_last_commit: 229aef4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/affy_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affy_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affy_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affy_1.88.0.tgz vignettes: vignettes/affy/inst/doc/affy.pdf, vignettes/affy/inst/doc/builtinMethods.pdf, vignettes/affy/inst/doc/customMethods.pdf, vignettes/affy/inst/doc/vim.pdf vignetteTitles: 1. Primer, 2. Built-in Processing Methods, 3. Custom Processing Methods, 4. Import Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affy/inst/doc/affy.R, vignettes/affy/inst/doc/builtinMethods.R, vignettes/affy/inst/doc/customMethods.R, vignettes/affy/inst/doc/vim.R dependsOnMe: affyContam, affyPLM, AffyRNADegradation, altcdfenvs, arrayMvout, Cormotif, DrugVsDisease, ExiMiR, frmaTools, gcrma, maskBAD, panp, prebs, qpcrNorm, RPA, SCAN.UPC, webbioc, affydata, ALLMLL, AmpAffyExample, bronchialIL13, CLL, curatedBladderData, ecoliLeucine, Hiiragi2013, MAQCsubset, mvoutData, PREDAsampledata, SpikeIn, SpikeInSubset, XhybCasneuf importsMe: affycoretools, affyILM, affylmGUI, bnem, CAFE, ChIPXpress, Cormotif, Doscheda, ffpe, frma, gcrma, GEOsubmission, HTqPCR, iCheck, lumi, makecdfenv, mimager, MSnbase, PECA, plier, puma, pvac, Rnits, STATegRa, tilingArray, TurboNorm, vsn, rat2302frmavecs, DeSousa2013, signatureSearchData, bapred suggestsMe: AnnotationForge, ArrayExpress, autonomics, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, factDesign, GeneRegionScan, limma, made4, piano, PREDA, qcmetrics, runibic, siggenes, TCGAbiolinks, ath1121501frmavecs, estrogen, ffpeExampleData, arrays, aroma.affymetrix, hexbin, isatabr, maGUI dependencyCount: 11 Package: affycomp Version: 1.86.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: ff4a5b27813a38986751e4dbd92d7d2a NeedsCompilation: no Title: Graphics Toolbox for Assessment of Affymetrix Expression Measures Description: The package contains functions that can be used to compare expression measures for Affymetrix Oligonucleotide Arrays. biocViews: OneChannel, Microarray, Preprocessing Author: Rafael A. Irizarry and Zhijin Wu with contributions from Simon Cawley Maintainer: Robert D. Shear URL: https://bioconductor.org/packages/affycomp BugReports: https://github.com/rafalab/affycomp/issues git_url: https://git.bioconductor.org/packages/affycomp git_branch: RELEASE_3_22 git_last_commit: b3e40b0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/affycomp_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affycomp_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affycomp_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affycomp_1.86.0.tgz vignettes: vignettes/affycomp/inst/doc/affycomp.pdf vignetteTitles: affycomp primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycomp/inst/doc/affycomp.R dependsOnMe: affycompData dependencyCount: 7 Package: affyContam Version: 1.68.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata Suggests: hgu95av2cdf License: Artistic-2.0 MD5sum: 7bf130f7e63b764326edbbb96a7e7699 NeedsCompilation: no Title: structured corruption of affymetrix cel file data Description: structured corruption of cel file data to demonstrate QA effectiveness biocViews: Infrastructure Author: V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/affyContam git_branch: RELEASE_3_22 git_last_commit: 5898469 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/affyContam_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affyContam_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affyContam_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affyContam_1.68.0.tgz vignettes: vignettes/affyContam/inst/doc/affyContam.pdf vignetteTitles: affy contamination tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyContam/inst/doc/affyContam.R importsMe: arrayMvout dependencyCount: 14 Package: affycoretools Version: 1.82.0 Depends: Biobase, methods Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi, ggplot2, gplots, oligoClasses, ReportingTools, hwriter, lattice, S4Vectors, edgeR, RSQLite, BiocGenerics, DBI, Glimma Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl, rmarkdown License: Artistic-2.0 MD5sum: add35bc25eac9df59059c82189a1dfec NeedsCompilation: no Title: Functions useful for those doing repetitive analyses with Affymetrix GeneChips Description: Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see. biocViews: ReportWriting, Microarray, OneChannel, GeneExpression Author: James W. MacDonald Maintainer: James W. MacDonald VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/affycoretools git_branch: RELEASE_3_22 git_last_commit: c96a089 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/affycoretools_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affycoretools_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affycoretools_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affycoretools_1.82.0.tgz vignettes: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.html vignetteTitles: Creating annotated output with \Biocpkg{affycoretools} and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R suggestsMe: EnMCB dependencyCount: 172 Package: affyILM Version: 1.62.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, Biobase Suggests: AffymetrixDataTestFiles, hgfocusprobe License: GPL-3 MD5sum: 5741032dc19d25712ac82b05fa120e24 NeedsCompilation: no Title: Linear Model of background subtraction and the Langmuir isotherm Description: affyILM is a preprocessing tool which estimates gene expression levels for Affymetrix Gene Chips. Input from physical chemistry is employed to first background subtract intensities before calculating concentrations on behalf of the Langmuir model. biocViews: Microarray, OneChannel, Preprocessing Author: K. Myriam Kroll, Fabrice Berger, Gerard Barkema, Enrico Carlon Maintainer: Myriam Kroll and Fabrice Berger git_url: https://git.bioconductor.org/packages/affyILM git_branch: RELEASE_3_22 git_last_commit: 4d9facc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/affyILM_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affyILM_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affyILM_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affyILM_1.62.0.tgz vignettes: vignettes/affyILM/inst/doc/affyILM.pdf vignetteTitles: affyILM1.3.0 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyILM/inst/doc/affyILM.R dependencyCount: 23 Package: affyio Version: 1.80.0 Depends: R (>= 2.6.0) Imports: methods License: LGPL (>= 2) Archs: x64 MD5sum: 0c5147bb9f0b683c9296156ee07a77d0 NeedsCompilation: yes Title: Tools for parsing Affymetrix data files Description: Routines for parsing Affymetrix data files based upon file format information. Primary focus is on accessing the CEL and CDF file formats. biocViews: Microarray, DataImport, Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyio git_url: https://git.bioconductor.org/packages/affyio git_branch: RELEASE_3_22 git_last_commit: eb747bb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/affyio_1.80.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affyio_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affyio_1.80.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affyio_1.80.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: makecdfenv, SCAN.UPC importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods dependencyCount: 1 Package: affylmGUI Version: 1.84.0 Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma, affy, affyio, affyPLM, gcrma, BiocGenerics, AnnotationDbi, BiocManager, R2HTML, xtable License: GPL (>=2) MD5sum: 3bebc9bde7a601726de610a020650714 NeedsCompilation: no Title: GUI for limma Package with Affymetrix Microarrays Description: A Graphical User Interface (GUI) for analysis of Affymetrix microarray gene expression data using the affy and limma packages. biocViews: GUI, GeneExpression, Transcription, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [cre,aut], Gordon Smyth [aut], Ken Simpson [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/affylmGUI/ git_url: https://git.bioconductor.org/packages/affylmGUI git_branch: RELEASE_3_22 git_last_commit: cb9ab7b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/affylmGUI_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affylmGUI_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affylmGUI_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affylmGUI_1.84.0.tgz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf, vignettes/affylmGUI/inst/doc/about.html, vignettes/affylmGUI/inst/doc/CustMenu.html, vignettes/affylmGUI/inst/doc/index.html, vignettes/affylmGUI/inst/doc/windowsFocus.html vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files, about.html, CustMenu.html, index.html, windowsFocus.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R dependencyCount: 56 Package: affyPLM Version: 1.86.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS, hgu95av2cdf License: GPL (>= 2) Archs: x64 MD5sum: 8d650502f7f4064cf957f80e5b031783 NeedsCompilation: yes Title: Methods for fitting probe-level models Description: A package that extends and improves the functionality of the base affy package. Routines that make heavy use of compiled code for speed. Central focus is on implementation of methods for fitting probe-level models and tools using these models. PLM based quality assessment tools. biocViews: Microarray, OneChannel, Preprocessing, QualityControl Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyPLM git_url: https://git.bioconductor.org/packages/affyPLM git_branch: RELEASE_3_22 git_last_commit: 8c46661 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/affyPLM_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affyPLM_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affyPLM_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affyPLM_1.86.0.tgz vignettes: vignettes/affyPLM/inst/doc/AffyExtensions.pdf, vignettes/affyPLM/inst/doc/MAplots.pdf, vignettes/affyPLM/inst/doc/QualityAssess.pdf, vignettes/affyPLM/inst/doc/ThreeStep.pdf vignetteTitles: affyPLM: Fitting Probe Level Models, affyPLM: Advanced use of the MAplot function, affyPLM: Model Based QC Assessment of Affymetrix GeneChips, affyPLM: the threestep function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPLM/inst/doc/AffyExtensions.R, vignettes/affyPLM/inst/doc/MAplots.R, vignettes/affyPLM/inst/doc/QualityAssess.R, vignettes/affyPLM/inst/doc/ThreeStep.R dependsOnMe: bapred importsMe: affylmGUI, mimager suggestsMe: arrayMvout, BiocGenerics, frmaTools, metahdep, piano, aroma.affymetrix dependencyCount: 22 Package: AffyRNADegradation Version: 1.56.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample, hgu133acdf License: GPL-2 MD5sum: e3234c4166a062c2a19391253154c703 NeedsCompilation: no Title: Analyze and correct probe positional bias in microarray data due to RNA degradation Description: The package helps with the assessment and correction of RNA degradation effects in Affymetrix 3' expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation. biocViews: GeneExpression, Microarray, OneChannel, Preprocessing, QualityControl Author: Mario Fasold Maintainer: Mario Fasold git_url: https://git.bioconductor.org/packages/AffyRNADegradation git_branch: RELEASE_3_22 git_last_commit: 61c4f11 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AffyRNADegradation_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AffyRNADegradation_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AffyRNADegradation_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AffyRNADegradation_1.56.0.tgz vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf vignetteTitles: AffyRNADegradation Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyRNADegradation/inst/doc/vignette.R dependencyCount: 12 Package: AGDEX Version: 1.58.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: abc327bbd2f998b3a63bc2cf23fd5925 NeedsCompilation: no Title: Agreement of Differential Expression Analysis Description: A tool to evaluate agreement of differential expression for cross-species genomics biocViews: Microarray, Genetics, GeneExpression Author: Stan Pounds ; Cuilan Lani Gao Maintainer: Cuilan lani Gao git_url: https://git.bioconductor.org/packages/AGDEX git_branch: RELEASE_3_22 git_last_commit: 63cfecd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AGDEX_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AGDEX_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AGDEX_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AGDEX_1.58.0.tgz vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf vignetteTitles: AGDEX.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AGDEX/inst/doc/AGDEX.R dependencyCount: 48 Package: aggregateBioVar Version: 1.20.0 Depends: R (>= 4.0) Imports: stats, methods, S4Vectors, SummarizedExperiment, SingleCellExperiment, Matrix, tibble, rlang Suggests: BiocStyle, magick, knitr, rmarkdown, testthat, BiocGenerics, DESeq2, magrittr, dplyr, ggplot2, cowplot, ggtext, RColorBrewer, pheatmap, viridis License: GPL-3 MD5sum: 7ef27faf74dd0ea6dfba82cf1a91d7e3 NeedsCompilation: no Title: Differential Gene Expression Analysis for Multi-subject scRNA-seq Description: For single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates), this package contains tools to summarize single cell gene expression profiles at the level of subject. A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools. biocViews: Software, SingleCell, RNASeq, Transcriptomics, Transcription, GeneExpression, DifferentialExpression Author: Jason Ratcliff [aut, cre] (ORCID: ), Andrew Thurman [aut], Michael Chimenti [ctb], Alejandro Pezzulo [ctb] Maintainer: Jason Ratcliff URL: https://github.com/jasonratcliff/aggregateBioVar VignetteBuilder: knitr BugReports: https://github.com/jasonratcliff/aggregateBioVar/issues git_url: https://git.bioconductor.org/packages/aggregateBioVar git_branch: RELEASE_3_22 git_last_commit: d53356b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/aggregateBioVar_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/aggregateBioVar_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/aggregateBioVar_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/aggregateBioVar_1.20.0.tgz vignettes: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.html vignetteTitles: Multi-subject scRNA-seq Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.R dependencyCount: 36 Package: agilp Version: 3.42.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: 59ba607c29a7fd2e3547e81fc6fc9254 NeedsCompilation: no Title: Agilent expression array processing package Description: More about what it does (maybe more than one line) Author: Benny Chain Maintainer: Benny Chain git_url: https://git.bioconductor.org/packages/agilp git_branch: RELEASE_3_22 git_last_commit: 9fe7b08 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/agilp_3.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/agilp_3.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/agilp_3.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/agilp_3.42.0.tgz vignettes: vignettes/agilp/inst/doc/agilp_manual.pdf vignetteTitles: An R Package for processing expression microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/agilp/inst/doc/agilp_manual.R dependencyCount: 0 Package: AgiMicroRna Version: 2.60.0 Depends: R (>= 2.10),methods,Biobase,limma,affy (>= 1.22),preprocessCore,affycoretools Imports: Biobase Suggests: geneplotter,marray,gplots,gtools,gdata,codelink License: GPL-3 MD5sum: e273adf8c7691e8a67897166ae451f54 NeedsCompilation: no Title: Processing and Differential Expression Analysis of Agilent microRNA chips Description: Processing and Analysis of Agilent microRNA data biocViews: Microarray, AgilentChip, OneChannel, Preprocessing, DifferentialExpression Author: Pedro Lopez-Romero Maintainer: Pedro Lopez-Romero git_url: https://git.bioconductor.org/packages/AgiMicroRna git_branch: RELEASE_3_22 git_last_commit: 9611ae4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AgiMicroRna_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AgiMicroRna_2.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AgiMicroRna_2.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AgiMicroRna_2.60.0.tgz vignettes: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.pdf vignetteTitles: AgiMicroRna hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.R dependencyCount: 173 Package: AHMassBank Version: 1.10.0 Depends: R (>= 4.2) Imports: AnnotationHubData (>= 1.5.24) Suggests: BiocStyle, knitr, AnnotationHub (>= 2.7.13), rmarkdown, methods, CompoundDb (>= 1.1.4) License: Artistic-2.0 MD5sum: 382e24d8529bd16f192d873eff40ff36 NeedsCompilation: no Title: MassBank Annotation Resources for AnnotationHub Description: Supplies AnnotationHub with MassBank metabolite/compound annotations bundled in CompDb SQLite databases. CompDb SQLite databases contain general compound annotation as well as fragment spectra representing fragmentation patterns of compounds' ions. MassBank data is retrieved from https://massbank.eu/MassBank and processed using helper functions from the CompoundDb Bioconductor package into redistributable SQLite databases. biocViews: MassSpectrometry, AnnotationHubSoftware Author: Johannes Rainer [cre] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/jorainer/AHMassBank VignetteBuilder: knitr BugReports: https://github.com/jorainer/AHMassBank/issues git_url: https://git.bioconductor.org/packages/AHMassBank git_branch: RELEASE_3_22 git_last_commit: 347350a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AHMassBank_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AHMassBank_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AHMassBank_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AHMassBank_1.10.0.tgz vignettes: vignettes/AHMassBank/inst/doc/creating-MassBank-CompDbs.html vignetteTitles: Provide EnsDb databases for AnnotationHub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AHMassBank/inst/doc/creating-MassBank-CompDbs.R dependencyCount: 117 Package: AIMS Version: 1.42.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 62ce46c7e30983c705b0a8d607239940 NeedsCompilation: no Title: AIMS : Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype Description: This package contains the AIMS implementation. It contains necessary functions to assign the five intrinsic molecular subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like, Normal-like). Assignments could be done on individual samples as well as on dataset of gene expression data. biocViews: ImmunoOncology, Classification, RNASeq, Microarray, Software, GeneExpression Author: Eric R. Paquet, Michael T. Hallett Maintainer: Eric R Paquet URL: http://www.bci.mcgill.ca/AIMS git_url: https://git.bioconductor.org/packages/AIMS git_branch: RELEASE_3_22 git_last_commit: 7c67c1d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AIMS_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AIMS_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AIMS_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AIMS_1.42.0.tgz vignettes: vignettes/AIMS/inst/doc/AIMS.pdf vignetteTitles: AIMS An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AIMS/inst/doc/AIMS.R dependsOnMe: genefu dependencyCount: 12 Package: airpart Version: 1.18.0 Depends: R (>= 4.1) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, scater, stats, smurf, apeglm (>= 1.13.3), emdbook, mclust, clue, dynamicTreeCut, matrixStats, dplyr, plyr, ggplot2, ComplexHeatmap, forestplot, RColorBrewer, rlang, lpSolve, grid, grDevices, graphics, utils, pbapply Suggests: knitr, rmarkdown, roxygen2 (>= 6.0.0), testthat (>= 3.0.0), gplots, tidyr License: GPL-2 MD5sum: 2552b6267db4c1460f915308e8e3e228 NeedsCompilation: no Title: Differential cell-type-specific allelic imbalance Description: Airpart identifies sets of genes displaying differential cell-type-specific allelic imbalance across cell types or states, utilizing single-cell allelic counts. It makes use of a generalized fused lasso with binomial observations of allelic counts to partition cell types by their allelic imbalance. Alternatively, a nonparametric method for partitioning cell types is offered. The package includes a number of visualizations and quality control functions for examining single cell allelic imbalance datasets. biocViews: SingleCell, RNASeq, ATACSeq, ChIPSeq, Sequencing, GeneRegulation, GeneExpression, Transcription, TranscriptomeVariant, CellBiology, FunctionalGenomics, DifferentialExpression, GraphAndNetwork, Regression, Clustering, QualityControl Author: Wancen Mu [aut, cre] (ORCID: ), Michael Love [aut, ctb] (ORCID: ) Maintainer: Wancen Mu URL: https://github.com/Wancen/airpart VignetteBuilder: knitr BugReports: https://github.com/Wancen/airpart/issues git_url: https://git.bioconductor.org/packages/airpart git_branch: RELEASE_3_22 git_last_commit: 1715ade git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/airpart_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/airpart_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/airpart_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/airpart_1.18.0.tgz vignettes: vignettes/airpart/inst/doc/airpart.html vignetteTitles: Differential allelic imbalance with airpart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/airpart/inst/doc/airpart.R dependencyCount: 134 Package: alabaster Version: 1.10.0 Depends: alabaster.base Imports: alabaster.matrix, alabaster.ranges, alabaster.se, alabaster.sce, alabaster.spatial, alabaster.string, alabaster.vcf, alabaster.bumpy, alabaster.mae Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: f1b5c29e4cdefe3822ed552a6496dd01 NeedsCompilation: no Title: Umbrella for the Alabaster Framework Description: Umbrella for the alabaster suite, providing a single-line import for all alabaster.* packages. Installing this package ensures that all known alabaster.* packages are also installed, avoiding problems with missing packages when a staging method or loading function is dynamically requested. Obviously, this comes at the cost of needing to install more packages, so advanced users and application developers may prefer to install the required alabaster.* packages individually. biocViews: DataRepresentation, DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster git_branch: RELEASE_3_22 git_last_commit: a1c102b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster_1.10.0.tgz vignettes: vignettes/alabaster/inst/doc/userguide.html vignetteTitles: alabaster umbrella hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster/inst/doc/userguide.R dependencyCount: 120 Package: alabaster.base Version: 1.10.0 Imports: alabaster.schemas, methods, utils, S4Vectors, rhdf5 (>= 2.47.6), jsonlite, jsonvalidate, Rcpp LinkingTo: Rcpp, assorthead (>= 1.1.2), Rhdf5lib Suggests: BiocStyle, rmarkdown, knitr, testthat, digest, Matrix, alabaster.matrix License: MIT + file LICENSE Archs: x64 MD5sum: e71ef6abb2fa73aafb03ece1e19cc66d NeedsCompilation: yes Title: Save Bioconductor Objects to File Description: Save Bioconductor data structures into file artifacts, and load them back into memory. This is a more robust and portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataRepresentation, DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/ArtifactDB/alabaster.base SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/ArtifactDB/alabaster.base/issues git_url: https://git.bioconductor.org/packages/alabaster.base git_branch: RELEASE_3_22 git_last_commit: 6b49bcc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.base_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.base_1.9.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.base_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.base_1.10.0.tgz vignettes: vignettes/alabaster.base/inst/doc/userguide.html vignetteTitles: Saving and loading artifacts hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.base/inst/doc/userguide.R dependsOnMe: alabaster, alabaster.bumpy, alabaster.mae, alabaster.matrix, alabaster.ranges, alabaster.sce, alabaster.se, alabaster.sfe, alabaster.spatial, alabaster.string, alabaster.vcf importsMe: celldex, scRNAseq dependencyCount: 19 Package: alabaster.bumpy Version: 1.10.0 Depends: BumpyMatrix, alabaster.base Imports: methods, rhdf5, Matrix, BiocGenerics, S4Vectors, IRanges Suggests: BiocStyle, rmarkdown, knitr, testthat, jsonlite License: MIT + file LICENSE MD5sum: 4152ed0f241fc5aab73e58af9f012533 NeedsCompilation: no Title: Save and Load BumpyMatrices to/from file Description: Save BumpyMatrix objects into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.bumpy git_branch: RELEASE_3_22 git_last_commit: 88d92ad git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.bumpy_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.bumpy_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.bumpy_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.bumpy_1.10.0.tgz vignettes: vignettes/alabaster.bumpy/inst/doc/userguide.html vignetteTitles: Saving and loading BumpyMatrices hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.bumpy/inst/doc/userguide.R importsMe: alabaster dependencyCount: 26 Package: alabaster.files Version: 1.8.0 Depends: alabaster.base, Imports: methods, S4Vectors, BiocGenerics, Rsamtools Suggests: BiocStyle, rmarkdown, knitr, testthat, VariantAnnotation, rtracklayer, Biostrings License: MIT + file LICENSE MD5sum: c1625e110b3fb5e3ac651fbff866bf2f NeedsCompilation: no Title: Wrappers to Save Common File Formats Description: Save common bioinformatics file formats within the alabaster framework. This includes BAM, BED, VCF, bigWig, bigBed, FASTQ, FASTA and so on. We save and load additional metadata for each file, and we support linkage between each file and its corresponding index. biocViews: DataRepresentation, DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.files git_branch: RELEASE_3_22 git_last_commit: d6447bf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.files_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.files_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.files_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.files_1.8.0.tgz vignettes: vignettes/alabaster.files/inst/doc/userguide.html vignetteTitles: Saving common file formats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.files/inst/doc/userguide.R dependencyCount: 41 Package: alabaster.mae Version: 1.10.0 Depends: MultiAssayExperiment, alabaster.base Imports: methods, alabaster.se, S4Vectors, jsonlite, rhdf5 Suggests: testthat, knitr, SummarizedExperiment, BiocParallel, BiocStyle, rmarkdown License: MIT + file LICENSE MD5sum: 8084a76594f870bd4629dc50755b636c NeedsCompilation: no Title: Load and Save MultiAssayExperiments Description: Save MultiAssayExperiments into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.mae git_branch: RELEASE_3_22 git_last_commit: 5209c04 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.mae_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.mae_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.mae_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.mae_1.10.0.tgz vignettes: vignettes/alabaster.mae/inst/doc/userguide.html vignetteTitles: Saving and loading MultiAssayExperiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.mae/inst/doc/userguide.R importsMe: alabaster dependencyCount: 62 Package: alabaster.matrix Version: 1.10.0 Depends: alabaster.base Imports: methods, BiocGenerics, S4Vectors, DelayedArray (>= 0.33.3), S4Arrays, SparseArray (>= 1.5.22), rhdf5 (>= 2.47.1), HDF5Array, Matrix, Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, chihaya, BiocSingular, ResidualMatrix License: MIT + file LICENSE Archs: x64 MD5sum: 3f6d7da84ea1a77000d50919a344710b NeedsCompilation: yes Title: Load and Save Artifacts from File Description: Save matrices, arrays and similar objects into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.matrix git_branch: RELEASE_3_22 git_last_commit: 8892772 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.matrix_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.matrix_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.matrix_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.matrix_1.10.0.tgz vignettes: vignettes/alabaster.matrix/inst/doc/userguide.html vignetteTitles: Saving and loading arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.matrix/inst/doc/userguide.R importsMe: alabaster, alabaster.se, celldex, scMultiome, scRNAseq suggestsMe: alabaster.base dependencyCount: 35 Package: alabaster.ranges Version: 1.10.0 Depends: GenomicRanges, alabaster.base Imports: methods, S4Vectors, BiocGenerics, IRanges, Seqinfo, rhdf5 Suggests: testthat, knitr, BiocStyle, jsonlite License: MIT + file LICENSE MD5sum: f33117b343950e77ade983bfb39e00a7 NeedsCompilation: no Title: Load and Save Ranges-related Artifacts from File Description: Save GenomicRanges, IRanges and related data structures into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.ranges git_branch: RELEASE_3_22 git_last_commit: e35375b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.ranges_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.ranges_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.ranges_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.ranges_1.10.0.tgz vignettes: vignettes/alabaster.ranges/inst/doc/userguide.html vignetteTitles: Saving and loading genomic ranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.ranges/inst/doc/userguide.R importsMe: alabaster, alabaster.se dependencyCount: 23 Package: alabaster.sce Version: 1.10.0 Depends: SingleCellExperiment, alabaster.base Imports: methods, alabaster.se, jsonlite Suggests: knitr, testthat, BiocStyle, rmarkdown License: MIT + file LICENSE MD5sum: 8052cea21ca045b210c2b20e4f476f88 NeedsCompilation: no Title: Load and Save SingleCellExperiment from File Description: Save SingleCellExperiment into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.sce git_branch: RELEASE_3_22 git_last_commit: a6eed09 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.sce_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.sce_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.sce_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.sce_1.10.0.tgz vignettes: vignettes/alabaster.sce/inst/doc/userguide.html vignetteTitles: Saving and loading SingleCellExperiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.sce/inst/doc/userguide.R importsMe: alabaster, alabaster.sfe, alabaster.spatial, scRNAseq dependencyCount: 43 Package: alabaster.schemas Version: 1.10.0 Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 8d1caf02385b413a86ba8e068723b977 NeedsCompilation: no Title: Schemas for the Alabaster Framework Description: Stores all schemas required by various alabaster.* packages. No computation should be performed by this package, as that is handled by alabaster.base. We use a separate package instead of storing the schemas in alabaster.base itself, to avoid conflating management of the schemas with code maintenence. biocViews: DataRepresentation, DataImport Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.schemas git_branch: RELEASE_3_22 git_last_commit: f89d374 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.schemas_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.schemas_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.schemas_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.schemas_1.10.0.tgz vignettes: vignettes/alabaster.schemas/inst/doc/userguide.html vignetteTitles: Metadata schemas for Bioconductor hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: alabaster.base dependencyCount: 0 Package: alabaster.se Version: 1.10.0 Depends: SummarizedExperiment, alabaster.base Imports: methods, alabaster.ranges, alabaster.matrix, BiocGenerics, S4Vectors, IRanges, GenomicRanges, jsonlite Suggests: rmarkdown, knitr, testthat, BiocStyle License: MIT + file LICENSE MD5sum: b8eb6e4d5b0d3b8786c973b796ff38bb NeedsCompilation: no Title: Load and Save SummarizedExperiments from File Description: Save SummarizedExperiments into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.se git_branch: RELEASE_3_22 git_last_commit: 9f5dc20 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.se_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.se_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.se_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.se_1.10.0.tgz vignettes: vignettes/alabaster.se/inst/doc/userguide.html vignetteTitles: Saving and loading SummarizedExperiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.se/inst/doc/userguide.R importsMe: alabaster, alabaster.mae, alabaster.sce, alabaster.vcf, celldex dependencyCount: 41 Package: alabaster.sfe Version: 1.2.0 Depends: R (>= 4.1.0), SpatialFeatureExperiment (>= 1.9.3), alabaster.base Imports: alabaster.sce, alabaster.spatial (>= 1.5.2), EBImage, jsonlite, methods, RBioFormats, S4Vectors, sfarrow, SingleCellExperiment, spatialreg, spdep, SummarizedExperiment, terra, xml2 Suggests: BiocStyle, fs, knitr, rmarkdown, scater, sf, SFEData, testthat (>= 3.0.0), Voyager (>= 1.9.1) License: MIT + file LICENSE MD5sum: 4bd5f5365b2cf432ee93f2fd2b6d8903 NeedsCompilation: no Title: Language agnostic on disk serialization of SpatialFeatureExperiment Description: Builds upon the existing ArtifactDB project, expending alabaster.spatial for language agnostic on disk serialization of SpatialFeatureExperiment. biocViews: DataRepresentation, Spatial Author: Lambda Moses [aut, cre] (ORCID: ) Maintainer: Lambda Moses URL: https://pachterlab.github.io/alabaster.sfe/ VignetteBuilder: knitr BugReports: https://github.com/pachterlab/alabaster.sfe/issues git_url: https://git.bioconductor.org/packages/alabaster.sfe git_branch: RELEASE_3_22 git_last_commit: cb337e4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.sfe_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.sfe_1.1.0.zip vignettes: vignettes/alabaster.sfe/inst/doc/Overview.html vignetteTitles: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.sfe/inst/doc/Overview.R dependencyCount: 169 Package: alabaster.spatial Version: 1.10.0 Depends: SpatialExperiment, alabaster.base Imports: methods, utils, grDevices, S4Vectors, alabaster.sce, rhdf5 Suggests: testthat, knitr, rmarkdown, BiocStyle, DropletUtils, magick, png, digest License: MIT + file LICENSE MD5sum: 98985b5d38fe869188cc8447a5847591 NeedsCompilation: no Title: Save and Load Spatial 'Omics Data to/from File Description: Save SpatialExperiment objects and their images into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.spatial git_branch: RELEASE_3_22 git_last_commit: d3f2737 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.spatial_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.spatial_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.spatial_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.spatial_1.10.0.tgz vignettes: vignettes/alabaster.spatial/inst/doc/userguide.html vignetteTitles: Saving spatial experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.spatial/inst/doc/userguide.R importsMe: alabaster, alabaster.sfe dependencyCount: 82 Package: alabaster.string Version: 1.10.0 Depends: Biostrings, alabaster.base Imports: utils, methods, S4Vectors Suggests: BiocStyle, rmarkdown, knitr, testthat License: MIT + file LICENSE MD5sum: 207bc34d6e4950a598a02bca241ebfc6 NeedsCompilation: no Title: Save and Load Biostrings to/from File Description: Save Biostrings objects to file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.string git_branch: RELEASE_3_22 git_last_commit: 1014c54 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.string_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.string_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.string_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.string_1.10.0.tgz vignettes: vignettes/alabaster.string/inst/doc/userguide.html vignetteTitles: Saving and loading XStringSets hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.string/inst/doc/userguide.R importsMe: alabaster, alabaster.vcf dependencyCount: 27 Package: alabaster.vcf Version: 1.10.0 Depends: alabaster.base, VariantAnnotation Imports: methods, S4Vectors, alabaster.se, alabaster.string, Rsamtools Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: d3dd2014e6672652bbb1216dd8495c89 NeedsCompilation: no Title: Save and Load Variant Data to/from File Description: Save variant calling SummarizedExperiment to file and load them back as VCF objects. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.vcf git_branch: RELEASE_3_22 git_last_commit: 5ff4dad git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alabaster.vcf_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.vcf_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.vcf_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.vcf_1.10.0.tgz vignettes: vignettes/alabaster.vcf/inst/doc/userguide.html vignetteTitles: Saving and loading VCFs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.vcf/inst/doc/userguide.R importsMe: alabaster dependencyCount: 93 Package: ALDEx2 Version: 1.42.0 Depends: methods, stats, zCompositions, lattice, latticeExtra Imports: Rfast, BiocParallel, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, multtest, directlabels Suggests: testthat, BiocStyle, knitr, rmarkdown, purrr, ggpattern, ggplot2, cowplot, tidyverse, magick License: GPL (>=3) MD5sum: e329f896ab42170932d71f15e448c921 NeedsCompilation: no Title: Analysis Of Differential Abundance Taking Sample and Scale Variation Into Account Description: A differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a Kruskal-Wallis test (via aldex.kw), a generalized linear model (via aldex.glm), or a correlation test (via aldex.corr). All tests report predicted p-values and posterior Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs. ALDEx2 can now be used to estimate the effect of scale on the results and report on the scale-dependent robustness of results. biocViews: DifferentialExpression, RNASeq, Transcriptomics, GeneExpression, DNASeq, ChIPSeq, Bayesian, Sequencing, Software, Microbiome, Metagenomics, ImmunoOncology, Scale simulation, Posterior p-value Author: Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert, Matt Links, Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon Lieng, Michelle Nixon Maintainer: Greg Gloor URL: https://github.com/ggloor/ALDEx_bioc VignetteBuilder: knitr BugReports: https://github.com/ggloor/ALDEx_bioc/issues git_url: https://git.bioconductor.org/packages/ALDEx2 git_branch: RELEASE_3_22 git_last_commit: 4f92414 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ALDEx2_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ALDEx2_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ALDEx2_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ALDEx2_1.42.0.tgz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html, vignettes/ALDEx2/inst/doc/scaleSim_vignette.html vignetteTitles: ANOVA-Like Differential Expression tool for high throughput sequencing data, Incorporating Scale Uncertainty into ALDEx2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R, vignettes/ALDEx2/inst/doc/scaleSim_vignette.R dependsOnMe: omicplotR importsMe: aIc suggestsMe: dar, ggpicrust2, pctax dependencyCount: 55 Package: alevinQC Version: 1.26.0 Depends: R (>= 4.0) Imports: rmarkdown (>= 2.5), tools, methods, ggplot2 (>= 3.4.0), GGally, dplyr, rjson, shiny, shinydashboard, DT, stats, utils, tximport (>= 1.17.4), cowplot, rlang, Rcpp LinkingTo: Rcpp Suggests: knitr, BiocStyle, testthat (>= 3.0.0), BiocManager License: MIT + file LICENSE Archs: x64 MD5sum: 32c51a6cc456a519e7ed8914b3b9fd23 NeedsCompilation: yes Title: Generate QC Reports For Alevin Output Description: Generate QC reports summarizing the output from an alevin, alevin-fry, or simpleaf run. Reports can be generated as html or pdf files, or as shiny applications. biocViews: QualityControl, SingleCell Author: Charlotte Soneson [aut, cre] (ORCID: ), Avi Srivastava [aut], Rob Patro [aut], Dongze He [aut] Maintainer: Charlotte Soneson URL: https://github.com/csoneson/alevinQC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/csoneson/alevinQC/issues git_url: https://git.bioconductor.org/packages/alevinQC git_branch: RELEASE_3_22 git_last_commit: e9f801e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/alevinQC_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alevinQC_1.25.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alevinQC_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alevinQC_1.26.0.tgz vignettes: vignettes/alevinQC/inst/doc/alevinqc.html vignetteTitles: alevinQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alevinQC/inst/doc/alevinqc.R dependencyCount: 82 Package: AllelicImbalance Version: 1.48.0 Depends: R (>= 4.0.0), grid, GenomicRanges (>= 1.31.8), SummarizedExperiment (>= 0.2.0), GenomicAlignments (>= 1.15.6) Imports: methods, BiocGenerics, AnnotationDbi, BSgenome (>= 1.47.3), VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Rsamtools (>= 1.99.3), GenomicFeatures (>= 1.31.3), Gviz, lattice, latticeExtra, gridExtra, seqinr, GenomeInfoDb, nlme Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: a34f028cd8bb13fea7320f429904cc11 NeedsCompilation: no Title: Investigates Allele Specific Expression Description: Provides a framework for allelic specific expression investigation using RNA-seq data. biocViews: Genetics, Infrastructure, Sequencing Author: Jesper R Gadin, Lasse Folkersen Maintainer: Jesper R Gadin URL: https://github.com/pappewaio/AllelicImbalance VignetteBuilder: knitr BugReports: https://github.com/pappewaio/AllelicImbalance/issues git_url: https://git.bioconductor.org/packages/AllelicImbalance git_branch: RELEASE_3_22 git_last_commit: 556446f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AllelicImbalance_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AllelicImbalance_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AllelicImbalance_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AllelicImbalance_1.48.0.tgz vignettes: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf vignetteTitles: AllelicImbalance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.R dependencyCount: 159 Package: AlphaBeta Version: 1.24.0 Depends: R (>= 3.6.0) Imports: dplyr (>= 0.7), data.table (>= 1.10), stringr (>= 1.3), utils (>= 3.6.0), gtools (>= 3.8.0), optimx (>= 2018-7.10), expm (>= 0.999-4), stats (>= 3.6), BiocParallel (>= 1.18), igraph (>= 1.2.4), graphics (>= 3.6), ggplot2 (>= 3.2), grDevices (>= 3.6), plotly (>= 4.9) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 4eb1ae8b5f12296d08138cf3cb759c88 NeedsCompilation: no Title: Computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants Description: AlphaBeta is a computational method for estimating epimutation rates and spectra from high-throughput DNA methylation data in plants. The method has been specifically designed to: 1. analyze 'germline' epimutations in the context of multi-generational mutation accumulation lines (MA-lines). 2. analyze 'somatic' epimutations in the context of plant development and aging. biocViews: Epigenetics, FunctionalGenomics, Genetics, MathematicalBiology Author: Yadollah Shahryary Dizaji [cre, aut], Frank Johannes [aut], Rashmi Hazarika [aut] Maintainer: Yadollah Shahryary Dizaji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AlphaBeta git_branch: RELEASE_3_22 git_last_commit: 15e89c7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AlphaBeta_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AlphaBeta_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AlphaBeta_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AlphaBeta_1.24.0.tgz vignettes: vignettes/AlphaBeta/inst/doc/AlphaBeta.pdf vignetteTitles: AlphaBeta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlphaBeta/inst/doc/AlphaBeta.R dependencyCount: 89 Package: AlphaMissenseR Version: 1.6.0 Depends: R (>= 4.3.0), dplyr Imports: rjsoncons (>= 1.0.1), DBI, duckdb (>= 1.3.1), rlang, curl, BiocFileCache, spdl, memoise, BiocBaseUtils, utils, stats, tools, methods, whisker, ggplot2 Suggests: BiocManager, BiocGenerics, S4Vectors, Seqinfo, GenomeInfoDb, GenomicRanges, AnnotationHub, ExperimentHub, ensembldb, httr, tidyr, r3dmol, bio3d, shiny, shiny.gosling, ggdist, colorspace, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 9558ced6f0d7a344a6fe23e6e068b7c2 NeedsCompilation: no Title: Accessing AlphaMissense Data Resources in R Description: The AlphaMissense publication outlines how a variant of AlphaFold / DeepMind was used to predict missense variant pathogenicity. Supporting data on Zenodo include, for instance, 71M variants across hg19 and hg38 genome builds. The 'AlphaMissenseR' package allows ready access to the data, downloading individual files to DuckDB databases for exploration and integration into *R* and *Bioconductor* workflows. biocViews: SNP, Annotation, FunctionalGenomics, StructuralPrediction, Transcriptomics, VariantAnnotation, GenePrediction, ImmunoOncology Author: Martin Morgan [aut, cre] (ORCID: ), Tram Nguyen [aut] (ORCID: ), Tyrone Lee [ctb], Nitesh Turaga [ctb], Chan Zuckerberg Initiative DAF CZF2019-002443 [fnd], NIH NCI ITCR U24CA180996 [fnd], NIH NCI IOTN U24CA232979 [fnd], NIH NCI ARTNet U24CA274159 [fnd] Maintainer: Martin Morgan URL: https://mtmorgan.github.io/AlphaMissenseR/ VignetteBuilder: knitr BugReports: https://github.com/mtmorgan/AlphaMissenseR/issues git_url: https://git.bioconductor.org/packages/AlphaMissenseR git_branch: RELEASE_3_22 git_last_commit: 869abf7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AlphaMissenseR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AlphaMissenseR_1.5.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AlphaMissenseR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AlphaMissenseR_1.6.0.tgz vignettes: vignettes/AlphaMissenseR/inst/doc/alphafold.html, vignettes/AlphaMissenseR/inst/doc/benchmarking.html, vignettes/AlphaMissenseR/inst/doc/clinvar.html, vignettes/AlphaMissenseR/inst/doc/introduction.html, vignettes/AlphaMissenseR/inst/doc/issues.html vignetteTitles: B. AlphaFold Integration, D. Benchmarking with ProteinGym, C. ClinVar Integration, A. Introduction, E. Issues & Solutions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AlphaMissenseR/inst/doc/alphafold.R, vignettes/AlphaMissenseR/inst/doc/benchmarking.R, vignettes/AlphaMissenseR/inst/doc/clinvar.R, vignettes/AlphaMissenseR/inst/doc/introduction.R, vignettes/AlphaMissenseR/inst/doc/issues.R dependencyCount: 61 Package: AlpsNMR Version: 4.12.0 Depends: R (>= 4.2) Imports: utils, generics, graphics, stats, grDevices, cli, magrittr (>= 1.5), dplyr (>= 1.1.0), signal (>= 0.7-6), rlang (>= 0.3.0.1), scales (>= 1.2.0), stringr (>= 1.3.1), tibble(>= 1.3.4), tidyr (>= 1.0.0), tidyselect, readxl (>= 1.1.0), purrr (>= 0.2.5), glue (>= 1.2.0), reshape2 (>= 1.4.3), mixOmics (>= 6.22.0), matrixStats (>= 0.54.0), fs (>= 1.2.6), rmarkdown (>= 1.10), speaq (>= 2.4.0), htmltools (>= 0.3.6), pcaPP (>= 1.9-73), ggplot2 (>= 3.1.0), baseline (>= 1.2-1), vctrs (>= 0.3.0), BiocParallel (>= 1.34.0) Suggests: ASICS, BiocStyle, ChemoSpec, cowplot, curl, DT (>= 0.5), GGally (>= 1.4.0), ggrepel (>= 0.8.0), gridExtra, knitr, NMRphasing, plotly (>= 4.7.1), progressr, SummarizedExperiment, S4Vectors, testthat (>= 2.0.0), writexl (>= 1.0), zip (>= 2.0.4) License: MIT + file LICENSE MD5sum: f175650b7f431614239dfe2d39114d9e NeedsCompilation: no Title: Automated spectraL Processing System for NMR Description: Reads Bruker NMR data directories both zipped and unzipped. It provides automated and efficient signal processing for untargeted NMR metabolomics. It is able to interpolate the samples, detect outliers, exclude regions, normalize, detect peaks, align the spectra, integrate peaks, manage metadata and visualize the spectra. After spectra proccessing, it can apply multivariate analysis on extracted data. Efficient plotting with 1-D data is also available. Basic reading of 1D ACD/Labs exported JDX samples is also available. biocViews: Software, Preprocessing, Visualization, Classification, Cheminformatics, Metabolomics, DataImport Author: Ivan Montoliu Roura [aut], Sergio Oller Moreno [aut, cre] (ORCID: ), Francisco Madrid Gambin [aut] (ORCID: ), Luis Fernandez [aut] (ORCID: ), Laura López Sánchez [ctb], Héctor Gracia Cabrera [aut], Santiago Marco Colás [aut] (ORCID: ), Nestlé Institute of Health Sciences [cph], Institute for Bioengineering of Catalonia [cph], Miller Jack [ctb] (ORCID: , Autophase wrapper, ASICS export) Maintainer: Sergio Oller Moreno URL: https://sipss.github.io/AlpsNMR/, https://github.com/sipss/AlpsNMR VignetteBuilder: knitr BugReports: https://github.com/sipss/AlpsNMR/issues git_url: https://git.bioconductor.org/packages/AlpsNMR git_branch: RELEASE_3_22 git_last_commit: a1d41e5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AlpsNMR_4.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AlpsNMR_4.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AlpsNMR_4.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AlpsNMR_4.12.0.tgz vignettes: vignettes/AlpsNMR/inst/doc/Vig01-introduction-to-alpsnmr.pdf, vignettes/AlpsNMR/inst/doc/Vig01b-introduction-to-alpsnmr-old-api.pdf, vignettes/AlpsNMR/inst/doc/Vig02-handling-metadata-and-annotations.pdf vignetteTitles: Vignette 01: Introduction to AlpsNMR (start here), Older Introduction to AlpsNMR (soft-deprecated API), Vignette 02: Handling metadata and annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlpsNMR/inst/doc/Vig01-introduction-to-alpsnmr.R, vignettes/AlpsNMR/inst/doc/Vig01b-introduction-to-alpsnmr-old-api.R, vignettes/AlpsNMR/inst/doc/Vig02-handling-metadata-and-annotations.R dependencyCount: 126 Package: altcdfenvs Version: 2.72.0 Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.25), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings, hypergraph Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db, hgu133acdf, Rgraphviz, RColorBrewer License: GPL (>= 2) MD5sum: 469d22fe54bb97f58ab9447fb8bc622c NeedsCompilation: no Title: alternative CDF environments (aka probeset mappings) Description: Convenience data structures and functions to handle cdfenvs biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Annotation, ProprietaryPlatforms, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/altcdfenvs git_branch: RELEASE_3_22 git_last_commit: 0c41761 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/altcdfenvs_2.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/altcdfenvs_2.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/altcdfenvs_2.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/altcdfenvs_2.72.0.tgz vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf, vignettes/altcdfenvs/inst/doc/modify.pdf, vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf vignetteTitles: altcdfenvs, Modifying existing CDF environments to make alternative CDF environments, Alternative CDF environments for 2(or more)-genomes chips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/altcdfenvs/inst/doc/altcdfenvs.R, vignettes/altcdfenvs/inst/doc/modify.R, vignettes/altcdfenvs/inst/doc/ngenomeschips.R dependencyCount: 23 Package: AMARETTO Version: 1.26.0 Depends: R (>= 3.6), impute, doParallel, grDevices, dplyr, methods, ComplexHeatmap Imports: callr (>= 3.0.0.9001), Matrix, Rcpp, BiocFileCache, DT, MultiAssayExperiment, circlize, curatedTCGAData, foreach, glmnet, httr, limma, matrixStats, readr, reshape2, tibble, rmarkdown, graphics, grid, parallel, stats, knitr, ggplot2, gridExtra, utils LinkingTo: Rcpp Suggests: testthat, MASS, knitr, BiocStyle License: Apache License (== 2.0) + file LICENSE MD5sum: e7fee204dcb64f903d3b9da70383f0c3 NeedsCompilation: no Title: Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression Description: Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways. biocViews: StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,Transcription,Preprocessing,BatchEffect,DataImport,mRNAMicroarray,MicroRNAArray,Regression,Clustering,RNASeq,CopyNumberVariation,Sequencing,Microarray,Normalization,Network,Bayesian,ExonArray,OneChannel,TwoChannel,ProprietaryPlatforms,AlternativeSplicing,DifferentialExpression,DifferentialSplicing,GeneSetEnrichment,MultipleComparison,QualityControl,TimeCourse Author: Jayendra Shinde, Celine Everaert, Shaimaa Bakr, Mohsen Nabian, Jishu Xu, Vincent Carey, Nathalie Pochet and Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMARETTO git_branch: RELEASE_3_22 git_last_commit: aa11071 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AMARETTO_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AMARETTO_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AMARETTO_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AMARETTO_1.26.0.tgz vignettes: vignettes/AMARETTO/inst/doc/amaretto.html vignetteTitles: "1. Introduction" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AMARETTO/inst/doc/amaretto.R dependencyCount: 148 Package: AMOUNTAIN Version: 1.36.0 Depends: R (>= 3.3.0) Imports: stats Suggests: BiocStyle, qgraph, knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: b6d4fd0808f6cbaae5940cd859536898 NeedsCompilation: yes Title: Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach Description: A pure data-driven gene network, weighted gene co-expression network (WGCN) could be constructed only from expression profile. Different layers in such networks may represent different time points, multiple conditions or various species. AMOUNTAIN aims to search active modules in multi-layer WGCN using a continuous optimization approach. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, Shan He, Zhisong Pan and Guyu Hu Maintainer: Dong Li SystemRequirements: gsl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMOUNTAIN git_branch: RELEASE_3_22 git_last_commit: 5e47cdb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AMOUNTAIN_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AMOUNTAIN_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AMOUNTAIN_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AMOUNTAIN_1.36.0.tgz vignettes: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.R importsMe: MODA dependencyCount: 1 Package: anansi Version: 1.0.0 Depends: R (>= 4.5.0) Imports: S7, stats, methods, igraph, Matrix, forcats, S4Vectors, SummarizedExperiment, MultiAssayExperiment, SingleCellExperiment, TreeSummarizedExperiment, rlang, ggplot2, ggforce, patchwork, ggraph, tidygraph Suggests: BiocStyle, dplyr, tidyr, graph, mia, KEGGREST, testthat (>= 3.0.0), knitr, rmarkdown License: GPL-3 MD5sum: 946813a8d5e7c2f07be052d94fc3ffa2 NeedsCompilation: no Title: Annotation-Based Analysis of Specific Interactions Description: Studies including both microbiome and metabolomics data are becoming more common. Often, it would be helpful to integrate both datasets in order to see if they corroborate each others patterns. All vs all association is imprecise and likely to yield spurious associations. This package takes a knowledge-based approach to constrain association search space, only considering metabolite-function pairs that have been recorded in a pathway database. This package also provides a framework to assess differential association. biocViews: Microbiome, Metabolomics, Regression, Pathways, KEGG Author: Thomaz Bastiaanssen [aut, cre] (ORCID: ), Thomas Quinn [aut] (ORCID: ), Giulio Benedetti [aut] (ORCID: ), Tuomas Borman [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ) Maintainer: Thomaz Bastiaanssen URL: https://github.com/thomazbastiaanssen/anansi, https://thomazbastiaanssen.github.io/anansi VignetteBuilder: knitr BugReports: https://github.com/thomazbastiaanssen/anansi/issues git_url: https://git.bioconductor.org/packages/anansi git_branch: RELEASE_3_22 git_last_commit: 78b4ccb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/anansi_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/anansi_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/anansi_1.0.0.tgz vignettes: vignettes/anansi/inst/doc/adjacency_matrices.html, vignettes/anansi/inst/doc/anansi.html, vignettes/anansi/inst/doc/differential_associations.html vignetteTitles: 2. Working with (bi)adjacency matrices, 1. Getting started with anansi, 3. Differential associations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anansi/inst/doc/adjacency_matrices.R, vignettes/anansi/inst/doc/anansi.R, vignettes/anansi/inst/doc/differential_associations.R dependencyCount: 98 Package: Anaquin Version: 2.34.0 Depends: R (>= 3.3), ggplot2 (>= 2.2.0) Imports: ggplot2, ROCR, knitr, qvalue, locfit, methods, stats, utils, plyr, DESeq2 Suggests: RUnit, rmarkdown License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 7ac72d0b0b355869daa0b7dddf37c67d NeedsCompilation: no Title: Statistical analysis of sequins Description: The project is intended to support the use of sequins (synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard open source library for quantitative analysis, modelling and visualization of spike-in controls. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, GeneExpression, Software Author: Ted Wong Maintainer: Ted Wong URL: www.sequin.xyz VignetteBuilder: knitr BugReports: https://github.com/student-t/RAnaquin/issues git_url: https://git.bioconductor.org/packages/Anaquin git_branch: RELEASE_3_22 git_last_commit: d616308 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Anaquin_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Anaquin_2.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Anaquin_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Anaquin_2.34.0.tgz vignettes: vignettes/Anaquin/inst/doc/Anaquin.pdf vignetteTitles: Anaquin - Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Anaquin/inst/doc/Anaquin.R dependencyCount: 73 Package: ANCOMBC Version: 2.12.0 Depends: R (>= 4.5.0) Imports: stats, CVXR, DescTools, Hmisc, MASS, Matrix, Rdpack, doParallel, doRNG, energy, foreach, gtools, lme4, lmerTest, multcomp, nloptr, parallel, utils Suggests: mia (>= 1.6.0), DT, S4Vectors, SingleCellExperiment, SummarizedExperiment, TreeSummarizedExperiment, dplyr, knitr, magrittr, microbiome, phyloseq, rmarkdown, testthat, tidyr, tidyverse License: Artistic-2.0 MD5sum: 510fcf30fb4105da098313d20bc57266 NeedsCompilation: no Title: Microbiome differential abudance and correlation analyses with bias correction Description: ANCOMBC is a package containing differential abundance (DA) and correlation analyses for microbiome data. Specifically, the package includes Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2), Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), and Analysis of Composition of Microbiomes (ANCOM) for DA analysis, and Sparse Estimation of Correlations among Microbiomes (SECOM) for correlation analysis. Microbiome data are typically subject to two sources of biases: unequal sampling fractions (sample-specific biases) and differential sequencing efficiencies (taxon-specific biases). Methodologies included in the ANCOMBC package are designed to correct these biases and construct statistically consistent estimators. biocViews: DifferentialExpression, Microbiome, Normalization, Sequencing, Software Author: Huang Lin [cre, aut] (ORCID: ) Maintainer: Huang Lin URL: https://github.com/FrederickHuangLin/ANCOMBC VignetteBuilder: knitr BugReports: https://github.com/FrederickHuangLin/ANCOMBC/issues git_url: https://git.bioconductor.org/packages/ANCOMBC git_branch: RELEASE_3_22 git_last_commit: 5ceba73 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ANCOMBC_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ANCOMBC_2.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ANCOMBC_2.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ANCOMBC_2.12.0.tgz vignettes: vignettes/ANCOMBC/inst/doc/ANCOM.html, vignettes/ANCOMBC/inst/doc/ANCOMBC.html, vignettes/ANCOMBC/inst/doc/ANCOMBC2.html, vignettes/ANCOMBC/inst/doc/data_sanity_check.html, vignettes/ANCOMBC/inst/doc/SECOM.html vignetteTitles: ANCOM Tutorial, ANCOM-BC Tutorial, ANCOM-BC2 Tutorial, Tutorial on Data Sanity and Integrity Checks, SECOM Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANCOMBC/inst/doc/ANCOM.R, vignettes/ANCOMBC/inst/doc/ANCOMBC.R, vignettes/ANCOMBC/inst/doc/ANCOMBC2.R, vignettes/ANCOMBC/inst/doc/data_sanity_check.R, vignettes/ANCOMBC/inst/doc/SECOM.R suggestsMe: dar, MiscMetabar dependencyCount: 135 Package: ANF Version: 1.32.0 Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 81c235b381e5909bc722880e039a30f8 NeedsCompilation: no Title: Affinity Network Fusion for Complex Patient Clustering Description: This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion. biocViews: Clustering, GraphAndNetwork, Network Author: Tianle Ma, Aidong Zhang Maintainer: Tianle Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ANF git_branch: RELEASE_3_22 git_last_commit: 58724a9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ANF_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ANF_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ANF_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ANF_1.32.0.tgz vignettes: vignettes/ANF/inst/doc/ANF.html vignetteTitles: Cancer Patient Clustering with ANF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANF/inst/doc/ANF.R suggestsMe: HarmonizedTCGAData dependencyCount: 24 Package: anglemania Version: 1.0.0 Depends: R (>= 4.5.0) Imports: bigparallelr, bigstatsr, checkmate, digest, dplyr, Matrix, pbapply, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tidyr, withr LinkingTo: Rcpp, rmio, bigstatsr Suggests: batchelor, BiocStyle, bluster, knitr, magick, matrixStats, patchwork, RcppArmadillo, rmarkdown, scater, scran, Seurat, splatter, testthat (>= 3.0.0), UpSetR License: GPL (>= 3) MD5sum: 9d2f6286b744063e9a9305854c054df3 NeedsCompilation: yes Title: Feature Extraction for scRNA-seq Dataset Integration Description: anglemania extracts genes from multi-batch scRNA-seq experiments for downstream dataset integration. It shows improvement over the conventional usage of highly-variable genes for many integration tasks. We leverage gene-gene correlations that are stable across batches to identify biologically informative genes which are less affected by batch effects. Currently, its main use is for single-cell RNA-seq dataset integration, but it can be applied for other multi-batch downstream analyses such as NMF. biocViews: SingleCell, BatchEffect, MultipleComparison, FeatureExtraction Author: Aaron Kollotzek [aut, cre] (ORCID: ), Vedran Franke [aut] (ORCID: ), Artem Baranovskii [aut], Altuna Akalin [aut], SFB1588 [fnd] (Funded by the DFG – Deutsche Forschungsgemeinschaft) Maintainer: Aaron Kollotzek URL: https://github.com/BIMSBbioinfo/anglemania/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/anglemania/issues git_url: https://git.bioconductor.org/packages/anglemania git_branch: RELEASE_3_22 git_last_commit: 3acc9da git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/anglemania_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/anglemania_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/anglemania_1.0.0.tgz vignettes: vignettes/anglemania/inst/doc/anglemania_tutorial.html vignetteTitles: anglemania tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anglemania/inst/doc/anglemania_tutorial.R dependencyCount: 78 Package: animalcules Version: 1.26.0 Depends: R (>= 4.3.0) Imports: ape, assertthat, caret, covr, DESeq2, dplyr, DT, forcats, ggforce, ggplot2, GUniFrac, lattice, limma, magrittr, Matrix, methods, MultiAssayExperiment, plotly, rentrez, reshape2, ROCit, S4Vectors (>= 0.23.19), scales, shiny, shinyjs, stats, SummarizedExperiment, tibble, tidyr, tsne, umap, utils, vegan, XML Suggests: BiocStyle, biomformat, devtools, glmnet, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: f18353261bbb700aa28d68f619408d17 NeedsCompilation: no Title: Interactive microbiome analysis toolkit Description: animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights. biocViews: Microbiome, Metagenomics, Coverage, Visualization Author: Jessica McClintock [cre], Yue Zhao [aut] (ORCID: ), Anthony Federico [aut] (ORCID: ), W. Evan Johnson [aut] (ORCID: ) Maintainer: Jessica McClintock URL: https://github.com/wejlab/animalcules VignetteBuilder: knitr BugReports: https://github.com/wejlab/animalcules/issues git_url: https://git.bioconductor.org/packages/animalcules git_branch: RELEASE_3_22 git_last_commit: 353d97a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/animalcules_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/animalcules_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/animalcules_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/animalcules_1.26.0.tgz vignettes: vignettes/animalcules/inst/doc/animalcules.html vignetteTitles: animalcules hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/animalcules/inst/doc/animalcules.R importsMe: LegATo suggestsMe: MetaScope dependencyCount: 190 Package: annaffy Version: 1.82.0 Depends: R (>= 2.5.0), methods, Biobase, BiocManager, GO.db Imports: AnnotationDbi (>= 0.1.15), DBI Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: 053d4bf1d8d7514224a1e6e5ce8b44d9 NeedsCompilation: no Title: Annotation tools for Affymetrix biological metadata Description: Functions for handling data from Bioconductor Affymetrix annotation data packages. Produces compact HTML and text reports including experimental data and URL links to many online databases. Allows searching biological metadata using various criteria. biocViews: OneChannel, Microarray, Annotation, GO, Pathways, ReportWriting Author: Colin A. Smith Maintainer: Colin A. Smith git_url: https://git.bioconductor.org/packages/annaffy git_branch: RELEASE_3_22 git_last_commit: 897daa0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/annaffy_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/annaffy_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/annaffy_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/annaffy_1.82.0.tgz vignettes: vignettes/annaffy/inst/doc/annaffy.pdf vignetteTitles: annaffy Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annaffy/inst/doc/annaffy.R dependsOnMe: webbioc importsMe: a4Base suggestsMe: metaMA dependencyCount: 45 Package: anndataR Version: 1.0.0 Depends: R (>= 4.5.0) Imports: cli, lifecycle, Matrix, methods, purrr, R6 (>= 2.4.0), reticulate (>= 1.41.1), rlang, stats Suggests: BiocFileCache, BiocStyle, knitr, processx, rhdf5 (>= 2.52.1), rmarkdown, S4Vectors, Seurat, SeuratObject, SingleCellExperiment, spelling, SummarizedExperiment, testthat (>= 3.0.0), vctrs, withr, yaml License: MIT + file LICENSE MD5sum: 5db508c164cc0f5208ea9600fca3c8a2 NeedsCompilation: no Title: AnnData interoperability in R Description: Bring the power and flexibility of AnnData to the R ecosystem, allowing you to effortlessly manipulate and analyse your single-cell data. This package lets you work with backed h5ad and zarr files, directly access various slots (e.g. X, obs, var), or convert the data into SingleCellExperiment and Seurat objects. biocViews: SingleCell, DataImport, DataRepresentation Author: Robrecht Cannoodt [aut, cre] (ORCID: , github: rcannood), Luke Zappia [aut] (ORCID: , github: lazappi), Martin Morgan [aut] (ORCID: , github: mtmorgan), Louise Deconinck [aut] (ORCID: , github: LouiseDck), Danila Bredikhin [ctb] (ORCID: , github: gtca), Isaac Virshup [ctb] (ORCID: , github: ivirshup), Brian Schilder [ctb] (ORCID: , github: bschilder), Chananchida Sang-aram [ctb] (ORCID: , github: csangara), Data Intuitive [fnd, cph], Chan Zuckerberg Initiative [fnd], scverse consortium [spn] Maintainer: Robrecht Cannoodt URL: https://anndatar.data-intuitive.com, https://github.com/scverse/anndataR VignetteBuilder: knitr BugReports: https://github.com/scverse/anndataR/issues git_url: https://git.bioconductor.org/packages/anndataR git_branch: RELEASE_3_22 git_last_commit: 243ac84 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/anndataR_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/anndataR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/anndataR_1.0.0.tgz vignettes: vignettes/anndataR/inst/doc/anndataR.html, vignettes/anndataR/inst/doc/usage_python.html, vignettes/anndataR/inst/doc/usage_seurat.html, vignettes/anndataR/inst/doc/usage_singlecellexperiment.html vignetteTitles: Using anndataR to read and convert, Python integration with anndataR, Read/write Seurat objects using anndataR, Read/write SingleCellExperiment objects using anndataR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/anndataR/inst/doc/anndataR.R, vignettes/anndataR/inst/doc/usage_python.R, vignettes/anndataR/inst/doc/usage_seurat.R, vignettes/anndataR/inst/doc/usage_singlecellexperiment.R dependencyCount: 25 Package: annmap Version: 1.52.0 Depends: R (>= 2.15.0), methods, GenomicRanges Imports: DBI, RMySQL (>= 0.6-0), digest, Biobase, grid, lattice, Rsamtools, genefilter, IRanges, BiocGenerics Suggests: RUnit, rjson, Gviz License: GPL-2 MD5sum: ad37f93bc1eca669e0ab2e28fb5c6306 NeedsCompilation: no Title: Genome annotation and visualisation package pertaining to Affymetrix arrays and NGS analysis. Description: annmap provides annotation mappings for Affymetrix exon arrays and coordinate based queries to support deep sequencing data analysis. Database access is hidden behind the API which provides a set of functions such as genesInRange(), geneToExon(), exonDetails(), etc. Functions to plot gene architecture and BAM file data are also provided. Underlying data are from Ensembl. The annmap database can be downloaded from: https://figshare.manchester.ac.uk/account/articles/16685071 biocViews: Annotation, Microarray, OneChannel, ReportWriting, Transcription, Visualization Author: Tim Yates Maintainer: Chris Wirth URL: https://github.com/cruk-mi/annmap git_url: https://git.bioconductor.org/packages/annmap git_branch: RELEASE_3_22 git_last_commit: 2222171 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/annmap_1.52.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/annmap_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/annmap_1.52.0.tgz vignettes: vignettes/annmap/inst/doc/annmap.pdf, vignettes/annmap/inst/doc/cookbook.pdf, vignettes/annmap/inst/doc/INSTALL.pdf vignetteTitles: annmap primer, The Annmap Cookbook, annmap installation instruction hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 69 Package: annotate Version: 1.88.0 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8), httr Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges, rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db, org.Mm.eg.db, humanCHRLOC, Rgraphviz, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 500c1e93ab69e40ae1747f06007c1c77 NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/annotate git_branch: RELEASE_3_22 git_last_commit: bc0d5a0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/annotate_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/annotate_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/annotate_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/annotate_1.88.0.tgz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf, vignettes/annotate/inst/doc/chromLOC.html, vignettes/annotate/inst/doc/useDataPkgs.html vignetteTitles: Annotation Overview, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Affymetrix Probe Level Data, HowTo: Build 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canine.db, canine2.db, celegans.db, chicken.db, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, drosgenome1.db, drosophila2.db, ecoli2.db, GGHumanMethCancerPanelv1.db, h10kcod.db, h20kcod.db, hcg110.db, hgfocus.db, hgu133a.db, hgu133a2.db, hgu133b.db, hgu133plus2.db, hgu219.db, hgu95a.db, hgu95av2.db, hgu95b.db, hgu95c.db, hgu95d.db, hgu95e.db, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133b.db, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, htmg430a.db, htmg430b.db, htmg430pm.db, htrat230pm.db, htratfocus.db, hu35ksuba.db, hu35ksubb.db, hu35ksubc.db, hu35ksubd.db, hu6800.db, huex10stprobeset.db, huex10sttranscriptcluster.db, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiMouseAll.db, lumiRatAll.db, m10kcod.db, m20kcod.db, mgu74a.db, mgu74av2.db, mgu74b.db, mgu74bv2.db, mgu74c.db, mgu74cv2.db, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, miRBaseVersions.db, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430b.db, moex10stprobeset.db, moex10sttranscriptcluster.db, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse4302.db, mouse430a2.db, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubb.db, Mu15v1.db, mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db, mwgcod.db, Norway981.db, nugohs1a520180.db, nugomm1a520177.db, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hbacteriophora.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, POCRCannotation.db, porcine.db, r10kcod.db, rae230a.db, rae230b.db, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rgu34a.db, rgu34b.db, rgu34c.db, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, ri16cod.db, RnAgilentDesign028282.db, rnu34.db, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rwgcod.db, SHDZ.db, SomaScan.db, u133x3p.db, xlaevis.db, yeast2.db, ygs98.db, zebrafish.db, clValid, limorhyde, maGUI dependencyCount: 45 Package: AnnotationDbi Version: 1.72.0 Depends: R (>= 2.7.0), methods, stats4, BiocGenerics (>= 0.29.2), Biobase (>= 1.17.0), IRanges Imports: DBI, RSQLite, S4Vectors (>= 0.9.25), stats, KEGGREST Suggests: utils, hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr License: Artistic-2.0 MD5sum: dab5c3c6d38999d7f3d0c16dd93d1780 NeedsCompilation: no Title: Manipulation of SQLite-based annotations in Bioconductor Description: Implements a user-friendly interface for querying SQLite-based annotation data packages. biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation Author: Hervé Pagès, Marc Carlson, Seth Falcon, Nianhua Li Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationDbi VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik BugReports: https://github.com/Bioconductor/AnnotationDbi/issues git_url: https://git.bioconductor.org/packages/AnnotationDbi git_branch: RELEASE_3_22 git_last_commit: ffdaf5d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnnotationDbi_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnnotationDbi_1.71.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationDbi_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnnotationDbi_1.72.0.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: 2. (Deprecated) How to use bimaps from the ".db" annotation packages, 1. Introduction To Bioconductor Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.R, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.R dependsOnMe: annotate, AnnotationForge, ASpli, attract, Category, ChromHeatMap, DEXSeq, EGSEA, EpiTxDb, GenomicFeatures, goProfiles, GSReg, ipdDb, miRNAtap, OrganismDbi, pathRender, proBAMr, safe, SemDist, topGO, adme16cod.db, ag.db, agprobe, anopheles.db0, arabidopsis.db0, ath1121501.db, ath1121501probe, barley1probe, bovine.db, bovine.db0, bovineprobe, bsubtilisprobe, canine.db, canine.db0, canine2.db, canine2probe, canineprobe, celegans.db, celegansprobe, chicken.db, chicken.db0, chickenprobe, chimp.db0, citrusprobe, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottonprobe, DO.db, drosgenome1.db, drosgenome1probe, drosophila2.db, drosophila2probe, ecoli2.db, ecoli2probe, ecoliasv2probe, ecoliK12.db0, ecoliprobe, ecoliSakai.db0, fly.db0, GGHumanMethCancerPanelv1.db, GO.db, h10kcod.db, h20kcod.db, hcg110.db, hcg110probe, hgfocus.db, hgfocusprobe, hgu133a.db, hgu133a2.db, hgu133a2probe, hgu133aprobe, hgu133atagprobe, hgu133b.db, hgu133bprobe, hgu133plus2.db, hgu133plus2probe, hgu219.db, hgu219probe, hgu95a.db, hgu95aprobe, hgu95av2.db, hgu95av2probe, hgu95b.db, hgu95bprobe, hgu95c.db, hgu95cprobe, hgu95d.db, hgu95dprobe, hgu95e.db, hgu95eprobe, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, Homo.sapiens, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133aprobe, hthgu133b.db, hthgu133bprobe, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmprobe, htmg430a.db, htmg430aprobe, htmg430b.db, htmg430bprobe, htmg430pm.db, htmg430pmprobe, htrat230pm.db, htrat230pmprobe, htratfocus.db, htratfocusprobe, hu35ksuba.db, hu35ksubaprobe, hu35ksubb.db, hu35ksubbprobe, hu35ksubc.db, hu35ksubcprobe, hu35ksubd.db, hu35ksubdprobe, hu6800.db, hu6800probe, huex10stprobeset.db, huex10sttranscriptcluster.db, HuExExonProbesetLocation, HuExExonProbesetLocationHg18, HuExExonProbesetLocationHg19, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1probe, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, human.db0, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, IlluminaHumanMethylation450kprobe, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizeprobe, malaria.db0, medicagoprobe, mgu74a.db, mgu74aprobe, mgu74av2.db, mgu74av2probe, mgu74b.db, mgu74bprobe, mgu74bv2.db, mgu74bv2probe, mgu74c.db, mgu74cprobe, mgu74cv2.db, mgu74cv2probe, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirna10probe, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430aprobe, moe430b.db, moe430bprobe, moex10stprobeset.db, moex10sttranscriptcluster.db, MoExExonProbesetLocation, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1probe, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse.db0, mouse4302.db, mouse4302probe, mouse430a2.db, mouse430a2probe, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubaprobe, mu11ksubb.db, mu11ksubbprobe, Mu15v1.db, mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180probe, nugomm1a520177.db, nugomm1a520177probe, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hbacteriophora.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Mxanthus.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db, paeg1aprobe, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, PFAM.db, pig.db0, plasmodiumanophelesprobe, POCRCannotation.db, poplarprobe, porcine.db, porcineprobe, primeviewprobe, r10kcod.db, rae230a.db, rae230aprobe, rae230b.db, rae230bprobe, raex10stprobeset.db, raex10sttranscriptcluster.db, RaExExonProbesetLocation, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1probe, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat.db0, rat2302.db, rat2302probe, rattoxfxprobe, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34aprobe, rgu34b.db, rgu34bprobe, rgu34c.db, rgu34cprobe, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesus.db0, rhesusprobe, ri16cod.db, riceprobe, RnAgilentDesign028282.db, rnu34.db, rnu34probe, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34probe, rwgcod.db, saureusprobe, SHDZ.db, SomaScan.db, soybeanprobe, sugarcaneprobe, test3probe, tomatoprobe, u133x3p.db, u133x3pprobe, vitisviniferaprobe, wheatprobe, worm.db0, xenopus.db0, xenopuslaevisprobe, xlaevis.db, xlaevis2probe, xtropicalisprobe, yeast.db0, yeast2.db, yeast2probe, ygs98.db, ygs98probe, zebrafish.db, zebrafish.db0, zebrafishprobe, tinesath1probe, rnaseqGene, convertid importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, AnnotationHub, AnnotationHubData, annotatr, artMS, BgeeCall, bioCancer, BiocSet, biomaRt, BioNAR, BioNet, biovizBase, bumphunter, BUSpaRse, categoryCompare, cellity, chimeraviz, chipenrich, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, Cogito, compEpiTools, consensusDE, crisprDesign, CrispRVariants, cTRAP, Damsel, debrowser, derfinder, DominoEffect, DOSE, doubletrouble, EasyCellType, EDASeq, EnrichmentBrowser, ensembldb, EpiMix, epimutacions, esATAC, FRASER, funOmics, GA4GHshiny, gage, gDNAx, genefilter, geneplotter, geneXtendeR, GenomicInteractionNodes, ggbio, GlobalAncova, globaltest, GmicR, GOfuncR, GOpro, GOSemSim, goseq, goSTAG, GOstats, goTools, graphite, GSEABase, GSEABenchmarkeR, Gviz, gwascat, ideal, interactiveDisplay, IVAS, karyoploteR, keggorthology, linkSet, LRBaseDbi, lumi, magpie, mastR, MCbiclust, MeSHDbi, meshes, MesKit, MetaboSignal, methylGSA, methylumi, MineICA, MiRaGE, mirIntegrator, MIRit, miRNAmeConverter, missMethyl, mitology, MLP, MOSClip, mosdef, MSnID, multiGSEA, multiMiR, NanoMethViz, NetSAM, ORFik, Organism.dplyr, OutSplice, PADOG, pathview, pcaExplorer, phantasus, phenoTest, proActiv, psichomics, qpgraph, QuasR, RAIDS, ReactomePA, REDseq, regutools, RFLOMICS, rGREAT, rgsepd, ribosomeProfilingQC, RNAAgeCalc, rrvgo, rTRM, SBGNview, scanMiRApp, scPipe, scruff, scTensor, SGSeq, signatureSearch, signifinder, simplifyEnrichment, SMITE, SPICEY, SubCellBarCode, SVMDO, TCGAutils, tenXplore, TFutils, tigre, trackViewer, TRESS, tricycle, txcutr, txdbmaker, tximeta, Ularcirc, UniProt.ws, VariantAnnotation, VariantFiltering, ViSEAGO, adme16cod.db, ag.db, agcdf, ath1121501.db, ath1121501cdf, barley1cdf, bovine.db, bovinecdf, bsubtiliscdf, canine.db, canine2.db, canine2cdf, caninecdf, celegans.db, celeganscdf, chicken.db, chickencdf, citruscdf, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottoncdf, cyp450cdf, DO.db, drosgenome1.db, drosgenome1cdf, drosophila2.db, drosophila2cdf, ecoli2.db, ecoli2cdf, ecoliasv2cdf, ecolicdf, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState, GGHumanMethCancerPanelv1.db, gp53cdf, h10kcod.db, h20kcod.db, hcg110.db, hcg110cdf, HDO.db, hgfocus.db, hgfocuscdf, hgu133a.db, hgu133a2.db, hgu133a2cdf, hgu133acdf, hgu133atagcdf, hgu133b.db, hgu133bcdf, hgu133plus2.db, hgu133plus2cdf, hgu219.db, hgu219cdf, hgu95a.db, hgu95acdf, hgu95av2.db, hgu95av2cdf, hgu95b.db, hgu95bcdf, hgu95c.db, hgu95ccdf, hgu95d.db, hgu95dcdf, hgu95e.db, hgu95ecdf, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hivprtplus2cdf, Homo.sapiens, HPO.db, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, Hspec, hspeccdf, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133acdf, hthgu133b.db, hthgu133bcdf, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmcdf, htmg430a.db, htmg430acdf, htmg430b.db, htmg430bcdf, htmg430pm.db, htmg430pmcdf, htrat230pm.db, htrat230pmcdf, htratfocus.db, htratfocuscdf, hu35ksuba.db, hu35ksubacdf, hu35ksubb.db, hu35ksubbcdf, hu35ksubc.db, hu35ksubccdf, hu35ksubd.db, hu35ksubdcdf, hu6800.db, hu6800cdf, hu6800subacdf, hu6800subbcdf, hu6800subccdf, hu6800subdcdf, huex10stprobeset.db, huex10sttranscriptcluster.db, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1cdf, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizecdf, medicagocdf, mgu74a.db, mgu74acdf, mgu74av2.db, mgu74av2cdf, mgu74b.db, mgu74bcdf, mgu74bv2.db, mgu74bv2cdf, mgu74c.db, mgu74ccdf, mgu74cv2.db, mgu74cv2cdf, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, miRBaseVersions.db, mirna102xgaincdf, mirna10cdf, mirna20cdf, miRNAtap.db, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430acdf, moe430b.db, moe430bcdf, moex10stprobeset.db, moex10sttranscriptcluster.db, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1cdf, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse4302.db, mouse4302cdf, mouse430a2.db, mouse430a2cdf, mpedbarray.db, MPO.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubacdf, mu11ksubb.db, mu11ksubbcdf, Mu15v1.db, mu19ksuba.db, mu19ksubacdf, mu19ksubb.db, mu19ksubbcdf, mu19ksubc.db, mu19ksubccdf, Mu22v3.db, mu6500subacdf, mu6500subbcdf, mu6500subccdf, mu6500subdcdf, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180cdf, nugomm1a520177.db, nugomm1a520177cdf, OperonHumanV3.db, paeg1acdf, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, plasmodiumanophelescdf, POCRCannotation.db, PolyPhen.Hsapiens.dbSNP131, poplarcdf, porcine.db, porcinecdf, primeviewcdf, r10kcod.db, rae230a.db, rae230acdf, rae230b.db, rae230bcdf, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1cdf, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rat2302cdf, rattoxfxcdf, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34acdf, rgu34b.db, rgu34bcdf, rgu34c.db, rgu34ccdf, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesuscdf, ri16cod.db, ricecdf, RmiR.Hs.miRNA, RmiR.hsa, RnAgilentDesign028282.db, rnu34.db, rnu34cdf, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34cdf, rwgcod.db, saureuscdf, SHDZ.db, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, soybeancdf, sugarcanecdf, test1cdf, test2cdf, test3cdf, tomatocdf, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Cfamiliaris.UCSC.canFam4.refGene, TxDb.Cfamiliaris.UCSC.canFam5.refGene, TxDb.Cfamiliaris.UCSC.canFam6.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg38.refGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, u133aaofav2cdf, u133x3p.db, u133x3pcdf, vitisviniferacdf, wheatcdf, xenopuslaeviscdf, xlaevis.db, xlaevis2cdf, xtropicaliscdf, ye6100subacdf, ye6100subbcdf, ye6100subccdf, ye6100subdcdf, yeast2.db, yeast2cdf, ygs98.db, ygs98cdf, zebrafish.db, zebrafishcdf, celldex, chipenrich.data, DeSousa2013, msigdb, scRNAseq, ExpHunterSuite, aliases2entrez, BiSEp, CAMML, DIscBIO, g3viz, HiCociety, jetset, netgsa, pathfindR, PathwayVote, prioGene, RCPA, SurprisalAnalysis, WayFindR, WGCNA suggestsMe: ASURAT, autonomics, bambu, BiocGenerics, CellTrails, cicero, cola, csaw, DAPAR, DEGreport, edgeR, eisaR, enrichplot, esetVis, FELLA, FGNet, fgsea, fishpond, GA4GHclient, gatom, gCrisprTools, GeneRegionScan, GenomicPlot, GenomicRanges, ggkegg, gsean, hpar, iSEEu, limma, MutationalPatterns, NetActivity, oligo, ontoProc, OUTRIDER, pathlinkR, piano, plotgardener, pRoloc, ProteoDisco, quantiseqr, R3CPET, recount, scDotPlot, scGraphVerse, simona, SingleCellAlleleExperiment, sparrow, SpliceWiz, SummarizedExperiment, systemPipeR, TFEA.ChIP, tidybulk, topconfects, weitrix, wiggleplotr, BioPlex, BloodCancerMultiOmics2017, curatedAdipoChIP, RforProteomics, bulkAnalyseR, CALANGO, conos, easylabel, genekitr, goat, pagoda2, Platypus, rliger, scITD dependencyCount: 42 Package: AnnotationFilter Version: 1.34.0 Depends: R (>= 3.4.0) Imports: utils, methods, GenomicRanges, lazyeval Suggests: BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db, rmarkdown License: Artistic-2.0 MD5sum: 5bc2245699890c08ae66669120851b8b NeedsCompilation: no Title: Facilities for Filtering Bioconductor Annotation Resources Description: This package provides class and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters will be used by ensembldb, Organism.dplyr, and other packages. biocViews: Annotation, Infrastructure, Software Author: Martin Morgan [aut], Johannes Rainer [aut], Joachim Bargsten [ctb], Daniel Van Twisk [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/AnnotationFilter VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationFilter/issues git_url: https://git.bioconductor.org/packages/AnnotationFilter git_branch: RELEASE_3_22 git_last_commit: 730457f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnnotationFilter_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnnotationFilter_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationFilter_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnnotationFilter_1.34.0.tgz vignettes: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.html vignetteTitles: Facilities for Filtering Bioconductor Annotation resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.R dependsOnMe: chimeraviz, CompoundDb, ensembldb, Organism.dplyr importsMe: biovizBase, BUSpaRse, CleanUpRNAseq, drugTargetInteractions, ggbio, QFeatures, RAIDS, RITAN, scanMiRApp, TVTB, GenomicDistributionsData, locuszoomr, RNAseqQC suggestsMe: GenomicDistributions, GenomicFeatures, TFutils, wiggleplotr dependencyCount: 12 Package: AnnotationForge Version: 1.52.0 Depends: R (>= 3.5.0), methods, utils, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), AnnotationDbi (>= 1.33.14) Imports: DBI, RSQLite, XML, S4Vectors, RCurl Suggests: biomaRt, httr, GenomeInfoDb (>= 1.17.1), Biostrings, affy, hgu95av2.db, human.db0, org.Hs.eg.db, Homo.sapiens, GO.db, rmarkdown, BiocStyle, knitr, BiocManager, BiocFileCache, RUnit License: Artistic-2.0 MD5sum: 58f29899312d02f6898e0bfad0868ffb NeedsCompilation: no Title: Tools for building SQLite-based annotation data packages Description: Provides code for generating Annotation packages and their databases. Packages produced are intended to be used with AnnotationDbi. biocViews: Annotation, Infrastructure Author: Marc Carlson [aut], Hervé Pagès [aut], Madelyn Carlson [ctb] ('Creating probe packages' vignette translation from Sweave to Rmarkdown / HTML), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationForge VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationForge/issues git_url: https://git.bioconductor.org/packages/AnnotationForge git_branch: RELEASE_3_22 git_last_commit: c5f7358 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnnotationForge_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnnotationForge_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationForge_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnnotationForge_1.52.0.tgz vignettes: vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf, vignettes/AnnotationForge/inst/doc/makeProbePackage.html, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html vignetteTitles: AnnotationForge: Creating select Interfaces for custom Annotation resources, SQLForge: An easy way to create a new annotation package with a standard database schema., Creating probe packages, Making New Organism Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationForge/inst/doc/makeProbePackage.R, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.R, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.R, vignettes/AnnotationForge/inst/doc/SQLForge.R importsMe: AnnotationHubData, GOstats, ViSEAGO, GGHumanMethCancerPanelv1.db suggestsMe: AnnotationDbi, AnnotationHub dependencyCount: 46 Package: AnnotationHub Version: 4.0.0 Depends: BiocGenerics (>= 0.15.10), BiocFileCache (>= 2.99.3) Imports: utils, methods, grDevices, RSQLite, BiocManager, BiocVersion, curl, rappdirs, AnnotationDbi (>= 1.31.19), S4Vectors, httr2, yaml, dplyr, BiocBaseUtils Suggests: IRanges, Seqinfo, GenomeInfoDb, GenomicRanges, VariantAnnotation, Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit, txdbmaker, MSnbase, mzR, Biostrings, CompoundDb, keras, ensembldb, SummarizedExperiment, ExperimentHub, gdsfmt, rmarkdown, HubPub Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: 49b6caf594ce60d2c833a61105fbff76 NeedsCompilation: yes Title: Client to access AnnotationHub resources Description: This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, Ensembl) can be discovered. The resource includes metadata about each resource, e.g., a textual description, tags, and date of modification. The client creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationHub/issues git_url: https://git.bioconductor.org/packages/AnnotationHub git_branch: RELEASE_3_22 git_last_commit: e27e804 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnnotationHub_4.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnnotationHub_3.99.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationHub_4.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnnotationHub_4.0.0.tgz vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/TroubleshootingTheHubs.html vignetteTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub: Access the AnnotationHub Web Service, Troubleshooting The Hubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.R, vignettes/AnnotationHub/inst/doc/AnnotationHub.R, vignettes/AnnotationHub/inst/doc/TroubleshootingTheHubs.R dependsOnMe: adductomicsR, AnnotationHubData, ExperimentHub, ipdDb, LRcell, octad, AlphaMissense.v2023.hg19, AlphaMissense.v2023.hg38, cadd.v1.6.hg19, cadd.v1.6.hg38, EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB, GenomicState, hpAnnot, org.Mxanthus.db, phastCons30way.UCSC.hg38, phastCons35way.UCSC.mm39, phyloP35way.UCSC.mm39, rGenomeTracksData, synaptome.data, UCSCRepeatMasker, MetaGxBreast, NestLink, scMultiome, sesameData, tartare, annotation, sequencing, OSCA.advanced, OSCA.basic, OSCA.workflows, SingleRBook importsMe: annotatr, atena, BiocHubsShiny, BUSpaRse, circRNAprofiler, coMethDMR, cTRAP, customCMPdb, DeconvoBuddies, DMRcate, dmrseq, EpiCompare, EpiMix, epimutacions, epiregulon, gDNAx, GenomicScores, GRaNIE, GSEABenchmarkeR, gwascat, iSEEhub, MACSr, meshes, MetaboAnnotation, methodical, MethReg, Moonlight2R, MSnID, OGRE, ontoProc, orthos, partCNV, psichomics, regutools, REMP, scanMiRApp, scAnnotatR, scmeth, scTensor, shinyDSP, singleCellTK, SpliceWiz, TENET, tximeta, Ularcirc, xCell2, AHLRBaseDbs, AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, CENTREannotation, EPICv2manifest, grasp2db, HPO.db, metaboliteIDmapping, MPO.db, synaptome.db, TENET.AnnotationHub, adductData, BioImageDbs, biscuiteerData, celldex, chipseqDBData, crisprScoreData, curatedMetagenomicData, curatedPCaData, curatedTBData, curatedTCGAData, depmap, DoReMiTra, DropletTestFiles, easierData, FieldEffectCrc, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, GenomicDistributionsData, HCAData, HiBED, HiContactsData, HMP16SData, HMP2Data, mcsurvdata, MerfishData, MetaGxPancreas, MouseAgingData, msigdb, orthosData, ProteinGymR, scpdata, scRNAseq, SFEData, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, RNAseqQC suggestsMe: AHMassBank, AlphaMissenseR, autonomics, BgeeCall, Chicago, ChIPpeakAnno, clusterProfiler, CNVRanger, COCOA, crisprViz, DNAshapeR, dupRadar, ELMER, ensembldb, epiNEM, EpiTxDb, epivizrChart, epivizrData, GenomicRanges, Glimma, GOSemSim, LRBaseDbi, maser, MIRA, motifTestR, MSnbase, multicrispr, muscat, nullranges, OrganismDbi, peakCombiner, plotgardener, raer, recountmethylation, satuRn, simona, TCGAbiolinks, TCGAutils, tidyCoverage, VariantAnnotation, xcore, AHEnsDbs, CTCF, ENCODExplorerData, excluderanges, gwascatData, ontoProcData, org.Hbacteriophora.eg.db, BioPlex, ChIPDBData, CoSIAdata, HarmonizedTCGAData, homosapienDEE2CellScore, GeneSelectR, locuszoomr dependencyCount: 63 Package: AnnotationHubData Version: 1.40.0 Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub (>= 2.15.15) Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics, jsonlite, BiocManager, biocViews, BiocCheck, graph, AnnotationDbi, Biobase, Biostrings, DBI, Seqinfo, GenomeInfoDb (>= 1.45.5), OrganismDbi, RSQLite, AnnotationForge, futile.logger (>= 1.3.0), XML, RCurl Suggests: RUnit, knitr, BiocStyle, grasp2db, GenomeInfoDbData, rmarkdown, HubPub License: Artistic-2.0 MD5sum: 0d19d71a0c716d4b6d62e064bee0e860 NeedsCompilation: no Title: Transform public data resources into Bioconductor Data Structures Description: These recipes convert a wide variety and a growing number of public bioinformatic data sets into easily-used standard Bioconductor data structures. biocViews: DataImport Author: Martin Morgan [ctb], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Paul Shannon [ctb], Lori Shepherd [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationHubData git_branch: RELEASE_3_22 git_last_commit: 077effd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnnotationHubData_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnnotationHubData_1.39.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationHubData_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnnotationHubData_1.40.0.tgz vignettes: vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.html vignetteTitles: Introduction to AnnotationHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ExperimentHubData importsMe: AHMassBank, AHEnsDbs, EuPathDB suggestsMe: HubPub, EPICv2manifest, GenomicState, TENET.AnnotationHub, homosapienDEE2CellScore, humanHippocampus2024, smokingMouse dependencyCount: 116 Package: annotationTools Version: 1.84.0 Imports: Biobase, stats Suggests: BiocStyle License: GPL MD5sum: 097b9c49b28c643a81c89dedf6560ff1 NeedsCompilation: no Title: Annotate microarrays and perform cross-species gene expression analyses using flat file databases Description: Functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files). biocViews: Microarray, Annotation Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/annotationTools git_branch: RELEASE_3_22 git_last_commit: 906b275 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/annotationTools_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/annotationTools_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/annotationTools_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/annotationTools_1.84.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R dependencyCount: 7 Package: annotatr Version: 1.36.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, AnnotationHub, dplyr, GenomicFeatures (>= 1.61.4), GenomicRanges (>= 1.61.1), Seqinfo, ggplot2 (>= 3.5.0), IRanges, methods, readr, regioneR, reshape2, rlang, rtracklayer (>= 1.69.1), S4Vectors (>= 0.23.10), stats, utils Suggests: GenomeInfoDb, BiocStyle, devtools, knitr, org.Dm.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, rmarkdown, roxygen2, testthat, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene License: GPL-3 MD5sum: 8e8fbf6d9d31a3689e4228f57c11a222 NeedsCompilation: no Title: Annotation of Genomic Regions to Genomic Annotations Description: Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations. biocViews: Software, Annotation, GenomeAnnotation, FunctionalGenomics, Visualization Author: Raymond G. Cavalcante [aut, cre], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://www.github.com/rcavalcante/annotatr/issues git_url: https://git.bioconductor.org/packages/annotatr git_branch: RELEASE_3_22 git_last_commit: 6850c77 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/annotatr_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/annotatr_1.35.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/annotatr_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/annotatr_1.36.0.tgz vignettes: vignettes/annotatr/inst/doc/annotatr-vignette.html vignetteTitles: annotatr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R importsMe: dmrseq, methodical, scmeth, SOMNiBUS, ExpHunterSuite suggestsMe: borealis, ramr dependencyCount: 119 Package: anota Version: 1.58.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: 8307f53c0342b1e37a647194c7283d2c NeedsCompilation: no Title: ANalysis Of Translational Activity (ANOTA). Description: Genome wide studies of translational control is emerging as a tool to study verious biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the levl of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e. differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallell the library performs a number of tests to assure that the data set is suitable for such analysis. biocViews: GeneExpression, DifferentialExpression, Microarray, Sequencing Author: Ola Larsson , Nahum Sonenberg , Robert Nadon Maintainer: Ola Larsson git_url: https://git.bioconductor.org/packages/anota git_branch: RELEASE_3_22 git_last_commit: f642b32 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/anota_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/anota_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/anota_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/anota_1.58.0.tgz vignettes: vignettes/anota/inst/doc/anota.pdf vignetteTitles: ANalysis Of Translational Activity (anota) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota/inst/doc/anota.R dependsOnMe: tRanslatome dependencyCount: 40 Package: anota2seq Version: 1.32.0 Depends: R (>= 3.4.0), methods Imports: multtest,qvalue,limma,DESeq2,edgeR,RColorBrewer, grDevices, graphics, stats, utils, SummarizedExperiment Suggests: BiocStyle,knitr License: GPL-3 MD5sum: 636847cb5242b69e461c2529a0d629f7 NeedsCompilation: no Title: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq Description: anota2seq provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources; translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source (i.e. translated mRNA or total mRNA). Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels (i.e. changes in translated mRNA that are independent of levels of total mRNA) or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels (i.e. changes in total mRNA that are independent of levels of translated mRNA). anota2seq applies analysis of partial variance and the random variance model to fulfill these tasks. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Microarray,GenomeWideAssociation, BatchEffect, Normalization, RNASeq, Sequencing, GeneRegulation, Regression Author: Christian Oertlin , Julie Lorent , Ola Larsson Maintainer: Christian Oertlin , Ola Larsson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/anota2seq git_branch: RELEASE_3_22 git_last_commit: 8720150 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/anota2seq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/anota2seq_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/anota2seq_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/anota2seq_1.32.0.tgz vignettes: vignettes/anota2seq/inst/doc/anota2seq.pdf vignetteTitles: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota2seq/inst/doc/anota2seq.R dependencyCount: 68 Package: antiProfiles Version: 1.50.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: 3d54d5391d53938f594495229ee4a838 NeedsCompilation: no Title: Implementation of gene expression anti-profiles Description: Implements gene expression anti-profiles as described in Corrada Bravo et al., BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272. biocViews: GeneExpression,Classification Author: Hector Corrada Bravo, Rafael A. Irizarry and Jeffrey T. Leek Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/antiProfiles git_url: https://git.bioconductor.org/packages/antiProfiles git_branch: RELEASE_3_22 git_last_commit: bac01fd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/antiProfiles_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/antiProfiles_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/antiProfiles_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/antiProfiles_1.50.0.tgz vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf vignetteTitles: Introduction to antiProfiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R dependencyCount: 9 Package: AnVIL Version: 1.22.0 Depends: R (>= 4.5.0), dplyr, AnVILBase Imports: stats, utils, methods, futile.logger, GCPtools, jsonlite, httr, rapiclient, yaml, tibble, shiny, DT, miniUI, htmltools, BiocBaseUtils Suggests: knitr, rmarkdown, testthat, withr, readr, BiocStyle, devtools, AnVILAz, AnVILGCP, lifecycle License: Artistic-2.0 MD5sum: 55fa30a2069ad6897808937cbf29282c NeedsCompilation: no Title: Bioconductor on the AnVIL compute environment Description: The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVIL package provides programatic access to the Dockstore, Leonardo, Rawls, TDR, and Terra RESTful programming interfaces. For platform-specific user-level functionality, see either the AnVILGCP or AnVILAz package. biocViews: Infrastructure Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut] (ORCID: ), Kayla Interdonato [aut], Yubo Cheng [aut], Nitesh Turaga [aut], BJ Stubbs [ctb], Vincent Carey [ctb], Sehyun Oh [ctb], Sweta Gopaulakrishnan [ctb], Valerie Obenchain [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVIL git_branch: RELEASE_3_22 git_last_commit: d87ee9c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnVIL_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVIL_1.21.9.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVIL_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVIL_1.22.0.tgz vignettes: vignettes/AnVIL/inst/doc/BiocDockstore.html, vignettes/AnVIL/inst/doc/Introduction.html, vignettes/AnVIL/inst/doc/RunningWorkflow.html vignetteTitles: Dockstore and Bioconductor for AnVIL, Introduction to the AnVIL package, Running an AnVIL workflow within R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVIL/inst/doc/BiocDockstore.R, vignettes/AnVIL/inst/doc/Introduction.R, vignettes/AnVIL/inst/doc/RunningWorkflow.R dependsOnMe: cBioPortalData importsMe: AnVILPublish, AnVILWorkflow, bedbaser, terraTCGAdata suggestsMe: AnVILBase, AnVILGCP dependencyCount: 75 Package: AnVILAz Version: 1.4.0 Depends: R (>= 4.5.0) Imports: AnVILBase, BiocBaseUtils, curl, httr2, jsonlite, methods, rjsoncons, tibble, utils Suggests: BiocStyle, dplyr, knitr, readr, rmarkdown, tinytest License: Artistic-2.0 MD5sum: 2820283f92783d30188835126122ec34 NeedsCompilation: no Title: R / Bioconductor Support for the AnVIL Azure Platform Description: The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVILAz package supports end-users and developers using the AnVIL platform in the Azure cloud. The package provides a programmatic interface to AnVIL resources, including workspaces, notebooks, tables, and workflows. The package also provides utilities for managing resources, including copying files to and from Azure Blob Storage, and creating shared access signatures (SAS) for secure access to Azure resources. biocViews: Software, Infrastructure, ThirdPartyClient Author: Martin Morgan [aut, ctb] (ORCID: ), Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/AnVILAz SystemRequirements: az, azcopy VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnVILAz/issues git_url: https://git.bioconductor.org/packages/AnVILAz git_branch: RELEASE_3_22 git_last_commit: b9b0191 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnVILAz_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVILAz_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVILAz_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVILAz_1.4.0.tgz vignettes: vignettes/AnVILAz/inst/doc/AnVILAzWorkspaces.html, vignettes/AnVILAz/inst/doc/IntroductionToAnVILAz.html vignetteTitles: Working with Workspaces on AnVIL Azure, Introduction to the AnVILAz package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILAz/inst/doc/AnVILAzWorkspaces.R, vignettes/AnVILAz/inst/doc/IntroductionToAnVILAz.R suggestsMe: AnVIL, AnVILBase dependencyCount: 33 Package: AnVILBase Version: 1.4.0 Depends: R (>= 4.5.0) Imports: httr, httr2, dplyr, jsonlite, methods, tibble Suggests: AnVIL, AnVILAz, AnVILGCP, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), tinytest License: Artistic-2.0 MD5sum: 14d636c9cc9f4fcbdb7751b8d70233e2 NeedsCompilation: no Title: Generic functions for interacting with the AnVIL ecosystem Description: Provides generic functions for interacting with the AnVIL ecosystem. Packages that use either GCP or Azure in AnVIL are built on top of AnVILBase. Extension packages will provide methods for interacting with other cloud providers. biocViews: Software, Infrastructure Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut, ctb] (ORCID: ), NIH NHGRI U24HG004059 [fnd] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/AnVILBase VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnVILBase/issues git_url: https://git.bioconductor.org/packages/AnVILBase git_branch: RELEASE_3_22 git_last_commit: fc4d3f3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnVILBase_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVILBase_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVILBase_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVILBase_1.4.0.tgz vignettes: vignettes/AnVILBase/inst/doc/AnVILBaseIntroduction.html vignetteTitles: Introduction to AnVILBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILBase/inst/doc/AnVILBaseIntroduction.R dependsOnMe: AnVIL, AnVILWorkflow importsMe: AnVILAz, AnVILGCP, GCPtools suggestsMe: terraTCGAdata dependencyCount: 29 Package: AnVILBilling Version: 1.20.0 Depends: R (>= 4.1) Imports: methods, DT, shiny, bigrquery, shinytoastr, DBI, magrittr, dplyr, lubridate, plotly, ggplot2 Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 107b5177099992a15d461589e68a313c NeedsCompilation: no Title: Provide functions to retrieve and report on usage expenses in NHGRI AnVIL (anvilproject.org). Description: AnVILBilling helps monitor AnVIL-related costs in R, using queries to a BigQuery table to which costs are exported daily. Functions are defined to help categorize tasks and associated expenditures, and to visualize and explore expense profiles over time. This package will be expanded to help users estimate costs for specific task sets. biocViews: Infrastructure, Software Author: BJ Stubbs [aut], Vince Carey [aut, cre] Maintainer: Vince Carey VignetteBuilder: knitr BugReports: https://github.com/vjcitn/AnVILBilling/issues git_url: https://git.bioconductor.org/packages/AnVILBilling git_branch: RELEASE_3_22 git_last_commit: dadda5b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnVILBilling_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVILBilling_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVILBilling_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVILBilling_1.20.0.tgz vignettes: vignettes/AnVILBilling/inst/doc/billing.html vignetteTitles: Software for reckoning AnVIL/terra usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILBilling/inst/doc/billing.R dependencyCount: 91 Package: AnVILGCP Version: 1.4.0 Depends: R (>= 4.5.0) Imports: AnVILBase, BiocBaseUtils, dplyr, GCPtools (>= 0.99.4), httr, jsonlite, methods, rlang, stats, tibble, tidyr, utils Suggests: AnVIL, BiocStyle, knitr, rmarkdown, testthat, withr License: Artistic-2.0 MD5sum: e00b1697142cc1b882d2aabcc4e572ca NeedsCompilation: no Title: The GCP R Client for the AnVIL Description: The package provides a set of functions to interact with the Google Cloud Platform (GCP) services on the AnVIL platform. The package is designed to use the API calls from the AnVIL package. It coordinates AnVIL workspace functionality with native GCP tools. biocViews: Software, Infrastructure, ThirdPartyClient, DataImport Author: Marcel Ramos [aut, cre] (ORCID: ), Nitesh Turaga [aut], Martin Morgan [aut] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/AnVILGCP VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnVILGCP/issues git_url: https://git.bioconductor.org/packages/AnVILGCP git_branch: RELEASE_3_22 git_last_commit: 4361fc4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnVILGCP_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVILGCP_1.3.8.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVILGCP_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVILGCP_1.4.0.tgz vignettes: vignettes/AnVILGCP/inst/doc/AnVILGCPIntroduction.html vignetteTitles: Working with AnVIL on GCP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILGCP/inst/doc/AnVILGCPIntroduction.R dependsOnMe: AnVILWorkflow, terraTCGAdata importsMe: AnVILPublish suggestsMe: AnVIL, AnVILBase dependencyCount: 38 Package: AnVILPublish Version: 1.20.0 Imports: AnVIL, AnVILGCP, BiocBaseUtils, BiocManager, httr, jsonlite, rmarkdown, yaml, readr, whisker, tools, utils, stats Suggests: knitr, BiocStyle, GCPtools, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 2ca2978c71dc3d573f527fac5b9c3b1a NeedsCompilation: no Title: Publish Packages and Other Resources to AnVIL Workspaces Description: Use this package to create or update AnVIL workspaces from resources such as R / Bioconductor packages. The metadata about the package (e.g., select information from the package DESCRIPTION file and from vignette YAML headings) are used to populate the 'DASHBOARD'. Vignettes are translated to python notebooks ready for evaluation in AnVIL. biocViews: Infrastructure, Software Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut] (ORCID: ), Kayla Interdonato [aut], Vincent Carey [ctb] (ORCID: ) Maintainer: Marcel Ramos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVILPublish git_branch: RELEASE_3_22 git_last_commit: d3ede23 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnVILPublish_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVILPublish_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVILPublish_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVILPublish_1.20.0.tgz vignettes: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.html vignetteTitles: Publishing R / Bioconductor packages to AnVIL Workspaces hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.R dependencyCount: 89 Package: AnVILWorkflow Version: 1.10.0 Depends: R (>= 4.4.0), AnVILGCP, AnVILBase, httr Imports: AnVIL, dplyr, jsonlite, rlang, tibble, tidyr, utils, methods, plyr, stringr Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: 5df30a3eca680da3586da73a8ed90c68 NeedsCompilation: no Title: Run workflows implemented in Terra/AnVIL workspace Description: The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The main cloud-based genomics platform deported by the AnVIL project is Terra. The AnVILWorkflow package allows remote access to Terra implemented workflows, enabling end-user to utilize Terra/ AnVIL provided resources - such as data, workflows, and flexible/scalble computing resources - through the conventional R functions. biocViews: Infrastructure, Software Author: Sehyun Oh [aut, cre] (ORCID: ), Marcel Ramos [ctb] (ORCID: ), Kai Gravel-Pucillo [aut] Maintainer: Sehyun Oh URL: https://github.com/shbrief/AnVILWorkflow VignetteBuilder: knitr BugReports: https://github.com/shbrief/AnVILWorkflow/issues git_url: https://git.bioconductor.org/packages/AnVILWorkflow git_branch: RELEASE_3_22 git_last_commit: 453f0b4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AnVILWorkflow_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVILWorkflow_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVILWorkflow_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVILWorkflow_1.10.0.tgz vignettes: vignettes/AnVILWorkflow/inst/doc/salmon.html vignetteTitles: Quickstart - RNAseq analysis using salmon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILWorkflow/inst/doc/salmon.R dependencyCount: 78 Package: apComplex Version: 2.76.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: e368146b4e534b4097841e3305937aed NeedsCompilation: no Title: Estimate protein complex membership using AP-MS protein data Description: Functions to estimate a bipartite graph of protein complex membership using AP-MS data. biocViews: ImmunoOncology, NetworkInference, MassSpectrometry, GraphAndNetwork Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/apComplex git_branch: RELEASE_3_22 git_last_commit: c8b447e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/apComplex_2.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/apComplex_2.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/apComplex_2.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/apComplex_2.76.0.tgz vignettes: vignettes/apComplex/inst/doc/apComplex.pdf vignetteTitles: apComplex hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apComplex/inst/doc/apComplex.R dependencyCount: 49 Package: apeglm Version: 1.32.0 Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats, utils, Rcpp LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: DESeq2, airway, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 844f77046de2ac9d9e92d6ae42a8189e NeedsCompilation: yes Title: Approximate posterior estimation for GLM coefficients Description: apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, GeneExpression, Bayesian Author: Anqi Zhu [aut, cre], Joshua Zitovsky [ctb], Joseph Ibrahim [aut], Michael Love [aut] Maintainer: Anqi Zhu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/apeglm git_branch: RELEASE_3_22 git_last_commit: 8940580 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/apeglm_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/apeglm_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/apeglm_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/apeglm_1.32.0.tgz vignettes: vignettes/apeglm/inst/doc/apeglm.html vignetteTitles: Effect size estimation with apeglm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apeglm/inst/doc/apeglm.R dependsOnMe: rnaseqGene importsMe: airpart, debrowser, DiffBind, ERSSA, phantasus, Rmmquant, TEKRABber suggestsMe: bambu, dar, DeeDeeExperiment, DESeq2, extraChIPs, fishpond, terapadog, NanoporeRNASeq, RNAseqQC dependencyCount: 36 Package: APL Version: 1.14.0 Depends: R (>= 4.4.0) Imports: Matrix, RSpectra, ggrepel, ggplot2, viridisLite, plotly, SeuratObject, SingleCellExperiment, magrittr, SummarizedExperiment, topGO, methods, stats, utils, org.Hs.eg.db, org.Mm.eg.db, rlang Suggests: BiocStyle, knitr, rmarkdown, scRNAseq, scater, scran, sparseMatrixStats, testthat License: GPL (>= 3) MD5sum: 286f0a652356da5553a0f30431f8649b NeedsCompilation: no Title: Association Plots Description: APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest. biocViews: StatisticalMethod, DimensionReduction, SingleCell, Sequencing, RNASeq, GeneExpression Author: Clemens Kohl [cre, aut], Elzbieta Gralinska [aut], Martin Vingron [aut] Maintainer: Clemens Kohl URL: https://vingronlab.github.io/APL/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/APL git_branch: RELEASE_3_22 git_last_commit: 3bb5ce8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/APL_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/APL_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/APL_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/APL_1.14.0.tgz vignettes: vignettes/APL/inst/doc/APL.html vignetteTitles: Analyzing data with APL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/APL/inst/doc/APL.R dependencyCount: 121 Package: appreci8R Version: 1.28.0 Imports: shiny, shinyjs, DT, VariantAnnotation, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, Homo.sapiens, SNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, Biostrings, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.hs37d5, MafDb.gnomADex.r2.1.hs37d5, COSMIC.67, rentrez, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137, seqinr, openxlsx, Rsamtools, stringr, stats, GenomicRanges, S4Vectors, GenomicFeatures, IRanges, GenomicScores, SummarizedExperiment Suggests: GO.db, org.Hs.eg.db, utils License: LGPL-3 Archs: x64 MD5sum: f07f6671203afbb091ca449a3a42b0c0 NeedsCompilation: no Title: appreci8R: an R/Bioconductor package for filtering SNVs and short indels with high sensitivity and high PPV Description: The appreci8R is an R version of our appreci8-algorithm - A Pipeline for PREcise variant Calling Integrating 8 tools. Variant calling results of our standard appreci8-tools (GATK, Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and VarDict), as well as up to 5 additional tools is combined, evaluated and filtered. biocViews: VariantDetection, GeneticVariability, SNP, VariantAnnotation, Sequencing, Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/appreci8R git_branch: RELEASE_3_22 git_last_commit: 43ec6e7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/appreci8R_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/appreci8R_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/appreci8R_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/appreci8R_1.28.0.tgz vignettes: vignettes/appreci8R/inst/doc/appreci8R.pdf vignetteTitles: Using appreci8R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/appreci8R/inst/doc/appreci8R.R dependencyCount: 161 Package: aroma.light Version: 3.40.0 Depends: R (>= 2.15.2) Imports: stats, R.methodsS3 (>= 1.7.1), R.oo (>= 1.23.0), R.utils (>= 2.9.0), matrixStats (>= 0.55.0) Suggests: princurve (>= 2.1.4) License: GPL (>= 2) MD5sum: 27b5fe2b7335391beef623707a49cae8 NeedsCompilation: no Title: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types Description: Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. biocViews: Infrastructure, Microarray, OneChannel, TwoChannel, MultiChannel, Visualization, Preprocessing Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb], Aaron Lun [ctb] Maintainer: Henrik Bengtsson URL: https://github.com/HenrikBengtsson/aroma.light, https://www.aroma-project.org BugReports: https://github.com/HenrikBengtsson/aroma.light/issues git_url: https://git.bioconductor.org/packages/aroma.light git_branch: RELEASE_3_22 git_last_commit: e3e2939 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/aroma.light_3.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/aroma.light_3.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/aroma.light_3.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/aroma.light_3.40.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq, scone, PSCBS suggestsMe: TIN, aroma.affymetrix, aroma.cn, aroma.core dependencyCount: 8 Package: ArrayExpress Version: 1.70.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: oligo, limma, httr, utils, jsonlite, rlang, tools, methods Suggests: affy License: Artistic-2.0 MD5sum: 9f1259df45f5f3c3a518d360fc3aff60 NeedsCompilation: no Title: Access the ArrayExpress Collection at EMBL-EBI Biostudies and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet Description: Access the ArrayExpress Collection at EMBL-EBI Biostudies and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet. biocViews: Microarray, DataImport, OneChannel, TwoChannel Author: Audrey Kauffmann, Ibrahim Emam, Michael Schubert, Jose Marugan Maintainer: Jose Marugan git_url: https://git.bioconductor.org/packages/ArrayExpress git_branch: RELEASE_3_22 git_last_commit: 01e7144 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ArrayExpress_1.70.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ArrayExpress_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ArrayExpress_1.70.0.tgz vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets into R object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R dependsOnMe: DrugVsDisease, maEndToEnd suggestsMe: bapred dependencyCount: 64 Package: arrayMvout Version: 1.68.0 Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy Imports: mdqc, affyContam, lumi Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata, hgu133atagcdf License: Artistic-2.0 MD5sum: 1cc10943071b504c9419ced1231ef6e0 NeedsCompilation: no Title: multivariate outlier detection for expression array QA Description: This package supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate biocViews: Infrastructure, Microarray, QualityControl Author: Z. Gao, A. Asare, R. Wang, V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/arrayMvout git_branch: RELEASE_3_22 git_last_commit: d58d26c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/arrayMvout_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/arrayMvout_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/arrayMvout_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/arrayMvout_1.68.0.tgz vignettes: vignettes/arrayMvout/inst/doc/arrayMvout.pdf vignetteTitles: arrayMvout -- multivariate outlier algorithm for expression arrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayMvout/inst/doc/arrayMvout.R dependencyCount: 170 Package: arrayQuality Version: 1.88.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: 17688d4690482aeecf11e56319629f05 NeedsCompilation: no Title: Assessing array quality on spotted arrays Description: Functions for performing print-run and array level quality assessment. biocViews: Microarray,TwoChannel,QualityControl,Visualization Author: Agnes Paquet and Jean Yee Hwa Yang Maintainer: Agnes Paquet URL: http://arrays.ucsf.edu/ git_url: https://git.bioconductor.org/packages/arrayQuality git_branch: RELEASE_3_22 git_last_commit: 8c022a8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/arrayQuality_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/arrayQuality_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/arrayQuality_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/arrayQuality_1.88.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: ARRmNormalization Version: 1.50.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: 18afc86b2ad195a417a2393582ff125a NeedsCompilation: no Title: Adaptive Robust Regression normalization for Illumina methylation data Description: Perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay. biocViews: DNAMethylation, TwoChannel, Preprocessing, Microarray Author: Jean-Philippe Fortin, Celia M.T. Greenwood, Aurelie Labbe. Maintainer: Jean-Philippe Fortin git_url: https://git.bioconductor.org/packages/ARRmNormalization git_branch: RELEASE_3_22 git_last_commit: 6501c1b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ARRmNormalization_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ARRmNormalization_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ARRmNormalization_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ARRmNormalization_1.50.0.tgz vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf vignetteTitles: ARRmNormalization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.R dependencyCount: 1 Package: artMS Version: 1.28.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, bit64, circlize, cluster, corrplot, data.table, dplyr, getopt, ggdendro, ggplot2, gplots, ggrepel, graphics, grDevices, grid, limma, MSstats, openxlsx, org.Hs.eg.db, pheatmap, plotly, plyr, RColorBrewer, scales, seqinr, stats, stringr, tidyr, UpSetR, utils, VennDiagram, yaml Suggests: BiocStyle, ComplexHeatmap, factoextra, FactoMineR, gProfileR, knitr, PerformanceAnalytics, org.Mm.eg.db, rmarkdown, testthat License: GPL (>= 3) + file LICENSE MD5sum: 55c3f7af0d97cc89ba8d32b87f4ef359 NeedsCompilation: no Title: Analytical R tools for Mass Spectrometry Description: artMS provides a set of tools for the analysis of proteomics label-free datasets. It takes as input the MaxQuant search result output (evidence.txt file) and performs quality control, relative quantification using MSstats, downstream analysis and integration. artMS also provides a set of functions to re-format and make it compatible with other analytical tools, including, SAINTq, SAINTexpress, Phosfate, and PHOTON. Check [http://artms.org](http://artms.org) for details. biocViews: Proteomics, DifferentialExpression, BiomedicalInformatics, SystemsBiology, MassSpectrometry, Annotation, QualityControl, GeneSetEnrichment, Clustering, Normalization, ImmunoOncology, MultipleComparison Author: David Jimenez-Morales [aut, cre] (ORCID: ), Alexandre Rosa Campos [aut, ctb] (ORCID: ), John Von Dollen [aut], Nevan Krogan [aut] (ORCID: ), Danielle Swaney [aut, ctb] (ORCID: ) Maintainer: David Jimenez-Morales URL: http://artms.org VignetteBuilder: knitr BugReports: https://github.com/biodavidjm/artMS/issues git_url: https://git.bioconductor.org/packages/artMS git_branch: RELEASE_3_22 git_last_commit: 9313c4c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/artMS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/artMS_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/artMS_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/artMS_1.28.0.tgz vignettes: vignettes/artMS/inst/doc/artMS_vignette.html vignetteTitles: Learn to use artMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/artMS/inst/doc/artMS_vignette.R dependencyCount: 145 Package: ASAFE Version: 1.36.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: ab36a62a66b7b3e93e29cd5442c171aa NeedsCompilation: no Title: Ancestry Specific Allele Frequency Estimation Description: Given admixed individuals' bi-allelic SNP genotypes and ancestry pairs (where each ancestry can take one of three values) for multiple SNPs, perform an EM algorithm to deal with the fact that SNP genotypes are unphased with respect to ancestry pairs, in order to estimate ancestry-specific allele frequencies for all SNPs. biocViews: SNP, GenomeWideAssociation, LinkageDisequilibrium, BiomedicalInformatics, Genetics, ExperimentalDesign Author: Qian Zhang Maintainer: Qian Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASAFE git_branch: RELEASE_3_22 git_last_commit: aebca4a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ASAFE_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASAFE_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASAFE_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASAFE_1.36.0.tgz vignettes: vignettes/ASAFE/inst/doc/ASAFE.pdf vignetteTitles: ASAFE (Ancestry Specific Allele Frequency Estimation) hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASAFE/inst/doc/ASAFE.R dependencyCount: 0 Package: ASEB Version: 1.54.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) MD5sum: a35458bb56b49cd145565773e7948db5 NeedsCompilation: yes Title: Predict Acetylated Lysine Sites Description: ASEB is an R package to predict lysine sites that can be acetylated by a specific KAT-family. biocViews: Proteomics Author: Likun Wang and Tingting Li . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/ASEB git_branch: RELEASE_3_22 git_last_commit: 9e46d6a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ASEB_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASEB_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASEB_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASEB_1.54.0.tgz vignettes: vignettes/ASEB/inst/doc/ASEB.pdf vignetteTitles: ASEB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASEB/inst/doc/ASEB.R dependencyCount: 3 Package: ASGSCA Version: 1.44.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: 11f2643464453174a6901d518ca68edd NeedsCompilation: no Title: Association Studies for multiple SNPs and multiple traits using Generalized Structured Equation Models Description: The package provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables. The method is based on Generalized Structured Component Analysis (GSCA). biocViews: StructuralEquationModels Author: Hela Romdhani, Stepan Grinek , Heungsun Hwang and Aurelie Labbe. Maintainer: Hela Romdhani git_url: https://git.bioconductor.org/packages/ASGSCA git_branch: RELEASE_3_22 git_last_commit: a6dbcb3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ASGSCA_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASGSCA_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASGSCA_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASGSCA_1.44.0.tgz vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf vignetteTitles: Association Studies using Generalized Structured Equation Models. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASGSCA/inst/doc/ASGSCA.R dependencyCount: 9 Package: ASICS Version: 2.26.0 Depends: R (>= 3.5) Imports: BiocParallel, ggplot2, glmnet, grDevices, gridExtra, methods, mvtnorm, PepsNMR, plyr, quadprog, ropls, stats, SummarizedExperiment, utils, Matrix, zoo Suggests: knitr, rmarkdown, BiocStyle, testthat, ASICSdata License: GPL (>= 2) MD5sum: 8f978edb609f3ed682e8f08dfbdbf77d NeedsCompilation: no Title: Automatic Statistical Identification in Complex Spectra Description: With a set of pure metabolite reference spectra, ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty. The method and its statistical properties are described in Tardivel et al. (2017) . biocViews: Software, DataImport, Cheminformatics, Metabolomics Author: Gaëlle Lefort [aut, cre], Rémi Servien [aut], Patrick Tardivel [aut], Nathalie Vialaneix [aut] Maintainer: Gaëlle Lefort VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASICS git_branch: RELEASE_3_22 git_last_commit: 3e46722 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ASICS_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASICS_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASICS_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASICS_2.26.0.tgz vignettes: vignettes/ASICS/inst/doc/ASICS.html, vignettes/ASICS/inst/doc/ASICSUsersGuide.html vignetteTitles: ASICS, ASICS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASICS/inst/doc/ASICS.R, vignettes/ASICS/inst/doc/ASICSUsersGuide.R suggestsMe: AlpsNMR dependencyCount: 124 Package: ASpli Version: 2.20.0 Depends: methods, grDevices, stats, utils, parallel, edgeR, limma, AnnotationDbi Imports: GenomicRanges, GenomicFeatures, BiocGenerics, IRanges, GenomicAlignments, Gviz, S4Vectors, Rsamtools, BiocStyle, igraph, htmltools, data.table, UpSetR, tidyr, DT, MASS, grid, graphics, pbmcapply, txdbmaker License: GPL Archs: x64 MD5sum: f9f55f6c64a7d7a4c4725c37ae2ed8b3 NeedsCompilation: no Title: Analysis of Alternative Splicing Using RNA-Seq Description: Integrative pipeline for the analysis of alternative splicing using RNAseq. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, TimeCourse, RNASeq, GenomeAnnotation, Sequencing, Alignment Author: Estefania Mancini, Andres Rabinovich, Javier Iserte, Marcelo Yanovsky and Ariel Chernomoretz Maintainer: Ariel Chernomoretz git_url: https://git.bioconductor.org/packages/ASpli git_branch: RELEASE_3_22 git_last_commit: 3c6d496 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ASpli_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASpli_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASpli_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASpli_2.20.0.tgz vignettes: vignettes/ASpli/inst/doc/ASpli.pdf vignetteTitles: ASpli hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpli/inst/doc/ASpli.R importsMe: saseR dependencyCount: 169 Package: AssessORF Version: 1.28.0 Depends: R (>= 3.5.0), DECIPHER (>= 2.10.0) Imports: Biostrings, GenomicRanges, IRanges, graphics, grDevices, methods, stats, utils Suggests: AssessORFData, BiocStyle, knitr, rmarkdown, RSQLite (>= 1.1) License: GPL-3 MD5sum: 2656882949d2ef38feaa3fc234cdf929 NeedsCompilation: no Title: Assess Gene Predictions Using Proteomics and Evolutionary Conservation Description: In order to assess the quality of a set of predicted genes for a genome, evidence must first be mapped to that genome. Next, each gene must be categorized based on how strong the evidence is for or against that gene. The AssessORF package provides the functions and class structures necessary for accomplishing those tasks, using proteomic hits and evolutionarily conserved start codons as the forms of evidence. biocViews: ComparativeGenomics, GenePrediction, GenomeAnnotation, Genetics, Proteomics, QualityControl, Visualization Author: Deepank Korandla [aut, cre], Erik Wright [aut] Maintainer: Deepank Korandla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AssessORF git_branch: RELEASE_3_22 git_last_commit: 06260fd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AssessORF_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AssessORF_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AssessORF_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AssessORF_1.28.0.tgz vignettes: vignettes/AssessORF/inst/doc/UsingAssessORF.pdf vignetteTitles: Using AssessORF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AssessORF/inst/doc/UsingAssessORF.R suggestsMe: AssessORFData dependencyCount: 18 Package: ASSET Version: 2.28.0 Depends: R (>= 3.5.0), stats, graphics Imports: MASS, msm, rmeta Suggests: RUnit, BiocGenerics, knitr License: GPL-2 + file LICENSE MD5sum: ff4c62a50c3c4b1d395e08d7e5e6040d NeedsCompilation: no Title: An R package for subset-based association analysis of heterogeneous traits and subtypes Description: An R package for subset-based analysis of heterogeneous traits and disease subtypes. The package allows the user to search through all possible subsets of z-scores to identify the subset of traits giving the best meta-analyzed z-score. Further, it returns a p-value adjusting for the multiple-testing involved in the search. It also allows for searching for the best combination of disease subtypes associated with each variant. biocViews: StatisticalMethod, SNP, GenomeWideAssociation, MultipleComparison Author: Samsiddhi Bhattacharjee [aut, cre], Guanghao Qi [aut], Nilanjan Chatterjee [aut], William Wheeler [aut] Maintainer: Samsiddhi Bhattacharjee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASSET git_branch: RELEASE_3_22 git_last_commit: 7481559 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ASSET_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASSET_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASSET_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASSET_2.28.0.tgz vignettes: vignettes/ASSET/inst/doc/vignette.pdf vignetteTitles: ASSET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSET/inst/doc/vignette.R dependsOnMe: REBET dependencyCount: 26 Package: ASSIGN Version: 1.46.0 Depends: R (>= 3.4) Imports: gplots, graphics, grDevices, msm, Rlab, stats, sva, utils, ggplot2, yaml Suggests: testthat, BiocStyle, lintr, knitr, rmarkdown License: MIT + file LICENSE MD5sum: f03ac46c891add8e5607feb63a122e2b NeedsCompilation: no Title: Adaptive Signature Selection and InteGratioN (ASSIGN) Description: ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literature or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature. biocViews: Software, GeneExpression, Pathways, Bayesian Author: Ying Shen, Andrea H. Bild, W. Evan Johnson, and Mumtehena Rahman Maintainer: Ying Shen , W. Evan Johnson , David Jenkins , Mumtehena Rahman URL: https://compbiomed.github.io/ASSIGN/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/ASSIGN/issues git_url: https://git.bioconductor.org/packages/ASSIGN git_branch: RELEASE_3_22 git_last_commit: 09ba421 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ASSIGN_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASSIGN_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASSIGN_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASSIGN_1.46.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.html vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R importsMe: TBSignatureProfiler dependencyCount: 94 Package: assorthead Version: 1.4.0 Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 8ec7798fdb26c24973ba4da2944122ed NeedsCompilation: no Title: Assorted Header-Only C++ Libraries Description: Vendors an assortment of useful header-only C++ libraries. Bioconductor packages can use these libraries in their own C++ code by LinkingTo this package without introducing any additional dependencies. The use of a central repository avoids duplicate vendoring of libraries across multiple R packages, and enables better coordination of version updates across cohorts of interdependent C++ libraries. biocViews: SingleCell, QualityControl, Normalization, DataRepresentation, DataImport, DifferentialExpression, Alignment Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun URL: https://github.com/LTLA/assorthead VignetteBuilder: knitr BugReports: https://github.com/LTLA/assorthead/issues git_url: https://git.bioconductor.org/packages/assorthead git_branch: RELEASE_3_22 git_last_commit: 9255f2f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/assorthead_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/assorthead_1.3.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/assorthead_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/assorthead_1.4.0.tgz vignettes: vignettes/assorthead/inst/doc/userguide.html vignetteTitles: User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/assorthead/inst/doc/userguide.R linksToMe: alabaster.base, beachmat, beachmat.hdf5, beachmat.tiledb, BiocNeighbors, BiocSingular, bluster, bsseq, DropletUtils, glmGamPoi, scrapper, SingleR dependencyCount: 0 Package: ASURAT Version: 1.13.1 Depends: R (>= 4.0.0) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, Rcpp (>= 1.0.7), cluster, utils, plot3D, ComplexHeatmap, circlize, grid, grDevices, graphics LinkingTo: Rcpp Suggests: ggplot2, TENxPBMCData, dplyr, Rtsne, Seurat, AnnotationDbi, BiocGenerics, stringr, org.Hs.eg.db, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: efca495ca6332a806873f6ef5999deef NeedsCompilation: yes Title: Functional annotation-driven unsupervised clustering for single-cell data Description: ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs). biocViews: GeneExpression, SingleCell, Sequencing, Clustering, GeneSignaling Author: Keita Iida [aut, cre] (ORCID: ), Johannes Nicolaus Wibisana [ctb] Maintainer: Keita Iida VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASURAT git_branch: devel git_last_commit: 1e68e9e git_last_commit_date: 2025-10-14 Date/Publication: 2025-10-15 source.ver: src/contrib/ASURAT_1.13.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASURAT_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASURAT_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASURAT_1.14.0.tgz vignettes: vignettes/ASURAT/inst/doc/ASURAT.html vignetteTitles: ASURAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASURAT/inst/doc/ASURAT.R dependencyCount: 48 Package: ATACseqQC Version: 1.34.0 Depends: R (>= 3.5.0), BiocGenerics, S4Vectors Imports: BSgenome, Biostrings, ChIPpeakAnno, IRanges, GenomicRanges, GenomicAlignments, GenomeInfoDb, GenomicScores, graphics, grid, limma, Rsamtools (>= 1.31.2), randomForest, rtracklayer, stats, motifStack, preseqR, utils, KernSmooth, edgeR, BiocParallel Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, MotifDb, trackViewer, testthat, rmarkdown License: GPL (>= 2) MD5sum: 36db3b93f1ce666640b9e518726e2b4c NeedsCompilation: no Title: ATAC-seq Quality Control Description: ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints. biocViews: Sequencing, DNASeq, ATACSeq, GeneRegulation, QualityControl, Coverage, NucleosomePositioning, ImmunoOncology Author: Jianhong Ou, Haibo Liu, Feng Yan, Jun Yu, Michelle Kelliher, Lucio Castilla, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ATACseqQC git_branch: RELEASE_3_22 git_last_commit: 9b61612 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ATACseqQC_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ATACseqQC_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ATACseqQC_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ATACseqQC_1.34.0.tgz vignettes: vignettes/ATACseqQC/inst/doc/ATACseqQC.html vignetteTitles: ATACseqQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqQC/inst/doc/ATACseqQC.R suggestsMe: ATACseqTFEA dependencyCount: 172 Package: ATACseqTFEA Version: 1.12.0 Depends: R (>= 4.2) Imports: BiocGenerics, S4Vectors, IRanges, Matrix, GenomicRanges, GenomicAlignments, Seqinfo, SummarizedExperiment, Rsamtools, motifmatchr, TFBSTools, stats, pracma, ggplot2, ggrepel, dplyr, limma, methods, rtracklayer Suggests: BSgenome.Drerio.UCSC.danRer10, knitr, testthat, ATACseqQC, rmarkdown, BiocStyle License: GPL-3 MD5sum: 67e8b262cbdf0b3517834888f6af6a66 NeedsCompilation: no Title: Transcription Factor Enrichment Analysis for ATAC-seq Description: Assay for Transpose-Accessible Chromatin using sequencing (ATAC-seq) is a technique to assess genome-wide chromatin accessibility by probing open chromatin with hyperactive mutant Tn5 Transposase that inserts sequencing adapters into open regions of the genome. ATACseqTFEA is an improvement of the current computational method that detects differential activity of transcription factors (TFs). ATACseqTFEA not only uses the difference of open region information, but also (or emphasizes) the difference of TFs footprints (cutting sites or insertion sites). ATACseqTFEA provides an easy, rigorous way to broadly assess TF activity changes between two conditions. biocViews: Sequencing, DNASeq, ATACSeq, MNaseSeq, GeneRegulation Author: Jianhong Ou [aut, cre] (ORCID: ) Maintainer: Jianhong Ou URL: https://github.com/jianhong/ATACseqTFEA VignetteBuilder: knitr BugReports: https://github.com/jianhong/ATACseqTFEA/issues git_url: https://git.bioconductor.org/packages/ATACseqTFEA git_branch: RELEASE_3_22 git_last_commit: 77ec3ae git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ATACseqTFEA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ATACseqTFEA_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ATACseqTFEA_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ATACseqTFEA_1.12.0.tgz vignettes: vignettes/ATACseqTFEA/inst/doc/ATACseqTFEA.html vignetteTitles: ATACseqTFEA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqTFEA/inst/doc/ATACseqTFEA.R dependencyCount: 103 Package: atena Version: 1.16.0 Depends: R (>= 4.3.0), SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, MatrixGenerics, BiocParallel, S4Vectors, IRanges, Seqinfo, GenomicFeatures, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, SQUAREM, sparseMatrixStats, AnnotationHub, matrixStats, cli Suggests: covr, BiocStyle, knitr, rmarkdown, RUnit, TxDb.Dmelanogaster.UCSC.dm6.ensGene, RColorBrewer License: Artistic-2.0 Archs: x64 MD5sum: 92e13f45f9000b719c908d8c41d30a08 NeedsCompilation: no Title: Analysis of Transposable Elements Description: Quantify expression of transposable elements (TEs) from RNA-seq data through different methods, including ERVmap, TEtranscripts and Telescope. A common interface is provided to use each of these methods, which consists of building a parameter object, calling the quantification function with this object and getting a SummarizedExperiment object as output container of the quantified expression profiles. The implementation allows one to quantify TEs and gene transcripts in an integrated manner. biocViews: Transcription, Transcriptomics, RNASeq, Sequencing, Preprocessing, Software, GeneExpression, Coverage, DifferentialExpression, FunctionalGenomics Author: Beatriz Calvo-Serra [aut], Robert Castelo [aut, cre] Maintainer: Robert Castelo URL: https://github.com/rcastelo/atena VignetteBuilder: knitr BugReports: https://github.com/rcastelo/atena/issues git_url: https://git.bioconductor.org/packages/atena git_branch: RELEASE_3_22 git_last_commit: 93211ad git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/atena_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/atena_1.15.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/atena_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/atena_1.16.0.tgz vignettes: vignettes/atena/inst/doc/atena.html vignetteTitles: An introduction to the atena package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atena/inst/doc/atena.R dependencyCount: 101 Package: atSNP Version: 1.26.0 Depends: R (>= 3.6) Imports: BSgenome, BiocFileCache, BiocParallel, Rcpp, data.table, ggplot2, grDevices, graphics, grid, motifStack, rappdirs, stats, testthat, utils, lifecycle LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: bdd42947e43bc4e1e378b1a1cdab575f NeedsCompilation: yes Title: Affinity test for identifying regulatory SNPs Description: atSNP performs affinity tests of motif matches with the SNP or the reference genomes and SNP-led changes in motif matches. biocViews: Software, ChIPSeq, GenomeAnnotation, MotifAnnotation, Visualization Author: Chandler Zuo [aut], Sunyoung Shin [aut, cre], Sunduz Keles [aut] Maintainer: Sunyoung Shin URL: https://github.com/sunyoungshin/atSNP VignetteBuilder: knitr BugReports: https://github.com/sunyoungshin/atSNP/issues git_url: https://git.bioconductor.org/packages/atSNP git_branch: RELEASE_3_22 git_last_commit: fab0a78 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/atSNP_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/atSNP_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/atSNP_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/atSNP_1.26.0.tgz vignettes: vignettes/atSNP/inst/doc/atsnp-vignette.html vignetteTitles: atsnp-vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atSNP/inst/doc/atsnp-vignette.R dependencyCount: 140 Package: attract Version: 1.62.0 Depends: R (>= 3.4.0), AnnotationDbi Imports: Biobase, limma, cluster, GOstats, graphics, stats, reactome.db, KEGGREST, org.Hs.eg.db, utils, methods Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) MD5sum: e82efa70a1770ae9ceab1111b0ae05e4 NeedsCompilation: no Title: Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape Description: This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression. biocViews: ImmunoOncology, KEGG, Reactome, GeneExpression, Pathways, GeneSetEnrichment, Microarray, RNASeq Author: Jessica Mar Maintainer: Samuel Zimmerman git_url: https://git.bioconductor.org/packages/attract git_branch: RELEASE_3_22 git_last_commit: c4f9ff4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/attract_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/attract_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/attract_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/attract_1.62.0.tgz vignettes: vignettes/attract/inst/doc/attract.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{attract} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/attract/inst/doc/attract.R dependencyCount: 70 Package: AUCell Version: 1.32.0 Imports: DelayedArray, DelayedMatrixStats, data.table, graphics, grDevices, GSEABase, Matrix, methods, mixtools, R.utils, stats, SummarizedExperiment, BiocGenerics, utils Suggests: Biobase, BiocStyle, doSNOW, dynamicTreeCut, DT, GEOquery, knitr, NMF, plyr, R2HTML, rmarkdown, reshape2, plotly, Rtsne, testthat, zoo Enhances: doMC, doRNG, doParallel, foreach License: GPL-3 MD5sum: f65aac910be023f1ae26ba53a1ed3f8c NeedsCompilation: no Title: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures) Description: AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, Transcription, GeneExpression, WorkflowStep, Normalization Author: Sara Aibar, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium. Maintainer: Gert Hulselmans URL: http://scenic.aertslab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AUCell git_branch: RELEASE_3_22 git_last_commit: a57f250 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AUCell_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AUCell_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AUCell_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AUCell_1.32.0.tgz vignettes: vignettes/AUCell/inst/doc/AUCell.html vignetteTitles: AUCell: Identifying cells with active gene sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AUCell/inst/doc/AUCell.R dependsOnMe: OSCA.basic importsMe: RcisTarget, OSTA suggestsMe: decoupleR, escape, pathMED, scDiagnostics dependencyCount: 116 Package: autonomics Version: 1.18.0 Depends: R (>= 4.0) Imports: abind, arrow, BiocFileCache, BiocGenerics, bit64, cluster, codingMatrices, colorspace, data.table, dplyr, edgeR, ggforce, ggplot2, ggrepel, graphics, grDevices, grid, gridExtra, limma, lme4, magrittr, matrixStats, methods, MultiAssayExperiment, parallel, RColorBrewer, rlang, R.utils, readxl, S4Vectors, scales, stats, stringi, SummarizedExperiment, survival, tidyr, tidyselect, tools, utils, vsn Suggests: affy, AnnotationDbi, AnnotationHub, apcluster, Biobase, BiocManager, BiocStyle, Biostrings, coin, diagram, DBI, e1071, ensembldb, GenomicDataCommons, GenomicRanges, GEOquery, ggstance, ggridges, ggtext, hgu95av2.db, ICSNP, jsonlite, knitr, lmerTest, MASS, mclust, mixOmics, mixtools, mpm, nlme, OlinkAnalyze, org.Hs.eg.db, org.Mm.eg.db, patchwork, pcaMethods, pheatmap, progeny, propagate, RCurl, RSQLite, remotes, rmarkdown, ropls, Rsubread, readODS, rtracklayer, statmod, testthat, UniProt.ws, writexl, XML License: GPL-3 Archs: x64 MD5sum: 368f7b10c0c23e49c951cdf6cb3c0642 NeedsCompilation: no Title: Unified Statistical Modeling of Omics Data Description: This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). Across survival methods (coxph, survdiff, coin). It provides a fast enrichment analysis implementation. biocViews: Software, DataImport, Preprocessing, DimensionReduction, PrincipalComponent, Regression, DifferentialExpression, GeneSetEnrichment, Transcriptomics, Transcription, GeneExpression, RNASeq, Microarray, Proteomics, Metabolomics, MassSpectrometry, Author: Aditya Bhagwat [aut, cre], Richard Cotton [aut], Vanessa Beutgen [ctb], Witold Szymanski [ctb], Shahina Hayat [ctb], Laure Cougnaud [ctb], Hinrich Goehlmann [sad], Karsten Suhre [sad], Johannes Graumann [aut, sad] Maintainer: Aditya Bhagwat VignetteBuilder: knitr BugReports: https://gitlab.uni-marburg.de/fb20/ag-graumann/software/autonomics/issues git_url: https://git.bioconductor.org/packages/autonomics git_branch: RELEASE_3_22 git_last_commit: c22fce2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/autonomics_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/autonomics_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/autonomics_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/autonomics_1.18.0.tgz vignettes: vignettes/autonomics/inst/doc/autonomics_platformaware_analysis.html vignetteTitles: autonomics_platformaware_analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/autonomics/inst/doc/autonomics_platformaware_analysis.R dependencyCount: 121 Package: AWAggregator Version: 1.0.0 Depends: R (>= 4.5.0) Imports: dplyr, Peptides, progress, purrr, ranger, rlang, stats, stringr, tidyr, toOrdinal, utils Suggests: AWAggregatorData, BiocStyle, ExperimentHub, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 670a92cfd73303d5c42f58d082f7cc5d NeedsCompilation: no Title: Attribute-Weighted Aggregation Description: This package implements an attribute-weighted aggregation algorithm which leverages peptide-spectrum match (PSM) attributes to provide a more accurate estimate of protein abundance compared to conventional aggregation methods. This algorithm employs pre-trained random forest models to predict the quantitative inaccuracy of PSMs based on their attributes. PSMs are then aggregated to the protein level using a weighted average, taking the predicted inaccuracy into account. Additionally, the package allows users to construct their own training sets that are more relevant to their specific experimental conditions if desired. biocViews: Software, MassSpectrometry, Preprocessing, Proteomics, Regression Author: Jiahua Tan [aut, cre] (ORCID: ), Gian L. Negri [aut] (ORCID: ), Gregg B. Morin [aut] (ORCID: ), David D. Y. Chen [aut] (ORCID: ) Maintainer: Jiahua Tan URL: https://github.com/Tan-Jiahua/AWAggregator VignetteBuilder: knitr BugReports: https://github.com/Tan-Jiahua/AWAggregator/issues git_url: https://git.bioconductor.org/packages/AWAggregator git_branch: RELEASE_3_22 git_last_commit: 8fdf64b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AWAggregator_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AWAggregator_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AWAggregator_1.0.0.tgz vignettes: vignettes/AWAggregator/inst/doc/AWAggregator-vignette.html vignetteTitles: AWAggregator vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AWAggregator/inst/doc/AWAggregator-vignette.R dependencyCount: 54 Package: AWFisher Version: 1.24.0 Depends: R (>= 3.6) Imports: edgeR, limma, stats Suggests: knitr, tightClust License: GPL-3 MD5sum: a1001f89412da67f27e4456e68878f70 NeedsCompilation: yes Title: An R package for fast computing for adaptively weighted fisher's method Description: Implementation of the adaptively weighted fisher's method, including fast p-value computing, variability index, and meta-pattern. biocViews: StatisticalMethod, Software Author: Zhiguang Huo Maintainer: Zhiguang Huo VignetteBuilder: knitr BugReports: https://github.com/Caleb-Huo/AWFisher/issues git_url: https://git.bioconductor.org/packages/AWFisher git_branch: RELEASE_3_22 git_last_commit: f7b0edc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/AWFisher_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AWFisher_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AWFisher_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AWFisher_1.24.0.tgz vignettes: vignettes/AWFisher/inst/doc/AWFisher.html vignetteTitles: AWFisher hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AWFisher/inst/doc/AWFisher.R dependencyCount: 11 Package: awst Version: 1.18.0 Imports: stats, methods, SummarizedExperiment Suggests: airway, ggplot2, testthat, EDASeq, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown License: MIT + file LICENSE MD5sum: 3d2a4a4b7e9c6ac7bed7c5f2e9ed5c3d NeedsCompilation: no Title: Asymmetric Within-Sample Transformation Description: We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts. biocViews: Normalization, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph] (ORCID: ), Stefano Pagnotta [aut, cph] (ORCID: ) Maintainer: Davide Risso URL: https://github.com/drisso/awst VignetteBuilder: knitr BugReports: https://github.com/drisso/awst/issues git_url: https://git.bioconductor.org/packages/awst git_branch: RELEASE_3_22 git_last_commit: fa8583c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/awst_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/awst_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/awst_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/awst_1.18.0.tgz vignettes: vignettes/awst/inst/doc/awst_intro.html vignetteTitles: Introduction to awst hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/awst/inst/doc/awst_intro.R dependencyCount: 25 Package: BaalChIP Version: 1.36.0 Depends: R (>= 3.3.1), GenomicRanges, IRanges, Rsamtools, Imports: GenomicAlignments, GenomeInfoDb, doParallel, parallel, doBy, reshape2, scales, coda, foreach, ggplot2, methods, utils, graphics, stats Suggests: RUnit, BiocGenerics, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 80430e4f4313bd04fff6a297daa390ea NeedsCompilation: no Title: BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes Description: The package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. Computes allele counts at individual variants (SNPs/SNVs), implements extensive QC steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples. biocViews: Software, ChIPSeq, Bayesian, Sequencing Author: Ines de Santiago, Wei Liu, Ke Yuan, Martin O'Reilly, Chandra SR Chilamakuri, Bruce Ponder, Kerstin Meyer, Florian Markowetz Maintainer: Ines de Santiago VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BaalChIP git_branch: RELEASE_3_22 git_last_commit: 4f4fba9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BaalChIP_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BaalChIP_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BaalChIP_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BaalChIP_1.36.0.tgz vignettes: vignettes/BaalChIP/inst/doc/BaalChIP.html vignetteTitles: Analyzing ChIP-seq and FAIRE-seq data with the BaalChIP package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaalChIP/inst/doc/BaalChIP.R dependencyCount: 94 Package: bacon Version: 1.38.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) Archs: x64 MD5sum: 5744d0b66c111f404bc38b3ccf0f1452 NeedsCompilation: yes Title: Controlling bias and inflation in association studies using the empirical null distribution Description: Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores. biocViews: ImmunoOncology, StatisticalMethod, Bayesian, Regression, GenomeWideAssociation, Transcriptomics, RNASeq, MethylationArray, BatchEffect, MultipleComparison Author: Maarten van Iterson [aut, cre], Erik van Zwet [ctb] Maintainer: Maarten van Iterson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bacon git_branch: RELEASE_3_22 git_last_commit: 7b49c5b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bacon_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bacon_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bacon_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bacon_1.38.0.tgz vignettes: vignettes/bacon/inst/doc/bacon.html vignetteTitles: Controlling bias and inflation in association studies using the empirical null distribution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bacon/inst/doc/bacon.R dependencyCount: 33 Package: BADER Version: 1.48.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 MD5sum: 98fe8b0130cfb8bfd2279d7cdf99d911 NeedsCompilation: yes Title: Bayesian Analysis of Differential Expression in RNA Sequencing Data Description: For RNA sequencing count data, BADER fits a Bayesian hierarchical model. The algorithm returns the posterior probability of differential expression for each gene between two groups A and B. The joint posterior distribution of the variables in the model can be returned in the form of posterior samples, which can be used for further down-stream analyses such as gene set enrichment. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, Software, SAGE Author: Andreas Neudecker, Matthias Katzfuss Maintainer: Andreas Neudecker git_url: https://git.bioconductor.org/packages/BADER git_branch: RELEASE_3_22 git_last_commit: 9b68706 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BADER_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BADER_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BADER_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BADER_1.48.0.tgz vignettes: vignettes/BADER/inst/doc/BADER.pdf vignetteTitles: Analysing RNA-Seq data with the "BADER" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BADER/inst/doc/BADER.R dependencyCount: 0 Package: BadRegionFinder Version: 1.38.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: fccdb9433eb364e57978443f4bebd051 NeedsCompilation: no Title: BadRegionFinder: an R/Bioconductor package for identifying regions with bad coverage Description: BadRegionFinder is a package for identifying regions with a bad, acceptable and good coverage in sequence alignment data available as bam files. The whole genome may be considered as well as a set of target regions. Various visual and textual types of output are available. biocViews: Coverage, Sequencing, Alignment, WholeGenome, Classification Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BadRegionFinder git_branch: RELEASE_3_22 git_last_commit: 6395c38 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BadRegionFinder_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BadRegionFinder_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BadRegionFinder_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BadRegionFinder_1.38.0.tgz vignettes: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.pdf vignetteTitles: Using BadRegionFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.R dependencyCount: 99 Package: BAGS Version: 2.50.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: x64 MD5sum: 8b4495813d2b53e9fbd50ffae5e8ce24 NeedsCompilation: yes Title: A Bayesian Approach for Geneset Selection Description: R package providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest. biocViews: Bayesian Author: Alejandro Quiroz-Zarate Maintainer: Alejandro Quiroz-Zarate git_url: https://git.bioconductor.org/packages/BAGS git_branch: RELEASE_3_22 git_last_commit: 28661fe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BAGS_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BAGS_2.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BAGS_2.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BAGS_2.50.0.tgz vignettes: vignettes/BAGS/inst/doc/BAGS.pdf vignetteTitles: BAGS: A Bayesian Approach for Geneset Selection. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAGS/inst/doc/BAGS.R dependencyCount: 8 Package: ballgown Version: 2.42.0 Depends: R (>= 3.5.0), methods Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors (>= 0.9.39), RColorBrewer, splines, sva, limma, rtracklayer (>= 1.29.25), Biobase (>= 2.25.0), Seqinfo Suggests: testthat, knitr, markdown License: Artistic-2.0 Archs: x64 MD5sum: 6a89b7f0daafa3db14013c7c55897dac NeedsCompilation: no Title: Flexible, isoform-level differential expression analysis Description: Tools for statistical analysis of assembled transcriptomes, including flexible differential expression analysis, visualization of transcript structures, and matching of assembled transcripts to annotation. biocViews: ImmunoOncology, RNASeq, StatisticalMethod, Preprocessing, DifferentialExpression Author: Jack Fu [aut], Alyssa C. Frazee [aut, cre], Leonardo Collado-Torres [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Jack Fu VignetteBuilder: knitr BugReports: https://github.com/alyssafrazee/ballgown/issues git_url: https://git.bioconductor.org/packages/ballgown git_branch: RELEASE_3_22 git_last_commit: f1b4171 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ballgown_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ballgown_2.41.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ballgown_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ballgown_2.42.0.tgz vignettes: vignettes/ballgown/inst/doc/ballgown.html vignetteTitles: Flexible isoform-level differential expression analysis with Ballgown hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ballgown/inst/doc/ballgown.R dependsOnMe: VaSP suggestsMe: variancePartition dependencyCount: 88 Package: bambu Version: 3.12.0 Depends: R(>= 4.1), SummarizedExperiment(>= 1.1.6), S4Vectors(>= 0.22.1), BSgenome, IRanges Imports: BiocGenerics, BiocParallel, data.table, dplyr, tidyr, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, stats, Rsamtools, methods, Rcpp, xgboost LinkingTo: Rcpp, RcppArmadillo Suggests: AnnotationDbi, Biostrings, rmarkdown, BiocFileCache, ggplot2, ComplexHeatmap, circlize, ggbio, gridExtra, knitr, testthat, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, ExperimentHub (>= 1.15.3), DESeq2, NanoporeRNASeq, purrr, apeglm, utils, DEXSeq Enhances: parallel License: GPL-3 + file LICENSE MD5sum: 924c2738c10d92786303d487ded6ecc8 NeedsCompilation: yes Title: Context-Aware Transcript Quantification from Long Read RNA-Seq data Description: bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage. biocViews: Alignment, Coverage, DifferentialExpression, FeatureExtraction, GeneExpression, GenomeAnnotation, GenomeAssembly, ImmunoOncology, LongRead, MultipleComparison, Normalization, RNASeq, Regression, Sequencing, Software, Transcription, Transcriptomics Author: Ying Chen [cre, aut], Andre Sim [aut], Yuk Kei Wan [aut], Jonathan Goeke [aut] Maintainer: Ying Chen URL: https://github.com/GoekeLab/bambu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bambu git_branch: RELEASE_3_22 git_last_commit: 4b0b622 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bambu_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bambu_3.11.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bambu_3.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bambu_3.12.0.tgz vignettes: vignettes/bambu/inst/doc/bambu.html vignetteTitles: bambu hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bambu/inst/doc/bambu.R importsMe: FLAMES suggestsMe: NanoporeRNASeq dependencyCount: 94 Package: bamsignals Version: 1.42.0 Depends: R (>= 3.5.0) Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges, GenomicRanges LinkingTo: Rcpp, Rhtslib (>= 1.13.1) Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 4db62ea81f938998d82b3b9b537b781f NeedsCompilation: yes Title: Extract read count signals from bam files Description: This package allows to efficiently obtain count vectors from indexed bam files. It counts the number of reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data. biocViews: DataImport, Sequencing, Coverage, Alignment Author: Alessandro Mammana [aut, cre], Johannes Helmuth [aut] Maintainer: Johannes Helmuth URL: https://github.com/lamortenera/bamsignals SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/lamortenera/bamsignals/issues git_url: https://git.bioconductor.org/packages/bamsignals git_branch: RELEASE_3_22 git_last_commit: 47952a1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bamsignals_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bamsignals_1.41.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bamsignals_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bamsignals_1.42.0.tgz vignettes: vignettes/bamsignals/inst/doc/bamsignals.html vignetteTitles: Introduction to the bamsignals package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R importsMe: crupR, epigraHMM, karyoploteR, normr, segmenter, hoardeR dependencyCount: 14 Package: BANDITS Version: 1.26.0 Depends: R (>= 4.3.0) Imports: Rcpp, doRNG, MASS, data.table, R.utils, doParallel, parallel, foreach, methods, stats, graphics, ggplot2, DRIMSeq, BiocParallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, tximport, BiocStyle, GenomicFeatures, Biostrings License: GPL (>= 3) MD5sum: dfed1bdc2d6d2d282f873addabee33c1 NeedsCompilation: yes Title: BANDITS: Bayesian ANalysis of DIfferenTial Splicing Description: BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via differential transcript usage (DTU), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts. biocViews: DifferentialSplicing, AlternativeSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization Author: Simone Tiberi [aut, cre]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/BANDITS SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/BANDITS/issues git_url: https://git.bioconductor.org/packages/BANDITS git_branch: RELEASE_3_22 git_last_commit: eafee14 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BANDITS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BANDITS_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BANDITS_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BANDITS_1.26.0.tgz vignettes: vignettes/BANDITS/inst/doc/BANDITS.html vignetteTitles: BANDITS: Bayesian ANalysis of DIfferenTial Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BANDITS/inst/doc/BANDITS.R importsMe: DifferentialRegulation dependencyCount: 64 Package: bandle Version: 1.14.0 Depends: R (>= 4.1), S4Vectors, Biobase, MSnbase, pRoloc Imports: Rcpp (>= 1.0.4.6), pRolocdata, lbfgs, ggplot2, dplyr, plyr, knitr, methods, BiocParallel, robustbase, BiocStyle, ggalluvial, ggrepel, tidyr, circlize, graphics, stats, utils, grDevices, rlang, RColorBrewer, gtools, gridExtra, coda (>= 0.19-4) LinkingTo: Rcpp, RcppArmadillo, BH Suggests: testthat, interp, fields, pheatmap, viridis, rmarkdown, spelling License: Artistic-2.0 Archs: x64 MD5sum: 0de9832a934d8b49592edf68cf721b65 NeedsCompilation: yes Title: An R package for the Bayesian analysis of differential subcellular localisation experiments Description: The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental methods supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data. biocViews: Bayesian, Classification, Clustering, ImmunoOncology, QualityControl,DataImport, Proteomics, MassSpectrometry Author: Oliver M. Crook [aut, cre] (ORCID: ), Lisa Breckels [aut] (ORCID: ) Maintainer: Oliver M. Crook URL: http://github.com/ococrook/bandle VignetteBuilder: knitr BugReports: https://github.com/ococrook/bandle/issues git_url: https://git.bioconductor.org/packages/bandle git_branch: RELEASE_3_22 git_last_commit: 9a9a1bc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bandle_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bandle_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bandle_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bandle_1.14.0.tgz vignettes: vignettes/bandle/inst/doc/v01-getting-started.html, vignettes/bandle/inst/doc/v02-workflow.html vignetteTitles: Analysing differential localisation experiments with BANDLE: Vignette 1, Analysing differential localisation experiments with BANDLE: Vignette 2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bandle/inst/doc/v01-getting-started.R, vignettes/bandle/inst/doc/v02-workflow.R dependencyCount: 237 Package: Banksy Version: 1.6.0 Depends: R (>= 4.4.0) Imports: aricode, BiocParallel, data.table, dbscan, SpatialExperiment, SingleCellExperiment, SummarizedExperiment, S4Vectors, stats, Matrix, MatrixGenerics, mclust, igraph, irlba, leidenAlg (>= 1.1.0), utils, uwot, RcppHungarian, GenomeInfoDb Suggests: knitr, rmarkdown, pals, scuttle, scater, scran, cowplot, ggplot2, testthat (>= 3.0.0), harmony, Seurat, ExperimentHub, spatialLIBD, BiocStyle License: file LICENSE Archs: x64 MD5sum: 7ae4066a2b7b3b480bb16e9cd16d46fd NeedsCompilation: no Title: Spatial transcriptomic clustering Description: Banksy is an R package that incorporates spatial information to cluster cells in a feature space (e.g. gene expression). To incorporate spatial information, BANKSY computes the mean neighborhood expression and azimuthal Gabor filters that capture gene expression gradients. These features are combined with the cell's own expression to embed cells in a neighbor-augmented product space which can then be clustered, allowing for accurate and spatially-aware cell typing and tissue domain segmentation. biocViews: Clustering, Spatial, SingleCell, GeneExpression, DimensionReduction Author: Vipul Singhal [aut], Joseph Lee [aut, cre] (ORCID: ) Maintainer: Joseph Lee URL: https://github.com/prabhakarlab/Banksy VignetteBuilder: knitr BugReports: https://github.com/prabhakarlab/Banksy/issues git_url: https://git.bioconductor.org/packages/Banksy git_branch: RELEASE_3_22 git_last_commit: 6847ab5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Banksy_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Banksy_1.5.8.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Banksy_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Banksy_1.6.0.tgz vignettes: vignettes/Banksy/inst/doc/batch-correction.html, vignettes/Banksy/inst/doc/domain-segment.html, vignettes/Banksy/inst/doc/multi-sample.html, vignettes/Banksy/inst/doc/parameter-selection.html vignetteTitles: Spatial data integration with Harmony (10x Visium Human DLPFC), Domain segmentation (STARmap PLUS mouse brain), Multi-sample analysis (10x Visium Human DLPFC), Parameter selection (VeraFISH Mouse Hippocampus) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Banksy/inst/doc/batch-correction.R, vignettes/Banksy/inst/doc/domain-segment.R, vignettes/Banksy/inst/doc/multi-sample.R, vignettes/Banksy/inst/doc/parameter-selection.R importsMe: OSTA dependencyCount: 195 Package: banocc Version: 1.34.0 Depends: R (>= 3.5.1), rstan (>= 2.17.4) Imports: coda (>= 0.18.1), mvtnorm, stringr Suggests: knitr, rmarkdown, methods, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 6942a8f8b360acfee6542ac1cb57c852 NeedsCompilation: no Title: Bayesian ANalysis Of Compositional Covariance Description: BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with `rstan`. It provides as output the `stanfit` object as well as posterior median and credible interval estimates for each correlation element. biocViews: ImmunoOncology, Metagenomics, Software, Bayesian Author: Emma Schwager [aut, cre], Curtis Huttenhower [aut] Maintainer: George Weingart , Curtis Huttenhower VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/banocc git_branch: RELEASE_3_22 git_last_commit: a81f897 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/banocc_1.34.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/banocc_1.34.0.tgz vignettes: vignettes/banocc/inst/doc/banocc-vignette.html vignetteTitles: BAnOCC (Bayesian Analysis of Compositional Covariance) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/banocc/inst/doc/banocc-vignette.R dependencyCount: 59 Package: barbieQ Version: 1.2.0 Depends: R (>= 4.5) Imports: magrittr, tidyr, dplyr, grid, circlize, ComplexHeatmap, ggplot2, logistf, limma, stats, igraph, utils, data.table, S4Vectors, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: GPL-3 Archs: x64 MD5sum: 6aa75f8cb3fc853dc85a2432978bba7f NeedsCompilation: no Title: Analyze Barcode Data from Clonal Tracking Experiments Description: The barbieQ package provides a series of robust statistical tools for analysing barcode count data generated from cell clonal tracking (i.e., lineage tracing) experiments. In these experiments, an initial cell and its offspring collectively form a clone (i.e., lineage). A unique barcode sequence, incorporated into the DNA of the inital cell, is inherited within the clone. This one-to-one mapping of barcodes to clones enables clonal tracking of their behaviors. By counting barcodes, researchers can quantify the population abundance of individual clones under specific experimental perturbations. barbieQ supports barcode count data preprocessing, statistical testing, and visualization. biocViews: Sequencing, Software, Regression, Preprocessing, Visualization Author: Liyang Fei [aut, cre] (ORCID: ) Maintainer: Liyang Fei URL: https://github.com/Oshlack/barbieQ/issues VignetteBuilder: knitr BugReports: https://github.com/Oshlack/barbieQ git_url: https://git.bioconductor.org/packages/barbieQ git_branch: RELEASE_3_22 git_last_commit: 713137b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/barbieQ_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/barbieQ_1.1.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/barbieQ_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/barbieQ_1.2.0.tgz vignettes: vignettes/barbieQ/inst/doc/barbieQ.html vignetteTitles: Quick start to barbieQ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/barbieQ/inst/doc/barbieQ.R dependencyCount: 114 Package: basecallQC Version: 1.34.0 Depends: R (>= 3.4), stats, utils, methods, rmarkdown, knitr, prettydoc, yaml Imports: ggplot2, stringr, XML, raster, dplyr, data.table, tidyr, magrittr, DT, lazyeval, ShortRead Suggests: testthat, BiocStyle License: GPL (>= 3) Archs: x64 MD5sum: 1a9f8a9b64cf4e166432e3f1bde34cae NeedsCompilation: no Title: Working with Illumina Basecalling and Demultiplexing input and output files Description: The basecallQC package provides tools to work with Illumina bcl2Fastq (versions >= 2.1.7) software.Prior to basecalling and demultiplexing using the bcl2Fastq software, basecallQC functions allow the user to update Illumina sample sheets from versions <= 1.8.9 to >= 2.1.7 standards, clean sample sheets of common problems such as invalid sample names and IDs, create read and index basemasks and the bcl2Fastq command. Following the generation of basecalled and demultiplexed data, the basecallQC packages allows the user to generate HTML tables, plots and a self contained report of summary metrics from Illumina XML output files. biocViews: Sequencing, Infrastructure, DataImport, QualityControl Author: Thomas Carroll and Marian Dore Maintainer: Thomas Carroll SystemRequirements: bcl2Fastq (versions >= 2.1.7) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basecallQC git_branch: RELEASE_3_22 git_last_commit: 7d04b5c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/basecallQC_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/basecallQC_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/basecallQC_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/basecallQC_1.34.0.tgz vignettes: vignettes/basecallQC/inst/doc/basecallQC.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basecallQC/inst/doc/basecallQC.R dependencyCount: 114 Package: BaseSpaceR Version: 1.54.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: 86755a4a14ef442307081a31bc947338 NeedsCompilation: no Title: R SDK for BaseSpace RESTful API Description: A rich R interface to Illumina's BaseSpace cloud computing environment, enabling the fast development of data analysis and visualisation tools. biocViews: Infrastructure, DataRepresentation, ConnectTools, Software, DataImport, HighThroughputSequencing, Sequencing, Genetics Author: Adrian Alexa Maintainer: Jared O'Connell git_url: https://git.bioconductor.org/packages/BaseSpaceR git_branch: RELEASE_3_22 git_last_commit: b3a083f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BaseSpaceR_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BaseSpaceR_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BaseSpaceR_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BaseSpaceR_1.54.0.tgz vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf vignetteTitles: BaseSpaceR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.R dependencyCount: 4 Package: Basic4Cseq Version: 1.46.0 Depends: R (>= 3.5.0), Biostrings, GenomicAlignments, caTools, GenomicRanges, grDevices, graphics, stats, utils Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 930965c15021d15caac2e201cf573309 NeedsCompilation: no Title: Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data Description: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile. biocViews: ImmunoOncology, Visualization, QualityControl, Sequencing, Coverage, Alignment, RNASeq, SequenceMatching, DataImport Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Basic4Cseq git_branch: RELEASE_3_22 git_last_commit: 9e614cc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Basic4Cseq_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Basic4Cseq_1.45.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Basic4Cseq_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Basic4Cseq_1.46.0.tgz vignettes: vignettes/Basic4Cseq/inst/doc/vignette.pdf vignetteTitles: Basic4Cseq: an R/Bioconductor package for the analysis of 4C-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Basic4Cseq/inst/doc/vignette.R dependencyCount: 61 Package: BASiCS Version: 2.22.0 Depends: R (>= 4.1), SingleCellExperiment Imports: Biobase, BiocGenerics, coda, cowplot, ggExtra, ggplot2, graphics, grDevices, MASS, methods, Rcpp (>= 0.11.3), S4Vectors, scran, scuttle, stats, stats4, SummarizedExperiment, viridis, utils, Matrix (>= 1.5.0), matrixStats, assertthat, reshape2, BiocParallel, posterior, hexbin LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, magick License: GPL-3 MD5sum: 620a0160af8ab2b99e95eee0b96118ce NeedsCompilation: yes Title: Bayesian Analysis of Single-Cell Sequencing data Description: Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology, ImmunoOncology Author: Catalina Vallejos [aut, cre] (ORCID: ), Nils Eling [aut], Alan O'Callaghan [aut], Sylvia Richardson [ctb], John Marioni [ctb] Maintainer: Catalina Vallejos URL: https://github.com/catavallejos/BASiCS VignetteBuilder: knitr BugReports: https://github.com/catavallejos/BASiCS/issues git_url: https://git.bioconductor.org/packages/BASiCS git_branch: RELEASE_3_22 git_last_commit: d0fd928 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BASiCS_2.22.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BASiCS_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BASiCS_2.22.0.tgz vignettes: vignettes/BASiCS/inst/doc/BASiCS.html vignetteTitles: Introduction to BASiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BASiCS/inst/doc/BASiCS.R dependsOnMe: BASiCStan suggestsMe: splatter dependencyCount: 129 Package: BASiCStan Version: 1.12.0 Depends: R (>= 4.2), BASiCS, rstan (>= 2.18.1) Imports: methods, glmGamPoi, scran, scuttle, stats, utils, SingleCellExperiment, SummarizedExperiment, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstantools (>= 2.1.1) LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: testthat (>= 3.0.0), knitr, rmarkdown License: GPL-3 MD5sum: 4dc3ff09cdc46af33528b533a8b57027 NeedsCompilation: yes Title: Stan implementation of BASiCS Description: Provides an interface to infer the parameters of BASiCS using the variational inference (ADVI), Markov chain Monte Carlo (NUTS), and maximum a posteriori (BFGS) inference engines in the Stan programming language. BASiCS is a Bayesian hierarchical model that uses an adaptive Metropolis within Gibbs sampling scheme. Alternative inference methods provided by Stan may be preferable in some situations, for example for particularly large data or posterior distributions with difficult geometries. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology Author: Alan O'Callaghan [aut, cre], Catalina Vallejos [aut] Maintainer: Alan O'Callaghan URL: https://github.com/Alanocallaghan/BASiCStan SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/BASiCStan/issues git_url: https://git.bioconductor.org/packages/BASiCStan git_branch: RELEASE_3_22 git_last_commit: 6e9027c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BASiCStan_1.12.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BASiCStan_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BASiCStan_1.12.0.tgz vignettes: vignettes/BASiCStan/inst/doc/BASiCStan.html vignetteTitles: An introduction to BASiCStan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BASiCStan/inst/doc/BASiCStan.R dependencyCount: 152 Package: BasicSTARRseq Version: 1.38.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,Seqinfo,stats Suggests: knitr License: LGPL-3 MD5sum: 73e4089af213eba0d905e7de91805773 NeedsCompilation: no Title: Basic peak calling on STARR-seq data Description: Basic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17. biocViews: PeakDetection, GeneRegulation, FunctionalPrediction, FunctionalGenomics, Coverage Author: Annika Buerger Maintainer: Annika Buerger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BasicSTARRseq git_branch: RELEASE_3_22 git_last_commit: e00f13d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BasicSTARRseq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BasicSTARRseq_1.37.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BasicSTARRseq_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BasicSTARRseq_1.38.0.tgz vignettes: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.pdf vignetteTitles: BasicSTARRseq.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.R dependencyCount: 42 Package: basilisk Version: 1.22.0 Depends: reticulate Imports: utils, methods, parallel, dir.expiry Suggests: knitr, rmarkdown, BiocStyle, testthat, callr License: GPL-3 MD5sum: b7097501b21ad7bedf2831a8883bafc0 NeedsCompilation: no Title: Freezing Python Dependencies Inside Bioconductor Packages Description: Installs a self-contained conda instance that is managed by the R/Bioconductor installation machinery. This aims to provide a consistent Python environment that can be used reliably by Bioconductor packages. Functions are also provided to enable smooth interoperability of multiple Python environments in a single R session. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph], Vince Carey [ctb] Maintainer: Aaron Lun VignetteBuilder: knitr BugReports: https://github.com/LTLA/basilisk/issues git_url: https://git.bioconductor.org/packages/basilisk git_branch: RELEASE_3_22 git_last_commit: 18be55f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/basilisk_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/basilisk_1.21.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/basilisk_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/basilisk_1.22.0.tgz vignettes: vignettes/basilisk/inst/doc/motivation.html vignetteTitles: Motivation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basilisk/inst/doc/motivation.R dependsOnMe: scviR importsMe: BiocHail, BiocSklearn, cbpManager, cfTools, crisprScore, densvis, DNAcycP2, DOtools, FLAMES, Ibex, MACSr, MOFA2, ontoProc, orthos, Rcwl, recountmethylation, ReUseData, scifer, scPipe, SimBu, sketchR, snifter, spatialDE, stPipe, velociraptor, zellkonverter, OSTA suggestsMe: CuratedAtlasQueryR, SUMO dependencyCount: 21 Package: basilisk.utils Version: 1.22.0 Imports: utils, methods, tools, dir.expiry Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 76aac1d78e6fbc380bce0ca6cf85403a NeedsCompilation: no Title: Centralized Conda Installation for Bioconductor Packages Description: Provides a centralized conda installation for use by other Bioconductor packages. If conda is not already available on the system, it is downloaded and installed from the Miniforge project; otherwise, no action is performed. Historically, this package was used to provide a Python installation for basilisk, hence the name. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basilisk.utils git_branch: RELEASE_3_22 git_last_commit: 30f426a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/basilisk.utils_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/basilisk.utils_1.21.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/basilisk.utils_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/basilisk.utils_1.22.0.tgz vignettes: vignettes/basilisk.utils/inst/doc/purpose.html vignetteTitles: conda for Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basilisk.utils/inst/doc/purpose.R importsMe: scifer suggestsMe: Ibex dependencyCount: 5 Package: batchCorr Version: 1.0.0 Depends: R (>= 4.4.0), SummarizedExperiment Imports: reshape, mclust, BiocParallel, methods Suggests: BiocStyle, knitr, testthat License: GPL-2 MD5sum: 767d2d7216e771158ae42e15ca98684c NeedsCompilation: no Title: Within And Between Batch Correction Of LC-MS Metabolomics Data Description: From the perspective of metabolites as the continuation of the central dogma of biology, metabolomics provides the closest link to many phenotypes of interest. This makes metabolomics research promising in teasing apart the complexities of living systems. However, due to experimental reasons, the data includes non-biological variation which limits quality and reproducibility, especially if the data is obtained from several batches. The batchCorr package reduces unwanted variation by way of between-batch alignment, within-batch drift correction and between-batch normalization using batch-specific quality control samples and long-term reference QC samples. Please see the associated article for more thorough descriptions of algorithms. biocViews: BiomedicalInformatics, Metabolomics, MassSpectrometry, BatchEffect, Normalization, QualityControl Author: Anton Ribbenstedt [cre] (ORCID: ), Carl Brunius [aut] (ORCID: ), Vilhelm Suksi [aut] Maintainer: Anton Ribbenstedt URL: https://github.com/MetaboComp/batchCorr VignetteBuilder: knitr BugReports: https://github.com/MetaboComp/batchCorr/issues git_url: https://git.bioconductor.org/packages/batchCorr git_branch: RELEASE_3_22 git_last_commit: 25f7e5b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/batchCorr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/batchCorr_0.99.8.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/batchCorr_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/batchCorr_1.0.0.tgz vignettes: vignettes/batchCorr/inst/doc/Introduction.html vignetteTitles: Introduction.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/batchCorr/inst/doc/Introduction.R suggestsMe: notameViz dependencyCount: 39 Package: batchelor Version: 1.26.0 Depends: SingleCellExperiment Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats, methods, utils, igraph, BiocNeighbors, BiocSingular, Matrix, SparseArray, DelayedArray (>= 0.31.5), DelayedMatrixStats, BiocParallel, scuttle, ResidualMatrix, ScaledMatrix, beachmat LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, scran, scater, bluster, scRNAseq License: GPL-3 MD5sum: d00d5f2a0f4cb77dbbb97849bbe2d1f9 NeedsCompilation: yes Title: Single-Cell Batch Correction Methods Description: Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression values. Functions are also provided to perform global rescaling to remove differences in depth between batches, and to perform a principal components analysis that is robust to differences in the numbers of cells across batches. biocViews: Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Normalization Author: Aaron Lun [aut, cre], Laleh Haghverdi [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/batchelor git_branch: RELEASE_3_22 git_last_commit: 8a9df0d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/batchelor_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/batchelor_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/batchelor_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/batchelor_1.26.0.tgz vignettes: vignettes/batchelor/inst/doc/correction.html, vignettes/batchelor/inst/doc/extension.html vignetteTitles: 1. Correcting batch effects, 2. Extending methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/batchelor/inst/doc/correction.R, vignettes/batchelor/inst/doc/extension.R dependsOnMe: OSCA.advanced, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: chevreulProcess, ChromSCape, mumosa, scMerge, singleCellTK, scPipeline suggestsMe: anglemania, TSCAN, Canek, RaceID dependencyCount: 56 Package: BatchQC Version: 2.6.0 Depends: R (>= 4.5.0) Imports: data.table, DESeq2, dplyr, EBSeq, edgeR, FNN, ggdendro, ggnewscale, ggplot2, limma, matrixStats, methods, MASS, pheatmap, RColorBrewer, reader, reshape2, scran, shiny, shinyjs, shinythemes, stats, SummarizedExperiment, sva, S4Vectors, tibble, tidyr, tidyverse, umap, utils Suggests: BiocManager, BiocStyle, bladderbatch, curatedTBData, devtools, knitr, lintr, MultiAssayExperiment, plotly, rmarkdown, spelling, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 99482c92e524c4f7c278e18173b09c23 NeedsCompilation: no Title: Batch Effects Quality Control Software Description: Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA. biocViews: BatchEffect, GraphAndNetwork, Microarray, Normalization, PrincipalComponent, Sequencing, Software, Visualization, QualityControl, RNASeq, Preprocessing, DifferentialExpression, ImmunoOncology Author: Jessica Anderson [aut, cre] (ORCID: ), W. Evan Johnson [aut] (ORCID: ), Solaiappan Manimaran [aut], Heather Selby [ctb], Claire Ruberman [ctb], Kwame Okrah [ctb], Hector Corrada Bravo [ctb], Michael Silverstein [ctb], Regan Conrad [ctb], Zhaorong Li [ctb], Evan Holmes [ctb], Solomon Joseph [ctb], Yaoan Leng [ctb] (ORCID: ), Howard Fan [ctb] Maintainer: Jessica Anderson URL: https://github.com/wejlab/BatchQC VignetteBuilder: knitr BugReports: https://github.com/wejlab/BatchQC/issues git_url: https://git.bioconductor.org/packages/BatchQC git_branch: RELEASE_3_22 git_last_commit: dce5259 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BatchQC_2.6.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BatchQC_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BatchQC_2.6.0.tgz vignettes: vignettes/BatchQC/inst/doc/BatchQC_examples.html, vignettes/BatchQC/inst/doc/BatchQC_Intro.html vignetteTitles: BatchQC Examples, Introdution to BatchQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BatchQC/inst/doc/BatchQC_examples.R, vignettes/BatchQC/inst/doc/BatchQC_Intro.R dependencyCount: 210 Package: BatchSVG Version: 1.2.0 Depends: R (>= 4.5.0) Imports: scry, dplyr, stats, rlang, cowplot, ggrepel, ggplot2, RColorBrewer, scales, SummarizedExperiment Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, spatialLIBD License: Artistic-2.0 MD5sum: 3455a5131f43bceda212e2af3dad0700 NeedsCompilation: no Title: Identify Batch-Biased Features in Spatially Variable Genes Description: `BatchSVG` is a feature-based Quality Control (QC) to identify SVGs on spatial transcriptomics data with specific types of batch effect. Regarding to the spatial transcriptomics data experiments, the batch can be defined as "sample", "sex", and etc.The `BatchSVG` method is based on binomial deviance model (Townes et al, 2019) and applies cutoffs based on the number of standard deviation (nSD) of relative change in deviance and rank difference as the data-driven thresholding approach to detect the batch-biased outliers. biocViews: Spatial, Transcriptomics, BatchEffect, QualityControl Author: Christine Hou [aut] (ORCID: ), Kinnary Shah [aut, cre], Jacqui Thompson [aut], Stephanie C. Hicks [aut, fnd] (ORCID: ) Maintainer: Kinnary Shah URL: https://github.com/christinehou11/BatchSVG, https://christinehou11.github.io/BatchSVG VignetteBuilder: knitr BugReports: https://github.com/christinehou11/BatchSVG/issues git_url: https://git.bioconductor.org/packages/BatchSVG git_branch: RELEASE_3_22 git_last_commit: 9593ada git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BatchSVG_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BatchSVG_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BatchSVG_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BatchSVG_1.2.0.tgz vignettes: vignettes/BatchSVG/inst/doc/spe.html vignetteTitles: 01 Tutorial for spe data object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BatchSVG/inst/doc/spe.R dependencyCount: 71 Package: BayesKnockdown Version: 1.36.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 MD5sum: ee211bdf1eb84fdc6010d1ab368a540e NeedsCompilation: no Title: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data Description: A simple, fast Bayesian method for computing posterior probabilities for relationships between a single predictor variable and multiple potential outcome variables, incorporating prior probabilities of relationships. In the context of knockdown experiments, the predictor variable is the knocked-down gene, while the other genes are potential targets. Can also be used for differential expression/2-class data. biocViews: NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: William Chad Young Maintainer: William Chad Young git_url: https://git.bioconductor.org/packages/BayesKnockdown git_branch: RELEASE_3_22 git_last_commit: 508624d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BayesKnockdown_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BayesKnockdown_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BayesKnockdown_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BayesKnockdown_1.36.0.tgz vignettes: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.pdf vignetteTitles: BayesKnockdown.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.R dependencyCount: 7 Package: BayesSpace Version: 1.20.0 Depends: R (>= 4.0.0), SingleCellExperiment Imports: Rcpp (>= 1.0.4.6), stats, methods, purrr, scater, scran, SummarizedExperiment, coda, rhdf5, S4Vectors, Matrix, magrittr, assertthat, arrow, mclust, RCurl, DirichletReg, xgboost (< 2.0.0), utils, dplyr, rlang, ggplot2, tibble, rjson, tidyr, scales, microbenchmark, BiocFileCache, BiocSingular, BiocParallel LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress Suggests: testthat, knitr, rmarkdown, igraph, spatialLIBD, viridis, patchwork, RColorBrewer, Seurat License: MIT + file LICENSE MD5sum: 44dc0a7a96334f8aa1137c74a4c0e805 NeedsCompilation: yes Title: Clustering and Resolution Enhancement of Spatial Transcriptomes Description: Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed. biocViews: Software, Clustering, Transcriptomics, GeneExpression, SingleCell, ImmunoOncology, DataImport Author: Edward Zhao [aut], Senbai Kang [aut, cre], Matt Stone [aut], Xing Ren [ctb], Raphael Gottardo [ctb] Maintainer: Senbai Kang URL: edward130603.github.io/BayesSpace SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/edward130603/BayesSpace/issues git_url: https://git.bioconductor.org/packages/BayesSpace git_branch: RELEASE_3_22 git_last_commit: b8f514b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BayesSpace_1.20.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BayesSpace_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BayesSpace_1.20.0.tgz vignettes: vignettes/BayesSpace/inst/doc/BayesSpace.html vignetteTitles: BayesSpace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BayesSpace/inst/doc/BayesSpace.R importsMe: RegionalST, OSTA dependencyCount: 147 Package: bayNorm Version: 1.28.0 Depends: R (>= 3.5), Imports: Rcpp (>= 0.12.12), BB, foreach, iterators, doSNOW, Matrix, parallel, MASS, locfit, fitdistrplus, stats, methods, graphics, grDevices, SingleCellExperiment, SummarizedExperiment, BiocParallel, utils LinkingTo: Rcpp, RcppArmadillo,RcppProgress Suggests: knitr, rmarkdown, BiocStyle, devtools, testthat License: GPL (>= 2) MD5sum: 7f3046db731ac3a04a1adf336231ec9f NeedsCompilation: yes Title: Single-cell RNA sequencing data normalization Description: bayNorm is used for normalizing single-cell RNA-seq data. biocViews: ImmunoOncology, Normalization, RNASeq, SingleCell,Sequencing Author: Wenhao Tang [aut, cre], Franois Bertaux [aut], Philipp Thomas [aut], Claire Stefanelli [aut], Malika Saint [aut], Samuel Marguerat [aut], Vahid Shahrezaei [aut] Maintainer: Wenhao Tang URL: https://github.com/WT215/bayNorm VignetteBuilder: knitr BugReports: https://github.com/WT215/bayNorm/issues git_url: https://git.bioconductor.org/packages/bayNorm git_branch: RELEASE_3_22 git_last_commit: a368043 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bayNorm_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bayNorm_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bayNorm_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bayNorm_1.28.0.tgz vignettes: vignettes/bayNorm/inst/doc/bayNorm.html vignetteTitles: Introduction to bayNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bayNorm/inst/doc/bayNorm.R dependencyCount: 50 Package: baySeq Version: 2.44.0 Depends: R (>= 2.3.0), methods Imports: edgeR, GenomicRanges, abind, parallel, graphics, stats, utils Suggests: BiocStyle, BiocGenerics License: GPL-3 Archs: x64 MD5sum: afd7608b27a147dd7c814b7bf48763ee NeedsCompilation: no Title: Empirical Bayesian analysis of patterns of differential expression in count data Description: This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE, Bayesian, Coverage Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/baySeq BugReports: https://github.com/samgg/baySeq/issues git_url: https://git.bioconductor.org/packages/baySeq git_branch: RELEASE_3_22 git_last_commit: 9204ac0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/baySeq_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/baySeq_2.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/baySeq_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/baySeq_2.44.0.tgz vignettes: vignettes/baySeq/inst/doc/baySeq_generic.pdf, vignettes/baySeq/inst/doc/baySeq.pdf vignetteTitles: Advanced baySeq analyses, baySeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/baySeq/inst/doc/baySeq_generic.R, vignettes/baySeq/inst/doc/baySeq.R dependsOnMe: clusterSeq, segmentSeq importsMe: riboSeqR dependencyCount: 20 Package: BBCAnalyzer Version: 1.40.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 6804e980fc33d11791b7cbf5632ba061 NeedsCompilation: no Title: BBCAnalyzer: an R/Bioconductor package for visualizing base counts Description: BBCAnalyzer is a package for visualizing the relative or absolute number of bases, deletions and insertions at defined positions in sequence alignment data available as bam files in comparison to the reference bases. Markers for the relative base frequencies, the mean quality of the detected bases, known mutations or polymorphisms and variants called in the data may additionally be included in the plots. biocViews: Sequencing, Alignment, Coverage, GeneticVariability, SNP Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BBCAnalyzer git_branch: RELEASE_3_22 git_last_commit: d118157 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BBCAnalyzer_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BBCAnalyzer_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BBCAnalyzer_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BBCAnalyzer_1.40.0.tgz vignettes: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.pdf vignetteTitles: Using BBCAnalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.R dependencyCount: 78 Package: BCRANK Version: 1.72.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 MD5sum: 338f808a2c1b1d67a232551a4d83f928 NeedsCompilation: yes Title: Predicting binding site consensus from ranked DNA sequences Description: Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search biocViews: MotifDiscovery, GeneRegulation Author: Adam Ameur Maintainer: Adam Ameur git_url: https://git.bioconductor.org/packages/BCRANK git_branch: RELEASE_3_22 git_last_commit: b625b23 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BCRANK_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BCRANK_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BCRANK_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BCRANK_1.72.0.tgz vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf vignetteTitles: BCRANK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BCRANK/inst/doc/BCRANK.R dependencyCount: 15 Package: bcSeq Version: 1.32.0 Depends: R (>= 3.4.0) Imports: Rcpp (>= 0.12.12), Matrix, Biostrings LinkingTo: Rcpp, Matrix Suggests: knitr License: GPL (>= 2) MD5sum: c894c15874b09f4edb211b7d32498057 NeedsCompilation: yes Title: Fast Sequence Mapping in High-Throughput shRNA and CRISPR Screens Description: This Rcpp-based package implements a highly efficient data structure and algorithm for performing alignment of short reads from CRISPR or shRNA screens to reference barcode library. Sequencing error are considered and matching qualities are evaluated based on Phred scores. A Bayes' classifier is employed to predict the originating barcode of a read. The package supports provision of user-defined probability models for evaluating matching qualities. The package also supports multi-threading. biocViews: ImmunoOncology, Alignment, CRISPR, Sequencing, SequenceMatching, MultipleSequenceAlignment, Software, ATACSeq Author: Jiaxing Lin [aut, cre], Jeremy Gresham [aut], Jichun Xie [aut], Kouros Owzar [aut], Tongrong Wang [ctb], So Young Kim [ctb], James Alvarez [ctb], Jeffrey S. Damrauer [ctb], Scott Floyd [ctb], Joshua Granek [ctb], Andrew Allen [ctb], Cliburn Chan [ctb] Maintainer: Jiaxing Lin URL: https://github.com/jl354/bcSeq VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/bcSeq git_branch: RELEASE_3_22 git_last_commit: 306f3a8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bcSeq_1.32.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bcSeq_1.32.0.tgz vignettes: vignettes/bcSeq/inst/doc/bcSeq.pdf vignetteTitles: bcSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bcSeq/inst/doc/bcSeq.R dependencyCount: 19 Package: beachmat Version: 2.26.0 Imports: methods, DelayedArray (>= 0.27.2), SparseArray, BiocGenerics, Matrix, Rcpp LinkingTo: Rcpp, assorthead (>= 1.3.3) Suggests: testthat, BiocStyle, knitr, rmarkdown, rcmdcheck, BiocParallel, HDF5Array, beachmat.hdf5 License: GPL-3 Archs: x64 MD5sum: 11a56ce6354e9e5b48f4854c7a328175 NeedsCompilation: yes Title: Compiling Bioconductor to Handle Each Matrix Type Description: Provides a consistent C++ class interface for reading from a variety of commonly used matrix types. Ordinary matrices and several sparse/dense Matrix classes are directly supported, along with a subset of the delayed operations implemented in the DelayedArray package. All other matrix-like objects are supported by calling back into R. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre], Hervé Pagès [aut], Mike Smith [aut] Maintainer: Aaron Lun URL: https://github.com/tatami-inc/beachmat SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/tatami-inc/beachmat/issues git_url: https://git.bioconductor.org/packages/beachmat git_branch: RELEASE_3_22 git_last_commit: 016c55e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/beachmat_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/beachmat_2.25.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/beachmat_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/beachmat_2.26.0.tgz vignettes: vignettes/beachmat/inst/doc/linking.html vignetteTitles: Developer guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat/inst/doc/linking.R importsMe: batchelor, beachmat.hdf5, beachmat.tiledb, BiocSingular, bsseq, DropletUtils, glmGamPoi, mumosa, omicsGMF, scater, scran, scrapper, scuttle, SingleR suggestsMe: mbkmeans, scCB2 linksToMe: beachmat.hdf5, beachmat.tiledb, BiocSingular, bsseq, dreamlet, DropletUtils, glmGamPoi, mbkmeans, scran, scrapper, scuttle, SingleR dependencyCount: 23 Package: beachmat.hdf5 Version: 1.8.0 Imports: methods, beachmat, HDF5Array, DelayedArray, Rcpp LinkingTo: Rcpp, assorthead, beachmat, Rhdf5lib Suggests: testthat, BiocStyle, knitr, rmarkdown, rhdf5, Matrix License: GPL-3 MD5sum: ef1dceb5ff5596aa3ca533aeab253058 NeedsCompilation: yes Title: beachmat bindings for HDF5-backed matrices Description: Extends beachmat to support initialization of tatami matrices from HDF5-backed arrays. This allows C++ code in downstream packages to directly call the HDF5 C/C++ library to access array data, without the need for block processing via DelayedArray. Some utilities are also provided for direct creation of an in-memory tatami matrix from a HDF5 file. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beachmat.hdf5 git_branch: RELEASE_3_22 git_last_commit: e3de22b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/beachmat.hdf5_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/beachmat.hdf5_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/beachmat.hdf5_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/beachmat.hdf5_1.8.0.tgz vignettes: vignettes/beachmat.hdf5/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat.hdf5/inst/doc/userguide.R suggestsMe: beachmat, sketchR dependencyCount: 29 Package: beachmat.tiledb Version: 1.2.0 Imports: methods, beachmat, tiledb, TileDBArray, DelayedArray, Rcpp LinkingTo: Rcpp, assorthead, beachmat Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix License: GPL-3 MD5sum: 039c55f2c690a0cdaed31b7b8475e0b2 NeedsCompilation: yes Title: beachmat bindings for TileDB-backed matrices Description: Extends beachmat to initialize tatami matrices from TileDB-backed arrays. This allows C++ code in downstream packages to directly call the TileDB C/C++ library to access array data, without the need for block processing via DelayedArray. Developers only need to import this package to automatically extend the capabilities of beachmat::initializeCpp to TileDBArray instances. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/tatami-inc/beachmat.tiledb SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/tatami-inc/beachmat.tiledb/issues git_url: https://git.bioconductor.org/packages/beachmat.tiledb git_branch: RELEASE_3_22 git_last_commit: 70c0ac3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/beachmat.tiledb_1.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/beachmat.tiledb_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/beachmat.tiledb_1.2.0.tgz vignettes: vignettes/beachmat.tiledb/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat.tiledb/inst/doc/userguide.R dependencyCount: 36 Package: BeadDataPackR Version: 1.62.0 Imports: stats, utils Suggests: BiocStyle, knitr License: GPL-2 MD5sum: 6a02231bae47780f26f0f0c753188d35 NeedsCompilation: yes Title: Compression of Illumina BeadArray data Description: Provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform. biocViews: Microarray Author: Mike Smith, Andy Lynch Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BeadDataPackR git_branch: RELEASE_3_22 git_last_commit: eed1f34 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BeadDataPackR_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BeadDataPackR_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BeadDataPackR_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BeadDataPackR_1.62.0.tgz vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf vignetteTitles: BeadDataPackR.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.R dependencyCount: 2 Package: BEAT Version: 1.48.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) Archs: x64 MD5sum: 4fd369cc44cff96eb48ff476746625db NeedsCompilation: no Title: BEAT - BS-Seq Epimutation Analysis Toolkit Description: Model-based analysis of single-cell methylation data biocViews: ImmunoOncology, Genetics, MethylSeq, Software, DNAMethylation, Epigenetics Author: Kemal Akman Maintainer: Kemal Akman git_url: https://git.bioconductor.org/packages/BEAT git_branch: RELEASE_3_22 git_last_commit: 6d10f1c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BEAT_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BEAT_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BEAT_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BEAT_1.48.0.tgz vignettes: vignettes/BEAT/inst/doc/BEAT.pdf vignetteTitles: Analysing single-cell BS-Seq data with the "BEAT" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEAT/inst/doc/BEAT.R dependencyCount: 70 Package: BEclear Version: 2.26.0 Depends: BiocParallel (>= 1.14.2) Imports: futile.logger, Rdpack, Matrix, data.table (>= 1.11.8), Rcpp, abind, stats, graphics, utils, methods, dixonTest, ids LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, pander, seewave License: GPL-3 Archs: x64 MD5sum: 5f15c51098664b6d2539e28bd4e20cb5 NeedsCompilation: yes Title: Correction of batch effects in DNA methylation data Description: Provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers. biocViews: BatchEffect, DNAMethylation, Software, Preprocessing, StatisticalMethod Author: Livia Rasp [aut, cre] (ORCID: ), Markus Merl [aut], Ruslan Akulenko [aut] Maintainer: Livia Rasp URL: https://github.com/uds-helms/BEclear SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/uds-helms/BEclear/issues git_url: https://git.bioconductor.org/packages/BEclear git_branch: RELEASE_3_22 git_last_commit: 575a6df git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BEclear_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BEclear_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BEclear_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BEclear_2.26.0.tgz vignettes: vignettes/BEclear/inst/doc/BEclear.html vignetteTitles: BEclear tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BEclear/inst/doc/BEclear.R dependencyCount: 30 Package: bedbaser Version: 1.2.0 Depends: R (>= 4.5.0) Imports: AnVIL (>= 1.16.0), BiocFileCache, dplyr, GenomeInfoDb, GenomicRanges, httr, methods, purrr, rtracklayer, rlang, R.utils, stats, stringr, tibble, tidyr, tools, utils Suggests: BiocStyle, knitr, liftOver, testthat (>= 3.0.0) License: Artistic License 2.0 Archs: x64 MD5sum: 37ce0c0aaa466ca6e7580c33373d6f6f NeedsCompilation: no Title: A BEDbase client Description: A client for BEDbase. bedbaser provides access to the API at api.bedbase.org. It also includes convenience functions to import BED files into GRanges objects and BEDsets into GRangesLists. biocViews: Software, DataImport, ThirdPartyClient Author: Andres Wokaty [aut, cre] (ORCID: ), Levi Waldron [aut] (ORCID: ) Maintainer: Andres Wokaty URL: https://github.com/waldronlab/bedbaser VignetteBuilder: knitr BugReports: https://github.com/waldronlab/bedbaser/issues git_url: https://git.bioconductor.org/packages/bedbaser git_branch: RELEASE_3_22 git_last_commit: 4ef0719 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bedbaser_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bedbaser_1.1.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bedbaser_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bedbaser_1.2.0.tgz vignettes: vignettes/bedbaser/inst/doc/bedbaser.html vignetteTitles: bedbaser hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bedbaser/inst/doc/bedbaser.R dependencyCount: 126 Package: beer Version: 1.14.0 Depends: R (>= 4.2.0), PhIPData (>= 1.1.1), rjags Imports: cli, edgeR, BiocParallel, methods, progressr, stats, SummarizedExperiment, utils Suggests: testthat (>= 3.0.0), BiocStyle, covr, codetools, knitr, rmarkdown, dplyr, ggplot2, spelling License: MIT + file LICENSE Archs: x64 MD5sum: 436d5f2f23837ccb0183219092e80302 NeedsCompilation: no Title: Bayesian Enrichment Estimation in R Description: BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses. biocViews: Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Athena Chen [aut, cre] (ORCID: ), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen URL: https://github.com/athchen/beer/ SystemRequirements: JAGS (4.3.0) VignetteBuilder: knitr BugReports: https://github.com/athchen/beer/issues git_url: https://git.bioconductor.org/packages/beer git_branch: RELEASE_3_22 git_last_commit: 820bb40 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/beer_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/beer_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/beer_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/beer_1.14.0.tgz vignettes: vignettes/beer/inst/doc/beer.html vignetteTitles: beer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/beer/inst/doc/beer.R dependencyCount: 80 Package: BERT Version: 1.6.0 Depends: R (>= 4.3.0) Imports: cluster, comprehenr, foreach (>= 1.5.2), invgamma, iterators (>= 1.0.14), janitor (>= 2.2.0), limma (>= 3.46.0), logging (>= 0.10-108), sva (>= 3.38.0), SummarizedExperiment, methods, BiocParallel Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: 519707326ab8ba0c8e1552735b82d66b NeedsCompilation: no Title: High Performance Data Integration for Large-Scale Analyses of Incomplete Omic Profiles Using Batch-Effect Reduction Trees (BERT) Description: Provides efficient batch-effect adjustment of data with missing values. BERT orders all batch effect correction to a tree of pairwise computations. BERT allows parallelization over sub-trees. biocViews: BatchEffect, Preprocessing, ExperimentalDesign, QualityControl Author: Yannis Schumann [aut, cre] (ORCID: ), Simon Schlumbohm [aut] (ORCID: ) Maintainer: Yannis Schumann URL: https://github.com/HSU-HPC/BERT/ VignetteBuilder: knitr BugReports: https://github.com/HSU-HPC/BERT/issues git_url: https://git.bioconductor.org/packages/BERT git_branch: RELEASE_3_22 git_last_commit: 49dbe48 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BERT_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BERT_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BERT_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BERT_1.6.0.tgz vignettes: vignettes/BERT/inst/doc/BERT-Vignette.html vignetteTitles: BERT-Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BERT/inst/doc/BERT-Vignette.R dependencyCount: 98 Package: betaHMM Version: 1.6.0 Depends: R (>= 4.3.0), SummarizedExperiment, S4Vectors, GenomicRanges Imports: stats, ggplot2, scales, methods, pROC, foreach, doParallel, parallel, cowplot, dplyr, tidyr, tidyselect, stringr, utils Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-3 MD5sum: e4451fd4efdd74e1c3c787390fabc097 NeedsCompilation: no Title: A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data Description: A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites. biocViews: DNAMethylation, DifferentialMethylation, ImmunoOncology, BiomedicalInformatics, MethylationArray, Software, MultipleComparison, Sequencing, Spatial, Coverage, GeneTarget, HiddenMarkovModel, Microarray Author: Koyel Majumdar [cre, aut] (ORCID: ), Romina Silva [aut], Antoinette Sabrina Perry [aut], Ronald William Watson [aut], Isobel Claire Gorley [aut] (ORCID: ), Thomas Brendan Murphy [aut] (ORCID: ), Florence Jaffrezic [aut], Andrea Rau [aut] (ORCID: ) Maintainer: Koyel Majumdar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/betaHMM git_branch: RELEASE_3_22 git_last_commit: 5800324 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/betaHMM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/betaHMM_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/betaHMM_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/betaHMM_1.6.0.tgz vignettes: vignettes/betaHMM/inst/doc/betaHMM.html vignetteTitles: betaHMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/betaHMM/inst/doc/betaHMM.R dependencyCount: 61 Package: bettr Version: 1.6.0 Depends: R (>= 4.4.0) Imports: dplyr (>= 1.0), tidyr, ggplot2 (>= 3.4.1), shiny (>= 1.6), tibble, ComplexHeatmap, bslib, rlang, circlize, stats, grid, methods, cowplot, Hmisc, sortable, shinyjqui, grDevices, scales, DT, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: c2f5a3f91503ce1cbb8806fc4a175341 NeedsCompilation: no Title: A Better Way To Explore What Is Best Description: bettr provides a set of interactive visualization methods to explore the results of a benchmarking study, where typically more than a single performance measures are computed. The user can weight the performance measures according to their preferences. Performance measures can also be grouped and aggregated according to additional annotations. biocViews: Visualization, ShinyApps, GUI Author: Federico Marini [aut] (ORCID: ), Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/federicomarini/bettr VignetteBuilder: knitr BugReports: https://github.com/federicomarini/bettr/issues git_url: https://git.bioconductor.org/packages/bettr git_branch: RELEASE_3_22 git_last_commit: 46da753 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bettr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bettr_1.5.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bettr_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bettr_1.6.0.tgz vignettes: vignettes/bettr/inst/doc/bettr.html vignetteTitles: bettr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bettr/inst/doc/bettr.R dependencyCount: 124 Package: BG2 Version: 1.10.0 Depends: R (>= 4.2.0) Imports: GA (>= 3.2), caret (>= 6.0-86), memoise (>= 1.1.0), Matrix (>= 1.2-18), MASS (>= 7.3-58.1), stats (>= 4.2.2) Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: ce53e5c51ae65c0a155f5938bf2076f6 NeedsCompilation: no Title: Performs Bayesian GWAS analysis for non-Gaussian data using BG2 Description: This package is built to perform GWAS analysis for non-Gaussian data using BG2. The BG2 method uses penalized quasi-likelihood along with nonlocal priors in a two step manner to identify SNPs in GWAS analysis. The research related to this package was supported in part by National Science Foundation awards DMS 1853549 and DMS 2054173. biocViews: Bayesian, AssayDomain, SNP, GenomeWideAssociation Author: Jacob Williams [aut, cre] (ORCID: ), Shuangshuang Xu [aut], Marco Ferreira [aut] (ORCID: ) Maintainer: Jacob Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BG2 git_branch: RELEASE_3_22 git_last_commit: 68e255f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BG2_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BG2_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BG2_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BG2_1.10.0.tgz vignettes: vignettes/BG2/inst/doc/BG2.html vignetteTitles: BG2 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BG2/inst/doc/BG2.R dependencyCount: 89 Package: BgeeCall Version: 1.26.0 Depends: R (>= 3.6) Imports: AnnotationDbi, curl, ggplot2, scales, GenomicFeatures, tximport, Biostrings, readr, sjmisc, RCurl, RSQLite, tools, stringr, rtracklayer, jsonlite, methods, dplyr, data.table, sjmisc, grDevices, graphics, stats, utils, rslurm, rhdf5, txdbmaker, IRanges, spatstat.univar Suggests: knitr, testthat, rmarkdown, AnnotationHub, GenomeInfoDb, httr License: GPL-3 + file LICENSE MD5sum: 5f09ab597ecd81ad8bf808280901e685 NeedsCompilation: no Title: Automatic RNA-Seq present/absent gene expression calls generation Description: BgeeCall allows to generate present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee (https://bgee.org). biocViews: Software, GeneExpression, RNASeq Author: Julien Wollbrett [aut, cre], Alessandro Brandulas Cammarata [aut], Sara Fonseca Costa [aut], Julien Roux [aut], Marc Robinson Rechavi [ctb], Frederic Bastian [aut] Maintainer: Julien Wollbrett URL: https://github.com/BgeeDB/BgeeCall SystemRequirements: kallisto VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeCall/issues git_url: https://git.bioconductor.org/packages/BgeeCall git_branch: RELEASE_3_22 git_last_commit: 0d3bcb0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BgeeCall_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BgeeCall_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BgeeCall_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BgeeCall_1.26.0.tgz vignettes: vignettes/BgeeCall/inst/doc/bgeecall-manual.html vignetteTitles: automatic RNA-Seq present/absent gene expression calls generation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BgeeCall/inst/doc/bgeecall-manual.R dependencyCount: 126 Package: BgeeDB Version: 2.36.0 Depends: R (>= 3.6.0), topGO, tidyr Imports: R.utils, data.table, curl, RCurl, digest, methods, stats, utils, dplyr, RSQLite, graph, Biobase, zellkonverter, anndata, HDF5Array, bread Suggests: knitr, BiocStyle, testthat, rmarkdown, markdown License: GPL-3 + file LICENSE MD5sum: cdd09ca6c6863fc56116f5063520b179 NeedsCompilation: no Title: Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology Description: A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns. biocViews: Software, DataImport, Sequencing, GeneExpression, Microarray, GO, GeneSetEnrichment Author: Andrea Komljenovic [aut, cre], Julien Roux [aut, cre] Maintainer: Julien Wollbrett , Julien Roux , Andrea Komljenovic , Frederic Bastian URL: https://github.com/BgeeDB/BgeeDB_R VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeDB_R/issues git_url: https://git.bioconductor.org/packages/BgeeDB git_branch: RELEASE_3_22 git_last_commit: 5af2650 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BgeeDB_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BgeeDB_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BgeeDB_2.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BgeeDB_2.36.0.tgz vignettes: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.html vignetteTitles: BgeeDB,, an R package for retrieval of curated expression datasets and for gene list enrichment tests hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.R importsMe: RITAN suggestsMe: RITAN dependencyCount: 96 Package: BicARE Version: 1.68.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase, GO.db Imports: methods Suggests: hgu95av2 License: GPL-2 MD5sum: ee0f977f242885d7dc48dbae3af28bcd NeedsCompilation: yes Title: Biclustering Analysis and Results Exploration Description: Biclustering Analysis and Results Exploration. biocViews: Microarray, Transcription, Clustering Author: Pierre Gestraud Maintainer: Pierre Gestraud URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/BicARE git_branch: RELEASE_3_22 git_last_commit: 58c1cf1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BicARE_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BicARE_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BicARE_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BicARE_1.68.0.tgz vignettes: vignettes/BicARE/inst/doc/BicARE.pdf vignetteTitles: BicARE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BicARE/inst/doc/BicARE.R importsMe: miRSM dependencyCount: 56 Package: BiFET Version: 1.30.0 Depends: R (>= 3.5.0) Imports: stats, poibin, GenomicRanges Suggests: rmarkdown, testthat, knitr License: GPL-3 MD5sum: d24ec2bc9e18a86663269cb4d37648fc NeedsCompilation: no Title: Bias-free Footprint Enrichment Test Description: BiFET identifies TFs whose footprints are over-represented in target regions compared to background regions after correcting for the bias arising from the imbalance in read counts and GC contents between the target and background regions. For a given TF k, BiFET tests the null hypothesis that the target regions have the same probability of having footprints for the TF k as the background regions while correcting for the read count and GC content bias. For this, we use the number of target regions with footprints for TF k, t_k as a test statistic and calculate the p-value as the probability of observing t_k or more target regions with footprints under the null hypothesis. biocViews: ImmunoOncology, Genetics, Epigenetics, Transcription, GeneRegulation, ATACSeq, DNaseSeq, RIPSeq, Software Author: Ahrim Youn [aut, cre], Eladio Marquez [aut], Nathan Lawlor [aut], Michael Stitzel [aut], Duygu Ucar [aut] Maintainer: Ahrim Youn VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiFET git_branch: RELEASE_3_22 git_last_commit: 42f9430 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiFET_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiFET_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiFET_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiFET_1.30.0.tgz vignettes: vignettes/BiFET/inst/doc/BiFET.html vignetteTitles: "A Guide to using BiFET" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiFET/inst/doc/BiFET.R dependencyCount: 12 Package: bigmelon Version: 1.36.0 Depends: R (>= 3.3), wateRmelon (>= 1.25.0), gdsfmt (>= 1.0.4), methods, minfi (>= 1.21.0), Biobase, methylumi Imports: stats, utils, GEOquery, graphics, BiocGenerics, illuminaio Suggests: BiocGenerics, RUnit, BiocStyle, minfiData, parallel, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, bumphunter License: GPL-3 MD5sum: adce1bfc650d789b93ecb1778390c0f1 NeedsCompilation: no Title: Illumina methylation array analysis for large experiments Description: Methods for working with Illumina arrays using gdsfmt. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl, MethylationArray, DataImport, CpGIsland Author: Tyler J. Gorrie-Stone [aut], Ayden Saffari [aut], Karim Malki [aut], Leonard C. Schalkwyk [cre, aut] Maintainer: Leonard C. Schalkwyk git_url: https://git.bioconductor.org/packages/bigmelon git_branch: RELEASE_3_22 git_last_commit: 8527321 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bigmelon_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bigmelon_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bigmelon_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bigmelon_1.36.0.tgz vignettes: vignettes/bigmelon/inst/doc/bigmelon.pdf vignetteTitles: The bigmelon Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigmelon/inst/doc/bigmelon.R dependencyCount: 173 Package: BindingSiteFinder Version: 2.8.0 Depends: GenomicRanges, R (>= 4.2) Imports: tidyr, tibble, plyr, matrixStats, stats, ggplot2, methods, rtracklayer, S4Vectors, ggforce, GenomeInfoDb, ComplexHeatmap, RColorBrewer, lifecycle, rlang, forcats, dplyr, GenomicFeatures, IRanges, kableExtra, ggdist Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicAlignments, scales, Gviz, xlsx, GGally, patchwork, viridis, ggplotify, SummarizedExperiment, DESeq2, ggpointdensity, ggrastr, ashr, txdbmaker, ggrepel, stringr License: Artistic-2.0 MD5sum: 1b30166f54cfefc97ea4450a249d02ab NeedsCompilation: no Title: Binding site defintion based on iCLIP data Description: Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette. biocViews: Sequencing, GeneExpression, GeneRegulation, FunctionalGenomics, Coverage, DataImport Author: Mirko Brüggemann [aut, cre] (ORCID: ), Melina Klostermann [aut] (ORCID: ), Kathi Zarnack [aut] (ORCID: ) Maintainer: Mirko Brüggemann VignetteBuilder: knitr BugReports: https://github.com/ZarnackGroup/BindingSiteFinder/issues git_url: https://git.bioconductor.org/packages/BindingSiteFinder git_branch: RELEASE_3_22 git_last_commit: 74a4d9c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BindingSiteFinder_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BindingSiteFinder_2.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BindingSiteFinder_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BindingSiteFinder_2.8.0.tgz vignettes: vignettes/BindingSiteFinder/inst/doc/vignette.html vignetteTitles: Definition of binding sites from iCLIP signal hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BindingSiteFinder/inst/doc/vignette.R dependencyCount: 141 Package: bioassayR Version: 1.48.0 Depends: R (>= 3.5.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods, Matrix, rjson, BiocGenerics (>= 0.13.8) Imports: XML, ChemmineR Suggests: BiocStyle, RCurl, biomaRt, knitr, knitcitations, knitrBootstrap, testthat, ggplot2, rmarkdown License: Artistic-2.0 MD5sum: d6049421c9bc717b4be9b5b3f1ba5465 NeedsCompilation: no Title: Cross-target analysis of small molecule bioactivity Description: bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data. biocViews: ImmunoOncology, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics, Metabolomics Author: Tyler Backman, Ronly Schlenk, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/bioassayR VignetteBuilder: knitr BugReports: https://github.com/girke-lab/bioassayR/issues git_url: https://git.bioconductor.org/packages/bioassayR git_branch: RELEASE_3_22 git_last_commit: 42cc73b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bioassayR_1.48.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bioassayR_1.48.0.tgz vignettes: vignettes/bioassayR/inst/doc/bioassayR.html vignetteTitles: bioassayR Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R dependencyCount: 75 Package: Biobase Version: 2.70.0 Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets, BiocStyle, knitr, limma License: Artistic-2.0 MD5sum: 1972f4f090998033acd1d3bfdec77961 NeedsCompilation: yes Title: Biobase: Base functions for Bioconductor Description: Functions that are needed by many other packages or which replace R functions. biocViews: Infrastructure Author: R. Gentleman [aut], V. Carey [aut], M. Morgan [aut], S. Falcon [aut], Haleema Khan [ctb] ('esApply' and 'BiobaseDevelopment' vignette translation from Sweave to Rmarkdown / HTML), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Biobase VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Biobase/issues git_url: https://git.bioconductor.org/packages/Biobase git_branch: RELEASE_3_22 git_last_commit: 9964e15 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Biobase_2.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Biobase_2.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Biobase_2.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Biobase_2.70.0.tgz vignettes: vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf, vignettes/Biobase/inst/doc/BiobaseDevelopment.html, vignettes/Biobase/inst/doc/esApply.html vignetteTitles: An introduction to Biobase and ExpressionSets, Notes for eSet developers, esApply Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biobase/inst/doc/BiobaseDevelopment.R, vignettes/Biobase/inst/doc/esApply.R, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.R dependsOnMe: ACME, affy, affycomp, affyContam, affycoretools, affyPLM, AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi, AnnotationForge, ArrayExpress, arrayMvout, BAGS, bandle, BicARE, bigmelon, BioMVCClass, BioQC, BLMA, borealis, CAMERA, cancerclass, casper, Category, categoryCompare, CCPROMISE, CGHbase, CGHcall, CGHregions, clippda, clusterStab, CMA, cn.farms, codelink, convert, copa, covEB, covRNA, CytoMDS, DEXSeq, DFP, diggit, doppelgangR, DSS, dyebias, EBarrays, EDASeq, edge, EGSEA, epivizrData, ExiMiR, ExpressionAtlas, fabia, factDesign, fastseg, flowBeads, frma, gaga, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, geNetClassifier, GeoDiff, GeomxTools, GEOquery, GOexpress, 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clustComp, coseq, cypress, dar, DART, dcanr, dearseq, DeconvoBuddies, DspikeIn, edgeR, EnMCB, EpiDISH, epivizr, epivizrChart, epivizrStandalone, genefu, GENIE3, GenomicPlot, GenomicRanges, GSAR, GSgalgoR, Heatplus, interactiveDisplay, kebabs, les, limma, M3Drop, messina, mitology, MOSim, msa, multiClust, OSAT, pathMED, RcisTarget, ribosomeProfilingQC, ROC, RTCGA, scater, scmeth, SeqArray, sparrow, stageR, survcomp, TargetScore, TCGAbiolinks, TFutils, tkWidgets, TOP, vbmp, widgetTools, biotmleData, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, dorothea, dyebiasexamples, HMP16SData, HMP2Data, homosapienDEE2CellScore, mammaPrintData, mtbls2, RegParallel, rheumaticConditionWOLLBOLD, seventyGeneData, yeastExpData, yeastRNASeq, amap, aroma.affymetrix, BaseSet, ClusterGVis, clValid, CrossValidate, distrDoc, evanverse, GenAlgo, ggpicrust2, hexbin, HTSCluster, isatabr, MetabolSSMF, mi4p, Modeler, multiclassPairs, NACHO, ordinalbayes, Patterns, rsconnect, Seurat, sigminer, SomaDataIO, tinyarray dependencyCount: 6 Package: biobroom Version: 1.42.0 Depends: R (>= 3.0.0), broom Imports: dplyr, tidyr, Biobase Suggests: limma, DESeq2, airway, ggplot2, plyr, GenomicRanges, testthat, magrittr, edgeR, qvalue, knitr, data.table, MSnbase, rmarkdown, SummarizedExperiment License: LGPL Archs: x64 MD5sum: 770fad11e88d92be599eeaba3b9d0ba7 NeedsCompilation: no Title: Turn Bioconductor objects into tidy data frames Description: This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses. biocViews: MultipleComparison, DifferentialExpression, Regression, GeneExpression, Proteomics, DataImport Author: Andrew J. Bass, David G. Robinson, Steve Lianoglou, Emily Nelson, John D. Storey, with contributions from Laurent Gatto Maintainer: John D. Storey and Andrew J. Bass URL: https://github.com/StoreyLab/biobroom VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/biobroom/issues git_url: https://git.bioconductor.org/packages/biobroom git_branch: RELEASE_3_22 git_last_commit: 3f4802c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biobroom_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biobroom_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biobroom_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biobroom_1.42.0.tgz vignettes: vignettes/biobroom/inst/doc/biobroom_vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biobroom/inst/doc/biobroom_vignette.R importsMe: TPP dependencyCount: 30 Package: biobtreeR Version: 1.22.0 Imports: httr, httpuv, stringi,jsonlite,methods,utils Suggests: BiocStyle, knitr,testthat,rmarkdown,markdown License: MIT + file LICENSE Archs: x64 MD5sum: e83d5439974802c1758cd63b6c0e4df9 NeedsCompilation: no Title: Using biobtree tool from R Description: The biobtreeR package provides an interface to [biobtree](https://github.com/tamerh/biobtree) tool which covers large set of bioinformatics datasets and allows search and chain mappings functionalities. biocViews: Annotation Author: Tamer Gur Maintainer: Tamer Gur URL: https://github.com/tamerh/biobtreeR VignetteBuilder: knitr BugReports: https://github.com/tamerh/biobtreeR/issues git_url: https://git.bioconductor.org/packages/biobtreeR git_branch: RELEASE_3_22 git_last_commit: 8d2f8ff git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biobtreeR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biobtreeR_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biobtreeR_1.22.0.tgz vignettes: vignettes/biobtreeR/inst/doc/biobtreeR.html vignetteTitles: The biobtreeR users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biobtreeR/inst/doc/biobtreeR.R dependencyCount: 24 Package: Bioc.gff Version: 1.0.0 Depends: R (>= 4.5.0) Imports: BiocBaseUtils, BiocGenerics, BiocIO, curl, GenomicRanges, IRanges, methods, Rsamtools, S4Vectors, Seqinfo, stats, utils, XVector LinkingTo: S4Vectors, XVector, IRanges Suggests: BiocFileCache, BiocStyle, GenomicFeatures, GenomeInfoDbData, knitr, httr2, rmarkdown, rvest, tinytest, txdbmaker, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 295d94837d3a40ee372aefb3c3b94c81 NeedsCompilation: yes Title: Read and write GFF and GTF files Description: Parse GFF and GTF files using C++ classes. The package also provides utilities to read and write GFF3 files. The GFF (General Feature Format) format is a tab-delimited file format for describing genes and other features of DNA, RNA, and protein sequences. GFF files are often used to describe the features of genomes. biocViews: Software, Infrastructure, DataImport Author: Michael Lawrence [aut], Hervé Pagès [aut], Marcel Ramos [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/Bioc.gff VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Bioc.gff/issues git_url: https://git.bioconductor.org/packages/Bioc.gff git_branch: RELEASE_3_22 git_last_commit: 5bbebde git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Bioc.gff_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Bioc.gff_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Bioc.gff_1.0.0.tgz vignettes: vignettes/Bioc.gff/inst/doc/Bioc.gff.html vignetteTitles: Bioc.gff: GFF3 File Format Support hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Bioc.gff/inst/doc/Bioc.gff.R dependencyCount: 32 Package: bioCancer Version: 1.38.0 Depends: R (>= 4.1.0), radiant.data (>= 0.9.1), cBioPortalData, XML (>= 3.98) Imports: R.oo, R.methodsS3, DT (>= 0.3), dplyr (>= 0.7.2), tidyr, shiny (>= 1.0.5), AlgDesign (>= 1.1.7.3), import (>= 1.1.0), methods, AnnotationDbi, shinythemes, Biobase, geNetClassifier, org.Hs.eg.db, org.Bt.eg.db, DOSE, clusterProfiler, reactome.db, ReactomePA, DiagrammeR(<= 1.01), visNetwork, htmlwidgets, plyr, tibble, GO.db Suggests: BiocStyle, prettydoc, rmarkdown, knitr, testthat (>= 0.10.0) License: AGPL-3 | file LICENSE MD5sum: df48eec917d6e9f2290290644cba68fc NeedsCompilation: no Title: Interactive Multi-Omics Cancers Data Visualization and Analysis Description: This package is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data. biocViews: GUI, DataRepresentation, Network, MultipleComparison, Pathways, Reactome, Visualization,GeneExpression,GeneTarget Author: Karim Mezhoud [aut, cre] Maintainer: Karim Mezhoud URL: https://kmezhoud.github.io/bioCancer/ VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/bioCancer/issues git_url: https://git.bioconductor.org/packages/bioCancer git_branch: RELEASE_3_22 git_last_commit: 7997387 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bioCancer_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bioCancer_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bioCancer_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bioCancer_1.38.0.tgz vignettes: vignettes/bioCancer/inst/doc/bioCancer.html vignetteTitles: bioCancer: Interactive Multi-OMICS Cancers Data Visualization and Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bioCancer/inst/doc/bioCancer.R dependencyCount: 264 Package: BioCartaImage Version: 1.8.0 Depends: R (>= 4.3.0) Imports: magick, grid, stats, grDevices, utils Suggests: testthat, knitr, BiocStyle, ragg License: MIT + file LICENSE MD5sum: 7dd355b26d1ec9d829fbbd736b029bdb NeedsCompilation: no Title: BioCarta Pathway Images Description: The core functionality of the package is to provide coordinates of genes on the BioCarta pathway images and to provide methods to add self-defined graphics to the genes of interest. biocViews: Software, Pathways, BioCarta, Visualization Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/BioCartaImage VignetteBuilder: knitr BugReports: https://github.com/jokergoo/BioCartaImage/issues git_url: https://git.bioconductor.org/packages/BioCartaImage git_branch: RELEASE_3_22 git_last_commit: 9cf9b96 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BioCartaImage_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioCartaImage_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioCartaImage_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioCartaImage_1.8.0.tgz vignettes: vignettes/BioCartaImage/inst/doc/BioCartaImage.html vignetteTitles: Customize BioCarta Pathway Images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioCartaImage/inst/doc/BioCartaImage.R dependencyCount: 9 Package: BiocBaseUtils Version: 1.12.0 Depends: R (>= 4.2.0) Imports: methods, utils Suggests: knitr, rmarkdown, BiocStyle, tinytest License: Artistic-2.0 MD5sum: d82175a5a77950261e1f0efa6f60e5b6 NeedsCompilation: no Title: General utility functions for developing Bioconductor packages Description: The package provides utility functions related to package development. These include functions that replace slots, and selectors for show methods. It aims to coalesce the various helper functions often re-used throughout the Bioconductor ecosystem. biocViews: Software, Infrastructure Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [ctb], Hervé Pagès [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://www.github.com/Bioconductor/BiocBaseUtils/issues git_url: https://git.bioconductor.org/packages/BiocBaseUtils git_branch: RELEASE_3_22 git_last_commit: 756163a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocBaseUtils_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocBaseUtils_1.11.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocBaseUtils_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocBaseUtils_1.12.0.tgz vignettes: vignettes/BiocBaseUtils/inst/doc/BiocBaseUtils.html vignetteTitles: BiocBaseUtils Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocBaseUtils/inst/doc/BiocBaseUtils.R importsMe: AlphaMissenseR, AnnotationHub, AnVIL, AnVILAz, AnVILGCP, AnVILPublish, Bioc.gff, BiocCheck, BiocFHIR, cBioPortalData, DNAfusion, GCPtools, GenomicFiles, iSEEfier, looking4clusters, MultiAssayExperiment, RaggedExperiment, scGraphVerse, TCGAutils, TENxIO, UniProt.ws, VisiumIO, visiumStitched, XeniumIO, SingleCellMultiModal suggestsMe: scifer dependencyCount: 2 Package: BiocBook Version: 1.8.0 Depends: R (>= 4.3) Imports: BiocGenerics, pak, cli, glue, gert, gh, gitcreds, httr, usethis, dplyr, purrr, tibble, methods, rprojroot, stringr, yaml, tools, utils, rlang, quarto, renv Suggests: BiocStyle, knitr, testthat (>= 3.0.0), rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: c25a6f865b8e9c8fee2936410996759e NeedsCompilation: no Title: Write, containerize, publish and version Quarto books with Bioconductor Description: A BiocBook can be created by authors (e.g. R developers, but also scientists, teachers, communicators, ...) who wish to 1) write (compile a body of biological and/or bioinformatics knowledge), 2) containerize (provide Docker images to reproduce the examples illustrated in the compendium), 3) publish (deploy an online book to disseminate the compendium), and 4) version (automatically generate specific online book versions and Docker images for specific Bioconductor releases). biocViews: Infrastructure, ReportWriting, Software Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://bioconductor.org/packages/BiocBook VignetteBuilder: knitr BugReports: https://github.com/js2264/BiocBook/issues git_url: https://git.bioconductor.org/packages/BiocBook git_branch: RELEASE_3_22 git_last_commit: 7c3194e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocBook_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocBook_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocBook_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocBook_1.8.0.tgz vignettes: vignettes/BiocBook/inst/doc/BiocBook.html vignetteTitles: BiocBook hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocBook/inst/doc/BiocBook.R dependencyCount: 72 Package: BiocCheck Version: 1.46.0 Depends: R (>= 4.4.0) Imports: BiocBaseUtils, BiocFileCache, BiocManager, biocViews, callr, cli, codetools, graph, httr2, knitr, methods, rvest, stringdist, tools, utils Suggests: BiocStyle, devtools, gert, jsonlite, rmarkdown, tinytest, usethis License: Artistic-2.0 MD5sum: bb96cb70ff53b55142384803fd23c406 NeedsCompilation: no Title: Bioconductor-specific package checks Description: BiocCheck guides maintainers through Bioconductor best practicies. It runs Bioconductor-specific package checks by searching through package code, examples, and vignettes. Maintainers are required to address all errors, warnings, and most notes produced. biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut], Lori Shepherd [aut], Daniel von Twisk [ctb], Kevin Rue [ctb], Marcel Ramos [aut, cre] (ORCID: ), Leonardo Collado-Torres [ctb], Federico Marini [ctb] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/BiocCheck VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocCheck/issues git_url: https://git.bioconductor.org/packages/BiocCheck git_branch: RELEASE_3_22 git_last_commit: 79a3619 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocCheck_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocCheck_1.45.12.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocCheck_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocCheck_1.46.0.tgz vignettes: vignettes/BiocCheck/inst/doc/BiocCheck.html vignetteTitles: BiocCheck: Ensuring Bioconductor package guidelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocCheck/inst/doc/BiocCheck.R importsMe: AnnotationHubData, gDRstyle, methodical suggestsMe: GEOfastq, packFinder, preciseTAD, ReducedExperiment, SpectralTAD, HMP16SData, HMP2Data, scpdata, MainExistingDatasets dependencyCount: 74 Package: BiocFHIR Version: 1.12.0 Depends: R (>= 4.2) Imports: DT, shiny, jsonlite, graph, tidyr, visNetwork, dplyr, utils, methods, BiocBaseUtils Suggests: knitr, testthat, rjsoncons, igraph, BiocStyle License: Artistic-2.0 MD5sum: c638db36762057076b2bd5802933edbf NeedsCompilation: no Title: Illustration of FHIR ingestion and transformation using R Description: FHIR R4 bundles in JSON format are derived from https://synthea.mitre.org/downloads. Transformation inspired by a kaggle notebook published by Dr Alexander Scarlat, https://www.kaggle.com/code/drscarlat/fhir-starter-parse-healthcare-bundles-into-tables. This is a very limited illustration of some basic parsing and reorganization processes. Additional tooling will be required to move beyond the Synthea data illustrations. biocViews: Infrastructure, DataImport, DataRepresentation Author: Vincent Carey [aut, cre] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/BiocFHIR VignetteBuilder: knitr BugReports: https://github.com/vjcitn/BiocFHIR/issues git_url: https://git.bioconductor.org/packages/BiocFHIR git_branch: RELEASE_3_22 git_last_commit: 53cafbd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocFHIR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocFHIR_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocFHIR_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocFHIR_1.12.0.tgz vignettes: vignettes/BiocFHIR/inst/doc/A_upper.html, vignettes/BiocFHIR/inst/doc/B_handling.html, vignettes/BiocFHIR/inst/doc/BiocFHIR.html, vignettes/BiocFHIR/inst/doc/C_tables.html, vignettes/BiocFHIR/inst/doc/D_linking.html vignetteTitles: Upper level FHIR concepts, Handling FHIR documents with BiocFHIR, BiocFHIR -- infrastructure for parsing and analyzing FHIR data, Transforming FHIR documents to tables with BiocFHIR, Linking information between FHIR resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFHIR/inst/doc/A_upper.R, vignettes/BiocFHIR/inst/doc/B_handling.R, vignettes/BiocFHIR/inst/doc/BiocFHIR.R, vignettes/BiocFHIR/inst/doc/C_tables.R, vignettes/BiocFHIR/inst/doc/D_linking.R dependencyCount: 65 Package: BiocFileCache Version: 3.0.0 Depends: R (>= 3.4.0), dbplyr (>= 1.0.0) Imports: methods, stats, utils, dplyr, RSQLite, DBI, filelock, curl, httr2 Suggests: testthat, knitr, BiocStyle, rmarkdown, rtracklayer License: Artistic-2.0 MD5sum: ab689154f452b7f076467111dcda6a2f NeedsCompilation: no Title: Manage Files Across Sessions Description: This package creates a persistent on-disk cache of files that the user can add, update, and retrieve. It is useful for managing resources (such as custom Txdb objects) that are costly or difficult to create, web resources, and data files used across sessions. biocViews: DataImport Author: Lori Shepherd [aut, cre], Martin Morgan [aut] Maintainer: Lori Shepherd VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocFileCache/issues git_url: https://git.bioconductor.org/packages/BiocFileCache git_branch: RELEASE_3_22 git_last_commit: 81fd6e0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocFileCache_3.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocFileCache_2.99.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocFileCache_3.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocFileCache_3.0.0.tgz vignettes: vignettes/BiocFileCache/inst/doc/BiocFileCache_Troubleshooting.html, vignettes/BiocFileCache/inst/doc/BiocFileCache_UseCases.html, vignettes/BiocFileCache/inst/doc/BiocFileCache.html vignetteTitles: 3. BiocFileCache Troubleshooting, 2. BiocFileCache: Use Cases, 1. BiocFileCache Overview: Managing File Resources Across Sessions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFileCache/inst/doc/BiocFileCache_Troubleshooting.R, vignettes/BiocFileCache/inst/doc/BiocFileCache_UseCases.R, vignettes/BiocFileCache/inst/doc/BiocFileCache.R dependsOnMe: AnnotationHub, easylift, ExperimentHub, RcwlPipelines, JASPAR2022, JASPAR2024, scATAC.Explorer, TMExplorer, csawBook, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.workflows importsMe: AlphaMissenseR, AMARETTO, atSNP, autonomics, BayesSpace, bedbaser, BiocCheck, BiocHail, BiocPkgTools, biomaRt, brendaDb, bugsigdbr, BulkSignalR, cbaf, cBioPortalData, CBNplot, CellBench, CTDquerier, customCMPdb, CytoPipeline, DeconvoBuddies, easyRNASeq, enhancerHomologSearch, EnMCB, EnrichmentBrowser, EpiTxDb, fenr, fgga, GenomicScores, GenomicSuperSignature, ggkegg, GSEABenchmarkeR, gwascat, iSEEindex, MBQN, MIRit, motifbreakR, MotifPeeker, MsBackendMetaboLights, ontoProc, ORFik, Organism.dplyr, OSTA.data, PhIPData, PMScanR, psichomics, rBLAST, recount3, recountmethylation, regutools, ReUseData, RiboDiPA, rpx, scviR, sesame, signeR, spacexr, SpatialExperiment, SpatialOmicsOverlay, SpliceWiz, SurfR, tenXplore, terraTCGAdata, TFutils, tomoseqr, tximeta, UMI4Cats, UniProt.ws, waddR, xenLite, geneplast.data, HPO.db, MPO.db, org.Mxanthus.db, PANTHER.db, BioPlex, bugphyzz, depmap, DNAZooData, fourDNData, HiContactsData, MetaScope, MicrobiomeBenchmarkData, NxtIRFdata, orthosData, SFEData, SingleCellMultiModal, spatialLIBD, OSTA, convertid suggestsMe: anndataR, AnnotationForge, bambu, Bioc.gff, BiocSet, ChIPpeakAnno, CoGAPS, CRISPRseek, dominoSignal, EpiCompare, fastreeR, GRaNIE, HicAggR, HiCDCPlus, HiCExperiment, iscream, MethReg, Nebulosa, nipalsMCIA, progeny, qsvaR, seqsetvis, TREG, visiumStitched, XeniumIO, zellkonverter, emtdata, HighlyReplicatedRNASeq, MethylSeqData, msigdb, TENxBrainData, TENxPBMCData, chipseqDB, fluentGenomics, simpleSingleCell, scCustomize dependencyCount: 43 Package: BiocGenerics Version: 0.56.0 Depends: R (>= 4.0.0), methods, utils, graphics, stats, generics Imports: methods, utils, graphics, stats Suggests: Biobase, S4Vectors, IRanges, S4Arrays, SparseArray, DelayedArray, HDF5Array, GenomicRanges, pwalign, Rsamtools, AnnotationDbi, affy, affyPLM, DESeq2, flowClust, MSnbase, annotate, MultiAssayExperiment, RUnit License: Artistic-2.0 MD5sum: 5d6a2059f5c9559acfd2bd95d0db93bf NeedsCompilation: no Title: S4 generic functions used in Bioconductor Description: The package defines many S4 generic functions used in Bioconductor. biocViews: Infrastructure Author: The Bioconductor Dev Team [aut], Hervé Pagès [aut, cre] (ORCID: ), Laurent Gatto [ctb] (ORCID: ), Nathaniel Hayden [ctb], James Hester [ctb], Wolfgang Huber [ctb], Michael Lawrence [ctb], Martin Morgan [ctb] (ORCID: ), Valerie Obenchain [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/BiocGenerics BugReports: https://github.com/Bioconductor/BiocGenerics/issues git_url: https://git.bioconductor.org/packages/BiocGenerics git_branch: RELEASE_3_22 git_last_commit: 16cf16d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocGenerics_0.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocGenerics_0.55.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocGenerics_0.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocGenerics_0.56.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, affyPLM, altcdfenvs, AnnotationDbi, AnnotationForge, AnnotationHub, ATACseqQC, bioassayR, Biobase, Biostrings, bnbc, BSgenome, BSgenomeForge, bsseq, Cardinal, Category, categoryCompare, chipseq, ChIPseqR, ChromHeatMap, cigarillo, clusterExperiment, codelink, consensusDE, consensusSeekeR, CoreGx, CRISPRseek, DelayedArray, ensembldb, ExperimentHub, ExperimentHubData, GDSArray, geneplotter, GenomeInfoDb, genomeIntervals, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges, GenomicScores, ggbio, graph, GSEABase, GUIDEseq, h5mread, HelloRanges, interactiveDisplay, interactiveDisplayBase, IRanges, ISLET, MBASED, MGnifyR, MineICA, minfi, MLInterfaces, MotifDb, mpra, MSnbase, multtest, NADfinder, ngsReports, oligo, OrganismDbi, plyranges, pwalign, PWMEnrich, QSutils, RareVariantVis, Rarr, REDseq, RnBeads, RPA, rsbml, S4Arrays, S4Vectors, Seqinfo, ShortRead, SparseArray, spqn, StructuralVariantAnnotation, svaNUMT, svaRetro, TEQC, tigre, topdownr, topGO, txdbmaker, UNDO, updateObject, VanillaICE, VariantAnnotation, VariantFiltering, VCFArray, XVector, yamss, ChAMPdata, liftOver, rsolr importsMe: a4Preproc, affycoretools, affylmGUI, alabaster.bumpy, alabaster.files, alabaster.matrix, alabaster.ranges, alabaster.se, AllelicImbalance, annmap, annotate, AnnotationHubData, ASpli, ATACseqTFEA, atena, AUCell, autonomics, bambu, bamsignals, BASiCS, batchelor, beachmat, bigmelon, Bioc.gff, BiocBook, biocGraph, BiocHail, BiocIO, BiocSingular, biotmle, biovizBase, biscuiteer, BiSeq, breakpointR, BrowserViz, bumphunter, BUSpaRse, CAGEfightR, CAGEr, casper, celaref, CellBench, CellMixS, CellTrails, cfDNAPro, cghMCR, ChemmineOB, ChemmineR, chipenrich, ChIPpeakAnno, ChIPseeker, chipseq, chromVAR, cicero, CircSeqAlignTk, CleanUpRNAseq, clusterSeq, cn.mops, CNEr, CNVPanelizer, CNVRanger, COCOA, cola, compEpiTools, CompoundDb, concordexR, crisprBase, crisprBowtie, crisprBwa, crisprDesign, crisprScore, crisprShiny, crisprViz, crlmm, csaw, CTexploreR, CuratedAtlasQueryR, cydar, dada2, dagLogo, DAMEfinder, ddCt, decompTumor2Sig, deconvR, DegCre, DEGreport, DelayedDataFrame, demuxSNP, derfinder, DEScan2, DESeq2, DESpace, DEWSeq, DEXSeq, DFplyr, diffcoexp, diffHic, dinoR, DirichletMultinomial, DiscoRhythm, DNAfusion, dreamlet, DRIMSeq, DropletUtils, DrugVsDisease, easyRNASeq, EBImage, EDASeq, eiR, eisaR, ELViS, enhancerHomologSearch, EnrichDO, epialleleR, EpiCompare, epimutacions, epistack, EpiTxDb, epivizrChart, epivizrStandalone, esATAC, FamAgg, fastseg, ffpe, FindIT2, FLAMES, flowBin, flowClust, flowCore, flowFP, FlowSOM, flowSpecs, flowStats, flowWorkspace, fmcsR, FRASER, frma, GA4GHclient, GA4GHshiny, gcapc, gDNAx, geneAttribution, geneClassifiers, GENESIS, GenomAutomorphism, GenomicAlignments, GenomicInteractions, GenomicPlot, GenomicTuples, geomeTriD, GeomxTools, glmGamPoi, gmapR, gmoviz, goseq, GOTHiC, GSVA, Gviz, HDF5Array, heatmaps, hermes, HicAggR, HiCDOC, HiCExperiment, HiContacts, HiCParser, HiLDA, hopach, icetea, igvR, igvShiny, IHW, infercnv, INSPEcT, intansv, InteractionSet, IntEREst, IONiseR, iSEE, IsoformSwitchAnalyzeR, IVAS, KCsmart, ldblock, lefser, lemur, linkSet, lisaClust, LOLA, maaslin3, mariner, maser, MAST, matter, MEAL, meshr, MetaboAnnotation, metaMS, metaseqR2, methInheritSim, MethylAid, methylPipe, methylumi, mia, miaViz, miloR, mimager, MinimumDistance, MIRA, MiRaGE, missMethyl, mist, mobileRNA, Modstrings, mogsa, monaLisa, monocle, Moonlight2R, Motif2Site, motifbreakR, msa, MsBackendSql, MsExperiment, MSnID, MultiAssayExperiment, multicrispr, MultiDataSet, multiMiR, MultimodalExperiment, mumosa, MutationalPatterns, mutscan, mzR, NanoStringNCTools, nearBynding, notame, notameStats, notameViz, npGSEA, nucleR, oligoClasses, openCyto, openPrimeR, ORFik, OUTRIDER, parglms, pcaMethods, PDATK, pdInfoBuilder, PharmacoGx, PhIPData, PhosR, phyloseq, piano, PIPETS, plyinteractions, podkat, pram, primirTSS, proDA, profileScoreDist, pRoloc, pRolocGUI, ProteoDisco, PSMatch, PureCN, QDNAseq, QFeatures, qPLEXanalyzer, qsea, QTLExperiment, QuasR, R3CPET, RadioGx, raer, RaggedExperiment, ramr, ramwas, RCAS, RcisTarget, RCy3, RCyjs, recoup, ReducedExperiment, REMP, ReportingTools, RGSEA, RiboCrypt, RiboDiPA, RiboProfiling, ribosomeProfilingQC, RJMCMCNucleosomes, rnaEditr, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RNAseqCovarImpute, roar, Rqc, rqubic, Rsamtools, rsbml, rScudo, RTCGAToolbox, rtracklayer, SanityR, saseR, SC3, SCArray.sat, scater, scDblFinder, scDotPlot, scmap, scmeth, SCnorm, SCOPE, scPipe, scran, scruff, scuttle, SeqVarTools, sevenC, SGSeq, SharedObject, shinyDSP, shinyMethyl, signatureSearch, signeR, signifinder, simPIC, SingleCellExperiment, SingleMoleculeFootprinting, sitadela, Site2Target, SNPhood, snpStats, sparrow, SpatialExperiment, SpatialFeatureExperiment, Spectra, splatter, SpliceWiz, SplicingGraphs, SplineDV, sRACIPE, sscu, StabMap, standR, strandCheckR, Streamer, Structstrings, SummarizedExperiment, SVP, SynMut, systemPipeR, tadar, TAPseq, target, TCGAutils, TCseq, TENxIO, TFBSTools, tidySpatialExperiment, ToxicoGx, trackViewer, transcriptR, transite, TreeSummarizedExperiment, tRNA, tRNAscanImport, TVTB, txcutr, Ularcirc, UMI4Cats, unifiedWMWqPCR, UniProt.ws, universalmotif, uSORT, VariantTools, velociraptor, VisiumIO, visiumStitched, wavClusteR, weitrix, xcms, XDE, XeniumIO, XVector, zitools, CENTREannotation, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, CENTREprecomputed, chipenrich.data, curatedOvarianData, gDNAinRNAseqData, homosapienDEE2CellScore, IHWpaper, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, microbiomeDataSets, MouseGastrulationData, MouseThymusAgeing, raerdata, scRNAseq, spatialLIBD, systemPipeRdata, TENxBUSData, VariantToolsData, ExpHunterSuite, GeoMxWorkflows, crispRdesignR, DCLEAR, decompDL, EEMDlstm, geno2proteo, HiCociety, hicream, locuszoomr, RNAseqQC, scPOEM, Signac, TaxaNorm, toxpiR, treediff, TSdeeplearning suggestsMe: acde, adverSCarial, aggregateBioVar, AIMS, AlphaMissenseR, ASSET, ASURAT, BaalChIP, baySeq, bigmelon, BiocParallel, BiocStyle, biocViews, biosigner, BiRewire, BLMA, BloodGen3Module, bnem, borealis, BUScorrect, BUSseq, CAFE, CAMERA, CausalR, ccrepe, CDI, cellmigRation, CexoR, chihaya, ChIPanalyser, ChIPXpress, CHRONOS, cleanUpdTSeq, clipper, ClustAll, clustComp, CNORfeeder, CNORfuzzy, consensus, cosmiq, COSNet, cpvSNP, crumblr, cypress, DEsubs, DExMA, DMRcaller, DMRcate, DNAcycP2, DspikeIn, ENmix, EpiMix, epiNEM, EventPointer, fCCAC, fcScan, fgga, FGNet, flowCut, flowTime, fmrs, GateFinder, gCrisprTools, gdsfmt, GEM, GeneNetworkBuilder, GeneOverlap, geneplast, geneRxCluster, geNetClassifier, genomation, GEOquery, GeoTcgaData, ginmappeR, GMRP, GOstats, GrafGen, GreyListChIP, GWASTools, h5vc, Harman, HiCDCPlus, hierGWAS, HIREewas, hypergraph, iCARE, iClusterPlus, IFAA, illuminaio, immunotation, INPower, IPO, kebabs, KEGGREST, LACE, MAGAR, magpie, massiR, MatrixQCvis, MatrixRider, MBttest, Mergeomics, MetaboSignal, metagene2, metagenomeSeq, MetCirc, methylCC, methylInheritance, MetNet, microbiome, miRBaseConverter, miRcomp, mirIntegrator, mnem, mosbi, MOSClip, motifStack, MsQuality, multiClust, MultiMed, MultiRNAflow, MungeSumstats, MWASTools, ncRNAtools, nempi, NetSAM, nondetects, nucleoSim, omicsGMF, OMICsPCA, OncoScore, PAA, panelcn.mops, Path2PPI, pathMED, PathNet, pathview, pepXMLTab, phenomis, powerTCR, proBAMr, qpgraph, quantro, RBGL, rBiopaxParser, RbowtieCuda, rcellminer, rCGH, REBET, RESOLVE, rfaRm, RGraph2js, Rgraphviz, rgsepd, riboSeqR, ROntoTools, ropls, ROSeq, RTN, RTNduals, RTNsurvival, rTRM, SAIGEgds, sangerseqR, SANTA, sarks, SCArray, scDataviz, scLANE, scp, screenCounter, scry, segmentSeq, SeqArray, seqPattern, SICtools, sigFeature, sigsquared, SIMAT, similaRpeak, SIMLR, singleCellTK, SingleR, slingshot, SNPRelate, SparseSignatures, specL, STATegRa, STRINGdb, SUITOR, systemPipeTools, TCC, TFEA.ChIP, TIN, transcriptogramer, traseR, TreeAndLeaf, tripr, tRNAdbImport, TRONCO, Uniquorn, variancePartition, VERSO, XAItest, xcore, zenith, ENCODExplorerData, geneplast.data, ConnectivityMap, FieldEffectCrc, grndata, HarmanData, healthyControlsPresenceChecker, microRNAome, RegParallel, scMultiome, sesameData, xcoredata, adjclust, aroma.affymetrix, ggpicrust2, gkmSVM, GSEMA, inDAGO, MarZIC, NutrienTrackeR, pagoda2, Platypus, polyRAD, Rediscover, Seurat dependencyCount: 5 Package: biocGraph Version: 1.72.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: 8c37735c6b79ce7118b6586cba9a3e08 NeedsCompilation: no Title: Graph examples and use cases in Bioinformatics Description: This package provides examples and code that make use of the different graph related packages produced by Bioconductor. biocViews: Visualization, GraphAndNetwork Author: Li Long , Robert Gentleman , Seth Falcon Florian Hahne Maintainer: Florian Hahne git_url: https://git.bioconductor.org/packages/biocGraph git_branch: RELEASE_3_22 git_last_commit: c7492ba git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biocGraph_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biocGraph_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biocGraph_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biocGraph_1.72.0.tgz vignettes: vignettes/biocGraph/inst/doc/biocGraph.pdf, vignettes/biocGraph/inst/doc/layingOutPathways.pdf vignetteTitles: Examples of plotting graphs Using Rgraphviz, HOWTO layout pathways hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocGraph/inst/doc/biocGraph.R, vignettes/biocGraph/inst/doc/layingOutPathways.R suggestsMe: EnrichmentBrowser dependencyCount: 52 Package: BiocHail Version: 1.10.0 Depends: R (>= 4.3.0), graphics, stats, utils Imports: reticulate, basilisk, BiocFileCache, methods, dplyr, BiocGenerics Suggests: knitr, testthat, BiocStyle, ggplot2, DT License: Artistic-2.0 MD5sum: d396d94ee8ebb289b958f32a16a5b8d7 NeedsCompilation: no Title: basilisk and hail Description: Use hail via basilisk when appropriate, or via reticulate. This package can be used in terra.bio to interact with UK Biobank resources processed by hail.is. biocViews: Infrastructure Author: Vincent Carey [aut, cre] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/BiocHail VignetteBuilder: knitr BugReports: https://github.com/vjcitn/BiocHail/issues git_url: https://git.bioconductor.org/packages/BiocHail git_branch: RELEASE_3_22 git_last_commit: dc7f0b8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocHail_1.10.0.tar.gz vignettes: vignettes/BiocHail/inst/doc/gwas_tut.html, vignettes/BiocHail/inst/doc/large_t2t.html, vignettes/BiocHail/inst/doc/ukbb.html vignetteTitles: 01 BiocHail -- GWAS tutorial, 02 Working with larger VCF: T2T by chromosome, 03 Working with UK Biobank summary statistics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocHail/inst/doc/gwas_tut.R, vignettes/BiocHail/inst/doc/large_t2t.R, vignettes/BiocHail/inst/doc/ukbb.R dependencyCount: 58 Package: BiocHubsShiny Version: 1.10.0 Depends: R (>= 4.3.0), shiny Imports: AnnotationHub, ExperimentHub, DT, htmlwidgets, rclipboard, S4Vectors, shinyAce, shinybiocloader, shinyjs, shinythemes, utils Suggests: BiocManager, BiocStyle, curl, glue, knitr, rmarkdown, sessioninfo, shinytest2 License: Artistic-2.0 MD5sum: 439c5dfe057c0e2123d3b218470b7bb5 NeedsCompilation: no Title: View AnnotationHub and ExperimentHub Resources Interactively Description: A package that allows interactive exploration of AnnotationHub and ExperimentHub resources. It uses DT / DataTable to display resources for multiple organisms. It provides template code for reproducibility and for downloading resources via the indicated Hub package. biocViews: Software, ShinyApps Author: Marcel Ramos [aut, cre] (ORCID: ), Vincent Carey [ctb] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/BiocHubsShiny VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocHubsShiny/issues git_url: https://git.bioconductor.org/packages/BiocHubsShiny git_branch: RELEASE_3_22 git_last_commit: 2adb511 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocHubsShiny_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocHubsShiny_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocHubsShiny_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocHubsShiny_1.10.0.tgz vignettes: vignettes/BiocHubsShiny/inst/doc/BiocHubsShiny.html vignetteTitles: BiocHubsShiny Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocHubsShiny/inst/doc/BiocHubsShiny.R dependencyCount: 97 Package: BiocIO Version: 1.20.0 Depends: R (>= 4.3.0) Imports: BiocGenerics, S4Vectors, methods, tools Suggests: testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 82d3b94b4df3eb96dcc8614e06ba380e NeedsCompilation: no Title: Standard Input and Output for Bioconductor Packages Description: The `BiocIO` package contains high-level abstract classes and generics used by developers to build IO funcionality within the Bioconductor suite of packages. Implements `import()` and `export()` standard generics for importing and exporting biological data formats. `import()` supports whole-file as well as chunk-wise iterative import. The `import()` interface optionally provides a standard mechanism for 'lazy' access via `filter()` (on row or element-like components of the file resource), `select()` (on column-like components of the file resource) and `collect()`. The `import()` interface optionally provides transparent access to remote (e.g. via https) as well as local access. Developers can register a file extension, e.g., `.loom` for dispatch from character-based URIs to specific `import()` / `export()` methods based on classes representing file types, e.g., `LoomFile()`. biocViews: Annotation,DataImport Author: Martin Morgan [aut], Michael Lawrence [aut], Daniel Van Twisk [aut], Marcel Ramos [cre] (ORCID: ) Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocIO/issues git_url: https://git.bioconductor.org/packages/BiocIO git_branch: RELEASE_3_22 git_last_commit: 2810f6a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocIO_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocIO_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocIO_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocIO_1.20.0.tgz vignettes: vignettes/BiocIO/inst/doc/BiocIO.html vignetteTitles: BiocIO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocIO/inst/doc/BiocIO.R dependsOnMe: BSgenome, HelloRanges, LoomExperiment importsMe: Bioc.gff, BiocSet, BSgenomeForge, gmapR, HiCExperiment, HiContacts, rtracklayer, TENxIO, tidyCoverage, txdbmaker, VisiumIO, XeniumIO dependencyCount: 9 Package: biocmake Version: 1.2.0 Imports: utils, tools, dir.expiry Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: bd5bb06d493c0ce944fc91be6206eb97 NeedsCompilation: no Title: CMake for Bioconductor Description: Manages the installation of CMake for building Bioconductor packages. This avoids the need for end-users to manually install CMake on their system. No action is performed if a suitable version of CMake is already available. biocViews: Infrastructure Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun URL: https://github.com/LTLA/biocmake VignetteBuilder: knitr BugReports: https://github.com/LTLA/biocmake/issues git_url: https://git.bioconductor.org/packages/biocmake git_branch: RELEASE_3_22 git_last_commit: ee210d0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biocmake_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biocmake_1.1.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biocmake_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biocmake_1.2.0.tgz vignettes: vignettes/biocmake/inst/doc/userguide.html vignetteTitles: Cmake for Bioconductor hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biocmake/inst/doc/userguide.R linksToMe: Rigraphlib dependencyCount: 4 Package: BiocNeighbors Version: 2.4.0 Imports: Rcpp, methods LinkingTo: Rcpp, assorthead Suggests: BiocParallel, testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 9d9be69d472844e36fa6e340400c534b NeedsCompilation: yes Title: Nearest Neighbor Detection for Bioconductor Packages Description: Implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Exact searches can be performed using the k-means for k-nearest neighbors algorithm or with vantage point trees. Approximate searches can be performed using the Annoy or HNSW libraries. Searching on either Euclidean or Manhattan distances is supported. Parallelization is achieved for all methods by using BiocParallel. Functions are also provided to search for all neighbors within a given distance. biocViews: Clustering, Classification Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun SystemRequirements: C++17 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocNeighbors git_branch: RELEASE_3_22 git_last_commit: c2ff286 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocNeighbors_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocNeighbors_2.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocNeighbors_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocNeighbors_2.4.0.tgz vignettes: vignettes/BiocNeighbors/inst/doc/userguide.html vignetteTitles: Finding neighbors in high-dimensional space hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocNeighbors/inst/doc/userguide.R dependsOnMe: OSCA.advanced, OSCA.workflows, SingleRBook importsMe: batchelor, bluster, CellMixS, clustSIGNAL, concordexR, cydar, imcRtools, lemur, miloR, mumosa, poem, scater, scDblFinder, scider, scMerge, scrapper, SingleR, smoothclust, SpatialFeatureExperiment, SpotSweeper, StabMap, SVP, UCell suggestsMe: ClassifyR, scLANE, TrajectoryUtils, TSCAN linksToMe: scrapper, SingleR dependencyCount: 4 Package: BioCor Version: 1.34.0 Depends: R (>= 4.4) Imports: BiocParallel, GSEABase, Matrix, methods Suggests: airway, BiocStyle, boot, DESeq2, ggplot2 (>= 3.4.1), GOSemSim, Hmisc, knitr (>= 1.43), org.Hs.eg.db, reactome.db, rmarkdown, spelling, testthat (>= 3.1.5), WGCNA License: MIT + file LICENSE MD5sum: 8d865c6f646e239dcd68626e9ee7b6f8 NeedsCompilation: no Title: Functional Similarities Description: Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships... biocViews: StatisticalMethod, Clustering, GeneExpression, Network, Pathways, NetworkEnrichment, SystemsBiology Author: Lluís Revilla Sancho [aut, cre] (ORCID: ), Pau Sancho-Bru [ths] (ORCID: ), Juan José Salvatella Lozano [ths] (ORCID: ) Maintainer: Lluís Revilla Sancho URL: https://bioconductor.org/packages/BioCor, https://biocor.llrs.dev VignetteBuilder: knitr BugReports: https://github.com/llrs/BioCor/issues git_url: https://git.bioconductor.org/packages/BioCor git_branch: RELEASE_3_22 git_last_commit: 252c6ff git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BioCor_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioCor_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioCor_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioCor_1.34.0.tgz vignettes: vignettes/BioCor/inst/doc/BioCor_1_basics.html, vignettes/BioCor/inst/doc/BioCor_2_advanced.html vignetteTitles: About BioCor, Advanced usage of BioCor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioCor/inst/doc/BioCor_1_basics.R, vignettes/BioCor/inst/doc/BioCor_2_advanced.R dependencyCount: 60 Package: BiocParallel Version: 1.44.0 Depends: methods, R (>= 4.1.0) Imports: stats, utils, futile.logger, parallel, snow, codetools LinkingTo: BH (>= 1.87.0), cpp11 Suggests: BiocGenerics, tools, foreach, BBmisc, doParallel, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, RUnit, BiocStyle, knitr, batchtools, data.table Enhances: Rmpi License: GPL-2 | GPL-3 | BSL-1.0 MD5sum: 75ab7c6895d7c10cbf15f4335adc6f08 NeedsCompilation: yes Title: Bioconductor facilities for parallel evaluation Description: This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga [aut], Aaron Lun [ctb], Henrik Bengtsson [ctb], Madelyn Carlson [ctb] (Translated 'Random Numbers' vignette from Sweave to RMarkdown / HTML.), Phylis Atieno [ctb] (Translated 'Introduction to BiocParallel' vignette from Sweave to Rmarkdown / HTML.), Sergio Oller [ctb] (Improved bpmapply() efficiency., ORCID: ) Maintainer: Jiefei Wang URL: https://github.com/Bioconductor/BiocParallel SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocParallel/issues git_url: https://git.bioconductor.org/packages/BiocParallel git_branch: RELEASE_3_22 git_last_commit: 3d6f2f6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocParallel_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocParallel_1.43.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocParallel_1.43.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocParallel_1.44.0.tgz vignettes: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.html, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.html, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.html, vignettes/BiocParallel/inst/doc/Random_Numbers.html vignetteTitles: 2. Introduction to BatchtoolsParam, 3. Errors,, Logs and Debugging, 1. Introduction to BiocParallel, 4. Random Numbers in BiocParallel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.R, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.R, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.R, vignettes/BiocParallel/inst/doc/Random_Numbers.R dependsOnMe: bacon, BEclear, Cardinal, CardinalIO, Chromatograms, ClassifyR, clusterSeq, consensusSeekeR, DEWSeq, DEXSeq, DMCFB, DMCHMM, doppelgangR, DSS, extraChIPs, FEAST, FRASER, GenomicFiles, INSPEcT, ISLET, matter, MBASED, metagene2, metapone, ncGTW, Oscope, OUTRIDER, PCAN, periodicDNA, pRoloc, RedisParam, Rqc, ShortRead, SigCheck, Spectra, sva, variancePartition, xcms, sequencing, OSCA.advanced, OSCA.workflows, SingleRBook importsMe: abseqR, ADImpute, AffiXcan, ALDEx2, AlphaBeta, AlpsNMR, ASICS, ATACseqQC, atena, atSNP, bambu, BANDITS, bandle, Banksy, BASiCS, batchCorr, batchelor, BayesSpace, bayNorm, beer, BERT, BioCor, BiocSingular, BioNERO, biotmle, biscuiteer, blase, bluster, brendaDb, bsseq, CAGEfightR, CAGEr, CARDspa, CBN2Path, ccImpute, CDI, cellbaseR, CellBench, CelliD, CellMixS, censcyt, Cepo, ChIPexoQual, ChromSCape, chromVAR, ClusterFoldSimilarity, clustSIGNAL, CNVMetrics, CNVRanger, CoGAPS, coMethDMR, CompoundDb, concordexR, condiments, consensusDE, consensusOV, consICA, Coralysis, CoreGx, coseq, cpvSNP, CrispRVariants, crupR, csaw, CTSV, cydar, cypress, CytoGLMM, cytoKernel, cytomapper, CytoMDS, CytoPipeline, dcGSA, decoupleR, DeepTarget, DegCre, DepInfeR, derfinder, DEScan2, DESeq2, DEsingle, DESpace, DiffBind, Dino, DMRcaller, dmrseq, DNEA, DOSE, dreamlet, DRIMSeq, DropletUtils, Dune, easier, easyRNASeq, EMDomics, enhancerHomologSearch, epimutacions, epiregulon, epistasisGA, ERSSA, EWCE, faers, fgsea, findIPs, FindIT2, FLAMES, flowcatchR, flowSpecs, GDCRNATools, gDNAx, gDRcore, gDRutils, GENESIS, GenomAutomorphism, GenomicAlignments, GloScope, gmapR, gscreend, GSEABenchmarkeR, GSVA, h5vc, HicAggR, HiCBricks, HiCcompare, HiCDOC, HiCExperiment, HiContacts, HTSFilter, HybridExpress, iasva, icetea, ideal, imcRtools, IntEREst, IONiseR, IPO, IsoformSwitchAnalyzeR, jazzPanda, katdetectr, KinSwingR, LimROTS, lisaClust, loci2path, LRcell, magpie, magrene, mariner, mbkmeans, MCbiclust, MetaboAnnotation, MetaboCoreUtils, metabomxtr, metaseqR2, methodical, MethylAid, methylGSA, methyLImp2, methylInheritance, methylscaper, MetNet, mia, miaViz, MICSQTL, miloR, minfi, MIRit, mist, mixOmics, MOGAMUN, MoleculeExperiment, monaLisa, motifbreakR, MotifPeeker, MPAC, MPRAnalyze, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MSnbase, msqrob2, MsQuality, MSstatsResponse, multiHiCcompare, mumosa, muscat, NBAMSeq, nnSVG, notame, notameStats, NPARC, omicsGMF, ORFik, orthos, OVESEG, PAIRADISE, pairedGSEA, pathMED, PDATK, pengls, PharmacoGx, pipeComp, poem, pram, proActiv, ProteoDisco, PSMatch, qpgraph, QRscore, qsea, QuasR, RadioGx, raer, rawDiag, Rcwl, recount, ReducedExperiment, RegEnrich, REMP, RiboCrypt, RJMCMCNucleosomes, RNAmodR, RNAseqCovarImpute, ROTS, Rsamtools, RUVcorr, SanityR, saseR, satuRn, scanMiR, scanMiRApp, SCArray, SCArray.sat, scater, scBubbletree, scClassify, scDblFinder, scDD, scDDboost, scde, scDesign3, SCFA, scGraphVerse, scHiCcompare, scHOT, scMerge, scMultiSim, SCnorm, scone, scoreInvHap, scPCA, scran, scRecover, screenCounter, scruff, scShapes, scTHI, scuttle, SEraster, sesame, SEtools, sigFeature, signatureSearch, SimBu, simpleSeg, SingleCellAlleleExperiment, singleCellTK, SingleR, singscore, SmartPhos, SNPhood, spacexr, spARI, sparrow, SpatialFeatureExperiment, SpectralTAD, spicyR, splatter, SpliceWiz, SplicingGraphs, spoon, SpotSweeper, srnadiff, StabMap, Statial, SUITOR, SuperCellCyto, SVP, syntenet, TAPseq, TBSignatureProfiler, ternarynet, TFBSTools, tidyCoverage, TMixClust, ToxicoGx, TPP2D, tpSVG, tradeSeq, TreeSummarizedExperiment, Trendy, TVTB, txcutr, UCell, UPDhmm, VariantFiltering, VariantTools, VDJdive, velociraptor, vmrseq, Voyager, waddR, weitrix, xCell2, zinbwave, CytoMethIC, IHWpaper, JohnsonKinaseData, ExpHunterSuite, seqpac, OSTA, causalBatch, DCLEAR, DTSEA, DysPIA, enviGCMS, GSEMA, Holomics, LDM, minSNPs, oosse, robin, scGate, spatialGE suggestsMe: alabaster.mae, beachmat, BiocNeighbors, cliqueMS, DelayedArray, EpiCompare, escape, GenomicDataCommons, ggsc, glmGamPoi, GRaNIE, h5mread, HDF5Array, ISAnalytics, MungeSumstats, netSmooth, omicsPrint, plyinteractions, PureCN, randRotation, RcisTarget, rebook, rhdf5, S4Arrays, scGPS, scLANE, SeqArray, survClust, TFutils, TileDBArray, TrajectoryUtils, TSCAN, universalmotif, xcore, MethylAidData, Single.mTEC.Transcriptomes, TENxBrainData, TENxPBMCData, CAGEWorkflow, clustermq, conos, pagoda2, phase1RMD, RaMS, SpatialDDLS, survBootOutliers, wrTopDownFrag dependencyCount: 12 Package: BiocPkgTools Version: 1.27.12 Depends: htmlwidgets, R (>= 4.1.0) Imports: BiocFileCache, BiocManager, biocViews, tibble, methods, rlang, stringr, stats, rvest, dplyr, xml2, readr, httr, httr2, htmltools, DT, tools, utils, igraph (>= 2.0.0), jsonlite, gh, RBGL, graph, curl, glue, lubridate, purrr, tidyr, yaml Suggests: BiocStyle, knitr, rmarkdown, testthat, tm, networkD3, visNetwork, clipr, blastula, kableExtra, DiagrammeR, SummarizedExperiment License: MIT + file LICENSE MD5sum: 712fa74d87f407b4fa6e05d53c23b0f7 NeedsCompilation: no Title: Collection of simple tools for learning about Bioconductor Packages Description: Bioconductor has a rich ecosystem of metadata around packages, usage, and build status. This package is a simple collection of functions to access that metadata from R. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages. biocViews: Software, Infrastructure Author: Shian Su [aut, ctb], Lori Shepherd [ctb], Marcel Ramos [aut, ctb] (ORCID: ), Felix G.M. Ernst [ctb], Jennifer Wokaty [ctb], Charlotte Soneson [ctb], Martin Morgan [ctb], Vince Carey [ctb], Sean Davis [aut, cre] Maintainer: Sean Davis URL: https://github.com/seandavi/BiocPkgTools SystemRequirements: mailsend-go VignetteBuilder: knitr BugReports: https://github.com/seandavi/BiocPkgTools/issues/new git_url: https://git.bioconductor.org/packages/BiocPkgTools git_branch: devel git_last_commit: 68dd50d git_last_commit_date: 2025-08-27 Date/Publication: 2025-10-07 source.ver: src/contrib/BiocPkgTools_1.27.12.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocPkgTools_1.27.9.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocPkgTools_1.27.12.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocPkgTools_1.27.12.tgz vignettes: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.html vignetteTitles: Overview of BiocPkgTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.R suggestsMe: biocViews, OSTA, rworkflows dependencyCount: 102 Package: biocroxytest Version: 1.6.0 Depends: R (>= 4.4.0) Imports: cli, glue, roxygen2, stringr Suggests: BiocStyle, here, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: cd0caf1bca950f00b0099fde1d662404 NeedsCompilation: no Title: Handle Long Tests in Bioconductor Packages Description: This package provides a roclet for roxygen2 that identifies and processes code blocks in your documentation marked with `@longtests`. These blocks should contain tests that take a long time to run and thus cannot be included in the regular test suite of the package. When you run `roxygen2::roxygenise` with the `longtests_roclet`, it will extract these long tests from your documentation and save them in a separate directory. This allows you to run these long tests separately from the rest of your tests, for example, on a continuous integration server that is set up to run long tests. biocViews: Software, Infrastructure Author: Francesc Catala-Moll [aut, cre] (ORCID: ) Maintainer: Francesc Catala-Moll URL: https://github.com/xec-cm/biocroxytest VignetteBuilder: knitr BugReports: https://github.com/xec-cm/biocroxytest/issues git_url: https://git.bioconductor.org/packages/biocroxytest git_branch: RELEASE_3_22 git_last_commit: bbd5ac6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biocroxytest_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biocroxytest_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biocroxytest_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biocroxytest_1.6.0.tgz vignettes: vignettes/biocroxytest/inst/doc/biocroxytest.html vignetteTitles: Introduction to biocroxytest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocroxytest/inst/doc/biocroxytest.R dependencyCount: 35 Package: BiocSet Version: 1.24.0 Depends: R (>= 3.6), dplyr Imports: methods, tibble, utils, rlang, plyr, S4Vectors, BiocIO, AnnotationDbi, KEGGREST, ontologyIndex, tidyr Suggests: GSEABase, airway, org.Hs.eg.db, DESeq2, limma, BiocFileCache, GO.db, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 5e0b623177b7d02a98d887195f6c99ea NeedsCompilation: no Title: Representing Different Biological Sets Description: BiocSet displays different biological sets in a triple tibble format. These three tibbles are `element`, `set`, and `elementset`. The user has the abilty to activate one of these three tibbles to perform common functions from the dplyr package. 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JASPAR2024, org.Hbacteriophora.eg.db, phastCons30way.UCSC.hg38, phastCons35way.UCSC.mm39, phyloP35way.UCSC.mm39, rat2302frmavecs, SomaScan.db, synaptome.data, TENET.AnnotationHub, UCSCRepeatMasker, ASICSdata, AssessORFData, AWAggregatorData, BioImageDbs, BioPlex, blimaTestingData, BloodCancerMultiOmics2017, bodymapRat, brgedata, bugphyzz, CardinalWorkflows, celldex, CellMapperData, CENTREprecomputed, cfToolsData, ChIPDBData, chipenrich.data, ChIPexoQualExample, chipseqDBData, CLLmethylation, clustifyrdatahub, CopyhelpeR, CoSIAdata, COSMIC.67, crisprScoreData, curatedBladderData, curatedMetagenomicData, curatedOvarianData, curatedPCaData, curatedTBData, curatedTCGAData, CytoMethIC, DAPARdata, depmap, derfinderData, DExMAdata, DNAZooData, DoReMiTra, dorothea, DropletTestFiles, DuoClustering2018, easierData, ELMER.data, emtdata, eoPredData, epimutacionsData, ewceData, fourDNData, furrowSeg, gDNAinRNAseqData, gDRtestData, GenomicDistributionsData, GeuvadisTranscriptExpr, GSE103322, GSE13015, GSE159526, GSE62944, HarmanData, HCAData, HCATonsilData, HD2013SGI, HDCytoData, healthyControlsPresenceChecker, HelloRangesData, HiCDataHumanIMR90, HiContactsData, HighlyReplicatedRNASeq, Hiiragi2013, HMP16SData, HMP2Data, HumanAffyData, humanHippocampus2024, IHWpaper, imcdatasets, JohnsonKinaseData, LegATo, LRcellTypeMarkers, mCSEAdata, mcsurvdata, MerfishData, MetaGxPancreas, MetaScope, MethylAidData, methylclockData, MethylSeqData, MicrobiomeBenchmarkData, microbiomeDataSets, minionSummaryData, MOFAdata, MouseAgingData, MouseGastrulationData, MouseThymusAgeing, msigdb, MSMB, msqc1, multiWGCNAdata, muscData, muSpaData, nanotubes, NestLink, NetActivityData, nmrdata, OnassisJavaLibs, optimalFlowData, orthosData, pasilla, PasillaTranscriptExpr, PCHiCdata, PepsNMRData, preciseTADhub, ProteinGymR, ptairData, raerdata, rcellminerData, RforProteomics, RGMQLlib, RNAmodR.Data, RnaSeqSampleSizeData, sampleClassifierData, scaeData, scanMiRData, scATAC.Explorer, SCLCBam, scMultiome, scpdata, scRNAseq, seventyGeneData, SFEData, SimBenchData, Single.mTEC.Transcriptomes, SingleCellMultiModal, smokingMouse, SpatialDatasets, spatialDmelxsim, spatialLIBD, STexampleData, SubcellularSpatialData, systemPipeRdata, TabulaMurisData, TabulaMurisSenisData, tartare, TCGAbiolinksGUI.data, TCGAWorkflowData, TENET.ExperimentHub, TENxBrainData, TENxBUSData, TENxPBMCData, TENxVisiumData, TENxXeniumData, timecoursedata, TimerQuant, tissueTreg, TMExplorer, TransOmicsData, tuberculosis, tweeDEseqCountData, VariantToolsData, VectraPolarisData, WeberDivechaLCdata, zebrafishRNASeq, annotation, arrays, CAGEWorkflow, chipseqDB, csawUsersGuide, EGSEA123, ExpressionNormalizationWorkflow, generegulation, highthroughputassays, liftOver, maEndToEnd, recountWorkflow, RNAseq123, seqpac, sequencing, spicyWorkflow, variants, BiocBookDemo, OSTA, aIc, asteRisk, BiocManager, corrmeta, cyjShiny, DEHOGT, EHRtemporalVariability, genetic.algo.optimizeR, ggBubbles, GSEMA, ipsRdbs, magmaR, MariNET, MarZIC, multiclassPairs, net4pg, NutrienTrackeR, openSkies, PlackettLuce, Rediscover, rjsoncons, rworkflows, StepReg, TFactSR dependencyCount: 33 Package: biocthis Version: 1.20.0 Imports: BiocManager, fs, glue, rlang, styler, usethis (>= 2.0.1) Suggests: BiocStyle, covr, devtools, knitr, pkgdown, RefManageR, rmarkdown, sessioninfo, testthat, utils License: Artistic-2.0 MD5sum: a7578d60d10e5df3c7d92a19bff1acb1 NeedsCompilation: no Title: Automate package and project setup for Bioconductor packages Description: This package expands the usethis package with the goal of helping automate the process of creating R packages for Bioconductor or making them Bioconductor-friendly. biocViews: Software, ReportWriting Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Marcel Ramos [ctb] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/biocthis VignetteBuilder: knitr BugReports: https://github.com/lcolladotor/biocthis/issues git_url: https://git.bioconductor.org/packages/biocthis git_branch: RELEASE_3_22 git_last_commit: a03c941 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biocthis_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biocthis_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biocthis_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biocthis_1.20.0.tgz vignettes: vignettes/biocthis/inst/doc/biocthis_dev_notes.html, vignettes/biocthis/inst/doc/biocthis.html vignetteTitles: biocthis developer notes, Introduction to biocthis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocthis/inst/doc/biocthis_dev_notes.R, vignettes/biocthis/inst/doc/biocthis.R importsMe: HubPub suggestsMe: tripr dependencyCount: 44 Package: BiocVersion Version: 3.22.0 Depends: R (>= 4.5.0) License: Artistic-2.0 MD5sum: a5bcfb771bbfac2e0993b84ba9c6374c NeedsCompilation: no Title: Set the appropriate version of Bioconductor packages Description: This package provides repository information for the appropriate version of Bioconductor. biocViews: Infrastructure Author: Martin Morgan [aut], Marcel Ramos [ctb], Bioconductor Package Maintainer [ctb, cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/BiocVersion git_branch: devel git_last_commit: fea53ac git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/BiocVersion_3.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocVersion_3.22.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocVersion_3.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocVersion_3.22.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHub, pkgndep suggestsMe: BiocBookDemo, OSTA, BiocManager dependencyCount: 0 Package: biocViews Version: 1.78.0 Depends: R (>= 3.6.0) Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools, utils, XML, RCurl, RUnit, BiocManager Suggests: BiocGenerics, BiocPkgTools, knitr, commonmark, BiocStyle License: Artistic-2.0 MD5sum: acf440627c2560bacb74ed7d661d10ba NeedsCompilation: no Title: Categorized views of R package repositories Description: Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and 'experiment data' packages. biocViews: Infrastructure Author: Vincent Carey [aut], Benjamin Harshfield [aut], Seth Falcon [aut], Sonali Arora [aut], Lori Shepherd [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/biocViews VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/biocViews/issues git_url: https://git.bioconductor.org/packages/biocViews git_branch: RELEASE_3_22 git_last_commit: 2adaab7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biocViews_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biocViews_1.77.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biocViews_1.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biocViews_1.78.0.tgz vignettes: vignettes/biocViews/inst/doc/createReposHtml.html, vignettes/biocViews/inst/doc/HOWTO-BCV.html vignetteTitles: biocViews-CreateRepositoryHTML, biocViews-HOWTO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocViews/inst/doc/createReposHtml.R, vignettes/biocViews/inst/doc/HOWTO-BCV.R importsMe: AnnotationHubData, BiocCheck, BiocPkgTools, BioGA, monocle, sigFeature, RforProteomics, genetic.algo.optimizeR suggestsMe: packFinder, plasmut, ReducedExperiment, rworkflows dependencyCount: 17 Package: BiocWorkflowTools Version: 1.36.0 Depends: R (>= 3.4) Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown, rstudioapi, stringr, tools, utils, usethis License: MIT + file LICENSE Archs: x64 MD5sum: a4620d16f29615793ba5a093c9a9cfe7 NeedsCompilation: no Title: Tools to aid the development of Bioconductor Workflow packages Description: Provides functions to ease the transition between Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow. biocViews: Software, ReportWriting Author: Mike Smith [aut, cre], Andrzej Oleś [aut] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocWorkflowTools git_branch: RELEASE_3_22 git_last_commit: 49b903f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiocWorkflowTools_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocWorkflowTools_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocWorkflowTools_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocWorkflowTools_1.36.0.tgz vignettes: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.html vignetteTitles: Converting Rmarkdown to F1000Research LaTeX Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.R dependsOnMe: RNAseq123 suggestsMe: CAGEWorkflow, recountWorkflow dependencyCount: 61 Package: biodb Version: 1.18.0 Depends: R (>= 4.1.0) Imports: R6, RSQLite, Rcpp, XML, chk, fscache (>= 1.0.2), jsonlite, lgr, lifecycle, methods, openssl, plyr, progress, rappdirs, sched (>= 1.0.1), sqlq, stats, stringr, tools, withr, yaml LinkingTo: Rcpp, testthat Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, xml2 License: AGPL-3 MD5sum: 5dba370f5249ce4a46133054a693071d NeedsCompilation: yes Title: Biodb, a Library and a Development Framework for Connecting to Chemical and Biological Databases Description: The biodb package provides access to standard remote chemical and biological databases (ChEBI, KEGG, HMDB, ...), as well as to in-house local database files (CSV, SQLite), with easy retrieval of entries, access to web services, search of compounds by mass and/or name, and mass spectra matching for LCMS and MSMS. Its architecture as a development framework facilitates the development of new database connectors for local projects or inside separate published packages. biocViews: Software, Infrastructure, DataImport, KEGG Author: Pierrick Roger [aut, cre] (ORCID: ), Alexis Delabrière [ctb] (ORCID: ) Maintainer: Pierrick Roger URL: https://gitlab.com/rbiodb/biodb VignetteBuilder: knitr BugReports: https://gitlab.com/rbiodb/biodb/-/issues git_url: https://git.bioconductor.org/packages/biodb git_branch: RELEASE_3_22 git_last_commit: cb4e359 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biodb_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biodb_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biodb_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biodb_1.18.0.tgz vignettes: vignettes/biodb/inst/doc/biodb.html, vignettes/biodb/inst/doc/details.html, vignettes/biodb/inst/doc/entries.html vignetteTitles: Introduction to the biodb package., Details on general *biodb* usage and principles, Manipulating entry objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodb/inst/doc/biodb.R, vignettes/biodb/inst/doc/details.R, vignettes/biodb/inst/doc/entries.R importsMe: biodbChebi, phenomis dependencyCount: 65 Package: biodbChebi Version: 1.16.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.3.1) Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, lgr License: AGPL-3 MD5sum: 7f09e47bf56e4f45c2a408b8e02537a3 NeedsCompilation: no Title: biodbChebi, a library for connecting to the ChEBI Database Description: The biodbChebi library provides access to the ChEBI Database, using biodb package framework. It allows to retrieve entries by their accession number. Web services can be accessed for searching the database by name, mass or other fields. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] (ORCID: ) Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbChebi VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbChebi/issues git_url: https://git.bioconductor.org/packages/biodbChebi git_branch: RELEASE_3_22 git_last_commit: 28c1b2d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biodbChebi_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biodbChebi_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biodbChebi_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biodbChebi_1.16.0.tgz vignettes: vignettes/biodbChebi/inst/doc/biodbChebi.html vignetteTitles: Introduction to the biodbChebi package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbChebi/inst/doc/biodbChebi.R importsMe: phenomis dependencyCount: 66 Package: bioDist Version: 1.82.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: 33e375ce8913781ec704dee6ecf5e499 NeedsCompilation: no Title: Different distance measures Description: A collection of software tools for calculating distance measures. biocViews: Clustering, Classification Author: B. Ding, R. Gentleman and Vincent Carey Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/bioDist git_branch: RELEASE_3_22 git_last_commit: a093830 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bioDist_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bioDist_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bioDist_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bioDist_1.82.0.tgz vignettes: vignettes/bioDist/inst/doc/bioDist.pdf vignetteTitles: bioDist Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioDist/inst/doc/bioDist.R importsMe: CHETAH, PhyloProfile dependencyCount: 8 Package: BioGA Version: 1.4.0 Depends: R (>= 4.4) Imports: ggplot2, graphics, Rcpp, SummarizedExperiment, animation, rlang, biocViews, sessioninfo, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: e7659a070844422d82736ad55acd2bd0 NeedsCompilation: yes Title: Bioinformatics Genetic Algorithm (BioGA) Description: Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package allows users to analyze and optimize high throughput genomic data using genetic algorithms. The functions provided are implemented in C++ for improved speed and efficiency, with an easy-to-use interface for use within R. biocViews: ExperimentalDesign, Technology Author: Dany Mukesha [aut, cre] (ORCID: ) Maintainer: Dany Mukesha URL: https://danymukesha.github.io/BioGA/ VignetteBuilder: knitr BugReports: https://github.com/danymukesha/BioGA/issues git_url: https://git.bioconductor.org/packages/BioGA git_branch: RELEASE_3_22 git_last_commit: d779378 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BioGA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioGA_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioGA_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioGA_1.4.0.tgz vignettes: vignettes/BioGA/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioGA/inst/doc/Introduction.R dependencyCount: 79 Package: biomaRt Version: 2.66.0 Depends: methods, R (>= 4.5.0) Imports: AnnotationDbi, BiocFileCache, curl, httr2, progress, stringr, utils, xml2 Suggests: BiocStyle, httptest2, knitr, mockery, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 0d06bd30f9225b9223db6f75008cce4b NeedsCompilation: no Title: Interface to BioMart databases (i.e. Ensembl) Description: In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite (). The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. The most prominent examples of BioMart databases are maintained by Ensembl, which provides biomaRt users direct access to a diverse set of data and enables a wide range of powerful online queries from gene annotation to database mining. biocViews: Annotation Author: Steffen Durinck [aut], Wolfgang Huber [aut], Sean Davis [ctb], Francois Pepin [ctb], Vince S Buffalo [ctb], Mike Smith [ctb] (ORCID: ), Hugo Gruson [ctb, cre] (ORCID: ), German Network for Bioinformatics Infrastructure - de.NBI [fnd] Maintainer: Hugo Gruson URL: https://github.com/Huber-group-EMBL/biomaRt, https://huber-group-embl.github.io/biomaRt/ VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/biomaRt/issues git_url: https://git.bioconductor.org/packages/biomaRt git_branch: RELEASE_3_22 git_last_commit: 6757ed1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biomaRt_2.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biomaRt_2.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biomaRt_2.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biomaRt_2.66.0.tgz vignettes: vignettes/biomaRt/inst/doc/accessing_ensembl.html, vignettes/biomaRt/inst/doc/accessing_other_marts.html vignetteTitles: Accessing Ensembl annotation with biomaRt, Using a BioMart other than Ensembl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/accessing_ensembl.R, vignettes/biomaRt/inst/doc/accessing_other_marts.R dependsOnMe: chromPlot, DrugVsDisease, genefu, GenomicOZone, MineICA, NetSAM, PPInfer, VegaMC, annotation importsMe: BadRegionFinder, BUSpaRse, ChIPpeakAnno, CHRONOS, dagLogo, DEXSeq, DMRcate, DominoEffect, dominoSignal, easyRNASeq, EDASeq, ELMER, EpiMix, epimutacions, FRASER, GDCRNATools, glmSparseNet, GOexpress, goSTAG, GRaNIE, Gviz, hermes, InterCellar, isobar, LACE, MEDIPS, MetaboSignal, metaseqR2, motifbreakR, MouseFM, OncoScore, oposSOM, ORFik, pcaExplorer, phenoTest, pRoloc, ProteoMM, ramwas, recoup, ReducedExperiment, rgsepd, scafari, scPipe, SeqGSEA, sitadela, SPLINTER, SPONGE, surfaltr, SurfR, SWATH2stats, TCGAbiolinks, TEKRABber, terapadog, TFEA.ChIP, transcriptogramer, txdbmaker, ViSEAGO, yarn, ExpHunterSuite, biomartr, BioVenn, convertid, DiNAMIC.Duo, GOxploreR, HiCociety, scGOclust, scPipeline, snplinkage, snplist suggestsMe: AnnotationForge, bioassayR, celda, ClusterJudge, crisprDesign, cTRAP, Damsel, epistack, fedup, FELLA, h5vc, martini, massiR, MethReg, MineICA, MiRaGE, MIRit, MutationalPatterns, netSmooth, oligo, OrganismDbi, pathlinkR, piano, progeny, R3CPET, RnBeads, rTRM, scater, ShortRead, SIM, sincell, tidysbml, trackViewer, wiggleplotr, zinbwave, BioMartGOGeneSets, BloodCancerMultiOmics2017, leeBamViews, RegParallel, RforProteomics, BED, BioInsight, DGEobj, DGEobj.utils, evanverse, gaawr2, geneviewer, grandR, GRIN2, kangar00, MoBPS, Patterns, Platypus, ProFAST, scDiffCom, SNPassoc dependencyCount: 63 Package: biomformat Version: 1.38.0 Depends: R (>= 3.2), methods Imports: plyr (>= 1.8), jsonlite (>= 0.9.16), Matrix (>= 1.2), rhdf5 Suggests: testthat (>= 0.10), knitr (>= 1.10), BiocStyle (>= 1.6), rmarkdown (>= 0.7) License: GPL-2 MD5sum: 8715d34976b538cd46c9a5b9eb0cc776 NeedsCompilation: no Title: An interface package for the BIOM file format Description: This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods. biocViews: ImmunoOncology, DataImport, Metagenomics, Microbiome Author: Paul J. McMurdie and Joseph N Paulson Maintainer: Paul J. McMurdie URL: https://github.com/joey711/biomformat/, http://biom-format.org/ VignetteBuilder: knitr BugReports: https://github.com/joey711/biomformat/issues git_url: https://git.bioconductor.org/packages/biomformat git_branch: RELEASE_3_22 git_last_commit: dc389e0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biomformat_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biomformat_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biomformat_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biomformat_1.38.0.tgz vignettes: vignettes/biomformat/inst/doc/biomformat.html vignetteTitles: The biomformat package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomformat/inst/doc/biomformat.R importsMe: microbiomeExplorer, phyloseq suggestsMe: animalcules, iSEEtree, metagenomeSeq, MGnifyR, mia, MicrobiotaProcess, MetaScope dependencyCount: 14 Package: BioMVCClass Version: 1.78.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL Archs: x64 MD5sum: bb22cbace88b5fd39f57e92af47a6dc9 NeedsCompilation: no Title: Model-View-Controller (MVC) Classes That Use Biobase Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/BioMVCClass git_branch: RELEASE_3_22 git_last_commit: ff8dda8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BioMVCClass_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioMVCClass_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioMVCClass_1.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioMVCClass_1.78.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: biomvRCNS Version: 1.50.0 Depends: IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 4fe79f3088f373388841578b3a036d2d NeedsCompilation: yes Title: Copy Number study and Segmentation for multivariate biological data Description: In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing. biocViews: aCGH, CopyNumberVariation, Microarray, Sequencing, Visualization, Genetics Author: Yang Du Maintainer: Yang Du git_url: https://git.bioconductor.org/packages/biomvRCNS git_branch: RELEASE_3_22 git_last_commit: c716e09 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biomvRCNS_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biomvRCNS_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biomvRCNS_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biomvRCNS_1.50.0.tgz vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf vignetteTitles: biomvRCNS package introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomvRCNS/inst/doc/biomvRCNS.R dependencyCount: 153 Package: BioNAR Version: 1.11.0 Depends: R (>= 3.5.0), igraph (>= 2.0.1.1), poweRlaw, latex2exp, RSpectra, Rdpack Imports: stringr, viridis, fgsea, grid, methods, AnnotationDbi, dplyr, GO.db, org.Hs.eg.db (>= 3.19.1), rSpectral, WGCNA, ggplot2, ggrepel, minpack.lm, cowplot, data.table, scales, stats, Matrix Suggests: knitr, BiocStyle, magick, rmarkdown, igraphdata, testthat (>= 3.0.0), vdiffr, devtools, pander, plotly, randomcoloR License: Artistic-2.0 Archs: x64 MD5sum: 9aef840d1ccbb65905e1afc4481cd751 NeedsCompilation: no Title: Biological Network Analysis in R Description: the R package BioNAR, developed to step by step analysis of PPI network. The aim is to quantify and rank each protein’s simultaneous impact into multiple complexes based on network topology and clustering. Package also enables estimating of co-occurrence of diseases across the network and specific clusters pointing towards shared/common mechanisms. biocViews: Software, GraphAndNetwork, Network Author: Colin Mclean [aut], Anatoly Sorokin [aut, cre], Oksana Sorokina [aut], J. Douglas Armstrong [aut, fnd], T. Ian Simpson [ctb, fnd] Maintainer: Anatoly Sorokin VignetteBuilder: knitr BugReports: https://github.com/lptolik/BioNAR/issues/ git_url: https://git.bioconductor.org/packages/BioNAR git_branch: devel git_last_commit: ac4d792 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/BioNAR_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioNAR_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioNAR_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioNAR_1.11.0.tgz vignettes: vignettes/BioNAR/inst/doc/BioNAR_overview.html vignetteTitles: BioNAR: Biological Network Analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNAR/inst/doc/BioNAR_overview.R dependencyCount: 134 Package: BioNERO Version: 1.18.0 Depends: R (>= 4.1) Imports: WGCNA, dynamicTreeCut, ggdendro, matrixStats, sva, RColorBrewer, ComplexHeatmap, ggplot2, rlang, ggrepel, patchwork, reshape2, igraph, ggnetwork, intergraph, NetRep, stats, grDevices, utils, methods, BiocParallel, minet, GENIE3, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle, DESeq2, networkD3, covr License: GPL-3 MD5sum: 70eacade117064fbd5dc09238186bf01 NeedsCompilation: no Title: Biological Network Reconstruction Omnibus Description: BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks. biocViews: Software, GeneExpression, GeneRegulation, SystemsBiology, GraphAndNetwork, Preprocessing, Network, NetworkInference Author: Fabricio Almeida-Silva [cre, aut] (ORCID: ), Thiago Venancio [aut] (ORCID: ) Maintainer: Fabricio Almeida-Silva URL: https://github.com/almeidasilvaf/BioNERO VignetteBuilder: knitr BugReports: https://github.com/almeidasilvaf/BioNERO/issues git_url: https://git.bioconductor.org/packages/BioNERO git_branch: RELEASE_3_22 git_last_commit: 33be27e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BioNERO_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioNERO_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioNERO_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioNERO_1.18.0.tgz vignettes: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.html, vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.html, vignettes/BioNERO/inst/doc/vignette_03_network_comparison.html vignetteTitles: Gene coexpression network inference, Gene regulatory network inference with BioNERO, Network comparison: consensus modules and module preservation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.R, vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.R, vignettes/BioNERO/inst/doc/vignette_03_network_comparison.R importsMe: cageminer dependencyCount: 160 Package: BioNet Version: 1.70.0 Depends: R (>= 2.10.0), graph, RBGL Imports: igraph (>= 1.0.1), AnnotationDbi, Biobase Suggests: rgl, impute, DLBCL, genefilter, xtable, ALL, limma, hgu95av2.db, XML License: GPL (>= 2) Archs: x64 MD5sum: ced1bcb98a95f6bc333c9864796216c0 NeedsCompilation: no Title: Routines for the functional analysis of biological networks Description: This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork. biocViews: Microarray, DataImport, GraphAndNetwork, Network, NetworkEnrichment, GeneExpression, DifferentialExpression Author: Marcus Dittrich and Daniela Beisser Maintainer: Marcus Dittrich URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/ git_url: https://git.bioconductor.org/packages/BioNet git_branch: RELEASE_3_22 git_last_commit: 4ac64e0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BioNet_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioNet_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioNet_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioNet_1.70.0.tgz vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf vignetteTitles: BioNet Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNet/inst/doc/Tutorial.R importsMe: gatom, SMITE suggestsMe: SANTA, mwcsr dependencyCount: 51 Package: BioQC Version: 1.38.0 Depends: R (>= 3.5.0), Biobase Imports: edgeR, Rcpp, methods, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, lattice, latticeExtra, rbenchmark, gplots, gridExtra, org.Hs.eg.db, hgu133plus2.db, ggplot2, reshape2, plyr, ineq, covr, limma, RColorBrewer License: GPL (>=3) + file LICENSE MD5sum: 218e54f2c0d515bd337351ef7659fb40 NeedsCompilation: yes Title: Detect tissue heterogeneity in expression profiles with gene sets Description: BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance. biocViews: GeneExpression,QualityControl,StatisticalMethod, GeneSetEnrichment Author: Jitao David Zhang [cre, aut], Laura Badi [aut], Gregor Sturm [aut], Roland Ambs [aut], Iakov Davydov [aut] Maintainer: Jitao David Zhang URL: https://accio.github.io/BioQC VignetteBuilder: knitr BugReports: https://accio.github.io/BioQC/issues git_url: https://git.bioconductor.org/packages/BioQC git_branch: RELEASE_3_22 git_last_commit: d2191e5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BioQC_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioQC_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioQC_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioQC_1.38.0.tgz vignettes: vignettes/BioQC/inst/doc/bioqc-efficiency.html, vignettes/BioQC/inst/doc/bioqc-introduction.html, vignettes/BioQC/inst/doc/bioqc-signedGenesets.html, vignettes/BioQC/inst/doc/bioqc-simulation.html, vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.html, vignettes/BioQC/inst/doc/BioQC.html vignetteTitles: BioQC Algorithm: Speeding up the Wilcoxon-Mann-Whitney Test, BioQC: Detect tissue heterogeneity in gene expression data, Using BioQC with signed genesets, BioQC-benchmark: Testing Efficiency,, Sensitivity and Specificity of BioQC on simulated and real-world data, Comparing the Wilcoxon-Mann-Whitney to alternative statistical tests, BioQC-kidney: The kidney expression example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioQC/inst/doc/bioqc-efficiency.R, vignettes/BioQC/inst/doc/bioqc-introduction.R, vignettes/BioQC/inst/doc/bioqc-signedGenesets.R, vignettes/BioQC/inst/doc/bioqc-simulation.R, vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.R, vignettes/BioQC/inst/doc/BioQC.R dependencyCount: 15 Package: biosigner Version: 1.38.0 Imports: Biobase, methods, e1071, grDevices, graphics, MultiAssayExperiment, MultiDataSet, randomForest, ropls, stats, SummarizedExperiment, utils Suggests: BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 1356ca1a8d2078e97e5dd88cc9ac5ab7 NeedsCompilation: no Title: Signature discovery from omics data Description: Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics. biocViews: Classification, FeatureExtraction, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry Author: Philippe Rinaudo [aut], Etienne A. Thevenot [aut, cre] (ORCID: ) Maintainer: Etienne A. Thevenot URL: http://dx.doi.org/10.3389/fmolb.2016.00026 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biosigner git_branch: RELEASE_3_22 git_last_commit: 0c86c9b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biosigner_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biosigner_1.37.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biosigner_1.38.0.tgz vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.html vignetteTitles: biosigner-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R suggestsMe: phenomis dependencyCount: 102 Package: Biostrings Version: 2.78.0 Depends: R (>= 4.1.0), BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.31.2), XVector (>= 0.37.1), Seqinfo Imports: methods, utils, grDevices, stats, crayon LinkingTo: S4Vectors, IRanges, XVector Suggests: graphics, pwalign, BSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe, hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy (>= 1.41.3), affydata (>= 1.11.5), RUnit, BiocStyle, knitr, testthat (>= 3.0.0), covr License: Artistic-2.0 Archs: x64 MD5sum: b4ec1bf212646114ef729393f6ff3855 NeedsCompilation: yes Title: Efficient manipulation of biological strings Description: Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. biocViews: SequenceMatching, Alignment, Sequencing, Genetics, DataImport, DataRepresentation, Infrastructure Author: Hervé Pagès [aut, cre], Patrick Aboyoun [aut], Robert Gentleman [aut], Saikat DebRoy [aut], Vince Carey [ctb], Nicolas Delhomme [ctb], Felix Ernst [ctb], Wolfgang Huber [ctb] ('matchprobes' vignette), Beryl Kanali [ctb] (Converted 'MultipleAlignments' vignette from Sweave to RMarkdown), Haleema Khan [ctb] (Converted 'matchprobes' vignette from Sweave to RMarkdown), Aidan Lakshman [ctb], Kieran O'Neill [ctb], Valerie Obenchain [ctb], Marcel Ramos [ctb], Albert Vill [ctb], Jen Wokaty [ctb] (Converted 'matchprobes' vignette from Sweave to RMarkdown), Erik Wright [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/Biostrings VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Biostrings/issues git_url: https://git.bioconductor.org/packages/Biostrings git_branch: RELEASE_3_22 git_last_commit: eda5d66 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Biostrings_2.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Biostrings_2.77.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Biostrings_2.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Biostrings_2.78.0.tgz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf, vignettes/Biostrings/inst/doc/matchprobes.html, vignettes/Biostrings/inst/doc/MultipleAlignments.html vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Pairwise Sequence Alignments, Handling probe sequence information, Multiple Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R, vignettes/Biostrings/inst/doc/matchprobes.R, vignettes/Biostrings/inst/doc/MultipleAlignments.R dependsOnMe: alabaster.string, altcdfenvs, Basic4Cseq, BRAIN, BSgenome, BSgenomeForge, chimeraviz, ChIPanalyser, ChIPsim, cigarillo, cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, GenomicAlignments, GOTHiC, HelloRanges, igblastr, kebabs, MethTargetedNGS, minfi, Modstrings, MotifDb, motifTestR, 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pd.zebgene.1.1.st, pd.zebrafish, harbChIP, JASPAR2014, NestLink, generegulation, sequencing, CleanBSequences, SubVis importsMe: AllelicImbalance, AnnotationHubData, appreci8R, AssessORF, ATACseqQC, BBCAnalyzer, BCRANK, bcSeq, BEAT, BgeeCall, biovizBase, bsseq, BUMHMM, BUSpaRse, CAGEr, CellBarcode, ChIPpeakAnno, ChIPseqR, ChIPsim, chromVAR, circRNAprofiler, CircSeqAlignTk, cleanUpdTSeq, CleanUpRNAseq, cliProfiler, CNEr, CNVfilteR, cogeqc, compEpiTools, consensusDE, coRdon, crisprBase, crisprBowtie, crisprDesign, crisprScore, crisprShiny, CrispRVariants, crisprViz, dada2, dagLogo, DAMEfinder, Damsel, decompTumor2Sig, diffHic, DMRcaller, DNAshapeR, DominoEffect, doubletrouble, DspikeIn, DuplexDiscovereR, easyRNASeq, EDASeq, enhancerHomologSearch, ensembldb, EpiTxDb, esATAC, eudysbiome, EventPointer, FastqCleaner, FLAMES, G4SNVHunter, GA4GHclient, gcapc, gcrma, gDNAx, GeneRegionScan, genomation, GenomAutomorphism, GenomicAlignments, GenomicDistributions, GenomicFeatures, GenomicScores, 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AntibodyForests, BASiNET, BASiNETEntropy, BIGr, biomartr, copyseparator, crispRdesignR, CSESA, cubar, deepredeff, DNAmotif, dowser, eDNAfuns, ensembleTax, EpiSemble, GB5mcPred, genBaRcode, GencoDymo2, geneHapR, GenomicSig, hoardeR, ICAMS, iimi, kmeRtone, longreadvqs, metaCluster, MIC, MitoHEAR, MixviR, ogrdbstats, OpEnHiMR, PACVr, Platypus, refseqR, revert, SATS, seqmagick, SMITIDstruct, SQMtools, SVAlignR, vhcub, VIProDesign suggestsMe: alabaster.files, annotate, AnnotationForge, AnnotationHub, autonomics, bambu, BANDITS, CSAR, DNAcycP2, eisaR, GenomicFiles, GenomicRanges, GenomicTuples, ggseqalign, ggtree, GWASTools, HiContacts, maftools, methrix, methylumi, MiRaGE, mitoClone2, mutscan, nuCpos, plyinteractions, RNAmodR.AlkAnilineSeq, rpx, rTRM, screenCounter, splatter, systemPipeTools, treeio, tripr, XVector, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BeadArrayUseCases, bbl, bio3d, BOLDconnectR, DDPNA, demulticoder, file2meco, geneviewer, gkmSVM, gwas2crispr, inDAGO, karyotapR, maGUI, MiscMetabar, msaR, NameNeedle, orthGS, phangorn, polyRAD, protr, sigminer, Signac, tidysq linksToMe: DECIPHER, kebabs, MatrixRider, pwalign, Rsamtools, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 14 Package: BioTIP Version: 1.24.0 Depends: R (>= 3.6) Imports: igraph, cluster, psych, stringr, GenomicRanges, MASS, scran, methods, stats, utils, grDevices, graphics, foreach, doParallel Suggests: knitr, markdown, base, rmarkdown, ggplot2 License: GPL-2 MD5sum: e64b98d3815884bbd56bb6200dae69bb NeedsCompilation: no Title: BioTIP: An R package for characterization of Biological Tipping-Point Description: Adopting tipping-point theory to transcriptome profiles to unravel disease regulatory trajectory. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Software Author: Zhezhen Wang, Andrew Goldstein, Yuxi Sun, Biniam Feleke, Qier An, Antonio Feliciano, Xinan Yang Maintainer: Felix Yu and X Holly Yang URL: https://github.com/xyang2uchicago/BioTIP VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioTIP git_branch: RELEASE_3_22 git_last_commit: 34f169d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BioTIP_1.24.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioTIP_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioTIP_1.24.0.tgz vignettes: vignettes/BioTIP/inst/doc/BioTIP.html vignetteTitles: BioTIP- an R package for characterization of Biological Tipping-Point hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioTIP/inst/doc/BioTIP.R dependencyCount: 73 Package: biotmle Version: 1.34.0 Depends: R (>= 4.0) Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, drtmle (>= 1.0.4), S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma Suggests: testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger, SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1) License: MIT + file LICENSE MD5sum: 8ffe35974070800b6c3f19032c938a2d NeedsCompilation: no Title: Targeted Learning with Moderated Statistics for Biomarker Discovery Description: Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions. biocViews: Regression, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq, ImmunoOncology Author: Nima Hejazi [aut, cre, cph] (ORCID: ), Alan Hubbard [aut, ths] (ORCID: ), Mark van der Laan [aut, ths] (ORCID: ), Weixin Cai [ctb] (ORCID: ), Philippe Boileau [ctb] (ORCID: ) Maintainer: Nima Hejazi URL: https://code.nimahejazi.org/biotmle VignetteBuilder: knitr BugReports: https://github.com/nhejazi/biotmle/issues git_url: https://git.bioconductor.org/packages/biotmle git_branch: RELEASE_3_22 git_last_commit: 0d668f1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biotmle_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biotmle_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biotmle_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biotmle_1.34.0.tgz vignettes: vignettes/biotmle/inst/doc/exposureBiomarkers.html vignetteTitles: Identifying Biomarkers from an Exposure Variable hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biotmle/inst/doc/exposureBiomarkers.R dependencyCount: 96 Package: biovizBase Version: 1.58.0 Depends: R (>= 3.5.0), methods Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.23.19), IRanges (>= 1.99.28), Seqinfo, GenomeInfoDb (>= 1.45.5), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), Biostrings (>= 2.77.2), Rsamtools (>= 2.25.1), GenomicAlignments (>= 1.45.1), GenomicFeatures (>= 1.61.4), AnnotationDbi, VariantAnnotation (>= 1.55.1), ensembldb (>= 2.33.1), AnnotationFilter (>= 0.99.8), rlang Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer, EnsDb.Hsapiens.v75, RUnit License: Artistic-2.0 MD5sum: 8acce840d7124b7c4903eab85aa479cd NeedsCompilation: yes Title: Basic graphic utilities for visualization of genomic data. Description: The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency. biocViews: Infrastructure, Visualization, Preprocessing Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/biovizBase git_branch: RELEASE_3_22 git_last_commit: 1f6087b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biovizBase_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biovizBase_1.57.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biovizBase_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biovizBase_1.58.0.tgz vignettes: vignettes/biovizBase/inst/doc/intro.pdf vignetteTitles: An Introduction to biovizBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biovizBase/inst/doc/intro.R dependsOnMe: CAFE importsMe: ChIPexoQual, ggbio, Gviz, karyoploteR, Pviz, Rqc suggestsMe: Damsel, derfinderPlot, FRASER, NanoStringNCTools, OUTRIDER, R3CPET, regionReport, StructuralVariantAnnotation, Signac dependencyCount: 127 Package: BiRewire Version: 3.41.0 Depends: igraph, slam, Rtsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 MD5sum: 0d3846049d2b667304d4c7287a1293f8 NeedsCompilation: yes Title: High-performing routines for the randomization of a bipartite graph (or a binary event matrix), undirected and directed signed graph preserving degree distribution (or marginal totals) Description: Fast functions for bipartite network rewiring through N consecutive switching steps (See References) and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. Includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks and directed signed networks is also provided. Starting from version 1.9.7 a more precise bound (especially for small network) has been implemented. Starting from version 2.2.0 the analysis routine is more complete and a visual montioring of the underlying Markov Chain has been implemented. Starting from 3.6.0 the library can handle also matrices with NA (not for the directed signed graphs). Since version 3.27.1 it is possible to add a constraint for dsg generation: usually positive and negative arc between two nodes could be not accepted. biocViews: Network Author: Andrea Gobbi [aut], Francesco Iorio [aut], Giuseppe Jurman [cbt], Davide Albanese [cbt], Julio Saez-Rodriguez [cbt]. Maintainer: Andrea Gobbi URL: http://www.ebi.ac.uk/~iorio/BiRewire git_url: https://git.bioconductor.org/packages/BiRewire git_branch: devel git_last_commit: fc05c96 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/BiRewire_3.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiRewire_3.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiRewire_3.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiRewire_3.41.0.tgz vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf vignetteTitles: BiRewire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiRewire/inst/doc/BiRewire.R dependencyCount: 20 Package: biscuiteer Version: 1.24.0 Depends: R (>= 4.1.0), biscuiteerData, bsseq Imports: readr, qualV, Matrix, impute, HDF5Array, S4Vectors, Rsamtools, data.table, Biobase, GenomicRanges, IRanges, BiocGenerics, VariantAnnotation, DelayedMatrixStats, SummarizedExperiment, GenomeInfoDb, Mus.musculus, Homo.sapiens, matrixStats, rtracklayer, QDNAseq, dmrseq, methods, utils, R.utils, gtools, BiocParallel Suggests: DSS, covr, knitr, rmarkdown, markdown, rlang, scmeth, pkgdown, roxygen2, testthat, QDNAseq.hg19, QDNAseq.mm10, BiocStyle License: GPL-3 Archs: x64 MD5sum: 4becc9f8e970d898f834538735c0942e NeedsCompilation: no Title: Convenience Functions for Biscuit Description: A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc. biocViews: DataImport, MethylSeq, DNAMethylation Author: Tim Triche [aut], Wanding Zhou [aut], Benjamin Johnson [aut], Jacob Morrison [aut, cre], Lyong Heo [aut], James Eapen [aut] Maintainer: Jacob Morrison URL: https://github.com/trichelab/biscuiteer VignetteBuilder: knitr BugReports: https://github.com/trichelab/biscuiteer/issues git_url: https://git.bioconductor.org/packages/biscuiteer git_branch: RELEASE_3_22 git_last_commit: 29c89c4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/biscuiteer_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biscuiteer_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biscuiteer_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biscuiteer_1.24.0.tgz vignettes: vignettes/biscuiteer/inst/doc/biscuiteer.html vignetteTitles: Biscuiteer User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biscuiteer/inst/doc/biscuiteer.R dependencyCount: 176 Package: BiSeq Version: 1.50.0 Depends: R (>= 3.5.0), methods, S4Vectors, IRanges (>= 1.17.24), GenomicRanges, SummarizedExperiment (>= 0.2.0), Formula Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, Seqinfo, GenomicRanges, SummarizedExperiment, rtracklayer, parallel, betareg, lokern, Formula, globaltest License: LGPL-3 MD5sum: c4356b31461cbd5fea824b13e5c2e102 NeedsCompilation: no Title: Processing and analyzing bisulfite sequencing data Description: The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples. biocViews: Genetics, Sequencing, MethylSeq, DNAMethylation Author: Katja Hebestreit, Hans-Ulrich Klein Maintainer: Katja Hebestreit git_url: https://git.bioconductor.org/packages/BiSeq git_branch: RELEASE_3_22 git_last_commit: 626afbf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BiSeq_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiSeq_1.49.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiSeq_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiSeq_1.50.0.tgz vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf vignetteTitles: An Introduction to BiSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiSeq/inst/doc/BiSeq.R dependsOnMe: RRBSdata suggestsMe: updateObject dependencyCount: 90 Package: blacksheepr Version: 1.24.0 Depends: R (>= 3.6) Imports: grid, stats, grDevices, utils, circlize, viridis, RColorBrewer, ComplexHeatmap, SummarizedExperiment, pasilla Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, curl License: MIT + file LICENSE MD5sum: a6508ab8446429cd171f493f10e6ade6 NeedsCompilation: no Title: Outlier Analysis for pairwise differential comparison Description: Blacksheep is a tool designed for outlier analysis in the context of pairwise comparisons in an effort to find distinguishing characteristics from two groups. This tool was designed to be applied for biological applications such as phosphoproteomics or transcriptomics, but it can be used for any data that can be represented by a 2D table, and has two sub populations within the table to compare. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, DifferentialExpression, Transcriptomics Author: MacIntosh Cornwell [aut], RugglesLab [cre] Maintainer: RugglesLab VignetteBuilder: knitr BugReports: https://github.com/ruggleslab/blacksheepr/issues git_url: https://git.bioconductor.org/packages/blacksheepr git_branch: RELEASE_3_22 git_last_commit: 9710e23 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/blacksheepr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/blacksheepr_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/blacksheepr_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/blacksheepr_1.24.0.tgz vignettes: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.html vignetteTitles: Outlier Analysis using blacksheepr - Phosphoprotein hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.R dependencyCount: 126 Package: blase Version: 1.0.0 Depends: R (>= 4.5.0) Imports: SummarizedExperiment, SingleCellExperiment, ggplot2, viridis, patchwork, Matrix, scater, methods, rlang, BiocParallel, boot, dplyr, mgcv, stats, MatrixGenerics, Seurat (>= 4.0.0) Suggests: knitr, rmarkdown, testthat (>= 3.2.3), covr, tradeSeq, scran, slingshot, tools, ami, reshape2, plyr, fs, sparseMatrixStats, ggVennDiagram, uwot, BiocStyle, DelayedMatrixStats, limma License: GPL (>= 3) MD5sum: 04da772b121ec145905d4b573b098d01 NeedsCompilation: no Title: Bulk Linking Analysis for Single-cell Experiments Description: BLASE is a method for finding where bulk RNA-seq data lies on a single-cell pseudotime trajectory. It uses a fast and understandable approach based on Spearman correlation, with bootstrapping to provide confidence. BLASE can be used to "date" bulk RNA-seq data, annotate cell types in scRNA-seq, and help correct for developmental phenotype differences in bulk RNA-seq experiments. biocViews: Transcriptomics, SingleCell, Sequencing, GeneExpression, Transcription, RNASeq, TimeCourse, CellBiology, Software, CellBasedAssays Author: Andrew McCluskey [aut, cre] (ORCID: ), Toby Kettlewell [aut] (ORCID: ), Adrian M. Smith [aut] (ORCID: ), Rhiannon Kundu [aut] (ORCID: ), David A. Gunn [aut] (ORCID: ), Thomas D. Otto [aut, ths] (ORCID: ) Maintainer: Andrew McCluskey <2117532m@student.gla.ac.uk> URL: https://andrewmccluskey-uog.github.io/blase/ VignetteBuilder: knitr BugReports: https://andrewmccluskey-uog.github.io/blase/issues git_url: https://git.bioconductor.org/packages/blase git_branch: RELEASE_3_22 git_last_commit: 874f31b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/blase_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/blase_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/blase_1.0.0.tgz vignettes: vignettes/blase/inst/doc/assign-bulk-to-pseudotime.html, vignettes/blase/inst/doc/BLASE-for-annotating-scRNA-seq.html, vignettes/blase/inst/doc/BLASE-for-excluding-developmental-genes-from-bulk-RNA-seq.html vignetteTitles: Assigning bulk RNA-seq to pseudotime, BLASE for annotating scRNA-seq, BLASE for excluding developmental genes from bulk RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/blase/inst/doc/assign-bulk-to-pseudotime.R, vignettes/blase/inst/doc/BLASE-for-annotating-scRNA-seq.R, vignettes/blase/inst/doc/BLASE-for-excluding-developmental-genes-from-bulk-RNA-seq.R dependencyCount: 193 Package: BLMA Version: 1.34.0 Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel, Biobase, metafor, methods Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 859ff084ae48006cbc83f46b39f13393 NeedsCompilation: no Title: BLMA: A package for bi-level meta-analysis Description: Suit of tools for bi-level meta-analysis. The package can be used in a wide range of applications, including general hypothesis testings, differential expression analysis, functional analysis, and pathway analysis. biocViews: GeneSetEnrichment, Pathways, DifferentialExpression, Microarray Author: Tin Nguyen , Hung Nguyen , and Sorin Draghici Maintainer: Van-Dung Pham git_url: https://git.bioconductor.org/packages/BLMA git_branch: RELEASE_3_22 git_last_commit: 32ae9d5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BLMA_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BLMA_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BLMA_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BLMA_1.34.0.tgz vignettes: vignettes/BLMA/inst/doc/BLMA.pdf vignetteTitles: BLMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BLMA/inst/doc/BLMA.R dependencyCount: 75 Package: BloodGen3Module Version: 1.18.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, ExperimentHub, methods, grid, graphics, stats, grDevices, circlize, testthat, ComplexHeatmap(>= 1.99.8), ggplot2, matrixStats, gtools, reshape2, preprocessCore, randomcoloR, V8, limma Suggests: RUnit, devtools, BiocGenerics, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 6f25b869fe51c10d6aa1f56b6cac7fa4 NeedsCompilation: no Title: This R package for performing module repertoire analyses and generating fingerprint representations Description: The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.1101/525709v2 and https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of module repertoire analyses can be represented in a fingerprint format, where red and blue spots indicate increases or decreases in module activity. These spots are subsequently represented either on a grid, with each position being assigned to a given module, or in a heatmap where the samples are arranged in columns and the modules in rows. biocViews: Software, Visualization, GeneExpression Author: Darawan Rinchai [aut, cre] (ORCID: ) Maintainer: Darawan Rinchai VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BloodGen3Module git_branch: RELEASE_3_22 git_last_commit: 6d48c98 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BloodGen3Module_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BloodGen3Module_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BloodGen3Module_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BloodGen3Module_1.18.0.tgz vignettes: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.html vignetteTitles: BloodGen3Module: Modular Repertoire Analysis and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.R dependencyCount: 124 Package: bluster Version: 1.20.0 Imports: stats, methods, utils, cluster, Matrix, Rcpp, igraph, S4Vectors, BiocParallel, BiocNeighbors LinkingTo: Rcpp, assorthead Suggests: knitr, rmarkdown, testthat, BiocStyle, dynamicTreeCut, scRNAseq, scuttle, scater, scran, pheatmap, viridis, mbkmeans, kohonen, apcluster, DirichletMultinomial, vegan, fastcluster License: GPL-3 MD5sum: 4d0ebac62dfa944bef94c149904f26a4 NeedsCompilation: yes Title: Clustering Algorithms for Bioconductor Description: Wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results. biocViews: ImmunoOncology, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Aaron Lun [aut, cre], Stephanie Hicks [ctb], Basil Courbayre [ctb], Tuomas Borman [ctb], Leo Lahti [ctb] Maintainer: Aaron Lun SystemRequirements: C++17 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bluster git_branch: RELEASE_3_22 git_last_commit: b47a2df git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bluster_1.20.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bluster_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bluster_1.20.0.tgz vignettes: vignettes/bluster/inst/doc/clusterRows.html, vignettes/bluster/inst/doc/diagnostics.html vignetteTitles: 1. Clustering algorithms, 2. Clustering diagnostics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bluster/inst/doc/clusterRows.R, vignettes/bluster/inst/doc/diagnostics.R dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: chevreulProcess, clustSIGNAL, concordexR, mia, miaDash, MPAC, poem, scDblFinder, scDiagnostics, scran, Voyager, Canek suggestsMe: anglemania, batchelor, ChromSCape, Coralysis, dittoSeq, Ibex, mbkmeans, miaViz, MOSim, mumosa, scLANE, sketchR, SuperCellCyto, SpatialDDLS, SuperCell dependencyCount: 34 Package: bnbc Version: 1.32.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, GenomicRanges Imports: Rcpp (>= 0.12.12), IRanges, rhdf5, data.table, Seqinfo, S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage, utils, HiCBricks LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, RUnit, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 4554452f2d1a7e27e6c2ec21598cff1d NeedsCompilation: yes Title: Bandwise normalization and batch correction of Hi-C data Description: Tools to normalize (several) Hi-C data from replicates. biocViews: HiC, Preprocessing, Normalization, Software Author: Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Kipper Fletez-Brant URL: https://github.com/hansenlab/bnbc VignetteBuilder: knitr BugReports: https://github.com/hansenlab/bnbc/issues git_url: https://git.bioconductor.org/packages/bnbc git_branch: RELEASE_3_22 git_last_commit: b9c6b77 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bnbc_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bnbc_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bnbc_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bnbc_1.32.0.tgz vignettes: vignettes/bnbc/inst/doc/bnbc.html vignetteTitles: bnbc User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnbc/inst/doc/bnbc.R dependencyCount: 137 Package: bnem Version: 1.18.0 Depends: R (>= 4.1) Imports: CellNOptR, matrixStats, snowfall, Rgraphviz, cluster, flexclust, stats, RColorBrewer, epiNEM, mnem, Biobase, methods, utils, graphics, graph, affy, binom, limma, sva, vsn, rmarkdown Suggests: knitr, BiocGenerics, MatrixGenerics, BiocStyle, RUnit License: GPL-3 Archs: x64 MD5sum: 0cfb60850a01067ff7a75644408c4464 NeedsCompilation: no Title: Training of logical models from indirect measurements of perturbation experiments Description: bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate). biocViews: Pathways, SystemsBiology, NetworkInference, Network, GeneExpression, GeneRegulation, Preprocessing Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/MartinFXP/bnem/ VignetteBuilder: knitr BugReports: https://github.com/MartinFXP/bnem/issues git_url: https://git.bioconductor.org/packages/bnem git_branch: RELEASE_3_22 git_last_commit: c751314 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bnem_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bnem_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bnem_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bnem_1.18.0.tgz vignettes: vignettes/bnem/inst/doc/bnem.html vignetteTitles: bnem.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnem/inst/doc/bnem.R dependencyCount: 173 Package: BOBaFIT Version: 1.14.0 Depends: R (>= 2.10) Imports: dplyr, NbClust, ggplot2, ggbio, grDevices, stats, tidyr, GenomicRanges, ggforce, stringr, plyranges, methods, utils, magrittr Suggests: rmarkdown, markdown, BiocStyle, knitr, testthat (>= 3.0.0), utils, testthat License: GPL (>= 3) MD5sum: ad4a4ed82290d52e2195db63a29b8cd8 NeedsCompilation: no Title: Refitting diploid region profiles using a clustering procedure Description: This package provides a method to refit and correct the diploid region in copy number profiles. It uses a clustering algorithm to identify pathology-specific normal (diploid) chromosomes and then use their copy number signal to refit the whole profile. The package is composed by three functions: DRrefit (the main function), ComputeNormalChromosome and PlotCluster. biocViews: CopyNumberVariation, Clustering, Visualization, Normalization, Software Author: Andrea Poletti [aut], Gaia Mazzocchetti [aut, cre], Vincenza Solli [aut] Maintainer: Gaia Mazzocchetti URL: https://github.com/andrea-poletti-unibo/BOBaFIT VignetteBuilder: knitr BugReports: https://github.com/andrea-poletti-unibo/BOBaFIT/issues git_url: https://git.bioconductor.org/packages/BOBaFIT git_branch: RELEASE_3_22 git_last_commit: b498546 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BOBaFIT_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BOBaFIT_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BOBaFIT_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BOBaFIT_1.14.0.tgz vignettes: vignettes/BOBaFIT/inst/doc/BOBaFIT.html, vignettes/BOBaFIT/inst/doc/Data-Preparation.html vignetteTitles: BOBaFIT.Rmd, Data preparation using TCGA-BRCA database.Rmd hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BOBaFIT/inst/doc/BOBaFIT.R, vignettes/BOBaFIT/inst/doc/Data-Preparation.R dependencyCount: 150 Package: borealis Version: 1.14.0 Depends: R (>= 4.2.0), Biobase Imports: doParallel, snow, purrr, plyr, foreach, gamlss, gamlss.dist, bsseq, methods, DSS, R.utils, utils, stats, ggplot2, cowplot, dplyr, rlang, GenomicRanges Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, annotatr, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL-3 Archs: x64 MD5sum: 79d87c6d18be8575ccbec8c9661cf59d NeedsCompilation: no Title: Bisulfite-seq OutlieR mEthylation At singLe-sIte reSolution Description: Borealis is an R library performing outlier analysis for count-based bisulfite sequencing data. It detectes outlier methylated CpG sites from bisulfite sequencing (BS-seq). The core of Borealis is modeling Beta-Binomial distributions. This can be useful for rare disease diagnoses. biocViews: Sequencing, Coverage, DNAMethylation, DifferentialMethylation Author: Garrett Jenkinson [aut, cre] (ORCID: ) Maintainer: Garrett Jenkinson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/borealis git_branch: RELEASE_3_22 git_last_commit: eac9f5e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/borealis_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/borealis_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/borealis_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/borealis_1.14.0.tgz vignettes: vignettes/borealis/inst/doc/borealis.html vignetteTitles: Borealis outlier methylation detection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/borealis/inst/doc/borealis.R dependencyCount: 114 Package: BRAIN Version: 1.56.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 Archs: x64 MD5sum: f55e0ad4ef0a4f1a3c3edc14072e1e44 NeedsCompilation: no Title: Baffling Recursive Algorithm for Isotope distributioN calculations Description: Package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Piotr Dittwald, with contributions of Dirk Valkenborg and Jurgen Claesen Maintainer: Piotr Dittwald git_url: https://git.bioconductor.org/packages/BRAIN git_branch: RELEASE_3_22 git_last_commit: 514ccdf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BRAIN_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BRAIN_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BRAIN_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BRAIN_1.56.0.tgz vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf vignetteTitles: BRAIN Usage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRAIN/inst/doc/BRAIN-vignette.R suggestsMe: cleaver, RforProteomics dependencyCount: 19 Package: breakpointR Version: 1.28.0 Depends: R (>= 3.5), GenomicRanges, cowplot, breakpointRdata Imports: methods, utils, grDevices, stats, S4Vectors, GenomeInfoDb (>= 1.12.3), IRanges, Rsamtools, GenomicAlignments, ggplot2, BiocGenerics, gtools, doParallel, foreach Suggests: knitr, BiocStyle, testthat License: file LICENSE MD5sum: 55cc8f1e96964dbb25fbba7bf71f74ec NeedsCompilation: no Title: Find breakpoints in Strand-seq data Description: This package implements functions for finding breakpoints, plotting and export of Strand-seq data. biocViews: Software, Sequencing, DNASeq, SingleCell, Coverage Author: David Porubsky, Ashley Sanders, Aaron Taudt Maintainer: David Porubsky URL: https://github.com/daewoooo/BreakPointR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/breakpointR git_branch: RELEASE_3_22 git_last_commit: 65b23a9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/breakpointR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/breakpointR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/breakpointR_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/breakpointR_1.28.0.tgz vignettes: vignettes/breakpointR/inst/doc/breakpointR.pdf vignetteTitles: How to use breakpointR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/breakpointR/inst/doc/breakpointR.R dependencyCount: 73 Package: BreastSubtypeR Version: 1.2.0 Depends: R (>= 4.5.0) Imports: methods, Biobase, tidyselect, dplyr, ggplot2, magrittr, rlang, stringr, withr, edgeR, ComplexHeatmap, impute (>= 1.80.0), data.table (>= 1.16.0), RColorBrewer (>= 1.1-3), circlize (>= 0.4.16), ggrepel (>= 0.9.6), e1071 (>= 1.7-8), SummarizedExperiment, utils Suggests: lifecycle, tidyverse, shiny (>= 1.9.1), bslib (>= 0.8.0), BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: c1c0e61db454195a3086e5a34c9dfa0a NeedsCompilation: no Title: Cohort-aware methods for intrinsic molecular subtyping of breast cancer Description: BreastSubtypeR provides an assumption-aware, multi-method framework for intrinsic molecular subtyping of breast cancer. The package harmonizes several published nearest-centroid (NC) and single-sample predictor (SSP) classifiers, supplies method-specific preprocessing and robust probe-to-gene mapping, and implements a cohort-aware AUTO mode that selectively enables classifiers compatible with the cohort composition. A local Shiny app (iBreastSubtypeR) is included for interactive analyses and to support users without programming experience. biocViews: RNASeq, Software, GeneExpression, Classification, Preprocessing, Visualization Author: Qiao Yang [aut, cre] (ORCID: ), Emmanouil G. Sifakis [aut] (ORCID: ) Maintainer: Qiao Yang URL: https://doi.org/10.18129/B9.bioc.BreastSubtypeR,https://github.com/yqkiuo/BreastSubtypeR,https://github.com/JohanHartmanGroupBioteam/BreastSubtypeR VignetteBuilder: knitr BugReports: https://github.com/yqkiuo/BreastSubtypeR/issues git_url: https://git.bioconductor.org/packages/BreastSubtypeR git_branch: RELEASE_3_22 git_last_commit: f1af123 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BreastSubtypeR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BreastSubtypeR_1.1.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BreastSubtypeR_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BreastSubtypeR_1.2.0.tgz vignettes: vignettes/BreastSubtypeR/inst/doc/BreastSubtypeR.html vignetteTitles: BreastSubtypeR: Introduction and Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BreastSubtypeR/inst/doc/BreastSubtypeR.R dependencyCount: 79 Package: brendaDb Version: 1.24.0 Imports: dplyr, Rcpp, tibble, stringr, magrittr, purrr, BiocParallel, crayon, utils, tidyr, grDevices, rlang, BiocFileCache, rappdirs LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, devtools License: MIT + file LICENSE MD5sum: d2066ec3917bfb6e755dd0ac92c9ce84 NeedsCompilation: yes Title: The BRENDA Enzyme Database Description: R interface for importing and analyzing enzyme information from the BRENDA database. biocViews: ThirdPartyClient, Annotation, DataImport Author: Yi Zhou [aut, cre] (ORCID: ) Maintainer: Yi Zhou URL: https://github.com/y1zhou/brendaDb SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/y1zhou/brendaDb/issues git_url: https://git.bioconductor.org/packages/brendaDb git_branch: RELEASE_3_22 git_last_commit: 2520fae git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/brendaDb_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/brendaDb_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/brendaDb_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/brendaDb_1.24.0.tgz vignettes: vignettes/brendaDb/inst/doc/brendaDb.html vignetteTitles: brendaDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/brendaDb/inst/doc/brendaDb.R dependencyCount: 55 Package: BREW3R.r Version: 1.6.0 Imports: GenomicRanges, methods, rlang, S4Vectors, utils Suggests: testthat (>= 3.0.0), IRanges, knitr, rmarkdown, BiocStyle, rtracklayer License: GPL-3 Archs: x64 MD5sum: fab8f0f6e9f3becf6abc0ac72e3a6006 NeedsCompilation: no Title: R package associated to BREW3R Description: This R package provide functions that are used in the BREW3R workflow. This mainly contains a function that extend a gtf as GRanges using information from another gtf (also as GRanges). The process allows to extend gene annotation without increasing the overlap between gene ids. biocViews: GenomeAnnotation Author: Lucille Lopez-Delisle [aut, cre] (ORCID: ) Maintainer: Lucille Lopez-Delisle URL: https://github.com/lldelisle/BREW3R.r VignetteBuilder: knitr BugReports: https://github.com/lldelisle/BREW3R.r/issues/ git_url: https://git.bioconductor.org/packages/BREW3R.r git_branch: RELEASE_3_22 git_last_commit: d8919db git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BREW3R.r_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BREW3R.r_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BREW3R.r_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BREW3R.r_1.6.0.tgz vignettes: vignettes/BREW3R.r/inst/doc/BREW3R.r.html vignetteTitles: BREW3R.r hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BREW3R.r/inst/doc/BREW3R.r.R dependencyCount: 12 Package: BridgeDbR Version: 2.20.0 Depends: R (>= 3.3.0), rJava Imports: curl Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 MD5sum: c6fe3d37ebcfc47442a0f46cd921ab18 NeedsCompilation: no Title: Code for using BridgeDb identifier mapping framework from within R Description: Use BridgeDb functions and load identifier mapping databases in R. It uses GitHub, Zenodo, and Figshare if you use this package to download identifier mappings files. biocViews: Software, Annotation, Metabolomics, Cheminformatics Author: Christ Leemans , Egon Willighagen , Denise Slenter, Anwesha Bohler , Lars Eijssen , Tooba Abbassi-Daloii Maintainer: Egon Willighagen URL: https://github.com/bridgedb/BridgeDbR VignetteBuilder: knitr BugReports: https://github.com/bridgedb/BridgeDbR/issues git_url: https://git.bioconductor.org/packages/BridgeDbR git_branch: RELEASE_3_22 git_last_commit: 7ed5d27 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BridgeDbR_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BridgeDbR_2.19.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BridgeDbR_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BridgeDbR_2.20.0.tgz vignettes: vignettes/BridgeDbR/inst/doc/secondary.html, vignettes/BridgeDbR/inst/doc/tutorial.html vignetteTitles: 2. Secondary IDs, 1. Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/secondary.R, vignettes/BridgeDbR/inst/doc/tutorial.R dependencyCount: 3 Package: BrowserViz Version: 2.32.0 Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: e5540161a75c0d23fbaa2af09343beef NeedsCompilation: no Title: BrowserViz: interactive R/browser graphics using websockets and JSON Description: Interactvive graphics in a web browser from R, using websockets and JSON. biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Arkadiusz Gladki URL: https://gladkia.github.io/BrowserViz/ VignetteBuilder: knitr BugReports: https://github.com/gladkia/BrowserViz/issues git_url: https://git.bioconductor.org/packages/BrowserViz git_branch: RELEASE_3_22 git_last_commit: ae98426 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BrowserViz_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BrowserViz_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BrowserViz_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BrowserViz_2.32.0.tgz vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.html vignetteTitles: "BrowserViz: support programmatic access to javascript apps running in your web browser" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R dependsOnMe: igvR, RCyjs dependencyCount: 19 Package: BSgenome Version: 1.78.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.47.6), IRanges (>= 2.13.16), Seqinfo, GenomicRanges (>= 1.61.1), Biostrings (>= 2.77.2), BiocIO, rtracklayer (>= 1.69.1) Imports: utils, stats, matrixStats, XVector, Rsamtools (>= 2.25.1) Suggests: BiocManager, GenomeInfoDb, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, hgu95av2probe, RUnit, BSgenomeForge License: Artistic-2.0 Archs: x64 MD5sum: cd45c3077014361f118fc82733205c10 NeedsCompilation: no Title: Software infrastructure for efficient representation of full genomes and their SNPs Description: Infrastructure shared by all the Biostrings-based genome data packages. biocViews: Genetics, Infrastructure, DataRepresentation, SequenceMatching, Annotation, SNP Author: Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/BSgenome BugReports: https://github.com/Bioconductor/BSgenome/issues git_url: https://git.bioconductor.org/packages/BSgenome git_branch: RELEASE_3_22 git_last_commit: 8f40a31 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BSgenome_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BSgenome_1.77.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BSgenome_1.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BSgenome_1.78.0.tgz vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf, vignettes/BSgenome/inst/doc/GenomeSearching.pdf vignetteTitles: How to forge a BSgenome data package, Efficient genome searching with Biostrings and the BSgenome data packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenome/inst/doc/GenomeSearching.R dependsOnMe: bambu, BSgenomeForge, ChIPanalyser, GOTHiC, HelloRanges, MEDIPS, periodicDNA, REDseq, VarCon, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4, BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.major, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.minor, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Hsapiens.UCSC.hs1, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, leeBamViews, annotation importsMe: AllelicImbalance, appreci8R, ATACseqQC, atSNP, BEAT, bsseq, BUSpaRse, CAGEr, chromVAR, cleanUpdTSeq, CleanUpRNAseq, cliProfiler, crisprBowtie, crisprBwa, crisprDesign, CRISPRseek, crisprShiny, crisprViz, diffHic, DMRcaller, enhancerHomologSearch, esATAC, EventPointer, FRASER, gcapc, genomation, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, HiCaptuRe, IsoformSwitchAnalyzeR, katdetectr, m6Aboost, methodical, methrix, MethylSeekR, MMDiff2, monaLisa, Motif2Site, motifbreakR, motifmatchr, MotifPeeker, msgbsR, multicrispr, MungeSumstats, musicatk, MutationalPatterns, ORFik, pipeFrame, podkat, qsea, QuasR, raer, RAIDS, RareVariantVis, RCAS, regioneR, REMP, RESOLVE, ribosomeProfilingQC, RNAmodR, scmeth, SCOPE, signeR, SigsPack, SingleMoleculeFootprinting, SparseSignatures, spiky, SpliceWiz, TAPseq, TFBSTools, transmogR, tRNAscanImport, Ularcirc, UMI4Cats, VariantAnnotation, VariantFiltering, VariantTools, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4, BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hs1, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, GenomicDistributionsData, GencoDymo2, ICAMS, revert suggestsMe: Biostrings, biovizBase, ChIPpeakAnno, chipseq, DegCre, DiffBind, easyRNASeq, eisaR, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, maftools, metaseqR2, MiRaGE, PICB, plotgardener, ProteoDisco, PWMEnrich, QDNAseq, recoup, RiboCrypt, rtracklayer, Seqinfo, sitadela, gkmSVM, sigminer, Signac dependencyCount: 57 Package: BSgenomeForge Version: 1.10.0 Depends: R (>= 4.3.0), methods, BiocGenerics, IRanges, Seqinfo, GenomeInfoDb (>= 1.45.5), Biostrings (>= 2.77.2), BSgenome (>= 1.77.1) Imports: utils, stats, Biobase, S4Vectors (>= 0.47.6), GenomicRanges (>= 1.61.1), BiocIO, rtracklayer (>= 1.69.1) Suggests: GenomicFeatures, Rsamtools, testthat, knitr, rmarkdown, BiocStyle, devtools, BSgenome.Celegans.UCSC.ce2 License: Artistic-2.0 MD5sum: 955c7a837ff9982c63a09b20acf74810 NeedsCompilation: no Title: Forge your own BSgenome data package Description: A set of tools to forge BSgenome data packages. Supersedes the old seed-based tools from the BSgenome software package. This package allows the user to create a BSgenome data package in one function call, simplifying the old seed-based process. biocViews: Infrastructure, DataRepresentation, GenomeAssembly, Annotation, GenomeAnnotation, Sequencing, Alignment, DataImport, SequenceMatching Author: Hervé Pagès [aut, cre], Atuhurira Kirabo Kakopo [aut], Emmanuel Chigozie Elendu [ctb], Prisca Chidimma Maduka [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/BSgenomeForge VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BSgenomeForge/issues git_url: https://git.bioconductor.org/packages/BSgenomeForge git_branch: RELEASE_3_22 git_last_commit: 5634fdd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BSgenomeForge_1.10.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BSgenomeForge_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BSgenomeForge_1.10.0.tgz vignettes: vignettes/BSgenomeForge/inst/doc/AdvancedBSgenomeForge.pdf, vignettes/BSgenomeForge/inst/doc/QuickBSgenomeForge.html vignetteTitles: Advanced BSgenomeForge usage, A quick introduction to the BSgenomeForge package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenomeForge/inst/doc/AdvancedBSgenomeForge.R, vignettes/BSgenomeForge/inst/doc/QuickBSgenomeForge.R suggestsMe: BSgenome dependencyCount: 60 Package: bsseq Version: 1.46.0 Depends: R (>= 4.0), methods, BiocGenerics, GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5) Imports: IRanges (>= 2.23.9), Seqinfo, scales, stats, parallel, tools, graphics, Biobase, locfit, gtools, data.table (>= 1.11.8), S4Vectors (>= 0.27.12), R.utils (>= 2.0.0), DelayedMatrixStats (>= 1.5.2), permute, limma, DelayedArray (>= 0.15.16), Rcpp, BiocParallel, BSgenome, Biostrings, utils, HDF5Array (>= 1.19.11), rhdf5, beachmat (>= 2.23.2) LinkingTo: Rcpp, beachmat, assorthead (>= 1.1.4) Suggests: testthat, bsseqData, BiocStyle, rmarkdown, knitr, Matrix, doParallel, rtracklayer, BSgenome.Hsapiens.UCSC.hg38, batchtools License: Artistic-2.0 MD5sum: 9c3ba5fad7ed1023a2510c41debbe495 NeedsCompilation: yes Title: Analyze, manage and store whole-genome methylation data Description: A collection of tools for analyzing and visualizing whole-genome methylation data from sequencing. This includes whole-genome bisulfite sequencing and Oxford nanopore data. biocViews: DNAMethylation Author: Kasper Daniel Hansen [aut, cre] (ORCID: ), Peter Hickey [aut] (ORCID: ), Hervé Pagès [ctb], Aaron Lun [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues git_url: https://git.bioconductor.org/packages/bsseq git_branch: RELEASE_3_22 git_last_commit: ea21812 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bsseq_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bsseq_1.45.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bsseq_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bsseq_1.46.0.tgz vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.html, vignettes/bsseq/inst/doc/bsseq.html vignetteTitles: Analyzing WGBS data with bsseq, bsseq User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bsseq/inst/doc/bsseq_analysis.R, vignettes/bsseq/inst/doc/bsseq.R dependsOnMe: biscuiteer, dmrseq, DSS, bsseqData importsMe: borealis, DMRcate, methylCC, methylSig, MIRA, NanoMethViz, scmeth, SOMNiBUS suggestsMe: iscream, methrix, tissueTreg dependencyCount: 86 Package: BufferedMatrix Version: 1.74.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) MD5sum: 287e1471dceb53c0ae477bebd1fede6e NeedsCompilation: yes Title: A matrix data storage object held in temporary files Description: A tabular style data object where most data is stored outside main memory. A buffer is used to speed up access to data. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/BufferedMatrix git_url: https://git.bioconductor.org/packages/BufferedMatrix git_branch: RELEASE_3_22 git_last_commit: d2ce144 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BufferedMatrix_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BufferedMatrix_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BufferedMatrix_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BufferedMatrix_1.74.0.tgz vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf vignetteTitles: BufferedMatrix: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.R dependsOnMe: BufferedMatrixMethods linksToMe: BufferedMatrixMethods dependencyCount: 1 Package: BufferedMatrixMethods Version: 1.74.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) MD5sum: ebcfdbf9cab205fe6b62040018eea716 NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.bom/bmbolstad/BufferedMatrixMethods git_url: https://git.bioconductor.org/packages/BufferedMatrixMethods git_branch: RELEASE_3_22 git_last_commit: e42f5a3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BufferedMatrixMethods_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BufferedMatrixMethods_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BufferedMatrixMethods_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BufferedMatrixMethods_1.74.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 2 Package: bugsigdbr Version: 1.16.0 Depends: R (>= 4.1) Imports: BiocFileCache, methods, vroom, utils Suggests: BiocStyle, knitr, ontologyIndex, rmarkdown, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 7f597e0bc08d70b34882d6f338684908 NeedsCompilation: no Title: R-side access to published microbial signatures from BugSigDB Description: The bugsigdbr package implements convenient access to bugsigdb.org from within R/Bioconductor. The goal of the package is to facilitate import of BugSigDB data into R/Bioconductor, provide utilities for extracting microbe signatures, and enable export of the extracted signatures to plain text files in standard file formats such as GMT. biocViews: DataImport, GeneSetEnrichment, Metagenomics, Microbiome Author: Ludwig Geistlinger [aut, cre], Jennifer Wokaty [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/bugsigdbr VignetteBuilder: knitr BugReports: https://github.com/waldronlab/bugsigdbr/issues git_url: https://git.bioconductor.org/packages/bugsigdbr git_branch: RELEASE_3_22 git_last_commit: fb143e0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bugsigdbr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bugsigdbr_1.15.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bugsigdbr_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bugsigdbr_1.16.0.tgz vignettes: vignettes/bugsigdbr/inst/doc/bugsigdbr.html vignetteTitles: R-side access to BugSigDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bugsigdbr/inst/doc/bugsigdbr.R suggestsMe: TaxSEA dependencyCount: 50 Package: BulkSignalR Version: 1.2.0 Depends: R (>= 4.5) Imports: BiocFileCache, httr2, RCurl, cli, curl, rlang, jsonlite, matrixStats, methods, doParallel, glmnet, ggalluvial, ggplot2, gridExtra, grid, Rtsne, ggrepel, foreach, multtest, igraph, orthogene, stabledist, circlize (>= 0.4.14), ComplexHeatmap (>= 2.0.0), stats, scales, RANN, SpatialExperiment, SummarizedExperiment, tools Suggests: knitr, markdown, rmarkdown, STexampleData, testthat (>= 3.0.0), codetools, Matrix, lattice, cluster, survival, MASS, nlme License: CeCILL | file LICENSE MD5sum: 999aca12dd60a4c55840caf07039522a NeedsCompilation: no Title: Infer Ligand-Receptor Interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics Description: Inference of ligand-receptor (LR) interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics. BulkSignalR bases its inferences on the LRdb database included in our other package, SingleCellSignalR available from Bioconductor. It relies on a statistical model that is specific to bulk data sets. Different visualization and data summary functions are proposed to help navigating prediction results. biocViews: Network, RNASeq, Software, Proteomics, Transcriptomics, NetworkInference, Spatial Author: Jacques Colinge [aut] (ORCID: ), Jean-Philippe Villemin [cre] (ORCID: ) Maintainer: Jean-Philippe Villemin URL: https://github.com/ZheFrench/BulkSignalR VignetteBuilder: knitr BugReports: https://github.com/ZheFrench/BulkSignalR/issues git_url: https://git.bioconductor.org/packages/BulkSignalR git_branch: RELEASE_3_22 git_last_commit: 4e95251 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BulkSignalR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BulkSignalR_1.1.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BulkSignalR_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BulkSignalR_1.2.0.tgz vignettes: vignettes/BulkSignalR/inst/doc/BulkSignalR-Configure.html, vignettes/BulkSignalR/inst/doc/BulkSignalR-Differential.html, vignettes/BulkSignalR/inst/doc/BulkSignalR-Main.html vignetteTitles: BulkSignalR-Configure, BulkSignalR-Differential, BulkSignalR-Main hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BulkSignalR/inst/doc/BulkSignalR-Configure.R, vignettes/BulkSignalR/inst/doc/BulkSignalR-Differential.R, vignettes/BulkSignalR/inst/doc/BulkSignalR-Main.R importsMe: SingleCellSignalR dependencyCount: 197 Package: BUMHMM Version: 1.34.0 Depends: R (>= 3.5.0) Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges Suggests: testthat, knitr, BiocStyle License: GPL-3 MD5sum: 0f676bdd629ff0990e1e567eeade3185 NeedsCompilation: no Title: Computational pipeline for computing probability of modification from structure probing experiment data Description: This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment. biocViews: ImmunoOncology, GeneticVariability, Transcription, GeneExpression, GeneRegulation, Coverage, Genetics, StructuralPrediction, Transcriptomics, Bayesian, Classification, FeatureExtraction, HiddenMarkovModel, Regression, RNASeq, Sequencing Author: Alina Selega (alina.selega@gmail.com), Sander Granneman, Guido Sanguinetti Maintainer: Alina Selega VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUMHMM git_branch: RELEASE_3_22 git_last_commit: 63f1678 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BUMHMM_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BUMHMM_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BUMHMM_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BUMHMM_1.34.0.tgz vignettes: vignettes/BUMHMM/inst/doc/BUMHMM.pdf vignetteTitles: An Introduction to the BUMHMM pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUMHMM/inst/doc/BUMHMM.R dependencyCount: 122 Package: bumphunter Version: 1.52.0 Depends: R (>= 3.5), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), Seqinfo, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils, GenomicFeatures, AnnotationDbi, stats Suggests: testthat, RUnit, doParallel, GenomeInfoDb, txdbmaker, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 53f9e578add6a4e7ed68d0a4e6371488 NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison, ImmunoOncology Author: Rafael A. Irizarry [aut], Martin Aryee [aut], Kasper Daniel Hansen [aut], Hector Corrada Bravo [aut], Shan Andrews [ctb], Andrew E. Jaffe [ctb], Harris Jaffee [ctb], Leonardo Collado-Torres [ctb], Tamilselvi Guharaj [cre] Maintainer: Tamilselvi Guharaj URL: https://github.com/rafalab/bumphunter git_url: https://git.bioconductor.org/packages/bumphunter git_branch: RELEASE_3_22 git_last_commit: 2f99897 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/bumphunter_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bumphunter_1.51.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bumphunter_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bumphunter_1.52.0.tgz vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf vignetteTitles: The bumphunter user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bumphunter/inst/doc/bumphunter.R dependsOnMe: minfi importsMe: coMethDMR, DAMEfinder, derfinder, dmrseq, epimutacions, epivizr, methylCC, rnaEditr, vmrseq, GenomicState, recountWorkflow suggestsMe: bigmelon, derfinderPlot, epivizrData, regionReport dependencyCount: 84 Package: BumpyMatrix Version: 1.18.0 Imports: utils, methods, Matrix, S4Vectors, IRanges Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 2296a780f61269a868c5cf2f1d475d7e NeedsCompilation: no Title: Bumpy Matrix of Non-Scalar Objects Description: Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects. biocViews: Software, Infrastructure, DataRepresentation Author: Aaron Lun [aut, cre], Genentech, Inc. [cph] Maintainer: Aaron Lun URL: https://bioconductor.org/packages/BumpyMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/BumpyMatrix/issues git_url: https://git.bioconductor.org/packages/BumpyMatrix git_branch: RELEASE_3_22 git_last_commit: d16b5ea git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BumpyMatrix_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BumpyMatrix_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BumpyMatrix_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BumpyMatrix_1.18.0.tgz vignettes: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.html vignetteTitles: The BumpyMatrix class hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.R dependsOnMe: alabaster.bumpy importsMe: CoreGx, gDRcore, gDRimport, gDRutils, MerfishData, MouseGastrulationData, TENxXeniumData suggestsMe: escheR, gDR, ggspavis, SpatialExperiment, tpSVG, STexampleData dependencyCount: 13 Package: BUS Version: 1.66.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: x64 MD5sum: 71fd39dea4c7aaa1c3181e2e04627926 NeedsCompilation: yes Title: Gene network reconstruction Description: This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical). biocViews: Preprocessing Author: Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/BUS git_branch: RELEASE_3_22 git_last_commit: 13ca259 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BUS_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BUS_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BUS_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BUS_1.66.0.tgz vignettes: vignettes/BUS/inst/doc/bus.pdf vignetteTitles: bus.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUS/inst/doc/bus.R dependencyCount: 3 Package: BUScorrect Version: 1.28.0 Depends: R (>= 3.5.0) Imports: gplots, methods, grDevices, stats, SummarizedExperiment Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: de3142a1290b94587b8aa4b4a2bc596e NeedsCompilation: yes Title: Batch Effects Correction with Unknown Subtypes Description: High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity. biocViews: GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect Author: Xiangyu Luo , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUScorrect git_branch: RELEASE_3_22 git_last_commit: f811af6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BUScorrect_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BUScorrect_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BUScorrect_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BUScorrect_1.28.0.tgz vignettes: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.R dependencyCount: 30 Package: BUSpaRse Version: 1.24.0 Depends: R (>= 3.6) Imports: AnnotationDbi, AnnotationFilter, AnnotationHub, biomaRt, BiocGenerics, Biostrings, BSgenome, dplyr, ensembldb, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, IRanges, lifecycle, magrittr, Matrix, methods, plyranges, Rcpp, S4Vectors, stats, stringr, tibble, tidyr, utils, zeallot LinkingTo: Rcpp, RcppArmadillo, RcppProgress, BH Suggests: knitr, rmarkdown, testthat, BiocStyle, txdbmaker, TENxBUSData, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: BSD_2_clause + file LICENSE MD5sum: a5e16b737dbbb588ff778a3a2ae6c5a1 NeedsCompilation: yes Title: kallisto | bustools R utilities Description: The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Here biotypes can be filtered and scaffolds and haplotypes can be removed, and the filtered transcriptome can be extracted and written to disk. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices. biocViews: SingleCell, RNASeq, WorkflowStep Author: Lambda Moses [aut, cre] (ORCID: ), Lior Pachter [aut, ths] (ORCID: ) Maintainer: Lambda Moses URL: https://github.com/BUStools/BUSpaRse SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/BUStools/BUSpaRse/issues git_url: https://git.bioconductor.org/packages/BUSpaRse git_branch: RELEASE_3_22 git_last_commit: 757e3e4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BUSpaRse_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BUSpaRse_1.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BUSpaRse_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BUSpaRse_1.24.0.tgz vignettes: vignettes/BUSpaRse/inst/doc/sparse-matrix.html, vignettes/BUSpaRse/inst/doc/tr2g.html vignetteTitles: Converting BUS format into sparse matrix, Transcript to gene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUSpaRse/inst/doc/sparse-matrix.R, vignettes/BUSpaRse/inst/doc/tr2g.R dependencyCount: 122 Package: BUSseq Version: 1.16.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, gplots, grDevices, methods, stats, utils Suggests: BiocStyle, knitr, BiocGenerics License: Artistic-2.0 MD5sum: 808bf53ccf93c6e138ffdbfbed8357b6 NeedsCompilation: yes Title: Batch Effect Correction with Unknow Subtypes for scRNA-seq data Description: BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. BUSseq can integrate read count matrices obtained from different scRNA-seq platforms and allow cell types to be measured in some but not all of the batches as long as the experimental design fulfills the conditions listed in our manuscript. biocViews: ExperimentalDesign, GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect, SingleCell, Sequencing Author: Fangda Song [aut, cre] (ORCID: ), Ga Ming Chan [aut], Yingying Wei [aut] (ORCID: ) Maintainer: Fangda Song URL: https://github.com/songfd2018/BUSseq VignetteBuilder: knitr BugReports: https://github.com/songfd2018/BUSseq/issues git_url: https://git.bioconductor.org/packages/BUSseq git_branch: RELEASE_3_22 git_last_commit: 95429aa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/BUSseq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BUSseq_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BUSseq_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BUSseq_1.16.0.tgz vignettes: vignettes/BUSseq/inst/doc/BUSseq_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUSseq/inst/doc/BUSseq_user_guide.R dependencyCount: 31 Package: CaDrA Version: 1.8.0 Depends: R (>= 4.4.0) Imports: doParallel, ggplot2, gplots, graphics, grid, gtable, knnmi, MASS, methods, misc3d, plyr, ppcor, R.cache, reshape2, stats, SummarizedExperiment Suggests: BiocManager, devtools, knitr, pheatmap, rmarkdown, testthat (>= 3.1.6) License: GPL-3 + file LICENSE MD5sum: 6fcf34cb4ddd2946cc00cb210f033748 NeedsCompilation: yes Title: Candidate Driver Analysis Description: Performs both stepwise and backward heuristic search for candidate (epi)genetic drivers based on a binary multi-omics dataset. CaDrA's main objective is to identify features which, together, are significantly skewed or enriched pertaining to a given vector of continuous scores (e.g. sample-specific scores representing a phenotypic readout of interest, such as protein expression, pathway activity, etc.), based on the union occurence (i.e. logical OR) of the events. biocViews: Microarray, RNASeq, GeneExpression, Software, FeatureExtraction Author: Reina Chau [aut, cre] (ORCID: ), Katia Bulekova [aut] (ORCID: ), Vinay Kartha [aut], Stefano Monti [aut] (ORCID: ) Maintainer: Reina Chau URL: https://github.com/montilab/CaDrA/ VignetteBuilder: knitr BugReports: https://github.com/montilab/CaDrA/issues git_url: https://git.bioconductor.org/packages/CaDrA git_branch: RELEASE_3_22 git_last_commit: 2878e83 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CaDrA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CaDrA_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CaDrA_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CaDrA_1.8.0.tgz vignettes: vignettes/CaDrA/inst/doc/docker.html, vignettes/CaDrA/inst/doc/permutation_based_testing.html, vignettes/CaDrA/inst/doc/scoring_functions.html vignetteTitles: How to run CaDrA within a Docker Environment, Permutation-Based Testing, Scoring Functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CaDrA/inst/doc/permutation_based_testing.R, vignettes/CaDrA/inst/doc/scoring_functions.R dependencyCount: 67 Package: CAEN Version: 1.18.0 Depends: R (>= 4.1) Imports: stats,PoiClaClu,SummarizedExperiment,methods Suggests: knitr,rmarkdown,BiocManager,SummarizedExperiment,BiocStyle License: GPL-2 MD5sum: 0d3c0699f0a3058ae6151f1c1b572d70 NeedsCompilation: no Title: Category encoding method for selecting feature genes for the classification of single-cell RNA-seq Description: With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples. biocViews: DifferentialExpression, Sequencing, Classification, RNASeq, ATACSeq, SingleCell, GeneExpression, RIPSeq Author: Zhou Yan [aut, cre] Maintainer: Zhou Yan <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAEN git_branch: RELEASE_3_22 git_last_commit: e5e75f7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CAEN_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CAEN_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CAEN_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CAEN_1.18.0.tgz vignettes: vignettes/CAEN/inst/doc/CAEN.html vignetteTitles: CAEN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAEN/inst/doc/CAEN.R dependencyCount: 26 Package: CAFE Version: 1.46.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: x64 MD5sum: d4e4a5f68b358577c0e407a76758f262 NeedsCompilation: no Title: Chromosmal Aberrations Finder in Expression data Description: Detection and visualizations of gross chromosomal aberrations using Affymetrix expression microarrays as input biocViews: GeneExpression, Microarray, OneChannel, GeneSetEnrichment Author: Sander Bollen Maintainer: Sander Bollen git_url: https://git.bioconductor.org/packages/CAFE git_branch: RELEASE_3_22 git_last_commit: d968725 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CAFE_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CAFE_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CAFE_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CAFE_1.46.0.tgz vignettes: vignettes/CAFE/inst/doc/CAFE-manual.pdf vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAFE/inst/doc/CAFE-manual.R dependencyCount: 142 Package: CAGEfightR Version: 1.30.0 Depends: R (>= 3.5), GenomicRanges (>= 1.61.1), rtracklayer (>= 1.69.1), SummarizedExperiment (>= 1.39.1) Imports: pryr(>= 0.1.3), assertthat(>= 0.2.0), methods(>= 3.6.3), Matrix(>= 1.2-12), BiocGenerics(>= 0.24.0), S4Vectors(>= 0.16.0), IRanges(>= 2.12.0), Seqinfo, GenomicFeatures(>= 1.61.4), GenomicAlignments(>= 1.45.1), BiocParallel(>= 1.12.0), GenomicFiles(>= 1.14.0), Gviz(>= 1.22.2), InteractionSet(>= 1.9.4), GenomicInteractions(>= 1.15.1) Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 + file LICENSE MD5sum: 91c715482a771965d4288f88101d9768 NeedsCompilation: no Title: Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor Description: CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization. biocViews: Software, Transcription, Coverage, GeneExpression, GeneRegulation, PeakDetection, DataImport, DataRepresentation, Transcriptomics, Sequencing, Annotation, GenomeBrowsers, Normalization, Preprocessing, Visualization Author: Malte Thodberg Maintainer: Malte Thodberg URL: https://github.com/MalteThodberg/CAGEfightR VignetteBuilder: knitr BugReports: https://github.com/MalteThodberg/CAGEfightR/issues git_url: https://git.bioconductor.org/packages/CAGEfightR git_branch: RELEASE_3_22 git_last_commit: eda75c3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CAGEfightR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CAGEfightR_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CAGEfightR_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CAGEfightR_1.30.0.tgz vignettes: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.html vignetteTitles: Introduction to CAGEfightR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.R dependsOnMe: CAGEWorkflow importsMe: CAGEr suggestsMe: nanotubes dependencyCount: 160 Package: cageminer Version: 1.16.0 Depends: R (>= 4.1) Imports: ggplot2, rlang, ggbio, ggtext, GenomeInfoDb, GenomicRanges, IRanges, reshape2, methods, BioNERO Suggests: testthat (>= 3.0.0), SummarizedExperiment, knitr, BiocStyle, rmarkdown, covr, sessioninfo License: GPL-3 Archs: x64 MD5sum: 5141dcbb4459cf87858e5f4fed829dce NeedsCompilation: no Title: Candidate Gene Miner Description: This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments. biocViews: Software, SNP, FunctionalPrediction, GenomeWideAssociation, GeneExpression, NetworkEnrichment, VariantAnnotation, FunctionalGenomics, Network Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Thiago Venancio [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/cageminer VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/cageminer git_url: https://git.bioconductor.org/packages/cageminer git_branch: RELEASE_3_22 git_last_commit: 304c1d0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cageminer_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cageminer_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cageminer_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cageminer_1.16.0.tgz vignettes: vignettes/cageminer/inst/doc/cageminer.html vignetteTitles: Mining high-confidence candidate genes with cageminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cageminer/inst/doc/cageminer.R dependencyCount: 193 Package: CAGEr Version: 2.16.0 Depends: methods, MultiAssayExperiment, R (>= 4.1.0) Imports: BiocGenerics, BiocParallel, Biostrings, BSgenome, CAGEfightR, data.table, formula.tools, Seqinfo, GenomicAlignments (>= 1.45.1), GenomicFeatures (>= 1.61.4), GenomicRanges (>= 1.61.1), ggplot2 (>= 4.0.0), gtools, IRanges (>= 2.18.0), KernSmooth, Matrix, memoise, plyr, rlang, Rsamtools (>= 2.25.1), reshape2, rtracklayer (>= 1.69.1), S4Vectors (>= 0.27.5), scales, som, stringdist, stringi, SummarizedExperiment (>= 1.39.1), utils, vegan, VGAM Suggests: BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Drerio.UCSC.danRer7, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9, DESeq2, FANTOM3and4CAGE, ggseqlogo, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 680da7a0a818f28f96ce0c09838234b9 NeedsCompilation: no Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining Description: The _CAGEr_ package identifies transcription start sites (TSS) and their usage frequency from CAGE (Cap Analysis Gene Expression) sequencing data. It normalises raw CAGE tag count, clusters TSSs into tag clusters (TC) and aggregates them across multiple CAGE experiments to construct consensus clusters (CC) representing the promoterome. CAGEr provides functions to profile expression levels of these clusters by cumulative expression and rarefaction analysis, and outputs the plots in ggplot2 format for further facetting and customisation. After clustering, CAGEr performs analyses of promoter width and detects differential usage of TSSs (promoter shifting) between samples. CAGEr also exports its data as genome browser tracks, and as R objects for downsteam expression analysis by other Bioconductor packages such as DESeq2, CAGEfightR, or seqArchR. biocViews: Preprocessing, Sequencing, Normalization, FunctionalGenomics, Transcription, GeneExpression, Clustering, Visualization Author: Vanja Haberle [aut], Charles Plessy [cre], Damir Baranasic [ctb], Katalin Ferenc [ctb], Sarvesh Nikumbh [ctb] Maintainer: Charles Plessy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEr git_branch: RELEASE_3_22 git_last_commit: b717389 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CAGEr_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CAGEr_2.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CAGEr_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CAGEr_2.16.0.tgz vignettes: vignettes/CAGEr/inst/doc/CAGE_Resources.html, vignettes/CAGEr/inst/doc/CAGEexp.html vignetteTitles: Use of CAGE resources with CAGEr, CAGEr: an R package for CAGE data analysis and promoterome mining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGE_Resources.R, vignettes/CAGEr/inst/doc/CAGEexp.R suggestsMe: seqPattern dependencyCount: 176 Package: CalibraCurve Version: 1.0.0 Depends: R (>= 4.5.0) Imports: checkmate, dplyr, ggplot2, magrittr, openxlsx, scales, SummarizedExperiment, tidyr Suggests: BiocStyle, knitr, msqc1, RefManageR, rmarkdown, sessioninfo, testthat, vdiffr License: BSD 3-clause License + file LICENSE MD5sum: b4ca7e1b5caa07aab5596b3781699732 NeedsCompilation: no Title: Calibration curves for targeted proteomics, lipidomics and metabolomics data Description: CalibraCurve is a computational tool designed to generate calibration curves for targeted mass spectrometry-based quantitative data. It is applicable to various omics disciplines, including proteomics, lipidomics, and metabolomics. The package also offers functionalities for data and calibration curve visualization and concentration prediction from new datasets based on the established curves. biocViews: Proteomics, Lipidomics, Metabolomics, Regression, MassSpectrometry, Visualization Author: Karin Schork [aut, cre] (ORCID: ), Robin Grugel [aut], Michael Kohl [aut], Markus Stepath [aut], Martin Eisenacher [aut, fnd] Maintainer: Karin Schork URL: https://github.com/mpc-bioinformatics/CalibraCurve VignetteBuilder: knitr BugReports: https://github.com/mpc-bioinformatics/CalibraCurve/issues git_url: https://git.bioconductor.org/packages/CalibraCurve git_branch: RELEASE_3_22 git_last_commit: f737cac git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CalibraCurve_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CalibraCurve_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CalibraCurve_1.0.0.tgz vignettes: vignettes/CalibraCurve/inst/doc/CalibraCurve_Visualization.html, vignettes/CalibraCurve/inst/doc/CalibraCurve.html vignetteTitles: 2. Customizing the visualizations of CalibraCurve, 1. Introduction to CalibraCurve hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CalibraCurve/inst/doc/CalibraCurve_Visualization.R, vignettes/CalibraCurve/inst/doc/CalibraCurve.R dependencyCount: 58 Package: calm Version: 1.24.0 Imports: mgcv, stats, graphics Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 2294475aedb11cf37d5cd8a3bb1c9fd2 NeedsCompilation: no Title: Covariate Assisted Large-scale Multiple testing Description: Statistical methods for multiple testing with covariate information. Traditional multiple testing methods only consider a list of test statistics, such as p-values. Our methods incorporate the auxiliary information, such as the lengths of gene coding regions or the minor allele frequencies of SNPs, to improve power. biocViews: Bayesian, DifferentialExpression, GeneExpression, Regression, Microarray, Sequencing, RNASeq, MultipleComparison, Genetics, ImmunoOncology, Metabolomics, Proteomics, Transcriptomics Author: Kun Liang [aut, cre] Maintainer: Kun Liang VignetteBuilder: knitr BugReports: https://github.com/k22liang/calm/issues git_url: https://git.bioconductor.org/packages/calm git_branch: RELEASE_3_22 git_last_commit: 2913384 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/calm_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/calm_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/calm_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/calm_1.24.0.tgz vignettes: vignettes/calm/inst/doc/calm_intro.html vignetteTitles: Userguide for calm package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/calm/inst/doc/calm_intro.R dependencyCount: 11 Package: CAMERA Version: 1.66.0 Depends: R (>= 3.5.0), methods, Biobase, xcms (>= 1.13.5) Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils, Hmisc, igraph Suggests: faahKO, RUnit, BiocGenerics, multtest Enhances: Rmpi, snow License: GPL (>= 2) MD5sum: fe342c0af7bd42be4897399d9857ba9e NeedsCompilation: yes Title: Collection of annotation related methods for mass spectrometry data Description: Annotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, isotope validation, EIC correlation based tagging of unknown adducts and fragments biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Carsten Kuhl, Ralf Tautenhahn, Hendrik Treutler, Steffen Neumann {ckuhl|htreutle|sneumann}@ipb-halle.de, rtautenh@scripps.edu Maintainer: Steffen Neumann URL: http://msbi.ipb-halle.de/msbi/CAMERA/ BugReports: https://github.com/sneumann/CAMERA/issues/new git_url: https://git.bioconductor.org/packages/CAMERA git_branch: RELEASE_3_22 git_last_commit: fcd3b86 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CAMERA_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CAMERA_1.65.1.zip vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf, vignettes/CAMERA/inst/doc/compoundQuantilesVignette.pdf, vignettes/CAMERA/inst/doc/IsotopeDetectionVignette.pdf vignetteTitles: Molecule Identification with CAMERA, Atom count expectations with compoundQuantiles, Isotope pattern validation with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R dependsOnMe: flagme, IPO, LOBSTAHS, MAIT, metaMS, PtH2O2lipids suggestsMe: cliqueMS, msPurity, RMassBank, mtbls2 dependencyCount: 153 Package: CaMutQC Version: 1.6.0 Depends: R (>= 4.5.0) Imports: ggplot2, dplyr, org.Hs.eg.db, vcfR, clusterProfiler, stringr, DT, MesKit, maftools, data.table, utils, stats, methods, tidyr Suggests: knitr, rmarkdown, BiocStyle, shiny License: GPL-3 MD5sum: d67fcbed267392e06934d609899386d6 NeedsCompilation: no Title: An R Package for Comprehensive Filtration and Selection of Cancer Somatic Mutations Description: CaMutQC is able to filter false positive mutations generated due to technical issues, as well as to select candidate cancer mutations through a series of well-structured functions by labeling mutations with various flags. And a detailed and vivid filter report will be offered after completing a whole filtration or selection section. Also, CaMutQC integrates serveral methods and gene panels for Tumor Mutational Burden (TMB) estimation. biocViews: Software, QualityControl, GeneTarget Author: Xin Wang [aut, cre] (ORCID: ) Maintainer: Xin Wang URL: https://github.com/likelet/CaMutQC VignetteBuilder: knitr BugReports: https://github.com/likelet/CaMutQC/issues git_url: https://git.bioconductor.org/packages/CaMutQC git_branch: RELEASE_3_22 git_last_commit: 77bf433 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CaMutQC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CaMutQC_1.5.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CaMutQC_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CaMutQC_1.6.0.tgz vignettes: vignettes/CaMutQC/inst/doc/CaMutQC-manual.html vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CaMutQC/inst/doc/CaMutQC-manual.R dependencyCount: 173 Package: canceR Version: 1.44.0 Depends: R (>= 4.3), tcltk, cBioPortalData Imports: GSEABase, tkrplot, geNetClassifier, RUnit, Formula, rpart, survival, Biobase, phenoTest, circlize, plyr, tidyr, dplyr, graphics, stats, utils, grDevices, R.oo, R.methodsS3 Suggests: testthat (>= 3.1), knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 211bf388a571156bb7062877b94ded60 NeedsCompilation: no Title: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC Description: The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC). biocViews: GUI, GeneExpression, Clustering, GO, GeneSetEnrichment, KEGG, MultipleComparison Author: Karim Mezhoud. Nuclear Safety & Security Department. Nuclear Science Center of Tunisia. Maintainer: Karim Mezhoud SystemRequirements: Tktable, BWidget VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/canceR/issues git_url: https://git.bioconductor.org/packages/canceR git_branch: RELEASE_3_22 git_last_commit: fa7857a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/canceR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/canceR_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/canceR_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/canceR_1.44.0.tgz vignettes: vignettes/canceR/inst/doc/canceR.html vignetteTitles: canceR: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/canceR/inst/doc/canceR.R dependencyCount: 214 Package: cancerclass Version: 1.54.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 MD5sum: a37471ec270457f7f35cf4125309edf7 NeedsCompilation: yes Title: Development and validation of diagnostic tests from high-dimensional molecular data Description: The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements. biocViews: Cancer, Microarray, Classification, Visualization Author: Jan Budczies, Daniel Kosztyla Maintainer: Daniel Kosztyla git_url: https://git.bioconductor.org/packages/cancerclass git_branch: RELEASE_3_22 git_last_commit: 16058a6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cancerclass_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cancerclass_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cancerclass_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cancerclass_1.54.0.tgz vignettes: vignettes/cancerclass/inst/doc/vignette_cancerclass.pdf vignetteTitles: Cancerclass: An R package for development and validation of diagnostic tests from high-dimensional molecular data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cancerclass/inst/doc/vignette_cancerclass.R dependencyCount: 8 Package: cardelino Version: 1.11.0 Depends: R (>= 4.2), stats Imports: combinat, GenomeInfoDb, GenomicRanges, ggplot2, ggtree, Matrix, matrixStats, methods, pheatmap, snpStats, S4Vectors, utils, VariantAnnotation, vcfR Suggests: BiocStyle, foreach, knitr, pcaMethods, rmarkdown, testthat, VGAM Enhances: doMC License: GPL-3 Archs: x64 MD5sum: 654bb2cdc7146fcf7805f980794bf525 NeedsCompilation: yes Title: Clone Identification from Single Cell Data Description: Methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stochastic dropout events as well as systematic allelic imbalance is used. biocViews: SingleCell, RNASeq, Visualization, Transcriptomics, GeneExpression, Sequencing, Software, ExomeSeq Author: Jeffrey Pullin [aut], Yuanhua Huang [aut], Davis McCarthy [aut, cre] Maintainer: Davis McCarthy URL: https://github.com/single-cell-genetics/cardelino VignetteBuilder: knitr BugReports: https://github.com/single-cell-genetics/cardelino/issues git_url: https://git.bioconductor.org/packages/cardelino git_branch: devel git_last_commit: cabff04 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/cardelino_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cardelino_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cardelino_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cardelino_1.11.0.tgz vignettes: vignettes/cardelino/inst/doc/vignette-cloneid.html vignetteTitles: Clone ID with cardelino hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cardelino/inst/doc/vignette-cloneid.R dependencyCount: 148 Package: Cardinal Version: 3.12.0 Depends: R (>= 4.4), BiocParallel, BiocGenerics, ProtGenerics, S4Vectors, methods, stats, stats4 Imports: CardinalIO, Biobase, EBImage, graphics, grDevices, irlba, Matrix, matter (>= 2.7.10), nlme, parallel, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, emmeans, lme4, lmerTest License: Artistic-2.0 | file LICENSE Archs: x64 MD5sum: 54bc5bf0d5129949524dccc24616df69 NeedsCompilation: no Title: A mass spectrometry imaging toolbox for statistical analysis Description: Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification. biocViews: Software, Infrastructure, Proteomics, Lipidomics, MassSpectrometry, ImagingMassSpectrometry, ImmunoOncology, Normalization, Clustering, Classification, Regression Author: Kylie Ariel Bemis [aut, cre] Maintainer: Kylie Ariel Bemis URL: http://www.cardinalmsi.org VignetteBuilder: knitr BugReports: https://github.com/kuwisdelu/Cardinal/issues git_url: https://git.bioconductor.org/packages/Cardinal git_branch: RELEASE_3_22 git_last_commit: 859d28b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Cardinal_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Cardinal_3.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Cardinal_3.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Cardinal_3.12.0.tgz vignettes: vignettes/Cardinal/inst/doc/Cardinal3-guide.html, vignettes/Cardinal/inst/doc/Cardinal3-stats.html vignetteTitles: 1. Cardinal 3: User guide for mass spectrometry imaging analysis, 2. Cardinal 3: Statistical methods for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Cardinal/inst/doc/Cardinal3-guide.R, vignettes/Cardinal/inst/doc/Cardinal3-stats.R dependsOnMe: CardinalWorkflows dependencyCount: 66 Package: CardinalIO Version: 1.8.0 Depends: R (>= 4.4), BiocParallel, matter, ontologyIndex Imports: methods, S4Vectors, stats, utils, tools Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 | file LICENSE Archs: x64 MD5sum: 974a5f2553a1f60f0ee1ca0978b4745b NeedsCompilation: yes Title: Read and write mass spectrometry imaging files Description: Fast and efficient reading and writing of mass spectrometry imaging data files. Supports imzML and Analyze 7.5 formats. Provides ontologies for mass spectrometry imaging. biocViews: Software, Infrastructure, DataImport, MassSpectrometry, ImagingMassSpectrometry Author: Kylie Ariel Bemis [aut, cre] Maintainer: Kylie Ariel Bemis URL: http://www.cardinalmsi.org VignetteBuilder: knitr BugReports: https://github.com/kuwisdelu/CardinalIO/issues git_url: https://git.bioconductor.org/packages/CardinalIO git_branch: RELEASE_3_22 git_last_commit: 0a719c9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CardinalIO_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CardinalIO_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CardinalIO_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CardinalIO_1.8.0.tgz vignettes: vignettes/CardinalIO/inst/doc/CardinalIO-guide.html vignetteTitles: Parsing and writing imzML files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CardinalIO/inst/doc/CardinalIO-guide.R importsMe: Cardinal dependencyCount: 28 Package: CARDspa Version: 1.2.0 Depends: R (>= 4.3.0) Imports: Rcpp (>= 1.0.7),RcppArmadillo, SummarizedExperiment, methods, MCMCpack, fields, wrMisc, concaveman, sp, dplyr, sf, Matrix, RANN, ggplot2, reshape2, RColorBrewer, S4Vectors, scatterpie, grDevices,ggcorrplot, stats, nnls, BiocParallel, RcppML, NMF, spatstat.random, gtools, SingleCellExperiment, SpatialExperiment LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 + file LICENSE Archs: x64 MD5sum: 2fd7d702893d20899d23cd6c5ecfbf06 NeedsCompilation: yes Title: Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics Description: CARD is a reference-based deconvolution method that estimates cell type composition in spatial transcriptomics based on cell type specific expression information obtained from a reference scRNA-seq data. A key feature of CARD is its ability to accommodate spatial correlation in the cell type composition across tissue locations, enabling accurate and spatially informed cell type deconvolution as well as refined spatial map construction. CARD relies on an efficient optimization algorithm for constrained maximum likelihood estimation and is scalable to spatial transcriptomics with tens of thousands of spatial locations and tens of thousands of genes. biocViews: Spatial, SingleCell, Transcriptomics, Visualization Author: Ying Ma [aut], Jing Fu [cre] Maintainer: Jing Fu URL: https://github.com/YMa-lab/CARDspa VignetteBuilder: knitr BugReports: https://github.com/YMa-lab/CARDspa/issues git_url: https://git.bioconductor.org/packages/CARDspa git_branch: RELEASE_3_22 git_last_commit: 980a743 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CARDspa_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CARDspa_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CARDspa_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CARDspa_1.2.0.tgz vignettes: vignettes/CARDspa/inst/doc/Example_Analysis.html vignetteTitles: Example_Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CARDspa/inst/doc/Example_Analysis.R importsMe: OSTA dependencyCount: 147 Package: CARNIVAL Version: 2.20.0 Depends: R (>= 4.0) Imports: readr, stringr, lpSolve, igraph, dplyr, tibble, tidyr, rjson, rmarkdown Suggests: RefManageR, BiocStyle, covr, knitr, testthat (>= 3.0.0), sessioninfo License: GPL-3 Archs: x64 MD5sum: 4b6bca0b7c75f93a8e7d0d235f290c7d NeedsCompilation: no Title: A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming Description: An upgraded causal reasoning tool from Melas et al in R with updated assignments of TFs' weights from PROGENy scores. Optimization parameters can be freely adjusted and multiple solutions can be obtained and aggregated. biocViews: Transcriptomics, GeneExpression, Network Author: Enio Gjerga [aut] (ORCID: ), Panuwat Trairatphisan [aut], Anika Liu [ctb], Alberto Valdeolivas [ctb], Nikolas Peschke [ctb], Aurelien Dugourd [ctb], Attila Gabor [cre], Olga Ivanova [aut] Maintainer: Attila Gabor URL: https://github.com/saezlab/CARNIVAL VignetteBuilder: knitr BugReports: https://github.com/saezlab/CARNIVAL/issues git_url: https://git.bioconductor.org/packages/CARNIVAL git_branch: RELEASE_3_22 git_last_commit: c4cdaf4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CARNIVAL_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CARNIVAL_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CARNIVAL_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CARNIVAL_2.20.0.tgz vignettes: vignettes/CARNIVAL/inst/doc/CARNIVAL.html vignetteTitles: Contextualizing large scale signalling networks from expression footprints with CARNIVAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CARNIVAL/inst/doc/CARNIVAL.R importsMe: cosmosR dependencyCount: 63 Package: casper Version: 2.44.0 Depends: R (>= 3.6.0), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics (>= 0.31.6), coda, EBarrays, gaga, gtools, Seqinfo, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors (>= 0.9.25), sqldf, survival, VGAM Enhances: parallel License: GPL (>=2) MD5sum: 68dcf59e0be037d94127b40877b37aef NeedsCompilation: yes Title: Characterization of Alternative Splicing based on Paired-End Reads Description: Infer alternative splicing from paired-end RNA-seq data. The model is based on counting paths across exons, rather than pairwise exon connections, and estimates the fragment size and start distributions non-parametrically, which improves estimation precision. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Transcription, RNASeq, Sequencing Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda Stobbe, Victor Pena Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/casper git_branch: RELEASE_3_22 git_last_commit: 9f5a6ac git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/casper_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/casper_2.43.1.zip vignettes: vignettes/casper/inst/doc/casper.pdf, vignettes/casper/inst/doc/DesignRNASeq.pdf vignetteTitles: Manual for the casper library, DesignRNASeq.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/casper/inst/doc/casper.R dependencyCount: 92 Package: CATALYST Version: 1.34.0 Depends: R (>= 4.5), SingleCellExperiment Imports: circlize, ComplexHeatmap, ConsensusClusterPlus, cowplot, data.table, dplyr, drc, flowCore, FlowSOM, ggplot2, ggrepel, ggridges, graphics, grDevices, grid, gridExtra, Matrix, matrixStats, methods, nnls, purrr, RColorBrewer, reshape2, Rtsne, SummarizedExperiment, S4Vectors, scales, scater, stats Suggests: BiocStyle, diffcyt, flowWorkspace, ggcyto, knitr, openCyto, rmarkdown, testthat, uwot License: GPL (>=2) MD5sum: e45d4c5ee6d12ed776dc24d98f995566 NeedsCompilation: no Title: Cytometry dATa anALYSis Tools Description: CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively. biocViews: Clustering, DataImport, DifferentialExpression, ExperimentalDesign, FlowCytometry, ImmunoOncology, MassSpectrometry,Normalization, Preprocessing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre] (ORCID: ), Vito R.T. Zanotelli [aut] (ORCID: ), Stéphane Chevrier [aut, dtc] (ORCID: ), Mark D. Robinson [aut, fnd] (ORCID: ), Bernd Bodenmiller [fnd] (ORCID: ) Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/CATALYST VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/CATALYST/issues git_url: https://git.bioconductor.org/packages/CATALYST git_branch: RELEASE_3_22 git_last_commit: 8ec200e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CATALYST_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CATALYST_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CATALYST_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CATALYST_1.34.0.tgz vignettes: vignettes/CATALYST/inst/doc/differential.html, vignettes/CATALYST/inst/doc/preprocessing.html vignetteTitles: "2. Differential discovery", "1. Preprocessing" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CATALYST/inst/doc/differential.R, vignettes/CATALYST/inst/doc/preprocessing.R dependsOnMe: spillR, cytofWorkflow suggestsMe: diffcyt, imcRtools, treekoR dependencyCount: 173 Package: Category Version: 2.76.0 Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, DBI Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGGREST, karyoploteR, geneplotter, limma, lattice, RUnit, org.Sc.sgd.db, GOstats, GO.db License: Artistic-2.0 MD5sum: 191f432e1c8843d95f9e503a3d7fe914 NeedsCompilation: no Title: Category Analysis Description: A collection of tools for performing category (gene set enrichment) analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: Robert Gentleman [aut], Seth Falcon [ctb], Deepayan Sarkar [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Category git_branch: RELEASE_3_22 git_last_commit: df2c742 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Category_2.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Category_2.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Category_2.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Category_2.76.0.tgz vignettes: vignettes/Category/inst/doc/Category.pdf, vignettes/Category/inst/doc/ChromBand.pdf vignetteTitles: Using Categories to Analyze Microarray Data, Using Chromosome Bands as Categories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Category/inst/doc/Category.R, vignettes/Category/inst/doc/ChromBand.R dependsOnMe: GOstats importsMe: categoryCompare, GmicR, interactiveDisplay, meshr, miRLAB, phenoTest, scTensor suggestsMe: qpgraph, RnBeads, maGUI dependencyCount: 58 Package: categoryCompare Version: 1.54.0 Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8), Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1), GOstats, annotate, colorspace, graph, RCy3 (>= 1.99.29), methods, grDevices, utils Suggests: knitr, GO.db, KEGGREST, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter, rmarkdown License: GPL-2 MD5sum: 642d62ce45b79e4f25242db97037e4df NeedsCompilation: no Title: Meta-analysis of high-throughput experiments using feature annotations Description: Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested). biocViews: Annotation, GO, MultipleComparison, Pathways, GeneExpression Author: Robert M. Flight Maintainer: Robert M. Flight URL: https://github.com/rmflight/categoryCompare SystemRequirements: Cytoscape (>= 3.6.1) (if used for visualization of results, heavily suggested) VignetteBuilder: knitr BugReports: https://github.com/rmflight/categoryCompare/issues git_url: https://git.bioconductor.org/packages/categoryCompare git_branch: RELEASE_3_22 git_last_commit: 3381da0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/categoryCompare_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/categoryCompare_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/categoryCompare_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/categoryCompare_1.54.0.tgz vignettes: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html vignetteTitles: categoryCompare: High-throughput data meta-analysis using gene annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R dependencyCount: 89 Package: CatsCradle Version: 1.3.3 Depends: R (>= 4.4.0) Imports: Seurat (>= 5.0.1), ggplot2, networkD3, stringr, pracma, reshape2, rdist, igraph, geometry, Rfast, data.table, abind, pheatmap, EBImage, S4Vectors, SeuratObject, SingleCellExperiment, SpatialExperiment, Matrix, methods, SummarizedExperiment, msigdbr Suggests: fossil, interp, knitr, BiocStyle, tictoc License: MIT + file LICENSE MD5sum: 418ca5f67724ed170139947ad98edbc9 NeedsCompilation: no Title: This package provides methods for analysing spatial transcriptomics data and for discovering gene clusters Description: This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. 'CatsCradle' allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes. biocViews: BiologicalQuestion, StatisticalMethod, GeneExpression, SingleCell, Transcriptomics, Spatial Author: Anna Laddach [aut] (ORCID: ), Michael Shapiro [aut, cre] (ORCID: ) Maintainer: Michael Shapiro URL: https://github.com/AnnaLaddach/CatsCradle VignetteBuilder: knitr BugReports: https://github.com/AnnaLaddach/CatsCradle/issues git_url: https://git.bioconductor.org/packages/CatsCradle git_branch: devel git_last_commit: 00cf5a4 git_last_commit_date: 2025-10-25 Date/Publication: 2025-10-27 source.ver: src/contrib/CatsCradle_1.3.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/CatsCradle_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CatsCradle_1.3.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CatsCradle_1.3.3.tgz vignettes: vignettes/CatsCradle/inst/doc/CatsCradle.html, vignettes/CatsCradle/inst/doc/CatsCradleExampleData.html, vignettes/CatsCradle/inst/doc/CatsCradleQuickStart.html, vignettes/CatsCradle/inst/doc/CatsCradleSingleCellExperimentQuickStart.html, vignettes/CatsCradle/inst/doc/CatsCradleSpatial.html vignetteTitles: CatsCradle, CatsCradle Example Data, CatsCradle Quick Start, CatsCradle SingleCellExperiment Quick Start, CatsCradle Spatial Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CatsCradle/inst/doc/CatsCradle.R, vignettes/CatsCradle/inst/doc/CatsCradleExampleData.R, vignettes/CatsCradle/inst/doc/CatsCradleQuickStart.R, vignettes/CatsCradle/inst/doc/CatsCradleSingleCellExperimentQuickStart.R, vignettes/CatsCradle/inst/doc/CatsCradleSpatial.R dependencyCount: 200 Package: CausalR Version: 1.42.0 Depends: R (>= 3.2.0) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 4f134020f652af53be66dacee82cc044 NeedsCompilation: no Title: Causal network analysis methods Description: Causal network analysis methods for regulator prediction and network reconstruction from genome scale data. biocViews: ImmunoOncology, SystemsBiology, Network, GraphAndNetwork, Network Inference, Transcriptomics, Proteomics, DifferentialExpression, RNASeq, Microarray Author: Glyn Bradley, Steven Barrett, Chirag Mistry, Mark Pipe, David Wille, David Riley, Bhushan Bonde, Peter Woollard Maintainer: Glyn Bradley , Steven Barrett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CausalR git_branch: RELEASE_3_22 git_last_commit: 4d12003 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CausalR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CausalR_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CausalR_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CausalR_1.42.0.tgz vignettes: vignettes/CausalR/inst/doc/CausalR.pdf vignetteTitles: CausalR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CausalR/inst/doc/CausalR.R dependencyCount: 17 Package: cbaf Version: 1.32.0 Depends: R (>= 4.1) Imports: BiocFileCache, RColorBrewer, cBioPortalData, genefilter, gplots, grDevices, stats, utils, openxlsx, zip Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 6047bcca88c3a0f1c6ed5feedc9e5fdd NeedsCompilation: no Title: Automated functions for comparing various omic data from cbioportal.org Description: This package contains functions that allow analysing and comparing omic data across various cancers/cancer subgroups easily. So far, it is compatible with RNA-seq, microRNA-seq, microarray and methylation datasets that are stored on cbioportal.org. biocViews: Software, AssayDomain, DNAMethylation, GeneExpression, Transcription, Microarray,ResearchField, BiomedicalInformatics, ComparativeGenomics, Epigenetics, Genetics, Transcriptomics Author: Arman Shahrisa [aut, cre, cph], Maryam Tahmasebi Birgani [aut] Maintainer: Arman Shahrisa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cbaf git_branch: RELEASE_3_22 git_last_commit: b77921d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cbaf_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cbaf_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cbaf_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cbaf_1.32.0.tgz vignettes: vignettes/cbaf/inst/doc/cbaf.html vignetteTitles: cbaf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cbaf/inst/doc/cbaf.R dependencyCount: 156 Package: cBioPortalData Version: 2.21.5 Depends: R (>= 4.5.0), AnVIL (>= 1.19.5), MultiAssayExperiment Imports: BiocBaseUtils, BiocFileCache (>= 1.5.3), digest, dplyr, Seqinfo, GenomicRanges, httr, IRanges, methods, readr, RaggedExperiment, RTCGAToolbox (>= 2.19.7), S4Vectors, SummarizedExperiment, stats, tibble, tidyr, TCGAutils (>= 1.9.4), utils Suggests: BiocStyle, jsonlite, knitr, survival, survminer, rmarkdown, testthat License: AGPL-3 MD5sum: de6c7d8dc954b663c96b47db372111bd NeedsCompilation: no Title: Exposes and Makes Available Data from the cBioPortal Web Resources Description: The cBioPortalData R package accesses study datasets from the cBio Cancer Genomics Portal. It accesses the data either from the pre-packaged zip / tar files or from the API interface that was recently implemented by the cBioPortal Data Team. The package can provide data in either tabular format or with MultiAssayExperiment object that uses familiar Bioconductor data representations. biocViews: Software, Infrastructure, ThirdPartyClient Author: Levi Waldron [aut], Marcel Ramos [aut, cre] (ORCID: ), Karim Mezhoud [ctb] Maintainer: Marcel Ramos URL: https://github.com/waldronlab/cBioPortalData VignetteBuilder: knitr BugReports: https://github.com/waldronlab/cBioPortalData/issues git_url: https://git.bioconductor.org/packages/cBioPortalData git_branch: devel git_last_commit: acba625 git_last_commit_date: 2025-07-21 Date/Publication: 2025-10-07 source.ver: src/contrib/cBioPortalData_2.21.5.tar.gz win.binary.ver: bin/windows/contrib/4.5/cBioPortalData_2.21.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cBioPortalData_2.21.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cBioPortalData_2.21.5.tgz vignettes: vignettes/cBioPortalData/inst/doc/cBioPortalData.html, vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.html, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.html, vignettes/cBioPortalData/inst/doc/cgdsrMigration.html vignetteTitles: cBioPortalData User Guide, cBioPortal Data Build Errors, cBioPortal Developer Guide, cgdsr to cBioPortalData Migration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cBioPortalData/inst/doc/cBioPortalData.R, vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.R, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.R, vignettes/cBioPortalData/inst/doc/cgdsrMigration.R dependsOnMe: bioCancer, canceR importsMe: cbaf, GNOSIS dependencyCount: 144 Package: CBN2Path Version: 0.99.18 Depends: R (>= 4.1.0) Imports: R6, ggraph, tidygraph, ggplot2, patchwork, cowplot, magrittr, igraph, rlang, grDevices, coda, graphics, stats, TCGAbiolinks, BiocParallel Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE OS_type: unix MD5sum: ce0cb9e12d45ec5061d9a2de156a85a4 NeedsCompilation: yes Title: "CBN2Path: an R/Bioconductor package for the analysis of cancer progression pathways using Conjunctive Bayesian Networks Description: CBN2Path package provides a unifying interface to facilitate CBN-based quantification, analysis and visualization of cancer progression pathways. biocViews: Software, StatisticalMethod, GraphAndNetwork, Bayesian, Pathways Author: William Choi-Kim [aut, cre] (ORCID: ), Sayed-Rzgar Hosseini [aut] (ORCID: ) Maintainer: William Choi-Kim URL: https://github.com/rockwillck/CBN2Path, http://dx.doi.org/10.1093/biomet/asp023, http://dx.doi.org/10.1093/bioinformatics/btp505 SystemRequirements: GNU Scientific Library (GSL) VignetteBuilder: knitr BugReports: https://github.com/rockwillck/CBN2Path/issues git_url: https://git.bioconductor.org/packages/CBN2Path git_branch: devel git_last_commit: e7af674 git_last_commit_date: 2025-10-26 Date/Publication: 2025-10-27 source.ver: src/contrib/CBN2Path_0.99.18.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CBN2Path_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CBN2Path_1.0.0.tgz vignettes: vignettes/CBN2Path/inst/doc/CBN2Path.html vignetteTitles: CBN2Path Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/CBN2Path/inst/doc/CBN2Path.R dependencyCount: 132 Package: CBNplot Version: 1.10.0 Depends: R (>= 4.3.0) Imports: ggplot2, magrittr, graphite, ggraph, igraph, bnlearn (>= 4.7), patchwork, org.Hs.eg.db, clusterProfiler, utils, enrichplot, reshape2, ggforce, dplyr, tidyr, stringr, depmap, ExperimentHub, Rmpfr, graphlayouts, BiocFileCache, ggdist, purrr, pvclust, stats, rlang Suggests: knitr, arules, concaveman, ReactomePA, bnviewer, rmarkdown, withr, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: c45a84811a2b168417c1f36b273f30de NeedsCompilation: no Title: plot bayesian network inferred from gene expression data based on enrichment analysis results Description: This package provides the visualization of bayesian network inferred from gene expression data. The networks are based on enrichment analysis results inferred from packages including clusterProfiler and ReactomePA. The networks between pathways and genes inside the pathways can be inferred and visualized. biocViews: Visualization, Bayesian, GeneExpression, NetworkInference, Pathways, Reactome, Network, NetworkEnrichment, GeneSetEnrichment Author: Noriaki Sato [cre, aut] Maintainer: Noriaki Sato URL: https://github.com/noriakis/CBNplot VignetteBuilder: knitr BugReports: https://github.com/noriakis/CBNplot/issues git_url: https://git.bioconductor.org/packages/CBNplot git_branch: RELEASE_3_22 git_last_commit: 89ee1b1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CBNplot_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CBNplot_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CBNplot_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CBNplot_1.10.0.tgz vignettes: vignettes/CBNplot/inst/doc/CBNplot_basic_usage.html vignetteTitles: CBNplot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CBNplot/inst/doc/CBNplot_basic_usage.R dependencyCount: 165 Package: cbpManager Version: 1.18.0 Depends: shiny, shinydashboard Imports: utils, DT, htmltools, vroom, plyr, dplyr, magrittr, jsonlite, rapportools, basilisk, reticulate, shinyBS, shinycssloaders, rintrojs, rlang, markdown Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE Archs: x64 MD5sum: 7fb57917e91d82559f5fa195d26549ac NeedsCompilation: no Title: Generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics Description: This R package provides an R Shiny application that enables the user to generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics. Create cancer studies and edit its metadata. Upload mutation data of a patient that will be concatenated to the data_mutation_extended.txt file of the study. Create and edit clinical patient data, sample data, and timeline data. Create custom timeline tracks for patients. biocViews: ImmunoOncology, DataImport, DataRepresentation, GUI, ThirdPartyClient, Preprocessing, Visualization Author: Arsenij Ustjanzew [aut, cre, cph] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Arsenij Ustjanzew URL: https://arsenij-ust.github.io/cbpManager/index.html VignetteBuilder: knitr BugReports: https://github.com/arsenij-ust/cbpManager/issues git_url: https://git.bioconductor.org/packages/cbpManager git_branch: RELEASE_3_22 git_last_commit: 5565444 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cbpManager_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cbpManager_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cbpManager_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cbpManager_1.18.0.tgz vignettes: vignettes/cbpManager/inst/doc/intro.html vignetteTitles: intro.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cbpManager/inst/doc/intro.R dependencyCount: 89 Package: CCAFE Version: 1.2.0 Depends: R (>= 4.4.0) Imports: dplyr, VariantAnnotation Suggests: testthat (>= 3.0.0), rmarkdown, markdown, knitr, tidyverse, DescTools, cowplot, BiocStyle, GenomicRanges, SummarizedExperiment, S4Vectors, IRanges License: GPL-3 MD5sum: e3269bca87421e20731e5d0998e723ac NeedsCompilation: no Title: Case Control Allele Frequency Estimation Description: Functions to reconstruct case and control AFs from summary statistics. One function uses OR, NCase, NControl, and SE(log(OR)). The second function uses OR, NCase, NControl, and AF for the whole sample. biocViews: GenomeWideAssociation, ComparativeGenomics, Genetics, Preprocessing, SNP, Software, WholeGenome Author: Hayley Wolff [cre, aut] Maintainer: Hayley Wolff URL: https://github.com/wolffha/CCAFE/ VignetteBuilder: knitr BugReports: https://github.com/wolffha/CCAFE/issues git_url: https://git.bioconductor.org/packages/CCAFE git_branch: RELEASE_3_22 git_last_commit: 93ae3ad git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CCAFE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CCAFE_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CCAFE_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CCAFE_1.2.0.tgz vignettes: vignettes/CCAFE/inst/doc/CCAFE_Extra_Details.html, vignettes/CCAFE/inst/doc/CCAFE.html vignetteTitles: CCAFE Extra Details, CCAFE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CCAFE/inst/doc/CCAFE_Extra_Details.R, vignettes/CCAFE/inst/doc/CCAFE.R dependencyCount: 85 Package: ccfindR Version: 1.30.0 Depends: R (>= 3.6.0) Imports: stats, S4Vectors, utils, methods, Matrix, SummarizedExperiment, SingleCellExperiment, Rtsne, graphics, grDevices, gtools, RColorBrewer, ape, Rmpi, irlba, Rcpp, Rdpack (>= 0.7) LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 2cb7adf6d3e1db18b448d3385fcc79e0 NeedsCompilation: yes Title: Cancer Clone Finder Description: A collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters. biocViews: Transcriptomics, SingleCell, ImmunoOncology, Bayesian, Clustering Author: Jun Woo [aut, cre], Jinhua Wang [aut] Maintainer: Jun Woo URL: http://dx.doi.org/10.26508/lsa.201900443 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccfindR git_branch: RELEASE_3_22 git_last_commit: 2aa6151 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ccfindR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ccfindR_1.29.0.zip vignettes: vignettes/ccfindR/inst/doc/ccfindR.html vignetteTitles: ccfindR: single-cell RNA-seq analysis using Bayesian non-negative matrix factorization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccfindR/inst/doc/ccfindR.R suggestsMe: MutationalPatterns dependencyCount: 39 Package: ccImpute Version: 1.12.0 Imports: Rcpp, sparseMatrixStats, stats, BiocParallel, irlba, SingleCellExperiment, Matrix, SummarizedExperiment LinkingTo: Rcpp, RcppEigen Suggests: knitr, rmarkdown, BiocStyle, sessioninfo, scRNAseq, scater, mclust, testthat (>= 3.0.0), splatter License: GPL-3 Archs: x64 MD5sum: 925f594dc15cab50a2a906ea6e21d25a NeedsCompilation: yes Title: ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (https://doi.org/10.1186/s12859-022-04814-8) Description: Dropout events make the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult. ccImpute is an imputation algorithm that uses cell similarity established by consensus clustering to impute the most probable dropout events in the scRNA-seq datasets. ccImpute demonstrated performance which exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities. biocViews: SingleCell, Sequencing, PrincipalComponent, DimensionReduction, Clustering, RNASeq, Transcriptomics Author: Marcin Malec [cre, aut] (ORCID: ), Parichit Sharma [aut] (ORCID: ), Hasan Kurban [aut] (ORCID: ), Mehmet Dalkilic [aut] Maintainer: Marcin Malec URL: https://github.com/khazum/ccImpute/ VignetteBuilder: knitr BugReports: https://github.com/khazum/ccImpute/issues git_url: https://git.bioconductor.org/packages/ccImpute git_branch: RELEASE_3_22 git_last_commit: 4f91f58 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ccImpute_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ccImpute_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ccImpute_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ccImpute_1.12.0.tgz vignettes: vignettes/ccImpute/inst/doc/ccImpute.html vignetteTitles: ccImpute package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccImpute/inst/doc/ccImpute.R dependencyCount: 40 Package: CCPlotR Version: 1.8.0 Imports: plyr, tidyr, dplyr, ggplot2, forcats, ggraph, igraph, scatterpie, circlize, ComplexHeatmap, tibble, grid, ggbump, stringr, ggtext, ggh4x, patchwork, RColorBrewer, scales, viridis, grDevices, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 05d9b3c3b2caf4ce08d1a0d2efa8ce04 NeedsCompilation: no Title: Plots For Visualising Cell-Cell Interactions Description: CCPlotR is an R package for visualising results from tools that predict cell-cell interactions from single-cell RNA-seq data. These plots are generic and can be used to visualise results from multiple tools such as Liana, CellPhoneDB, NATMI etc. biocViews: SingleCell, Network, Visualization, CellBiology, SystemsBiology Author: Sarah Ennis [aut, cre] (ORCID: ), Pilib Ó Broin [aut], Eva Szegezdi [aut] Maintainer: Sarah Ennis URL: https://github.com/Sarah145/CCPlotR VignetteBuilder: knitr BugReports: https://github.com/Sarah145/CCPlotR/issues git_url: https://git.bioconductor.org/packages/CCPlotR git_branch: RELEASE_3_22 git_last_commit: c97d24c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CCPlotR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CCPlotR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CCPlotR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CCPlotR_1.8.0.tgz vignettes: vignettes/CCPlotR/inst/doc/CCPlotR_visualisations.html vignetteTitles: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CCPlotR/inst/doc/CCPlotR_visualisations.R importsMe: OSTA dependencyCount: 98 Package: CCPROMISE Version: 1.36.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: 518f2f498a64a045f937bf40a4a01301 NeedsCompilation: no Title: PROMISE analysis with Canonical Correlation for Two Forms of High Dimensional Genetic Data Description: Perform Canonical correlation between two forms of high demensional genetic data, and associate the first compoent of each form of data with a specific biologically interesting pattern of associations with multiple endpoints. A probe level analysis is also implemented. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao git_url: https://git.bioconductor.org/packages/CCPROMISE git_branch: RELEASE_3_22 git_last_commit: 99c9449 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CCPROMISE_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CCPROMISE_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CCPROMISE_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CCPROMISE_1.36.0.tgz vignettes: vignettes/CCPROMISE/inst/doc/CCPROMISE.pdf vignetteTitles: An introduction to CCPROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CCPROMISE/inst/doc/CCPROMISE.R dependencyCount: 50 Package: ccrepe Version: 1.46.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat, RUnit License: MIT + file LICENSE Archs: x64 MD5sum: e6813875af675cc311f1048dc8dcfbc7 NeedsCompilation: no Title: ccrepe_and_nc.score Description: The CCREPE (Compositionality Corrected by REnormalizaion and PErmutation) package is designed to assess the significance of general similarity measures in compositional datasets. In microbial abundance data, for example, the total abundances of all microbes sum to one; CCREPE is designed to take this constraint into account when assigning p-values to similarity measures between the microbes. The package has two functions: ccrepe: Calculates similarity measures, p-values and q-values for relative abundances of bugs in one or two body sites using bootstrap and permutation matrices of the data. nc.score: Calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data. biocViews: ImmunoOncology, Statistics, Metagenomics, Bioinformatics, Software Author: Emma Schwager ,Craig Bielski, George Weingart Maintainer: Emma Schwager ,Craig Bielski, George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccrepe git_branch: RELEASE_3_22 git_last_commit: 8e7bf32 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ccrepe_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ccrepe_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ccrepe_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ccrepe_1.46.0.tgz vignettes: vignettes/ccrepe/inst/doc/ccrepe.pdf vignetteTitles: ccrepe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccrepe/inst/doc/ccrepe.R dependencyCount: 1 Package: CDI Version: 1.8.0 Depends: R(>= 3.6) Imports: matrixStats, Seurat, SeuratObject, stats, BiocParallel, ggplot2, reshape2, grDevices, ggsci, SingleCellExperiment, SummarizedExperiment, methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, magick, BiocStyle License: GPL-3 + file LICENSE MD5sum: dd4d8081c3ce6630d7ca06b4246339b0 NeedsCompilation: no Title: Clustering Deviation Index (CDI) Description: Single-cell RNA-sequencing (scRNA-seq) is widely used to explore cellular variation. The analysis of scRNA-seq data often starts from clustering cells into subpopulations. This initial step has a high impact on downstream analyses, and hence it is important to be accurate. However, there have not been unsupervised metric designed for scRNA-seq to evaluate clustering performance. Hence, we propose clustering deviation index (CDI), an unsupervised metric based on the modeling of scRNA-seq UMI counts to evaluate clustering of cells. biocViews: SingleCell, Software, Clustering, Visualization, Sequencing, RNASeq, CellBasedAssays Author: Jiyuan Fang [cre, aut] (ORCID: ), Jichun Xie [ctb], Cliburn Chan [ctb], Kouros Owzar [ctb], Liuyang Wang [ctb], Diyuan Qin [ctb], Qi-Jing Li [ctb], Jichun Xie [ctb] Maintainer: Jiyuan Fang URL: https://github.com/jichunxie/CDI VignetteBuilder: knitr BugReports: https://github.com/jichunxie/CDI/issues git_url: https://git.bioconductor.org/packages/CDI git_branch: RELEASE_3_22 git_last_commit: 1b170d2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CDI_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CDI_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CDI_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CDI_1.8.0.tgz vignettes: vignettes/CDI/inst/doc/CDI.html vignetteTitles: Clustering Deviation Index (CDI) Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CDI/inst/doc/CDI.R dependencyCount: 173 Package: celaref Version: 1.28.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: MAST, ggplot2, Matrix, dplyr, magrittr, stats, utils, rlang, BiocGenerics, S4Vectors, readr, tibble, DelayedArray Suggests: limma, parallel, knitr, rmarkdown, ExperimentHub, testthat License: GPL-3 MD5sum: d01d3644f1df41ce0d645bd321a70636 NeedsCompilation: no Title: Single-cell RNAseq cell cluster labelling by reference Description: After the clustering step of a single-cell RNAseq experiment, this package aims to suggest labels/cell types for the clusters, on the basis of similarity to a reference dataset. It requires a table of read counts per cell per gene, and a list of the cells belonging to each of the clusters, (for both test and reference data). biocViews: SingleCell Author: Sarah Williams [aut, cre] Maintainer: Sarah Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/celaref git_branch: RELEASE_3_22 git_last_commit: 21285bc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/celaref_1.28.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/celaref_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/celaref_1.28.0.tgz vignettes: vignettes/celaref/inst/doc/celaref_doco.html vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/celaref/inst/doc/celaref_doco.R dependencyCount: 68 Package: celda Version: 1.26.0 Depends: R (>= 4.0), SingleCellExperiment, Matrix Imports: plyr, foreach, ggplot2, RColorBrewer, grid, scales, gtable, grDevices, graphics, matrixStats, doParallel, digest, methods, reshape2, S4Vectors, data.table, Rcpp, RcppEigen, uwot, enrichR, SummarizedExperiment, MCMCprecision, ggrepel, Rtsne, withr, scater (>= 1.14.4), scran, dbscan, DelayedArray, stringr, ComplexHeatmap, gridExtra, circlize, dendextend, ggdendro, pROC LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr, BiocManager, BiocStyle, TENxPBMCData, singleCellTK, M3DExampleData License: MIT + file LICENSE MD5sum: cb0e93f5b098a2843cb63617f9f754de NeedsCompilation: yes Title: CEllular Latent Dirichlet Allocation Description: Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included. biocViews: SingleCell, GeneExpression, Clustering, Sequencing, Bayesian, ImmunoOncology, DataImport Author: Joshua Campbell [aut, cre], Shiyi Yang [aut], Zhe Wang [aut], Sean Corbett [aut], Yusuke Koga [aut] Maintainer: Joshua Campbell VignetteBuilder: knitr BugReports: https://github.com/campbio/celda/issues git_url: https://git.bioconductor.org/packages/celda git_branch: RELEASE_3_22 git_last_commit: e1acfed git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/celda_1.26.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/celda_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/celda_1.26.0.tgz vignettes: vignettes/celda/inst/doc/celda.html, vignettes/celda/inst/doc/decontX.html vignetteTitles: Analysis of single-cell genomic data with celda, Estimate and remove cross-contamination from ambient RNA in single-cell data with DecontX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/celda/inst/doc/celda.R, vignettes/celda/inst/doc/decontX.R importsMe: decontX, singleCellTK dependencyCount: 133 Package: CellBarcode Version: 1.16.0 Depends: R (>= 4.1.0) Imports: methods, stats, Rcpp (>= 1.0.5), data.table (>= 1.12.6), plyr, ggplot2, stringr, magrittr, ShortRead (>= 1.48.0), Biostrings (>= 2.58.0), egg, Ckmeans.1d.dp, utils, S4Vectors, seqinr, Rsamtools LinkingTo: Rcpp, BH Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: Artistic-2.0 MD5sum: c53657f3e5071e70f07939dba9a97ef4 NeedsCompilation: yes Title: Cellular DNA Barcode Analysis toolkit Description: The package CellBarcode performs Cellular DNA Barcode analysis. It can handle all kinds of DNA barcodes, as long as the barcode is within a single sequencing read and has a pattern that can be matched by a regular expression. \code{CellBarcode} can handle barcodes with flexible lengths, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing, and metagenome data. biocViews: Preprocessing, QualityControl, Sequencing, CRISPR Author: Wenjie Sun [cre, aut] (ORCID: ), Anne-Marie Lyne [aut], Leila Perie [aut] Maintainer: Wenjie Sun URL: https://wenjie1991.github.io/CellBarcode/ VignetteBuilder: knitr BugReports: https://github.com/wenjie1991/CellBarcode/issues git_url: https://git.bioconductor.org/packages/CellBarcode git_branch: RELEASE_3_22 git_last_commit: 5db08a9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CellBarcode_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellBarcode_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellBarcode_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellBarcode_1.16.0.tgz vignettes: vignettes/CellBarcode/inst/doc/Barcode_in_10X_scRNASeq.html, vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.html vignetteTitles: 10X_Barcode, UMI_Barcode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellBarcode/inst/doc/Barcode_in_10X_scRNASeq.R, vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.R dependencyCount: 86 Package: cellbaseR Version: 1.34.0 Depends: R(>= 3.4) Imports: methods, jsonlite, httr, data.table, pbapply, tidyr, R.utils, Rsamtools, BiocParallel, foreach, utils, parallel, doParallel Suggests: BiocStyle, knitr, rmarkdown, Gviz, VariantAnnotation License: Apache License (== 2.0) MD5sum: ad89c53b7639cdbe598bf9c49428c01d NeedsCompilation: no Title: Querying annotation data from the high performance Cellbase web Description: This R package makes use of the exhaustive RESTful Web service API that has been implemented for the Cellabase database. It enable researchers to query and obtain a wealth of biological information from a single database saving a lot of time. Another benefit is that researchers can easily make queries about different biological topics and link all this information together as all information is integrated. biocViews: Annotation, VariantAnnotation Author: Mohammed OE Abdallah Maintainer: Mohammed OE Abdallah URL: https://github.com/melsiddieg/cellbaseR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellbaseR git_branch: RELEASE_3_22 git_last_commit: eb7c9ed git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cellbaseR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cellbaseR_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cellbaseR_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cellbaseR_1.34.0.tgz vignettes: vignettes/cellbaseR/inst/doc/cellbaseR.html vignetteTitles: "Simplifying Genomic Annotations in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellbaseR/inst/doc/cellbaseR.R dependencyCount: 62 Package: CellBench Version: 1.26.0 Depends: R (>= 3.6), SingleCellExperiment, magrittr, methods, stats, tibble, utils Imports: assertthat, BiocGenerics, BiocFileCache, BiocParallel, dplyr, rlang, glue, memoise, purrr (>= 0.3.0), rappdirs, tidyr, tidyselect, lubridate Suggests: BiocStyle, covr, knitr, rmarkdown, testthat, limma, ggplot2 License: GPL-3 MD5sum: fd09759d642f3db8e7435c537b67bbd6 NeedsCompilation: no Title: Construct Benchmarks for Single Cell Analysis Methods Description: This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods. biocViews: Software, Infrastructure, SingleCell Author: Shian Su [cre, aut], Saskia Freytag [aut], Luyi Tian [aut], Xueyi Dong [aut], Matthew Ritchie [aut], Peter Hickey [ctb], Stuart Lee [ctb] Maintainer: Shian Su URL: https://github.com/shians/cellbench VignetteBuilder: knitr BugReports: https://github.com/Shians/CellBench/issues git_url: https://git.bioconductor.org/packages/CellBench git_branch: RELEASE_3_22 git_last_commit: 452edea git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CellBench_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellBench_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellBench_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellBench_1.26.0.tgz vignettes: vignettes/CellBench/inst/doc/DataManipulation.html, vignettes/CellBench/inst/doc/Introduction.html, vignettes/CellBench/inst/doc/TidyversePatterns.html, vignettes/CellBench/inst/doc/Timing.html, vignettes/CellBench/inst/doc/WritingWrappers.html vignetteTitles: Data Manipulation, Introduction, Tidyverse Patterns, Timing, Writing Wrappers hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellBench/inst/doc/DataManipulation.R, vignettes/CellBench/inst/doc/Introduction.R, vignettes/CellBench/inst/doc/TidyversePatterns.R, vignettes/CellBench/inst/doc/Timing.R, vignettes/CellBench/inst/doc/WritingWrappers.R suggestsMe: corral, speckle dependencyCount: 75 Package: CelliD Version: 1.18.0 Depends: R (>= 4.1), Seurat (>= 4.0.1), SingleCellExperiment Imports: Rcpp, RcppArmadillo, stats, utils, Matrix, tictoc, scater, stringr, irlba, data.table, glue, pbapply, umap, Rtsne, reticulate, fastmatch, matrixStats, ggplot2, BiocParallel, SummarizedExperiment, fgsea LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, BiocStyle, testthat, tidyverse, ggpubr, destiny, ggrepel License: GPL-3 + file LICENSE MD5sum: 51aaefa1f92c4fd38e294a7fea37d622 NeedsCompilation: yes Title: Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis Description: CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data. biocViews: RNASeq, SingleCell, DimensionReduction, Clustering, GeneSetEnrichment, GeneExpression, ATACSeq Author: Akira Cortal [aut, cre], Antonio Rausell [aut, ctb] Maintainer: Akira Cortal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CelliD git_branch: RELEASE_3_22 git_last_commit: 097905e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CelliD_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CelliD_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CelliD_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CelliD_1.18.0.tgz vignettes: vignettes/CelliD/inst/doc/BioconductorVignette.html vignetteTitles: CelliD Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CelliD/inst/doc/BioconductorVignette.R dependencyCount: 195 Package: cellity Version: 1.38.0 Depends: R (>= 3.3) Imports: AnnotationDbi, e1071, ggplot2, graphics, grDevices, grid, mvoutlier, org.Hs.eg.db, org.Mm.eg.db, robustbase, stats, topGO, utils Suggests: BiocStyle, caret, knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: 4f44b9ef769d89a87038914075c2b4ba NeedsCompilation: no Title: Quality Control for Single-Cell RNA-seq Data Description: A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets. biocViews: ImmunoOncology, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, SupportVectorMachine Author: Tomislav Illicic, Davis McCarthy Maintainer: Tomislav Ilicic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellity git_branch: RELEASE_3_22 git_last_commit: e41648e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cellity_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cellity_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cellity_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cellity_1.38.0.tgz vignettes: vignettes/cellity/inst/doc/cellity_vignette.html vignetteTitles: An introduction to the cellity package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R dependencyCount: 70 Package: CellMapper Version: 1.36.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 MD5sum: d3348cb33ecdea4286ac75f9a145b510 NeedsCompilation: no Title: Predict genes expressed selectively in specific cell types Description: Infers cell type-specific expression based on co-expression similarity with known cell type marker genes. Can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before. biocViews: Microarray, Software, GeneExpression Author: Brad Nelms Maintainer: Brad Nelms git_url: https://git.bioconductor.org/packages/CellMapper git_branch: RELEASE_3_22 git_last_commit: 2f7af84 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CellMapper_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellMapper_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellMapper_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellMapper_1.36.0.tgz vignettes: vignettes/CellMapper/inst/doc/CellMapper.pdf vignetteTitles: CellMapper Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMapper/inst/doc/CellMapper.R dependsOnMe: CellMapperData dependencyCount: 8 Package: cellmig Version: 0.99.16 Depends: R (>= 4.5.0) Imports: base, ggplot2, ggforce, ggtree, patchwork, ape, methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), reshape2, rstan (>= 2.18.1), rstantools (>= 2.4.0), stats, utils, scales LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, knitr, testthat License: GPL-3 + file LICENSE MD5sum: 06feac5d69ffa66158525bde306c776c NeedsCompilation: yes Title: Uncertainty-aware quantitative analysis of high-throughput live cell migration data Description: High-throughput cell imaging facilitates the analysis of cell migration across many wells treated under different biological conditions. These workflows generate considerable technical noise and biological variability, and therefore technical and biological replicates are necessary, leading to large, hierarchically structured datasets, i.e., cells are nested within technical replicates that are nested within biological replicates. Current statistical analyses of such data usually ignore the hierarchical structure of the data and fail to explicitly quantify uncertainty arising from technical or biological variability. To address this gap, we present cellmig, an R package implementing Bayesian hierarchical models for migration analysis. cellmig quantifies condition- specific velocity changes (e.g., drug effects) while modeling nested data structures and technical artifacts. It further enables synthetic data generation for experimental design optimization. biocViews: SingleCell, CellBiology, Bayesian, ExperimentalDesign, Software, BatchEffect, Regression, Clustering Author: Simo Kitanovski [aut, cre] (ORCID: ) Maintainer: Simo Kitanovski URL: https://github.com/snaketron/cellmig SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/snaketron/cellmig/issues git_url: https://git.bioconductor.org/packages/cellmig git_branch: devel git_last_commit: 52a8964 git_last_commit_date: 2025-09-09 Date/Publication: 2025-10-07 source.ver: src/contrib/cellmig_0.99.16.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cellmig_0.99.16.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cellmig_0.99.16.tgz vignettes: vignettes/cellmig/inst/doc/User_manual_analysis.html, vignettes/cellmig/inst/doc/User_manual_simulation.html vignetteTitles: User Manual: cellmig, User manual: data simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cellmig/inst/doc/User_manual_analysis.R, vignettes/cellmig/inst/doc/User_manual_simulation.R dependencyCount: 110 Package: cellmigRation Version: 1.18.0 Depends: R (>= 4.1), methods, foreach Imports: tiff, graphics, stats, utils, reshape2, parallel, doParallel, grDevices, matrixStats, FME, SpatialTools, sp, vioplot, FactoMineR, Hmisc Suggests: knitr, rmarkdown, dplyr, ggplot2, RUnit, BiocGenerics, BiocManager, kableExtra, rgl License: GPL-2 MD5sum: 8303b96e259d9a9e310eb7183404b50a NeedsCompilation: no Title: Track Cells, Analyze Cell Trajectories and Compute Migration Statistics Description: Import TIFF images of fluorescently labeled cells, and track cell movements over time. Parallelization is supported for image processing and for fast computation of cell trajectories. In-depth analysis of cell trajectories is enabled by 15 trajectory analysis functions. biocViews: CellBiology, DataRepresentation, DataImport Author: Salim Ghannoum [aut, cph], Damiano Fantini [aut, cph], Waldir Leoncio [cre, aut], Øystein Sørensen [aut] Maintainer: Waldir Leoncio URL: https://github.com/ocbe-uio/cellmigRation/ VignetteBuilder: knitr BugReports: https://github.com/ocbe-uio/cellmigRation/issues git_url: https://git.bioconductor.org/packages/cellmigRation git_branch: RELEASE_3_22 git_last_commit: 995cd43 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cellmigRation_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cellmigRation_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cellmigRation_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cellmigRation_1.18.0.tgz vignettes: vignettes/cellmigRation/inst/doc/cellmigRation.html vignetteTitles: cellmigRation hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellmigRation/inst/doc/cellmigRation.R dependencyCount: 139 Package: CellMixS Version: 1.26.0 Depends: kSamples, R (>= 4.0) Imports: BiocNeighbors, ggplot2, scater, viridis, cowplot, SummarizedExperiment, SingleCellExperiment, tidyr, magrittr, dplyr, ggridges, stats, purrr, methods, BiocParallel, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, testthat, limma, Rtsne License: GPL (>=2) MD5sum: 81a4e1b65f9506eb8bb745d8eb1e669a NeedsCompilation: no Title: Evaluate Cellspecific Mixing Description: CellMixS provides metrics and functions to evaluate batch effects, data integration and batch effect correction in single cell trancriptome data with single cell resolution. Results can be visualized and summarised on different levels, e.g. on cell, celltype or dataset level. biocViews: SingleCell, Transcriptomics, GeneExpression, BatchEffect Author: Almut Lütge [aut, cre] Maintainer: Almut Lütge URL: https://github.com/almutlue/CellMixS VignetteBuilder: knitr BugReports: https://github.com/almutlue/CellMixS/issues git_url: https://git.bioconductor.org/packages/CellMixS git_branch: RELEASE_3_22 git_last_commit: 9f46a58 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CellMixS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellMixS_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellMixS_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellMixS_1.26.0.tgz vignettes: vignettes/CellMixS/inst/doc/CellMixS.html vignetteTitles: Explore data integration and batch effects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMixS/inst/doc/CellMixS.R dependencyCount: 102 Package: CellNOptR Version: 1.56.0 Depends: R (>= 4.0.0), RBGL, graph, methods, RCurl, Rgraphviz, XML, ggplot2, rmarkdown Imports: igraph, stringi, stringr Suggests: data.table, dplyr, tidyr, readr, knitr, RUnit, BiocGenerics, Enhances: doParallel, foreach License: GPL-3 MD5sum: ed79c2b94bcc10f7efeeb291cbc92644 NeedsCompilation: yes Title: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data Description: This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network. biocViews: CellBasedAssays, CellBiology, Proteomics, Pathways, Network, TimeCourse, ImmunoOncology Author: Thomas Cokelaer [aut], Federica Eduati [aut], Aidan MacNamara [aut], S Schrier [ctb], Camille Terfve [aut], Enio Gjerga [ctb], Attila Gabor [cre] Maintainer: Attila Gabor SystemRequirements: Graphviz version >= 2.2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellNOptR git_branch: RELEASE_3_22 git_last_commit: 2244640 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CellNOptR_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellNOptR_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellNOptR_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellNOptR_1.56.0.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.html vignetteTitles: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data with CellNOptR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R dependsOnMe: CNORdt, CNORfuzzy, CNORode importsMe: bnem, CNORfeeder suggestsMe: MEIGOR dependencyCount: 62 Package: cellscape Version: 1.34.0 Depends: R (>= 3.3) Imports: dplyr (>= 0.4.3), gtools (>= 3.5.0), htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), reshape2 (>= 1.4.1), stringr (>= 1.0.0) Suggests: knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: be048a57453b1817b97bbd4b8d41a697 NeedsCompilation: no Title: Explores single cell copy number profiles in the context of a single cell tree Description: CellScape facilitates interactive browsing of single cell clonal evolution datasets. The tool requires two main inputs: (i) the genomic content of each single cell in the form of either copy number segments or targeted mutation values, and (ii) a single cell phylogeny. Phylogenetic formats can vary from dendrogram-like phylogenies with leaf nodes to evolutionary model-derived phylogenies with observed or latent internal nodes. The CellScape phylogeny is flexibly input as a table of source-target edges to support arbitrary representations, where each node may or may not have associated genomic data. The output of CellScape is an interactive interface displaying a single cell phylogeny and a cell-by-locus genomic heatmap representing the mutation status in each cell for each locus. biocViews: Visualization Author: Shixiang Wang [aut, cre] (ORCID: ), Maia Smith [aut] Maintainer: Shixiang Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellscape git_branch: RELEASE_3_22 git_last_commit: d0e7c79 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cellscape_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cellscape_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cellscape_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cellscape_1.34.0.tgz vignettes: vignettes/cellscape/inst/doc/cellscape_vignette.html vignetteTitles: CellScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellscape/inst/doc/cellscape_vignette.R dependencyCount: 49 Package: CellTrails Version: 1.28.0 Depends: R (>= 3.5), SingleCellExperiment Imports: BiocGenerics, Biobase, cba, dendextend, dtw, EnvStats, ggplot2, ggrepel, grDevices, igraph, maptree, methods, mgcv, reshape2, Rtsne, stats, splines, SummarizedExperiment, utils Suggests: AnnotationDbi, destiny, RUnit, scater, scran, knitr, org.Mm.eg.db, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 8a9a49bb26704b588dbb83a858c74d20 NeedsCompilation: no Title: Reconstruction, visualization and analysis of branching trajectories Description: CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes. biocViews: ImmunoOncology, Clustering, DataRepresentation, DifferentialExpression, DimensionReduction, GeneExpression, Sequencing, SingleCell, Software, TimeCourse Author: Daniel Ellwanger [aut, cre, cph] Maintainer: Daniel Ellwanger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellTrails git_branch: RELEASE_3_22 git_last_commit: 0fc34ed git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CellTrails_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellTrails_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellTrails_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellTrails_1.28.0.tgz vignettes: vignettes/CellTrails/inst/doc/vignette.pdf vignetteTitles: CellTrails: Reconstruction,, visualization,, and analysis of branching trajectories from single-cell expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellTrails/inst/doc/vignette.R dependencyCount: 69 Package: cellxgenedp Version: 1.14.0 Depends: R (>= 4.1.0), dplyr Imports: httr, curl, utils, tools, cli, shiny, DT, rjsoncons Suggests: zellkonverter, SingleCellExperiment, HDF5Array, tidyr, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), mockery License: Artistic-2.0 MD5sum: 1fc6d67fe55c3d4423e0a6432a092144 NeedsCompilation: no Title: Discover and Access Single Cell Data Sets in the CELLxGENE Data Portal Description: The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading. biocViews: SingleCell, DataImport, ThirdPartyClient Author: Martin Morgan [aut, cre] (ORCID: ), Kayla Interdonato [aut] Maintainer: Martin Morgan URL: https://mtmorgan.github.io/cellxgenedp/, https://github.com/mtmorgan/cellxgenedp VignetteBuilder: knitr BugReports: https://github.com/mtmorgan/cellxgenedp/issues git_url: https://git.bioconductor.org/packages/cellxgenedp git_branch: RELEASE_3_22 git_last_commit: 4c68beb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cellxgenedp_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cellxgenedp_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cellxgenedp_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cellxgenedp_1.14.0.tgz vignettes: vignettes/cellxgenedp/inst/doc/a_using_cellxgenedp.html, vignettes/cellxgenedp/inst/doc/b_case_studies.html vignetteTitles: Discovery and retrieval, Case studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellxgenedp/inst/doc/a_using_cellxgenedp.R, vignettes/cellxgenedp/inst/doc/b_case_studies.R dependencyCount: 62 Package: CEMiTool Version: 1.34.0 Depends: R (>= 4.0) Imports: methods, scales, dplyr, data.table (>= 1.9.4), WGCNA, grid, ggplot2, ggpmisc, ggthemes, ggrepel, sna, clusterProfiler, fgsea, stringr, knitr, rmarkdown, igraph, DT, htmltools, pracma, intergraph, grDevices, utils, network, matrixStats, ggdendro, gridExtra, gtable, fastcluster Suggests: testthat, BiocManager License: GPL-3 MD5sum: f4a51249cd9c80c2cb06bb7c4b6945a1 NeedsCompilation: no Title: Co-expression Modules identification Tool Description: The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology Author: Pedro Russo [aut], Gustavo Ferreira [aut], Matheus Bürger [aut], Lucas Cardozo [aut], Diogenes Lima [aut], Thiago Hirata [aut], Melissa Lever [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CEMiTool git_branch: RELEASE_3_22 git_last_commit: a56e804 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CEMiTool_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CEMiTool_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CEMiTool_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CEMiTool_1.34.0.tgz vignettes: vignettes/CEMiTool/inst/doc/CEMiTool.html vignetteTitles: CEMiTool: Co-expression Modules Identification Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CEMiTool/inst/doc/CEMiTool.R dependencyCount: 193 Package: censcyt Version: 1.18.0 Depends: R (>= 4.0), diffcyt Imports: BiocParallel, broom.mixed, dirmult, dplyr, edgeR, fitdistrplus, lme4, magrittr, MASS, methods, mice, multcomp, purrr, rlang, S4Vectors, stats, stringr, SummarizedExperiment, survival, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2 License: MIT + file LICENSE MD5sum: 01d1583a9a14099385a6e9fc02e88116 NeedsCompilation: no Title: Differential abundance analysis with a right censored covariate in high-dimensional cytometry Description: Methods for differential abundance analysis in high-dimensional cytometry data when a covariate is subject to right censoring (e.g. survival time) based on multiple imputation and generalized linear mixed models. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software, Survival Author: Reto Gerber [aut, cre] (ORCID: ) Maintainer: Reto Gerber URL: https://github.com/retogerber/censcyt VignetteBuilder: knitr BugReports: https://github.com/retogerber/censcyt/issues git_url: https://git.bioconductor.org/packages/censcyt git_branch: RELEASE_3_22 git_last_commit: c86eeed git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/censcyt_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/censcyt_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/censcyt_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/censcyt_1.18.0.tgz vignettes: vignettes/censcyt/inst/doc/censored_covariate.html vignetteTitles: Censored covariate hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/censcyt/inst/doc/censored_covariate.R dependencyCount: 173 Package: Cepo Version: 1.16.0 Depends: GSEABase, R (>= 4.1) Imports: DelayedMatrixStats, DelayedArray, HDF5Array, S4Vectors, methods, SingleCellExperiment, SummarizedExperiment, ggplot2, rlang, grDevices, patchwork, reshape2, BiocParallel, stats, dplyr, purrr Suggests: knitr, rmarkdown, BiocStyle, testthat, covr, UpSetR, scater, scMerge, fgsea, escape, pheatmap License: MIT + file LICENSE MD5sum: 06c5ebaa3eab5204c77ebf598ca66759 NeedsCompilation: no Title: Cepo for the identification of differentially stable genes Description: Defining the identity of a cell is fundamental to understand the heterogeneity of cells to various environmental signals and perturbations. We present Cepo, a new method to explore cell identities from single-cell RNA-sequencing data using differential stability as a new metric to define cell identity genes. Cepo computes cell-type specific gene statistics pertaining to differential stable gene expression. biocViews: Classification, GeneExpression, SingleCell, Software, Sequencing, DifferentialExpression Author: Hani Jieun Kim [aut, cre] (ORCID: ), Kevin Wang [aut] (ORCID: ) Maintainer: Hani Jieun Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cepo git_branch: RELEASE_3_22 git_last_commit: 14015a6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Cepo_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Cepo_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Cepo_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Cepo_1.16.0.tgz vignettes: vignettes/Cepo/inst/doc/cepo.html vignetteTitles: Cepo method for differential stability analysis of scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Cepo/inst/doc/cepo.R importsMe: scClassify dependencyCount: 99 Package: ceRNAnetsim Version: 1.22.0 Depends: R (>= 4.0.0), dplyr, tidygraph Imports: furrr, rlang, tibble, ggplot2, ggraph, igraph, purrr, tidyr, future, stats Suggests: knitr, png, rmarkdown, testthat, covr License: GPL (>= 3.0) Archs: x64 MD5sum: fe52d5d4c8d6a667688ae30a2fc0c4fe NeedsCompilation: no Title: Regulation Simulator of Interaction between miRNA and Competing RNAs (ceRNA) Description: This package simulates regulations of ceRNA (Competing Endogenous) expression levels after a expression level change in one or more miRNA/mRNAs. The methodolgy adopted by the package has potential to incorparate any ceRNA (circRNA, lincRNA, etc.) into miRNA:target interaction network. The package basically distributes miRNA expression over available ceRNAs where each ceRNA attracks miRNAs proportional to its amount. But, the package can utilize multiple parameters that modify miRNA effect on its target (seed type, binding energy, binding location, etc.). The functions handle the given dataset as graph object and the processes progress via edge and node variables. biocViews: NetworkInference, SystemsBiology, Network, GraphAndNetwork, Transcriptomics Author: Selcen Ari Yuka [aut, cre] (ORCID: ), Alper Yilmaz [aut] (ORCID: ) Maintainer: Selcen Ari Yuka URL: https://github.com/selcenari/ceRNAnetsim VignetteBuilder: knitr BugReports: https://github.com/selcenari/ceRNAnetsim/issues git_url: https://git.bioconductor.org/packages/ceRNAnetsim git_branch: RELEASE_3_22 git_last_commit: 19914d6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ceRNAnetsim_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ceRNAnetsim_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ceRNAnetsim_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ceRNAnetsim_1.22.0.tgz vignettes: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.html, vignettes/ceRNAnetsim/inst/doc/basic_usage.html, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.html, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.html vignetteTitles: auxiliary_commands, basic_usage, A Suggestion: How to Find the Appropriate Iteration for Simulation, An TCGA dataset application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.R, vignettes/ceRNAnetsim/inst/doc/basic_usage.R, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.R, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.R dependencyCount: 65 Package: CeTF Version: 1.22.0 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, clusterProfiler, DESeq2, dplyr, GenomicTools.fileHandler, GGally, ggnetwork, ggplot2, ggpubr, ggrepel, graphics, grid, igraph, Matrix, network, Rcpp, RCy3, stats, SummarizedExperiment, S4Vectors, utils, methods LinkingTo: Rcpp, RcppArmadillo Suggests: airway, kableExtra, knitr, org.Hs.eg.db, rmarkdown, testthat License: GPL-3 MD5sum: 015a8eaf062528e3fa5ef7a664d68f1c NeedsCompilation: yes Title: Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis Description: This package provides the necessary functions for performing the Partial Correlation coefficient with Information Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network and can be applied to any correlation-based network including but not limited to gene co-expression networks, while the RIF algorithm identify critical Transcription Factors (TF) from gene expression data. These two algorithms when combined provide a very relevant layer of information for gene expression studies (Microarray, RNA-seq and single-cell RNA-seq data). biocViews: Sequencing, RNASeq, Microarray, GeneExpression, Transcription, Normalization, DifferentialExpression, SingleCell, Network, Regression, ChIPSeq, ImmunoOncology, Coverage Author: Carlos Alberto Oliveira de Biagi Junior [aut, cre], Ricardo Perecin Nociti [aut], Breno Osvaldo Funicheli [aut], João Paulo Bianchi Ximenez [ctb], Patrícia de Cássia Ruy [ctb], Marcelo Gomes de Paula [ctb], Rafael dos Santos Bezerra [ctb], Wilson Araújo da Silva Junior [aut, ths] Maintainer: Carlos Alberto Oliveira de Biagi Junior SystemRequirements: libcurl4-openssl-dev, libxml2-dev, libssl-dev, gfortran, build-essential, libz-dev, zlib1g-dev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CeTF git_branch: RELEASE_3_22 git_last_commit: 2408165 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CeTF_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CeTF_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CeTF_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CeTF_1.22.0.tgz vignettes: vignettes/CeTF/inst/doc/CeTF.html vignetteTitles: Analyzing Regulatory Impact Factors and Partial Correlation and Information Theory hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CeTF/inst/doc/CeTF.R dependencyCount: 221 Package: CexoR Version: 1.48.0 Depends: R (>= 4.2.0), S4Vectors, IRanges Imports: Rsamtools, Seqinfo, GenomicRanges, rtracklayer, idr, RColorBrewer, genomation Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | GPL-2 + file LICENSE MD5sum: 229fe290239ee989d314d0e2d8ac9085 NeedsCompilation: no Title: An R package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates Description: Strand specific peak-pair calling in ChIP-exo replicates. The cumulative Skellam distribution function is used to detect significant normalised count differences of opposed sign at each DNA strand (peak-pairs). Then, irreproducible discovery rate for overlapping peak-pairs across biological replicates is computed. biocViews: FunctionalGenomics, Sequencing, Coverage, ChIPSeq, PeakDetection Author: Pedro Madrigal [aut, cre] (ORCID: ) Maintainer: Pedro Madrigal URL: https://github.com/pmb59/CexoR BugReports: https://github.com/pmb59/CexoR/issues git_url: https://git.bioconductor.org/packages/CexoR git_branch: RELEASE_3_22 git_last_commit: 2ba0990 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CexoR_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CexoR_1.47.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CexoR_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CexoR_1.48.0.tgz vignettes: vignettes/CexoR/inst/doc/CexoR.pdf vignetteTitles: CexoR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CexoR/inst/doc/CexoR.R dependencyCount: 101 Package: CFAssay Version: 1.44.0 Depends: R (>= 2.10.0) License: LGPL Archs: x64 MD5sum: 791abd20cf3173959fb5ff5a13a86446 NeedsCompilation: no Title: Statistical analysis for the Colony Formation Assay Description: The package provides functions for calculation of linear-quadratic cell survival curves and for ANOVA of experimental 2-way designs along with the colony formation assay. biocViews: CellBasedAssays, CellBiology, ImmunoOncology, Regression, Survival Author: Herbert Braselmann Maintainer: Herbert Braselmann git_url: https://git.bioconductor.org/packages/CFAssay git_branch: RELEASE_3_22 git_last_commit: fcfe777 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CFAssay_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CFAssay_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CFAssay_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CFAssay_1.44.0.tgz vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf vignetteTitles: CFAssay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CFAssay/inst/doc/cfassay.R dependencyCount: 0 Package: cfdnakit Version: 1.8.0 Depends: R (>= 4.3) Imports: Biobase, dplyr, GenomicRanges, GenomeInfoDb, ggplot2, IRanges, magrittr, PSCBS, QDNAseq, Rsamtools, utils, S4Vectors, stats, rlang Suggests: rmarkdown, knitr, roxygen2, BiocStyle License: GPL-3 MD5sum: 091b6f181fdb6b7777a7bc71a0230e47 NeedsCompilation: no Title: Fragmen-length analysis package from high-throughput sequencing of cell-free DNA (cfDNA) Description: This package provides basic functions for analyzing shallow whole-genome sequencing (~0.3X or more) of cell-free DNA (cfDNA). The package basically extracts the length of cfDNA fragments and aids the vistualization of fragment-length information. The package also extract fragment-length information per non-overlapping fixed-sized bins and used it for calculating ctDNA estimation score (CES). biocViews: CopyNumberVariation, Sequencing, WholeGenome Author: Pitithat Puranachot [aut, cre] (ORCID: ) Maintainer: Pitithat Puranachot VignetteBuilder: knitr BugReports: https://github.com/Pitithat-pu/cfdnakit/issues git_url: https://git.bioconductor.org/packages/cfdnakit git_branch: RELEASE_3_22 git_last_commit: 7eac6e7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cfdnakit_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cfdnakit_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cfdnakit_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cfdnakit_1.8.0.tgz vignettes: vignettes/cfdnakit/inst/doc/cfdnakit-vignette.html vignetteTitles: cfdnakit vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cfdnakit/inst/doc/cfdnakit-vignette.R dependencyCount: 85 Package: cfDNAPro Version: 1.16.0 Depends: R (>= 4.1.0), magrittr (>= 1.5.0) Imports: tibble, GenomicAlignments, IRanges, plyranges, GenomeInfoDb, GenomicRanges, BiocGenerics, stats, utils, dplyr (>= 0.8.3), stringr (>= 1.4.0), quantmod (>= 0.4), ggplot2 (>= 3.2.1), Rsamtools (>= 2.4.0), rlang (>= 0.4.0), BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.NCBI.GRCh38 Suggests: scales, ggpubr, knitr (>= 1.23), rmarkdown (>= 1.14), devtools (>= 2.3.0), BiocStyle, testthat License: GPL-3 MD5sum: 8aabe47eeafab250c2298fb39c3d65d2 NeedsCompilation: no Title: cfDNAPro extracts and Visualises biological features from whole genome sequencing data of cell-free DNA Description: cfDNA fragments carry important features for building cancer sample classification ML models, such as fragment size, and fragment end motif etc. Analyzing and visualizing fragment size metrics, as well as other biological features in a curated, standardized, scalable, well-documented, and reproducible way might be time intensive. This package intends to resolve these problems and simplify the process. It offers two sets of functions for cfDNA feature characterization and visualization. biocViews: Visualization, Sequencing, WholeGenome Author: Haichao Wang [aut, cre], Hui Zhao [ctb], Elkie Chan [ctb], Christopher Smith [ctb], Tomer Kaplan [ctb], Florian Markowetz [ctb], Nitzan Rosenfeld [ctb] Maintainer: Haichao Wang URL: https://github.com/hw538/cfDNAPro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cfDNAPro git_branch: RELEASE_3_22 git_last_commit: 910ff77 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cfDNAPro_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cfDNAPro_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cfDNAPro_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cfDNAPro_1.16.0.tgz vignettes: vignettes/cfDNAPro/inst/doc/cfDNAPro.html vignetteTitles: cfDNAPro Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cfDNAPro/inst/doc/cfDNAPro.R dependencyCount: 92 Package: cfTools Version: 1.10.0 Imports: Rcpp, utils, GenomicRanges, basilisk, R.utils, stats, cfToolsData, grDevices, graphics LinkingTo: Rcpp, BH Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: file LICENSE MD5sum: 785aeedb18e11ed2e22cc626eb5af9db NeedsCompilation: yes Title: Informatics Tools for Cell-Free DNA Study Description: The cfTools R package provides methods for cell-free DNA (cfDNA) methylation data analysis to facilitate cfDNA-based studies. Given the methylation sequencing data of a cfDNA sample, for each cancer marker or tissue marker, we deconvolve the tumor-derived or tissue-specific reads from all reads falling in the marker region. Our read-based deconvolution algorithm exploits the pervasiveness of DNA methylation for signal enhancement, therefore can sensitively identify a trace amount of tumor-specific or tissue-specific cfDNA in plasma. cfTools provides functions for (1) cancer detection: sensitively detect tumor-derived cfDNA and estimate the tumor-derived cfDNA fraction (tumor burden); (2) tissue deconvolution: infer the tissue type composition and the cfDNA fraction of multiple tissue types for a plasma cfDNA sample. These functions can serve as foundations for more advanced cfDNA-based studies, including cancer diagnosis and disease monitoring. biocViews: Software, BiomedicalInformatics, Epigenetics, Sequencing, MethylSeq, DNAMethylation, DifferentialMethylation Author: Ran Hu [aut, cre] (ORCID: ), Mary Louisa Stackpole [aut] (ORCID: ), Shuo Li [aut] (ORCID: ), Xianghong Jasmine Zhou [aut] (ORCID: ), Wenyuan Li [aut] (ORCID: ) Maintainer: Ran Hu URL: https://github.com/jasminezhoulab/cfTools VignetteBuilder: knitr BugReports: https://github.com/jasminezhoulab/cfTools/issues git_url: https://git.bioconductor.org/packages/cfTools git_branch: RELEASE_3_22 git_last_commit: d9d259e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cfTools_1.10.0.tar.gz vignettes: vignettes/cfTools/inst/doc/cfTools-vignette.html vignetteTitles: cfTools-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cfTools/inst/doc/cfTools-vignette.R dependencyCount: 82 Package: CGEN Version: 3.46.0 Depends: R (>= 4.0), survival, mvtnorm Imports: stats, graphics, utils, grDevices Suggests: cluster License: GPL-2 + file LICENSE MD5sum: 9369ca067ed766863f6d355e84e78002 NeedsCompilation: yes Title: An R package for analysis of case-control studies in genetic epidemiology Description: This is a package for analysis of case-control data in genetic epidemiology. It provides a set of statistical methods for evaluating gene-environment (or gene-genes) interactions under multiplicative and additive risk models, with or without assuming gene-environment (or gene-gene) independence in the underlying population. biocViews: SNP, MultipleComparison, Clustering Author: Samsiddhi Bhattacharjee [aut], Nilanjan Chatterjee [aut], Summer Han [aut], Minsun Song [aut], William Wheeler [aut], Matthieu de Rochemonteix [aut], Nilotpal Sanyal [aut], Justin Lee [cre] Maintainer: Justin Lee git_url: https://git.bioconductor.org/packages/CGEN git_branch: RELEASE_3_22 git_last_commit: ed47109 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CGEN_3.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CGEN_3.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CGEN_3.46.0.tgz vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf, vignettes/CGEN/inst/doc/vignette.pdf vignetteTitles: CGEN Scan Vignette, CGEN Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CGEN/inst/doc/vignette_GxE.R, vignettes/CGEN/inst/doc/vignette.R dependencyCount: 11 Package: CGHbase Version: 1.70.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: a52ad364c2c47559aa8dbeded5e881ae NeedsCompilation: no Title: CGHbase: Base functions and classes for arrayCGH data analysis. Description: Contains functions and classes that are needed by arrayCGH packages. biocViews: Infrastructure, Microarray, CopyNumberVariation Author: Sjoerd Vosse, Mark van de Wiel Maintainer: Mark van de Wiel URL: https://github.com/tgac-vumc/CGHbase BugReports: https://github.com/tgac-vumc/CGHbase/issues git_url: https://git.bioconductor.org/packages/CGHbase git_branch: RELEASE_3_22 git_last_commit: 7351416 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CGHbase_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CGHbase_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CGHbase_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CGHbase_1.70.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak importsMe: CGHnormaliter, QDNAseq dependencyCount: 11 Package: CGHcall Version: 2.72.0 Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods, Biobase, CGHbase (>= 1.15.1), snowfall License: GPL (http://www.gnu.org/copyleft/gpl.html) Archs: x64 MD5sum: 30a7ddd1ae5d79c2a7e45c3128111b29 NeedsCompilation: no Title: Calling aberrations for array CGH tumor profiles. Description: Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. biocViews: Microarray,Preprocessing,Visualization Author: Mark van de Wiel, Sjoerd Vosse Maintainer: Mark van de Wiel git_url: https://git.bioconductor.org/packages/CGHcall git_branch: RELEASE_3_22 git_last_commit: 08e37a0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CGHcall_2.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CGHcall_2.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CGHcall_2.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CGHcall_2.72.0.tgz vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHcall/inst/doc/CGHcall.R dependsOnMe: CGHnormaliter, GeneBreak importsMe: CGHnormaliter, QDNAseq dependencyCount: 16 Package: cghMCR Version: 1.68.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 90e31a028e98883b33c2a8ec8fc9d980 NeedsCompilation: no Title: Find chromosome regions showing common gains/losses Description: This package provides functions to identify genomic regions of interests based on segmented copy number data from multiple samples. biocViews: Microarray, CopyNumberVariation Author: J. Zhang and B. Feng Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/cghMCR git_branch: RELEASE_3_22 git_last_commit: 5d9a32a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cghMCR_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cghMCR_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cghMCR_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cghMCR_1.68.0.tgz vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf vignetteTitles: cghMCR findMCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cghMCR/inst/doc/findMCR.R dependencyCount: 58 Package: CGHnormaliter Version: 1.64.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: a77995d4a97fc0f7ffbf1fb498338cee NeedsCompilation: no Title: Normalization of array CGH data with imbalanced aberrations. Description: Normalization and centralization of array comparative genomic hybridization (aCGH) data. The algorithm uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). biocViews: Microarray, Preprocessing Author: Bart P.P. van Houte, Thomas W. Binsl, Hannes Hettling Maintainer: Bart P.P. van Houte git_url: https://git.bioconductor.org/packages/CGHnormaliter git_branch: RELEASE_3_22 git_last_commit: 9bc2534 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CGHnormaliter_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CGHnormaliter_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CGHnormaliter_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CGHnormaliter_1.64.0.tgz vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf vignetteTitles: CGHnormaliter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.R dependencyCount: 17 Package: CGHregions Version: 1.68.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 41481a515ebc9dd5bee639f25170df61 NeedsCompilation: no Title: Dimension Reduction for Array CGH Data with Minimal Information Loss. Description: Dimension Reduction for Array CGH Data with Minimal Information Loss biocViews: Microarray, CopyNumberVariation, Visualization Author: Sjoerd Vosse & Mark van de Wiel Maintainer: Sjoerd Vosse git_url: https://git.bioconductor.org/packages/CGHregions git_branch: RELEASE_3_22 git_last_commit: 684ec3c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CGHregions_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CGHregions_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CGHregions_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CGHregions_1.68.0.tgz vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHregions/inst/doc/CGHregions.R suggestsMe: ADaCGH2 dependencyCount: 12 Package: ChAMP Version: 2.40.0 Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0),DMRcate, Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest, DT, RPMM Imports: prettydoc,Hmisc,globaltest,sva,illuminaio,rmarkdown,IlluminaHumanMethylation450kmanifest,IlluminaHumanMethylationEPICanno.ilm10b4.hg19, limma, DNAcopy, preprocessCore,impute, marray, wateRmelon, plyr,goseq,missMethyl,ggplot2, GenomicRanges,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly (>= 4.5.6),RColorBrewer,dendextend, matrixStats,combinat Suggests: knitr,rmarkdown License: GPL-3 MD5sum: 2e5089dfd6a36910c7261222e55b8287 NeedsCompilation: no Title: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC Description: The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber, DNAMethylation Author: Yuan Tian [cre,aut], Tiffany Morris [ctb], Lee Stirling [ctb], Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur Chakravarthy [ctb] Maintainer: Yuan Tian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChAMP git_branch: RELEASE_3_22 git_last_commit: fa068cf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChAMP_2.40.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChAMP_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChAMP_2.40.0.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.html vignetteTitles: ChAMP: The Chip Analysis Methylation Pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R suggestsMe: GeoTcgaData dependencyCount: 265 Package: ChemmineOB Version: 1.48.0 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp Suggests: ChemmineR, BiocStyle, knitr, knitrBootstrap, BiocManager, rmarkdown,RUnit,codetools Enhances: ChemmineR (>= 2.13.0) License: Artistic-2.0 MD5sum: 10a0e640f3e841e3d12ac9c75c55fa9d NeedsCompilation: yes Title: R interface to a subset of OpenBabel functionalities Description: ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineOB SystemRequirements: OpenBabel (>= 3.0.0) with headers (http://openbabel.org). Eigen3 with headers. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineOB git_branch: RELEASE_3_22 git_last_commit: 8110cf5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChemmineOB_1.48.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChemmineOB_1.48.0.tgz vignettes: vignettes/ChemmineOB/inst/doc/ChemmineOB.html vignetteTitles: ChemmineOB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R dependencyCount: 8 Package: ChemmineR Version: 3.62.0 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0), ggplot2,grid,gridExtra, png,base64enc,DT,rsvg,jsonlite,stringi LinkingTo: Rcpp, BH Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL, BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineDrugs, png,rmarkdown, BiocManager,bibtex,codetools Enhances: ChemmineOB License: Artistic-2.0 MD5sum: da6b2fbc405dd0942024076a14b22627 NeedsCompilation: yes Title: Cheminformatics Toolkit for R Description: ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics,Metabolomics Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineR SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineR git_branch: RELEASE_3_22 git_last_commit: 8667085 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChemmineR_3.62.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChemmineR_3.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChemmineR_3.62.0.tgz vignettes: vignettes/ChemmineR/inst/doc/ChemmineR.html vignetteTitles: ChemmineR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R dependsOnMe: eiR, fmcsR, ChemmineDrugs importsMe: bioassayR, CompoundDb, customCMPdb, eiR, fmcsR, MetID, RMassBank, chemodiv suggestsMe: ChemmineOB, xnet dependencyCount: 64 Package: CHETAH Version: 1.26.0 Depends: R (>= 4.2), ggplot2, SingleCellExperiment Imports: shiny, plotly, pheatmap, bioDist, dendextend, cowplot, corrplot, grDevices, stats, graphics, reshape2, S4Vectors, SummarizedExperiment Suggests: knitr, rmarkdown, Matrix, testthat, vdiffr License: file LICENSE MD5sum: 332ef94d78b97ff6b7cd46ff96068c6d NeedsCompilation: no Title: Fast and accurate scRNA-seq cell type identification Description: CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate, selective and fast scRNA-seq classifier. Classification is guided by a reference dataset, preferentially also a scRNA-seq dataset. By hierarchical clustering of the reference data, CHETAH creates a classification tree that enables a step-wise, top-to-bottom classification. Using a novel stopping rule, CHETAH classifies the input cells to the cell types of the references and to "intermediate types": more general classifications that ended in an intermediate node of the tree. biocViews: Classification, RNASeq, SingleCell, Clustering, GeneExpression, ImmunoOncology Author: Jurrian de Kanter [aut, cre], Philip Lijnzaad [aut] Maintainer: Jurrian de Kanter URL: https://github.com/jdekanter/CHETAH VignetteBuilder: knitr BugReports: https://github.com/jdekanter/CHETAH git_url: https://git.bioconductor.org/packages/CHETAH git_branch: RELEASE_3_22 git_last_commit: 00917fa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CHETAH_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CHETAH_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CHETAH_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CHETAH_1.26.0.tgz vignettes: vignettes/CHETAH/inst/doc/CHETAH_introduction.html vignetteTitles: Introduction to the CHETAH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CHETAH/inst/doc/CHETAH_introduction.R suggestsMe: adverSCarial dependencyCount: 105 Package: chevreulPlot Version: 1.2.0 Depends: R (>= 4.5.0), SingleCellExperiment, chevreulProcess Imports: base, cluster, clustree, ComplexHeatmap (>= 2.5.4), circlize, dplyr, EnsDb.Hsapiens.v86, forcats, fs, ggplot2, grid, plotly, purrr, S4Vectors, scales, scater, scran, scuttle, stats, stringr, tibble, tidyr, utils, wiggleplotr (>= 1.13.1), tidyselect, patchwork Suggests: BiocStyle, knitr, RefManageR, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 05b2609a3bf27696d30fc18724aaeafd NeedsCompilation: no Title: Plots used in the chevreulPlot package Description: Tools for plotting SingleCellExperiment objects in the chevreulPlot package. Includes functions for analysis and visualization of single-cell data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik. biocViews: Coverage, RNASeq, Sequencing, Visualization, GeneExpression, Transcription, SingleCell, Transcriptomics, Normalization, Preprocessing, QualityControl, DimensionReduction, DataImport Author: Kevin Stachelek [aut, cre] (ORCID: ), Bhavana Bhat [aut] Maintainer: Kevin Stachelek URL: https://github.com/whtns/chevreulPlot, https://whtns.github.io/chevreulPlot/ VignetteBuilder: knitr BugReports: https://github.com/cobriniklab/chevreulPlot/issues git_url: https://git.bioconductor.org/packages/chevreulPlot git_branch: RELEASE_3_22 git_last_commit: bc927ef git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/chevreulPlot_1.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chevreulPlot_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chevreulPlot_1.2.0.tgz vignettes: vignettes/chevreulPlot/inst/doc/chevreulPlot.html, vignettes/chevreulPlot/inst/doc/visualization.html vignetteTitles: Preprocessing, Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chevreulPlot/inst/doc/chevreulPlot.R, vignettes/chevreulPlot/inst/doc/visualization.R dependencyCount: 233 Package: chevreulProcess Version: 1.2.0 Depends: R (>= 4.5.0), SingleCellExperiment, scater Imports: batchelor, bluster, circlize, cluster, DBI, dplyr, EnsDb.Hsapiens.v86, ensembldb, fs, GenomicFeatures, glue, megadepth, methods, purrr, RSQLite, S4Vectors, scran, scuttle, stringr, tibble, tidyr, tidyselect, utils Suggests: BiocStyle, knitr, RefManageR, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 2a519549d5b8d2bac6966a83e998047f NeedsCompilation: no Title: Tools for managing SingleCellExperiment objects as projects Description: Tools for analyzing SingleCellExperiment objects as projects. for input into the chevreulShiny app downstream. Includes functions for analysis of single cell RNA sequencing data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik. biocViews: Coverage, RNASeq, Sequencing, Visualization, GeneExpression, Transcription, SingleCell, Transcriptomics, Normalization, Preprocessing, QualityControl, DimensionReduction, DataImport Author: Kevin Stachelek [aut, cre] (ORCID: ), Bhavana Bhat [aut] Maintainer: Kevin Stachelek URL: https://github.com/whtns/chevreulProcess, https://whtns.github.io/chevreulProcess/ VignetteBuilder: knitr BugReports: https://github.com/cobriniklab/chevreulProcess/issues git_url: https://git.bioconductor.org/packages/chevreulProcess git_branch: RELEASE_3_22 git_last_commit: a07dc13 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/chevreulProcess_1.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chevreulProcess_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chevreulProcess_1.2.0.tgz vignettes: vignettes/chevreulProcess/inst/doc/chevreulProcess.html vignetteTitles: Preprocessing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chevreulProcess/inst/doc/chevreulProcess.R dependsOnMe: chevreulPlot dependencyCount: 193 Package: Chicago Version: 1.38.0 Depends: R (>= 3.3.1), data.table Imports: matrixStats, MASS, Hmisc, Delaporte, methods, grDevices, graphics, stats, utils Suggests: argparser, BiocStyle, knitr, rmarkdown, PCHiCdata, testthat, GenomeInfoDb, Rsamtools, GenomicInteractions, GenomicRanges, IRanges, AnnotationHub License: Artistic-2.0 MD5sum: 2bdedfe7969e3633f93395c329a8e064 NeedsCompilation: no Title: CHiCAGO: Capture Hi-C Analysis of Genomic Organization Description: A pipeline for analysing Capture Hi-C data. biocViews: Epigenetics, HiC, Sequencing, Software Author: Jonathan Cairns, Paula Freire Pritchett, Steven Wingett, Mikhail Spivakov Maintainer: Mikhail Spivakov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Chicago git_branch: RELEASE_3_22 git_last_commit: dd51fe0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Chicago_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Chicago_1.37.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Chicago_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Chicago_1.38.0.tgz vignettes: vignettes/Chicago/inst/doc/Chicago.html vignetteTitles: CHiCAGO Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chicago/inst/doc/Chicago.R dependsOnMe: PCHiCdata dependencyCount: 65 Package: chihaya Version: 1.10.0 Depends: DelayedArray Imports: methods, Matrix, rhdf5, Rcpp, HDF5Array LinkingTo: Rcpp, Rhdf5lib Suggests: BiocGenerics, S4Vectors, BiocSingular, ResidualMatrix, BiocStyle, testthat, rmarkdown, knitr License: GPL-3 MD5sum: c514f17b2ed2c6544708206be95d14aa NeedsCompilation: yes Title: Save Delayed Operations to a HDF5 File Description: Saves the delayed operations of a DelayedArray to a HDF5 file. This enables efficient recovery of the DelayedArray's contents in other languages and analysis frameworks. biocViews: DataImport, DataRepresentation Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun URL: https://github.com/ArtifactDB/chihaya-R SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/ArtifactDB/chihaya-R/issues git_url: https://git.bioconductor.org/packages/chihaya git_branch: RELEASE_3_22 git_last_commit: 9c74cc8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/chihaya_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chihaya_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chihaya_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chihaya_1.10.0.tgz vignettes: vignettes/chihaya/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chihaya/inst/doc/userguide.R suggestsMe: alabaster.matrix dependencyCount: 27 Package: chimeraviz Version: 1.36.0 Depends: Biostrings, GenomicRanges, IRanges, Gviz, S4Vectors, ensembldb, AnnotationFilter, data.table Imports: methods, grid, Rsamtools, GenomeInfoDb, GenomicAlignments, RColorBrewer, graphics, AnnotationDbi, RCircos, org.Hs.eg.db, org.Mm.eg.db, rmarkdown, graph, Rgraphviz, DT, plyr, dplyr, BiocStyle, checkmate, gtools, magick Suggests: testthat, roxygen2, devtools, knitr, lintr License: Artistic-2.0 MD5sum: 4eb7427babacdca5a626bd3806d5cdae NeedsCompilation: no Title: Visualization tools for gene fusions Description: chimeraviz manages data from fusion gene finders and provides useful visualization tools. biocViews: Infrastructure, Alignment Author: Stian Lågstad [aut, cre], Sen Zhao [ctb], Andreas M. Hoff [ctb], Bjarne Johannessen [ctb], Ole Christian Lingjærde [ctb], Rolf Skotheim [ctb] Maintainer: Stian Lågstad URL: https://github.com/stianlagstad/chimeraviz SystemRequirements: bowtie, samtools, and egrep are required for some functionalities VignetteBuilder: knitr BugReports: https://github.com/stianlagstad/chimeraviz/issues git_url: https://git.bioconductor.org/packages/chimeraviz git_branch: RELEASE_3_22 git_last_commit: 36b8261 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/chimeraviz_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chimeraviz_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chimeraviz_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chimeraviz_1.36.0.tgz vignettes: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.html vignetteTitles: chimeraviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R dependencyCount: 168 Package: ChIPanalyser Version: 1.32.0 Depends: R (>= 3.5.0),GenomicRanges, Biostrings, BSgenome, RcppRoll, parallel Imports: methods, IRanges, S4Vectors,grDevices,graphics,stats,utils,rtracklayer,ROCR, BiocManager,GenomeInfoDb,RColorBrewer Suggests: BSgenome.Dmelanogaster.UCSC.dm6,knitr, RUnit, BiocGenerics License: GPL-3 Archs: x64 MD5sum: dd2937d88fa02a3c9ce6068445e009ee NeedsCompilation: no Title: ChIPanalyser: Predicting Transcription Factor Binding Sites Description: ChIPanalyser is a package to predict and understand TF binding by utilizing a statistical thermodynamic model. The model incorporates 4 main factors thought to drive TF binding: Chromatin State, Binding energy, Number of bound molecules and a scaling factor modulating TF binding affinity. Taken together, ChIPanalyser produces ChIP-like profiles that closely mimic the patterns seens in real ChIP-seq data. biocViews: Software, BiologicalQuestion, WorkflowStep, Transcription, Sequencing, ChipOnChip, Coverage, Alignment, ChIPSeq, SequenceMatching, DataImport ,PeakDetection Author: Patrick C.N.Martin & Nicolae Radu Zabet Maintainer: Patrick C.N. Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPanalyser git_branch: RELEASE_3_22 git_last_commit: 36e9429 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChIPanalyser_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPanalyser_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChIPanalyser_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChIPanalyser_1.32.0.tgz vignettes: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.pdf, vignettes/ChIPanalyser/inst/doc/GA_ChIPanalyser.pdf vignetteTitles: ChIPanalyser User's Guide, ChIPanalyser User's Guide for Genetic Algorithms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.R, vignettes/ChIPanalyser/inst/doc/GA_ChIPanalyser.R dependencyCount: 69 Package: ChIPComp Version: 1.40.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,rtracklayer,Seqinfo,S4Vectors Imports: Rsamtools,limma,BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9,BiocGenerics Suggests: BiocStyle,RUnit License: GPL MD5sum: fbf52e8eac68708209d3bc58cb183627 NeedsCompilation: yes Title: Quantitative comparison of multiple ChIP-seq datasets Description: ChIPComp detects differentially bound sharp binding sites across multiple conditions considering matching control. biocViews: ChIPSeq, Sequencing, Transcription, Genetics,Coverage, MultipleComparison, DataImport Author: Hao Wu, Li Chen, Zhaohui S.Qin, Chi Wang Maintainer: Li Chen git_url: https://git.bioconductor.org/packages/ChIPComp git_branch: RELEASE_3_22 git_last_commit: 0b95bdf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChIPComp_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPComp_1.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChIPComp_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChIPComp_1.40.0.tgz vignettes: vignettes/ChIPComp/inst/doc/ChIPComp.pdf vignetteTitles: ChIPComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPComp/inst/doc/ChIPComp.R dependencyCount: 62 Package: chipenrich Version: 2.34.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, BiocGenerics, chipenrich.data, Seqinfo, GenomicRanges, grDevices, grid, IRanges, lattice, latticeExtra, MASS, methods, mgcv, org.Dm.eg.db, org.Dr.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, parallel, plyr, rms, rtracklayer, S4Vectors (>= 0.23.10), stats, stringr, utils Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat License: GPL-3 MD5sum: 3226f0c4bb213452d275beee1a532df2 NeedsCompilation: no Title: Gene Set Enrichment For ChIP-seq Peak Data Description: ChIP-Enrich and Poly-Enrich perform gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes. biocViews: ImmunoOncology, ChIPSeq, Epigenetics, FunctionalGenomics, GeneSetEnrichment, HistoneModification, Regression Author: Ryan P. Welch [aut, cph], Chee Lee [aut], Raymond G. Cavalcante [aut], Kai Wang [cre], Chris Lee [aut], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Kai Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipenrich git_branch: RELEASE_3_22 git_last_commit: a610787 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/chipenrich_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chipenrich_2.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chipenrich_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chipenrich_2.34.0.tgz vignettes: vignettes/chipenrich/inst/doc/chipenrich-vignette.html vignetteTitles: chipenrich_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich-vignette.R dependencyCount: 158 Package: ChIPexoQual Version: 1.34.0 Depends: R (>= 3.5.0), GenomicAlignments (>= 1.45.1) Imports: methods, utils, Seqinfo, stats, BiocParallel, GenomicRanges (>= 1.61.1), ggplot2 (>= 1.0), data.table (>= 1.9.6), Rsamtools (>= 2.25.1), IRanges (>= 1.6), S4Vectors (>= 0.8), biovizBase (>= 1.18), broom (>= 0.4), RColorBrewer (>= 1.1), dplyr (>= 0.5), scales (>= 0.4.0), viridis (>= 0.3), hexbin (>= 1.27), rmarkdown Suggests: ChIPexoQualExample (>= 0.99.1), knitr (>= 1.10), BiocStyle, gridExtra (>= 2.2), testthat License: GPL (>=2) MD5sum: 699c34000358388dd7b06350a321aaad NeedsCompilation: no Title: ChIPexoQual Description: Package with a quality control pipeline for ChIP-exo/nexus data. biocViews: ChIPSeq, Sequencing, Transcription, Visualization, QualityControl, Coverage, Alignment Author: Rene Welch, Dongjun Chung, Sunduz Keles Maintainer: Rene Welch URL: https:github.com/keleslab/ChIPexoQual VignetteBuilder: knitr BugReports: https://github.com/welch16/ChIPexoQual/issues git_url: https://git.bioconductor.org/packages/ChIPexoQual git_branch: RELEASE_3_22 git_last_commit: fb0fa9b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChIPexoQual_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPexoQual_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChIPexoQual_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChIPexoQual_1.34.0.tgz vignettes: vignettes/ChIPexoQual/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPexoQual/inst/doc/vignette.R dependencyCount: 138 Package: ChIPpeakAnno Version: 3.44.0 Depends: R (>= 3.5), methods, IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), S4Vectors (>= 0.17.25) Imports: AnnotationDbi, BiocGenerics (>= 0.1.0), Biostrings (>= 2.47.6), pwalign, DBI, dplyr, GenomeInfoDb, GenomicAlignments, GenomicFeatures, RBGL, Rsamtools, SummarizedExperiment, VennDiagram, biomaRt, ggplot2, grDevices, graph, graphics, grid, InteractionSet, KEGGREST, matrixStats, multtest, regioneR, rtracklayer, stats, utils, universalmotif, stringr, tibble, tidyr, data.table, scales, ensembldb Suggests: AnnotationHub, BSgenome, limma, reactome.db, BiocManager, BiocStyle, BSgenome.Ecoli.NCBI.20080805, BSgenome.Hsapiens.UCSC.hg19, org.Ce.eg.db, org.Hs.eg.db, BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7, BSgenome.Hsapiens.UCSC.hg38, DelayedArray, idr, seqinr, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, GO.db, gplots, UpSetR, knitr, rmarkdown, reshape2, testthat, trackViewer, motifStack, OrganismDbi, BiocFileCache License: GPL (>= 2) MD5sum: 2aa83acac8b17458eac97db5641fd2cc NeedsCompilation: no Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments, or any experiments that result in large number of genomic interval data Description: The package encompasses a range of functions for identifying the closest gene, exon, miRNA, or custom features—such as highly conserved elements and user-supplied transcription factor binding sites. Additionally, users can retrieve sequences around the peaks and obtain enriched Gene Ontology (GO) or Pathway terms. In version 2.0.5 and beyond, new functionalities have been introduced. These include features for identifying peaks associated with bi-directional promoters along with summary statistics (peaksNearBDP), summarizing motif occurrences in peaks (summarizePatternInPeaks), and associating additional identifiers with annotated peaks or enrichedGO (addGeneIDs). The package integrates with various other packages such as biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest, and stat to enhance its analytical capabilities. biocViews: Annotation, ChIPSeq, ChIPchip Author: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Kai Hu, Haibo Liu, Junhui Li, Hervé Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe, Michael Green Maintainer: Jianhong Ou , Lihua Julie Zhu , Kai Hu , Junhui Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPpeakAnno git_branch: RELEASE_3_22 git_last_commit: 2b855fa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChIPpeakAnno_3.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPpeakAnno_3.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChIPpeakAnno_3.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChIPpeakAnno_3.44.0.tgz vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html vignetteTitles: ChIPpeakAnno: annotate,, visualize,, and compare peak data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R dependsOnMe: REDseq, csawBook importsMe: ATACseqQC, DEScan2, GUIDEseq suggestsMe: hicVennDiagram, R3CPET, seqsetvis, chipseqDB dependencyCount: 127 Package: ChIPseeker Version: 1.45.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, aplot, BiocGenerics, boot, dplyr, enrichplot, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, graphics, grDevices, gtools, magrittr, methods, plotrix, parallel, RColorBrewer, rlang, rtracklayer, S4Vectors, scales, stats, tibble, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, yulab.utils (>= 0.1.5) Suggests: clusterProfiler, ggimage, ggplotify, ggupset, ggVennDiagram, knitr, org.Hs.eg.db, prettydoc, ReactomePA, rmarkdown, testthat License: Artistic-2.0 Archs: x64 MD5sum: 5c45f94c6acdda2acd96daf0727e132a NeedsCompilation: no Title: ChIPseeker for ChIP peak Annotation, Comparison, and Visualization Description: This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. biocViews: Annotation, ChIPSeq, Software, Visualization, MultipleComparison Author: Guangchuang Yu [aut, cre] (ORCID: ), Ming Li [ctb], Qianwen Wang [ctb], Yun Yan [ctb], Hervé Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb], Zhougeng Xu [ctb], Chun-Hui Gao [ctb] (ORCID: ) Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ChIPseeker/issues git_url: https://git.bioconductor.org/packages/ChIPseeker git_branch: devel git_last_commit: 3fa2bfb git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/ChIPseeker_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPseeker_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChIPseeker_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChIPseeker_1.45.0.tgz vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.html vignetteTitles: ChIPseeker: an R package for ChIP peak Annotation,, Comparison and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R importsMe: EpiCompare, esATAC, segmenter, cinaR suggestsMe: GRaNIE, curatedAdipoChIP dependencyCount: 164 Package: chipseq Version: 1.60.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), ShortRead Imports: methods, stats, lattice, BiocGenerics, IRanges, GenomicRanges, ShortRead Suggests: BSgenome, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, BSgenome.Mmusculus.UCSC.mm9, BiocStyle, knitr License: Artistic-2.0 MD5sum: 625a8bf8d309bc86c16399d54ca2c3f3 NeedsCompilation: yes Title: chipseq: A package for analyzing chipseq data Description: Tools for helping process short read data for chipseq experiments. biocViews: ChIPSeq, Sequencing, Coverage, QualityControl, DataImport Author: Deepayan Sarkar [aut], Robert Gentleman [aut], Michael Lawrence [aut], Zizhen Yao [aut], Oluwabukola Bamigbade [ctb] (Converted vignette from Sweave to R Markdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipseq git_branch: RELEASE_3_22 git_last_commit: 1be0c9c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/chipseq_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chipseq_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chipseq_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chipseq_1.60.0.tgz vignettes: vignettes/chipseq/inst/doc/Workflow.html vignetteTitles: Some Basic Analysis of ChIP-Seq Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseq/inst/doc/Workflow.R importsMe: transcriptR dependencyCount: 54 Package: ChIPseqR Version: 1.64.0 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors (>= 0.9.25) Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.5.14), graphics, grDevices, HilbertVis, ShortRead, stats, timsac, utils License: GPL (>= 2) MD5sum: ce07f4d4e7dfa814fe43c594f0f96036 NeedsCompilation: yes Title: Identifying Protein Binding Sites in High-Throughput Sequencing Data Description: ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should flexible enough to handle other types of experiments as well. biocViews: ChIPSeq, Infrastructure Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPseqR git_branch: RELEASE_3_22 git_last_commit: 544d5eb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChIPseqR_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPseqR_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChIPseqR_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChIPseqR_1.64.0.tgz vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf vignetteTitles: Introduction to ChIPseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseqR/inst/doc/Introduction.R dependencyCount: 62 Package: ChIPsim Version: 1.64.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: fae807f7a6b8dd5b9363a4b2f8b0d612 NeedsCompilation: no Title: Simulation of ChIP-seq experiments Description: A general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments. biocViews: Infrastructure, ChIPSeq Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPsim git_branch: RELEASE_3_22 git_last_commit: 4288ced git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChIPsim_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPsim_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChIPsim_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChIPsim_1.64.0.tgz vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf vignetteTitles: Simulating ChIP-seq experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPsim/inst/doc/ChIPsimIntro.R dependencyCount: 54 Package: ChIPXpress Version: 1.54.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 18f8b9b6854fef0e1db02bd16b81888e NeedsCompilation: no Title: ChIPXpress: enhanced transcription factor target gene identification from ChIP-seq and ChIP-chip data using publicly available gene expression profiles Description: ChIPXpress takes as input predicted TF bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target. biocViews: ChIPchip, ChIPSeq Author: George Wu Maintainer: George Wu git_url: https://git.bioconductor.org/packages/ChIPXpress git_branch: RELEASE_3_22 git_last_commit: 3886eb0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChIPXpress_1.54.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChIPXpress_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChIPXpress_1.54.0.tgz vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf vignetteTitles: ChIPXpress hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPXpress/inst/doc/ChIPXpress.R dependencyCount: 105 Package: chopsticks Version: 1.76.0 Imports: graphics, stats, utils, methods, survival Suggests: hexbin License: GPL-3 MD5sum: b5b2ff314fbe0bcdc2c9d546d2e7db85 NeedsCompilation: yes Title: The 'snp.matrix' and 'X.snp.matrix' Classes Description: Implements classes and methods for large-scale SNP association studies biocViews: Microarray, SNPsAndGeneticVariability, SNP, GeneticVariability Author: Hin-Tak Leung Maintainer: Hin-Tak Leung URL: http://outmodedbonsai.sourceforge.net/ git_url: https://git.bioconductor.org/packages/chopsticks git_branch: RELEASE_3_22 git_last_commit: 0c26346 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/chopsticks_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chopsticks_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chopsticks_1.76.0.tgz vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf vignetteTitles: snpMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chopsticks/inst/doc/chopsticks-vignette.R dependencyCount: 10 Package: Chromatograms Version: 1.0.0 Depends: BiocParallel, ProtGenerics (>= 1.39.2), R (>= 4.5.0) Imports: methods, S4Vectors, MsCoreUtils (>= 1.7.5), Spectra Suggests: msdata (>= 0.19.3), roxygen2, BiocStyle (>= 2.5.19), testthat, knitr (>= 1.1.0), rmarkdown, mzR (>= 2.41.4), MsBackendMetaboLights (>= 1.3.1), vdiffr, RColorBrewer License: Artistic-2.0 MD5sum: 10357f83c4f6f3c3429bae0f5476b17d NeedsCompilation: no Title: Infrastructure for Chromatographic Mass Spectrometry Data Description: The Chromatograms packages defines an efficient infrastructure for storing and handling of chromatographic mass spectrometry data. It provides different implementations of *backends* to store and represent the data. Such backends can be optimized for small memory footprint or fast data access/processing. A lazy evaluation queue and chunk-wise processing capabilities ensure efficient analysis of also very large data sets. biocViews: Infrastructure, Metabolomics, MassSpectrometry, Proteomics Author: Johannes Rainer [aut] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Philippine Louail [aut, cre] (ORCID: , fnd: European Union HORIZON-MSCA-2021 project Grant No. 101073062: HUMAN – Harmonising and Unifying Blood Metabolic Analysis Networks) Maintainer: Philippine Louail URL: https://github.com/RforMassSpectrometry/Chromatograms VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/Chromatograms/issues git_url: https://git.bioconductor.org/packages/Chromatograms git_branch: RELEASE_3_22 git_last_commit: 74743d6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Chromatograms_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Chromatograms_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Chromatograms_1.0.0.tgz vignettes: vignettes/Chromatograms/inst/doc/creating-backend-classes.html, vignettes/Chromatograms/inst/doc/using-a-chromatograms-object.html vignetteTitles: Creating new `ChromBackend` class for Chromatograms, Using and understanding a Chromatograms object hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chromatograms/inst/doc/creating-backend-classes.R, vignettes/Chromatograms/inst/doc/using-a-chromatograms-object.R dependencyCount: 30 Package: chromDraw Version: 2.40.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 MD5sum: d39eaea80777ab4ba7f2d6e03b36a9b0 NeedsCompilation: yes Title: chromDraw is a R package for drawing the schemes of karyotypes in the linear and circular fashion. Description: ChromDraw is a R package for drawing the schemes of karyotype(s) in the linear and circular fashion. It is possible to visualized cytogenetic marsk on the chromosomes. This tool has own input data format. Input data can be imported from the GenomicRanges data structure. This package can visualized the data in the BED file format. Here is requirement on to the first nine fields of the BED format. Output files format are *.eps and *.svg. biocViews: Software Author: Jan Janecka, Ing., Mgr. CEITEC Masaryk University Maintainer: Jan Janecka URL: www.plantcytogenomics.org/chromDraw SystemRequirements: Rtools (>= 3.1) git_url: https://git.bioconductor.org/packages/chromDraw git_branch: RELEASE_3_22 git_last_commit: 424540b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/chromDraw_2.40.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chromDraw_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chromDraw_2.40.0.tgz vignettes: vignettes/chromDraw/inst/doc/chromDraw.pdf vignetteTitles: chromDraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromDraw/inst/doc/chromDraw.R dependencyCount: 12 Package: ChromHeatMap Version: 1.64.0 Depends: R (>= 2.9.0), BiocGenerics (>= 0.3.2), annotate (>= 1.20.0), AnnotationDbi (>= 1.4.0) Imports: Biobase (>= 2.17.8), graphics, grDevices, methods, stats, IRanges, rtracklayer, GenomicRanges Suggests: ALL, hgu95av2.db License: Artistic-2.0 MD5sum: 8b180ab0fe5feda638017ed886885926 NeedsCompilation: no Title: Heat map plotting by genome coordinate Description: The ChromHeatMap package can be used to plot genome-wide data (e.g. expression, CGH, SNP) along each strand of a given chromosome as a heat map. The generated heat map can be used to interactively identify probes and genes of interest. biocViews: Visualization Author: Tim F. Rayner Maintainer: Tim F. Rayner git_url: https://git.bioconductor.org/packages/ChromHeatMap git_branch: RELEASE_3_22 git_last_commit: 3e0a547 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChromHeatMap_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChromHeatMap_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChromHeatMap_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChromHeatMap_1.64.0.tgz vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf vignetteTitles: Plotting expression data with ChromHeatMap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.R dependencyCount: 77 Package: chromPlot Version: 1.38.0 Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt, GenomicRanges, R (>= 3.1.0) Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 24e3cd420b2641d08940a67356f6a1d1 NeedsCompilation: no Title: Global visualization tool of genomic data Description: Package designed to visualize genomic data along the chromosomes, where the vertical chromosomes are sorted by number, with sex chromosomes at the end. biocViews: DataRepresentation, FunctionalGenomics, Genetics, Sequencing, Annotation, Visualization Author: Ricardo A. Verdugo and Karen Y. Orostica Maintainer: Karen Y. Orostica git_url: https://git.bioconductor.org/packages/chromPlot git_branch: RELEASE_3_22 git_last_commit: 25372fc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/chromPlot_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chromPlot_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chromPlot_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chromPlot_1.38.0.tgz vignettes: vignettes/chromPlot/inst/doc/chromPlot.pdf vignetteTitles: General Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromPlot/inst/doc/chromPlot.R dependencyCount: 67 Package: ChromSCape Version: 1.20.0 Depends: R (>= 4.1) Imports: shiny, colourpicker, shinyjs, rtracklayer, shinyFiles, shinyhelper, shinyWidgets, shinydashboardPlus, shinycssloaders, Matrix, plotly, shinydashboard, colorRamps, kableExtra, viridis, batchelor, BiocParallel, parallel, Rsamtools, ggplot2, ggrepel, gggenes, gridExtra, qualV, stringdist, stringr, fs, qs, DT, scran, scater, ConsensusClusterPlus, Rtsne, dplyr, tidyr, GenomicRanges, IRanges, irlba, rlist, umap, tibble, methods, jsonlite, edgeR, stats, graphics, grDevices, utils, S4Vectors, SingleCellExperiment, SummarizedExperiment, msigdbr, forcats, Rcpp, coop, matrixTests, DelayedArray LinkingTo: Rcpp Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, Signac, future, igraph, bluster, httr License: GPL-3 MD5sum: e044a44ec564c93a0c22a7e88c301dba NeedsCompilation: yes Title: Analysis of single-cell epigenomics datasets with a Shiny App Description: ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis. biocViews: ShinyApps, Software, SingleCell, ChIPSeq, ATACSeq, MethylSeq, Classification, Clustering, Epigenetics, PrincipalComponent, SingleCell, ATACSeq, ChIPSeq, Annotation, BatchEffect, MultipleComparison, Normalization, Pathways, Preprocessing, QualityControl, ReportWriting, Visualization, GeneSetEnrichment, DifferentialPeakCalling Author: Pacome Prompsy [aut, cre] (ORCID: ), Celine Vallot [aut] (ORCID: ) Maintainer: Pacome Prompsy URL: https://github.com/vallotlab/ChromSCape VignetteBuilder: knitr BugReports: https://github.com/vallotlab/ChromSCape/issues git_url: https://git.bioconductor.org/packages/ChromSCape git_branch: RELEASE_3_22 git_last_commit: a5a7bda git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ChromSCape_1.20.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChromSCape_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChromSCape_1.20.0.tgz vignettes: vignettes/ChromSCape/inst/doc/vignette.html vignetteTitles: ChromSCape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromSCape/inst/doc/vignette.R dependencyCount: 206 Package: chromVAR Version: 1.32.0 Depends: R (>= 3.5.0) Imports: IRanges, Seqinfo, GenomicRanges, ggplot2, nabor, BiocParallel, BiocGenerics, Biostrings, TFBSTools, Rsamtools, S4Vectors, methods, Rcpp, grid, plotly, shiny, miniUI, stats, utils, graphics, DT, Rtsne, Matrix, SummarizedExperiment, RColorBrewer, BSgenome LinkingTo: Rcpp, RcppArmadillo Suggests: JASPAR2016, BSgenome.Hsapiens.UCSC.hg19, readr, testthat, knitr, rmarkdown, pheatmap, motifmatchr License: MIT + file LICENSE MD5sum: 238f7d11810853bc96646586689aeb6e NeedsCompilation: yes Title: Chromatin Variation Across Regions Description: Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. biocViews: SingleCell, Sequencing, GeneRegulation, ImmunoOncology Author: Alicia Schep [aut, cre], Jason Buenrostro [ctb], Caleb Lareau [ctb], William Greenleaf [ths], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chromVAR git_branch: RELEASE_3_22 git_last_commit: c67de98 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/chromVAR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chromVAR_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chromVAR_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chromVAR_1.32.0.tgz vignettes: vignettes/chromVAR/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromVAR/inst/doc/Introduction.R suggestsMe: Signac dependencyCount: 135 Package: CHRONOS Version: 1.38.0 Depends: R (>= 3.5) Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx, igraph, circlize, graph, stats, utils, grDevices, graphics, methods, biomaRt, rJava Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL-2 MD5sum: 3031a435c6af0cc4e9bc53dacf619623 NeedsCompilation: no Title: CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis Description: A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG Author: Aristidis G. Vrahatis, Konstantina Dimitrakopoulou, Panos Balomenos Maintainer: Panos Balomenos SystemRequirements: Java version >= 1.7, Pandoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CHRONOS git_branch: RELEASE_3_22 git_last_commit: 6ae799a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CHRONOS_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CHRONOS_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CHRONOS_1.38.0.tgz vignettes: vignettes/CHRONOS/inst/doc/CHRONOS.pdf vignetteTitles: CHRONOS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CHRONOS/inst/doc/CHRONOS.R dependencyCount: 87 Package: cicero Version: 1.28.0 Depends: R (>= 3.5.0), monocle, Gviz (>= 1.22.3) Imports: assertthat (>= 0.2.0), Biobase (>= 2.37.2), BiocGenerics (>= 0.23.0), data.table (>= 1.10.4), dplyr (>= 0.7.4), FNN (>= 1.1), GenomicRanges (>= 1.30.3), ggplot2 (>= 2.2.1), glasso (>= 1.8), grDevices, igraph (>= 1.1.0), IRanges (>= 2.10.5), Matrix (>= 1.2-12), methods, parallel, plyr (>= 1.8.4), reshape2 (>= 1.4.3), S4Vectors (>= 0.14.7), stats, stringi, stringr (>= 1.2.0), tibble (>= 1.4.2), tidyr, VGAM (>= 1.0-5), utils Suggests: AnnotationDbi (>= 1.38.2), knitr, markdown, rmarkdown, rtracklayer (>= 1.36.6), testthat, vdiffr (>= 0.2.3), covr License: MIT + file LICENSE Archs: x64 MD5sum: f9f96de92cb9b2e0cb928202425bc5c9 NeedsCompilation: no Title: Predict cis-co-accessibility from single-cell chromatin accessibility data Description: Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data. biocViews: Sequencing, Clustering, CellBasedAssays, ImmunoOncology, GeneRegulation, GeneTarget, Epigenetics, ATACSeq, SingleCell Author: Hannah Pliner [aut, cre], Cole Trapnell [aut] Maintainer: Hannah Pliner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cicero git_branch: RELEASE_3_22 git_last_commit: bcae37f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cicero_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cicero_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cicero_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cicero_1.28.0.tgz vignettes: vignettes/cicero/inst/doc/website.html vignetteTitles: Vignette from Cicero Website hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cicero/inst/doc/website.R importsMe: scPOEM dependencyCount: 180 Package: cigarillo Version: 1.0.0 Depends: methods, BiocGenerics, S4Vectors (>= 0.47.2), IRanges, Biostrings Imports: stats LinkingTo: S4Vectors, IRanges Suggests: Rsamtools, GenomicAlignments, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: bde1a74d1ed5bd8a2c0defed30cd1f42 NeedsCompilation: yes Title: Efficient manipulation of CIGAR strings Description: CIGAR stands for Concise Idiosyncratic Gapped Alignment Report. CIGAR strings are found in the BAM files produced by most aligners and in the AIRR-formatted output produced by IgBLAST. The cigarillo package provides functions to parse and inspect CIGAR strings, trim them, turn them into ranges of positions relative to the "query space" or "reference space", and project positions or sequences from one space to the other. Note that these operations are low-level operations that the user rarely needs to perform directly. More typically, they are performed behind the scene by higher-level functionality implemented in other packages like Bioconductor packages GenomicAlignments and igblastr. biocViews: Infrastructure, Alignment, SequenceMatching, Sequencing Author: Hervé Pagès [aut, cre] (ORCID: ), Valerie Obenchain [aut], Michael Lawrence [aut], Patrick Aboyoun [ctb], Fedor Bezrukov [ctb], Martin Morgan [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/cigarillo VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/cigarillo/issues git_url: https://git.bioconductor.org/packages/cigarillo git_branch: RELEASE_3_22 git_last_commit: 8775adf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cigarillo_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cigarillo_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cigarillo_1.0.0.tgz vignettes: vignettes/cigarillo/inst/doc/cigarillo.html vignetteTitles: The cigarillo package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cigarillo/inst/doc/cigarillo.R importsMe: GenomicAlignments dependencyCount: 15 Package: CIMICE Version: 1.18.0 Imports: dplyr, ggplot2, glue, tidyr, igraph, networkD3, visNetwork, ggcorrplot, purrr, ggraph, stats, utils, maftools, assertthat, tidygraph, expm, Matrix Suggests: BiocStyle, knitr, rmarkdown, testthat, webshot License: Artistic-2.0 MD5sum: 989b5afdba7cd5c49c8f22f60f96a0df NeedsCompilation: no Title: CIMICE-R: (Markov) Chain Method to Inferr Cancer Evolution Description: CIMICE is a tool in the field of tumor phylogenetics and its goal is to build a Markov Chain (called Cancer Progression Markov Chain, CPMC) in order to model tumor subtypes evolution. The input of CIMICE is a Mutational Matrix, so a boolean matrix representing altered genes in a collection of samples. These samples are assumed to be obtained with single-cell DNA analysis techniques and the tool is specifically written to use the peculiarities of this data for the CMPC construction. biocViews: Software, BiologicalQuestion, NetworkInference, ResearchField, Phylogenetics, StatisticalMethod, GraphAndNetwork, Technology, SingleCell Author: Nicolò Rossi [aut, cre] (Lab. of Computational Biology and Bioinformatics, Department of Mathematics, Computer Science and Physics, University of Udine, ORCID: ) Maintainer: Nicolò Rossi URL: https://github.com/redsnic/CIMICE VignetteBuilder: knitr BugReports: https://github.com/redsnic/CIMICE/issues git_url: https://git.bioconductor.org/packages/CIMICE git_branch: RELEASE_3_22 git_last_commit: 4b7030e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CIMICE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CIMICE_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CIMICE_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CIMICE_1.18.0.tgz vignettes: vignettes/CIMICE/inst/doc/CIMICE_SHORT.html, vignettes/CIMICE/inst/doc/CIMICER.html vignetteTitles: Quick guide, CIMICE-R: (Markov) Chain Method to Infer Cancer Evolution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CIMICE/inst/doc/CIMICE_SHORT.R, vignettes/CIMICE/inst/doc/CIMICER.R dependencyCount: 89 Package: circRNAprofiler Version: 1.24.0 Depends: R(>= 4.5.0) Imports: dplyr, magrittr, readr, rtracklayer, stringr, stringi, DESeq2, edgeR, GenomicRanges, IRanges, seqinr, R.utils, reshape2, ggplot2, utils, rlang, S4Vectors, stats, GenomeInfoDb, universalmotif, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, Biostrings, gwascat, BSgenome, Suggests: testthat, knitr, roxygen2, rmarkdown, devtools, gridExtra, ggpubr, VennDiagram, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BiocManager, License: GPL-3 MD5sum: e84f2dc9addbab024a470f0e6ff6f733 NeedsCompilation: no Title: circRNAprofiler: An R-Based Computational Framework for the Downstream Analysis of Circular RNAs Description: R-based computational framework for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis. biocViews: Annotation, StructuralPrediction, FunctionalPrediction, GenePrediction, GenomeAssembly, DifferentialExpression Author: Simona Aufiero Maintainer: Simona Aufiero URL: https://github.com/Aufiero/circRNAprofiler VignetteBuilder: knitr BugReports: https://github.com/Aufiero/circRNAprofiler/issues git_url: https://git.bioconductor.org/packages/circRNAprofiler git_branch: RELEASE_3_22 git_last_commit: 8f3c453 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/circRNAprofiler_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/circRNAprofiler_1.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/circRNAprofiler_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/circRNAprofiler_1.24.0.tgz vignettes: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.html vignetteTitles: circRNAprofiler hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.R dependencyCount: 143 Package: CircSeqAlignTk Version: 1.12.0 Depends: R (>= 4.2) Imports: stats, tools, utils, R.utils, methods, S4Vectors, rlang, magrittr, dplyr, tidyr, ggplot2, BiocGenerics, Biostrings, IRanges, ShortRead, Rsamtools, Rbowtie2, Rhisat2, shiny, shinyFiles, shinyjs, plotly, parallel, htmltools Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: a9437ae113da6043ca8069d06eb3a577 NeedsCompilation: no Title: End-to-End Analysis of Small RNA-Seq Data from Viroids Description: CircSeqAlignTk is a toolkit for the analysis of RNA-Seq data derived from circular genome sequences, with a primary focus on viroids, circular RNAs typically consisting of a few hundred nucleotides. The toolkit supports an end-to-end analysis pipeline, from alignment to visualization. biocViews: Sequencing, SmallRNA, Alignment, Software Author: Jianqiang Sun [cre, aut] (ORCID: ), Xi Fu [ctb], Wei Cao [ctb] Maintainer: Jianqiang Sun URL: https://github.com/bitdessin/CircSeqAlignTk VignetteBuilder: knitr BugReports: https://github.com/bitdessin/CircSeqAlignTk/issues git_url: https://git.bioconductor.org/packages/CircSeqAlignTk git_branch: RELEASE_3_22 git_last_commit: d5a1377 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CircSeqAlignTk_1.12.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CircSeqAlignTk_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CircSeqAlignTk_1.12.0.tgz vignettes: vignettes/CircSeqAlignTk/inst/doc/CircSeqAlignTk.html vignetteTitles: Documentation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CircSeqAlignTk/inst/doc/CircSeqAlignTk.R dependencyCount: 156 Package: CiteFuse Version: 1.22.0 Depends: R (>= 4.0) Imports: SingleCellExperiment (>= 1.8.0), SummarizedExperiment (>= 1.16.0), Matrix, mixtools, cowplot, ggplot2, gridExtra, grid, dbscan, uwot, Rtsne, S4Vectors (>= 0.24.0), igraph, scales, scran (>= 1.14.6), graphics, methods, stats, utils, reshape2, ggridges, randomForest, pheatmap, ggraph, grDevices, rhdf5, rlang, Rcpp, compositions LinkingTo: Rcpp Suggests: knitr, rmarkdown, DT, mclust, scater, ExPosition, BiocStyle, pkgdown License: GPL-3 MD5sum: 9a3e0f0cf51d1e0eba3bcc85cdcc7148 NeedsCompilation: yes Title: CiteFuse: multi-modal analysis of CITE-seq data Description: CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses. biocViews: SingleCell, GeneExpression Author: Yingxin Lin [aut, cre], Hani Kim [aut] Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/CiteFuse/issues git_url: https://git.bioconductor.org/packages/CiteFuse git_branch: RELEASE_3_22 git_last_commit: 7efd8aa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CiteFuse_1.22.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CiteFuse_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CiteFuse_1.22.0.tgz vignettes: vignettes/CiteFuse/inst/doc/CiteFuse.html vignetteTitles: CiteFuse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CiteFuse/inst/doc/CiteFuse.R suggestsMe: MuData dependencyCount: 157 Package: ClassifyR Version: 3.14.0 Depends: R (>= 4.1.0), generics, methods, S4Vectors, MultiAssayExperiment, BiocParallel, survival Imports: grid, genefilter, utils, dplyr, tidyr, rlang, ranger, ggplot2 (>= 3.5.0), ggpubr, reshape2, ggupset, broom, dcanr Suggests: limma, edgeR, car, Rmixmod, gridExtra (>= 2.0.0), cowplot, BiocStyle, pamr, PoiClaClu, knitr, htmltools, gtable, scales, e1071, rmarkdown, IRanges, robustbase, glmnet, class, randomForestSRC, MatrixModels, xgboost, data.tree, ggnewscale, TOP, BiocNeighbors License: GPL-3 Archs: x64 MD5sum: 3eb933f3595f4b4eabdbe575e0d264a6 NeedsCompilation: yes Title: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing Description: The software formalises a framework for classification and survival model evaluation in R. There are four stages; Data transformation, feature selection, model training, and prediction. The requirements of variable types and variable order are fixed, but specialised variables for functions can also be provided. The framework is wrapped in a driver loop that reproducibly carries out a number of cross-validation schemes. Functions for differential mean, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework. biocViews: Classification, Survival Author: Dario Strbenac [aut, cre], Ellis Patrick [aut], Sourish Iyengar [aut], Harry Robertson [aut], Andy Tran [aut], John Ormerod [aut], Graham Mann [aut], Jean Yang [aut] Maintainer: Dario Strbenac URL: https://sydneybiox.github.io/ClassifyR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClassifyR git_branch: RELEASE_3_22 git_last_commit: ff15d84 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ClassifyR_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClassifyR_3.13.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClassifyR_3.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClassifyR_3.14.0.tgz vignettes: vignettes/ClassifyR/inst/doc/ClassifyR.html, vignettes/ClassifyR/inst/doc/DevelopersGuide.html vignetteTitles: An Introduction to the ClassifyR Package, Developer's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R, vignettes/ClassifyR/inst/doc/DevelopersGuide.R importsMe: spicyR, TOP suggestsMe: Statial, spicyWorkflow dependencyCount: 142 Package: cleanUpdTSeq Version: 1.48.0 Depends: R (>= 3.5.0), BSgenome.Drerio.UCSC.danRer7, methods Imports: BSgenome, GenomicRanges, seqinr, e1071, Biostrings, Seqinfo, IRanges, utils, stringr, stats, S4Vectors Suggests: BiocStyle, rmarkdown, knitr, RUnit, BiocGenerics (>= 0.1.0) License: GPL-2 MD5sum: f696476effe4d8bc6c414eeb1c58211f NeedsCompilation: no Title: cleanUpdTSeq cleans up artifacts from polyadenylation sites from oligo(dT)-mediated 3' end RNA sequending data Description: This package implements a Naive Bayes classifier for accurately differentiating true polyadenylation sites (pA sites) from oligo(dT)-mediated 3' end sequencing such as PAS-Seq, PolyA-Seq and RNA-Seq by filtering out false polyadenylation sites, mainly due to oligo(dT)-mediated internal priming during reverse transcription. The classifer is highly accurate and outperforms other heuristic methods. biocViews: Sequencing, 3' end sequencing, polyadenylation site, internal priming Author: Sarah Sheppard, Haibo Liu, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou ; Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cleanUpdTSeq git_branch: RELEASE_3_22 git_last_commit: 4565da2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cleanUpdTSeq_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cleanUpdTSeq_1.47.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cleanUpdTSeq_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cleanUpdTSeq_1.48.0.tgz vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html vignetteTitles: cleanUpdTSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R dependencyCount: 79 Package: CleanUpRNAseq Version: 1.4.0 Depends: R (>= 4.4.0) Imports: AnnotationFilter, BiocGenerics, Biostrings, BSgenome, DESeq2, edgeR, ensembldb, Seqinfo, GenomicRanges, ggplot2, ggrepel, graphics, grDevices, KernSmooth, limma, methods, pheatmap, qsmooth, R6, RColorBrewer, Rsamtools, Rsubread, reshape2, SummarizedExperiment, stats, tximport, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86, ggplotify, knitr, patchwork, R.utils, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: b1e5c02f23482f75660f3e732db1a493 NeedsCompilation: no Title: Detect and Correct Genomic DNA Contamination in RNA-seq Data Description: RNA-seq data generated by some library preparation methods, such as rRNA-depletion-based method and the SMART-seq method, might be contaminated by genomic DNA (gDNA), if DNase I disgestion is not performed properly during RNA preparation. CleanUpRNAseq is developed to check if RNA-seq data is suffered from gDNA contamination. If so, it can perform correction for gDNA contamination and reduce false discovery rate of differentially expressed genes. biocViews: QualityControl, Sequencing, GeneExpression Author: Haibo Liu [aut, cre] (ORCID: ), Kevin O'Connor [ctb], Michelle Kelliher [ctb], Lihua Julie Zhu [aut], Kai Hu [aut] Maintainer: Haibo Liu VignetteBuilder: knitr BugReports: https://github.com/haibol2016/CleanUpRNAseq/issues git_url: https://git.bioconductor.org/packages/CleanUpRNAseq git_branch: RELEASE_3_22 git_last_commit: 3ccdf23 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CleanUpRNAseq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CleanUpRNAseq_1.3.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CleanUpRNAseq_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CleanUpRNAseq_1.4.0.tgz vignettes: vignettes/CleanUpRNAseq/inst/doc/CleanUpRNAseq.html vignetteTitles: CleanUpRNAseq: detecting and correcting for DNA contamination\nin RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CleanUpRNAseq/inst/doc/CleanUpRNAseq.R dependencyCount: 148 Package: cleaver Version: 1.48.0 Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8) Imports: S4Vectors, IRanges Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), rmarkdown, BRAIN, UniProt.ws (>= 2.36.5) License: GPL (>= 3) MD5sum: 96258c0c1f8e943fb62ad78ff80c46ee NeedsCompilation: no Title: Cleavage of Polypeptide Sequences Description: In-silico cleavage of polypeptide sequences. The cleavage rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html biocViews: Proteomics Author: Sebastian Gibb [aut, cre] (ORCID: ) Maintainer: Sebastian Gibb URL: https://codeberg.org/sgibb/cleaver/ VignetteBuilder: knitr BugReports: https://codeberg.org/sgibb/cleaver/issues/ git_url: https://git.bioconductor.org/packages/cleaver git_branch: RELEASE_3_22 git_last_commit: 2f4b3fc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cleaver_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cleaver_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cleaver_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cleaver_1.48.0.tgz vignettes: vignettes/cleaver/inst/doc/cleaver.html vignetteTitles: In-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleaver/inst/doc/cleaver.R importsMe: ProteoDisco suggestsMe: RforProteomics dependencyCount: 15 Package: clevRvis Version: 1.10.0 Imports: shiny, ggraph, igraph, ggiraph, cowplot, htmlwidgets, readxl, dplyr, readr, purrr, tibble, patchwork, R.utils, shinyWidgets, colorspace, shinyhelper, shinycssloaders, ggnewscale, shinydashboard, DT, colourpicker, grDevices, methods, utils, stats, ggplot2, magrittr, tools Suggests: knitr, rmarkdown, BiocStyle License: LGPL-3 MD5sum: 9d3bab94b6ad7749c1edb228aadfc8f4 NeedsCompilation: no Title: Visualization Techniques for Clonal Evolution Description: clevRvis provides a set of visualization techniques for clonal evolution. These include shark plots, dolphin plots and plaice plots. Algorithms for time point interpolation as well as therapy effect estimation are provided. Phylogeny-aware color coding is implemented. A shiny-app for generating plots interactively is additionally provided. biocViews: Software, ShinyApps, Visualization Author: Sarah Sandmann [aut, cre] (ORCID: ) Maintainer: Sarah Sandmann URL: https://github.com/sandmanns/clevRvis VignetteBuilder: knitr BugReports: https://github.com/sandmanns/clevRvis/issues git_url: https://git.bioconductor.org/packages/clevRvis git_branch: RELEASE_3_22 git_last_commit: fa805b9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clevRvis_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clevRvis_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clevRvis_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clevRvis_1.10.0.tgz vignettes: vignettes/clevRvis/inst/doc/clevRvis.html vignetteTitles: ClEvR Viz vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clevRvis/inst/doc/clevRvis.R dependencyCount: 119 Package: clippda Version: 1.60.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: 7c35b5c2d8c5d89c2228fa83e6a069c3 NeedsCompilation: no Title: A package for the clinical proteomic profiling data analysis Description: Methods for the nalysis of data from clinical proteomic profiling studies. The focus is on the studies of human subjects, which are often observational case-control by design and have technical replicates. A method for sample size determination for planning these studies is proposed. It incorporates routines for adjusting for the expected heterogeneities and imbalances in the data and the within-sample replicate correlations. biocViews: Proteomics, OneChannel, Preprocessing, DifferentialExpression, MultipleComparison Author: Stephen Nyangoma Maintainer: Stephen Nyangoma URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml git_url: https://git.bioconductor.org/packages/clippda git_branch: RELEASE_3_22 git_last_commit: 34fec11 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clippda_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clippda_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clippda_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clippda_1.60.0.tgz vignettes: vignettes/clippda/inst/doc/clippda.pdf vignetteTitles: Sample Size Calculation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clippda/inst/doc/clippda.R dependencyCount: 43 Package: clipper Version: 1.50.0 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor Suggests: RUnit, BiocGenerics, graphite, ALL, hgu95av2.db, MASS, BiocStyle Enhances: RCy3 License: AGPL-3 Archs: x64 MD5sum: f88ddbe41368cfbeeaa28789076116fd NeedsCompilation: no Title: Gene Set Analysis Exploiting Pathway Topology Description: Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype. Author: Paolo Martini , Gabriele Sales , Chiara Romualdi Maintainer: Paolo Martini git_url: https://git.bioconductor.org/packages/clipper git_branch: RELEASE_3_22 git_last_commit: a4ee17e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clipper_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clipper_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clipper_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clipper_1.50.0.tgz vignettes: vignettes/clipper/inst/doc/clipper.pdf vignetteTitles: clipper hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clipper/inst/doc/clipper.R suggestsMe: graphite dependencyCount: 91 Package: cliProfiler Version: 1.16.0 Depends: S4Vectors, methods, R (>= 4.1) Imports: dplyr, rtracklayer, GenomicRanges, ggplot2, BSgenome, Biostrings, utils Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 Archs: x64 MD5sum: 15c982e746ed13f1921b0857f8cf2fb7 NeedsCompilation: no Title: A package for the CLIP data visualization Description: An easy and fast way to visualize and profile the high-throughput IP data. This package generates the meta gene profile and other profiles. These profiles could provide valuable information for understanding the IP experiment results. biocViews: Sequencing, ChIPSeq, Visualization, Epigenetics, Genetics Author: You Zhou [aut, cre] (ORCID: ), Kathi Zarnack [aut] (ORCID: ) Maintainer: You Zhou URL: https://github.com/Codezy99/cliProfiler VignetteBuilder: knitr BugReports: https://github.com/Codezy99/cliProfiler/issues git_url: https://git.bioconductor.org/packages/cliProfiler git_branch: RELEASE_3_22 git_last_commit: 3ed7cb4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cliProfiler_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cliProfiler_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cliProfiler_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cliProfiler_1.16.0.tgz vignettes: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.html vignetteTitles: cliProfiler Vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.R dependencyCount: 80 Package: cliqueMS Version: 1.24.0 Depends: R (>= 4.3.0) Imports: Rcpp (>= 0.12.15), xcms(>= 3.0.0), MSnbase, igraph, coop, slam, matrixStats, methods LinkingTo: Rcpp, BH, RcppArmadillo Suggests: BiocParallel, knitr, rmarkdown, testthat, CAMERA License: GPL (>= 2) MD5sum: c015d39daf9895c5c07fde4cc93103b0 NeedsCompilation: yes Title: Annotation of Isotopes, Adducts and Fragmentation Adducts for in-Source LC/MS Metabolomics Data Description: Annotates data from liquid chromatography coupled to mass spectrometry (LC/MS) metabolomics experiments. Based on a network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O. Yanes, R.Guimerà and M. Sales-Pardo, Bioinformatics, 35(20), 2019), 'CliqueMS' builds a weighted similarity network where nodes are features and edges are weighted according to the similarity of this features. Then it searches for the most plausible division of the similarity network into cliques (fully connected components). Finally it annotates metabolites within each clique, obtaining for each annotated metabolite the neutral mass and their features, corresponding to isotopes, ionization adducts and fragmentation adducts of that metabolite. biocViews: Metabolomics, MassSpectrometry, Network, NetworkInference Author: Oriol Senan Campos [aut, cre], Antoni Aguilar-Mogas [aut], Jordi Capellades [aut], Miriam Navarro [aut], Oscar Yanes [aut], Roger Guimera [aut], Marta Sales-Pardo [aut] Maintainer: Oriol Senan Campos URL: http://cliquems.seeslab.net SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/osenan/cliqueMS/issues git_url: https://git.bioconductor.org/packages/cliqueMS git_branch: RELEASE_3_22 git_last_commit: ce55a9e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cliqueMS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cliqueMS_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cliqueMS_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cliqueMS_1.24.0.tgz vignettes: vignettes/cliqueMS/inst/doc/annotate_features.html vignetteTitles: Annotating LC/MS data with cliqueMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliqueMS/inst/doc/annotate_features.R dependencyCount: 143 Package: Clomial Version: 1.46.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) Archs: x64 MD5sum: 2af50fc04b927377303daf4ae6a11ad1 NeedsCompilation: no Title: Infers clonal composition of a tumor Description: Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor. biocViews: Genetics, GeneticVariability, Sequencing, Clustering, MultipleComparison, Bayesian, DNASeq, ExomeSeq, TargetedResequencing, ImmunoOncology Author: Habil Zare and Alex Hu Maintainer: Habil Zare git_url: https://git.bioconductor.org/packages/Clomial git_branch: RELEASE_3_22 git_last_commit: f59f698 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Clomial_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Clomial_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Clomial_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Clomial_1.46.0.tgz vignettes: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.pdf vignetteTitles: A likelihood maximization approach to infer the clonal structure of a cancer using multiple tumor samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.R dependencyCount: 4 Package: clst Version: 1.58.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: 56ff79fd6f3cc8bd6e7d6447d1a9b998 NeedsCompilation: no Title: Classification by local similarity threshold Description: Package for modified nearest-neighbor classification based on calculation of a similarity threshold distinguishing within-group from between-group comparisons. biocViews: Classification Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clst git_branch: RELEASE_3_22 git_last_commit: f4d2118 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clst_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clst_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clst_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clst_1.58.0.tgz vignettes: vignettes/clst/inst/doc/clstDemo.pdf vignetteTitles: clst hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clst/inst/doc/clstDemo.R dependsOnMe: clstutils dependencyCount: 14 Package: clstutils Version: 1.58.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit License: GPL-3 MD5sum: 0229d742c578dafdf96333bc8b9abb8d NeedsCompilation: no Title: Tools for performing taxonomic assignment Description: Tools for performing taxonomic assignment based on phylogeny using pplacer and clst. biocViews: Sequencing, Classification, Visualization, QualityControl Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clstutils git_branch: RELEASE_3_22 git_last_commit: a286166 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clstutils_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clstutils_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clstutils_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clstutils_1.58.0.tgz vignettes: vignettes/clstutils/inst/doc/pplacerDemo.pdf, vignettes/clstutils/inst/doc/refSet.pdf vignetteTitles: clst, clstutils hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clstutils/inst/doc/pplacerDemo.R, vignettes/clstutils/inst/doc/refSet.R dependencyCount: 37 Package: CluMSID Version: 1.26.0 Depends: R (>= 3.6) Imports: mzR, S4Vectors, dbscan, RColorBrewer, ape, network, GGally, ggplot2, plotly, methods, utils, stats, sna, grDevices, graphics, Biobase, gplots, MSnbase Suggests: knitr, rmarkdown, testthat, dplyr, readr, stringr, magrittr, CluMSIDdata, metaMS, metaMSdata, xcms License: MIT + file LICENSE Archs: x64 MD5sum: b9babe97bac465b039e96997f0f9157b NeedsCompilation: no Title: Clustering of MS2 Spectra for Metabolite Identification Description: CluMSID is a tool that aids the identification of features in untargeted LC-MS/MS analysis by the use of MS2 spectra similarity and unsupervised statistical methods. It offers functions for a complete and customisable workflow from raw data to visualisations and is interfaceable with the xmcs family of preprocessing packages. biocViews: Metabolomics, Preprocessing, Clustering Author: Tobias Depke [aut, cre], Raimo Franke [ctb], Mark Broenstrup [ths] Maintainer: Tobias Depke URL: https://github.com/tdepke/CluMSID VignetteBuilder: knitr BugReports: https://github.com/tdepke/CluMSID/issues git_url: https://git.bioconductor.org/packages/CluMSID git_branch: RELEASE_3_22 git_last_commit: 4d01b89 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CluMSID_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CluMSID_1.25.0.zip vignettes: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.html, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.html, vignettes/CluMSID/inst/doc/CluMSID_tutorial.html vignetteTitles: CluMSID DI-MS/MS Tutorial, CluMSID GC-EI-MS Tutorial, CluMSID LowRes Tutorial, CluMSID MTBLS Tutorial, CluMSID Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.R, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.R, vignettes/CluMSID/inst/doc/CluMSID_tutorial.R dependencyCount: 151 Package: ClustAll Version: 1.6.0 Depends: R (>= 4.2.0) Imports: FactoMineR, bigstatsr, clValid, doSNOW, parallel, foreach, dplyr, fpc, mice, modeest, flock, networkD3, methods, ComplexHeatmap, cluster, RColorBrewer, circlize, grDevices, ggplot2, grid, stats, utils, pbapply Suggests: RUnit, knitr, BiocGenerics, rmarkdown, BiocStyle, roxygen2 License: GPL-2 MD5sum: 3de36e501ad566d3a8e36b93df2cf192 NeedsCompilation: no Title: ClustAll: Data driven strategy to robustly identify stratification of patients within complex diseases Description: Data driven strategy to find hidden groups of patients with complex diseases using clinical data. ClustAll facilitates the unsupervised identification of multiple robust stratifications. ClustAll, is able to overcome the most common limitations found when dealing with clinical data (missing values, correlated data, mixed data types). biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: Asier Ortega-Legarreta [aut, cre] (ORCID: ), Sara Palomino-Echeverria [aut] Maintainer: Asier Ortega-Legarreta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClustAll git_branch: RELEASE_3_22 git_last_commit: 43762e5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ClustAll_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClustAll_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClustAll_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClustAll_1.6.0.tgz vignettes: vignettes/ClustAll/inst/doc/Vignette_Clustall.html vignetteTitles: ClustALL User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClustAll/inst/doc/Vignette_Clustall.R dependencyCount: 185 Package: clustComp Version: 1.38.0 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: d1d607c16f37a435762c0f099f20f4cf NeedsCompilation: no Title: Clustering Comparison Package Description: clustComp is a package that implements several techniques for the comparison and visualisation of relationships between different clustering results, either flat versus flat or hierarchical versus flat. These relationships among clusters are displayed using a weighted bi-graph, in which the nodes represent the clusters and the edges connect pairs of nodes with non-empty intersection; the weight of each edge is the number of elements in that intersection and is displayed through the edge thickness. The best layout of the bi-graph is provided by the barycentre algorithm, which minimises the weighted number of crossings. In the case of comparing a hierarchical and a non-hierarchical clustering, the dendrogram is pruned at different heights, selected by exploring the tree by depth-first search, starting at the root. Branches are decided to be split according to the value of a scoring function, that can be based either on the aesthetics of the bi-graph or on the mutual information between the hierarchical and the flat clusterings. A mapping between groups of clusters from each side is constructed with a greedy algorithm, and can be additionally visualised. biocViews: GeneExpression, Clustering, Visualization Author: Aurora Torrente and Alvis Brazma. Maintainer: Aurora Torrente git_url: https://git.bioconductor.org/packages/clustComp git_branch: RELEASE_3_22 git_last_commit: 0a8a5b4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clustComp_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clustComp_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clustComp_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clustComp_1.38.0.tgz vignettes: vignettes/clustComp/inst/doc/clustComp.pdf vignetteTitles: The clustComp Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clustComp/inst/doc/clustComp.R dependencyCount: 4 Package: clusterExperiment Version: 2.30.0 Depends: R (>= 3.6.0), SingleCellExperiment, SummarizedExperiment (>= 1.15.4), BiocGenerics Imports: methods, NMF, RColorBrewer, ape (>= 5.0), cluster, stats, limma, locfdr, matrixStats, graphics, parallel, BiocSingular, kernlab, stringr, S4Vectors, grDevices, DelayedArray (>= 0.7.48), HDF5Array (>= 1.7.10), Matrix, Rcpp, edgeR, scales, zinbwave, phylobase, pracma, mbkmeans LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, MAST, Rtsne, scran, igraph, rmarkdown License: Artistic-2.0 MD5sum: e5a706b3482c2284ebd5e9d21e365d0a NeedsCompilation: yes Title: Compare Clusterings for Single-Cell Sequencing Description: Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets. biocViews: Clustering, RNASeq, Sequencing, Software, SingleCell Author: Elizabeth Purdom [aut, cre, cph], Davide Risso [aut] Maintainer: Elizabeth Purdom VignetteBuilder: knitr BugReports: https://github.com/epurdom/clusterExperiment/issues git_url: https://git.bioconductor.org/packages/clusterExperiment git_branch: RELEASE_3_22 git_last_commit: 86a6cae git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clusterExperiment_2.30.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clusterExperiment_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clusterExperiment_2.30.0.tgz vignettes: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.html, vignettes/clusterExperiment/inst/doc/largeDataSets.html vignetteTitles: clusterExperiment Vignette, Working with Large Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.R, vignettes/clusterExperiment/inst/doc/largeDataSets.R dependsOnMe: netSmooth suggestsMe: slingshot, tradeSeq dependencyCount: 145 Package: ClusterFoldSimilarity Version: 1.6.0 Imports: methods, igraph, ggplot2, scales, BiocParallel, graphics, stats, utils, Matrix, cowplot, dplyr, reshape2, Seurat, SeuratObject, SingleCellExperiment, ggdendro Suggests: knitr, rmarkdown, kableExtra, scRNAseq, BiocStyle License: Artistic-2.0 MD5sum: 56d0a34326257101705243cbc70294ab NeedsCompilation: no Title: Calculate similarity of clusters from different single cell samples using foldchanges Description: This package calculates a similarity coefficient using the fold changes of shared features (e.g. genes) among clusters of different samples/batches/datasets. The similarity coefficient is calculated using the dot-product (Hadamard product) of every pairwise combination of Fold Changes between a source cluster i of sample/dataset n and all the target clusters j in sample/dataset m biocViews: SingleCell, Clustering, FeatureExtraction, GraphAndNetwork, GeneTarget, RNASeq Author: Oscar Gonzalez-Velasco [cre, aut] (ORCID: ) Maintainer: Oscar Gonzalez-Velasco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterFoldSimilarity git_branch: RELEASE_3_22 git_last_commit: 8775001 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ClusterFoldSimilarity_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClusterFoldSimilarity_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClusterFoldSimilarity_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClusterFoldSimilarity_1.6.0.tgz vignettes: vignettes/ClusterFoldSimilarity/inst/doc/ClusterFoldSimilarity.html vignetteTitles: ClusterFoldSimilarity: hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ClusterFoldSimilarity/inst/doc/ClusterFoldSimilarity.R dependencyCount: 173 Package: ClusterJudge Version: 1.32.0 Depends: R (>= 3.6), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt License: Artistic-2.0 MD5sum: 9c68488ff2daea4168b9ac361714def3 NeedsCompilation: no Title: Judging Quality of Clustering Methods using Mutual Information Description: ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview. biocViews: Software, StatisticalMethod, Clustering, GeneExpression, GO Author: Adrian Pasculescu Maintainer: Adrian Pasculescu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterJudge git_branch: RELEASE_3_22 git_last_commit: 4124b3f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ClusterJudge_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClusterJudge_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClusterJudge_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClusterJudge_1.32.0.tgz vignettes: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.R dependencyCount: 26 Package: clusterProfiler Version: 4.18.0 Depends: R (>= 4.2.0) Imports: AnnotationDbi, DOSE (>= 3.23.2), dplyr, enrichplot (>= 1.9.3), GO.db, GOSemSim (>= 2.27.2), gson (>= 0.0.7), httr, igraph, magrittr, methods, plyr, qvalue, rlang, stats, tidyr, utils, yulab.utils (>= 0.1.6) Suggests: AnnotationHub, knitr, jsonlite, readr, rmarkdown, org.Hs.eg.db, prettydoc, BiocManager, testthat License: Artistic-2.0 MD5sum: 7df0b23abf871093773fb79a78340242 NeedsCompilation: no Title: A universal enrichment tool for interpreting omics data Description: This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources and thus can be applied in diverse scenarios. It provides a tidy interface to access, manipulate, and visualize enrichment results to help users achieve efficient data interpretation. Datasets obtained from multiple treatments and time points can be analyzed and compared in a single run, easily revealing functional consensus and differences among distinct conditions. biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG, MultipleComparison, Pathways, Reactome, Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Li-Gen Wang [ctb], Xiao Luo [ctb], Meijun Chen [ctb], Giovanni Dall'Olio [ctb], Wanqian Wei [ctb], Chun-Hui Gao [ctb] (ORCID: ) Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/clusterProfiler/issues git_url: https://git.bioconductor.org/packages/clusterProfiler git_branch: RELEASE_3_22 git_last_commit: a1dbe09 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clusterProfiler_4.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clusterProfiler_4.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clusterProfiler_4.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clusterProfiler_4.18.0.tgz vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.html vignetteTitles: Statistical analysis and visualization of functional profiles for genes and gene clusters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R dependsOnMe: maEndToEnd importsMe: bioCancer, CaMutQC, CBNplot, CEMiTool, CeTF, debrowser, EasyCellType, epiregulon.extra, esATAC, GDCRNATools, goSorensen, MetaPhOR, methylGSA, MicrobiomeProfiler, miRSM, miRspongeR, mitology, Moonlight2R, MoonlightR, mosdef, PanomiR, pathlinkR, ReducedExperiment, RFLOMICS, signatureSearch, vsclust, ExpHunterSuite, recountWorkflow, DRviaSPCN, genekitr, Grouphmap, immcp, PathwayVote, PMAPscore, RVA, ssdGSA, SurprisalAnalysis, tinyarray suggestsMe: ChIPseeker, cola, DAPAR, DeeDeeExperiment, DOSE, enrichplot, EpiCompare, EpiMix, GenomicSuperSignature, GeoTcgaData, ggkegg, GOSemSim, GRaNIE, GSEAmining, mastR, MesKit, ReactomePA, rrvgo, scGPS, scGraphVerse, TCGAbiolinks, tidybulk, org.Mxanthus.db, ClusterGVis, GeneSelectR, ggpicrust2, grandR, OlinkAnalyze, ReporterScore dependencyCount: 136 Package: clusterSeq Version: 1.34.0 Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils Imports: BiocGenerics Suggests: BiocStyle License: GPL-3 Archs: x64 MD5sum: e5e597dbcd6619210e5f9b552a50e45d NeedsCompilation: no Title: Clustering of high-throughput sequencing data by identifying co-expression patterns Description: Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples. biocViews: Sequencing, DifferentialExpression, MultipleComparison, Clustering, GeneExpression Author: Thomas J. Hardcastle [aut], Irene Papatheodorou [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/clusterSeq BugReports: https://github.com/samgg/clusterSeq/issues git_url: https://git.bioconductor.org/packages/clusterSeq git_branch: RELEASE_3_22 git_last_commit: 0ccb56f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clusterSeq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clusterSeq_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clusterSeq_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clusterSeq_1.34.0.tgz vignettes: vignettes/clusterSeq/inst/doc/clusterSeq.pdf vignetteTitles: Advanced baySeq analyses hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterSeq/inst/doc/clusterSeq.R dependencyCount: 30 Package: ClusterSignificance Version: 1.38.0 Depends: R (>= 3.3.0) Imports: methods, pracma, princurve (>= 2.0.5), scatterplot3d, RColorBrewer, grDevices, graphics, utils, stats Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics, covr License: GPL-3 Archs: x64 MD5sum: 3c6f3d25b7f2ad6301f5bb9c769baba7 NeedsCompilation: no Title: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data Description: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method. biocViews: Clustering, Classification, PrincipalComponent, StatisticalMethod Author: Jason T. Serviss [aut, cre], Jesper R. Gadin [aut] Maintainer: Jason T Serviss URL: https://github.com/jasonserviss/ClusterSignificance/ VignetteBuilder: knitr BugReports: https://github.com/jasonserviss/ClusterSignificance/issues git_url: https://git.bioconductor.org/packages/ClusterSignificance git_branch: RELEASE_3_22 git_last_commit: c7fe714 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ClusterSignificance_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClusterSignificance_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClusterSignificance_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClusterSignificance_1.38.0.tgz vignettes: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html vignetteTitles: ClusterSignificance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R dependencyCount: 10 Package: clusterStab Version: 1.82.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: c0dd9cd413cc0ebbd292df747a7cc61a NeedsCompilation: no Title: Compute cluster stability scores for microarray data Description: This package can be used to estimate the number of clusters in a set of microarray data, as well as test the stability of these clusters. biocViews: Clustering Author: James W. MacDonald, Debashis Ghosh, Mark Smolkin Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/clusterStab git_branch: RELEASE_3_22 git_last_commit: 8b08aab git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clusterStab_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clusterStab_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clusterStab_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clusterStab_1.82.0.tgz vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf vignetteTitles: clusterStab Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterStab/inst/doc/clusterStab.R dependencyCount: 7 Package: clustifyr Version: 1.22.0 Depends: R (>= 2.10) Imports: cowplot, dplyr, entropy, fgsea, ggplot2, Matrix, rlang, scales, stringr, tibble, tidyr, stats, methods, SingleCellExperiment, SummarizedExperiment, SeuratObject, matrixStats, S4Vectors, proxy, httr, utils Suggests: ComplexHeatmap, covr, knitr, rmarkdown, testthat, ggrepel, BiocStyle, BiocManager, remotes, shiny, gprofiler2, purrr, data.table, R.utils License: MIT + file LICENSE MD5sum: 119772540df4719b72117b01c912e3ef NeedsCompilation: no Title: Classifier for Single-cell RNA-seq Using Cell Clusters Description: Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment. biocViews: SingleCell, Annotation, Sequencing, Microarray, GeneExpression Author: Rui Fu [cre, aut], Kent Riemondy [aut], Austin Gillen [ctb], Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb], Michelle Daya [ctb], Sidhant Puntambekar [ctb], RNA Bioscience Initiative [fnd, cph] (ROR: ) Maintainer: Rui Fu URL: https://github.com/rnabioco/clustifyr, https://rnabioco.github.io/clustifyr/ VignetteBuilder: knitr BugReports: https://github.com/rnabioco/clustifyr/issues git_url: https://git.bioconductor.org/packages/clustifyr git_branch: RELEASE_3_22 git_last_commit: 9338c89 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clustifyr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clustifyr_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clustifyr_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clustifyr_1.22.0.tgz vignettes: vignettes/clustifyr/inst/doc/clustifyr.html, vignettes/clustifyr/inst/doc/geo-annotations.html vignetteTitles: Introduction to clustifyr, geo-annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clustifyr/inst/doc/clustifyr.R, vignettes/clustifyr/inst/doc/geo-annotations.R suggestsMe: clustifyrdatahub dependencyCount: 89 Package: ClustIRR Version: 1.8.0 Depends: R (>= 4.3.0) Imports: blaster, future, future.apply, grDevices, igraph, methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), reshape2, rstan (>= 2.18.1), rstantools (>= 2.4.0), stats, stringdist, utils, posterior, visNetwork, dplyr, tidyr, ggplot2, ggforce, scales LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, knitr, testthat, ggplot2, ggrepel, patchwork License: GPL-3 + file LICENSE MD5sum: a813ad57e4542c52f377f34427cbd1e7 NeedsCompilation: yes Title: Clustering of immune receptor repertoires Description: ClustIRR analyzes repertoires of B- and T-cell receptors. It starts by identifying communities of immune receptors with similar specificities, based on the sequences of their complementarity-determining regions (CDRs). Next, it employs a Bayesian probabilistic models to quantify differential community occupancy (DCO) between repertoires, allowing the identification of expanding or contracting communities in response to e.g. infection or cancer treatment. biocViews: Clustering, ImmunoOncology, SingleCell, Software, Classification, Bayesian, BiomedicalInformatics, ImmunoOncology, MathematicalBiology Author: Simo Kitanovski [aut, cre] (ORCID: ), Kai Wollek [aut] (ORCID: ) Maintainer: Simo Kitanovski URL: https://github.com/snaketron/ClustIRR SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/snaketron/ClustIRR/issues git_url: https://git.bioconductor.org/packages/ClustIRR git_branch: RELEASE_3_22 git_last_commit: 0b886ef git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ClustIRR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClustIRR_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClustIRR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClustIRR_1.8.0.tgz vignettes: vignettes/ClustIRR/inst/doc/User_manual.html vignetteTitles: Decoding T- and B-cell receptor repertoires with ClustIRR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ClustIRR/inst/doc/User_manual.R dependencyCount: 103 Package: clustSIGNAL Version: 1.2.0 Depends: R (>= 4.4.0), SpatialExperiment Imports: BiocParallel, BiocNeighbors, bluster (>= 1.16.0), scater, harmony, SingleCellExperiment, SummarizedExperiment, methods, Matrix, reshape2 Suggests: knitr, BiocStyle, testthat (>= 3.0.0), aricode, ggplot2, patchwork, dplyr, scattermore License: GPL-2 MD5sum: d7af3d7c2a2fc2c68f1fe4b12b0910be NeedsCompilation: no Title: ClustSIGNAL: a spatial clustering method Description: clustSIGNAL: clustering of Spatially Informed Gene expression with Neighbourhood Adapted Learning. A tool for adaptively smoothing and clustering gene expression data. clustSIGNAL uses entropy to measure heterogeneity of cell neighbourhoods and performs a weighted, adaptive smoothing, where homogeneous neighbourhoods are smoothed more and heterogeneous neighbourhoods are smoothed less. This not only overcomes data sparsity but also incorporates spatial context into the gene expression data. The resulting smoothed gene expression data is used for clustering and could be used for other downstream analyses. biocViews: Clustering, Software, GeneExpression, Spatial, Transcriptomics, SingleCell Author: Pratibha Panwar [cre, aut, ctb] (ORCID: ), Boyi Guo [aut], Haowen Zhao [aut], Stephanie Hicks [aut], Shila Ghazanfar [aut, ctb] (ORCID: ) Maintainer: Pratibha Panwar URL: https://sydneybiox.github.io/clustSIGNAL/ VignetteBuilder: knitr BugReports: https://github.com/sydneybiox/clustSIGNAL/issues git_url: https://git.bioconductor.org/packages/clustSIGNAL git_branch: RELEASE_3_22 git_last_commit: 55aa2e1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/clustSIGNAL_1.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clustSIGNAL_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clustSIGNAL_1.2.0.tgz vignettes: vignettes/clustSIGNAL/inst/doc/clustSIGNAL.html vignetteTitles: ClustSIGNAL tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clustSIGNAL/inst/doc/clustSIGNAL.R dependencyCount: 128 Package: CMA Version: 1.68.0 Depends: R (>= 2.10), methods, stats, Biobase Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics, gbm, mgcv, corpcor, limma, st, mvtnorm License: GPL (>= 2) MD5sum: 7a358c91f45a6804fae6da7cfe9ce180 NeedsCompilation: no Title: Synthesis of microarray-based classification Description: This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment. biocViews: Classification, DecisionTree Author: Martin Slawski , Anne-Laure Boulesteix , Christoph Bernau . Maintainer: Roman Hornung git_url: https://git.bioconductor.org/packages/CMA git_branch: RELEASE_3_22 git_last_commit: 70c568b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CMA_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CMA_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CMA_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CMA_1.68.0.tgz vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf vignetteTitles: CMA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CMA/inst/doc/CMA_vignette.R dependencyCount: 7 Package: cmapR Version: 1.22.0 Depends: R (>= 4.0) Imports: methods, rhdf5, data.table, flowCore, SummarizedExperiment, matrixStats Suggests: knitr, testthat, BiocStyle, rmarkdown License: file LICENSE MD5sum: ffc2d5847fbb902ac7a3b68ead2eb9cd NeedsCompilation: no Title: CMap Tools in R Description: The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets. biocViews: DataImport, DataRepresentation, GeneExpression Author: Ted Natoli [aut, cre] (ORCID: ) Maintainer: Ted Natoli URL: https://github.com/cmap/cmapR VignetteBuilder: knitr BugReports: https://github.com/cmap/cmapR/issues git_url: https://git.bioconductor.org/packages/cmapR git_branch: RELEASE_3_22 git_last_commit: 7a01f1e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cmapR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cmapR_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cmapR_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cmapR_1.22.0.tgz vignettes: vignettes/cmapR/inst/doc/tutorial.html vignetteTitles: cmapR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cmapR/inst/doc/tutorial.R dependencyCount: 35 Package: cn.farms Version: 1.58.0 Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice License: LGPL (>= 2.0) MD5sum: 8c41c4b0cd4cc151fd600f5f03445ca8 NeedsCompilation: yes Title: cn.FARMS - factor analysis for copy number estimation Description: This package implements the cn.FARMS algorithm for copy number variation (CNV) analysis. cn.FARMS allows to analyze the most common Affymetrix (250K-SNP6.0) array types, supports high-performance computing using snow and ff. biocViews: Microarray, CopyNumberVariation Author: Andreas Mitterecker, Djork-Arne Clevert Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/cnfarms/cnfarms.html git_url: https://git.bioconductor.org/packages/cn.farms git_branch: RELEASE_3_22 git_last_commit: 547c019 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cn.farms_1.58.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cn.farms_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cn.farms_1.58.0.tgz vignettes: vignettes/cn.farms/inst/doc/cn.farms.pdf vignetteTitles: cn.farms: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.farms/inst/doc/cn.farms.R dependencyCount: 56 Package: cn.mops Version: 1.56.0 Depends: R (>= 3.5.0), methods, utils, stats, graphics, parallel, GenomicRanges Imports: BiocGenerics, Biobase, IRanges, Rsamtools, Seqinfo, S4Vectors Suggests: DNAcopy License: LGPL (>= 2.0) MD5sum: 59fe3a600df3d710473fd025ea16bdaf NeedsCompilation: yes Title: cn.mops - Mixture of Poissons for CNV detection in NGS data Description: cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++. biocViews: Sequencing, CopyNumberVariation, Homo_sapiens, CellBiology, HapMap, Genetics Author: Guenter Klambauer [aut], Gundula Povysil [cre] Maintainer: Gundula Povysil URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html git_url: https://git.bioconductor.org/packages/cn.mops git_branch: RELEASE_3_22 git_last_commit: 7e51624 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cn.mops_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cn.mops_1.55.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cn.mops_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cn.mops_1.56.0.tgz vignettes: vignettes/cn.mops/inst/doc/cn.mops.pdf vignetteTitles: cn.mops: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.mops/inst/doc/cn.mops.R dependsOnMe: panelcn.mops importsMe: CopyNumberPlots dependencyCount: 30 Package: CNAnorm Version: 1.56.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 MD5sum: ec48c85c7eae0238dd5988deb172a47d NeedsCompilation: yes Title: A normalization method for Copy Number Aberration in cancer samples Description: Performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found. biocViews: CopyNumberVariation, Sequencing, Coverage, Normalization, WholeGenome, DNASeq, GenomicVariation Author: Stefano Berri , Henry M. Wood , Arief Gusnanto Maintainer: Stefano Berri URL: http://www.r-project.org, git_url: https://git.bioconductor.org/packages/CNAnorm git_branch: RELEASE_3_22 git_last_commit: 33c151d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNAnorm_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNAnorm_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNAnorm_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNAnorm_1.56.0.tgz vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf vignetteTitles: CNAnorm.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNAnorm/inst/doc/CNAnorm.R dependencyCount: 2 Package: CNEr Version: 1.46.0 Depends: R (>= 3.5.0) Imports: Biostrings (>= 2.33.4), pwalign, DBI (>= 0.7), RSQLite (>= 0.11.4), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.23.16), Seqinfo (>= 0.99.2), rtracklayer (>= 1.25.5), XVector (>= 0.5.4), GenomicAlignments (>= 1.1.9), methods, S4Vectors (>= 0.13.13), IRanges (>= 2.5.27), readr (>= 0.2.2), BiocGenerics, tools, parallel, reshape2 (>= 1.4.1), ggplot2 (>= 2.1.0), poweRlaw (>= 0.60.3), annotate (>= 1.50.0), GO.db (>= 3.3.0), R.utils (>= 2.3.0), KEGGREST (>= 1.14.0) LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz (>= 1.7.4), BiocStyle, knitr, rmarkdown, testthat, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, TxDb.Drerio.UCSC.danRer10.refGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Ggallus.UCSC.galGal3 License: GPL-2 | file LICENSE License_restricts_use: yes Archs: x64 MD5sum: d2482a0fc43fcf0a960852a558dd728a NeedsCompilation: yes Title: CNE Detection and Visualization Description: Large-scale identification and advanced visualization of sets of conserved noncoding elements. biocViews: GeneRegulation, Visualization, DataImport Author: Ge Tan Maintainer: Boris Lenhard Damir Baranasic URL: https://github.com/ComputationalRegulatoryGenomicsICL/CNEr VignetteBuilder: knitr BugReports: https://github.com/ge11232002/CNEr/issues git_url: https://git.bioconductor.org/packages/CNEr git_branch: RELEASE_3_22 git_last_commit: a5d64f4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNEr_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNEr_1.45.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNEr_1.46.0.tgz vignettes: vignettes/CNEr/inst/doc/CNEr.html, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html vignetteTitles: CNE identification and visualisation, Pairwise whole genome alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R dependencyCount: 113 Package: CNORdt Version: 1.52.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 MD5sum: d0f5fb8d5c2368e0191f12aad3ea2c75 NeedsCompilation: yes Title: Add-on to CellNOptR: Discretized time treatments Description: This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: A. MacNamara Maintainer: A. MacNamara git_url: https://git.bioconductor.org/packages/CNORdt git_branch: RELEASE_3_22 git_last_commit: a0225ee git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNORdt_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNORdt_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNORdt_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNORdt_1.52.0.tgz vignettes: vignettes/CNORdt/inst/doc/CNORdt-vignette.pdf vignetteTitles: Using multiple time points to train logic models to data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORdt/inst/doc/CNORdt-vignette-example.R, vignettes/CNORdt/inst/doc/CNORdt-vignette.R dependencyCount: 64 Package: CNORfeeder Version: 1.50.0 Depends: R (>= 4.0.0), graph Imports: CellNOptR (>= 1.4.0) Suggests: minet, Rgraphviz, RUnit, BiocGenerics, igraph Enhances: MEIGOR License: GPL-3 MD5sum: 2db0397c3557a30b0847da393b40ed90 NeedsCompilation: no Title: Integration of CellNOptR to add missing links Description: This package integrates literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. It permits to extends a given network with links derived from the data via various inference methods and uses information on physical interactions of proteins to guide and validate the integration of links. biocViews: CellBasedAssays, CellBiology, Proteomics, NetworkInference Author: Federica Eduati [aut, cre] Maintainer: Attila Gabor git_url: https://git.bioconductor.org/packages/CNORfeeder git_branch: RELEASE_3_22 git_last_commit: 92dc988 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNORfeeder_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNORfeeder_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNORfeeder_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNORfeeder_1.50.0.tgz vignettes: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfeeder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.R dependencyCount: 63 Package: CNORfuzzy Version: 1.52.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 MD5sum: 835e90ce25fee812ae2eb4bae7cceb59 NeedsCompilation: yes Title: Addon to CellNOptR: Fuzzy Logic Description: This package is an extension to CellNOptR. It contains additional functionality needed to simulate and train a prior knowledge network to experimental data using constrained fuzzy logic (cFL, rather than Boolean logic as is the case in CellNOptR). Additionally, this package will contain functions to use for the compilation of multiple optimization results (either Boolean or cFL). biocViews: Network Author: M. Morris, T. Cokelaer Maintainer: T. Cokelaer git_url: https://git.bioconductor.org/packages/CNORfuzzy git_branch: RELEASE_3_22 git_last_commit: 201bfd0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNORfuzzy_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNORfuzzy_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNORfuzzy_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNORfuzzy_1.52.0.tgz vignettes: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfuzzyl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.R dependencyCount: 64 Package: CNORode Version: 1.52.0 Depends: CellNOptR, genalg Suggests: knitr, rmarkdown Enhances: doParallel, foreach License: GPL-2 MD5sum: dc369678f510ab99b5d7b4125b5c6101 NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: Logic based ordinary differential equation (ODE) add-on to CellNOptR. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer, Attila Gabor, Federica Eduati, Enio Gjerga Maintainer: Attila Gabor VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNORode git_branch: RELEASE_3_22 git_last_commit: 0bfa1d4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNORode_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNORode_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNORode_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNORode_1.52.0.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR dependencyCount: 64 Package: CNTools Version: 1.66.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL MD5sum: b111b97a9b1fe0b0a74fdea48c966c83 NeedsCompilation: yes Title: Convert segment data into a region by sample matrix to allow for other high level computational analyses. Description: This package provides tools to convert the output of segmentation analysis using DNAcopy to a matrix structure with overlapping segments as rows and samples as columns so that other computational analyses can be applied to segmented data biocViews: Microarray, CopyNumberVariation Author: Jianhua Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/CNTools git_branch: RELEASE_3_22 git_last_commit: e91ebea git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNTools_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNTools_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNTools_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNTools_1.66.0.tgz vignettes: vignettes/CNTools/inst/doc/HowTo.pdf vignetteTitles: NCTools HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNTools/inst/doc/HowTo.R dependsOnMe: cghMCR dependencyCount: 54 Package: CNVfilteR Version: 1.24.0 Depends: R (>= 4.3) Imports: IRanges, GenomicRanges, SummarizedExperiment, pracma, regioneR, assertthat, karyoploteR, CopyNumberPlots, graphics, utils, VariantAnnotation, Rsamtools, GenomeInfoDb, Biostrings, methods Suggests: knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, rmarkdown License: Artistic-2.0 MD5sum: 3393a5d50aee26f91e09e10b7c3aa633 NeedsCompilation: no Title: Identifies false positives of CNV calling tools by using SNV calls Description: CNVfilteR identifies those CNVs that can be discarded by using the single nucleotide variant (SNV) calls that are usually obtained in common NGS pipelines. biocViews: CopyNumberVariation, Sequencing, DNASeq, Visualization, DataImport Author: Jose Marcos Moreno-Cabrera [aut, cre] (ORCID: ), Bernat Gel [aut] Maintainer: Jose Marcos Moreno-Cabrera URL: https://github.com/jpuntomarcos/CNVfilteR VignetteBuilder: knitr BugReports: https://github.com/jpuntomarcos/CNVfilteR/issues git_url: https://git.bioconductor.org/packages/CNVfilteR git_branch: RELEASE_3_22 git_last_commit: ad670c2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNVfilteR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNVfilteR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNVfilteR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNVfilteR_1.24.0.tgz vignettes: vignettes/CNVfilteR/inst/doc/CNVfilteR.html vignetteTitles: CNVfilteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVfilteR/inst/doc/CNVfilteR.R dependencyCount: 140 Package: cnvGSA Version: 1.54.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: d2f286cabee5387c4cb998b749fcacc8 NeedsCompilation: no Title: Gene Set Analysis of (Rare) Copy Number Variants Description: This package is intended to facilitate gene-set association with rare CNVs in case-control studies. biocViews: MultipleComparison Author: Daniele Merico , Robert Ziman ; packaged by Joseph Lugo Maintainer: Joseph Lugo git_url: https://git.bioconductor.org/packages/cnvGSA git_branch: RELEASE_3_22 git_last_commit: 3a911c5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cnvGSA_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cnvGSA_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cnvGSA_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cnvGSA_1.54.0.tgz vignettes: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf, vignettes/cnvGSA/inst/doc/cnvGSAUsersGuide.pdf vignetteTitles: cnvGSA - Gene-Set Analysis of Rare Copy Number Variants, cnvGSAUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cnvGSAdata dependencyCount: 20 Package: CNViz Version: 1.18.0 Depends: R (>= 4.0), shiny (>= 1.5.0) Imports: dplyr, stats, utils, grDevices, plotly, karyoploteR, CopyNumberPlots, GenomicRanges, magrittr, DT, scales, graphics Suggests: rmarkdown, knitr License: Artistic-2.0 MD5sum: 6ca45315f997d6317f7a25d8d6920cc2 NeedsCompilation: no Title: Copy Number Visualization Description: CNViz takes probe, gene, and segment-level log2 copy number ratios and launches a Shiny app to visualize your sample's copy number profile. You can also integrate loss of heterozygosity (LOH) and single nucleotide variant (SNV) data. biocViews: Visualization, CopyNumberVariation, Sequencing, DNASeq Author: Rebecca Greenblatt [aut, cre] Maintainer: Rebecca Greenblatt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNViz git_branch: RELEASE_3_22 git_last_commit: 0c7d023 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNViz_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNViz_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNViz_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNViz_1.18.0.tgz vignettes: vignettes/CNViz/inst/doc/CNViz.html vignetteTitles: CNViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNViz/inst/doc/CNViz.R dependencyCount: 156 Package: CNVMetrics Version: 1.13.0 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, S4Vectors, BiocParallel, methods, magrittr, stats, pheatmap, gridExtra, grDevices, rBeta2009 Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 5bd070e8e36d065e01281a6efe7353b3 NeedsCompilation: no Title: Copy Number Variant Metrics Description: The CNVMetrics package calculates similarity metrics to facilitate copy number variant comparison among samples and/or methods. Similarity metrics can be employed to compare CNV profiles of genetically unrelated samples as well as those with a common genetic background. Some metrics are based on the shared amplified/deleted regions while other metrics rely on the level of amplification/deletion. The data type used as input is a plain text file containing the genomic position of the copy number variations, as well as the status and/or the log2 ratio values. Finally, a visualization tool is provided to explore resulting metrics. biocViews: BiologicalQuestion, Software, CopyNumberVariation Author: Astrid Deschênes [aut, cre] (ORCID: ), Pascal Belleau [aut] (ORCID: ), David A. Tuveson [aut] (ORCID: ), Alexander Krasnitz [aut] Maintainer: Astrid Deschênes URL: https://github.com/krasnitzlab/CNVMetrics, https://krasnitzlab.github.io/CNVMetrics/ VignetteBuilder: knitr BugReports: https://github.com/krasnitzlab/CNVMetrics/issues git_url: https://git.bioconductor.org/packages/CNVMetrics git_branch: devel git_last_commit: 89fe9c1 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/CNVMetrics_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNVMetrics_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNVMetrics_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNVMetrics_1.14.0.tgz vignettes: vignettes/CNVMetrics/inst/doc/CNVMetrics.html vignetteTitles: Copy number variant metrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVMetrics/inst/doc/CNVMetrics.R dependencyCount: 38 Package: CNVPanelizer Version: 1.42.0 Depends: R (>= 3.2.0), GenomicRanges Imports: BiocGenerics, S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, foreach, ggplot2, plyr, GenomeInfoDb, gplots, reshape2, stringr, testthat, graphics, methods, shiny, shinyFiles, shinyjs, grid, openxlsx Suggests: knitr, RUnit License: GPL-3 MD5sum: ccffe0f7494f0bcf1e9f2a1b6813e36a NeedsCompilation: no Title: Reliable CNV detection in targeted sequencing applications Description: A method that allows for the use of a collection of non-matched normal tissue samples. Our approach uses a non-parametric bootstrap subsampling of the available reference samples to estimate the distribution of read counts from targeted sequencing. As inspired by random forest, this is combined with a procedure that subsamples the amplicons associated with each of the targeted genes. The obtained information allows us to reliably classify the copy number aberrations on the gene level. biocViews: Classification, Sequencing, Normalization, CopyNumberVariation, Coverage Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb], Benedikt Brors [ths], Wilko Weichert [ths] Maintainer: Thomas Wolf VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVPanelizer git_branch: RELEASE_3_22 git_last_commit: 9b9e840 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNVPanelizer_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNVPanelizer_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNVPanelizer_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNVPanelizer_1.42.0.tgz vignettes: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.pdf vignetteTitles: CNVPanelizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.R dependencyCount: 113 Package: CNVRanger Version: 1.26.0 Depends: GenomicRanges, RaggedExperiment Imports: BiocGenerics, BiocParallel, GDSArray, GenomeInfoDb, IRanges, S4Vectors, SNPRelate, SummarizedExperiment, data.table, edgeR, gdsfmt, grDevices, lattice, limma, methods, plyr, qqman, rappdirs, reshape2, stats, utils Suggests: AnnotationHub, BSgenome.Btaurus.UCSC.bosTau6.masked, BiocStyle, ComplexHeatmap, Gviz, MultiAssayExperiment, TCGAutils, TxDb.Hsapiens.UCSC.hg19.knownGene, curatedTCGAData, ensembldb, grid, knitr, org.Hs.eg.db, regioneR, rmarkdown, statmod License: Artistic-2.0 MD5sum: 28c608793cbfa4c5a96def01355a254f NeedsCompilation: no Title: Summarization and expression/phenotype association of CNV ranges Description: The CNVRanger package implements a comprehensive tool suite for CNV analysis. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and association analysis with gene expression and quantitative phenotypes. biocViews: CopyNumberVariation, DifferentialExpression, GeneExpression, GenomeWideAssociation, GenomicVariation, Microarray, RNASeq, SNP Author: Ludwig Geistlinger [aut, cre] (ORCID: ), Vinicius Henrique da Silva [aut], Marcel Ramos [ctb] (ORCID: ), Levi Waldron [ctb] (ORCID: ) Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/waldronlab/CNVRanger/issues git_url: https://git.bioconductor.org/packages/CNVRanger git_branch: RELEASE_3_22 git_last_commit: 53ff90f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNVRanger_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNVRanger_1.25.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNVRanger_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNVRanger_1.26.0.tgz vignettes: vignettes/CNVRanger/inst/doc/CNVRanger.html vignetteTitles: Summarization and quantitative trait analysis of CNV ranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVRanger/inst/doc/CNVRanger.R dependencyCount: 75 Package: CNVrd2 Version: 1.48.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: e9fe4e9c66233a8cdf78e5bef3a2006e NeedsCompilation: no Title: CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data. Description: CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions. biocViews: CopyNumberVariation, SNP, Sequencing, Software, Coverage, LinkageDisequilibrium, Clustering. Author: Hoang Tan Nguyen, Tony R Merriman and Mik Black Maintainer: Hoang Tan Nguyen URL: https://github.com/hoangtn/CNVrd2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVrd2 git_branch: RELEASE_3_22 git_last_commit: b4c107b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CNVrd2_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNVrd2_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNVrd2_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNVrd2_1.48.0.tgz vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf vignetteTitles: A Markdown Vignette with knitr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVrd2/inst/doc/CNVrd2.R dependencyCount: 92 Package: CoCiteStats Version: 1.82.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 0ceb38fca92c4f4bbf4266a4e5b0af8c NeedsCompilation: no Title: Different test statistics based on co-citation. Description: A collection of software tools for dealing with co-citation data. biocViews: Software Author: B. Ding and R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/CoCiteStats git_branch: RELEASE_3_22 git_last_commit: e8750e6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CoCiteStats_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CoCiteStats_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CoCiteStats_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CoCiteStats_1.82.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 44 Package: COCOA Version: 2.24.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocGenerics, S4Vectors, IRanges, data.table, ggplot2, Biobase, stats, methods, ComplexHeatmap, MIRA, tidyr, grid, grDevices, simpleCache, fitdistrplus Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 Archs: x64 MD5sum: fd9e4941380b7590f286ebaed82c7448 NeedsCompilation: no Title: Coordinate Covariation Analysis Description: COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data. biocViews: Epigenetics, DNAMethylation, ATACSeq, DNaseSeq, MethylSeq, MethylationArray, PrincipalComponent, GenomicVariation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, Sequencing, ImmunoOncology Author: John Lawson [aut, cre], Nathan Sheffield [aut] (http://www.databio.org), Jason Smith [ctb] Maintainer: John Lawson URL: http://code.databio.org/COCOA/ VignetteBuilder: knitr BugReports: https://github.com/databio/COCOA git_url: https://git.bioconductor.org/packages/COCOA git_branch: RELEASE_3_22 git_last_commit: 8165f71 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/COCOA_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/COCOA_2.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/COCOA_2.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/COCOA_2.24.0.tgz vignettes: vignettes/COCOA/inst/doc/IntroToCOCOA.html vignetteTitles: Introduction to Coordinate Covariation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COCOA/inst/doc/IntroToCOCOA.R dependencyCount: 123 Package: codelink Version: 1.78.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: a600253cc2ee4f9f2189c728a05dc439 NeedsCompilation: no Title: Manipulation of Codelink microarray data Description: This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software. biocViews: Microarray, OneChannel, DataImport, Preprocessing Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/codelink VignetteBuilder: knitr BugReports: https://github.com/ddiez/codelink/issues git_url: https://git.bioconductor.org/packages/codelink git_branch: RELEASE_3_22 git_last_commit: d9a0b2d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/codelink_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/codelink_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/codelink_1.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/codelink_1.78.0.tgz vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf, vignettes/codelink/inst/doc/Codelink_Legacy.pdf vignetteTitles: Codelink Intruction, Codelink Legacy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/codelink/inst/doc/Codelink_Introduction.R, vignettes/codelink/inst/doc/Codelink_Legacy.R suggestsMe: MAQCsubset dependencyCount: 48 Package: CODEX Version: 1.42.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: d2b9af848193d343cf81468ca2c863de NeedsCompilation: no Title: A Normalization and Copy Number Variation Detection Method for Whole Exome Sequencing Description: A normalization and copy number variation calling procedure for whole exome DNA sequencing data. CODEX relies on the availability of multiple samples processed using the same sequencing pipeline for normalization, and does not require matched controls. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and targeting and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. biocViews: ImmunoOncology, ExomeSeq, Normalization, QualityControl, CopyNumberVariation Author: Yuchao Jiang, Nancy R. Zhang Maintainer: Yuchao Jiang git_url: https://git.bioconductor.org/packages/CODEX git_branch: RELEASE_3_22 git_last_commit: 27808f2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CODEX_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CODEX_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CODEX_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CODEX_1.42.0.tgz vignettes: vignettes/CODEX/inst/doc/CODEX_vignettes.pdf vignetteTitles: Using CODEX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CODEX/inst/doc/CODEX_vignettes.R dependsOnMe: iCNV dependencyCount: 61 Package: CoGAPS Version: 3.30.0 Depends: R (>= 3.5.0) Imports: BiocParallel, cluster, methods, gplots, graphics, grDevices, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tools, utils, rhdf5, dplyr, fgsea, forcats, ggplot2 LinkingTo: Rcpp, BH, testthat Suggests: testthat, knitr, rmarkdown, BiocStyle, SeuratObject, BiocFileCache, xml2 License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 817a4f23456f4e780fd4bc4cf3003b9c NeedsCompilation: yes Title: Coordinated Gene Activity in Pattern Sets Description: Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis. biocViews: GeneExpression, Transcription, GeneSetEnrichment, DifferentialExpression, Bayesian, Clustering, TimeCourse, RNASeq, Microarray, MultipleComparison, DimensionReduction, ImmunoOncology Author: Jeanette Johnson, Ashley Tsang, Jacob Mitchell, Thomas Sherman, Wai-shing Lee, Conor Kelton, Ondrej Maxian, Jacob Carey, Genevieve Stein-O'Brien, Michael Considine, Maggie Wodicka, John Stansfield, Shawn Sivy, Carlo Colantuoni, Alexander Favorov, Mike Ochs, Elana Fertig Maintainer: Elana J. Fertig , Thomas D. Sherman , Jeanette Johnson , Dmitrijs Lvovs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoGAPS git_branch: RELEASE_3_22 git_last_commit: 88653e4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CoGAPS_3.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CoGAPS_3.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CoGAPS_3.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CoGAPS_3.30.0.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPS.html vignetteTitles: CoGAPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPS.R suggestsMe: projectR, SpaceMarkers dependencyCount: 91 Package: cogena Version: 1.44.0 Depends: R (>= 3.6), cluster, ggplot2, kohonen Imports: methods, class, gplots, mclust, amap, apcluster, foreach, parallel, doParallel, fastcluster, corrplot, biwt, Biobase, reshape2, stringr, tibble, tidyr, dplyr, devtools Suggests: knitr, rmarkdown (>= 2.1) License: LGPL-3 Archs: x64 MD5sum: af2d44b5158471c11b721dfe2bfb76a4 NeedsCompilation: no Title: co-expressed gene-set enrichment analysis Description: cogena is a workflow for co-expressed gene-set enrichment analysis. It aims to discovery smaller scale, but highly correlated cellular events that may be of great biological relevance. A novel pipeline for drug discovery and drug repositioning based on the cogena workflow is proposed. Particularly, candidate drugs can be predicted based on the gene expression of disease-related data, or other similar drugs can be identified based on the gene expression of drug-related data. Moreover, the drug mode of action can be disclosed by the associated pathway analysis. In summary, cogena is a flexible workflow for various gene set enrichment analysis for co-expressed genes, with a focus on pathway/GO analysis and drug repositioning. biocViews: Clustering, GeneSetEnrichment, GeneExpression, Visualization, Pathways, KEGG, GO, Microarray, Sequencing, SystemsBiology, DataRepresentation, DataImport Author: Zhilong Jia [aut, cre], Michael Barnes [aut] Maintainer: Zhilong Jia URL: https://github.com/zhilongjia/cogena VignetteBuilder: knitr BugReports: https://github.com/zhilongjia/cogena/issues git_url: https://git.bioconductor.org/packages/cogena git_branch: RELEASE_3_22 git_last_commit: 5672c90 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cogena_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cogena_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cogena_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cogena_1.44.0.tgz vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf, vignettes/cogena/inst/doc/cogena-vignette_html.html vignetteTitles: a workflow of cogena, cogena,, a workflow for gene set enrichment analysis of co-expressed genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogena/inst/doc/cogena-vignette_html.R, vignettes/cogena/inst/doc/cogena-vignette_pdf.R dependencyCount: 143 Package: cogeqc Version: 1.13.0 Depends: R (>= 4.2.0) Imports: utils, graphics, stats, methods, reshape2, ggplot2, scales, ggtree, patchwork, igraph, rlang, ggbeeswarm, jsonlite, Biostrings Suggests: testthat (>= 3.0.0), sessioninfo, knitr, BiocStyle, rmarkdown, covr License: GPL-3 Archs: x64 MD5sum: 39e20d6fd7d02ec016599c491caa198f NeedsCompilation: no Title: Systematic quality checks on comparative genomics analyses Description: cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualization functions to explore QC summary statistics. biocViews: Software, GenomeAssembly, ComparativeGenomics, FunctionalGenomics, Phylogenetics, QualityControl, Network Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/cogeqc SystemRequirements: BUSCO (>= 5.1.3) VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/cogeqc git_url: https://git.bioconductor.org/packages/cogeqc git_branch: devel git_last_commit: 2630f82 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/cogeqc_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cogeqc_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cogeqc_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cogeqc_1.13.0.tgz vignettes: vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.html, vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.html, vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.html vignetteTitles: Assessing genome assembly and annotation quality, Assessing orthogroup inference, Assessing synteny identification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.R, vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.R, vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.R dependencyCount: 94 Package: Cogito Version: 1.16.0 Depends: R (>= 4.1), GenomicRanges, jsonlite, GenomicFeatures, entropy Imports: BiocManager, rmarkdown, Seqinfo, S4Vectors, AnnotationDbi, graphics, stats, utils, methods, magrittr, ggplot2, TxDb.Mmusculus.UCSC.mm9.knownGene Suggests: BiocStyle, knitr, markdown, testthat (>= 3.0.0) License: LGPL-3 Archs: x64 MD5sum: 660909353252c57df590b20be070e954 NeedsCompilation: no Title: Compare genomic intervals tool - Automated, complete, reproducible and clear report about genomic and epigenomic data sets Description: Biological studies often consist of multiple conditions which are examined with different laboratory set ups like RNA-sequencing or ChIP-sequencing. To get an overview about the whole resulting data set, Cogito provides an automated, complete, reproducible and clear report about all samples and basic comparisons between all different samples. This report can be used as documentation about the data set or as starting point for further custom analysis. biocViews: FunctionalGenomics, GeneRegulation, Software, Sequencing Author: Annika Bürger [cre, aut] Maintainer: Annika Bürger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cogito git_branch: RELEASE_3_22 git_last_commit: 2320bbf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Cogito_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Cogito_1.15.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Cogito_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Cogito_1.16.0.tgz vignettes: vignettes/Cogito/inst/doc/Cogito.html vignetteTitles: Cogito: Compare annotated genomic intervals tool hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cogito/inst/doc/Cogito.R dependencyCount: 105 Package: coGPS Version: 1.54.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: 2421a3b650e69448cdea848b86c65116 NeedsCompilation: no Title: cancer outlier Gene Profile Sets Description: Gene Set Enrichment Analysis of P-value based statistics for outlier gene detection in dataset merged from multiple studies biocViews: Microarray, DifferentialExpression Author: Yingying Wei, Michael Ochs Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/coGPS git_branch: RELEASE_3_22 git_last_commit: a264d73 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/coGPS_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/coGPS_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/coGPS_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/coGPS_1.54.0.tgz vignettes: vignettes/coGPS/inst/doc/coGPS.pdf vignetteTitles: coGPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coGPS/inst/doc/coGPS.R dependencyCount: 2 Package: cola Version: 2.16.0 Depends: R (>= 4.0.0) Imports: grDevices, graphics, grid, stats, utils, ComplexHeatmap (>= 2.5.4), matrixStats (>= 1.2.0), GetoptLong, circlize (>= 0.4.7), GlobalOptions (>= 0.1.0), clue, parallel, RColorBrewer, cluster, skmeans, png, mclust, crayon, methods, xml2, microbenchmark, httr, knitr (>= 1.4.0), markdown (>= 1.6), digest, impute, brew, Rcpp (>= 0.11.0), BiocGenerics, eulerr, foreach, doParallel, doRNG, irlba LinkingTo: Rcpp Suggests: genefilter, mvtnorm, testthat (>= 0.3), samr, pamr, kohonen, NMF, WGCNA, Rtsne, umap, clusterProfiler, ReactomePA, DOSE, AnnotationDbi, gplots, hu6800.db, BiocManager, data.tree, dendextend, Polychrome, rmarkdown, simplifyEnrichment, cowplot, flexclust, randomForest, e1071 License: MIT + file LICENSE Archs: x64 MD5sum: a350a2aa6fb9724f0797d3d9a279ec72 NeedsCompilation: yes Title: A Framework for Consensus Partitioning Description: Subgroup classification is a basic task in genomic data analysis, especially for gene expression and DNA methylation data analysis. It can also be used to test the agreement to known clinical annotations, or to test whether there exist significant batch effects. The cola package provides a general framework for subgroup classification by consensus partitioning. It has the following features: 1. It modularizes the consensus partitioning processes that various methods can be easily integrated. 2. It provides rich visualizations for interpreting the results. 3. It allows running multiple methods at the same time and provides functionalities to straightforward compare results. 4. It provides a new method to extract features which are more efficient to separate subgroups. 5. It automatically generates detailed reports for the complete analysis. 6. It allows applying consensus partitioning in a hierarchical manner. biocViews: Clustering, GeneExpression, Classification, Software Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/cola, https://jokergoo.github.io/cola_collection/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cola git_branch: RELEASE_3_22 git_last_commit: 796a58f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cola_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cola_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cola_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cola_2.16.0.tgz vignettes: vignettes/cola/inst/doc/cola.html vignetteTitles: The cola package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: InteractiveComplexHeatmap, simplifyEnrichment dependencyCount: 67 Package: combi Version: 1.22.0 Depends: R (>= 4.0), DBI Imports: ggplot2, nleqslv, phyloseq, tensor, stats, limma, Matrix (>= 1.6.0), BB, reshape2, alabama, cobs, Biobase, vegan, grDevices, graphics, methods, SummarizedExperiment Suggests: knitr, rmarkdown, testthat License: GPL-2 Archs: x64 MD5sum: e6f8be7d787cc4dcbe069f90e4121d20 NeedsCompilation: no Title: Compositional omics model based visual integration Description: This explorative ordination method combines quasi-likelihood estimation, compositional regression models and latent variable models for integrative visualization of several omics datasets. Both unconstrained and constrained integration are available. The results are shown as interpretable, compositional multiplots. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization, Metabolomics Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/combi/issues git_url: https://git.bioconductor.org/packages/combi git_branch: RELEASE_3_22 git_last_commit: e4cb095 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/combi_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/combi_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/combi_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/combi_1.22.0.tgz vignettes: vignettes/combi/inst/doc/combi.html vignetteTitles: Manual for the combi pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/combi/inst/doc/combi.R dependencyCount: 91 Package: coMethDMR Version: 1.14.0 Depends: R (>= 4.1) Imports: AnnotationHub, BiocParallel, bumphunter, ExperimentHub, GenomicRanges, IRanges, lmerTest, methods, stats, utils Suggests: BiocStyle, corrplot, knitr, rmarkdown, testthat, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 License: GPL-3 Archs: x64 MD5sum: 2659bae5e14ae3cc0a33a6f30cbe1608 NeedsCompilation: no Title: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies Description: coMethDMR identifies genomic regions associated with continuous phenotypes by optimally leverages covariations among CpGs within predefined genomic regions. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first without using any outcome information. Next, coMethDMR tests association between methylation within the sub-region and continuous phenotype using a random coefficient mixed effects model, which models both variations between CpG sites within the region and differential methylation simultaneously. biocViews: DNAMethylation, Epigenetics, MethylationArray, DifferentialMethylation, GenomeWideAssociation Author: Fernanda Veitzman [cre], Lissette Gomez [aut], Tiago Silva [aut], Ning Lijiao [ctb], Boissel Mathilde [ctb], Lily Wang [aut], Gabriel Odom [aut] Maintainer: Fernanda Veitzman URL: https://github.com/TransBioInfoLab/coMethDMR VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/coMethDMR/issues git_url: https://git.bioconductor.org/packages/coMethDMR git_branch: RELEASE_3_22 git_last_commit: 052092e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/coMethDMR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/coMethDMR_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/coMethDMR_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/coMethDMR_1.14.0.tgz vignettes: vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.html, vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.html vignetteTitles: "Introduction to coMethDMR", "coMethDMR with Parallel Computing" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.R, vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.R dependencyCount: 129 Package: COMPASS Version: 1.48.0 Depends: R (>= 3.0.3) Imports: methods, Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils, pdist, magrittr, reshape2, dplyr, tidyr, rlang, BiocStyle, rmarkdown, foreach, coda LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.33.1), flowCore, ncdfFlow, shiny, testthat, devtools, flowWorkspaceData, ggplot2, progress License: Artistic-2.0 MD5sum: fed375822e102b3f217c0f654f4f1153 NeedsCompilation: yes Title: Combinatorial Polyfunctionality Analysis of Single Cells Description: COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination. biocViews: ImmunoOncology, FlowCytometry Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap) Maintainer: Greg Finak VignetteBuilder: knitr BugReports: https://github.com/RGLab/COMPASS/issues git_url: https://git.bioconductor.org/packages/COMPASS git_branch: RELEASE_3_22 git_last_commit: 70d9f7d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/COMPASS_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/COMPASS_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/COMPASS_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/COMPASS_1.48.0.tgz vignettes: vignettes/COMPASS/inst/doc/SimpleCOMPASS.pdf, vignettes/COMPASS/inst/doc/COMPASS.html vignetteTitles: SimpleCOMPASS, COMPASS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R, vignettes/COMPASS/inst/doc/SimpleCOMPASS.R dependencyCount: 69 Package: compcodeR Version: 1.46.0 Depends: R (>= 4.0), sm Imports: knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods, stats, utils, ape, phylolm, matrixStats, grDevices, graphics, rmarkdown, shiny, shinydashboard Suggests: BiocStyle, EBSeq, DESeq2 (>= 1.1.31), genefilter, NOISeq, TCC, NBPSeq (>= 0.3.0), phytools, phangorn, testthat, ggtree, tidytree, statmod, covr, sva, tcltk Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: d93b9ca2e580de9e7029e6449f8fe996 NeedsCompilation: no Title: RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods Description: This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data. Finally, it provides convenient interfaces to several packages for performing the differential expression analysis. These can also be used as templates for setting up and running a user-defined differential analysis workflow within the framework of the package. biocViews: ImmunoOncology, RNASeq, DifferentialExpression Author: Charlotte Soneson [aut, cre] (ORCID: ), Paul Bastide [aut] (ORCID: ), Mélina Gallopin [aut] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/compcodeR VignetteBuilder: knitr BugReports: https://github.com/csoneson/compcodeR/issues git_url: https://git.bioconductor.org/packages/compcodeR git_branch: RELEASE_3_22 git_last_commit: 0bb9bee git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/compcodeR_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/compcodeR_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/compcodeR_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/compcodeR_1.46.0.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.html, vignettes/compcodeR/inst/doc/phylocompcodeR.html vignetteTitles: compcodeR, phylocompcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R, vignettes/compcodeR/inst/doc/phylocompcodeR.R dependencyCount: 98 Package: compEpiTools Version: 1.44.0 Depends: R (>= 3.5.0), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, Seqinfo Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: 1f9a215eaddc7075c44615cd6b3e4808 NeedsCompilation: no Title: Tools for computational epigenomics Description: Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples. biocViews: GeneExpression, Sequencing, Visualization, GenomeAnnotation, Coverage Author: Mattia Pelizzola [aut], Kamal Kishore [aut], Mattia Furlan [ctb, cre] Maintainer: Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/compEpiTools git_branch: RELEASE_3_22 git_last_commit: 75ae4a8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/compEpiTools_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/compEpiTools_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/compEpiTools_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/compEpiTools_1.44.0.tgz vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf vignetteTitles: compEpiTools.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compEpiTools/inst/doc/compEpiTools.R dependencyCount: 164 Package: ComplexHeatmap Version: 2.26.0 Depends: R (>= 4.0.0), methods, grid, graphics, stats, grDevices Imports: circlize (>= 0.4.14), GetoptLong, colorspace, clue, RColorBrewer, GlobalOptions (>= 0.1.0), png, digest, IRanges, matrixStats, foreach, doParallel, codetools Suggests: testthat (>= 1.0.0), knitr, markdown, dendsort, jpeg, tiff, fastcluster, EnrichedHeatmap, dendextend (>= 1.0.1), grImport, grImport2, glue, GenomicRanges, gridtext, pheatmap (>= 1.0.12), gridGraphics, gplots, rmarkdown, Cairo, magick License: MIT + file LICENSE MD5sum: f3bb08cb15a30d2c60bc85f7aca37345 NeedsCompilation: no Title: Make Complex Heatmaps Description: Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/ComplexHeatmap, https://jokergoo.github.io/ComplexHeatmap-reference/book/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComplexHeatmap git_branch: RELEASE_3_22 git_last_commit: 4803c6d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ComplexHeatmap_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ComplexHeatmap_2.25.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ComplexHeatmap_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ComplexHeatmap_2.26.0.tgz vignettes: vignettes/ComplexHeatmap/inst/doc/complex_heatmap.html, vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.html vignetteTitles: complex_heatmap.html, Most probably asked questions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.R dependsOnMe: AMARETTO, EnrichedHeatmap, InteractiveComplexHeatmap, multistateQTL, recoup, sechm, countToFPKM importsMe: airpart, ASURAT, barbieQ, bettr, BindingSiteFinder, BioNERO, blacksheepr, BloodGen3Module, BreastSubtypeR, BulkSignalR, CATALYST, CCPlotR, celda, CeTF, chevreulPlot, ClustAll, COCOA, cola, COTAN, CTexploreR, cytoKernel, Damsel, dar, DEGreport, DEP, diffcyt, diffUTR, dinoR, dominoSignal, ELMER, ELViS, epiregulon.extra, fCCAC, FLAMES, gCrisprTools, GenomicPlot, GenomicSuperSignature, geyser, gmoviz, GRaNIE, gVenn, hermes, hoodscanR, HybridExpress, iModMix, InterCellar, iSEE, MAPFX, markeR, MatrixQCvis, MesKit, mitology, MOMA, monaLisa, Moonlight2R, MOSClip, MPAC, MultiRNAflow, muscat, musicatk, MWASTools, nipalsMCIA, pathlinkR, PathoStat, PeacoQC, pipeComp, POMA, PRONE, RFLOMICS, RiboCrypt, RUCova, scafari, scRNAseqApp, segmenter, shinyDSP, signifinder, simona, simplifyEnrichment, singleCellTK, sparrow, SPONGE, TBSignatureProfiler, TMSig, ViSEAGO, Xeva, YAPSA, spatialLIBD, autoGO, bulkAnalyseR, cellGeometry, coda4microbiome, conos, DeSciDe, DiscreteGapStatistic, GAPR, GSSTDA, karyotapR, mineSweepR, missoNet, MitoHEAR, MKomics, ogrdbstats, Path.Analysis, PCAPAM50, pkgndep, RepeatedHighDim, rKOMICS, RNAseqQC, RVA, scITD, SeuratExplorer, sigQC, SingleCellComplexHeatMap, spatialGE, spiralize, tidyHeatmap, TransProR, visxhclust, wilson suggestsMe: artMS, bambu, clustifyr, CNVRanger, Coralysis, demuxSNP, dittoSeq, EnrichmentBrowser, gtrellis, HilbertCurve, mastR, miaViz, msImpute, plotgardener, projectR, QFeatures, raer, scDblFinder, scDiagnostics, scLANE, SpaceMarkers, SPIAT, TCGAbiolinks, TCGAutils, weitrix, curatedPCaData, LegATo, NanoporeRNASeq, ProteinGymR, BeeBDC, CIARA, circlize, circlizePlus, ClustAssess, ClusterGVis, ConsensusOPLS, eclust, ggpicrust2, ggsector, grandR, inferCSN, IOHanalyzer, metasnf, multipanelfigure, plotthis, rliger, scCustomize, SCpubr, sfcurve, singleCellHaystack, SpatialDDLS, tinyarray dependencyCount: 29 Package: CompoundDb Version: 1.14.0 Depends: R (>= 4.1), methods, AnnotationFilter, S4Vectors Imports: BiocGenerics, ChemmineR, tibble, jsonlite, dplyr, DBI, dbplyr, RSQLite, Biobase, ProtGenerics (>= 1.35.3), xml2, IRanges, Spectra (>= 1.15.10), MsCoreUtils, MetaboCoreUtils, BiocParallel, stringi Suggests: knitr, rmarkdown, testthat, BiocStyle (>= 2.5.19), MsBackendMgf License: Artistic-2.0 MD5sum: 2cd6e1e466d1f1289f79976693b6a589 NeedsCompilation: no Title: Creating and Using (Chemical) Compound Annotation Databases Description: CompoundDb provides functionality to create and use (chemical) compound annotation databases from a variety of different sources such as LipidMaps, HMDB, ChEBI or MassBank. The database format allows to store in addition MS/MS spectra along with compound information. The package provides also a backend for Bioconductor's Spectra package and allows thus to match experimetal MS/MS spectra against MS/MS spectra in the database. Databases can be stored in SQLite format and are thus portable. biocViews: MassSpectrometry, Metabolomics, Annotation Author: Jan Stanstrup [aut] (ORCID: ), Johannes Rainer [aut, cre] (ORCID: ), Josep M. Badia [ctb] (ORCID: ), Roger Gine [aut] (ORCID: ), Andrea Vicini [aut] (ORCID: ), Prateek Arora [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/CompoundDb VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/CompoundDb/issues git_url: https://git.bioconductor.org/packages/CompoundDb git_branch: RELEASE_3_22 git_last_commit: 287093d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CompoundDb_1.14.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CompoundDb_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CompoundDb_1.14.0.tgz vignettes: vignettes/CompoundDb/inst/doc/CompoundDb-usage.html, vignettes/CompoundDb/inst/doc/create-compounddb.html vignetteTitles: Usage of Annotation Resources with the CompoundDb Package, Creating CompoundDb annotation resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompoundDb/inst/doc/CompoundDb-usage.R, vignettes/CompoundDb/inst/doc/create-compounddb.R importsMe: MetaboAnnotation suggestsMe: AHMassBank, AnnotationHub dependencyCount: 104 Package: ComPrAn Version: 1.18.0 Imports: data.table, dplyr, forcats, ggplot2, magrittr, purrr, tidyr, rlang, stringr, shiny, DT, RColorBrewer, VennDiagram, rio, scales, shinydashboard, shinyjs, stats, tibble, grid Suggests: testthat (>= 2.1.0), knitr, rmarkdown License: MIT + file LICENSE MD5sum: b3254858a5f847fbfd30f2b6725a666b NeedsCompilation: no Title: Complexome Profiling Analysis package Description: This package is for analysis of SILAC labeled complexome profiling data. It uses peptide table in tab-delimited format as an input and produces ready-to-use tables and plots. biocViews: MassSpectrometry, Proteomics, Visualization Author: Rick Scavetta [aut], Petra Palenikova [aut, cre] (ORCID: ) Maintainer: Petra Palenikova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComPrAn git_branch: RELEASE_3_22 git_last_commit: 16928c0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ComPrAn_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ComPrAn_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ComPrAn_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ComPrAn_1.18.0.tgz vignettes: vignettes/ComPrAn/inst/doc/fileFormats.html, vignettes/ComPrAn/inst/doc/proteinWorkflow.html, vignettes/ComPrAn/inst/doc/SILACcomplexomics.html vignetteTitles: fileFormats.html, Protein workflow, SILAC complexomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComPrAn/inst/doc/fileFormats.R, vignettes/ComPrAn/inst/doc/proteinWorkflow.R, vignettes/ComPrAn/inst/doc/SILACcomplexomics.R dependencyCount: 100 Package: compSPOT Version: 1.8.0 Depends: R (>= 4.3.0) Imports: stats, base, ggplot2, plotly, magrittr, ggpubr, gridExtra, utils, data.table Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: a66384a7a2827f60e4dd871c4c5449f0 NeedsCompilation: no Title: compSPOT: Tool for identifying and comparing significantly mutated genomic hotspots Description: Clonal cell groups share common mutations within cancer, precancer, and even clinically normal appearing tissues. The frequency and location of these mutations may predict prognosis and cancer risk. It has also been well established that certain genomic regions have increased sensitivity to acquiring mutations. Mutation-sensitive genomic regions may therefore serve as markers for predicting cancer risk. This package contains multiple functions to establish significantly mutated hotspots, compare hotspot mutation burden between samples, and perform exploratory data analysis of the correlation between hotspot mutation burden and personal risk factors for cancer, such as age, gender, and history of carcinogen exposure. This package allows users to identify robust genomic markers to help establish cancer risk. biocViews: Software, Technology, Sequencing, DNASeq, WholeGenome, Classification, SingleCell, Survival, MultipleComparison Author: Sydney Grant [aut, cre] (ORCID: ), Ella Sampson [aut], Rhea Rodrigues [aut] (ORCID: ), Gyorgy Paragh [aut] (ORCID: ) Maintainer: Sydney Grant URL: https://github.com/sydney-grant/compSPOT VignetteBuilder: knitr BugReports: https://github.com/sydney-grant/compSPOT/issues git_url: https://git.bioconductor.org/packages/compSPOT git_branch: RELEASE_3_22 git_last_commit: 762cc92 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/compSPOT_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/compSPOT_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/compSPOT_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/compSPOT_1.8.0.tgz vignettes: vignettes/compSPOT/inst/doc/compSPOT-vignette.html vignetteTitles: compSPOT-Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compSPOT/inst/doc/compSPOT-vignette.R dependencyCount: 113 Package: concordexR Version: 1.10.0 Depends: R (>= 4.5.0) Imports: BiocGenerics, BiocNeighbors, BiocParallel, bluster, cli, DelayedArray, Matrix, methods, purrr, rlang, SingleCellExperiment, sparseMatrixStats, SpatialExperiment, SummarizedExperiment Suggests: BiocManager, BiocStyle, ggplot2, glue, knitr, mbkmeans, patchwork, rmarkdown, scater, SFEData, SpatialFeatureExperiment, TENxPBMCData, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: b49795b9ba2f99b1dab6cc3d9852dd62 NeedsCompilation: no Title: Identify Spatial Homogeneous Regions with concordex Description: Spatial homogeneous regions (SHRs) in tissues are domains that are homogenous with respect to cell type composition. We present a method for identifying SHRs using spatial transcriptomics data, and demonstrate that it is efficient and effective at finding SHRs for a wide variety of tissue types. concordex relies on analysis of k-nearest-neighbor (kNN) graphs. The tool is also useful for analysis of non-spatial transcriptomics data, and can elucidate the extent of concordance between partitions of cells derived from clustering algorithms, and transcriptomic similarity as represented in kNN graphs. biocViews: SingleCell, Clustering, Spatial, Transcriptomics Author: Kayla Jackson [aut, cre] (ORCID: ), A. Sina Booeshaghi [aut] (ORCID: ), Angel Galvez-Merchan [aut] (ORCID: ), Lambda Moses [aut] (ORCID: ), Alexandra Kim [ctb], Laura Luebbert [ctb] (ORCID: ), Lior Pachter [aut, rev, ths] (ORCID: ) Maintainer: Kayla Jackson URL: https://github.com/pachterlab/concordexR, https://pachterlab.github.io/concordexR/ VignetteBuilder: knitr BugReports: https://github.com/pachterlab/concordexR/issues git_url: https://git.bioconductor.org/packages/concordexR git_branch: RELEASE_3_22 git_last_commit: 9ea0373 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/concordexR_1.10.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/concordexR_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/concordexR_1.10.0.tgz vignettes: vignettes/concordexR/inst/doc/concordex-nonspatial.html, vignettes/concordexR/inst/doc/overview.html vignetteTitles: concordex-nonspatial, overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/concordexR/inst/doc/concordex-nonspatial.R, vignettes/concordexR/inst/doc/overview.R dependencyCount: 82 Package: condiments Version: 1.18.0 Depends: R (>= 4.0) Imports: slingshot (>= 1.9), mgcv, RANN, stats, SingleCellExperiment, SummarizedExperiment, utils, magrittr, dplyr (>= 1.0), Ecume (>= 0.9.1), methods, pbapply, matrixStats, BiocParallel, TrajectoryUtils, igraph, distinct Suggests: knitr, testthat, rmarkdown, covr, viridis, ggplot2, RColorBrewer, randomForest, tidyr, TSCAN, DelayedMatrixStats License: MIT + file LICENSE MD5sum: 4610734caf01bd72afe592f310e76530 NeedsCompilation: no Title: Differential Topology, Progression and Differentiation Description: This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an 'omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format. biocViews: RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Hector Roux de Bezieux [aut, cre] (ORCID: ), Koen Van den Berge [aut, ctb], Kelly Street [aut, ctb] Maintainer: Hector Roux de Bezieux URL: https://hectorrdb.github.io/condiments/index.html VignetteBuilder: knitr BugReports: https://github.com/HectorRDB/condiments/issues git_url: https://git.bioconductor.org/packages/condiments git_branch: RELEASE_3_22 git_last_commit: 02f3c88 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/condiments_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/condiments_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/condiments_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/condiments_1.18.0.tgz vignettes: vignettes/condiments/inst/doc/condiments.html, vignettes/condiments/inst/doc/controls.html, vignettes/condiments/inst/doc/examples.html vignetteTitles: The condiments workflow, Using condiments, Generating more examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/condiments/inst/doc/condiments.R, vignettes/condiments/inst/doc/controls.R, vignettes/condiments/inst/doc/examples.R dependencyCount: 158 Package: CONFESS Version: 1.38.0 Depends: R (>= 3.3),grDevices,utils,stats,graphics Imports: methods,changepoint,cluster,contrast,data.table(>= 1.9.7),ecp,EBImage,flexmix,flowCore,flowClust,flowMeans,flowMerge,flowPeaks,foreach,ggplot2,grid,limma,MASS,moments,outliers,parallel,plotrix,raster,readbitmap,reshape2,SamSPECTRAL,waveslim,wavethresh,zoo Suggests: BiocStyle, knitr, rmarkdown, CONFESSdata License: GPL-2 MD5sum: e1c89d621c8c6b375b6c94613c7432fa NeedsCompilation: no Title: Cell OrderiNg by FluorEScence Signal Description: Single Cell Fluidigm Spot Detector. biocViews: ImmunoOncology, GeneExpression,DataImport,CellBiology,Clustering,RNASeq,QualityControl,Visualization,TimeCourse,Regression,Classification Author: Diana LOW and Efthimios MOTAKIS Maintainer: Diana LOW VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CONFESS git_branch: RELEASE_3_22 git_last_commit: 5c1dbd9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CONFESS_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CONFESS_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CONFESS_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CONFESS_1.38.0.tgz vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf, vignettes/CONFESS/inst/doc/vignette.html vignetteTitles: CONFESS, CONFESS Walkthrough hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R, vignettes/CONFESS/inst/doc/vignette.R dependencyCount: 147 Package: consensus Version: 1.28.0 Depends: R (>= 3.5), RColorBrewer Imports: matrixStats, gplots, grDevices, methods, graphics, stats, utils Suggests: knitr, RUnit, rmarkdown, BiocGenerics License: BSD_3_clause + file LICENSE MD5sum: 12aa61a62a75bd36c747bbadbe003598 NeedsCompilation: no Title: Cross-platform consensus analysis of genomic measurements via interlaboratory testing method Description: An implementation of the American Society for Testing and Materials (ASTM) Standard E691 for interlaboratory testing procedures, designed for cross-platform genomic measurements. Given three (3) or more genomic platforms or laboratory protocols, this package provides interlaboratory testing procedures giving per-locus comparisons for sensitivity and precision between platforms. biocViews: QualityControl, Regression, DataRepresentation, GeneExpression, Microarray, RNASeq Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensus git_branch: RELEASE_3_22 git_last_commit: 41b16d0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/consensus_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/consensus_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/consensus_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/consensus_1.28.0.tgz vignettes: vignettes/consensus/inst/doc/consensus.pdf vignetteTitles: Fitting and visualising row-linear models with \texttt{consensus} hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/consensus/inst/doc/consensus.R dependencyCount: 12 Package: ConsensusClusterPlus Version: 1.74.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: cc937b82c2629f47e2153a409404293c NeedsCompilation: no Title: ConsensusClusterPlus Description: algorithm for determining cluster count and membership by stability evidence in unsupervised analysis biocViews: Software, Clustering Author: Matt Wilkerson , Peter Waltman Maintainer: Matt Wilkerson git_url: https://git.bioconductor.org/packages/ConsensusClusterPlus git_branch: RELEASE_3_22 git_last_commit: 4015c97 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ConsensusClusterPlus_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ConsensusClusterPlus_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ConsensusClusterPlus_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ConsensusClusterPlus_1.74.0.tgz vignettes: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf vignetteTitles: ConsensusClusterPlus Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.R importsMe: CATALYST, ChromSCape, DEGreport, FlowSOM, PDATK, DeSousa2013, iSubGen, longmixr, neatmaps, scRNAtools suggestsMe: TCGAbiolinks dependencyCount: 10 Package: consensusDE Version: 1.28.0 Depends: R (>= 3.5), BiocGenerics Imports: airway, AnnotationDbi, BiocParallel, Biobase, Biostrings, data.table, dendextend, DESeq2 (>= 1.20.0), EDASeq, ensembldb, edgeR, EnsDb.Hsapiens.v86, GenomicAlignments, GenomicFeatures, limma, org.Hs.eg.db, pcaMethods, RColorBrewer, Rsamtools, RUVSeq, S4Vectors, stats, SummarizedExperiment, TxDb.Dmelanogaster.UCSC.dm3.ensGene, utils Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 211e2c434199a20ea931aec10ec0d00e NeedsCompilation: no Title: RNA-seq analysis using multiple algorithms Description: This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports "Voom", "EdgeR" and "DESeq". It uses RUV-seq (optional) to remove unwanted sources of variation. biocViews: Transcriptomics, MultipleComparison, Clustering, Sequencing, Software Author: Ashley J. Waardenberg [aut, cre], Martha M. Cooper [ctb] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensusDE git_branch: RELEASE_3_22 git_last_commit: 7c6b138 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/consensusDE_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/consensusDE_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/consensusDE_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/consensusDE_1.28.0.tgz vignettes: vignettes/consensusDE/inst/doc/consensusDE.html vignetteTitles: consensusDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusDE/inst/doc/consensusDE.R dependencyCount: 145 Package: consensusOV Version: 1.32.0 Depends: R (>= 3.6) Imports: Biobase, GSVA (>= 1.50.0), gdata, genefu, limma, matrixStats, randomForest, stats, utils, methods, BiocParallel Suggests: BiocStyle, ggplot2, knitr, rmarkdown, magick License: Artistic-2.0 Archs: x64 MD5sum: b922bb9935762d89bc907d77c6f898f0 NeedsCompilation: no Title: Gene expression-based subtype classification for high-grade serous ovarian cancer Description: This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype. biocViews: Classification, Clustering, DifferentialExpression, GeneExpression, Microarray, Transcriptomics Author: Gregory M Chen [aut], Lavanya Kannan [aut], Ludwig Geistlinger [aut], Victor Kofia [aut], Levi Waldron [aut], Christopher Eeles [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/consensusOV VignetteBuilder: knitr BugReports: https://github.com/bhklab/consensusOV/issues git_url: https://git.bioconductor.org/packages/consensusOV git_branch: RELEASE_3_22 git_last_commit: 04a4249 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/consensusOV_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/consensusOV_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/consensusOV_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/consensusOV_1.32.0.tgz vignettes: vignettes/consensusOV/inst/doc/consensusOV.html vignetteTitles: Molecular subtyping for ovarian cancer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusOV/inst/doc/consensusOV.R importsMe: signifinder dependencyCount: 160 Package: consensusSeekeR Version: 1.38.0 Depends: R (>= 3.5.0), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: Seqinfo, rtracklayer, stringr, S4Vectors, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 628babaf25ce724e010695bef7004ec7 NeedsCompilation: no Title: Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges Description: This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. In genomic analysis where feature identification generates a position value surrounded by a genomic range, such as ChIP-Seq peaks and nucleosome positions, the replication of an experiment may result in slight differences between predicted values. This package enables the conciliation of the results into consensus regions. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, Transcription, PeakDetection, Sequencing, Coverage Author: Astrid Deschênes [cre, aut] (ORCID: ), Fabien Claude Lamaze [ctb], Pascal Belleau [aut] (ORCID: ), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/consensusSeekeR VignetteBuilder: knitr BugReports: https://github.com/adeschen/consensusSeekeR/issues git_url: https://git.bioconductor.org/packages/consensusSeekeR git_branch: RELEASE_3_22 git_last_commit: 0f072df git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/consensusSeekeR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/consensusSeekeR_1.37.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/consensusSeekeR_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/consensusSeekeR_1.38.0.tgz vignettes: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html vignetteTitles: Detection of consensus regions inside a group of experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R importsMe: RJMCMCNucleosomes suggestsMe: EpiCompare dependencyCount: 65 Package: consICA Version: 2.8.0 Depends: R (>= 4.2.0) Imports: fastICA (>= 1.2.1), sm, org.Hs.eg.db, GO.db, stats, SummarizedExperiment, BiocParallel, graph, ggplot2, methods, Rfast, pheatmap, survival, topGO, graphics, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat, Seurat License: MIT + file LICENSE MD5sum: 1eba2aac21b011fac4a8a5e6ad200d53 NeedsCompilation: no Title: consensus Independent Component Analysis Description: consICA implements a data-driven deconvolution method – consensus independent component analysis (ICA) to decompose heterogeneous omics data and extract features suitable for patient diagnostics and prognostics. The method separates biologically relevant transcriptional signals from technical effects and provides information about the cellular composition and biological processes. The implementation of parallel computing in the package ensures efficient analysis of modern multicore systems. biocViews: Technology, StatisticalMethod, Sequencing, RNASeq, Transcriptomics, Classification, FeatureExtraction Author: Petr V. Nazarov [aut, cre] (ORCID: ), Tony Kaoma [aut] (ORCID: ), Maryna Chepeleva [aut] (ORCID: ) Maintainer: Petr V. Nazarov VignetteBuilder: knitr BugReports: https://github.com/biomod-lih/consICA/issues git_url: https://git.bioconductor.org/packages/consICA git_branch: RELEASE_3_22 git_last_commit: e606646 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/consICA_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/consICA_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/consICA_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/consICA_2.8.0.tgz vignettes: vignettes/consICA/inst/doc/ConsICA.html vignetteTitles: The consICA package: User’s manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/consICA/inst/doc/ConsICA.R dependencyCount: 88 Package: CONSTANd Version: 1.18.0 Depends: R (>= 4.1) Suggests: BiocStyle, knitr, rmarkdown, tidyr, ggplot2, gridExtra, magick, Cairo, limma License: file LICENSE MD5sum: fcf17e9a407c2298a6cc3b1f724770c2 NeedsCompilation: no Title: Data normalization by matrix raking Description: Normalizes a data matrix `data` by raking (using the RAS method by Bacharach, see references) the Nrows by Ncols matrix such that the row means and column means equal 1. The result is a normalized data matrix `K=RAS`, a product of row mulipliers `R` and column multipliers `S` with the original matrix `A`. Missing information needs to be presented as `NA` values and not as zero values, because CONSTANd is able to ignore missing values when calculating the mean. Using CONSTANd normalization allows for the direct comparison of values between samples within the same and even across different CONSTANd-normalized data matrices. biocViews: MassSpectrometry, Cheminformatics, Normalization, Preprocessing, DifferentialExpression, Genetics, Transcriptomics, Proteomics Author: Joris Van Houtven [aut, trl], Geert Jan Bex [trl], Dirk Valkenborg [aut, cre] Maintainer: Dirk Valkenborg URL: qcquan.net/constand VignetteBuilder: knitr BugReports: https://github.com/PDiracDelta/CONSTANd/issues git_url: https://git.bioconductor.org/packages/CONSTANd git_branch: RELEASE_3_22 git_last_commit: 0419b0a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CONSTANd_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CONSTANd_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CONSTANd_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CONSTANd_1.18.0.tgz vignettes: vignettes/CONSTANd/inst/doc/CONSTANd.html vignetteTitles: CONSTANd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CONSTANd/inst/doc/CONSTANd.R dependencyCount: 0 Package: conumee Version: 1.44.0 Depends: R (>= 3.5.0), minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges, Seqinfo Suggests: BiocStyle, knitr, rmarkdown, minfiData, RCurl License: GPL (>= 2) MD5sum: ba4ba26a00a08ab6175bcc2a45199080 NeedsCompilation: no Title: Enhanced copy-number variation analysis using Illumina DNA methylation arrays Description: This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays. biocViews: CopyNumberVariation, DNAMethylation, MethylationArray, Microarray, Normalization, Preprocessing, QualityControl, Software Author: Volker Hovestadt, Marc Zapatka Maintainer: Volker Hovestadt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/conumee git_branch: RELEASE_3_22 git_last_commit: 9d7891e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/conumee_1.44.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/conumee_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/conumee_1.44.0.tgz vignettes: vignettes/conumee/inst/doc/conumee.html vignetteTitles: conumee hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conumee/inst/doc/conumee.R dependencyCount: 148 Package: convert Version: 1.86.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: 8fe13fef0eece104a44d69d86ac7d257 NeedsCompilation: no Title: Convert Microarray Data Objects Description: Define coerce methods for microarray data objects. biocViews: Infrastructure, Microarray, TwoChannel Author: Gordon Smyth , James Wettenhall , Yee Hwa (Jean Yang) , Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html git_url: https://git.bioconductor.org/packages/convert git_branch: RELEASE_3_22 git_last_commit: df54982 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/convert_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/convert_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/convert_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/convert_1.86.0.tgz vignettes: vignettes/convert/inst/doc/convert.pdf vignetteTitles: Converting Between Microarray Data Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: TurboNorm suggestsMe: dyebias, OLIN, dyebiasexamples dependencyCount: 11 Package: copa Version: 1.78.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: x64 MD5sum: 11ad10f59dee1224bb06852c355598de NeedsCompilation: yes Title: Functions to perform cancer outlier profile analysis. Description: COPA is a method to find genes that undergo recurrent fusion in a given cancer type by finding pairs of genes that have mutually exclusive outlier profiles. biocViews: OneChannel, TwoChannel, DifferentialExpression, Visualization Author: James W. MacDonald Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/copa git_branch: RELEASE_3_22 git_last_commit: d60925a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/copa_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/copa_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/copa_1.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/copa_1.78.0.tgz vignettes: vignettes/copa/inst/doc/copa.pdf vignetteTitles: copa Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copa/inst/doc/copa.R dependencyCount: 7 Package: CopyNumberPlots Version: 1.26.0 Depends: R (>= 3.6), karyoploteR Imports: regioneR, IRanges, Rsamtools, SummarizedExperiment, VariantAnnotation, methods, stats, GenomeInfoDb, GenomicRanges, cn.mops, rhdf5, utils Suggests: BiocStyle, knitr, rmarkdown, panelcn.mops, BSgenome.Hsapiens.UCSC.hg19.masked, DNAcopy, testthat License: Artistic-2.0 MD5sum: 27796bed1e8cbbc53994c6abdcd32ec5 NeedsCompilation: no Title: Create Copy-Number Plots using karyoploteR functionality Description: CopyNumberPlots have a set of functions extending karyoploteRs functionality to create beautiful, customizable and flexible plots of copy-number related data. biocViews: Visualization, CopyNumberVariation, Coverage, OneChannel, DataImport, Sequencing, DNASeq Author: Bernat Gel and Miriam Magallon Maintainer: Bernat Gel URL: https://github.com/bernatgel/CopyNumberPlots VignetteBuilder: knitr BugReports: https://github.com/bernatgel/CopyNumberPlots/issues git_url: https://git.bioconductor.org/packages/CopyNumberPlots git_branch: RELEASE_3_22 git_last_commit: 0687588 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CopyNumberPlots_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CopyNumberPlots_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CopyNumberPlots_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CopyNumberPlots_1.26.0.tgz vignettes: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.html vignetteTitles: CopyNumberPlots vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.R importsMe: CNVfilteR, CNViz dependencyCount: 137 Package: Coralysis Version: 1.0.0 Depends: R (>= 4.2.0) Imports: Matrix, aricode, LiblineaR, SparseM, ggplot2, umap, Rtsne, pheatmap, reshape2, dplyr, SingleCellExperiment, SummarizedExperiment, S4Vectors, methods, stats, utils, RANN, sparseMatrixStats, irlba, flexclust, scran, class, matrixStats, tidyr, cowplot, uwot, scatterpie, RColorBrewer, ggrastr, ggrepel, RSpectra, BiocParallel, withr Suggests: knitr, rmarkdown, bluster, ComplexHeatmap, circlize, scater, viridis, scRNAseq, SingleR, MouseGastrulationData, testthat (>= 3.0.0), BiocStyle, scrapper License: GPL-3 MD5sum: af322ed119441645a42b696e339eeadd NeedsCompilation: no Title: Coralysis sensitive identification of imbalanced cell types and states in single-cell data via multi-level integration Description: Coralysis is an R package featuring a multi-level integration algorithm for sensitive integration, reference-mapping, and cell-state identification in single-cell data. The multi-level integration algorithm is inspired by the process of assembling a puzzle - where one begins by grouping pieces based on low-to high-level features, such as color and shading, before looking into shape and patterns. This approach progressively blends the batch effects and separates cell types across multiple rounds of divisive clustering. biocViews: SingleCell, RNASeq, Proteomics, Transcriptomics, GeneExpression, BatchEffect, Clustering, Annotation, Classification, DifferentialExpression, DimensionReduction, Software Author: António Sousa [cre, aut] (ORCID: ), Johannes Smolander [ctb, aut] (ORCID: ), Sini Junttila [aut] (ORCID: ), Laura L Elo [aut] (ORCID: ) Maintainer: António Sousa URL: https://github.com/elolab/Coralysis, https://elolab.github.io/Coralysis/ VignetteBuilder: knitr BugReports: https://github.com/elolab/Coralysis/issues git_url: https://git.bioconductor.org/packages/Coralysis git_branch: RELEASE_3_22 git_last_commit: 146372a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Coralysis_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Coralysis_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Coralysis_1.0.0.tgz vignettes: vignettes/Coralysis/inst/doc/CellState.html, vignettes/Coralysis/inst/doc/Coralysis.html, vignettes/Coralysis/inst/doc/Integration.html, vignettes/Coralysis/inst/doc/RefMap.html vignetteTitles: Cell States, Get started, Integration, Reference-mapping hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Coralysis/inst/doc/CellState.R, vignettes/Coralysis/inst/doc/Coralysis.R, vignettes/Coralysis/inst/doc/Integration.R, vignettes/Coralysis/inst/doc/RefMap.R dependencyCount: 132 Package: coRdon Version: 1.28.0 Depends: R (>= 3.5) Imports: methods, stats, utils, Biostrings, Biobase, dplyr, stringr, purrr, ggplot2, data.table Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 78cc260e20fb3a90983f2bd619eef4f6 NeedsCompilation: no Title: Codon Usage Analysis and Prediction of Gene Expressivity Description: Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results. biocViews: Software, Metagenomics, GeneExpression, GeneSetEnrichment, GenePrediction, Visualization, KEGG, Pathways, Genetics CellBiology, BiomedicalInformatics, ImmunoOncology Author: Anamaria Elek [cre, aut], Maja Kuzman [aut], Kristian Vlahovicek [aut] Maintainer: Anamaria Elek URL: https://github.com/BioinfoHR/coRdon VignetteBuilder: knitr BugReports: https://github.com/BioinfoHR/coRdon/issues git_url: https://git.bioconductor.org/packages/coRdon git_branch: RELEASE_3_22 git_last_commit: 1ec9f4a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/coRdon_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/coRdon_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/coRdon_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/coRdon_1.28.0.tgz vignettes: vignettes/coRdon/inst/doc/coRdon.html vignetteTitles: coRdon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coRdon/inst/doc/coRdon.R importsMe: vhcub dependencyCount: 44 Package: CoreGx Version: 2.14.0 Depends: R (>= 4.1), BiocGenerics, SummarizedExperiment Imports: Biobase, S4Vectors, MultiAssayExperiment, MatrixGenerics, piano, BiocParallel, parallel, BumpyMatrix, checkmate, methods, stats, utils, graphics, grDevices, lsa, data.table, crayon, glue, rlang, bench Suggests: pander, markdown, BiocStyle, rmarkdown, knitr, formatR, testthat License: GPL (>= 3) MD5sum: 32b5d4c23e1c95c5c2b712e870d70260 NeedsCompilation: no Title: Classes and Functions to Serve as the Basis for Other 'Gx' Packages Description: A collection of functions and classes which serve as the foundation for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This package was created to abstract shared functionality from other lab package releases to increase ease of maintainability and reduce code repetition in current and future 'Gx' suite programs. Major features include a 'CoreSet' class, from which 'RadioSet' and 'PharmacoSet' are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, as well as: Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., Lupien, M., Goldenberg, A. (2015) . Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) . biocViews: Software, Pharmacogenomics, Classification, Survival Author: Jermiah Joseph [aut], Petr Smirnov [aut], Ian Smith [aut], Christopher Eeles [aut], Feifei Li [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoreGx git_branch: RELEASE_3_22 git_last_commit: d18e3bb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CoreGx_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CoreGx_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CoreGx_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CoreGx_2.14.0.tgz vignettes: vignettes/CoreGx/inst/doc/coreGx.html, vignettes/CoreGx/inst/doc/TreatmentResponseExperiment.html vignetteTitles: CoreGx: Class and Function Abstractions, The TreatmentResponseExperiment Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoreGx/inst/doc/coreGx.R, vignettes/CoreGx/inst/doc/TreatmentResponseExperiment.R dependsOnMe: PharmacoGx, RadioGx, ToxicoGx importsMe: gDRimport, PDATK dependencyCount: 127 Package: Cormotif Version: 1.56.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 Archs: x64 MD5sum: faef3ea9a98c0ff5acc7624838818e13 NeedsCompilation: no Title: Correlation Motif Fit Description: It fits correlation motif model to multiple studies to detect study specific differential expression patterns. biocViews: Microarray, DifferentialExpression Author: Hongkai Ji, Yingying Wei Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/Cormotif git_branch: RELEASE_3_22 git_last_commit: e616f53 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Cormotif_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Cormotif_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Cormotif_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Cormotif_1.56.0.tgz vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf vignetteTitles: Cormotif Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cormotif/inst/doc/CormotifVignette.R dependencyCount: 14 Package: corral Version: 1.20.0 Imports: ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix, methods, MultiAssayExperiment, pals, reshape2, SingleCellExperiment, SummarizedExperiment, transport Suggests: ade4, BiocStyle, CellBench, DuoClustering2018, knitr, rmarkdown, scater, testthat License: GPL-2 MD5sum: 675600d7505b54524d1d5e19f32447cf NeedsCompilation: no Title: Correspondence Analysis for Single Cell Data Description: Correspondence analysis (CA) is a matrix factorization method, and is similar to principal components analysis (PCA). Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale. The corral package implements CA for dimensionality reduction of a single matrix of single-cell data, as well as a multi-table adaptation of CA that leverages data-optimized scaling to align data generated from different sequencing platforms by projecting into a shared latent space. corral utilizes sparse matrices and a fast implementation of SVD, and can be called directly on Bioconductor objects (e.g., SingleCellExperiment) for easy pipeline integration. The package also includes additional options, including variations of CA to address overdispersion in count data (e.g., Freeman-Tukey chi-squared residual), as well as the option to apply CA-style processing to continuous data (e.g., proteomic TOF intensities) with the Hellinger distance adaptation of CA. biocViews: BatchEffect, DimensionReduction, GeneExpression, Preprocessing, PrincipalComponent, Sequencing, SingleCell, Software, Visualization Author: Lauren Hsu [aut, cre] (ORCID: ), Aedin Culhane [aut] (ORCID: ) Maintainer: Lauren Hsu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/corral git_branch: RELEASE_3_22 git_last_commit: 0d657a1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/corral_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/corral_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/corral_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/corral_1.20.0.tgz vignettes: vignettes/corral/inst/doc/corral_dimred.html, vignettes/corral/inst/doc/corralm_alignment.html vignetteTitles: dim reduction with corral, alignment with corralm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/corral/inst/doc/corral_dimred.R, vignettes/corral/inst/doc/corralm_alignment.R dependsOnMe: OSCA.advanced dependencyCount: 70 Package: coseq Version: 1.34.0 Depends: R (>= 4.0.0), SummarizedExperiment, S4Vectors Imports: edgeR, DESeq2, capushe, Rmixmod, e1071, BiocParallel, ggplot2, scales, HTSFilter, corrplot, HTSCluster, grDevices, graphics, stats, methods, compositions, mvtnorm Suggests: Biobase, knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: f980813592daf072a8ed9bdc14c2cfe8 NeedsCompilation: no Title: Co-Expression Analysis of Sequencing Data Description: Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided. biocViews: GeneExpression, RNASeq, Sequencing, Software, ImmunoOncology Author: Andrea Rau [cre, aut] (ORCID: ), Cathy Maugis-Rabusseau [ctb], Antoine Godichon-Baggioni [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coseq git_branch: RELEASE_3_22 git_last_commit: c25dfae git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/coseq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/coseq_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/coseq_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/coseq_1.34.0.tgz vignettes: vignettes/coseq/inst/doc/coseq.html vignetteTitles: coseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coseq/inst/doc/coseq.R dependsOnMe: RFLOMICS dependencyCount: 75 Package: cosmiq Version: 1.44.0 Depends: R (>= 3.6), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 1bea33b46caddc17d846257d63ad2c8e NeedsCompilation: yes Title: cosmiq - COmbining Single Masses Into Quantities Description: cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: David Fischer [aut, cre], Christian Panse [aut] (ORCID: ), Endre Laczko [ctb] Maintainer: David Fischer URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html git_url: https://git.bioconductor.org/packages/cosmiq git_branch: RELEASE_3_22 git_last_commit: b743b8d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cosmiq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cosmiq_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cosmiq_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cosmiq_1.44.0.tgz vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf vignetteTitles: cosmiq primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R dependencyCount: 142 Package: cosmosR Version: 1.18.0 Depends: R (>= 4.1) Imports: CARNIVAL, dorothea, dplyr, GSEABase, igraph, progress, purrr, rlang, stringr, utils, visNetwork, decoupleR Suggests: testthat, knitr, rmarkdown, piano, ggplot2 License: GPL-3 MD5sum: 275a79ff55c96349688245d8d312dfea NeedsCompilation: no Title: COSMOS (Causal Oriented Search of Multi-Omic Space) Description: COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets. biocViews: CellBiology, Pathways, Network, Proteomics, Metabolomics, Transcriptomics, GeneSignaling Author: Aurélien Dugourd [aut] (ORCID: ), Attila Gabor [cre] (ORCID: ), Katharina Zirngibl [aut] (ORCID: ) Maintainer: Attila Gabor URL: https://github.com/saezlab/COSMOSR VignetteBuilder: knitr BugReports: https://github.com/saezlab/COSMOSR/issues git_url: https://git.bioconductor.org/packages/cosmosR git_branch: RELEASE_3_22 git_last_commit: cbfdebc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cosmosR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cosmosR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cosmosR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cosmosR_1.18.0.tgz vignettes: vignettes/cosmosR/inst/doc/tutorial.html vignetteTitles: cosmosR tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cosmosR/inst/doc/tutorial.R dependencyCount: 106 Package: COSNet Version: 1.44.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: c7fd4e60608e702f5ce248bfa224de04 NeedsCompilation: yes Title: Cost Sensitive Network for node label prediction on graphs with highly unbalanced labelings Description: Package that implements the COSNet classification algorithm. The algorithm predicts node labels in partially labeled graphs where few positives are available for the class being predicted. biocViews: GraphAndNetwork, Classification,Network, NeuralNetwork Author: Marco Frasca and Giorgio Valentini -- Universita' degli Studi di Milano Maintainer: Marco Frasca URL: https://github.com/m1frasca/COSNet_GitHub git_url: https://git.bioconductor.org/packages/COSNet git_branch: RELEASE_3_22 git_last_commit: d4c8857 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/COSNet_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/COSNet_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/COSNet_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/COSNet_1.44.0.tgz vignettes: vignettes/COSNet/inst/doc/COSNet_v.pdf vignetteTitles: An R Package for Predicting Binary Labels in Partially-Labeled Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COSNet/inst/doc/COSNet_v.R dependencyCount: 0 Package: COTAN Version: 2.10.0 Depends: R (>= 4.3) Imports: stats, methods, grDevices, Matrix, ggplot2, ggrepel, gghalves, ggthemes, graphics, parallel, parallelly, tibble, tidyr, dplyr, BiocSingular, parallelDist, ComplexHeatmap, circlize, grid, scales, RColorBrewer, utils, rlang, Rfast, stringr, Seurat, dendextend, zeallot, assertthat, withr, SingleCellExperiment, proxy, RSpectra Suggests: testthat (>= 3.2.0), proto, spelling, knitr, data.table, gsubfn, R.utils, tidyverse, rmarkdown, htmlwidgets, MASS, Rtsne, plotly, BiocStyle, cowplot, qpdf, GEOquery, sf, torch, SummarizedExperiment, S4Vectors License: GPL-3 MD5sum: 289e0b1082cafbff81182b5b1ef03797 NeedsCompilation: no Title: COexpression Tables ANalysis Description: Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes. biocViews: SystemsBiology, Transcriptomics, GeneExpression, SingleCell Author: Galfrè Silvia Giulia [aut, cre] (ORCID: ), Morandin Francesco [aut] (ORCID: ), Fantozzi Marco [aut] (ORCID: ), Pietrosanto Marco [aut] (ORCID: ), Puttini Daniel [aut] (ORCID: ), Priami Corrado [aut] (ORCID: ), Cremisi Federico [aut] (ORCID: ), Helmer-Citterich Manuela [aut] (ORCID: ) Maintainer: Galfrè Silvia Giulia URL: https://github.com/seriph78/COTAN VignetteBuilder: knitr BugReports: https://github.com/seriph78/COTAN/issues git_url: https://git.bioconductor.org/packages/COTAN git_branch: RELEASE_3_22 git_last_commit: 9b1bfa0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/COTAN_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/COTAN_2.9.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/COTAN_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/COTAN_2.10.0.tgz vignettes: vignettes/COTAN/inst/doc/Guided_tutorial_v2.html vignetteTitles: Guided tutorial to COTAN V.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COTAN/inst/doc/Guided_tutorial_v2.R dependencyCount: 201 Package: countsimQC Version: 1.28.0 Depends: R (>= 3.5) Imports: rmarkdown (>= 2.5), edgeR, DESeq2 (>= 1.16.0), dplyr, tidyr, ggplot2, grDevices, tools, SummarizedExperiment, genefilter, DT, GenomeInfoDbData, caTools, randtests, stats, utils, methods, ragg Suggests: knitr, testthat License: GPL (>=2) MD5sum: 2bce13bfae53f9db0297e33b9cec6915 NeedsCompilation: no Title: Compare Characteristic Features of Count Data Sets Description: countsimQC provides functionality to create a comprehensive report comparing a broad range of characteristics across a collection of count matrices. One important use case is the comparison of one or more synthetic count matrices to a real count matrix, possibly the one underlying the simulations. However, any collection of count matrices can be compared. biocViews: Microbiome, RNASeq, SingleCell, ExperimentalDesign, QualityControl, ReportWriting, Visualization, ImmunoOncology Author: Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/countsimQC VignetteBuilder: knitr BugReports: https://github.com/csoneson/countsimQC/issues git_url: https://git.bioconductor.org/packages/countsimQC git_branch: RELEASE_3_22 git_last_commit: 5ad5ec4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/countsimQC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/countsimQC_1.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/countsimQC_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/countsimQC_1.28.0.tgz vignettes: vignettes/countsimQC/inst/doc/countsimQC.html vignetteTitles: countsimQC User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/countsimQC/inst/doc/countsimQC.R suggestsMe: muscat dependencyCount: 126 Package: covEB Version: 1.36.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats, LaplacesDemon, Matrix Suggests: curatedBladderData License: GPL-3 MD5sum: a430ecd86babf3457a29b048708a0c0d NeedsCompilation: no Title: Empirical Bayes estimate of block diagonal covariance matrices Description: Using bayesian methods to estimate correlation matrices assuming that they can be written and estimated as block diagonal matrices. These block diagonal matrices are determined using shrinkage parameters that values below this parameter to zero. biocViews: ImmunoOncology, Bayesian, Microarray, RNASeq, Preprocessing, Software, GeneExpression, StatisticalMethod Author: C. Pacini Maintainer: C. Pacini git_url: https://git.bioconductor.org/packages/covEB git_branch: RELEASE_3_22 git_last_commit: e8e7697 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/covEB_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/covEB_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/covEB_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/covEB_1.36.0.tgz vignettes: vignettes/covEB/inst/doc/covEB.pdf vignetteTitles: covEB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covEB/inst/doc/covEB.R dependencyCount: 24 Package: CoverageView Version: 1.48.0 Depends: R (>= 2.10), methods, Rsamtools (>= 1.19.17), rtracklayer Imports: S4Vectors (>= 0.7.21), IRanges(>= 2.3.23), GenomicRanges, GenomicAlignments, parallel, tools License: Artistic-2.0 MD5sum: 7ca10f02743a408c3f454778caf390c6 NeedsCompilation: no Title: Coverage visualization package for R Description: This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome biocViews: ImmunoOncology, Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software Author: Ernesto Lowy Maintainer: Ernesto Lowy git_url: https://git.bioconductor.org/packages/CoverageView git_branch: RELEASE_3_22 git_last_commit: 714038d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CoverageView_1.48.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CoverageView_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CoverageView_1.48.0.tgz vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf vignetteTitles: Easy visualization of the read coverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoverageView/inst/doc/CoverageView.R dependencyCount: 57 Package: covRNA Version: 1.36.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 7640e65abe9cde8eed102f1f36cf934f NeedsCompilation: no Title: Multivariate Analysis of Transcriptomic Data Description: This package provides the analysis methods fourthcorner and RLQ analysis for large-scale transcriptomic data. biocViews: GeneExpression, Transcription Author: Lara Urban Maintainer: Lara Urban VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/covRNA git_branch: RELEASE_3_22 git_last_commit: 1b430ba git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/covRNA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/covRNA_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/covRNA_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/covRNA_1.36.0.tgz vignettes: vignettes/covRNA/inst/doc/covRNA.html vignetteTitles: An Introduction to covRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covRNA/inst/doc/covRNA.R dependencyCount: 61 Package: CPSM Version: 1.2.0 Depends: R (>= 3.5.0) Imports: SummarizedExperiment, grDevices, reshape2 , survival , survminer , ggplot2 , MTLR , glmnet , rms , preprocessCore , Matrix , stats, Hmisc, ggfortify, randomForestSRC, caret, SurvMetrics, MASS, Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: GPL-3 | file LICENSE MD5sum: 07c3cdc3eb58a53243283db6db58adf3 NeedsCompilation: no Title: CPSM: Cancer patient survival model Description: CPSM provides a comprehensive computational pipeline for predicting survival probability and risk groups in cancer patients. The package includes steps for data preprocessing, training/test split, and normalization. It enables feature selection using univariate survival analysis and computes a LASSO-based prognostic index (PI) score. CPSM supports the development of predictive models using various feature sets and offers a suite of visualization tools, including survival curves based on predicted probabilities, barplots for predicted mean and median survival times, KM plots overlaid with individual survival predictions, and nomograms for estimating 1-, 3-, 5-, and 10-year survival probabilities. This makes CPSM a versatile tool for survival analysis in cancer research. biocViews: Normalization, Survival, GeneExpression, Preprocessing,FeatureExtraction, Software, Visualization Author: Harpreet Kaur [aut, cre] (ORCID: ), Pijush Das [aut], Kevin Camphausen [aut], Uma Shankavaram [aut, ctb] Maintainer: Harpreet Kaur URL: https://github.com/hks5august/CPSM/ VignetteBuilder: knitr BugReports: https://github.com/hks5august/CPSM/issues git_url: https://git.bioconductor.org/packages/CPSM git_branch: RELEASE_3_22 git_last_commit: 5c19f2f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CPSM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CPSM_1.1.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CPSM_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CPSM_1.2.0.tgz vignettes: vignettes/CPSM/inst/doc/CPSM.html vignetteTitles: CPSM: Cancer patient survival model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CPSM/inst/doc/CPSM.R dependencyCount: 208 Package: cpvSNP Version: 1.42.0 Depends: R (>= 3.5.0), GenomicFeatures, GSEABase (>= 1.24.0) Imports: methods, corpcor, BiocParallel, ggplot2, plyr Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: e7e0f63d98eea2366e6939a06cde3c15 NeedsCompilation: no Title: Gene set analysis methods for SNP association p-values that lie in genes in given gene sets Description: Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results. biocViews: Genetics, StatisticalMethod, Pathways, GeneSetEnrichment, GenomicVariation Author: Caitlin McHugh, Jessica Larson, and Jason Hackney Maintainer: Caitlin McHugh git_url: https://git.bioconductor.org/packages/cpvSNP git_branch: RELEASE_3_22 git_last_commit: 31700af git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cpvSNP_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cpvSNP_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cpvSNP_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cpvSNP_1.42.0.tgz vignettes: vignettes/cpvSNP/inst/doc/cpvSNP.pdf vignetteTitles: Running gene set analyses with the "cpvSNP" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cpvSNP/inst/doc/cpvSNP.R dependencyCount: 93 Package: cqn Version: 1.56.0 Depends: R (>= 2.10.0), mclust Imports: splines, graphics, nor1mix, stats, quantreg Suggests: scales, edgeR License: Artistic-2.0 MD5sum: 24be086ae7c20b99352c4d581afa3ab6 NeedsCompilation: no Title: Conditional quantile normalization Description: A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. biocViews: ImmunoOncology, RNASeq, Preprocessing, DifferentialExpression Author: Jean (Zhijin) Wu, Kasper Daniel Hansen Maintainer: Kasper Daniel Hansen git_url: https://git.bioconductor.org/packages/cqn git_branch: RELEASE_3_22 git_last_commit: b69409d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cqn_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cqn_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cqn_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cqn_1.56.0.tgz vignettes: vignettes/cqn/inst/doc/cqn.pdf vignetteTitles: CQN (Conditional Quantile Normalization) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cqn/inst/doc/cqn.R dependsOnMe: KnowSeq importsMe: GeoTcgaData, tweeDEseq dependencyCount: 16 Package: CRImage Version: 1.58.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: c36c89422334ac7ffe921ce0e2b5ca3c NeedsCompilation: no Title: CRImage a package to classify cells and calculate tumour cellularity Description: CRImage provides functionality to process and analyze images, in particular to classify cells in biological images. Furthermore, in the context of tumor images, it provides functionality to calculate tumour cellularity. biocViews: CellBiology, Classification Author: Henrik Failmezger , Yinyin Yuan , Oscar Rueda , Florian Markowetz Maintainer: Henrik Failmezger , Yinyin Yuan git_url: https://git.bioconductor.org/packages/CRImage git_branch: RELEASE_3_22 git_last_commit: 74fd591 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CRImage_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CRImage_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CRImage_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CRImage_1.58.0.tgz vignettes: vignettes/CRImage/inst/doc/CRImage.pdf vignetteTitles: CRImage Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRImage/inst/doc/CRImage.R dependencyCount: 63 Package: crisprBase Version: 1.14.0 Depends: utils, methods, R (>= 4.1) Imports: BiocGenerics, Biostrings, GenomicRanges, graphics, IRanges, S4Vectors, stringr Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 1681268546bcdabc6a43e708537928ae NeedsCompilation: no Title: Base functions and classes for CRISPR gRNA design Description: Provides S4 classes for general nucleases, CRISPR nucleases, CRISPR nickases, and base editors.Several CRISPR-specific genome arithmetic functions are implemented to help extract genomic coordinates of spacer and protospacer sequences. Commonly-used CRISPR nuclease objects are provided that can be readily used in other packages. Both DNA- and RNA-targeting nucleases are supported. biocViews: CRISPR, FunctionalGenomics Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBase VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBase/issues git_url: https://git.bioconductor.org/packages/crisprBase git_branch: RELEASE_3_22 git_last_commit: 6e22570 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/crisprBase_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprBase_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprBase_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprBase_1.14.0.tgz vignettes: vignettes/crisprBase/inst/doc/crisprBase.html vignetteTitles: Introduction to crisprBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBase/inst/doc/crisprBase.R dependsOnMe: crisprDesign, crisprViz importsMe: crisprBowtie, crisprBwa, crisprShiny, crisprVerse dependencyCount: 24 Package: crisprBowtie Version: 1.14.0 Depends: methods Imports: BiocGenerics, Biostrings, BSgenome, crisprBase (>= 0.99.15), Seqinfo, GenomicRanges, IRanges, Rbowtie, readr, stats, stringr, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: e6a8db7a25aa2d27b7c3b99f324e53f3 NeedsCompilation: no Title: Bowtie-based alignment of CRISPR gRNA spacer sequences Description: Provides a user-friendly interface to map on-targets and off-targets of CRISPR gRNA spacer sequences using bowtie. The alignment is fast, and can be performed using either commonly-used or custom CRISPR nucleases. The alignment can work with any reference or custom genomes. Both DNA- and RNA-targeting nucleases are supported. biocViews: CRISPR, FunctionalGenomics, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBowtie VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBowtie/issues git_url: https://git.bioconductor.org/packages/crisprBowtie git_branch: RELEASE_3_22 git_last_commit: 4840230 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/crisprBowtie_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprBowtie_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprBowtie_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprBowtie_1.14.0.tgz vignettes: vignettes/crisprBowtie/inst/doc/crisprBowtie.html vignetteTitles: Introduction to crisprBowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBowtie/inst/doc/crisprBowtie.R importsMe: crisprDesign, crisprVerse dependencyCount: 83 Package: crisprBwa Version: 1.14.0 Depends: methods Imports: BiocGenerics, BSgenome, crisprBase (>= 0.99.15), Seqinfo, Rbwa, readr, stats, stringr, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, testthat License: MIT + file LICENSE OS_type: unix MD5sum: df7217781a3a46c4458304b8d05fac6a NeedsCompilation: no Title: BWA-based alignment of CRISPR gRNA spacer sequences Description: Provides a user-friendly interface to map on-targets and off-targets of CRISPR gRNA spacer sequences using bwa. The alignment is fast, and can be performed using either commonly-used or custom CRISPR nucleases. The alignment can work with any reference or custom genomes. Currently not supported on Windows machines. biocViews: CRISPR, FunctionalGenomics, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBwa VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBwa/issues git_url: https://git.bioconductor.org/packages/crisprBwa git_branch: RELEASE_3_22 git_last_commit: 4aa579f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/crisprBwa_1.14.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprBwa_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprBwa_1.14.0.tgz vignettes: vignettes/crisprBwa/inst/doc/crisprBwa.html vignetteTitles: Introduction to crisprBwa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBwa/inst/doc/crisprBwa.R suggestsMe: crisprDesign dependencyCount: 83 Package: crisprDesign Version: 1.12.0 Depends: R (>= 4.2.0), crisprBase (>= 1.1.3) Imports: AnnotationDbi, BiocGenerics, Biostrings (>= 2.77.2), BSgenome (>= 1.77.1), crisprBowtie (>= 0.99.8), crisprScore (>= 1.1.6), GenomeInfoDb (>= 1.45.7), GenomicFeatures (>= 1.61.4), GenomicRanges (>= 1.61.1), IRanges, Matrix, MatrixGenerics, methods, rtracklayer (>= 1.69.1), S4Vectors, Seqinfo, stats, txdbmaker (>= 1.5.6), utils, VariantAnnotation (>= 1.55.1) Suggests: biomaRt, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BiocStyle, crisprBwa (>= 0.99.7), knitr, rmarkdown, Rbowtie, Rbwa, RCurl, testthat License: MIT + file LICENSE Archs: x64 MD5sum: f7e6019c5062a4a78d2f4c97de1aefa1 NeedsCompilation: no Title: Comprehensive design of CRISPR gRNAs for nucleases and base editors Description: Provides a comprehensive suite of functions to design and annotate CRISPR guide RNA (gRNAs) sequences. This includes on- and off-target search, on-target efficiency scoring, off-target scoring, full gene and TSS contextual annotations, and SNP annotation (human only). It currently support five types of CRISPR modalities (modes of perturbations): CRISPR knockout, CRISPR activation, CRISPR inhibition, CRISPR base editing, and CRISPR knockdown. All types of CRISPR nucleases are supported, including DNA- and RNA-target nucleases such as Cas9, Cas12a, and Cas13d. All types of base editors are also supported. gRNA design can be performed on reference genomes, transcriptomes, and custom DNA and RNA sequences. Both unpaired and paired gRNA designs are enabled. biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprDesign VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprDesign/issues git_url: https://git.bioconductor.org/packages/crisprDesign git_branch: RELEASE_3_22 git_last_commit: 143c0ce git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/crisprDesign_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprDesign_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprDesign_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprDesign_1.12.0.tgz vignettes: vignettes/crisprDesign/inst/doc/intro.html vignetteTitles: Introduction to crisprDesign hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprDesign/inst/doc/intro.R dependsOnMe: crisprViz importsMe: crisprShiny, crisprVerse dependencyCount: 124 Package: crisprScore Version: 1.14.0 Depends: R (>= 4.1), crisprScoreData (>= 1.1.3) Imports: basilisk (>= 1.9.2), BiocGenerics, Biostrings, IRanges, methods, randomForest, reticulate, stringr, utils, XVector Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 1afbfcfd63beb5ec786002709a804b0c NeedsCompilation: no Title: On-Target and Off-Target Scoring Algorithms for CRISPR gRNAs Description: Provides R wrappers of several on-target and off-target scoring methods for CRISPR guide RNAs (gRNAs). The following nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The available on-target cutting efficiency scoring methods are RuleSet1, Azimuth, DeepHF, DeepCpf1, enPAM+GB, and CRISPRscan. Both the CFD and MIT scoring methods are available for off-target specificity prediction. The package also provides a Lindel-derived score to predict the probability of a gRNA to produce indels inducing a frameshift for the Cas9 nuclease. Note that DeepHF, DeepCpf1 and enPAM+GB are not available on Windows machines. biocViews: CRISPR, FunctionalGenomics, FunctionalPrediction Author: Jean-Philippe Fortin [aut, cre, cph], Aaron Lun [aut], Luke Hoberecht [ctb], Pirunthan Perampalam [ctb] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprScore/issues VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprScore git_url: https://git.bioconductor.org/packages/crisprScore git_branch: RELEASE_3_22 git_last_commit: 86c5f68 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/crisprScore_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprScore_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprScore_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprScore_1.14.0.tgz vignettes: vignettes/crisprScore/inst/doc/crisprScore.html vignetteTitles: crisprScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprScore/inst/doc/crisprScore.R importsMe: crisprDesign, crisprShiny, crisprVerse dependencyCount: 78 Package: CRISPRseek Version: 1.50.0 Depends: R (>= 3.5.0), BiocGenerics, Biostrings, GenomicFeatures Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges, BSgenome, hash, methods,reticulate,rhdf5,XVector, DelayedArray, Seqinfo, GenomicRanges, dplyr, keras, mltools, gtools, openxlsx, rio, rlang, stringr Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, lattice, MASS, tensorflow, BSgenome.Hsapiens.UCSC.hg38, BiocFileCache, TxDb.Hsapiens.UCSC.hg38.knownGene, testthat, knitr License: file LICENSE MD5sum: 4367fd0ba0f8957df6383e6ed8e90f06 NeedsCompilation: no Title: Design of guide RNAs in CRISPR genome-editing systems Description: The package encompasses functions to find potential guide RNAs for the CRISPR-based genome-editing systems including the Base Editors and the Prime Editors when supplied with target sequences as input. Users have the flexibility to filter resulting guide RNAs based on parameters such as the absence of restriction enzyme cut sites or the lack of paired guide RNAs. The package also facilitates genome-wide exploration for off-targets, offering features to score and rank off-targets, retrieve flanking sequences, and indicate whether the hits are located within exon regions. All detected guide RNAs are annotated with the cumulative scores of the top5 and topN off-targets together with the detailed information such as mismatch sites and restrictuion enzyme cut sites. The package also outputs INDELs and their frequencies for Cas9 targeted sites. biocViews: ImmunoOncology, GeneRegulation, SequenceMatching, CRISPR Author: Lihua Julie Zhu Paul Scemama Benjamin R. Holmes Hervé Pagès Kai Hu Hui Mao Michael Lawrence Isana Veksler-Lublinsky Victor Ambros Neil Aronin Michael Brodsky Devin M Burris Maintainer: Lihua Julie Zhu Kai Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CRISPRseek git_branch: RELEASE_3_22 git_last_commit: fd2babc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CRISPRseek_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CRISPRseek_1.49.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CRISPRseek_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CRISPRseek_1.50.0.tgz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.html vignetteTitles: CRISPRseek: guide RNA design and off-target analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R importsMe: GUIDEseq, multicrispr dependencyCount: 139 Package: crisprShiny Version: 1.6.0 Depends: R (>= 4.4.0), shiny Imports: BiocGenerics, Biostrings, BSgenome, crisprBase, crisprDesign, crisprScore, crisprViz, DT, Seqinfo, htmlwidgets, methods, pwalign, S4Vectors, shinyBS, shinyjs, utils, waiter Suggests: BiocStyle, knitr, rmarkdown, shinyFeedback, testthat (>= 3.0.0), BSgenome.Hsapiens.UCSC.hg38 License: MIT + file LICENSE MD5sum: 658ed6a917fa744920e415124616ae17 NeedsCompilation: no Title: Exploring curated CRISPR gRNAs via Shiny Description: Provides means to interactively visualize guide RNAs (gRNAs) in GuideSet objects via Shiny application. This GUI can be self-contained or as a module within a larger Shiny app. The content of the app reflects the annotations present in the passed GuideSet object, and includes intuitive tools to examine, filter, and export gRNAs, thereby making gRNA design more user-friendly. biocViews: CRISPR, FunctionalGenomics, GeneTarget, GUI Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprShiny VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprShiny/issues git_url: https://git.bioconductor.org/packages/crisprShiny git_branch: RELEASE_3_22 git_last_commit: 00fd6fe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/crisprShiny_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprShiny_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprShiny_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprShiny_1.6.0.tgz vignettes: vignettes/crisprShiny/inst/doc/intro.html vignetteTitles: Introduction to crisprShiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprShiny/inst/doc/intro.R dependencyCount: 190 Package: CrispRVariants Version: 1.38.0 Depends: R (>= 4.3.0), ggplot2 (>= 2.2.0) Imports: AnnotationDbi, BiocParallel, Biostrings, methods, GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices, grid, gridExtra, IRanges, reshape2, Rsamtools, S4Vectors (>= 0.9.38), utils Suggests: BiocStyle, GenomicFeatures, knitr, rmarkdown, readxl, rtracklayer, sangerseqR, testthat, VariantAnnotation License: GPL-2 MD5sum: 84d428f01652e9a14fdfeab585727caa NeedsCompilation: no Title: Tools for counting and visualising mutations in a target location Description: CrispRVariants provides tools for analysing the results of a CRISPR-Cas9 mutagenesis sequencing experiment, or other sequencing experiments where variants within a given region are of interest. These tools allow users to localize variant allele combinations with respect to any genomic location (e.g. the Cas9 cut site), plot allele combinations and calculate mutation rates with flexible filtering of unrelated variants. biocViews: ImmunoOncology, CRISPR, GenomicVariation, VariantDetection, GeneticVariability, DataRepresentation, Visualization, Sequencing Author: Helen Lindsay [aut, cre] Maintainer: Helen Lindsay VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CrispRVariants git_branch: RELEASE_3_22 git_last_commit: d78780f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CrispRVariants_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CrispRVariants_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CrispRVariants_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CrispRVariants_1.38.0.tgz vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf, vignettes/CrispRVariants/inst/doc/user_guide.html vignetteTitles: CrispRVariants, CrispRVariants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R dependencyCount: 87 Package: crisprVerse Version: 1.12.0 Depends: R (>= 4.2.0) Imports: BiocManager, cli, crisprBase, crisprBowtie, crisprScore, crisprScoreData, crisprDesign, crisprViz, rlang, tools, utils Suggests: BiocStyle, knitr, testthat License: MIT + file LICENSE MD5sum: 9da03607c340091c27c1b6511e2d3e57 NeedsCompilation: no Title: Easily install and load the crisprVerse ecosystem for CRISPR gRNA design Description: The crisprVerse is a modular ecosystem of R packages developed for the design and manipulation of CRISPR guide RNAs (gRNAs). All packages share a common language and design principles. This package is designed to make it easy to install and load the crisprVerse packages in a single step. To learn more about the crisprVerse, visit . biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprVerse VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprVerse/issues git_url: https://git.bioconductor.org/packages/crisprVerse git_branch: RELEASE_3_22 git_last_commit: 4d6cc72 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/crisprVerse_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprVerse_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprVerse_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprVerse_1.12.0.tgz vignettes: vignettes/crisprVerse/inst/doc/crisprVerse.html vignetteTitles: crisprVerse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprVerse/inst/doc/crisprVerse.R dependencyCount: 176 Package: crisprViz Version: 1.12.0 Depends: R (>= 4.2.0), crisprBase (>= 0.99.15), crisprDesign (>= 0.99.77) Imports: BiocGenerics, Biostrings, BSgenome, Seqinfo, GenomicFeatures, GenomicRanges, grDevices, Gviz, IRanges, methods, S4Vectors, txdbmaker Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, rtracklayer, testthat, utils License: MIT + file LICENSE MD5sum: b1c75db09a2431d5ffd0b83d82a1d53d NeedsCompilation: no Title: Visualization Functions for CRISPR gRNAs Description: Provides functionalities to visualize and contextualize CRISPR guide RNAs (gRNAs) on genomic tracks across nucleases and applications. Works in conjunction with the crisprBase and crisprDesign Bioconductor packages. Plots are produced using the Gviz framework. biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprViz VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprViz/issues git_url: https://git.bioconductor.org/packages/crisprViz git_branch: RELEASE_3_22 git_last_commit: 3153352 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/crisprViz_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprViz_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprViz_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprViz_1.12.0.tgz vignettes: vignettes/crisprViz/inst/doc/intro.html vignetteTitles: Introduction to crisprViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprViz/inst/doc/intro.R importsMe: crisprShiny, crisprVerse dependencyCount: 175 Package: crlmm Version: 1.68.0 Depends: R (>= 2.14.0), oligoClasses (>= 1.21.12), preprocessCore (>= 1.17.7) Imports: methods, Biobase (>= 2.15.4), BiocGenerics, affyio (>= 1.23.2), illuminaio, ellipse, mvtnorm, splines, stats, utils, lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats, VGAM, parallel, graphics, limma, beanplot LinkingTo: preprocessCore (>= 1.17.7) Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), snpStats, RUnit License: Artistic-2.0 MD5sum: d010f7ea6b2f072060545a5da105a0dd NeedsCompilation: yes Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms. biocViews: Microarray, Preprocessing, SNP, CopyNumberVariation Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry Maintainer: Benilton S Carvalho , Robert Scharpf , Matt Ritchie git_url: https://git.bioconductor.org/packages/crlmm git_branch: RELEASE_3_22 git_last_commit: 7d8b5c6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/crlmm_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crlmm_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crlmm_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crlmm_1.68.0.tgz vignettes: vignettes/crlmm/inst/doc/AffyGW.pdf, vignettes/crlmm/inst/doc/CopyNumberOverview.pdf, vignettes/crlmm/inst/doc/genotyping.pdf, vignettes/crlmm/inst/doc/gtypeDownstream.pdf, vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf, vignettes/crlmm/inst/doc/Infrastructure.pdf vignetteTitles: Copy number estimation, Overview of copy number vignettes, crlmm Vignette - Genotyping, crlmm Vignette - Downstream Analysis, Preprocessing and genotyping Illumina arrays for copy number analysis, Infrastructure for copy number analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/crlmm/inst/doc/genotyping.R dependsOnMe: MAGAR importsMe: VanillaICE suggestsMe: oligoClasses, hapmap370k dependencyCount: 66 Package: crumblr Version: 1.1.0 Depends: R (>= 4.4.0), ggplot2, methods Imports: Rdpack, viridis, tidytree, variancePartition (>= 1.36.3), SingleCellExperiment, ggtree, dplyr, stats, MASS, Rfast Suggests: BiocStyle, RUnit, knitr, rmarkdown, dreamlet, muscat, ExperimentHub, scater, HMP, reshape2, glue, tidyverse, BiocGenerics, compositions License: Artistic-2.0 MD5sum: 272df97d350c4050015e180bc8a4fb56 NeedsCompilation: no Title: Count ratio uncertainty modeling base linear regression Description: Crumblr enables analysis of count ratio data using precision weighted linear (mixed) models. It uses an asymptotic normal approximation of the variance following the centered log ration transform (CLR) that is widely used in compositional data analysis. Crumblr provides a fast, flexible alternative to GLMs and GLMM's while retaining high power and controlling the false positive rate. biocViews: RNASeq, GeneExpression, DifferentialExpression, BatchEffect, QualityControl, SingleCell, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Clustering, DimensionReduction, Preprocessing, Software Author: Gabriel Hoffman [aut, cre] (ORCID: ) Maintainer: Gabriel Hoffman URL: https://DiseaseNeurogenomics.github.io/crumblr VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeurogenomics/crumblr/issues git_url: https://git.bioconductor.org/packages/crumblr git_branch: devel git_last_commit: 515361e git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/crumblr_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crumblr_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crumblr_1.1.0.tgz vignettes: vignettes/crumblr/inst/doc/crumblr_theory.html, vignettes/crumblr/inst/doc/crumblr.html, vignettes/crumblr/inst/doc/integration.html vignetteTitles: crumblr_theory, crumblr, crumblr_treeTest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/crumblr/inst/doc/crumblr_theory.R, vignettes/crumblr/inst/doc/crumblr.R, vignettes/crumblr/inst/doc/integration.R dependencyCount: 150 Package: crupR Version: 1.2.0 Depends: R (>= 4.4.0) Imports: bamsignals, Rsamtools, GenomicRanges, preprocessCore, randomForest, rtracklayer, Seqinfo, S4Vectors, ggplot2, matrixStats, dplyr, IRanges, GenomicAlignments, GenomicFeatures, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, reshape2, magrittr, stats, utils, grDevices, SummarizedExperiment, BiocParallel, fs, methods Suggests: GenomeInfoDb, testthat, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: b696f26a4657e550a78302189fb1edf7 NeedsCompilation: no Title: An R package to predict condition-specific enhancers from ChIP-seq data Description: An R package that offers a workflow to predict condition-specific enhancers from ChIP-seq data. The prediction of regulatory units is done in four main steps: Step 1 - the normalization of the ChIP-seq counts. Step 2 - the prediction of active enhancers binwise on the whole genome. Step 3 - the condition-specific clustering of the putative active enhancers. Step 4 - the detection of possible target genes of the condition-specific clusters using RNA-seq counts. biocViews: DifferentialPeakCalling, GeneTarget, FunctionalPrediction, HistoneModification, PeakDetection Author: Persia Akbari Omgba [cre], Verena Laupert [aut], Martin Vingron [aut] Maintainer: Persia Akbari Omgba URL: https://github.com/akbariomgba/crupR VignetteBuilder: knitr BugReports: https://github.com/akbariomgba/crupR/issues git_url: https://git.bioconductor.org/packages/crupR git_branch: RELEASE_3_22 git_last_commit: 01667b7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/crupR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crupR_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crupR_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crupR_1.2.0.tgz vignettes: vignettes/crupR/inst/doc/crupR-vignette.html vignetteTitles: crupR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/crupR/inst/doc/crupR-vignette.R dependencyCount: 105 Package: CSAR Version: 1.62.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, Seqinfo, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 MD5sum: 83fda92dd5ef6bb7bc0f98de95b2c333 NeedsCompilation: yes Title: Statistical tools for the analysis of ChIP-seq data Description: Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation. biocViews: ChIPSeq, Transcription, Genetics Author: Jose M Muino Maintainer: Jose M Muino git_url: https://git.bioconductor.org/packages/CSAR git_branch: RELEASE_3_22 git_last_commit: cb0f7e2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CSAR_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CSAR_1.61.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CSAR_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CSAR_1.62.0.tgz vignettes: vignettes/CSAR/inst/doc/CSAR.pdf vignetteTitles: CSAR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSAR/inst/doc/CSAR.R dependencyCount: 11 Package: csaw Version: 1.44.0 Depends: R (>= 3.5.0), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1) Imports: Rcpp, Matrix, BiocGenerics, Rsamtools, edgeR, limma, methods, S4Vectors, IRanges, Seqinfo, stats, BiocParallel, metapod, utils LinkingTo: Rhtslib, Rcpp Suggests: AnnotationDbi, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat, GenomicFeatures, GenomicAlignments, knitr, BiocStyle, rmarkdown, BiocManager License: GPL-3 Archs: x64 MD5sum: 86b5e3dfe0826e892924456638e959c3 NeedsCompilation: yes Title: ChIP-Seq Analysis with Windows Description: Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. biocViews: MultipleComparison, ChIPSeq, Normalization, Sequencing, Coverage, Genetics, Annotation, DifferentialPeakCalling Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/csaw git_branch: RELEASE_3_22 git_last_commit: 592497a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/csaw_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/csaw_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/csaw_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/csaw_1.44.0.tgz vignettes: vignettes/csaw/inst/doc/csaw.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csaw/inst/doc/csaw.R dependsOnMe: csawBook importsMe: diffHic, epigraHMM, extraChIPs, icetea, mutscan, NADfinder, vulcan, hicream, treediff suggestsMe: DiffBind, GRaNIE, chipseqDB dependencyCount: 46 Package: csdR Version: 1.16.0 Depends: R (>= 4.1.0) Imports: WGCNA, glue, RhpcBLASctl, matrixStats, Rcpp LinkingTo: Rcpp Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle, magrittr, igraph, dplyr License: GPL-3 MD5sum: 0cd2f805999ccb630344919d2e13dbbc NeedsCompilation: yes Title: Differential gene co-expression Description: This package contains functionality to run differential gene co-expression across two different conditions. The algorithm is inspired by Voigt et al. 2017 and finds Conserved, Specific and Differentiated genes (hence the name CSD). This package include efficient and variance calculation by bootstrapping and Welford's algorithm. biocViews: DifferentialExpression, GraphAndNetwork, GeneExpression, Network Author: Jakob Peder Pettersen [aut, cre] (ORCID: ) Maintainer: Jakob Peder Pettersen URL: https://almaaslab.github.io/csdR, https://github.com/AlmaasLab/csdR VignetteBuilder: knitr BugReports: https://github.com/AlmaasLab/csdR/issues git_url: https://git.bioconductor.org/packages/csdR git_branch: RELEASE_3_22 git_last_commit: 089bd25 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/csdR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/csdR_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/csdR_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/csdR_1.16.0.tgz vignettes: vignettes/csdR/inst/doc/csdR.html vignetteTitles: csdR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csdR/inst/doc/csdR.R dependencyCount: 105 Package: CSOA Version: 1.0.0 Imports: bayesbio, dplyr, ggplot2, henna, kerntools, methods, qs, reshape2, rlang, Seurat, SeuratObject, SummarizedExperiment, sgof, spatstat.utils, stats, textshape, wesanderson Suggests: BiocStyle, knitr, patchwork, rmarkdown, scRNAseq, scuttle, stringr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 01981ff6f66b309ef3fd464259c82a9b NeedsCompilation: no Title: Calculate per-cell gene signature scores in scRNA-seq data using cell set overlaps Description: Cell Set Overlap Analysis (CSOA) is a tool for calculating per-cell gene signature scores in an scRNA-seq dataset. CSOA constructs a set for each gene in the signature, consisting of the cells that highly express the gene. Next, all overlaps of pairs of cell sets are computed, ranked, filtered and scored. The CSOA per-cell score is calculated by summing up all products of the overlap scores and the min-max-normalized expression of the two involved genes. CSOA can run on a Seurat object, a SingleCellExperiment object, a matrix and a dgCMatrix. biocViews: Software, SingleCell, GeneSetEnrichment, GeneExpression Author: Andrei-Florian Stoica [aut, cre] (ORCID: ) Maintainer: Andrei-Florian Stoica URL: https://github.com/andrei-stoica26/CSOA VignetteBuilder: knitr BugReports: https://github.com/andrei-stoica26/CSOA/issues git_url: https://git.bioconductor.org/packages/CSOA git_branch: RELEASE_3_22 git_last_commit: 9b11f08 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CSOA_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CSOA_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CSOA_1.0.0.tgz vignettes: vignettes/CSOA/inst/doc/Advanced-CSOA.html, vignettes/CSOA/inst/doc/CSOA.html, vignettes/CSOA/inst/doc/The-CSOA-algorithm.html vignetteTitles: Advanced CSOA, Getting started with CSOA, The CSOA algorithm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CSOA/inst/doc/Advanced-CSOA.R, vignettes/CSOA/inst/doc/CSOA.R, vignettes/CSOA/inst/doc/The-CSOA-algorithm.R dependencyCount: 192 Package: CSSQ Version: 1.22.0 Depends: SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, rtracklayer Imports: GenomicAlignments, GenomicFeatures, Rsamtools, ggplot2, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, markdown License: Artistic-2.0 MD5sum: cae441b79520b8f90fdeed6c17cb2955 NeedsCompilation: no Title: Chip-seq Signal Quantifier Pipeline Description: This package is desgined to perform statistical analysis to identify statistically significant differentially bound regions between multiple groups of ChIP-seq dataset. biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Normalization Author: Ashwath Kumar [aut], Michael Y Hu [aut], Yajun Mei [aut], Yuhong Fan [aut] Maintainer: Fan Lab at Georgia Institute of Technology VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CSSQ git_branch: RELEASE_3_22 git_last_commit: 8bbe42d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CSSQ_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CSSQ_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CSSQ_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CSSQ_1.22.0.tgz vignettes: vignettes/CSSQ/inst/doc/CSSQ.html vignetteTitles: Introduction to CSSQ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSQ/inst/doc/CSSQ.R dependencyCount: 86 Package: ctc Version: 1.84.0 Depends: amap License: GPL-2 MD5sum: 2734b1b3dc55fe8995632a6ea268b53f NeedsCompilation: no Title: Cluster and Tree Conversion. Description: Tools for export and import classification trees and clusters to other programs biocViews: Microarray, Clustering, Classification, DataImport, Visualization Author: Antoine Lucas , Laurent Gautier Maintainer: Antoine Lucas URL: http://antoinelucas.free.fr/ctc git_url: https://git.bioconductor.org/packages/ctc git_branch: RELEASE_3_22 git_last_commit: b3efa5c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ctc_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ctc_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ctc_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ctc_1.84.0.tgz vignettes: vignettes/ctc/inst/doc/ctc.pdf vignetteTitles: Introduction to ctc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctc/inst/doc/ctc.R importsMe: miRLAB, multiClust dependencyCount: 1 Package: CTdata Version: 1.10.0 Depends: R (>= 4.2) Imports: ExperimentHub, utils Suggests: testthat (>= 3.0.0), DT, BiocStyle, knitr, rmarkdown, SummarizedExperiment, SingleCellExperiment License: Artistic-2.0 MD5sum: fcc3e7b463c0533b8c5c23baef4a9f05 NeedsCompilation: no Title: Data companion to CTexploreR Description: Data from publicly available databases (GTEx, CCLE, TCGA and ENCODE) that go with CTexploreR in order to re-define a comprehensive and thoroughly curated list of CT genes and their main characteristics. biocViews: Transcriptomics, Epigenetics, GeneExpression, DataImport, ExperimentHubSoftware Author: Axelle Loriot [aut] (ORCID: ), Julie Devis [aut] (ORCID: ), Anna Diacofotaki [ctb], Charles De Smet [ths], Laurent Gatto [aut, ths, cre] (ORCID: ) Maintainer: Laurent Gatto VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CTdata/issues git_url: https://git.bioconductor.org/packages/CTdata git_branch: RELEASE_3_22 git_last_commit: 6a89573 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CTdata_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CTdata_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CTdata_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CTdata_1.10.0.tgz vignettes: vignettes/CTdata/inst/doc/CTdata.html vignetteTitles: Cancer Testis Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CTdata/inst/doc/CTdata.R dependsOnMe: CTexploreR dependencyCount: 65 Package: CTDquerier Version: 2.18.0 Depends: R (>= 4.1) Imports: RCurl, stringr, S4Vectors, stringdist, ggplot2, igraph, utils, grid, gridExtra, methods, stats, BiocFileCache Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 4c9150bbf2449c3f5dc85e445bc636e9 NeedsCompilation: no Title: Package for CTDbase data query, visualization and downstream analysis Description: Package to retrieve and visualize data from the Comparative Toxicogenomics Database (http://ctdbase.org/). The downloaded data is formated as DataFrames for further downstream analyses. biocViews: Software, BiomedicalInformatics, Infrastructure, DataImport, DataRepresentation, GeneSetEnrichment, NetworkEnrichment, Pathways, Network, GO, KEGG Author: Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut], Xavier Escribà-Montagut [cre] Maintainer: Xavier Escribà-Montagut VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/CTDquerier git_branch: RELEASE_3_22 git_last_commit: 096f3dd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CTDquerier_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CTDquerier_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CTDquerier_2.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CTDquerier_2.18.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 65 Package: CTexploreR Version: 1.6.0 Depends: R (>= 4.3), CTdata (>= 1.5.3) Imports: BiocGenerics, ComplexHeatmap, grid, SummarizedExperiment, GenomicRanges, IRanges, dplyr, tidyr, tibble, ggplot2, rlang, grDevices, stats, circlize, ggrepel, SingleCellExperiment, MatrixGenerics Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), InteractiveComplexHeatmap License: Artistic-2.0 MD5sum: 9f62f0694bcaa71bae66f227557a6e9e NeedsCompilation: no Title: Explores Cancer Testis Genes Description: The CTexploreR package re-defines the list of Cancer Testis/Germline (CT) genes. It is based on publicly available RNAseq databases (GTEx, CCLE and TCGA) and summarises CT genes' main characteristics. Several visualisation functions allow to explore their expression in different types of tissues and cancer cells, or to inspect the methylation status of their promoters in normal tissues. biocViews: Transcriptomics, Epigenetics, DifferentialExpression, GeneExpression, DNAMethylation, ExperimentHubSoftware, DataImport Author: Axelle Loriot [aut, cre] (ORCID: ), Julie Devis [aut] (ORCID: ), Anna Diacofotaki [ctb], Charles De Smet [ths], Laurent Gatto [aut, ths] (ORCID: ) Maintainer: Axelle Loriot URL: https://github.com/UCLouvain-CBIO/CTexploreR VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CTexploreR/issues git_url: https://git.bioconductor.org/packages/CTexploreR git_branch: RELEASE_3_22 git_last_commit: c66423d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CTexploreR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CTexploreR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CTexploreR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CTexploreR_1.6.0.tgz vignettes: vignettes/CTexploreR/inst/doc/CTexploreR.html vignetteTitles: Cancer Testis Explorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CTexploreR/inst/doc/CTexploreR.R dependencyCount: 104 Package: cTRAP Version: 1.28.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationHub, binr, cowplot, data.table, dplyr, DT, fastmatch, fgsea, ggplot2, ggrepel, graphics, highcharter, htmltools, httr, limma, methods, parallel, pbapply, purrr, qs, R.utils, readxl, reshape2, rhdf5, rlang, scales, shiny (>= 1.7.0), shinycssloaders, stats, tibble, tools, utils Suggests: testthat, knitr, covr, rmarkdown, spelling, biomaRt, remotes License: MIT + file LICENSE MD5sum: ad07c689eb988072e141c86bca34c9a8 NeedsCompilation: no Title: Identification of candidate causal perturbations from differential gene expression data Description: Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations. biocViews: DifferentialExpression, GeneExpression, RNASeq, Transcriptomics, Pathways, ImmunoOncology, GeneSetEnrichment Author: Bernardo P. de Almeida [aut], Nuno Saraiva-Agostinho [aut, cre], Nuno L. Barbosa-Morais [aut, led] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/cTRAP, https://github.com/nuno-agostinho/cTRAP VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/cTRAP/issues git_url: https://git.bioconductor.org/packages/cTRAP git_branch: RELEASE_3_22 git_last_commit: 216e3fc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cTRAP_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cTRAP_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cTRAP_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cTRAP_1.28.0.tgz vignettes: vignettes/cTRAP/inst/doc/cTRAP.html vignetteTitles: cTRAP: identifying candidate causal perturbations from differential gene expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cTRAP/inst/doc/cTRAP.R dependencyCount: 154 Package: ctsGE Version: 1.36.0 Depends: R (>= 3.2) Imports: ccaPP, ggplot2, limma, reshape2, shiny, stats, stringr, utils Suggests: BiocStyle, dplyr, DT, GEOquery, knitr, pander, rmarkdown, testthat License: GPL-2 MD5sum: 93a71949a546505a624f82f5578540d3 NeedsCompilation: no Title: Clustering of Time Series Gene Expression data Description: Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Bayesian, Clustering, TimeCourse, Sequencing, RNASeq Author: Michal Sharabi-Schwager [aut, cre], Ron Ophir [aut] Maintainer: Michal Sharabi-Schwager URL: https://github.com/michalsharabi/ctsGE VignetteBuilder: knitr BugReports: https://github.com/michalsharabi/ctsGE/issues git_url: https://git.bioconductor.org/packages/ctsGE git_branch: RELEASE_3_22 git_last_commit: f0f5237 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ctsGE_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ctsGE_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ctsGE_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ctsGE_1.36.0.tgz vignettes: vignettes/ctsGE/inst/doc/ctsGE.html vignetteTitles: ctsGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctsGE/inst/doc/ctsGE.R dependencyCount: 60 Package: CTSV Version: 1.12.0 Depends: R (>= 4.2), Imports: stats, pscl, qvalue, BiocParallel, methods, knitr, SpatialExperiment, SummarizedExperiment Suggests: testthat, BiocStyle License: GPL-3 MD5sum: ad6678c041299598903c2d1803b3d300 NeedsCompilation: yes Title: Identification of cell-type-specific spatially variable genes accounting for excess zeros Description: The R package CTSV implements the CTSV approach developed by Jinge Yu and Xiangyu Luo that detects cell-type-specific spatially variable genes accounting for excess zeros. CTSV directly models sparse raw count data through a zero-inflated negative binomial regression model, incorporates cell-type proportions, and performs hypothesis testing based on R package pscl. The package outputs p-values and q-values for genes in each cell type, and CTSV is scalable to datasets with tens of thousands of genes measured on hundreds of spots. CTSV can be installed in Windows, Linux, and Mac OS. biocViews: GeneExpression, StatisticalMethod, Regression, Spatial, Genetics Author: Jinge Yu Developer [aut, cre], Xiangyu Luo Developer [aut] Maintainer: Jinge Yu Developer URL: https://github.com/jingeyu/CTSV VignetteBuilder: knitr BugReports: https://github.com/jingeyu/CTSV/issues git_url: https://git.bioconductor.org/packages/CTSV git_branch: RELEASE_3_22 git_last_commit: 862c792 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CTSV_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CTSV_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CTSV_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CTSV_1.12.0.tgz vignettes: vignettes/CTSV/inst/doc/CTSV.html vignetteTitles: Basic Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CTSV/inst/doc/CTSV.R dependencyCount: 97 Package: CuratedAtlasQueryR Version: 1.8.0 Depends: R (>= 4.2.0) Imports: dplyr, SummarizedExperiment, SingleCellExperiment, purrr (>= 1.0.0), BiocGenerics, glue, HDF5Array, DBI, tools, httr, cli, assertthat, SeuratObject, Seurat, methods, rlang, stats, S4Vectors, tibble, utils, dbplyr (>= 2.3.0), duckdb, stringr Suggests: zellkonverter, rmarkdown, knitr, testthat, basilisk, arrow, reticulate, spelling, forcats, ggplot2, tidySingleCellExperiment, rprojroot License: GPL-3 MD5sum: 516fd9b1933570ec454a1383beb22ced NeedsCompilation: no Title: Queries the Human Cell Atlas Description: Provides access to a copy of the Human Cell Atlas, but with harmonised metadata. This allows for uniform querying across numerous datasets within the Atlas using common fields such as cell type, tissue type, and patient ethnicity. Usage involves first querying the metadata table for cells of interest, and then downloading the corresponding cells into a SingleCellExperiment object. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre, rev] (ORCID: ), Michael Milton [aut, rev] (ORCID: ), Martin Morgan [ctb, rev], Vincent Carey [ctb, rev], Julie Iskander [rev], Tony Papenfuss [rev], Silicon Valley Foundation CZF2019-002443 [fnd], NIH NHGRI 5U24HG004059-18 [fnd], Victoria Cancer Agency ECRF21036 [fnd], NHMRC 1116955 [fnd] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/CuratedAtlasQueryR VignetteBuilder: knitr BugReports: https://github.com/stemangiola/CuratedAtlasQueryR/issues git_url: https://git.bioconductor.org/packages/CuratedAtlasQueryR git_branch: RELEASE_3_22 git_last_commit: d6fb5df git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CuratedAtlasQueryR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CuratedAtlasQueryR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CuratedAtlasQueryR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CuratedAtlasQueryR_1.8.0.tgz vignettes: vignettes/CuratedAtlasQueryR/inst/doc/Introduction.html vignetteTitles: CuratedAtlasQueryR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CuratedAtlasQueryR/inst/doc/Introduction.R dependencyCount: 176 Package: customCMPdb Version: 1.20.0 Depends: R (>= 4.0) Imports: AnnotationHub, RSQLite, XML, utils, ChemmineR, methods, stats, rappdirs, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: 95ffeee686b054206d92badcbec254e5 NeedsCompilation: no Title: Customize and Query Compound Annotation Database Description: This package serves as a query interface for important community collections of small molecules, while also allowing users to include custom compound collections. biocViews: Software, Cheminformatics,AnnotationHubSoftware Author: Yuzhu Duan [aut, cre], Thomas Girke [aut] Maintainer: Yuzhu Duan URL: https://github.com/yduan004/customCMPdb/ VignetteBuilder: knitr BugReports: https://github.com/yduan004/customCMPdb/issues git_url: https://git.bioconductor.org/packages/customCMPdb git_branch: RELEASE_3_22 git_last_commit: b3dd69a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/customCMPdb_1.20.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/customCMPdb_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/customCMPdb_1.20.0.tgz vignettes: vignettes/customCMPdb/inst/doc/customCMPdb.html vignetteTitles: customCMPdb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customCMPdb/inst/doc/customCMPdb.R dependencyCount: 104 Package: cyanoFilter Version: 1.18.0 Depends: R(>= 4.1.0) Imports: Biobase, flowCore, flowDensity, flowClust, cytometree, ggplot2, GGally, graphics, grDevices, methods, mrfDepth, stats, utils Suggests: magrittr, dplyr, purrr, knitr, stringr, rmarkdown, tidyr License: MIT + file LICENSE MD5sum: 184aea0bf4a40f65cf86659bcd196ac6 NeedsCompilation: no Title: Phytoplankton Population Identification using Cell Pigmentation and/or Complexity Description: An approach to filter out and/or identify phytoplankton cells from all particles measured via flow cytometry pigment and cell complexity information. It does this using a sequence of one-dimensional gates on pre-defined channels measuring certain pigmentation and complexity. The package is especially tuned for cyanobacteria, but will work fine for phytoplankton communities where there is at least one cell characteristic that differentiates every phytoplankton in the community. biocViews: FlowCytometry, Clustering, OneChannel Author: Oluwafemi Olusoji [cre, aut], Aerts Marc [ctb], Delaender Frederik [ctb], Neyens Thomas [ctb], Spaak jurg [aut] Maintainer: Oluwafemi Olusoji URL: https://github.com/fomotis/cyanoFilter VignetteBuilder: knitr BugReports: https://github.com/fomotis/cyanoFilter/issues git_url: https://git.bioconductor.org/packages/cyanoFilter git_branch: RELEASE_3_22 git_last_commit: e3c99a3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cyanoFilter_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cyanoFilter_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cyanoFilter_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cyanoFilter_1.18.0.tgz vignettes: vignettes/cyanoFilter/inst/doc/cyanoFilter.html vignetteTitles: cyanoFilter: A Semi-Automated Framework for Identifying Phytplanktons and Cyanobacteria Population in Flow Cytometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cyanoFilter/inst/doc/cyanoFilter.R dependencyCount: 118 Package: cycle Version: 1.64.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: 068e4f8bc6b51c3211e1acc12f43a63d NeedsCompilation: no Title: Significance of periodic expression pattern in time-series data Description: Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models biocViews: Microarray, TimeCourse Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://cycle.sysbiolab.eu git_url: https://git.bioconductor.org/packages/cycle git_branch: RELEASE_3_22 git_last_commit: 3e6fa8d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cycle_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cycle_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cycle_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cycle_1.64.0.tgz vignettes: vignettes/cycle/inst/doc/cycle.pdf vignetteTitles: Introduction to cycle hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cycle/inst/doc/cycle.R dependencyCount: 18 Package: cydar Version: 1.34.0 Depends: SingleCellExperiment Imports: viridis, methods, shiny, graphics, stats, grDevices, utils, BiocGenerics, S4Vectors, BiocParallel, SummarizedExperiment, flowCore, Biobase, Rcpp, BiocNeighbors LinkingTo: Rcpp Suggests: ncdfFlow, testthat, rmarkdown, knitr, edgeR, limma, glmnet, BiocStyle, flowStats License: GPL-3 MD5sum: 46926f487f43ce4e8a3136097d783ee6 NeedsCompilation: yes Title: Using Mass Cytometry for Differential Abundance Analyses Description: Identifies differentially abundant populations between samples and groups in mass cytometry data. Provides methods for counting cells into hyperspheres, controlling the spatial false discovery rate, and visualizing changes in abundance in the high-dimensional marker space. biocViews: ImmunoOncology, FlowCytometry, MultipleComparison, Proteomics, SingleCell Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cydar git_branch: RELEASE_3_22 git_last_commit: 92bf565 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cydar_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cydar_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cydar_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cydar_1.34.0.tgz vignettes: vignettes/cydar/inst/doc/cydar.html vignetteTitles: Detecting differential abundance hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cydar/inst/doc/cydar.R dependencyCount: 84 Package: cypress Version: 1.6.0 Depends: R(>= 4.4.0) Imports: stats, abind, sirt, MASS,TOAST, tibble, parallel, preprocessCore, SummarizedExperiment, TCA, PROPER, methods,dplyr, utils, RColorBrewer, graphics, edgeR, BiocParallel, checkmate, mvtnorm, DESeq2, rlang, e1071 Suggests: knitr, rmarkdown, MatrixGenerics, htmltools, RUnit, BiocGenerics, BiocManager, BiocStyle, Biobase License: GPL-2 | GPL-3 MD5sum: 280ea735877c08169eb1453c542d1a97 NeedsCompilation: no Title: Cell-Type-Specific Power Assessment Description: CYPRESS is a cell-type-specific power tool. This package aims to perform power analysis for the cell-type-specific data. It calculates FDR, FDC, and power, under various study design parameters, including but not limited to sample size, and effect size. It takes the input of a SummarizeExperimental(SE) object with observed mixture data (feature by sample matrix), and the cell-type mixture proportions (sample by cell-type matrix). It can solve the cell-type mixture proportions from the reference free panel from TOAST and conduct tests to identify cell-type-specific differential expression (csDE) genes. biocViews: Software, GeneExpression, DataImport, RNASeq, Sequencing Author: Shilin Yu [aut, cre] (ORCID: ), Guanqun Meng [aut], Wen Tang [aut] Maintainer: Shilin Yu URL: https://github.com/renlyly/cypress VignetteBuilder: knitr BugReports: https://github.com/renlyly/cypress/issues git_url: https://git.bioconductor.org/packages/cypress git_branch: RELEASE_3_22 git_last_commit: 2c7153e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cypress_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cypress_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cypress_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cypress_1.6.0.tgz vignettes: vignettes/cypress/inst/doc/cypress.html vignetteTitles: cypress Package User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cypress/inst/doc/cypress.R dependencyCount: 114 Package: CytoDx Version: 1.30.0 Depends: R (>= 3.5) Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats, flowCore,grDevices, graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: ea0e415e1898344f460aced5b4c0dd7a NeedsCompilation: no Title: Robust prediction of clinical outcomes using cytometry data without cell gating Description: This package provides functions that predict clinical outcomes using single cell data (such as flow cytometry data, RNA single cell sequencing data) without the requirement of cell gating or clustering. biocViews: ImmunoOncology, CellBiology, FlowCytometry, StatisticalMethod, Software, CellBasedAssays, Regression, Classification, Survival Author: Zicheng Hu Maintainer: Zicheng Hu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/CytoDx git_branch: RELEASE_3_22 git_last_commit: 95e9c9a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CytoDx_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoDx_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoDx_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoDx_1.30.0.tgz vignettes: vignettes/CytoDx/inst/doc/CytoDx_Vignette.pdf vignetteTitles: Introduction to CytoDx hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoDx/inst/doc/CytoDx_Vignette.R dependencyCount: 47 Package: CyTOFpower Version: 1.16.0 Depends: R (>= 4.1) Imports: CytoGLMM, diffcyt, DT, dplyr, ggplot2, magrittr, methods, rlang, stats, shiny, shinyFeedback, shinyjs, shinyMatrix, SummarizedExperiment, tibble, tidyr Suggests: testthat (>= 3.0.0), BiocStyle, knitr License: LGPL-3 MD5sum: 5cb0591938b33ead0d34e91692aa499e NeedsCompilation: no Title: Power analysis for CyTOF experiments Description: This package is a tool to predict the power of CyTOF experiments in the context of differential state analyses. The package provides a shiny app with two options to predict the power of an experiment: i. generation of in-sicilico CyTOF data, using users input ii. browsing in a grid of parameters for which the power was already precomputed. biocViews: FlowCytometry, SingleCell, CellBiology, StatisticalMethod, Software Author: Anne-Maud Ferreira [cre, aut] (ORCID: ), Catherine Blish [aut], Susan Holmes [aut] Maintainer: Anne-Maud Ferreira VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CyTOFpower git_branch: RELEASE_3_22 git_last_commit: 9cc6940 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CyTOFpower_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CyTOFpower_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CyTOFpower_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CyTOFpower_1.16.0.tgz vignettes: vignettes/CyTOFpower/inst/doc/CyTOFpower.html vignetteTitles: Power analysis for CyTOF experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CyTOFpower/inst/doc/CyTOFpower.R dependencyCount: 229 Package: CytoGLMM Version: 1.18.0 Imports: stats, methods, BiocParallel, RColorBrewer, cowplot, doParallel, dplyr, factoextra, flexmix, ggplot2, magrittr, mbest, pheatmap, stringr, strucchange, tibble, ggrepel, MASS, logging, Matrix, tidyr, caret, rlang, grDevices Suggests: knitr, rmarkdown, testthat, BiocStyle License: LGPL-3 MD5sum: da358a89249cc5bf0daeefef306e297d NeedsCompilation: no Title: Conditional Differential Analysis for Flow and Mass Cytometry Experiments Description: The CytoGLMM R package implements two multiple regression strategies: A bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM). Most current data analysis tools compare expressions across many computationally discovered cell types. CytoGLMM focuses on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. As a result, CytoGLMM finds differential proteins in flow and mass cytometry data while reducing biases arising from marker correlations and safeguarding against false discoveries induced by patient heterogeneity. biocViews: FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, ImmunoOncology, Regression, StatisticalMethod, Software Author: Christof Seiler [aut, cre] (ORCID: ) Maintainer: Christof Seiler URL: https://christofseiler.github.io/CytoGLMM, https://github.com/ChristofSeiler/CytoGLMM VignetteBuilder: knitr BugReports: https://github.com/ChristofSeiler/CytoGLMM/issues git_url: https://git.bioconductor.org/packages/CytoGLMM git_branch: RELEASE_3_22 git_last_commit: e50528b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CytoGLMM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoGLMM_1.17.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoGLMM_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoGLMM_1.18.0.tgz vignettes: vignettes/CytoGLMM/inst/doc/CytoGLMM.html vignetteTitles: CytoGLMM Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoGLMM/inst/doc/CytoGLMM.R importsMe: CyTOFpower dependencyCount: 175 Package: cytoKernel Version: 1.16.0 Depends: R (>= 4.1) Imports: Rcpp, SummarizedExperiment, utils, methods, ComplexHeatmap, circlize, ashr, data.table, BiocParallel, dplyr, stats, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 73662c2d7d42e27fa2b980c40715cff5 NeedsCompilation: yes Title: Differential expression using kernel-based score test Description: cytoKernel implements a kernel-based score test to identify differentially expressed features in high-dimensional biological experiments. This approach can be applied across many different high-dimensional biological data including gene expression data and dimensionally reduced cytometry-based marker expression data. In this R package, we implement functions that compute the feature-wise p values and their corresponding adjusted p values. Additionally, it also computes the feature-wise shrunk effect sizes and their corresponding shrunken effect size. Further, it calculates the percent of differentially expressed features and plots user-friendly heatmap of the top differentially expressed features on the rows and samples on the columns. biocViews: ImmunoOncology, Proteomics, SingleCell, Software, OneChannel, FlowCytometry, DifferentialExpression, GeneExpression, Clustering Author: Tusharkanti Ghosh [aut, cre], Victor Lui [aut], Pratyaydipta Rudra [aut], Souvik Seal [aut], Thao Vu [aut], Elena Hsieh [aut], Debashis Ghosh [aut, cph] Maintainer: Tusharkanti Ghosh VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/cytoKernel/issues git_url: https://git.bioconductor.org/packages/cytoKernel git_branch: RELEASE_3_22 git_last_commit: 3fcbac3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cytoKernel_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cytoKernel_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cytoKernel_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cytoKernel_1.16.0.tgz vignettes: vignettes/cytoKernel/inst/doc/cytoKernel.html vignetteTitles: The CytoK user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoKernel/inst/doc/cytoKernel.R dependencyCount: 75 Package: cytolib Version: 2.22.0 Depends: R (>= 3.4) Imports: RProtoBufLib LinkingTo: BH(>= 1.84.0.0), RProtoBufLib(>= 2.13.1),Rhdf5lib Suggests: knitr, rmarkdown License: AGPL-3.0-only License_restricts_use: no Archs: x64 MD5sum: 6a9e89e47f3d6d530999a39151ca4559 NeedsCompilation: yes Title: C++ infrastructure for representing and interacting with the gated cytometry data Description: This package provides the core data structure and API to represent and interact with the gated cytometry data. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytolib git_branch: RELEASE_3_22 git_last_commit: 13c3c9c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cytolib_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cytolib_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cytolib_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cytolib_2.22.0.tgz vignettes: vignettes/cytolib/inst/doc/cytolib.html vignetteTitles: Using cytolib hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cytolib/inst/doc/cytolib.R importsMe: CytoML, flowCore, flowWorkspace linksToMe: CytoML, flowCore, flowWorkspace dependencyCount: 3 Package: cytomapper Version: 1.22.0 Depends: R (>= 4.0), EBImage, SingleCellExperiment, methods Imports: SpatialExperiment, S4Vectors, BiocParallel, HDF5Array, DelayedArray, RColorBrewer, viridis, utils, SummarizedExperiment, tools, graphics, raster, grDevices, stats, ggplot2, ggbeeswarm, svgPanZoom, svglite, shiny, shinydashboard, matrixStats, rhdf5, nnls Suggests: BiocStyle, knitr, rmarkdown, markdown, cowplot, testthat, shinytest License: GPL (>= 2) MD5sum: 9e56120a5bec19953ad33036a67c8344 NeedsCompilation: no Title: Visualization of highly multiplexed imaging data in R Description: Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells. biocViews: ImmunoOncology, Software, SingleCell, OneChannel, TwoChannel, MultipleComparison, Normalization, DataImport Author: Nils Eling [aut] (ORCID: ), Nicolas Damond [aut] (ORCID: ), Tobias Hoch [ctb], Lasse Meyer [cre, ctb] (ORCID: ) Maintainer: Lasse Meyer URL: https://github.com/BodenmillerGroup/cytomapper VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/cytomapper/issues git_url: https://git.bioconductor.org/packages/cytomapper git_branch: RELEASE_3_22 git_last_commit: 0d3160a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cytomapper_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cytomapper_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cytomapper_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cytomapper_1.22.0.tgz vignettes: vignettes/cytomapper/inst/doc/cytomapper_ondisk.html, vignettes/cytomapper/inst/doc/cytomapper.html vignetteTitles: "On disk storage of images", "Visualization of imaging cytometry data in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytomapper/inst/doc/cytomapper_ondisk.R, vignettes/cytomapper/inst/doc/cytomapper.R importsMe: cytoviewer, imcRtools, simpleSeg suggestsMe: SpatialDatasets, spicyWorkflow dependencyCount: 138 Package: CytoMDS Version: 1.6.0 Depends: R (>= 4.4), Biobase Imports: methods, stats, rlang, pracma, withr, flowCore, reshape2, ggplot2, ggrepel, ggforce, patchwork, transport, smacof, BiocParallel, CytoPipeline Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown, BiocStyle, HDCytoData License: GPL-3 Archs: x64 MD5sum: 7997e3b4766b4c14f139a15dea175a66 NeedsCompilation: no Title: Low Dimensions projection of cytometry samples Description: This package implements a low dimensional visualization of a set of cytometry samples, in order to visually assess the 'distances' between them. This, in turn, can greatly help the user to identify quality issues like batch effects or outlier samples, and/or check the presence of potential sample clusters that might align with the exeprimental design. The CytoMDS algorithm combines, on the one hand, the concept of Earth Mover's Distance (EMD), a.k.a. Wasserstein metric and, on the other hand, the Multi Dimensional Scaling (MDS) algorithm for the low dimensional projection. Also, the package provides some diagnostic tools for both checking the quality of the MDS projection, as well as tools to help with the interpretation of the axes of the projection. biocViews: FlowCytometry, QualityControl, DimensionReduction, MultidimensionalScaling, Software, Visualization Author: Philippe Hauchamps [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Dan Lin [ctb] Maintainer: Philippe Hauchamps URL: https://uclouvain-cbio.github.io/CytoMDS VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CytoMDS/issues git_url: https://git.bioconductor.org/packages/CytoMDS git_branch: RELEASE_3_22 git_last_commit: 778219a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CytoMDS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoMDS_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoMDS_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoMDS_1.6.0.tgz vignettes: vignettes/CytoMDS/inst/doc/CytoMDS.html vignetteTitles: Low Dimensional Projection of Cytometry Samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoMDS/inst/doc/CytoMDS.R dependencyCount: 191 Package: cytoMEM Version: 1.14.0 Depends: R (>= 4.2.0) Imports: gplots, tools, flowCore, grDevices, stats, utils, matrixStats, methods Suggests: knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 7c578591ae91fa78465c0ca58000773d NeedsCompilation: no Title: Marker Enrichment Modeling (MEM) Description: MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry, mass cytometry, single cell RNA-seq, and spectral flow cytometry using RMSD. biocViews: Proteomics, SystemsBiology, Classification, FlowCytometry, DataRepresentation, DataImport, CellBiology, SingleCell, Clustering Author: Sierra Lima [aut] (ORCID: ), Kirsten Diggins [aut] (ORCID: ), Jonathan Irish [aut, cre] (ORCID: ) Maintainer: Jonathan Irish URL: https://github.com/cytolab/cytoMEM VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytoMEM git_branch: RELEASE_3_22 git_last_commit: e57b1e4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cytoMEM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cytoMEM_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cytoMEM_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cytoMEM_1.14.0.tgz vignettes: vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.html vignetteTitles: Intro_to_Marker_Enrichment_Modeling_Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.R dependencyCount: 24 Package: CytoML Version: 2.22.0 Depends: R (>= 3.5.0) Imports: cytolib(>= 2.3.10), flowCore (>= 1.99.10), flowWorkspace (>= 4.1.8), openCyto (>= 1.99.2), XML, data.table, jsonlite, RBGL, Rgraphviz, Biobase, methods, graph, graphics, utils, jsonlite, dplyr, grDevices, methods, ggcyto (>= 1.11.4), yaml, stats, tibble LinkingTo: cpp11, BH(>= 1.62.0-1), RProtoBufLib, cytolib, Rhdf5lib, flowWorkspace Suggests: testthat, flowWorkspaceData , knitr, rmarkdown, parallel License: AGPL-3.0-only License_restricts_use: no Archs: x64 MD5sum: 2a626fdc88d97752c953adfdb5a54f63 NeedsCompilation: yes Title: A GatingML Interface for Cross Platform Cytometry Data Sharing Description: Uses platform-specific implemenations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms. biocViews: ImmunoOncology, FlowCytometry, DataImport, DataRepresentation Author: Mike Jiang, Jake Wagner Maintainer: Mike Jiang URL: https://github.com/RGLab/CytoML SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/RGLab/CytoML/issues git_url: https://git.bioconductor.org/packages/CytoML git_branch: RELEASE_3_22 git_last_commit: 8902400 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CytoML_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoML_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoML_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoML_2.22.0.tgz vignettes: vignettes/CytoML/inst/doc/cytobank2GatingSet.html, vignettes/CytoML/inst/doc/flowjo_to_gatingset.html, vignettes/CytoML/inst/doc/HowToExportGatingSet.html vignetteTitles: How to import Cytobank into a GatingSet, flowJo parser, How to export a GatingSet to GatingML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoML/inst/doc/cytobank2GatingSet.R, vignettes/CytoML/inst/doc/flowjo_to_gatingset.R, vignettes/CytoML/inst/doc/HowToExportGatingSet.R suggestsMe: FlowSOM, flowWorkspace, openCyto dependencyCount: 78 Package: CytoPipeline Version: 1.10.0 Depends: R (>= 4.4) Imports: methods, stats, utils, withr, rlang, ggplot2 (>= 3.4.1), ggcyto, BiocFileCache, BiocParallel, flowCore, PeacoQC, flowAI, diagram, jsonlite, scales Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown, BiocStyle, reshape2, dplyr, CytoPipelineGUI License: GPL-3 MD5sum: cf46deaa98011e99d8fc2602c8b4b98a NeedsCompilation: no Title: Automation and visualization of flow cytometry data analysis pipelines Description: This package provides support for automation and visualization of flow cytometry data analysis pipelines. In the current state, the package focuses on the preprocessing and quality control part. The framework is based on two main S4 classes, i.e. CytoPipeline and CytoProcessingStep. The pipeline steps are linked to corresponding R functions - that are either provided in the CytoPipeline package itself, or exported from a third party package, or coded by the user her/himself. The processing steps need to be specified centrally and explicitly using either a json input file or through step by step creation of a CytoPipeline object with dedicated methods. After having run the pipeline, obtained results at all steps can be retrieved and visualized thanks to file caching (the running facility uses a BiocFileCache implementation). The package provides also specific visualization tools like pipeline workflow summary display, and 1D/2D comparison plots of obtained flowFrames at various steps of the pipeline. biocViews: FlowCytometry, Preprocessing, QualityControl, WorkflowStep, ImmunoOncology, Software, Visualization Author: Philippe Hauchamps [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Dan Lin [ctb] Maintainer: Philippe Hauchamps URL: https://uclouvain-cbio.github.io/CytoPipeline VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CytoPipeline/issues git_url: https://git.bioconductor.org/packages/CytoPipeline git_branch: RELEASE_3_22 git_last_commit: 4cb0101 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CytoPipeline_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoPipeline_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoPipeline_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoPipeline_1.10.0.tgz vignettes: vignettes/CytoPipeline/inst/doc/CytoPipeline.html, vignettes/CytoPipeline/inst/doc/Demo.html vignetteTitles: Automation and Visualization of Flow Cytometry Data Analysis Pipelines, Demonstration of the CytoPipeline R package suite functionalities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoPipeline/inst/doc/CytoPipeline.R, vignettes/CytoPipeline/inst/doc/Demo.R dependsOnMe: CytoPipelineGUI importsMe: CytoMDS dependencyCount: 130 Package: CytoPipelineGUI Version: 1.8.0 Depends: R (>= 4.4), CytoPipeline (>= 1.9.3) Imports: shiny, plotly, ggplot2, flowCore Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown, BiocStyle, patchwork License: GPL-3 MD5sum: 68f0cde0b017578622edb3967cf68504 NeedsCompilation: no Title: GUI's for visualization of flow cytometry data analysis pipelines Description: This package is the companion of the `CytoPipeline` package. It provides GUI's (shiny apps) for the visualization of flow cytometry data analysis pipelines that are run with `CytoPipeline`. Two shiny applications are provided, i.e. an interactive flow frame assessment and comparison tool and an interactive scale transformations visualization and adjustment tool. biocViews: FlowCytometry, Preprocessing, QualityControl, WorkflowStep, ImmunoOncology, Software, Visualization, GUI, ShinyApps Author: Philippe Hauchamps [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Dan Lin [ctb] Maintainer: Philippe Hauchamps URL: https://uclouvain-cbio.github.io/CytoPipelineGUI VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CytoPipelineGUI/issues git_url: https://git.bioconductor.org/packages/CytoPipelineGUI git_branch: RELEASE_3_22 git_last_commit: 9a3910e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/CytoPipelineGUI_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoPipelineGUI_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoPipelineGUI_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoPipelineGUI_1.8.0.tgz vignettes: vignettes/CytoPipelineGUI/inst/doc/CytoPipelineGUI.html, vignettes/CytoPipelineGUI/inst/doc/Demo.html vignetteTitles: CytoPipelineGUI : visualization of Flow Cytometry Data Analysis Pipelines run with CytoPipeline, Demonstration of the CytoPipeline R package suite functionalities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoPipelineGUI/inst/doc/CytoPipelineGUI.R, vignettes/CytoPipelineGUI/inst/doc/Demo.R suggestsMe: CytoPipeline dependencyCount: 144 Package: cytoviewer Version: 1.10.0 Imports: shiny, shinydashboard, utils, colourpicker, shinycssloaders, svgPanZoom, viridis, archive, grDevices, RColorBrewer, svglite, EBImage, methods, cytomapper, SingleCellExperiment, S4Vectors, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat License: GPL-3 MD5sum: 5d06c105dfe88eb5d861ef6eaf902375 NeedsCompilation: no Title: An interactive multi-channel image viewer for R Description: This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image downloads. biocViews: ImmunoOncology, Software, SingleCell, OneChannel, TwoChannel, MultiChannel, Spatial, DataImport Author: Lasse Meyer [aut, cre] (ORCID: ), Nils Eling [aut] (ORCID: ) Maintainer: Lasse Meyer URL: https://github.com/BodenmillerGroup/cytoviewer VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/cytoviewer/issues git_url: https://git.bioconductor.org/packages/cytoviewer git_branch: RELEASE_3_22 git_last_commit: 56765b4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/cytoviewer_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cytoviewer_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cytoviewer_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cytoviewer_1.10.0.tgz vignettes: vignettes/cytoviewer/inst/doc/cytoviewer.html vignetteTitles: "Interactive multi-channel image visualization in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoviewer/inst/doc/cytoviewer.R dependencyCount: 144 Package: dada2 Version: 1.38.0 Depends: R (>= 4.1.0), Rcpp (>= 0.12.0), methods (>= 3.4.0) Imports: Biostrings (>= 2.42.1), ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), ShortRead (>= 1.32.0), RcppParallel (>= 4.3.0), parallel (>= 3.2.0), IRanges (>= 2.6.0), XVector (>= 0.16.0), BiocGenerics (>= 0.22.0) LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, knitr, rmarkdown License: LGPL-2 MD5sum: 8675dab95ed519215a2662e1f71f914d NeedsCompilation: yes Title: Accurate, high-resolution sample inference from amplicon sequencing data Description: The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Paul McMurdie, Susan Holmes Maintainer: Benjamin Callahan URL: http://benjjneb.github.io/dada2/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/benjjneb/dada2/issues git_url: https://git.bioconductor.org/packages/dada2 git_branch: RELEASE_3_22 git_last_commit: 60234dd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dada2_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dada2_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dada2_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dada2_1.38.0.tgz vignettes: vignettes/dada2/inst/doc/dada2-intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dada2/inst/doc/dada2-intro.R dependsOnMe: MiscMetabar importsMe: Rbec, DBTC suggestsMe: mia, demulticoder dependencyCount: 75 Package: dagLogo Version: 1.48.0 Depends: R (>= 3.0.1), methods, grid Imports: pheatmap, Biostrings, UniProt.ws, BiocGenerics, utils, biomaRt, motifStack, httr Suggests: XML, grImport, grImport2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: 31adb09bf15ee118d8cb4a2461bebd96 NeedsCompilation: no Title: dagLogo: a Bioconductor package for visualizing conserved amino acid sequence pattern in groups based on probability theory Description: Visualize significant conserved amino acid sequence pattern in groups based on probability theory. biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Haibo Liu, Alexey Stukalov, Niraj Nirala, Usha Acharya, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dagLogo git_branch: RELEASE_3_22 git_last_commit: a5fc0da git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dagLogo_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dagLogo_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dagLogo_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dagLogo_1.48.0.tgz vignettes: vignettes/dagLogo/inst/doc/dagLogo.html vignetteTitles: dagLogo Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R dependencyCount: 139 Package: daMA Version: 1.82.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: 49a5609f637db120fafb17e8ed99ee56 NeedsCompilation: no Title: Efficient design and analysis of factorial two-colour microarray data Description: This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data. Statistical details are described in Bretz et al. (2003, submitted) biocViews: Microarray, TwoChannel, DifferentialExpression Author: Jobst Landgrebe and Frank Bretz Maintainer: Jobst Landgrebe URL: http://www.microarrays.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/daMA git_branch: RELEASE_3_22 git_last_commit: c18c4d3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/daMA_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/daMA_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/daMA_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/daMA_1.82.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: DAMEfinder Version: 1.22.0 Depends: R (>= 4.0) Imports: stats, Seqinfo, GenomicRanges, IRanges, S4Vectors, readr, SummarizedExperiment, GenomicAlignments, stringr, plyr, VariantAnnotation, parallel, ggplot2, Rsamtools, BiocGenerics, methods, limma, bumphunter, Biostrings, reshape2, cowplot, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: 3b76b5dd18766eabee123a89252bfb48 NeedsCompilation: no Title: Finds DAMEs - Differential Allelicly MEthylated regions Description: 'DAMEfinder' offers functionality for taking methtuple or bismark outputs to calculate ASM scores and compute DAMEs. It also offers nice visualization of methyl-circle plots. biocViews: DNAMethylation, DifferentialMethylation, Coverage Author: Stephany Orjuela [aut, cre] (ORCID: ), Dania Machlab [aut], Mark Robinson [aut] Maintainer: Stephany Orjuela VignetteBuilder: knitr BugReports: https://github.com/markrobinsonuzh/DAMEfinder/issues git_url: https://git.bioconductor.org/packages/DAMEfinder git_branch: RELEASE_3_22 git_last_commit: a1490db git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DAMEfinder_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DAMEfinder_1.21.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DAMEfinder_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DAMEfinder_1.22.0.tgz vignettes: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.html vignetteTitles: DAMEfinder Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.R dependencyCount: 115 Package: DaMiRseq Version: 2.22.0 Depends: R (>= 3.5.0), SummarizedExperiment, ggplot2 Imports: DESeq2, limma, EDASeq, RColorBrewer, sva, Hmisc, pheatmap, FactoMineR, corrplot, randomForest, e1071, caret, MASS, lubridate, plsVarSel, kknn, FSelector, methods, stats, utils, graphics, grDevices, reshape2, ineq, arm, pls, RSNNS, edgeR, plyr Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: ee34dab19a45595121d5d0f48e62e7f9 NeedsCompilation: no Title: Data Mining for RNA-seq data: normalization, feature selection and classification Description: The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot. biocViews: Sequencing, RNASeq, Classification, ImmunoOncology Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DaMiRseq git_branch: RELEASE_3_22 git_last_commit: 63d6fe2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DaMiRseq_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DaMiRseq_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DaMiRseq_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DaMiRseq_2.22.0.tgz vignettes: vignettes/DaMiRseq/inst/doc/DaMiRseq.pdf vignetteTitles: Data Mining for RNA-seq data: normalization,, features selection and classification - DaMiRseq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DaMiRseq/inst/doc/DaMiRseq.R importsMe: GARS dependencyCount: 251 Package: Damsel Version: 1.6.0 Depends: R (>= 4.4.0) Imports: AnnotationDbi, Biostrings, ComplexHeatmap, dplyr, edgeR, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggbio, ggplot2, goseq, magrittr, patchwork, plyranges, reshape2, rlang, Rsamtools, Rsubread, stats, stringr, tidyr, utils Suggests: BiocStyle, biomaRt, biovizBase, BSgenome.Dmelanogaster.UCSC.dm6, knitr, limma, org.Dm.eg.db, rmarkdown, testthat (>= 3.0.0), TxDb.Dmelanogaster.UCSC.dm6.ensGene License: MIT + file LICENSE MD5sum: 91b5f137f757e7eddfa2a5b38d710fe7 NeedsCompilation: no Title: Damsel: an end to end analysis of DamID Description: Damsel provides an end to end analysis of DamID data. Damsel takes bam files from Dam-only control and fusion samples and counts the reads matching to each GATC region. edgeR is utilised to identify regions of enrichment in the fusion relative to the control. Enriched regions are combined into peaks, and are associated with nearby genes. Damsel allows for IGV style plots to be built as the results build, inspired by ggcoverage, and using the functionality and layering ability of ggplot2. Damsel also conducts gene ontology testing with bias correction through goseq, and future versions of Damsel will also incorporate motif enrichment analysis. Overall, Damsel is the first package allowing for an end to end analysis with visual capabilities. The goal of Damsel was to bring all the analysis into one place, and allow for exploratory analysis within R. biocViews: DifferentialMethylation, PeakDetection, GenePrediction, GeneSetEnrichment Author: Caitlin Page [aut, cre] (ORCID: ) Maintainer: Caitlin Page URL: https://github.com/Oshlack/Damsel VignetteBuilder: knitr BugReports: https://github.com/Oshlack/Damsel git_url: https://git.bioconductor.org/packages/Damsel git_branch: RELEASE_3_22 git_last_commit: a70f6d8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Damsel_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Damsel_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Damsel_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Damsel_1.6.0.tgz vignettes: vignettes/Damsel/inst/doc/Damsel-workflow.html vignetteTitles: Damsel-workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Damsel/inst/doc/Damsel-workflow.R dependencyCount: 176 Package: DAPAR Version: 1.42.0 Depends: R (>= 4.3.0) Imports: Biobase, MSnbase, DAPARdata (>= 1.30.0), utils, highcharter, foreach Suggests: testthat, BiocStyle, AnnotationDbi, clusterProfiler, graph, diptest, cluster, vioplot, visNetwork, vsn, igraph, FactoMineR, factoextra, dendextend, parallel, doParallel, Mfuzz, apcluster, forcats, readxl, openxlsx, multcomp, purrr, tibble, knitr, norm, scales, tidyverse, cp4p, imp4p (>= 1.1),lme4, dplyr, limma, preprocessCore, stringr, tidyr, impute, gplots, grDevices, reshape2, graphics, stats, methods, ggplot2, RColorBrewer, Matrix, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 4c09d09c280db2edce905df99247bc3f NeedsCompilation: no Title: Tools for the Differential Analysis of Proteins Abundance with R Description: The package DAPAR is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required (see `Prostar` package). biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, GO, DataImport Author: c(person(given = "Samuel", family = "Wieczorek", email = "samuel.wieczorek@cea.fr", role = c("aut","cre")), person(given = "Florence", family ="Combes", email = "florence.combes@cea.fr", role = "aut"), person(given = "Thomas", family ="Burger", email = "thomas.burger@cea.fr", role = "aut"), person(given = "Vasile-Cosmin", family ="Lazar", email = "vcosminlazar@gmail.com", role = "ctb"), person(given = "Enora", family ="Fremy", email = "enora.fremy@cea.fr", role = "ctb"), person(given = "Helene", family ="Borges", email = "helene.borges@cea.fr", role = "ctb")) Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/edyp-lab/DAPAR/issues git_url: https://git.bioconductor.org/packages/DAPAR git_branch: RELEASE_3_22 git_last_commit: c9303a2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DAPAR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DAPAR_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DAPAR_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DAPAR_1.42.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: Prostar suggestsMe: DAPARdata, mi4p dependencyCount: 144 Package: dar Version: 1.6.0 Depends: R (>= 4.5.0) Imports: cli, ComplexHeatmap, crayon, dplyr, generics, ggplot2, glue, gplots, heatmaply, magrittr, methods, mia, phyloseq, purrr, readr, rlang (>= 0.4.11), scales, stringr, tibble, tidyr, UpSetR, waldo Suggests: ALDEx2, ANCOMBC, apeglm, ashr, Biobase, corncob, covr, DESeq2, devtools, furrr, future, knitr, lefser, limma, Maaslin2, metagenomeSeq, microbiome, rmarkdown, roxygen2, roxyglobals, roxytest, rstatix, SummarizedExperiment, TreeSummarizedExperiment, testthat (>= 3.0.0), GenomeInfoDb License: MIT + file LICENSE MD5sum: a94a0260ca14d8260b6b6cea6412fc14 NeedsCompilation: no Title: Differential Abundance Analysis by Consensus Description: Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way. biocViews: Software, Sequencing, Microbiome, Metagenomics, MultipleComparison, Normalization Author: Francesc Catala-Moll [aut, cre] (ORCID: ) Maintainer: Francesc Catala-Moll URL: https://github.com/MicrobialGenomics-IrsicaixaOrg/dar, https://microbialgenomics-irsicaixaorg.github.io/dar/ VignetteBuilder: knitr BugReports: https://github.com/MicrobialGenomics-IrsicaixaOrg/dar/issues git_url: https://git.bioconductor.org/packages/dar git_branch: RELEASE_3_22 git_last_commit: 0a164df git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dar_1.6.0.tar.gz vignettes: vignettes/dar/inst/doc/article.html, vignettes/dar/inst/doc/bioinformatics_vignette.html, vignettes/dar/inst/doc/dar.html, vignettes/dar/inst/doc/data_import.html, vignettes/dar/inst/doc/filtering_subsetting.html, vignettes/dar/inst/doc/import_export_recipes.html vignetteTitles: Workflow with real data, Workflow with real data, Introduction to dar, Data Import, Filtering and Subsetting, Reproducibility in Microbiome Data Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dar/inst/doc/article.R, vignettes/dar/inst/doc/bioinformatics_vignette.R, vignettes/dar/inst/doc/dar.R, vignettes/dar/inst/doc/data_import.R, vignettes/dar/inst/doc/filtering_subsetting.R, vignettes/dar/inst/doc/import_export_recipes.R dependencyCount: 231 Package: DART Version: 1.58.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: b20a0a3712cb7e18214e21273655110c NeedsCompilation: no Title: Denoising Algorithm based on Relevance network Topology Description: Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples. biocViews: GeneExpression, DifferentialExpression, GraphAndNetwork, Pathways Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff, Charles Shijie Zheng Maintainer: Charles Shijie Zheng git_url: https://git.bioconductor.org/packages/DART git_branch: RELEASE_3_22 git_last_commit: 9832e42 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DART_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DART_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DART_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DART_1.58.0.tgz vignettes: vignettes/DART/inst/doc/DART.pdf vignetteTitles: DART Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DART/inst/doc/DART.R dependencyCount: 17 Package: dcanr Version: 1.26.0 Depends: R (>= 3.6.0) Imports: igraph, foreach, plyr, stringr, reshape2, methods, Matrix, graphics, stats, RColorBrewer, circlize, doRNG Suggests: EBcoexpress, testthat, EBarrays, GeneNet, mclust, minqa, SummarizedExperiment, Biobase, knitr, rmarkdown, BiocStyle, edgeR Enhances: parallel, doSNOW, doParallel License: GPL-3 MD5sum: 266805f7c05b605461ac34fde73731dc NeedsCompilation: no Title: Differential co-expression/association network analysis Description: This package implements methods and an evaluation framework to infer differential co-expression/association networks. Various methods are implemented and can be evaluated using simulated datasets. Inference of differential co-expression networks can allow identification of networks that are altered between two conditions (e.g., health and disease). biocViews: NetworkInference, GraphAndNetwork, DifferentialExpression, Network Author: Dharmesh D. Bhuva [aut, cre] (ORCID: ) Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/dcanr/, https://github.com/DavisLaboratory/dcanr VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/dcanr/issues git_url: https://git.bioconductor.org/packages/dcanr git_branch: RELEASE_3_22 git_last_commit: 848b497 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dcanr_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dcanr_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dcanr_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dcanr_1.26.0.tgz vignettes: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.html, vignettes/dcanr/inst/doc/dcanr_vignette.html vignetteTitles: 2. DC method evaluation, 1. Differential co-expression analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.R, vignettes/dcanr/inst/doc/dcanr_vignette.R importsMe: ClassifyR, multiWGCNA dependencyCount: 35 Package: DCATS Version: 1.8.0 Depends: R (>= 4.1.0), stats Imports: MCMCpack, matrixStats, robustbase, aod, e1071 Suggests: testthat (>= 3.0.0), knitr, Seurat, SeuratObject, tidyverse, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 8e3c3bd1ec6a0808d9e9731ae523a1a1 NeedsCompilation: no Title: Differential Composition Analysis Transformed by a Similarity matrix Description: Methods to detect the differential composition abundances between conditions in singel-cell RNA-seq experiments, with or without replicates. It aims to correct bias introduced by missclaisification and enable controlling of confounding covariates. To avoid the influence of proportion change from big cell types, DCATS can use either total cell number or specific reference group as normalization term. biocViews: SingleCell, Normalization Author: Xinyi Lin [aut, cre] (ORCID: ), Chuen Chau [aut], Yuanhua Huang [aut], Joshua W.K. Ho [aut] Maintainer: Xinyi Lin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DCATS git_branch: RELEASE_3_22 git_last_commit: ed0c9f6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DCATS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DCATS_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DCATS_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DCATS_1.8.0.tgz vignettes: vignettes/DCATS/inst/doc/Intro_to_DCATS.html vignetteTitles: Differential Composition Analysis with DCATS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DCATS/inst/doc/Intro_to_DCATS.R dependencyCount: 24 Package: dcGSA Version: 1.38.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 Archs: x64 MD5sum: d00c8650484a7c9e2130eed0e99a8653 NeedsCompilation: no Title: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles Description: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes. biocViews: ImmunoOncology, GeneSetEnrichment,Microarray, StatisticalMethod, Sequencing, RNASeq, GeneExpression Author: Jiehuan Sun [aut, cre], Jose Herazo-Maya [aut], Xiu Huang [aut], Naftali Kaminski [aut], and Hongyu Zhao [aut] Maintainer: Jiehuan sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dcGSA git_branch: RELEASE_3_22 git_last_commit: 5013bd3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dcGSA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dcGSA_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dcGSA_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dcGSA_1.38.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 18 Package: ddCt Version: 1.66.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: testthat (>= 3.0.0), RUnit License: LGPL-3 MD5sum: e237a7cb6d94883390873734b1d48813 NeedsCompilation: no Title: The ddCt Algorithm for the Analysis of Quantitative Real-Time PCR (qRT-PCR) Description: The Delta-Delta-Ct (ddCt) Algorithm is an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. Compared to other approaches, it requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The ddCt package implements a pipeline to collect, analyse and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions. biocViews: GeneExpression, DifferentialExpression, MicrotitrePlateAssay, qPCR Author: Jitao David Zhang, Rudolf Biczok, and Markus Ruschhaupt Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/ddCt git_branch: RELEASE_3_22 git_last_commit: 7d1f599 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ddCt_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ddCt_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ddCt_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ddCt_1.66.0.tgz vignettes: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf, vignettes/ddCt/inst/doc/rtPCR-usage.pdf, vignettes/ddCt/inst/doc/rtPCR.pdf vignetteTitles: How to apply the ddCt method, Analyse RT-PCR data with the end-to-end script in ddCt package, Introduction to the ddCt method for qRT-PCR data analysis: background,, algorithm and example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R, vignettes/ddCt/inst/doc/rtPCR-usage.R, vignettes/ddCt/inst/doc/rtPCR.R dependencyCount: 12 Package: ddPCRclust Version: 1.30.0 Depends: R (>= 3.5) Imports: plotrix, clue, parallel, ggplot2, openxlsx, R.utils, flowCore, flowDensity (>= 1.13.3), SamSPECTRAL, flowPeaks Suggests: BiocStyle License: Artistic-2.0 MD5sum: 6bade4eebf1c0a0b31264bb552e19dda NeedsCompilation: no Title: Clustering algorithm for ddPCR data Description: The ddPCRclust algorithm can automatically quantify the CPDs of non-orthogonal ddPCR reactions with up to four targets. In order to determine the correct droplet count for each target, it is crucial to both identify all clusters and label them correctly based on their position. For more information on what data can be analyzed and how a template needs to be formatted, please check the vignette. biocViews: ddPCR, Clustering Author: Benedikt G. Brink [aut, cre], Justin Meskas [ctb], Ryan R. Brinkman [ctb] Maintainer: Benedikt G. Brink URL: https://github.com/bgbrink/ddPCRclust BugReports: https://github.com/bgbrink/ddPCRclust/issues git_url: https://git.bioconductor.org/packages/ddPCRclust git_branch: RELEASE_3_22 git_last_commit: 79d2c75 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ddPCRclust_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ddPCRclust_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ddPCRclust_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ddPCRclust_1.30.0.tgz vignettes: vignettes/ddPCRclust/inst/doc/ddPCRclust.pdf vignetteTitles: Bioconductor LaTeX Style hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddPCRclust/inst/doc/ddPCRclust.R suggestsMe: Polytect dependencyCount: 105 Package: dearseq Version: 1.22.0 Depends: R (>= 3.6.0) Imports: CompQuadForm, dplyr, ggplot2, KernSmooth, magrittr, matrixStats, methods, patchwork, parallel, pbapply, reshape2, rlang, scattermore, stats, statmod, survey, tibble, viridisLite Suggests: Biobase, BiocManager, BiocSet, edgeR, DESeq2, GEOquery, GSA, knitr, limma, readxl, rmarkdown, S4Vectors, SummarizedExperiment, testthat, covr License: GPL-2 | file LICENSE MD5sum: 8deb9fc598e9b11df35e5eb6200ddd92 NeedsCompilation: no Title: Differential Expression Analysis for RNA-seq data through a robust variance component test Description: Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093. biocViews: BiomedicalInformatics, CellBiology, DifferentialExpression, DNASeq, GeneExpression, Genetics, GeneSetEnrichment, ImmunoOncology, KEGG, Regression, RNASeq, Sequencing, SystemsBiology, TimeCourse, Transcription, Transcriptomics Author: Denis Agniel [aut], Boris P. Hejblum [aut, cre] (ORCID: ), Marine Gauthier [aut], Mélanie Huchon [ctb] Maintainer: Boris P. Hejblum VignetteBuilder: knitr BugReports: https://github.com/borishejblum/dearseq/issues git_url: https://git.bioconductor.org/packages/dearseq git_branch: RELEASE_3_22 git_last_commit: f876c6d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dearseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dearseq_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dearseq_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dearseq_1.22.0.tgz vignettes: vignettes/dearseq/inst/doc/dearseqUserguide.html vignetteTitles: dearseqUserguide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dearseq/inst/doc/dearseqUserguide.R suggestsMe: GeoTcgaData, TcGSA dependencyCount: 54 Package: debrowser Version: 1.38.0 Depends: R (>= 3.5.0), Imports: shiny, jsonlite, shinyjs, shinydashboard, shinyBS, gplots, DT, ggplot2, RColorBrewer, annotate, AnnotationDbi, DESeq2, DOSE, igraph, grDevices, graphics, stats, utils, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, stringi, reshape2, org.Hs.eg.db, org.Mm.eg.db, limma, edgeR, clusterProfiler, methods, sva, RCurl, enrichplot, colourpicker, plotly, heatmaply, Harman, pathview, apeglm, ashr Suggests: testthat, rmarkdown, knitr License: GPL-3 + file LICENSE MD5sum: dd730a64223fb42274a0afeebd968792 NeedsCompilation: no Title: Interactive Differential Expresion Analysis Browser Description: Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps. biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Clustering, ImmunoOncology Author: Alper Kucukural , Onur Yukselen , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/debrowser VignetteBuilder: knitr, rmarkdown BugReports: https://github.com/UMMS-Biocore/debrowser/issues/new git_url: https://git.bioconductor.org/packages/debrowser git_branch: RELEASE_3_22 git_last_commit: 5dcc10b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/debrowser_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/debrowser_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/debrowser_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/debrowser_1.38.0.tgz vignettes: vignettes/debrowser/inst/doc/DEBrowser.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R dependencyCount: 228 Package: DECIPHER Version: 3.6.0 Depends: R (>= 3.5.0), Biostrings (>= 2.59.1), stats Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, S4Vectors, IRanges, XVector Suggests: RSQLite (>= 1.1) License: GPL-3 MD5sum: 67ee5063f5795782c21c15f4e38ad2eb NeedsCompilation: yes Title: Tools for curating, analyzing, and manipulating biological sequences Description: A toolset for deciphering and managing biological sequences. biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization, Microarray, QualityControl, qPCR, Alignment, WholeGenome, Microbiome, ImmunoOncology, GenePrediction Author: Erik Wright Maintainer: Erik Wright URL: http://DECIPHER.codes git_url: https://git.bioconductor.org/packages/DECIPHER git_branch: RELEASE_3_22 git_last_commit: e807f36 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DECIPHER_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DECIPHER_3.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DECIPHER_3.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DECIPHER_3.6.0.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf, vignettes/DECIPHER/inst/doc/ClassifySequences.pdf, vignettes/DECIPHER/inst/doc/ClusteringSequences.pdf, vignettes/DECIPHER/inst/doc/DECIPHERing.pdf, vignettes/DECIPHER/inst/doc/DesignMicroarray.pdf, vignettes/DECIPHER/inst/doc/DesignPrimers.pdf, vignettes/DECIPHER/inst/doc/DesignProbes.pdf, vignettes/DECIPHER/inst/doc/DesignSignatures.pdf, vignettes/DECIPHER/inst/doc/FindChimeras.pdf, vignettes/DECIPHER/inst/doc/FindingGenes.pdf, vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.pdf, vignettes/DECIPHER/inst/doc/GrowingTrees.pdf, vignettes/DECIPHER/inst/doc/PopulationGenetics.pdf, vignettes/DECIPHER/inst/doc/RepeatRepeat.pdf, vignettes/DECIPHER/inst/doc/SearchForResearch.pdf vignetteTitles: The Art of Multiple Sequence Alignment in R, Classify Sequences in R, Upsize Your Clustering with Clusterize, Getting Started DECIPHERing, Design Microarray Probes in R, Design Group-Specific Primers in R, Design Group-Specific FISH Probes in R, Design Primers that Yield Group-Specific Signatures, Finding Chimeric Sequences in R, The Magic of Gene Finding, The Double Life of RNA: Uncovering Non-Coding RNAs, Growing Phylogenetic Trees in R with Treeline, Population Genetics Inference in R, Detecting Obscure Tandem Repeats in Sequences, Searching Biological Sequences for Research hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.R, vignettes/DECIPHER/inst/doc/ClassifySequences.R, vignettes/DECIPHER/inst/doc/ClusteringSequences.R, vignettes/DECIPHER/inst/doc/DECIPHERing.R, vignettes/DECIPHER/inst/doc/DesignMicroarray.R, vignettes/DECIPHER/inst/doc/DesignPrimers.R, vignettes/DECIPHER/inst/doc/DesignProbes.R, vignettes/DECIPHER/inst/doc/DesignSignatures.R, vignettes/DECIPHER/inst/doc/FindChimeras.R, vignettes/DECIPHER/inst/doc/FindingGenes.R, vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.R, vignettes/DECIPHER/inst/doc/GrowingTrees.R, vignettes/DECIPHER/inst/doc/PopulationGenetics.R, vignettes/DECIPHER/inst/doc/RepeatRepeat.R, vignettes/DECIPHER/inst/doc/SearchForResearch.R dependsOnMe: AssessORF, sangeranalyseR, SynExtend importsMe: DspikeIn, mia, openPrimeR, scifer, AssessORFData, copyseparator, ensembleTax, VIProDesign suggestsMe: MicrobiotaProcess, microbial, MiscMetabar dependencyCount: 16 Package: decompTumor2Sig Version: 2.26.0 Depends: R(>= 4.0), ggplot2 Imports: methods, Matrix, quadprog(>= 1.5-5), GenomicRanges, stats, GenomicFeatures, Biostrings, BiocGenerics, S4Vectors, plyr, utils, graphics, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, SummarizedExperiment, ggseqlogo, gridExtra, data.table, Seqinfo, readxl Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 Archs: x64 MD5sum: 116cf3c1bdfb53c9edd18eccc452d8ff NeedsCompilation: no Title: Decomposition of individual tumors into mutational signatures by signature refitting Description: Uses quadratic programming for signature refitting, i.e., to decompose the mutation catalog from an individual tumor sample into a set of given mutational signatures (either Alexandrov-model signatures or Shiraishi-model signatures), computing weights that reflect the contributions of the signatures to the mutation load of the tumor. biocViews: Software, SNP, Sequencing, DNASeq, GenomicVariation, SomaticMutation, BiomedicalInformatics, Genetics, BiologicalQuestion, StatisticalMethod Author: Rosario M. Piro [aut, cre], Sandra Krueger [ctb] Maintainer: Rosario M. Piro URL: http://rmpiro.net/decompTumor2Sig/, https://github.com/rmpiro/decompTumor2Sig VignetteBuilder: knitr BugReports: https://github.com/rmpiro/decompTumor2Sig/issues git_url: https://git.bioconductor.org/packages/decompTumor2Sig git_branch: RELEASE_3_22 git_last_commit: 1228e7c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/decompTumor2Sig_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/decompTumor2Sig_2.25.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/decompTumor2Sig_2.26.0.tgz vignettes: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.html vignetteTitles: A brief introduction to decompTumor2Sig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.R importsMe: musicatk dependencyCount: 106 Package: decontam Version: 1.30.0 Depends: R (>= 3.4.1), methods (>= 3.4.1) Imports: ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), stats Suggests: BiocStyle, knitr, rmarkdown, phyloseq License: Artistic-2.0 Archs: x64 MD5sum: 9f2a022e5853c63e8dc43b0339f1113f NeedsCompilation: no Title: Identify Contaminants in Marker-gene and Metagenomics Sequencing Data Description: Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan [aut, cre], Nicole Marie Davis [aut], Felix G.M. Ernst [ctb] (ORCID: ) Maintainer: Benjamin Callahan URL: https://github.com/benjjneb/decontam VignetteBuilder: knitr BugReports: https://github.com/benjjneb/decontam/issues git_url: https://git.bioconductor.org/packages/decontam git_branch: RELEASE_3_22 git_last_commit: 0351d36 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/decontam_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/decontam_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/decontam_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/decontam_1.30.0.tgz vignettes: vignettes/decontam/inst/doc/decontam_intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decontam/inst/doc/decontam_intro.R importsMe: mia dependencyCount: 29 Package: decontX Version: 1.8.0 Depends: R (>= 4.3.0) Imports: celda, dbscan, DelayedArray, ggplot2, Matrix (>= 1.5.3), MCMCprecision, methods, patchwork, plyr, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), reshape2, rstan (>= 2.18.1), rstantools (>= 2.2.0), S4Vectors, scater, Seurat, SingleCellExperiment, SummarizedExperiment, withr LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, dplyr, knitr, rmarkdown, scran, SingleCellMultiModal, TENxPBMCData, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 31eae98eb97c9dae738eaebdecd60390 NeedsCompilation: yes Title: Decontamination of single cell genomics data Description: This package contains implementation of DecontX (Yang et al. 2020), a decontamination algorithm for single-cell RNA-seq, and DecontPro (Yin et al. 2023), a decontamination algorithm for single cell protein expression data. DecontX is a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. DecontPro is a Bayesian method that estimates the level of contamination from ambient and background sources in CITE-seq ADT dataset and decontaminate the dataset. biocViews: SingleCell, Bayesian Author: Yuan Yin [aut] (ORCID: ), Masanao Yajima [aut] (ORCID: ), Joshua Campbell [aut, cre] (ORCID: ) Maintainer: Joshua Campbell SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/decontX git_branch: RELEASE_3_22 git_last_commit: e98b38c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/decontX_1.8.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/decontX_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/decontX_1.8.0.tgz vignettes: vignettes/decontX/inst/doc/decontPro.html, vignettes/decontX/inst/doc/decontX.html vignetteTitles: decontPro, Estimate and remove cross-contamination from ambient RNA in single-cell data with DecontX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/decontX/inst/doc/decontPro.R, vignettes/decontX/inst/doc/decontX.R dependencyCount: 238 Package: DeconvoBuddies Version: 1.2.0 Depends: R (>= 4.4.0) Imports: AnnotationHub, BiocFileCache, DelayedMatrixStats, dplyr, ExperimentHub, ggplot2, graphics, grDevices, MatrixGenerics, methods, purrr, rafalib, reshape2, S4Vectors, scran, SingleCellExperiment, spatialLIBD, stats, stringr, SummarizedExperiment, tibble, utils Suggests: Biobase, BiocStyle, covr, HDF5Array, knitr, RColorBrewer, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), tidyr, tidyverse License: Artistic-2.0 MD5sum: f5879abbc7b5fb7c610484b94e6873df NeedsCompilation: no Title: Helper Functions for LIBD Deconvolution Description: Funtions helpful for LIBD deconvolution project. Includes tools for marker finding with mean ratio, expression plotting, and plotting deconvolution results. Working to include DLPFC datasets. biocViews: Software, SingleCell, RNASeq, GeneExpression, Transcriptomics, ExperimentHubSoftware Author: Louise Huuki-Myers [aut, cre] (ORCID: ), Leonardo Collado-Torres [ctb] (ORCID: ) Maintainer: Louise Huuki-Myers URL: https://github.com/lahuuki/DeconvoBuddies VignetteBuilder: knitr BugReports: https://github.com/LieberInstitute/DeconvoBuddies/issues git_url: https://git.bioconductor.org/packages/DeconvoBuddies git_branch: RELEASE_3_22 git_last_commit: 1c038d1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DeconvoBuddies_1.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DeconvoBuddies_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DeconvoBuddies_1.2.0.tgz vignettes: vignettes/DeconvoBuddies/inst/doc/DeconvoBuddies.html, vignettes/DeconvoBuddies/inst/doc/Deconvolution_Benchmark_DLPFC.html, vignettes/DeconvoBuddies/inst/doc/Marker_Finding.html vignetteTitles: Get Started with DeconvoBuddies, Deconvolution Benchmark in Human DLPFC, Finding Marker Genes with DeconvoBuddies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeconvoBuddies/inst/doc/DeconvoBuddies.R, vignettes/DeconvoBuddies/inst/doc/Deconvolution_Benchmark_DLPFC.R, vignettes/DeconvoBuddies/inst/doc/Marker_Finding.R dependencyCount: 208 Package: deconvR Version: 1.16.0 Depends: R (>= 4.1), data.table (>= 1.14.0) Imports: S4Vectors (>= 0.30.0), methylKit (>= 1.18.0), IRanges (>= 2.26.0), GenomicRanges (>= 1.44.0), BiocGenerics (>= 0.38.0), stats, methods, foreach (>= 1.5.1), magrittr (>= 2.0.1), matrixStats (>= 0.61.0), e1071 (>= 1.7.9), quadprog (>= 1.5.8), nnls (>= 1.4), rsq (>= 2.2), MASS, utils, dplyr (>= 1.0.7), tidyr (>= 1.1.3), assertthat, minfi Suggests: testthat (>= 3.0.0), roxygen2 (>= 7.1.2), doParallel (>= 1.0.16), parallel, knitr (>= 1.34), BiocStyle (>= 2.20.2), reshape2 (>= 1.4.4), ggplot2 (>= 3.3.5), rmarkdown, devtools (>= 2.4.2), sessioninfo (>= 1.1.1), covr, granulator, RefManageR License: Artistic-2.0 MD5sum: 4ff1f4d4462ad815f5d12d711b54a2cc NeedsCompilation: no Title: Simulation and Deconvolution of Omic Profiles Description: This package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. Users are given the option to create or extend a reference atlas and,also simulate the desired size of the bulk signature profile of the reference cell types.The package includes the cell-type-specific methylation atlas and, Illumina Epic B5 probe ids that can be used in deconvolution. Additionally,we included BSmeth2Probe, to make mapping WGBS data to their probe IDs easier. biocViews: DNAMethylation, Regression, GeneExpression, RNASeq, SingleCell, StatisticalMethod, Transcriptomics Author: Irem B. Gündüz [aut, cre] (ORCID: ), Veronika Ebenal [aut] (ORCID: ), Altuna Akalin [aut] (ORCID: ) Maintainer: Irem B. Gündüz URL: https://github.com/BIMSBbioinfo/deconvR VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/deconvR git_url: https://git.bioconductor.org/packages/deconvR git_branch: RELEASE_3_22 git_last_commit: 8758aa2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/deconvR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/deconvR_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/deconvR_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/deconvR_1.16.0.tgz vignettes: vignettes/deconvR/inst/doc/deconvRVignette.html vignetteTitles: deconvRVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deconvR/inst/doc/deconvRVignette.R dependencyCount: 183 Package: decoupleR Version: 2.16.0 Depends: R (>= 4.0) Imports: BiocParallel, broom, dplyr, magrittr, Matrix, parallelly, purrr, rlang, stats, stringr, tibble, tidyr, tidyselect, withr Suggests: glmnet (>= 4.1-7), GSVA, viper, fgsea (>= 1.15.4), AUCell, SummarizedExperiment, rpart, ranger, BiocStyle, covr, knitr, pkgdown, RefManageR, rmarkdown, roxygen2, sessioninfo, pheatmap, testthat, OmnipathR, Seurat, ggplot2, ggrepel, patchwork, magick License: GPL-3 + file LICENSE MD5sum: 3461fdd6d25feb0c5034cdec6c53b9a4 NeedsCompilation: no Title: decoupleR: Ensemble of computational methods to infer biological activities from omics data Description: Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase. biocViews: DifferentialExpression, FunctionalGenomics, GeneExpression, GeneRegulation, Network, Software, StatisticalMethod, Transcription, Author: Pau Badia-i-Mompel [aut, cre] (ORCID: ), Jesús Vélez-Santiago [aut] (ORCID: ), Jana Braunger [aut] (ORCID: ), Celina Geiss [aut] (ORCID: ), Daniel Dimitrov [aut] (ORCID: ), Sophia Müller-Dott [aut] (ORCID: ), Petr Taus [aut] (ORCID: ), Aurélien Dugourd [aut] (ORCID: ), Christian H. Holland [aut] (ORCID: ), Ricardo O. Ramirez Flores [aut] (ORCID: ), Julio Saez-Rodriguez [aut] (ORCID: ) Maintainer: Pau Badia-i-Mompel URL: https://saezlab.github.io/decoupleR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/decoupleR/issues git_url: https://git.bioconductor.org/packages/decoupleR git_branch: RELEASE_3_22 git_last_commit: ff3aa1d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/decoupleR_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/decoupleR_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/decoupleR_2.16.0.tgz vignettes: vignettes/decoupleR/inst/doc/decoupleR.html, vignettes/decoupleR/inst/doc/pw_bk.html, vignettes/decoupleR/inst/doc/pw_sc.html, vignettes/decoupleR/inst/doc/tf_bk.html, vignettes/decoupleR/inst/doc/tf_sc.html vignetteTitles: Introduction, Pathway activity inference in bulk RNA-seq, Pathway activity activity inference from scRNA-seq, Transcription factor activity inference in bulk RNA-seq, Transcription factor activity inference from scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/decoupleR/inst/doc/decoupleR.R, vignettes/decoupleR/inst/doc/pw_bk.R, vignettes/decoupleR/inst/doc/pw_sc.R, vignettes/decoupleR/inst/doc/tf_bk.R, vignettes/decoupleR/inst/doc/tf_sc.R importsMe: cosmosR, easier, pathMED, progeny, SmartPhos suggestsMe: SCpubr dependencyCount: 41 Package: DeeDeeExperiment Version: 1.0.0 Depends: R (>= 4.5.0), SingleCellExperiment Imports: SummarizedExperiment, methods, S4Vectors, utils, DESeq2, edgeR, limma, cli Suggests: macrophage, knitr, BiocStyle, apeglm, mosdef, org.Hs.eg.db, topGO, clusterProfiler, DEFormats, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 64d15a7af0f6c5d0f7f2ba8a63bdb21b NeedsCompilation: no Title: DeeDeeExperiment: An S4 Class for managing and exploring omics analysis results Description: DeeDeeExperiment is an S4 class extending the SingleCellExperiment class, designed to integrate and manage omics analysis results. It introduces two dedicated slots to store Differential Expression Analysis (DEA) results and Functional Enrichment Analysis (FEA) results, providing a structured approach for downstream analysis. biocViews: Software, Infrastructure, DataRepresentation, GeneExpression, Transcription, Transcriptomics, DifferentialExpression, Pathways, GO Author: Najla Abassi [aut, cre] (ORCID: ), Lea Rothörl [aut] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Najla Abassi URL: https://github.com/imbeimainz/DeeDeeExperiment VignetteBuilder: knitr BugReports: https://github.com/imbeimainz/DeeDeeExperiment/issues git_url: https://git.bioconductor.org/packages/DeeDeeExperiment git_branch: RELEASE_3_22 git_last_commit: 8d413ac git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DeeDeeExperiment_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DeeDeeExperiment_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DeeDeeExperiment_1.0.0.tgz vignettes: vignettes/DeeDeeExperiment/inst/doc/DeeDeeExperiment_manual.html vignetteTitles: The DeeDeeExperiment User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DeeDeeExperiment/inst/doc/DeeDeeExperiment_manual.R dependencyCount: 59 Package: DeepPINCS Version: 1.18.0 Depends: keras, R (>= 4.1) Imports: tensorflow, CatEncoders, matlab, rcdk, stringdist, tokenizers, webchem, purrr, ttgsea, PRROC, reticulate, stats Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: 83b6079b0d3a1612ace8a000ea2634ef NeedsCompilation: no Title: Protein Interactions and Networks with Compounds based on Sequences using Deep Learning Description: The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences. biocViews: Software, Network, GraphAndNetwork, NeuralNetwork Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepPINCS git_branch: RELEASE_3_22 git_last_commit: 92177be git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DeepPINCS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DeepPINCS_1.17.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DeepPINCS_1.18.0.tgz vignettes: vignettes/DeepPINCS/inst/doc/DeepPINCS.html vignetteTitles: DeepPINCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepPINCS/inst/doc/DeepPINCS.R importsMe: GenProSeq, VAExprs dependencyCount: 142 Package: deepSNV Version: 1.56.0 Depends: R (>= 2.13.0), methods, graphics, parallel, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.27.6), Imports: Rhtslib LinkingTo: Rhtslib (>= 1.13.1) Suggests: RColorBrewer, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 950f7e6182701f383591211aa1cb6efb NeedsCompilation: yes Title: Detection of subclonal SNVs in deep sequencing data. Description: This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC. biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport Author: Niko Beerenwinkel [ths], Raul Alcantara [ctb], David Jones [ctb], John Marshall [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre] Maintainer: Moritz Gerstung SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deepSNV git_branch: RELEASE_3_22 git_last_commit: 50518b9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/deepSNV_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/deepSNV_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/deepSNV_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/deepSNV_1.56.0.tgz vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf, vignettes/deepSNV/inst/doc/shearwaterML.html vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater, Shearwater ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R, vignettes/deepSNV/inst/doc/shearwater.R, vignettes/deepSNV/inst/doc/shearwaterML.R importsMe: mitoClone2 suggestsMe: GenomicFiles dependencyCount: 80 Package: DeepTarget Version: 1.4.0 Depends: R (>= 4.2.0) Imports: fgsea, ggplot2, stringr, ggpubr, BiocParallel, pROC, stats, grDevices, graphics, depmap, readr, dplyr Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 94e1a45f0b107d395054f582ba4f22f0 NeedsCompilation: no Title: Deep characterization of cancer drugs Description: This package predicts a drug’s primary target(s) or secondary target(s) by integrating large-scale genetic and drug screens from the Cancer Dependency Map project run by the Broad Institute. It further investigates whether the drug specifically targets the wild-type or mutated target forms. To show how to use this package in practice, we provided sample data along with step-by-step example. biocViews: GeneTarget, GenePrediction,Pathways, GeneExpression, RNASeq, ImmunoOncology,DifferentialExpression, GeneSetEnrichment, ReportWriting,CRISPR Author: Sanju Sinha [aut], Trinh Nguyen [aut, cre] (ORCID: ), Ying Hu [aut] Maintainer: Trinh Nguyen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepTarget git_branch: RELEASE_3_22 git_last_commit: 5b8241e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DeepTarget_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DeepTarget_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DeepTarget_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DeepTarget_1.4.0.tgz vignettes: vignettes/DeepTarget/inst/doc/DeepTarget_Vignette.html vignetteTitles: Workflow Demonstration for Deep characterization of cancer drugs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepTarget/inst/doc/DeepTarget_Vignette.R dependencyCount: 137 Package: DEFormats Version: 1.38.0 Imports: checkmate, data.table, DESeq2, edgeR (>= 3.13.4), GenomicRanges, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat License: GPL-3 MD5sum: a2e14cebef6886c4dda54a0af1f78a19 NeedsCompilation: no Title: Differential gene expression data formats converter Description: Convert between different data formats used by differential gene expression analysis tools. biocViews: ImmunoOncology, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Transcription Author: Andrzej Oleś Maintainer: Andrzej Oleś URL: https://github.com/aoles/DEFormats VignetteBuilder: knitr BugReports: https://github.com/aoles/DEFormats/issues git_url: https://git.bioconductor.org/packages/DEFormats git_branch: RELEASE_3_22 git_last_commit: 430e20a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DEFormats_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEFormats_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEFormats_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEFormats_1.38.0.tgz vignettes: vignettes/DEFormats/inst/doc/DEFormats.html vignetteTitles: Differential gene expression data formats converter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R importsMe: regionReport suggestsMe: DeeDeeExperiment, ideal dependencyCount: 61 Package: DegCre Version: 1.6.0 Depends: R (>= 4.4) Imports: GenomicRanges, InteractionSet, plotgardener, S4Vectors, stats, graphics, grDevices, BiocGenerics, Seqinfo, IRanges, BiocParallel, qvalue, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, utils Suggests: BSgenome, BSgenome.Hsapiens.UCSC.hg38, BiocStyle, magick, knitr, rmarkdown, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 3b447777957916b79d31ac5c3a10d858 NeedsCompilation: no Title: Probabilistic association of DEGs to CREs from differential data Description: DegCre generates associations between differentially expressed genes (DEGs) and cis-regulatory elements (CREs) based on non-parametric concordance between differential data. The user provides GRanges of DEG TSS and CRE regions with differential p-value and optionally log-fold changes and DegCre returns an annotated Hits object with associations and their calculated probabilities. Additionally, the package provides functionality for visualization and conversion to other formats. biocViews: GeneExpression, GeneRegulation, ATACSeq, ChIPSeq, DNaseSeq, RNASeq Author: Brian S. Roberts [aut, cre] (ORCID: ) Maintainer: Brian S. Roberts URL: https://github.com/brianSroberts/DegCre VignetteBuilder: knitr BugReports: https://github.com/brianSroberts/DegCre/issues git_url: https://git.bioconductor.org/packages/DegCre git_branch: RELEASE_3_22 git_last_commit: b521002 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DegCre_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DegCre_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DegCre_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DegCre_1.6.0.tgz vignettes: vignettes/DegCre/inst/doc/degcre_introduction_and_examples.html vignetteTitles: DegCre Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DegCre/inst/doc/degcre_introduction_and_examples.R dependencyCount: 118 Package: DegNorm Version: 1.20.0 Depends: R (>= 4.0.0), methods Imports: Rcpp (>= 1.0.2),GenomicFeatures, txdbmaker, parallel, foreach, S4Vectors, doParallel, Rsamtools (>= 1.31.2), GenomicAlignments, heatmaply, data.table, stats, ggplot2, GenomicRanges, IRanges, plyr, plotly, utils,viridis LinkingTo: Rcpp, RcppArmadillo,S4Vectors,IRanges Suggests: knitr,rmarkdown,formatR License: LGPL (>= 3) Archs: x64 MD5sum: 46d3007d3d32800d95d3ea78c52d8209 NeedsCompilation: yes Title: DegNorm: degradation normalization for RNA-seq data Description: This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy. It provides estimates for each gene within each sample. biocViews: RNASeq, Normalization, GeneExpression, Alignment,Coverage, DifferentialExpression, BatchEffect,Software,Sequencing, ImmunoOncology, QualityControl, DataImport Author: Ji-Ping Wang [aut, cre] (ORCID: ) Maintainer: Ji-Ping Wang VignetteBuilder: knitr BugReports: https://github.com/jipingw/DegNorm/issues git_url: https://git.bioconductor.org/packages/DegNorm git_branch: RELEASE_3_22 git_last_commit: cca8a9e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DegNorm_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DegNorm_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DegNorm_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DegNorm_1.20.0.tgz vignettes: vignettes/DegNorm/inst/doc/DegNorm.html vignetteTitles: DegNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DegNorm/inst/doc/DegNorm.R dependencyCount: 163 Package: DEGraph Version: 1.62.0 Depends: R (>= 2.10.0), R.utils Imports: graph, KEGGgraph, lattice, mvtnorm, R.methodsS3, RBGL, Rgraphviz, rrcov, NCIgraph Suggests: corpcor, fields, graph, KEGGgraph, lattice, marray, RBGL, rrcov, Rgraphviz, NCIgraph License: GPL-3 MD5sum: fbdb97b817bf3759257028bc5a1772db NeedsCompilation: no Title: Two-sample tests on a graph Description: DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results. biocViews: Microarray, DifferentialExpression, GraphAndNetwork, Network, NetworkEnrichment, DecisionTree Author: Laurent Jacob, Pierre Neuvial and Sandrine Dudoit Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/DEGraph git_branch: RELEASE_3_22 git_last_commit: 1f09c9d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DEGraph_1.62.0.tar.gz vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf vignetteTitles: DEGraph: differential expression testing for gene networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGraph/inst/doc/DEGraph.R dependencyCount: 65 Package: DEGreport Version: 1.46.0 Depends: R (>= 4.0.0) Imports: utils, methods, Biobase, BiocGenerics, broom, circlize, ComplexHeatmap, cowplot, ConsensusClusterPlus, cluster, dendextend, DESeq2, dplyr, edgeR, ggplot2, ggdendro, grid, ggrepel, grDevices, knitr, logging, magrittr, psych, RColorBrewer, reshape, rlang, scales, stats, stringr, stringi, S4Vectors, SummarizedExperiment, tidyr, tibble Suggests: BiocStyle, AnnotationDbi, limma, pheatmap, rmarkdown, statmod, testthat License: MIT + file LICENSE MD5sum: e62f9e3f18b3a6579562eea8287cc851 NeedsCompilation: no Title: Report of DEG analysis Description: Creation of ready-to-share figures of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. biocViews: DifferentialExpression, Visualization, RNASeq, ReportWriting, GeneExpression, ImmunoOncology Author: Lorena Pantano [aut, cre], John Hutchinson [ctb], Victor Barrera [ctb], Mary Piper [ctb], Radhika Khetani [ctb], Kenneth Daily [ctb], Thanneer Malai Perumal [ctb], Rory Kirchner [ctb], Michael Steinbaugh [ctb], Ivo Zeller [ctb] Maintainer: Lorena Pantano URL: http://lpantano.github.io/DEGreport/ VignetteBuilder: knitr BugReports: https://github.com/lpantano/DEGreport/issues git_url: https://git.bioconductor.org/packages/DEGreport git_branch: RELEASE_3_22 git_last_commit: c98a779 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DEGreport_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEGreport_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEGreport_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEGreport_1.46.0.tgz vignettes: vignettes/DEGreport/inst/doc/DEGreport.html vignetteTitles: QC and downstream analysis for differential expression RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R dependencyCount: 108 Package: DEGseq Version: 1.64.0 Depends: R (>= 2.8.0), qvalue, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) MD5sum: ab9d39cfd7085af59add4ef95b8ea408 NeedsCompilation: yes Title: Identify Differentially Expressed Genes from RNA-seq data Description: DEGseq is an R package to identify differentially expressed genes from RNA-Seq data. biocViews: RNASeq, Preprocessing, GeneExpression, DifferentialExpression, ImmunoOncology Author: Likun Wang , Xiaowo Wang and Xuegong Zhang . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/DEGseq git_branch: RELEASE_3_22 git_last_commit: dc4cb8e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DEGseq_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEGseq_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEGseq_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEGseq_1.64.0.tgz vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf vignetteTitles: DEGseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGseq/inst/doc/DEGseq.R dependencyCount: 31 Package: DelayedArray Version: 0.36.0 Depends: R (>= 4.0.0), methods, stats4, Matrix, BiocGenerics (>= 0.53.3), MatrixGenerics (>= 1.1.3), S4Vectors (>= 0.47.6), IRanges (>= 2.17.3), S4Arrays (>= 1.9.3), SparseArray (>= 1.7.5) Imports: stats LinkingTo: S4Vectors Suggests: BiocParallel, HDF5Array (>= 1.17.12), genefilter, SummarizedExperiment, airway, lobstr, DelayedMatrixStats, knitr, rmarkdown, BiocStyle, RUnit License: Artistic-2.0 MD5sum: 57b2fd485d73f5dc5fcb65ec23e16429 NeedsCompilation: yes Title: A unified framework for working transparently with on-disk and in-memory array-like datasets Description: Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. biocViews: Infrastructure, DataRepresentation, Annotation, GenomeAnnotation Author: Hervé Pagès [aut, cre], Aaron Lun [ctb], Peter Hickey [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/DelayedArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedArray/issues git_url: https://git.bioconductor.org/packages/DelayedArray git_branch: RELEASE_3_22 git_last_commit: adde054 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DelayedArray_0.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DelayedArray_0.35.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DelayedArray_0.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DelayedArray_0.36.0.tgz vignettes: vignettes/DelayedArray/inst/doc/A-Working_with_large_arrays.pdf, vignettes/DelayedArray/inst/doc/C-DelayedArray_HDF5Array_update.pdf, vignettes/DelayedArray/inst/doc/B-Implementing_a_backend.html vignetteTitles: 1. Working with large arrays in R (slides from July 2017), 3. A DelayedArray / HDF5Array update (slides from April 2021), 2. Implementing A DelayedArray Backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedArray/inst/doc/A-Working_with_large_arrays.R, vignettes/DelayedArray/inst/doc/C-DelayedArray_HDF5Array_update.R dependsOnMe: chihaya, DelayedDataFrame, DelayedMatrixStats, DelayedRandomArray, GDSArray, HDF5Array, Rarr, rhdf5client, SCArray, singleCellTK, TileDBArray, VCFArray importsMe: adverSCarial, alabaster.matrix, AUCell, batchelor, beachmat, beachmat.hdf5, beachmat.tiledb, BiocSingular, bsseq, celaref, celda, Cepo, ChromSCape, clusterExperiment, concordexR, CRISPRseek, cytomapper, decontX, DelayedTensor, DEScan2, dreamlet, DropletUtils, ELMER, EWCE, flowWorkspace, FRASER, GenomicScores, glmGamPoi, GSVA, LoomExperiment, mariner, mbkmeans, methodical, MethReg, methrix, methylSig, mia, miaViz, minfi, MOFA2, MuData, MultiAssayExperiment, mumosa, mutscan, NetActivity, netSmooth, NewWave, omicsGMF, orthogene, orthos, ResidualMatrix, RTCGAToolbox, ScaledMatrix, SCArray.sat, scater, scDblFinder, scMerge, scmeth, scPCA, scran, scrapper, scry, scuttle, signatureSearch, SingleCellAlleleExperiment, SingleCellExperiment, SingleR, sketchR, SpliceWiz, SummarizedExperiment, transformGamPoi, TSCAN, VariantExperiment, velociraptor, vmrseq, Voyager, weitrix, xcore, zellkonverter, ZygosityPredictor, celldex, imcdatasets, scRNAseq, cellGeometry, ebvcube, rliger, scDiffCom, spatialGE suggestsMe: BiocGenerics, ChIPpeakAnno, gwascat, hermes, iSEE, MAST, MatrixGenerics, ProteoDisco, S4Arrays, S4Vectors, satuRn, scone, SPOTlight, TrajectoryUtils, Seurat, SeuratObject, SpatialDDLS dependencyCount: 20 Package: DelayedDataFrame Version: 1.26.0 Depends: R (>= 3.6), S4Vectors (>= 0.23.19), DelayedArray (>= 0.7.5) Imports: methods, stats, BiocGenerics Suggests: testthat, knitr, rmarkdown, BiocStyle, SeqArray, GDSArray License: GPL-3 MD5sum: f817d38d97f41c8543c92f4f0588f4c5 NeedsCompilation: no Title: Delayed operation on DataFrame using standard DataFrame metaphor Description: Based on the standard DataFrame metaphor, we are trying to implement the feature of delayed operation on the DelayedDataFrame, with a slot of lazyIndex, which saves the mapping indexes for each column of DelayedDataFrame. Methods like show, validity check, [/[[ subsetting, rbind/cbind are implemented for DelayedDataFrame to be operated around lazyIndex. The listData slot stays untouched until a realization call e.g., DataFrame constructor OR as.list() is invoked. biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/DelayedDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedDataFrame/issues git_url: https://git.bioconductor.org/packages/DelayedDataFrame git_branch: RELEASE_3_22 git_last_commit: 27e048f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DelayedDataFrame_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DelayedDataFrame_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DelayedDataFrame_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DelayedDataFrame_1.26.0.tgz vignettes: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.html vignetteTitles: DelayedDataFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.R importsMe: VariantExperiment dependencyCount: 21 Package: DelayedMatrixStats Version: 1.32.0 Depends: MatrixGenerics (>= 1.15.1), DelayedArray (>= 0.31.7) Imports: methods, sparseMatrixStats (>= 1.13.2), Matrix (>= 1.5-0), S4Vectors (>= 0.17.5), IRanges (>= 2.25.10), SparseArray (>= 1.5.19) Suggests: testthat, knitr, rmarkdown, BiocStyle, microbenchmark, profmem, HDF5Array, matrixStats (>= 1.0.0) License: MIT + file LICENSE MD5sum: b8e1a8f2eb6a62c6f61d7f5ae42a296a NeedsCompilation: no Title: Functions that Apply to Rows and Columns of 'DelayedMatrix' Objects Description: A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. biocViews: Infrastructure, DataRepresentation, Software Author: Peter Hickey [aut, cre] (ORCID: ), Hervé Pagès [ctb], Aaron Lun [ctb] Maintainer: Peter Hickey URL: https://github.com/PeteHaitch/DelayedMatrixStats VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/DelayedMatrixStats/issues git_url: https://git.bioconductor.org/packages/DelayedMatrixStats git_branch: RELEASE_3_22 git_last_commit: cbf7d75 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DelayedMatrixStats_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DelayedMatrixStats_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DelayedMatrixStats_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DelayedMatrixStats_1.32.0.tgz vignettes: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.html vignetteTitles: Overview of DelayedMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.R importsMe: AUCell, batchelor, biscuiteer, bsseq, Cepo, DeconvoBuddies, dmrseq, dreamlet, DropletUtils, FRASER, glmGamPoi, GSVA, lemur, methrix, methylSig, mia, minfi, mumosa, NetActivity, recountmethylation, SCArray, scMerge, scone, singleCellTK, SingleR, sparrow, SpliceWiz, SVP, weitrix, celldex, spatialGE suggestsMe: blase, condiments, DelayedArray, escape, EWCE, HDF5Array, MatrixGenerics, mbkmeans, ScaledMatrix, scater, scPCA, scran, scuttle, slingshot, SplineDV, tradeSeq, TrajectoryUtils, Voyager, ClustAssess, SpatialDDLS dependencyCount: 23 Package: DelayedRandomArray Version: 1.18.0 Depends: SparseArray (>= 1.5.15), DelayedArray (>= 0.31.6) Imports: methods, dqrng, Rcpp LinkingTo: dqrng, BH, Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, Matrix License: GPL-3 MD5sum: 2221cdb27b21580af33a5e2298243f0d NeedsCompilation: yes Title: Delayed Arrays of Random Values Description: Implements a DelayedArray of random values where the realization of the sampled values is delayed until they are needed. Reproducible sampling within any subarray is achieved by chunking where each chunk is initialized with a different random seed and stream. The usual distributions in the stats package are supported, along with scalar, vector and arrays for the parameters. biocViews: DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/LTLA/DelayedRandomArray SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/LTLA/DelayedRandomArray/issues git_url: https://git.bioconductor.org/packages/DelayedRandomArray git_branch: RELEASE_3_22 git_last_commit: a060a75 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DelayedRandomArray_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DelayedRandomArray_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DelayedRandomArray_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DelayedRandomArray_1.18.0.tgz vignettes: vignettes/DelayedRandomArray/inst/doc/userguide.html vignetteTitles: User's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedRandomArray/inst/doc/userguide.R importsMe: DelayedTensor dependencyCount: 25 Package: DelayedTensor Version: 1.16.0 Depends: R (>= 4.1.0) Imports: methods, utils, S4Arrays, SparseArray, DelayedArray (>= 0.31.8), HDF5Array, BiocSingular, rTensor, DelayedRandomArray (>= 1.13.1), irlba, Matrix, einsum, Suggests: markdown, rmarkdown, BiocStyle, knitr, testthat, magrittr, dplyr, reticulate License: Artistic-2.0 MD5sum: 5c7f2a574dc530af2c83b289cd935356 NeedsCompilation: no Title: R package for sparse and out-of-core arithmetic and decomposition of Tensor Description: DelayedTensor operates Tensor arithmetic directly on DelayedArray object. DelayedTensor provides some generic function related to Tensor arithmetic/decompotision and dispatches it on the DelayedArray class. DelayedTensor also suppors Tensor contraction by einsum function, which is inspired by numpy einsum. biocViews: Software, Infrastructure, DataRepresentation, DimensionReduction Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr BugReports: https://github.com/rikenbit/DelayedTensor/issues git_url: https://git.bioconductor.org/packages/DelayedTensor git_branch: RELEASE_3_22 git_last_commit: 363cba1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DelayedTensor_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DelayedTensor_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DelayedTensor_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DelayedTensor_1.16.0.tgz vignettes: vignettes/DelayedTensor/inst/doc/DelayedTensor_1.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_2.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_3.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_4.html vignetteTitles: DelayedTensor, TensorArithmetic, TensorDecomposition, Einsum hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedTensor/inst/doc/DelayedTensor_1.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_2.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_3.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_4.R dependencyCount: 50 Package: deltaCaptureC Version: 1.24.0 Depends: R (>= 3.6) Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2, tictoc Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 7253c0cf902b48fae036471e3bc84517 NeedsCompilation: no Title: This Package Discovers Meso-scale Chromatin Remodeling from 3C Data Description: This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred 'viewpoint' region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments. biocViews: BiologicalQuestion, StatisticalMethod Author: Michael Shapiro [aut, cre] (ORCID: ) Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaCaptureC git_branch: RELEASE_3_22 git_last_commit: 7e3bbf1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/deltaCaptureC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/deltaCaptureC_1.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/deltaCaptureC_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/deltaCaptureC_1.24.0.tgz vignettes: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.html vignetteTitles: Delta Capture-C hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.R dependencyCount: 56 Package: deltaGseg Version: 1.50.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: d756894cec4f12d365b5963b9e6d62ce NeedsCompilation: no Title: deltaGseg Description: Identifying distinct subpopulations through multiscale time series analysis biocViews: Proteomics, TimeCourse, Visualization, Clustering Author: Diana Low, Efthymios Motakis Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaGseg git_branch: RELEASE_3_22 git_last_commit: 4c0dc58 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/deltaGseg_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/deltaGseg_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/deltaGseg_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/deltaGseg_1.50.0.tgz vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf vignetteTitles: deltaGseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deltaGseg/inst/doc/deltaGseg.R dependencyCount: 44 Package: DeMAND Version: 1.40.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: a8857b32813eb30a59369297f2713c99 NeedsCompilation: no Title: DeMAND Description: DEMAND predicts Drug MoA by interrogating a cell context specific regulatory network with a small number (N >= 6) of compound-induced gene expression signatures, to elucidate specific proteins whose interactions in the network is dysregulated by the compound. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, StatisticalMethod, Network Author: Jung Hoon Woo , Yishai Shimoni Maintainer: Jung Hoon Woo , Mariano Alvarez git_url: https://git.bioconductor.org/packages/DeMAND git_branch: RELEASE_3_22 git_last_commit: 3c12837 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DeMAND_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DeMAND_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DeMAND_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DeMAND_1.40.0.tgz vignettes: vignettes/DeMAND/inst/doc/DeMAND.pdf vignetteTitles: Using DeMAND hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DeMAND/inst/doc/DeMAND.R dependencyCount: 3 Package: DeMixT Version: 1.26.0 Depends: R (>= 3.6.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment, knitr, KernSmooth, matrixcalc, rmarkdown, DSS, dendextend, psych, sva Imports: matrixStats, stats, truncdist, base64enc, ggplot2 LinkingTo: Rcpp License: GPL-3 MD5sum: 99515910974af4bbd62f44cc3c660596 NeedsCompilation: yes Title: Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms Description: DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components. biocViews: Software, StatisticalMethod, Classification, GeneExpression, Sequencing, Microarray, TissueMicroarray, Coverage Author: Zeya Wang , Shaolong Cao, Wenyi Wang Maintainer: Ruonan Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeMixT git_branch: RELEASE_3_22 git_last_commit: 9343d64 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DeMixT_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DeMixT_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DeMixT_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DeMixT_1.26.0.tgz vignettes: vignettes/DeMixT/inst/doc/demixt.html vignetteTitles: DeMixT.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeMixT/inst/doc/demixt.R dependencyCount: 143 Package: demuxmix Version: 1.12.0 Depends: R (>= 4.0.0) Imports: stats, MASS, Matrix, ggplot2, gridExtra, methods Suggests: BiocStyle, cowplot, DropletUtils, knitr, reshape2, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 862b1d0b58bb9a89020d1e5302f5101b NeedsCompilation: no Title: Demultiplexing oligo-barcoded scRNA-seq data using regression mixture models Description: A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided. biocViews: SingleCell, Sequencing, Preprocessing, Classification, Regression Author: Hans-Ulrich Klein [aut, cre] (ORCID: ) Maintainer: Hans-Ulrich Klein URL: https://github.com/huklein/demuxmix VignetteBuilder: knitr BugReports: https://github.com/huklein/demuxmix/issues git_url: https://git.bioconductor.org/packages/demuxmix git_branch: RELEASE_3_22 git_last_commit: 228c6f2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/demuxmix_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/demuxmix_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/demuxmix_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/demuxmix_1.12.0.tgz vignettes: vignettes/demuxmix/inst/doc/demuxmix.html vignetteTitles: Demultiplexing cells with demuxmix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/demuxmix/inst/doc/demuxmix.R importsMe: demuxSNP dependencyCount: 26 Package: demuxSNP Version: 1.8.0 Depends: R (>= 4.3.0), SingleCellExperiment, VariantAnnotation, ensembldb Imports: MatrixGenerics, BiocGenerics, class, Seqinfo, IRanges, Matrix, SummarizedExperiment, demuxmix, methods, KernelKnn, dplyr Suggests: knitr, rmarkdown, ComplexHeatmap, viridisLite, ggpubr, dittoSeq, EnsDb.Hsapiens.v86, BiocStyle, RefManageR, testthat (>= 3.0.0), Seurat License: GPL-3 MD5sum: 30a5dd14482dcef22ef8915ee3ec1bac NeedsCompilation: no Title: scRNAseq demultiplexing using cell hashing and SNPs Description: This package assists in demultiplexing scRNAseq data using both cell hashing and SNPs data. The SNP profile of each group os learned using high confidence assignments from the cell hashing data. Cells which cannot be assigned with high confidence from the cell hashing data are assigned to their most similar group based on their SNPs. We also provide some helper function to optimise SNP selection, create training data and merge SNP data into the SingleCellExperiment framework. biocViews: Classification, SingleCell Author: Michael Lynch [aut, cre] (ORCID: ), Aedin Culhane [aut] (ORCID: ) Maintainer: Michael Lynch URL: https://github.com/michaelplynch/demuxSNP VignetteBuilder: knitr BugReports: https://github.com/michaelplynch/demuxSNP/issues git_url: https://git.bioconductor.org/packages/demuxSNP git_branch: RELEASE_3_22 git_last_commit: 0c933a4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/demuxSNP_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/demuxSNP_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/demuxSNP_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/demuxSNP_1.8.0.tgz vignettes: vignettes/demuxSNP/inst/doc/supervised_demultiplexing.html vignetteTitles: Supervised Demultiplexing using Cell Hashing and SNPs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/demuxSNP/inst/doc/supervised_demultiplexing.R dependencyCount: 108 Package: densvis Version: 1.20.0 Imports: Rcpp, basilisk, assertthat, reticulate, Rtsne, irlba LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, ggplot2, uwot, testthat License: MIT + file LICENSE MD5sum: f3f13663455c07f7b75bd2dce64602a9 NeedsCompilation: yes Title: Density-Preserving Data Visualization via Non-Linear Dimensionality Reduction Description: Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) . The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space. biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Ashwinn Narayan [aut], Hyunghoon Cho [aut] Maintainer: Alan O'Callaghan URL: https://bioconductor.org/packages/densvis VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/densvis/issues git_url: https://git.bioconductor.org/packages/densvis git_branch: RELEASE_3_22 git_last_commit: 009e678 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/densvis_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/densvis_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/densvis_1.20.0.tgz vignettes: vignettes/densvis/inst/doc/densvis.html vignetteTitles: Introduction to densvis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/densvis/inst/doc/densvis.R dependsOnMe: OSCA.advanced suggestsMe: scater dependencyCount: 26 Package: DEP Version: 1.32.0 Depends: R (>= 3.5) Imports: ggplot2, dplyr, purrr, readr, tibble, tidyr, SummarizedExperiment (>= 1.11.5), MSnbase, limma, vsn, fdrtool, ggrepel, ComplexHeatmap, RColorBrewer, circlize, shiny, shinydashboard, DT, rmarkdown, assertthat, gridExtra, grid, stats, imputeLCMD, cluster Suggests: testthat, enrichR, knitr, BiocStyle License: Artistic-2.0 MD5sum: fb6ae3588acca38e37fe8d0a5102bbbe NeedsCompilation: no Title: Differential Enrichment analysis of Proteomics data Description: This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DifferentialExpression, DataRepresentation Author: Arne Smits [cre, aut], Wolfgang Huber [aut] Maintainer: Arne Smits VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/DEP git_branch: RELEASE_3_22 git_last_commit: 251eef4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DEP_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEP_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEP_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEP_1.32.0.tgz vignettes: vignettes/DEP/inst/doc/DEP.html, vignettes/DEP/inst/doc/MissingValues.html vignetteTitles: DEP: Introduction, DEP: Missing value handling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEP/inst/doc/DEP.R, vignettes/DEP/inst/doc/MissingValues.R suggestsMe: proDA dependencyCount: 167 Package: DepecheR Version: 1.26.0 Depends: R (>= 4.0) Imports: ggplot2 (>= 3.1.0), MASS (>= 7.3.51), Rcpp (>= 1.0.0), dplyr (>= 0.7.8), gplots (>= 3.0.1), viridis (>= 0.5.1), foreach (>= 1.4.4), doSNOW (>= 1.0.16), matrixStats (>= 0.54.0), mixOmics (>= 6.6.1), moments (>= 0.14), grDevices (>= 3.5.2), graphics (>= 3.5.2), stats (>= 3.5.2), utils (>= 3.5), methods (>= 3.5), parallel (>= 3.5.2), reshape2 (>= 1.4.3), beanplot (>= 1.2), FNN (>= 1.1.3), robustbase (>= 0.93.5), gmodels (>= 2.18.1), collapse (>= 1.9.2), ClusterR (>= 1.3.2) LinkingTo: Rcpp, RcppEigen Suggests: uwot, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: e1e17b2d8970d895974a6f034a3c8de4 NeedsCompilation: yes Title: Determination of essential phenotypic elements of clusters in high-dimensional entities Description: The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters. biocViews: Software,CellBasedAssays,Transcription,DifferentialExpression, DataRepresentation,ImmunoOncology,Transcriptomics,Classification,Clustering, DimensionReduction,FeatureExtraction,FlowCytometry,RNASeq,SingleCell, Visualization Author: Jakob Theorell [aut, cre] (ORCID: ), Axel Theorell [aut] Maintainer: Jakob Theorell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DepecheR git_branch: RELEASE_3_22 git_last_commit: 596b81e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DepecheR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DepecheR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DepecheR_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DepecheR_1.26.0.tgz vignettes: vignettes/DepecheR/inst/doc/DepecheR_test.html, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.html vignetteTitles: Example of a cytometry data analysis with DepecheR, Using the groupProbPlot plot function for single-cell probability display hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DepecheR/inst/doc/DepecheR_test.R, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.R suggestsMe: flowSpecs dependencyCount: 106 Package: DepInfeR Version: 1.14.0 Depends: R (>= 4.2.0) Imports: matrixStats, glmnet, stats, BiocParallel Suggests: testthat (>= 3.0.0), knitr, rmarkdown, dplyr, tidyr, tibble, ggplot2, missForest, pheatmap, RColorBrewer, ggrepel, BiocStyle, ggbeeswarm License: GPL-3 MD5sum: 63c6bd62901c1be3cfd4139c1fbc232e NeedsCompilation: no Title: Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling Description: DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864. biocViews: Software, Regression, Pharmacogenetics, Pharmacogenomics, FunctionalGenomics Author: Junyan Lu [aut, cre] (ORCID: ), Alina Batzilla [aut] Maintainer: Junyan Lu VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/DepInfeR/issues git_url: https://git.bioconductor.org/packages/DepInfeR git_branch: RELEASE_3_22 git_last_commit: ce2b9fb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DepInfeR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DepInfeR_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DepInfeR_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DepInfeR_1.14.0.tgz vignettes: vignettes/DepInfeR/inst/doc/vignette.html vignetteTitles: DepInfeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DepInfeR/inst/doc/vignette.R dependencyCount: 27 Package: DEqMS Version: 1.28.0 Depends: R(>= 3.5),graphics,stats,ggplot2,matrixStats,dplyr,limma(>= 3.34) Suggests: BiocStyle,knitr,rmarkdown,markdown,plyr,reshape2,utils,ggrepel,ExperimentHub,LSD License: LGPL MD5sum: b99726803a6b01cd4c798d8343a598b6 NeedsCompilation: no Title: a tool to perform statistical analysis of differential protein expression for quantitative proteomics data. Description: DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Preprocessing, DifferentialExpression, MultipleComparison,Normalization,Bayesian,ExperimentHubSoftware Author: Yafeng Zhu Maintainer: Yafeng Zhu VignetteBuilder: knitr BugReports: https://github.com/yafeng/DEqMS/issues git_url: https://git.bioconductor.org/packages/DEqMS git_branch: RELEASE_3_22 git_last_commit: 5bf2af4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DEqMS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEqMS_1.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEqMS_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEqMS_1.28.0.tgz vignettes: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.html vignetteTitles: DEqMS R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.R importsMe: PRONE dependencyCount: 33 Package: derfinder Version: 1.44.0 Depends: R (>= 3.5.0) Imports: BiocGenerics (>= 0.25.1), AnnotationDbi (>= 1.27.9), BiocParallel (>= 1.15.15), bumphunter (>= 1.9.2), derfinderHelper (>= 1.1.0), Seqinfo (>= 0.99.2), GenomeInfoDb (>= 1.45.9), GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges (>= 1.61.1), Hmisc, IRanges (>= 2.3.23), methods, qvalue (>= 1.99.0), Rsamtools (>= 2.25.1), rtracklayer, S4Vectors (>= 0.23.19), stats, utils Suggests: BiocStyle (>= 2.5.19), sessioninfo, derfinderData (>= 0.99.0), derfinderPlot, DESeq2, ggplot2, knitr (>= 1.6), limma, RefManageR, rmarkdown (>= 0.3.3), testthat (>= 2.1.0), TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 MD5sum: a8de4e6d817d20c96d93064223535486 NeedsCompilation: no Title: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach Description: This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks. biocViews: DifferentialExpression, Sequencing, RNASeq, ChIPSeq, DifferentialPeakCalling, Software, ImmunoOncology, Coverage Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Alyssa C. Frazee [ctb], Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinder VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinder/ git_url: https://git.bioconductor.org/packages/derfinder git_branch: RELEASE_3_22 git_last_commit: 7fd222b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/derfinder_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/derfinder_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/derfinder_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/derfinder_1.44.0.tgz vignettes: vignettes/derfinder/inst/doc/derfinder-quickstart.html, vignettes/derfinder/inst/doc/derfinder-users-guide.html vignetteTitles: derfinder quick start guide, derfinder users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R, vignettes/derfinder/inst/doc/derfinder-users-guide.R importsMe: derfinderPlot, recount, regionReport, GenomicState, recountWorkflow suggestsMe: megadepth dependencyCount: 138 Package: derfinderHelper Version: 1.44.0 Depends: R(>= 3.2.2) Imports: IRanges (>= 1.99.27), Matrix, methods, S4Vectors (>= 0.2.2) Suggests: sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), RefManageR, rmarkdown (>= 0.3.3), testthat, covr License: Artistic-2.0 MD5sum: 1b6f49b01dd2fdd0ef5a8be7d4f9ae62 NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. This package is particularly useful when using BiocParallel and it helps reduce the time spent loading the full derfinder package when running the F-statistics calculation in parallel. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderHelper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderHelper git_url: https://git.bioconductor.org/packages/derfinderHelper git_branch: RELEASE_3_22 git_last_commit: f623cf1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/derfinderHelper_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/derfinderHelper_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/derfinderHelper_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/derfinderHelper_1.44.0.tgz vignettes: vignettes/derfinderHelper/inst/doc/derfinderHelper.html vignetteTitles: Introduction to derfinderHelper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R importsMe: derfinder dependencyCount: 13 Package: derfinderPlot Version: 1.44.0 Depends: R(>= 3.2) Imports: derfinder (>= 1.1.0), Seqinfo, GenomeInfoDb (>= 1.45.9), GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>= 1.13.13), ggplot2, graphics, grDevices, IRanges (>= 1.99.28), limma, methods, plyr, RColorBrewer, reshape2, S4Vectors (>= 0.9.38), scales, utils Suggests: biovizBase (>= 1.27.2), bumphunter (>= 1.7.6), derfinderData (>= 0.99.0), sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), org.Hs.eg.db, RefManageR, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 MD5sum: bbe53d5e3b506815905f8e9b0abbd28a NeedsCompilation: no Title: Plotting functions for derfinder Description: This package provides plotting functions for results from the derfinder package. This helps separate the graphical dependencies required for making these plots from the core functionality of derfinder. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderPlot VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderPlot git_url: https://git.bioconductor.org/packages/derfinderPlot git_branch: RELEASE_3_22 git_last_commit: 84ed2cb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/derfinderPlot_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/derfinderPlot_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/derfinderPlot_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/derfinderPlot_1.44.0.tgz vignettes: vignettes/derfinderPlot/inst/doc/derfinderPlot.html vignetteTitles: Introduction to derfinderPlot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R importsMe: recountWorkflow suggestsMe: derfinder, regionReport, GenomicState dependencyCount: 150 Package: DEScan2 Version: 1.30.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocParallel, BiocGenerics, ChIPpeakAnno, data.table, DelayedArray, Seqinfo, GenomeInfoDb, GenomicAlignments, glue, IRanges, plyr, Rcpp (>= 0.12.13), rtracklayer, S4Vectors (>= 0.23.19), SummarizedExperiment, tools, utils LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, edgeR, limma, EDASeq, RUVSeq, RColorBrewer, statmod License: Artistic-2.0 MD5sum: d29d5adb3fe8a1488d97594b9d5f8083 NeedsCompilation: yes Title: Differential Enrichment Scan 2 Description: Integrated peak and differential caller, specifically designed for broad epigenomic signals. biocViews: ImmunoOncology, PeakDetection, Epigenetics, Software, Sequencing, Coverage Author: Dario Righelli [aut, cre], John Koberstein [aut], Bruce Gomes [aut], Nancy Zhang [aut], Claudia Angelini [aut], Lucia Peixoto [aut], Davide Risso [aut] Maintainer: Dario Righelli VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEScan2 git_branch: RELEASE_3_22 git_last_commit: 5e4b7c2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DEScan2_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEScan2_1.29.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEScan2_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEScan2_1.30.0.tgz vignettes: vignettes/DEScan2/inst/doc/DEScan2.html vignetteTitles: DEScan2 Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEScan2/inst/doc/DEScan2.R dependencyCount: 130 Package: DESeq2 Version: 1.50.0 Depends: S4Vectors (>= 0.23.18), IRanges, GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, matrixStats, methods, stats4, locfit, ggplot2 (>= 3.4.0), Rcpp (>= 0.11.0), MatrixGenerics LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, vsn, pheatmap, RColorBrewer, apeglm, ashr, tximport, tximeta, tximportData, readr, pbapply, airway, glmGamPoi, BiocManager License: LGPL (>= 3) Archs: x64 MD5sum: 68e9749dd71bbb7e0d7d2a4ff7a6739f NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. biocViews: Sequencing, RNASeq, ChIPSeq, GeneExpression, Transcription, Normalization, DifferentialExpression, Bayesian, Regression, PrincipalComponent, Clustering, ImmunoOncology Author: Michael Love [aut, cre], Constantin Ahlmann-Eltze [ctb], Kwame Forbes [ctb], Simon Anders [aut, ctb], Wolfgang Huber [aut, ctb], RADIANT EU FP7 [fnd], NIH NHGRI [fnd], CZI [fnd] Maintainer: Michael Love URL: https://github.com/thelovelab/DESeq2 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/DESeq2 git_branch: RELEASE_3_22 git_last_commit: adf0634 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DESeq2_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DESeq2_1.49.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DESeq2_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DESeq2_1.50.0.tgz vignettes: vignettes/DESeq2/inst/doc/DESeq2.html vignetteTitles: Analyzing RNA-seq data with DESeq2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: DEWSeq, DEXSeq, metaseqR2, octad, rgsepd, SeqGSEA, TCC, tRanslatome, rnaseqDTU, rnaseqGene, Anaconda, DRomics, ordinalbayes importsMe: Anaquin, animalcules, BatchQC, CeTF, circRNAprofiler, CleanUpRNAseq, consensusDE, coseq, countsimQC, cypress, DaMiRseq, debrowser, DeeDeeExperiment, DEFormats, DEGreport, deltaCaptureC, DEsubs, DiffBind, DOtools, DspikeIn, easier, EBSEA, ERSSA, GDCRNATools, gg4way, Glimma, GRaNIE, hermes, HTSFilter, HybridExpress, icetea, ideal, INSPEcT, IntEREst, iSEEde, kissDE, magpie, microbiomeExplorer, MIRit, MLSeq, mobileRNA, mosdef, MultiRNAflow, muscat, NBAMSeq, NetActivity, ORFik, OUTRIDER, pairedGSEA, PathoStat, pcaExplorer, phantasus, POMA, proActiv, RegEnrich, regionReport, ReportingTools, RiboDiPA, Rmmquant, saseR, scBFA, scGPS, SEtools, singleCellTK, SNPhood, srnadiff, SurfR, systemPipeTools, TBSignatureProfiler, TEKRABber, terapadog, UMI4Cats, vidger, vulcan, zitools, BloodCancerMultiOmics2017, FieldEffectCrc, homosapienDEE2CellScore, IHWpaper, ExpHunterSuite, recountWorkflow, autoGO, bulkAnalyseR, cinaR, ExpGenetic, HEssRNA, limorhyde2, microbial, RCPA, RNAseqQC, sRNAGenetic, TransProR, wilson suggestsMe: aggregateBioVar, apeglm, bambu, BindingSiteFinder, biobroom, BiocGenerics, BioCor, BiocSet, BioNERO, CAGEr, compcodeR, dar, dearseq, derfinder, dittoSeq, EDASeq, EnrichmentBrowser, EWCE, extraChIPs, fishpond, gage, GenomicAlignments, GenomicRanges, GeoTcgaData, geyser, glmGamPoi, HiCDCPlus, IHW, InteractiveComplexHeatmap, methodical, OPWeight, pathlinkR, phyloseq, progeny, QRscore, raer, recount, ribosomeProfilingQC, roastgsa, RUVSeq, Rvisdiff, scran, sparrow, SpliceWiz, subSeq, systemPipeR, systemPipeShiny, TFEA.ChIP, tidybulk, topconfects, tximeta, tximport, variancePartition, Wrench, zinbwave, ChIPDBData, curatedAdipoChIP, curatedAdipoRNA, GSE62944, RegParallel, Single.mTEC.Transcriptomes, CAGEWorkflow, fluentGenomics, seqpac, bakR, cellpypes, conos, dependentsimr, FateID, ggpicrust2, GiANT, glmmSeq, grandR, lfc, LorMe, metaRNASeq, MiscMetabar, pctax, pmartR, RaceID, rliger, SCdeconR, seqgendiff, Seurat, SeuratExplorer, volcano3D dependencyCount: 54 Package: DEsingle Version: 1.30.0 Depends: R (>= 3.4.0) Imports: stats, Matrix (>= 1.2-14), MASS (>= 7.3-45), VGAM (>= 1.0-2), bbmle (>= 1.0.18), gamlss (>= 4.4-0), maxLik (>= 1.3-4), pscl (>= 1.4.9), BiocParallel (>= 1.12.0), Suggests: knitr, rmarkdown, SingleCellExperiment License: GPL-2 MD5sum: 314dd28d30027ca961e2663a12b195c4 NeedsCompilation: no Title: DEsingle for detecting three types of differential expression in single-cell RNA-seq data Description: DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions. biocViews: DifferentialExpression, GeneExpression, SingleCell, ImmunoOncology, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao Maintainer: Zhun Miao URL: https://miaozhun.github.io/DEsingle/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsingle git_branch: RELEASE_3_22 git_last_commit: c41f24a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DEsingle_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEsingle_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEsingle_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEsingle_1.30.0.tgz vignettes: vignettes/DEsingle/inst/doc/DEsingle.html vignetteTitles: DEsingle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsingle/inst/doc/DEsingle.R dependencyCount: 39 Package: DESpace Version: 2.2.0 Depends: R (>= 4.5.0) Imports: edgeR, limma, dplyr, stats, Matrix, SpatialExperiment, ggplot2, SummarizedExperiment, S4Vectors, BiocGenerics, data.table, assertthat, terra, sf, spatstat.explore, spatstat.geom, ggforce, ggnewscale, patchwork, BiocParallel, methods, scales, scuttle Suggests: knitr, rmarkdown, testthat, BiocStyle, muSpaData, ExperimentHub, spatialLIBD, purrr, reshape2, tidyverse, concaveman License: GPL-3 MD5sum: e4e09da4bf5b688d6aff9c8c19ad324e NeedsCompilation: no Title: DESpace: a framework to discover spatially variable genes and differential spatial patterns across conditions Description: Intuitive framework for identifying spatially variable genes (SVGs) and differential spatial variable pattern (DSP) between conditions via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. For multi-sample, multi-condition datasets, we again fit a NB model via edgeR, incorporating spatial clusters, conditions and their interactions as covariates. DSP genes-representing differences in spatial gene expression patterns across experimental conditions-are identified by testing the interaction between spatial clusters and conditions. biocViews: Spatial, SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, DifferentialExpression,StatisticalMethod, Visualization Author: Peiying Cai [aut, cre] (ORCID: ), Simone Tiberi [aut] (ORCID: ) Maintainer: Peiying Cai URL: https://github.com/peicai/DESpace, https://peicai.github.io/DESpace/ VignetteBuilder: knitr BugReports: https://github.com/peicai/DESpace/issues git_url: https://git.bioconductor.org/packages/DESpace git_branch: RELEASE_3_22 git_last_commit: 1f1d532 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DESpace_2.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DESpace_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DESpace_2.2.0.tgz vignettes: vignettes/DESpace/inst/doc/DSP.html, vignettes/DESpace/inst/doc/SVG.html vignetteTitles: Differential Spatial Pattern between conditions, A framework to discover spatially variable genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESpace/inst/doc/DSP.R, vignettes/DESpace/inst/doc/SVG.R importsMe: OSTA dependencyCount: 124 Package: DEsubs Version: 1.36.0 Depends: R (>= 3.3), locfit Imports: graph, igraph, RBGL, circlize, limma, edgeR, EBSeq, NBPSeq, stats, grDevices, graphics, pheatmap, utils, ggplot2, Matrix, jsonlite, tools, DESeq2, methods Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL-3 MD5sum: ccc9e6069827b24bb3c9ab20f0f674a3 NeedsCompilation: no Title: DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments Description: DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG, GeneExpression, NetworkEnrichment, Network, RNASeq, DifferentialExpression, Normalization, ImmunoOncology Author: Aristidis G. Vrahatis and Panos Balomenos Maintainer: Aristidis G. Vrahatis , Panos Balomenos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsubs git_branch: RELEASE_3_22 git_last_commit: eacc80f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DEsubs_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEsubs_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEsubs_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEsubs_1.36.0.tgz vignettes: vignettes/DEsubs/inst/doc/DEsubs.pdf vignetteTitles: DEsubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsubs/inst/doc/DEsubs.R dependencyCount: 100 Package: DEWSeq Version: 1.24.0 Depends: R(>= 4.0.0), R.utils, DESeq2, BiocParallel Imports: BiocGenerics, data.table(>= 1.11.8), Seqinfo, GenomicRanges, methods, S4Vectors, SummarizedExperiment, stats, utils Suggests: knitr, tidyverse, rmarkdown, testthat, BiocStyle, IHW License: LGPL (>= 3) MD5sum: 737489f5023d1865eb7371447a9d06ee NeedsCompilation: no Title: Differential Expressed Windows Based on Negative Binomial Distribution Description: DEWSeq is a sliding window approach for the analysis of differentially enriched binding regions eCLIP or iCLIP next generation sequencing data. biocViews: Sequencing, GeneRegulation, FunctionalGenomics, DifferentialExpression Author: Sudeep Sahadevan [aut], Thomas Schwarzl [aut], bioinformatics team Hentze [aut, cre] Maintainer: bioinformatics team Hentze URL: https://github.com/EMBL-Hentze-group/DEWSeq/ VignetteBuilder: knitr BugReports: https://github.com/EMBL-Hentze-group/DEWSeq/issues git_url: https://git.bioconductor.org/packages/DEWSeq git_branch: RELEASE_3_22 git_last_commit: 02e5ed4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DEWSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEWSeq_1.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEWSeq_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEWSeq_1.24.0.tgz vignettes: vignettes/DEWSeq/inst/doc/DEWSeq.html vignetteTitles: Analyzing eCLIP/iCLIP data with DEWSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEWSeq/inst/doc/DEWSeq.R dependencyCount: 59 Package: DExMA Version: 1.18.0 Depends: R (>= 4.1), DExMAdata Imports: Biobase, GEOquery, impute, limma, pheatmap, plyr, scales, snpStats, sva, swamp, stats, methods, utils, bnstruct, RColorBrewer, grDevices Suggests: BiocStyle, qpdf, BiocGenerics, RUnit License: GPL-2 Archs: x64 MD5sum: 880023b9d99a5ff5ff2ce597de0a67fd NeedsCompilation: no Title: Differential Expression Meta-Analysis Description: performing all the steps of gene expression meta-analysis considering the possible existence of missing genes. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO datasets and show graphical representations of the results. biocViews: DifferentialExpression, GeneExpression, StatisticalMethod, QualityControl Author: Juan Antonio Villatoro-García [aut, cre], Pedro Carmona-Sáez [aut] Maintainer: Juan Antonio Villatoro-García git_url: https://git.bioconductor.org/packages/DExMA git_branch: RELEASE_3_22 git_last_commit: 55162f3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DExMA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DExMA_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DExMA_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DExMA_1.18.0.tgz vignettes: vignettes/DExMA/inst/doc/DExMA.pdf vignetteTitles: Differential Expression Meta-Analysis with DExMA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DExMA/inst/doc/DExMA.R dependencyCount: 127 Package: DEXSeq Version: 1.56.0 Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.39.6), AnnotationDbi, RColorBrewer, S4Vectors (>= 0.23.18) Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter Suggests: GenomeInfoDb, GenomicFeatures, txdbmaker, pasilla (>= 0.2.22), BiocStyle, knitr, rmarkdown, testthat, pasillaBamSubset, GenomicAlignments, roxygen2, glmGamPoi License: GPL (>= 3) MD5sum: ad35dfd1127a4f36de54a113b6f55e00 NeedsCompilation: no Title: Inference of differential exon usage in RNA-Seq Description: The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, AlternativeSplicing, DifferentialSplicing, GeneExpression, Visualization Author: Simon Anders and Alejandro Reyes Maintainer: Alejandro Reyes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEXSeq git_branch: RELEASE_3_22 git_last_commit: 0f430e0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DEXSeq_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEXSeq_1.55.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEXSeq_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEXSeq_1.56.0.tgz vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.html vignetteTitles: Inferring differential exon usage in RNA-Seq data with the DEXSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R dependsOnMe: IsoformSwitchAnalyzeR, pasilla, rnaseqDTU importsMe: diffUTR, IntEREst, pairedGSEA suggestsMe: bambu, GenomicRanges, satuRn, stageR, subSeq, BioPlex dependencyCount: 109 Package: DFP Version: 1.68.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 Archs: x64 MD5sum: bc022331c6124157e1112936d48d1d1c NeedsCompilation: no Title: Gene Selection Description: This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values. biocViews: Microarray, DifferentialExpression Author: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola Maintainer: Rodrigo Alvarez-Glez git_url: https://git.bioconductor.org/packages/DFP git_branch: RELEASE_3_22 git_last_commit: 0c65d66 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DFP_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DFP_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DFP_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DFP_1.68.0.tgz vignettes: vignettes/DFP/inst/doc/DFP.pdf vignetteTitles: Howto: Discriminat Fuzzy Pattern hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFP/inst/doc/DFP.R dependencyCount: 7 Package: DFplyr Version: 1.4.0 Depends: R (>= 4.1.0), dplyr Imports: BiocGenerics, methods, rlang, S4Vectors, tidyselect Suggests: BiocStyle, GenomeInfoDb, GenomicRanges, IRanges, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0), tibble License: GPL-3 Archs: x64 MD5sum: 5388c747f7e0c48d6004af81820650f8 NeedsCompilation: no Title: A `DataFrame` (`S4Vectors`) backend for `dplyr` Description: Provides `dplyr` verbs (`mutate`, `select`, `filter`, etc...) supporting `S4Vectors::DataFrame` objects. Importantly, this is achieved without conversion to an intermediate `tibble`. Adds grouping infrastructure to `DataFrame` which is respected by the transformation verbs. biocViews: DataRepresentation, Infrastructure, Software Author: Jonathan Carroll [aut, cre] (ORCID: ), Pierre-Paul Axisa [ctb] Maintainer: Jonathan Carroll URL: https://github.com/jonocarroll/DFplyr VignetteBuilder: knitr BugReports: https://github.com/jonocarroll/DFplyr/issues git_url: https://git.bioconductor.org/packages/DFplyr git_branch: RELEASE_3_22 git_last_commit: 3ff628e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DFplyr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DFplyr_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DFplyr_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DFplyr_1.4.0.tgz vignettes: vignettes/DFplyr/inst/doc/example_usage.html vignetteTitles: Example Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFplyr/inst/doc/example_usage.R dependencyCount: 23 Package: DiffBind Version: 3.20.0 Depends: R (>= 4.0), GenomicRanges, SummarizedExperiment Imports: RColorBrewer, amap, gplots, grDevices, limma, GenomicAlignments, locfit, stats, utils, IRanges, lattice, systemPipeR, tools, Rcpp, dplyr, ggplot2, BiocParallel, parallel, S4Vectors, Rsamtools (>= 2.13.1), DESeq2, methods, graphics, ggrepel, apeglm, ashr, GreyListChIP LinkingTo: Rhtslib (>= 1.99.1), Rcpp Suggests: BiocStyle, testthat, xtable, rgl, XLConnect, edgeR, csaw, BSgenome, GenomeInfoDb, profileplyr, rtracklayer, grid License: Artistic-2.0 MD5sum: 17f1e9b15ba00c0743bc46d8f6cc0243 NeedsCompilation: yes Title: Differential Binding Analysis of ChIP-Seq Peak Data Description: Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions. biocViews: Sequencing, ChIPSeq,ATACSeq, DNaseSeq, MethylSeq, RIPSeq, DifferentialPeakCalling, DifferentialMethylation, GeneRegulation, HistoneModification, PeakDetection, BiomedicalInformatics, CellBiology, MultipleComparison, Normalization, ReportWriting, Epigenetics, FunctionalGenomics Author: Rory Stark [aut, cre], Gord Brown [aut] Maintainer: Rory Stark URL: https://www.cruk.cam.ac.uk/core-facilities/bioinformatics-core/software/DiffBind SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/DiffBind git_branch: RELEASE_3_22 git_last_commit: fc696a0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DiffBind_3.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DiffBind_3.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DiffBind_3.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DiffBind_3.20.0.tgz vignettes: vignettes/DiffBind/inst/doc/DiffBind.pdf vignetteTitles: DiffBind: Differential binding analysis of ChIP-Seq peak data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffBind/inst/doc/DiffBind.R dependsOnMe: vulcan dependencyCount: 141 Package: diffcoexp Version: 1.30.0 Depends: R (>= 3.5), WGCNA, SummarizedExperiment Imports: stats, DiffCorr, psych, igraph, BiocGenerics Suggests: GEOquery, RUnit License: GPL (>2) MD5sum: fcc5753d68f43055ef6b63ee0dc2a1f7 NeedsCompilation: no Title: Differential Co-expression Analysis Description: A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance. biocViews: GeneExpression, DifferentialExpression, Transcription, Microarray, OneChannel, TwoChannel, RNASeq, Sequencing, Coverage, ImmunoOncology Author: Wenbin Wei, Sandeep Amberkar, Winston Hide Maintainer: Wenbin Wei URL: https://github.com/hidelab/diffcoexp git_url: https://git.bioconductor.org/packages/diffcoexp git_branch: RELEASE_3_22 git_last_commit: b77a67f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/diffcoexp_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diffcoexp_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diffcoexp_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diffcoexp_1.30.0.tgz vignettes: vignettes/diffcoexp/inst/doc/diffcoexp.pdf vignetteTitles: About diffcoexp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffcoexp/inst/doc/diffcoexp.R importsMe: ExpHunterSuite dependencyCount: 121 Package: diffcyt Version: 1.30.0 Depends: R (>= 3.4.0) Imports: flowCore, FlowSOM, SummarizedExperiment, S4Vectors, limma, edgeR, lme4, multcomp, dplyr, tidyr, reshape2, magrittr, stats, methods, utils, grDevices, graphics, ComplexHeatmap, circlize, grid Suggests: BiocStyle, knitr, rmarkdown, testthat, HDCytoData, CATALYST License: MIT + file LICENSE Archs: x64 MD5sum: c0d2cfbbeb05bb639083007dadce5d3d NeedsCompilation: no Title: Differential discovery in high-dimensional cytometry via high-resolution clustering Description: Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software Author: Lukas M. Weber [aut, cre] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/diffcyt VignetteBuilder: knitr BugReports: https://github.com/lmweber/diffcyt/issues git_url: https://git.bioconductor.org/packages/diffcyt git_branch: RELEASE_3_22 git_last_commit: d8003b3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/diffcyt_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diffcyt_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diffcyt_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diffcyt_1.30.0.tgz vignettes: vignettes/diffcyt/inst/doc/diffcyt_workflow.html vignetteTitles: diffcyt workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffcyt/inst/doc/diffcyt_workflow.R dependsOnMe: censcyt, cytofWorkflow importsMe: CyTOFpower, treeclimbR, treekoR suggestsMe: CATALYST dependencyCount: 138 Package: DifferentialRegulation Version: 2.8.0 Depends: R (>= 4.3.0) Imports: methods, Rcpp, doRNG, MASS, data.table, doParallel, parallel, foreach, stats, BANDITS, Matrix, SingleCellExperiment, SummarizedExperiment, ggplot2, tximport, gridExtra LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 7fd063d6bb144d62cd468d3ea79298bc NeedsCompilation: yes Title: Differentially regulated genes from scRNA-seq data Description: DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) 'ambiguous' reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs). biocViews: DifferentialSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, GeneTarget Author: Simone Tiberi [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ) Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/DifferentialRegulation SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/DifferentialRegulation/issues git_url: https://git.bioconductor.org/packages/DifferentialRegulation git_branch: RELEASE_3_22 git_last_commit: 9d1063a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DifferentialRegulation_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DifferentialRegulation_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DifferentialRegulation_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DifferentialRegulation_2.8.0.tgz vignettes: vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.html vignetteTitles: DifferentialRegulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.R dependencyCount: 78 Package: diffGeneAnalysis Version: 1.92.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: 0647db9dff3471d1648345aa5faeb396 NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi git_url: https://git.bioconductor.org/packages/diffGeneAnalysis git_branch: RELEASE_3_22 git_last_commit: a8f03c2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/diffGeneAnalysis_1.92.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diffGeneAnalysis_1.91.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diffGeneAnalysis_1.92.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diffGeneAnalysis_1.92.0.tgz vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf vignetteTitles: Documentation on diffGeneAnalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.R dependencyCount: 5 Package: diffHic Version: 1.42.0 Depends: R (>= 3.5), GenomicRanges, InteractionSet, SummarizedExperiment Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics, grDevices, graphics, stats, utils, Rcpp, rtracklayer LinkingTo: Rhtslib (>= 1.13.1), Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix, testthat License: GPL-3 MD5sum: e2e4e0e659374e29ee8e44c752a7a120 NeedsCompilation: yes Title: Differential Analysis of Hi-C Data Description: Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available. biocViews: MultipleComparison, Preprocessing, Sequencing, Coverage, Alignment, Normalization, Clustering, HiC Author: Aaron Lun, Gordon Smyth Maintainer: Aaron Lun , Gordon Smyth , Hannah Coughlin SystemRequirements: C++, GNU make git_url: https://git.bioconductor.org/packages/diffHic git_branch: RELEASE_3_22 git_last_commit: d1e2c99 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/diffHic_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diffHic_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diffHic_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diffHic_1.42.0.tgz vignettes: vignettes/diffHic/inst/doc/diffHic.pdf, vignettes/diffHic/inst/doc/diffHicUsersGuide.pdf vignetteTitles: diffHic Vignette, diffHicUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: OHCA, hicream dependencyCount: 71 Package: DiffLogo Version: 2.34.0 Depends: R (>= 3.4), stats, cba Imports: grDevices, graphics, utils, tools Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: a24eb0f3aa553a4ca0df5d68ffc5cb25 NeedsCompilation: no Title: DiffLogo: A comparative visualisation of biooligomer motifs Description: DiffLogo is an easy-to-use tool to visualize motif differences. biocViews: Software, SequenceMatching, MultipleComparison, MotifAnnotation, Visualization, Alignment Author: c( person("Martin", "Nettling", role = c("aut", "cre"), email = "martin.nettling@informatik.uni-halle.de"), person("Hendrik", "Treutler", role = c("aut", "cre"), email = "hendrik.treutler@ipb-halle.de"), person("Jan", "Grau", role = c("aut", "ctb"), email = "grau@informatik.uni-halle.de"), person("Andrey", "Lando", role = c("aut", "ctb"), email = "dronte@autosome.ru"), person("Jens", "Keilwagen", role = c("aut", "ctb"), email = "jens.keilwagen@julius-kuehn.de"), person("Stefan", "Posch", role = "aut", email = "posch@informatik.uni-halle.de"), person("Ivo", "Grosse", role = "aut", email = "grosse@informatik.uni-halle.de")) Maintainer: Hendrik Treutler URL: https://github.com/mgledi/DiffLogo/ BugReports: https://github.com/mgledi/DiffLogo/issues git_url: https://git.bioconductor.org/packages/DiffLogo git_branch: RELEASE_3_22 git_last_commit: 6dbe09c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DiffLogo_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DiffLogo_2.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DiffLogo_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DiffLogo_2.34.0.tgz vignettes: vignettes/DiffLogo/inst/doc/DiffLogoBasics.pdf vignetteTitles: Basics of the DiffLogo package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffLogo/inst/doc/DiffLogoBasics.R dependencyCount: 9 Package: diffuStats Version: 1.30.0 Depends: R (>= 3.4) Imports: grDevices, stats, methods, Matrix, MASS, checkmate, expm, igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata, BiocStyle, reshape2, utils License: GPL-3 MD5sum: d66467c23ce2aaf97842b36f26985f92 NeedsCompilation: yes Title: Diffusion scores on biological networks Description: Label propagation approaches are a widely used procedure in computational biology for giving context to molecular entities using network data. Node labels, which can derive from gene expression, genome-wide association studies, protein domains or metabolomics profiling, are propagated to their neighbours in the network, effectively smoothing the scores through prior annotated knowledge and prioritising novel candidates. The R package diffuStats contains a collection of diffusion kernels and scoring approaches that facilitates their computation, characterisation and benchmarking. biocViews: Network, GeneExpression, GraphAndNetwork, Metabolomics, Transcriptomics, Proteomics, Genetics, GenomeWideAssociation, Normalization Author: Sergio Picart-Armada [aut, cre], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/diffuStats git_branch: RELEASE_3_22 git_last_commit: 84e475b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/diffuStats_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diffuStats_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diffuStats_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diffuStats_1.30.0.tgz vignettes: vignettes/diffuStats/inst/doc/diffuStats.pdf, vignettes/diffuStats/inst/doc/intro.html vignetteTitles: Case study: predicting protein function, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffuStats/inst/doc/diffuStats.R, vignettes/diffuStats/inst/doc/intro.R dependencyCount: 41 Package: diffUTR Version: 1.18.0 Depends: R (>= 4.0) Imports: S4Vectors, SummarizedExperiment, limma, edgeR, DEXSeq, GenomicRanges, Rsubread, ggplot2, rtracklayer, ComplexHeatmap, ggrepel, stringi, methods, stats, GenomeInfoDb, dplyr, matrixStats, IRanges, ensembldb, viridisLite Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 35f646411550d86ec22425d0a8a1d908 NeedsCompilation: no Title: diffUTR: Streamlining differential exon and 3' UTR usage Description: The diffUTR package provides a uniform interface and plotting functions for limma/edgeR/DEXSeq -powered differential bin/exon usage. It includes in addition an improved version of the limma::diffSplice method. Most importantly, diffUTR further extends the application of these frameworks to differential UTR usage analysis using poly-A site databases. biocViews: GeneExpression Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Stefan Gerber [aut] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/ETHZ-INS/diffUTR git_url: https://git.bioconductor.org/packages/diffUTR git_branch: RELEASE_3_22 git_last_commit: 6ffc717 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/diffUTR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diffUTR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diffUTR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diffUTR_1.18.0.tgz vignettes: vignettes/diffUTR/inst/doc/diffSplice2.html, vignettes/diffUTR/inst/doc/diffUTR.html vignetteTitles: diffUTR_diffSplice2, 1_diffUTR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffUTR/inst/doc/diffSplice2.R, vignettes/diffUTR/inst/doc/diffUTR.R dependencyCount: 141 Package: diggit Version: 1.42.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE MD5sum: 66a86026cc8bcb0efa16218899f8d208 NeedsCompilation: no Title: Inference of Genetic Variants Driving Cellular Phenotypes Description: Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/diggit git_branch: RELEASE_3_22 git_last_commit: a6a13ed git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/diggit_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diggit_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diggit_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diggit_1.42.0.tgz vignettes: vignettes/diggit/inst/doc/diggit.pdf vignetteTitles: Using DIGGIT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diggit/inst/doc/diggit.R dependencyCount: 95 Package: Dino Version: 1.16.0 Depends: R (>= 4.0.0) Imports: BiocParallel, BiocSingular, SummarizedExperiment, SingleCellExperiment, S4Vectors, Matrix, Seurat, matrixStats, parallel, scran, grDevices, stats, methods Suggests: testthat (>= 2.1.0), knitr, rmarkdown, BiocStyle, devtools, ggplot2, gridExtra, ggpubr, grid, magick, hexbin License: GPL-3 MD5sum: 701bff6f3d753baa2a80a6617c6fbb40 NeedsCompilation: no Title: Normalization of Single-Cell mRNA Sequencing Data Description: Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge. biocViews: Software, Normalization, RNASeq, SingleCell, Sequencing, GeneExpression, Transcriptomics, Regression, CellBasedAssays Author: Jared Brown [aut, cre] (ORCID: ), Christina Kendziorski [ctb] Maintainer: Jared Brown URL: https://github.com/JBrownBiostat/Dino VignetteBuilder: knitr BugReports: https://github.com/JBrownBiostat/Dino/issues git_url: https://git.bioconductor.org/packages/Dino git_branch: RELEASE_3_22 git_last_commit: 479c993 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Dino_1.16.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Dino_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Dino_1.16.0.tgz vignettes: vignettes/Dino/inst/doc/Dino.html vignetteTitles: Normalization by distributional resampling of high throughput single-cell RNA-sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Dino/inst/doc/Dino.R dependencyCount: 186 Package: dinoR Version: 1.6.0 Depends: R (>= 4.3.0), SummarizedExperiment Imports: BiocGenerics, circlize, ComplexHeatmap, cowplot, dplyr, edgeR, GenomicRanges, ggplot2, Matrix, methods, rlang, stats, stringr, tibble, tidyr, tidyselect Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: feba6a71afd57b3a657cef949c2b17bf NeedsCompilation: no Title: Differential NOMe-seq analysis Description: dinoR tests for significant differences in NOMe-seq footprints between two conditions, using genomic regions of interest (ROI) centered around a landmark, for example a transcription factor (TF) motif. This package takes NOMe-seq data (GCH methylation/protection) in the form of a Ranged Summarized Experiment as input. dinoR can be used to group sequencing fragments into 3 or 5 categories representing characteristic footprints (TF bound, nculeosome bound, open chromatin), plot the percentage of fragments in each category in a heatmap, or averaged across different ROI groups, for example, containing a common TF motif. It is designed to compare footprints between two sample groups, using edgeR's quasi-likelihood methods on the total fragment counts per ROI, sample, and footprint category. biocViews: NucleosomePositioning, Epigenetics, MethylSeq, DifferentialMethylation, Coverage, Transcription, Sequencing, Software Author: Michaela Schwaiger [aut, cre] (ORCID: ) Maintainer: Michaela Schwaiger URL: https://github.com/xxxmichixxx/dinoR VignetteBuilder: knitr BugReports: https://github.com/xxxmichixxx/dinoR/issues git_url: https://git.bioconductor.org/packages/dinoR git_branch: RELEASE_3_22 git_last_commit: 5d0a090 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dinoR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dinoR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dinoR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dinoR_1.6.0.tgz vignettes: vignettes/dinoR/inst/doc/dinoR-vignette.html vignetteTitles: dinoR-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dinoR/inst/doc/dinoR-vignette.R dependencyCount: 75 Package: dir.expiry Version: 1.18.0 Imports: utils, filelock Suggests: rmarkdown, knitr, testthat, BiocStyle License: GPL-3 Archs: x64 MD5sum: 7134336be4b60092a0b3a7c55848fabf NeedsCompilation: no Title: Managing Expiration for Cache Directories Description: Implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages. biocViews: Software, Infrastructure Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dir.expiry git_branch: RELEASE_3_22 git_last_commit: 6d768b3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dir.expiry_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dir.expiry_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dir.expiry_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dir.expiry_1.18.0.tgz vignettes: vignettes/dir.expiry/inst/doc/userguide.html vignetteTitles: Managing directory expiration hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dir.expiry/inst/doc/userguide.R importsMe: basilisk, basilisk.utils, biocmake, graphite, rebook dependencyCount: 2 Package: DirichletMultinomial Version: 1.52.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, DT, knitr, rmarkdown, BiocStyle License: LGPL-3 MD5sum: 4d353fe6ad98fdf909ca031b98cde9bb NeedsCompilation: yes Title: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data Description: Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. biocViews: ImmunoOncology, Microbiome, Sequencing, Clustering, Classification, Metagenomics Author: Martin Morgan [aut, cre] (ORCID: ) Maintainer: Martin Morgan URL: https://mtmorgan.github.io/DirichletMultinomial/ SystemRequirements: gsl VignetteBuilder: knitr BugReports: https://github.com/mtmorgan/DirichletMultinomial/issues git_url: https://git.bioconductor.org/packages/DirichletMultinomial git_branch: RELEASE_3_22 git_last_commit: bfdb1c5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DirichletMultinomial_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DirichletMultinomial_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DirichletMultinomial_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DirichletMultinomial_1.52.0.tgz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.html vignetteTitles: DirichletMultinomial for Clustering and Classification of Microbiome Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.R importsMe: mia, miaViz, TFBSTools suggestsMe: bluster, MicrobiotaProcess dependencyCount: 9 Package: discordant Version: 1.34.0 Depends: R (>= 4.1.0) Imports: Rcpp, Biobase, stats, biwt, gtools, MASS, tools, dplyr, methods, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 0b3231c548e36621a530fbe9bb85f9ba NeedsCompilation: yes Title: The Discordant Method: A Novel Approach for Differential Correlation Description: Discordant is an R package that identifies pairs of features that correlate differently between phenotypic groups, with application to -omics data sets. Discordant uses a mixture model that “bins” molecular feature pairs based on their type of coexpression or coabbundance. Algorithm is explained further in "Differential Correlation for Sequencing Data"" (Siska et al. 2016). biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod, mRNAMicroarray, Microarray, Genetics, RNASeq Author: Charlotte Siska [aut], McGrath Max [aut, cre], Katerina Kechris [aut, cph, ths] Maintainer: McGrath Max URL: https://github.com/siskac/discordant VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/discordant git_branch: RELEASE_3_22 git_last_commit: 278d71f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/discordant_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/discordant_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/discordant_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/discordant_1.34.0.tgz vignettes: vignettes/discordant/inst/doc/Using_discordant.html vignetteTitles: The discordant R Package: A Novel Approach to Differential Correlation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/discordant/inst/doc/Using_discordant.R dependencyCount: 29 Package: DiscoRhythm Version: 1.26.0 Depends: R (>= 3.6.0) Imports: matrixTests, matrixStats, MetaCycle (>= 1.2.0), data.table, ggplot2, ggExtra, dplyr, broom, shiny, shinyBS, shinycssloaders, shinydashboard, shinyjs, BiocStyle, rmarkdown, knitr, kableExtra, magick, VennDiagram, UpSetR, heatmaply, viridis, plotly, DT, gridExtra, methods, stats, SummarizedExperiment, BiocGenerics, S4Vectors, zip, reshape2 Suggests: testthat License: GPL-3 MD5sum: 129dd232d07a1cf6b3199100f27fc5cf NeedsCompilation: no Title: Interactive Workflow for Discovering Rhythmicity in Biological Data Description: Set of functions for estimation of cyclical characteristics, such as period, phase, amplitude, and statistical significance in large temporal datasets. Supporting functions are available for quality control, dimensionality reduction, spectral analysis, and analysis of experimental replicates. Contains a R Shiny web interface to execute all workflow steps. biocViews: Software, TimeCourse, QualityControl, Visualization, GUI, PrincipalComponent Author: Matthew Carlucci [aut, cre], Algimantas Kriščiūnas [aut], Haohan Li [aut], Povilas Gibas [aut], Karolis Koncevičius [aut], Art Petronis [aut], Gabriel Oh [aut] Maintainer: Matthew Carlucci URL: https://github.com/matthewcarlucci/DiscoRhythm SystemRequirements: To generate html reports pandoc (http://pandoc.org/installing.html) is required. VignetteBuilder: knitr BugReports: https://github.com/matthewcarlucci/DiscoRhythm/issues git_url: https://git.bioconductor.org/packages/DiscoRhythm git_branch: RELEASE_3_22 git_last_commit: 30a30eb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DiscoRhythm_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DiscoRhythm_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DiscoRhythm_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DiscoRhythm_1.26.0.tgz vignettes: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.html vignetteTitles: Introduction to DiscoRhythm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.R dependencyCount: 156 Package: distinct Version: 1.22.0 Depends: R (>= 4.3) Imports: Rcpp, stats, SummarizedExperiment, SingleCellExperiment, methods, Matrix, foreach, parallel, doParallel, doRNG, ggplot2, limma, scater LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, UpSetR, BiocStyle License: GPL (>= 3) MD5sum: f6adf23310d1315aed1a79a262bae6e2 NeedsCompilation: yes Title: distinct: a method for differential analyses via hierarchical permutation tests Description: distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group. biocViews: Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, FlowCytometry, GeneTarget Author: Simone Tiberi [aut, cre]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/distinct SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/distinct/issues git_url: https://git.bioconductor.org/packages/distinct git_branch: RELEASE_3_22 git_last_commit: 8dca3b7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/distinct_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/distinct_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/distinct_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/distinct_1.22.0.tgz vignettes: vignettes/distinct/inst/doc/distinct.html vignetteTitles: distinct: a method for differential analyses via hierarchical permutation tests hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/distinct/inst/doc/distinct.R importsMe: condiments dependencyCount: 97 Package: dittoSeq Version: 1.22.0 Depends: ggplot2 Imports: methods, colorspace (>= 1.4), gridExtra, cowplot, reshape2, pheatmap, grDevices, ggrepel, ggridges, stats, utils, SummarizedExperiment, SingleCellExperiment, S4Vectors Suggests: plotly, testthat, Seurat (>= 2.2), DESeq2, edgeR, ggplot.multistats, knitr, rmarkdown, BiocStyle, scRNAseq, ggrastr (>= 0.2.0), ComplexHeatmap, bluster, scater, scran, MASS License: MIT + file LICENSE MD5sum: 5cf6ed5c0a759e21fdc8ec2230fd2d6c NeedsCompilation: no Title: User Friendly Single-Cell and Bulk RNA Sequencing Visualization Description: A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors(). biocViews: Software, Visualization, RNASeq, SingleCell, GeneExpression, Transcriptomics, DataImport Author: Daniel Bunis [aut, cre], Jared Andrews [aut, ctb] Maintainer: Daniel Bunis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dittoSeq git_branch: RELEASE_3_22 git_last_commit: d79c5f2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dittoSeq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dittoSeq_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dittoSeq_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dittoSeq_1.22.0.tgz vignettes: vignettes/dittoSeq/inst/doc/dittoSeq.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dittoSeq/inst/doc/dittoSeq.R importsMe: SPIAT suggestsMe: demuxSNP, tidySingleCellExperiment, magmaR, scCustomize dependencyCount: 54 Package: divergence Version: 1.26.0 Depends: R (>= 3.6), SummarizedExperiment Suggests: knitr, rmarkdown License: GPL-2 MD5sum: c36ec8bc25d34d9c1250681c91705388 NeedsCompilation: no Title: Divergence: Functionality for assessing omics data by divergence with respect to a baseline Description: This package provides functionality for performing divergence analysis as presented in Dinalankara et al, "Digitizing omics profiles by divergence from a baseline", PANS 2018. This allows the user to simplify high dimensional omics data into a binary or ternary format which encapsulates how the data is divergent from a specified baseline group with the same univariate or multivariate features. biocViews: Software, StatisticalMethod Author: Wikum Dinalankara , Luigi Marchionni , Qian Ke Maintainer: Wikum Dinalankara , Luigi Marchionni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/divergence git_branch: RELEASE_3_22 git_last_commit: f1b336f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/divergence_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/divergence_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/divergence_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/divergence_1.26.0.tgz vignettes: vignettes/divergence/inst/doc/divergence.html vignetteTitles: Performing Divergence Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/divergence/inst/doc/divergence.R dependencyCount: 25 Package: dks Version: 1.56.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: 7b4549d9b83d2dafc6841a7f62254e20 NeedsCompilation: no Title: The double Kolmogorov-Smirnov package for evaluating multiple testing procedures. Description: The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated. biocViews: MultipleComparison, QualityControl Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/dks git_branch: RELEASE_3_22 git_last_commit: d5a1582 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dks_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dks_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dks_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dks_1.56.0.tgz vignettes: vignettes/dks/inst/doc/dks.pdf vignetteTitles: dksTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dks/inst/doc/dks.R dependencyCount: 4 Package: DMCFB Version: 1.24.0 Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges Imports: utils, stats, speedglm, MASS, data.table, splines, arm, rtracklayer, benchmarkme, tibble, matrixStats, fastDummies, graphics Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: 4776509534074a349035b66cc078a793 NeedsCompilation: no Title: Differentially Methylated Cytosines via a Bayesian Functional Approach Description: DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method. biocViews: DifferentialMethylation, Sequencing, Coverage, Bayesian, Regression Author: Farhad Shokoohi [aut, cre] (ORCID: ) Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCFB/issues git_url: https://git.bioconductor.org/packages/DMCFB git_branch: RELEASE_3_22 git_last_commit: 0f829bf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DMCFB_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DMCFB_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DMCFB_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DMCFB_1.24.0.tgz vignettes: vignettes/DMCFB/inst/doc/DMCFB.html vignetteTitles: Identifying DMCs using Bayesian functional regressions in BS-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCFB/inst/doc/DMCFB.R dependencyCount: 97 Package: DMCHMM Version: 1.32.0 Depends: R (>= 4.1.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool Imports: utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics Suggests: testthat, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: c063e7f10136eba9ed07df5b2b2a2e61 NeedsCompilation: no Title: Differentially Methylated CpG using Hidden Markov Model Description: A pipeline for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. biocViews: DifferentialMethylation, Sequencing, HiddenMarkovModel, Coverage Author: Farhad Shokoohi Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCHMM/issues git_url: https://git.bioconductor.org/packages/DMCHMM git_branch: RELEASE_3_22 git_last_commit: 8994431 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DMCHMM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DMCHMM_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DMCHMM_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DMCHMM_1.32.0.tgz vignettes: vignettes/DMCHMM/inst/doc/DMCHMM.html vignetteTitles: DMCHMM: Differentially Methylated CpG using Hidden Markov Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCHMM/inst/doc/DMCHMM.R dependencyCount: 67 Package: dmGsea Version: 1.0.0 Depends: utils,stats,parallel,Matrix,SummarizedExperiment,methods,R(>= 3.5.0) Imports: dqrng,AnnotationDbi,poolr,BiasedUrn,Seqinfo Suggests: msigdbr, org.Hs.eg.db, org.Mm.eg.db, minfi, knitr, rmarkdown, GO.db, KEGGREST, testthat, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylation450kanno.ilmn12.hg19, BiocStyle, RUnit License: Artistic-2.0 MD5sum: c074bed237a451f7fa0090dc81a01172 NeedsCompilation: no Title: Efficient Gene Set Enrichment Analysis for DNA Methylation Data Description: The R package dmGsea provides efficient gene set enrichment analysis specifically for DNA methylation data. It addresses key biases, including probe dependency and varying probe numbers per gene. The package supports Illumina 450K, EPIC, and mouse methylation arrays. Users can also apply it to other omics data by supplying custom probe-to-gene mapping annotations. dmGsea is flexible, fast, and well-suited for large-scale epigenomic studies. biocViews: GeneSetEnrichment, Pathways,DNAMethylation,Proteomics,Sequencing, CopyNumberVariation, GeneExpression, GenomicVariation, Coverage Author: Zongli Xu [cre, aut] (ORCID: ), Alison Motsinger-Reif [aut], Liang Niu [aut] Maintainer: Zongli Xu URL: https://github.com/Bioconductor/dmGsea VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/dmGsea/issues git_url: https://git.bioconductor.org/packages/dmGsea git_branch: RELEASE_3_22 git_last_commit: 7cc868c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dmGsea_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dmGsea_0.99.11.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dmGsea_1.0.0.tgz vignettes: vignettes/dmGsea/inst/doc/dmGsea.html vignetteTitles: dmGsea User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dmGsea/inst/doc/dmGsea.R dependencyCount: 62 Package: DMRcaller Version: 1.42.0 Depends: R (>= 3.5), GenomicRanges, IRanges, S4Vectors Imports: parallel, Rcpp, RcppRoll, betareg, grDevices, graphics, methods, stats, utils, Rsamtools, GenomicRanges, GenomicAlignments, Biostrings, BSgenome, BiocManager, S4Vectors, IRanges, InteractionSet, stringr, inflection, BiocParallel, Seqinfo, GenomeInfoDb Suggests: knitr, RUnit, BiocGenerics, rmarkdown, bookdown, BiocStyle, betareg, rtracklayer, BSgenome.Hsapiens.UCSC.hg38 License: GPL-3 Archs: x64 MD5sum: 72d6899da11108bc4ba4cfbd805c1610 NeedsCompilation: no Title: Differentially Methylated Regions Caller Description: Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects. biocViews: DifferentialMethylation, DNAMethylation, Software, Sequencing, Coverage Author: Nicolae Radu Zabet , Jonathan Michael Foonlan Tsang , Alessandro Pio Greco , Ryan Merritt and Young Jun Kim Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcaller git_branch: RELEASE_3_22 git_last_commit: ba466d4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DMRcaller_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DMRcaller_1.41.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DMRcaller_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DMRcaller_1.42.0.tgz vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.html vignetteTitles: Overview of the DMRcaller package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRcaller/inst/doc/DMRcaller.R dependencyCount: 81 Package: DMRcate Version: 3.6.0 Depends: R (>= 4.3.0) Imports: AnnotationHub, ExperimentHub, bsseq, Seqinfo, limma, edgeR, minfi, missMethyl, GenomicRanges, plyr, Gviz, IRanges, stats, utils, S4Vectors, methods, graphics, SummarizedExperiment, biomaRt, grDevices Suggests: knitr, RUnit, BiocGenerics, GenomeInfoDb, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICv2anno.20a1.hg38, FlowSorted.Blood.EPIC, tissueTreg, DMRcatedata, EPICv2manifest License: file LICENSE MD5sum: 9614a22c0cd020a1d238ce4459b6d28d NeedsCompilation: no Title: Methylation array and sequencing spatial analysis methods Description: De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions. biocViews: DifferentialMethylation, GeneExpression, Microarray, MethylationArray, Genetics, DifferentialExpression, GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel, MultipleComparison, QualityControl, TimeCourse, Sequencing, WholeGenome, Epigenetics, Coverage, Preprocessing, DataImport Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcate git_branch: RELEASE_3_22 git_last_commit: d522a97 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DMRcate_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DMRcate_3.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DMRcate_3.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DMRcate_3.6.0.tgz vignettes: vignettes/DMRcate/inst/doc/EPICv1_and_450K.pdf, vignettes/DMRcate/inst/doc/EPICv2.pdf, vignettes/DMRcate/inst/doc/sequencing.pdf vignetteTitles: DMRcate for EPICv1 and 450K assays, DMR calling from EPICv2 arrays, DMRcate for bisulfite sequencing assays (WGBS and RRBS) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DMRcate/inst/doc/EPICv1_and_450K.R, vignettes/DMRcate/inst/doc/EPICv2.R, vignettes/DMRcate/inst/doc/sequencing.R dependsOnMe: methylationArrayAnalysis suggestsMe: missMethyl dependencyCount: 223 Package: DMRScan Version: 1.32.0 Depends: R (>= 3.6.0) Imports: Matrix, MASS, RcppRoll,GenomicRanges, IRanges, Seqinfo, methods, mvtnorm, stats, parallel Suggests: knitr, rmarkdown, BiocStyle, BiocManager License: GPL-3 MD5sum: 6f83c3c738ef09a32cf1a537472096a9 NeedsCompilation: no Title: Detection of Differentially Methylated Regions Description: This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG. biocViews: Software, Technology, Sequencing, WholeGenome Author: Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge [ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut] Maintainer: Christian M Page URL: https://github.com/christpa/DMRScan VignetteBuilder: knitr BugReports: https://github.com/christpa/DMRScan/issues PackageStatus: Active git_url: https://git.bioconductor.org/packages/DMRScan git_branch: RELEASE_3_22 git_last_commit: 93867b8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DMRScan_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DMRScan_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DMRScan_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DMRScan_1.32.0.tgz vignettes: vignettes/DMRScan/inst/doc/DMRScan_vignette.html vignetteTitles: DMR Scan Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRScan/inst/doc/DMRScan_vignette.R dependencyCount: 20 Package: dmrseq Version: 1.30.0 Depends: R (>= 3.5), bsseq Imports: GenomicRanges, nlme, ggplot2, S4Vectors, RColorBrewer, bumphunter, DelayedMatrixStats (>= 1.1.13), matrixStats, BiocParallel, outliers, methods, locfit, IRanges, grDevices, graphics, stats, utils, annotatr, AnnotationHub, rtracklayer, Seqinfo, splines Suggests: knitr, rmarkdown, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: MIT + file LICENSE MD5sum: bc5a7435d0d0f3c2260acb4ed73c1fe4 NeedsCompilation: no Title: Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing Description: This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, MultipleComparison, Software, Sequencing, DifferentialMethylation, WholeGenome, Regression, FunctionalGenomics Author: Keegan Korthauer [cre, aut] (ORCID: ), Rafael Irizarry [aut] (ORCID: ), Yuval Benjamini [aut], Sutirtha Chakraborty [aut] Maintainer: Keegan Korthauer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dmrseq git_branch: RELEASE_3_22 git_last_commit: 10b6432 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dmrseq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dmrseq_1.29.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dmrseq_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dmrseq_1.30.0.tgz vignettes: vignettes/dmrseq/inst/doc/dmrseq.html vignetteTitles: Analyzing Bisulfite-seq data with dmrseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dmrseq/inst/doc/dmrseq.R importsMe: biscuiteer dependencyCount: 148 Package: DNABarcodeCompatibility Version: 1.26.0 Depends: R (>= 3.6.0) Imports: dplyr, tidyr, numbers, purrr, stringr, stats, utils, methods, Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: file LICENSE MD5sum: e9e49803b9f31eac02e059f57946bce1 NeedsCompilation: yes Title: A Tool for Optimizing Combinations of DNA Barcodes Used in Multiplexed Experiments on Next Generation Sequencing Platforms Description: The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage. biocViews: Preprocessing, Sequencing Author: Céline Trébeau [cre] (ORCID: ), Jacques Boutet de Monvel [aut] (ORCID: ), Fabienne Wong Jun Tai [ctb], Raphaël Etournay [aut] (ORCID: ) Maintainer: Céline Trébeau URL: https://dnabarcodecompatibility.pasteur.fr/ VignetteBuilder: knitr BugReports: https://gitlab.pasteur.fr/ida-public/dnabarcodecompatibility/-/issues git_url: https://git.bioconductor.org/packages/DNABarcodeCompatibility git_branch: RELEASE_3_22 git_last_commit: 9063093 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DNABarcodeCompatibility_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DNABarcodeCompatibility_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNABarcodeCompatibility_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNABarcodeCompatibility_1.26.0.tgz vignettes: vignettes/DNABarcodeCompatibility/inst/doc/introduction.html vignetteTitles: Introduction to DNABarcodeCompatibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DNABarcodeCompatibility/inst/doc/introduction.R dependencyCount: 29 Package: DNABarcodes Version: 1.40.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 MD5sum: 03f8505d0ff7e0d4f7f1c95d6868bca5 NeedsCompilation: yes Title: A tool for creating and analysing DNA barcodes used in Next Generation Sequencing multiplexing experiments Description: The package offers a function to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analysed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e., assigned to their original reference barcode. biocViews: Preprocessing, Sequencing Author: Tilo Buschmann Maintainer: Tilo Buschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNABarcodes git_branch: RELEASE_3_22 git_last_commit: 3d5853f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DNABarcodes_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DNABarcodes_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNABarcodes_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNABarcodes_1.40.0.tgz vignettes: vignettes/DNABarcodes/inst/doc/DNABarcodes.html vignetteTitles: DNABarcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R dependencyCount: 11 Package: DNAcopy Version: 1.84.0 License: GPL (>= 2) MD5sum: 2f6ecfaf23882e5d8fc23759dad5e876 NeedsCompilation: yes Title: DNA Copy Number Data Analysis Description: Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number. biocViews: Microarray, CopyNumberVariation Author: Venkatraman E. Seshan, Adam Olshen Maintainer: Venkatraman E. Seshan git_url: https://git.bioconductor.org/packages/DNAcopy git_branch: RELEASE_3_22 git_last_commit: 174ee61 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DNAcopy_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DNAcopy_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNAcopy_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNAcopy_1.84.0.tgz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R dependsOnMe: CGHcall, cghMCR, CRImage, PureCN, CSclone, ParDNAcopy, saasCNV importsMe: ADaCGH2, ChAMP, cn.farms, CNAnorm, CNVrd2, conumee, GWASTools, maftools, MDTS, MEDIPS, MinimumDistance, QDNAseq, SCOPE, jointseg, PSCBS suggestsMe: cn.mops, CopyNumberPlots, fastseg, nullranges, sesame, ACNE, aroma.cn, aroma.core, calmate dependencyCount: 0 Package: DNAcycP2 Version: 1.2.0 Depends: R (>= 4.4.0) Imports: basilisk, reticulate Suggests: knitr, rmarkdown, BiocGenerics, RUnit, tinytest, BiocStyle, Biostrings License: Artistic-2.0 MD5sum: 601de72b09b14f55ad7682188ca59725 NeedsCompilation: no Title: DNA Cyclizability Prediction Description: This package performs prediction of intrinsic cyclizability of of every 50-bp subsequence in a DNA sequence. The input could be a file either in FASTA or text format. The output will be the C-score, the estimated intrinsic cyclizability score for each 50 bp sequences in each entry of the sequence set. biocViews: NeuralNetwork, StructuralPrediction Author: Ji-Ping Wang [aut, cre] (ORCID: ) Maintainer: Ji-Ping Wang URL: https://github.com/jipingw/DNAcycP2 VignetteBuilder: knitr BugReports: https://github.com/jipingw/DNAcycP2 git_url: https://git.bioconductor.org/packages/DNAcycP2 git_branch: RELEASE_3_22 git_last_commit: 05e80ac git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DNAcycP2_1.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNAcycP2_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNAcycP2_1.1.0.tgz vignettes: vignettes/DNAcycP2/inst/doc/dnacycp2.html vignetteTitles: DNAcycP2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcycP2/inst/doc/dnacycp2.R dependencyCount: 22 Package: DNAfusion Version: 1.12.0 Depends: R (>= 4.4.0) Imports: GenomicRanges, IRanges, Rsamtools, GenomicAlignments, BiocBaseUtils, S4Vectors, GenomicFeatures, TxDb.Hsapiens.UCSC.hg38.knownGene, BiocGenerics Suggests: knitr, rmarkdown, testthat, sessioninfo, BiocStyle License: GPL-3 Archs: x64 MD5sum: 7af117f6cfbb5deee502237b3c15985c NeedsCompilation: no Title: Identification of gene fusions using paired-end sequencing Description: DNAfusion can identify gene fusions such as EML4-ALK based on paired-end sequencing results. This package was developed using position deduplicated BAM files generated with the AVENIO Oncology Analysis Software. These files are made using the AVENIO ctDNA surveillance kit and Illumina Nextseq 500 sequencing. This is a targeted hybridization NGS approach and includes ALK-specific but not EML4-specific probes. biocViews: TargetedResequencing, Genetics, GeneFusionDetection, Sequencing Author: Christoffer Trier Maansson [aut, cre] (ORCID: ), Emma Roger Andersen [ctb, rev], Maiken Parm Ulhoi [dtc], Peter Meldgaard [dtc], Boe Sandahl Sorensen [rev, fnd] Maintainer: Christoffer Trier Maansson URL: https://github.com/CTrierMaansson/DNAfusion VignetteBuilder: knitr BugReports: https://github.com/CTrierMaansson/DNAfusion/issues git_url: https://git.bioconductor.org/packages/DNAfusion git_branch: RELEASE_3_22 git_last_commit: 7226d68 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DNAfusion_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DNAfusion_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNAfusion_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNAfusion_1.12.0.tgz vignettes: vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.html vignetteTitles: Introduction to DNAfusion hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.R dependencyCount: 78 Package: DNAshapeR Version: 1.38.0 Depends: R (>= 3.4), GenomicRanges Imports: Rcpp (>= 0.12.1), Biostrings, fields LinkingTo: Rcpp Suggests: AnnotationHub, knitr, rmarkdown, testthat, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Hsapiens.UCSC.hg19, caret License: GPL-2 MD5sum: 322992ace01a818c0c3e133f89aa596c NeedsCompilation: yes Title: High-throughput prediction of DNA shape features Description: DNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. The package allows to predict, visualize and encode DNA shape features for statistical learning. biocViews: StructuralPrediction, DNA3DStructure, Software Author: Tsu-Pei Chiu and Federico Comoglio Maintainer: Tsu-Pei Chiu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNAshapeR git_branch: RELEASE_3_22 git_last_commit: c0aba6b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DNAshapeR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DNAshapeR_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNAshapeR_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNAshapeR_1.38.0.tgz vignettes: vignettes/DNAshapeR/inst/doc/DNAshapeR.html vignetteTitles: DNAshapeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R dependencyCount: 24 Package: DNEA Version: 1.0.0 Depends: R (>= 4.2) Imports: BiocParallel, dplyr, gdata, glasso, igraph (>= 2.0.3), janitor, Matrix, methods, netgsa, stats, stringr, utils, SummarizedExperiment Suggests: BiocStyle, ggplot2, Hmisc, kableExtra, knitr, pheatmap, rmarkdown, testthat (>= 3.0.0), withr, airway Enhances: massdataset License: MIT + file LICENSE MD5sum: 55a5050328ffbe1bc764aa0f7b78245c NeedsCompilation: no Title: Differential Network Enrichment Analysis for Biological Data Description: The DNEA R package is the latest implementation of the Differential Network Enrichment Analysis algorithm and is the successor to the Filigree Java-application described in Iyer et al. (2020). The package is designed to take as input an m x n expression matrix for some -omics modality (ie. metabolomics, lipidomics, proteomics, etc.) and jointly estimate the biological network associations of each condition using the DNEA algorithm described in Ma et al. (2019). This approach provides a framework for data-driven enrichment analysis across two experimental conditions that utilizes the underlying correlation structure of the data to determine feature-feature interactions. biocViews: Metabolomics, Proteomics, Lipidomics, DifferentialExpression, NetworkEnrichment, Network, Clustering, DataImport Author: Christopher Patsalis [cre, aut] (ORCID: ), Gayatri Iyer [aut], Alla Karnovsky [fnd] (NIH_GRANT: 1U01CA235487), George Michailidis [fnd] (NIH_GRANT: 1U01CA235487) Maintainer: Christopher Patsalis URL: https://github.com/Karnovsky-Lab/DNEA VignetteBuilder: knitr BugReports: https://github.com/Karnovsky-Lab/DNEA/issues git_url: https://git.bioconductor.org/packages/DNEA git_branch: RELEASE_3_22 git_last_commit: a831e55 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DNEA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DNEA_0.99.14.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNEA_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNEA_1.0.0.tgz vignettes: vignettes/DNEA/inst/doc/DNEA.html vignetteTitles: DNEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DNEA/inst/doc/DNEA.R dependencyCount: 131 Package: DominoEffect Version: 1.30.0 Depends: R(>= 3.5) Imports: biomaRt, data.table, utils, stats, Biostrings, pwalign, SummarizedExperiment, VariantAnnotation, AnnotationDbi, Seqinfo, IRanges, GenomicRanges, methods Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: ad7a07991ef0868b7d8f76899be50498 NeedsCompilation: no Title: Identification and Annotation of Protein Hotspot Residues Description: The functions support identification and annotation of hotspot residues in proteins. These are individual amino acids that accumulate mutations at a much higher rate than their surrounding regions. biocViews: Software, SomaticMutation, Proteomics, SequenceMatching, Alignment Author: Marija Buljan and Peter Blattmann Maintainer: Marija Buljan , Peter Blattmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DominoEffect git_branch: RELEASE_3_22 git_last_commit: 6694070 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DominoEffect_1.30.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DominoEffect_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DominoEffect_1.30.0.tgz vignettes: vignettes/DominoEffect/inst/doc/Vignette.html vignetteTitles: Vignette for DominoEffect package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DominoEffect/inst/doc/Vignette.R dependencyCount: 101 Package: dominoSignal Version: 1.4.0 Depends: R(>= 4.2.0), Imports: biomaRt, ComplexHeatmap, circlize, ggpubr, grDevices, grid, igraph, Matrix, methods, plyr, stats, utils, magrittr, purrr, dplyr Suggests: knitr, patchwork, rmarkdown, Seurat, testthat, formatR, BiocFileCache, SingleCellExperiment License: GPL-3 | file LICENSE Archs: x64 MD5sum: 84b698b2c3e7ea17a3743ecbb287064f NeedsCompilation: no Title: Cell Communication Analysis for Single Cell RNA Sequencing Description: dominoSignal is a package developed to analyze cell signaling through ligand - receptor - transcription factor networks in scRNAseq data. It takes as input information transcriptomic data, requiring counts, z-scored counts, and cluster labels, as well as information on transcription factor activation (such as from SCENIC) and a database of ligand and receptor pairings (such as from CellPhoneDB). This package creates an object storing ligand - receptor - transcription factor linkages by cluster and provides several methods for exploring, summarizing, and visualizing the analysis. biocViews: SystemsBiology, SingleCell, Transcriptomics, Network Author: Christopher Cherry [aut] (ORCID: ), Jacob T Mitchell [aut, cre] (ORCID: ), Sushma Nagaraj [aut] (ORCID: ), Kavita Krishnan [aut] (ORCID: ), Dmitrijs Lvovs [aut], Elana Fertig [ctb] (ORCID: ), Jennifer Elisseeff [ctb] (ORCID: ) Maintainer: Jacob T Mitchell URL: https://fertiglab.github.io/dominoSignal/ VignetteBuilder: knitr BugReports: https://github.com/FertigLab/dominoSignal/issues git_url: https://git.bioconductor.org/packages/dominoSignal git_branch: RELEASE_3_22 git_last_commit: 37df5de git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dominoSignal_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dominoSignal_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dominoSignal_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dominoSignal_1.4.0.tgz vignettes: vignettes/dominoSignal/inst/doc/domino_object_vignette.html, vignettes/dominoSignal/inst/doc/dominoSignal.html, vignettes/dominoSignal/inst/doc/plotting_vignette.html vignetteTitles: Interacting with domino Objects, Get Started with dominoSignal, Plotting Functions and Options hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dominoSignal/inst/doc/domino_object_vignette.R, vignettes/dominoSignal/inst/doc/dominoSignal.R, vignettes/dominoSignal/inst/doc/plotting_vignette.R dependencyCount: 133 Package: doppelgangR Version: 1.38.0 Depends: R (>= 3.5.0), Biobase, BiocParallel Imports: sva, impute, digest, mnormt, methods, grDevices, graphics, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, curatedOvarianData, testthat License: GPL (>=2.0) MD5sum: 682302822d2f01ae881bdb636443a74d NeedsCompilation: no Title: Identify likely duplicate samples from genomic or meta-data Description: The main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset). biocViews: ImmunoOncology, RNASeq, Microarray, GeneExpression, QualityControl Author: Levi Waldron [aut, cre], Markus Reister [aut, ctb], Marcel Ramos [ctb] Maintainer: Levi Waldron URL: https://github.com/lwaldron/doppelgangR, https://waldronlab.github.io/doppelgangR VignetteBuilder: knitr BugReports: https://github.com/lwaldron/doppelgangR/issues git_url: https://git.bioconductor.org/packages/doppelgangR git_branch: RELEASE_3_22 git_last_commit: deddc39 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/doppelgangR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/doppelgangR_1.37.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/doppelgangR_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/doppelgangR_1.38.0.tgz vignettes: vignettes/doppelgangR/inst/doc/doppelgangR.html vignetteTitles: doppelgangR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R dependencyCount: 79 Package: Doscheda Version: 1.32.0 Depends: R (>= 3.4) Imports: methods, drc, stats, httr, jsonlite, reshape2 , vsn, affy, limma, stringr, ggplot2, graphics, grDevices, calibrate, corrgram, gridExtra, DT, shiny, shinydashboard, readxl, prodlim, matrixStats Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 5dc3d04f97061093e70cd0dc3782cc55 NeedsCompilation: no Title: A DownStream Chemo-Proteomics Analysis Pipeline Description: Doscheda focuses on quantitative chemoproteomics used to determine protein interaction profiles of small molecules from whole cell or tissue lysates using Mass Spectrometry data. The package provides a shiny application to run the pipeline, several visualisations and a downloadable report of an experiment. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, DataImport, Regression Author: Bruno Contrino, Piero Ricchiuto Maintainer: Bruno Contrino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Doscheda git_branch: RELEASE_3_22 git_last_commit: a361fa2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Doscheda_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Doscheda_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Doscheda_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Doscheda_1.32.0.tgz vignettes: vignettes/Doscheda/inst/doc/Doscheda.html vignetteTitles: Doscheda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Doscheda/inst/doc/Doscheda.R dependencyCount: 153 Package: DOSE Version: 4.4.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocParallel, fgsea, ggplot2, GOSemSim (>= 2.31.2), methods, qvalue, reshape2, stats, utils, yulab.utils (>= 0.1.6) Suggests: prettydoc, clusterProfiler, gson (>= 0.0.5), knitr, memoise, org.Hs.eg.db, rmarkdown, testthat License: Artistic-2.0 MD5sum: fd97f21bbbce18162934eb4b2023892b NeedsCompilation: no Title: Disease Ontology Semantic and Enrichment analysis Description: This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. biocViews: Annotation, Visualization, MultipleComparison, GeneSetEnrichment, Pathways, Software Author: Guangchuang Yu [aut, cre], Li-Gen Wang [ctb], Vladislav Petyuk [ctb], Giovanni Dall'Olio [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues git_url: https://git.bioconductor.org/packages/DOSE git_branch: RELEASE_3_22 git_last_commit: 963047a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DOSE_4.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DOSE_4.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DOSE_4.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DOSE_4.4.0.tgz vignettes: vignettes/DOSE/inst/doc/DOSE.html vignetteTitles: DOSE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOSE/inst/doc/DOSE.R importsMe: bioCancer, clusterProfiler, debrowser, enrichplot, enrichViewNet, GDCRNATools, meshes, miRSM, miRspongeR, Moonlight2R, MoonlightR, ReactomePA, RegEnrich, scTensor, signatureSearch, SVMDO, TDbasedUFEadv, vsclust, ExpHunterSuite, GseaVis, immcp suggestsMe: cola, GOSemSim, GRaNIE, rrvgo, scGPS, scGraphVerse, ggpicrust2 dependencyCount: 86 Package: doseR Version: 1.26.0 Depends: R (>= 3.6) Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4, RUnit, SummarizedExperiment, digest, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: GPL Archs: x64 MD5sum: 837be3347caa542f7ae8523294f3a5cc NeedsCompilation: no Title: doseR Description: doseR package is a next generation sequencing package for sex chromosome dosage compensation which can be applied broadly to detect shifts in gene expression among an arbitrary number of pre-defined groups of loci. doseR is a differential gene expression package for count data, that detects directional shifts in expression for multiple, specific subsets of genes, broad utility in systems biology research. doseR has been prepared to manage the nature of the data and the desired set of inferences. doseR uses S4 classes to store count data from sequencing experiment. It contains functions to normalize and filter count data, as well as to plot and calculate statistics of count data. It contains a framework for linear modeling of count data. The package has been tested using real and simulated data. biocViews: Infrastructure, Software, DataRepresentation, Sequencing, GeneExpression, SystemsBiology, DifferentialExpression Author: AJ Vaestermark, JR Walters. Maintainer: ake.vastermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/doseR git_branch: RELEASE_3_22 git_last_commit: 119605b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/doseR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/doseR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/doseR_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/doseR_1.26.0.tgz vignettes: vignettes/doseR/inst/doc/doseR.html vignetteTitles: "doseR" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doseR/inst/doc/doseR.R dependencyCount: 45 Package: DOtools Version: 1.0.0 Depends: R (>= 4.5.0) Imports: Seurat (>= 5.2.0), SeuratObject (>= 5.1.0), ggplot2 (>= 3.5.0), ggpubr (>= 0.6.0), ggtext (>= 0.1.2), ggalluvial (>= 0.12.5), tidyverse (>= 2.0.0), reshape2 (>= 1.4.4), dplyr (>= 1.1.4), tidyr (>= 1.3.1), rstatix (>= 0.7.2), cowplot (>= 1.1.3), reticulate (>= 1.41.0.1), zellkonverter (>= 1.16.0), progress (>= 1.2.3), ggiraphExtra (>= 0.3.0), grid (>= 4.4.3), SCpubr (>= 2.0.2), DropletUtils (>= 1.26.0), scCustomize (>= 3.0.1), openxlsx (>= 4.2.8), tibble (>= 3.2.1), scDblFinder (>= 1.20.0), ggcorrplot (>= 0.1.4.1), DESeq2 (>= 1.48.1), enrichR (>= 3.4), cli (>= 3.6.5), curl(>= 6.3.0), magrittr (>= 2.0.3), Matrix (>= 1.7.3), purrr(>= 1.0.4), rlang(>= 1.1.6), scales (>= 1.4.0), SingleCellExperiment (>= 1.30.1), S4Vectors (>= 0.46.0), basilisk (>= 1.20.0), methods, stats, utils Suggests: SummarizedExperiment, knitr, kableExtra, pkgdown, RefManageR, BiocStyle, roxygen2, httr, magick, rmarkdown, assertthat, plyr, rsvg, scran, scater, igraph, sessioninfo, testthat (>= 3.0.0), mockery License: MIT + file LICENSE MD5sum: 8f44bad45fee93bfe5826f28eb21f2fa NeedsCompilation: no Title: Convenient functions to streamline your single cell data analysis workflow Description: This package provides functions for creating various visualizations, convenient wrappers, and quality-of-life utilities for single cell experiment objects. It offers a streamlined approach to visualize results and integrates different tools for easy use. biocViews: SingleCell, RNASeq, Visualization, Clustering, Annotation, WorkflowStep, QualityControl, GeneExpression Author: Mariano Ruz Jurado [aut, cre] (ORCID: ), David Rodriguez Morales [aut] (ORCID: ), David John [aut] (ORCID: ), DFG SFB 1366, Project B04 [fnd], DFG SFB 1531, Project 456687919 [fnd] Maintainer: Mariano Ruz Jurado URL: https://marianoruzjurado.github.io/DOtools/ VignetteBuilder: knitr BugReports: https://github.com/MarianoRuzJurado/DOtools/issues git_url: https://git.bioconductor.org/packages/DOtools git_branch: RELEASE_3_22 git_last_commit: 95a2404 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DOtools_1.0.0.tar.gz vignettes: vignettes/DOtools/inst/doc/DOtools.html vignetteTitles: Quality control of sc/snRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DOtools/inst/doc/DOtools.R dependencyCount: 329 Package: doubletrouble Version: 1.10.0 Depends: R (>= 4.2.0) Imports: syntenet, GenomicRanges, Biostrings, mclust, MSA2dist (>= 1.1.5), ggplot2, rlang, stats, utils, AnnotationDbi, GenomicFeatures Suggests: txdbmaker, testthat (>= 3.0.0), knitr, feature, patchwork, BiocStyle, rmarkdown, covr, sessioninfo License: GPL-3 MD5sum: c81e742dcc854db6c7e8568990928c40 NeedsCompilation: no Title: Identification and classification of duplicated genes Description: doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a simpler classification scheme, duplicates can also be classified into SD- and SSD-derived (small-scale duplication) gene pairs. Besides classifying gene pairs, users can also classify genes, so that each gene is assigned a unique mode of duplication. Users can also calculate substitution rates per substitution site (i.e., Ka and Ks) from duplicate pairs, find peaks in Ks distributions with Gaussian Mixture Models (GMMs), and classify gene pairs into age groups based on Ks peaks. biocViews: Software, WholeGenome, ComparativeGenomics, FunctionalGenomics, Phylogenetics, Network, Classification Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/doubletrouble VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/doubletrouble git_url: https://git.bioconductor.org/packages/doubletrouble git_branch: RELEASE_3_22 git_last_commit: 555edc5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/doubletrouble_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/doubletrouble_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/doubletrouble_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/doubletrouble_1.10.0.tgz vignettes: vignettes/doubletrouble/inst/doc/doubletrouble_vignette.html vignetteTitles: Identification and classification of duplicated genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doubletrouble/inst/doc/doubletrouble_vignette.R dependencyCount: 138 Package: dreamlet Version: 1.8.0 Depends: R (>= 4.3.0), variancePartition (>= 1.36.1), SingleCellExperiment, ggplot2 Imports: edgeR, SummarizedExperiment, DelayedMatrixStats, sparseMatrixStats, MatrixGenerics, Matrix, methods, purrr, GSEABase, data.table, zenith (>= 1.1.2), mashr (>= 0.2.52), ashr, dplyr, BiocParallel, ggbeeswarm, S4Vectors, IRanges, irlba, limma, metafor, remaCor, broom, tidyr, rlang, BiocGenerics, S4Arrays, SparseArray, DelayedArray, gtools, reshape2, ggrepel, scattermore, Rcpp, lme4 (>= 1.1-33), MASS, Rdpack, utils, stats LinkingTo: Rcpp, beachmat Suggests: BiocStyle, knitr, pander, rmarkdown, muscat, ExperimentHub, RUnit, muscData, scater, scuttle License: Artistic-2.0 MD5sum: 776c8d6f8d7a69b8e433e26ddac637cc NeedsCompilation: yes Title: Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs Description: Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample. biocViews: RNASeq, GeneExpression, DifferentialExpression, BatchEffect, QualityControl, Regression, GeneSetEnrichment, GeneRegulation, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, SingleCell, Preprocessing, Sequencing, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] (ORCID: ) Maintainer: Gabriel Hoffman URL: https://DiseaseNeurogenomics.github.io/dreamlet SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeurogenomics/dreamlet/issues git_url: https://git.bioconductor.org/packages/dreamlet git_branch: RELEASE_3_22 git_last_commit: 1eb467d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dreamlet_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dreamlet_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dreamlet_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dreamlet_1.8.0.tgz vignettes: vignettes/dreamlet/inst/doc/cell_covs.html, vignettes/dreamlet/inst/doc/dreamlet.html, vignettes/dreamlet/inst/doc/errors.html, vignettes/dreamlet/inst/doc/h5ad_on_disk.html, vignettes/dreamlet/inst/doc/mashr.html, vignettes/dreamlet/inst/doc/non_lin_eff.html vignetteTitles: Modeling continuous cell-level covariates, Dreamlet analysis of single cell RNA-seq, Error handling, Loading large-scale H5AD datasets, mashr analysis following dreamlet, Testing non-linear effects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dreamlet/inst/doc/cell_covs.R, vignettes/dreamlet/inst/doc/dreamlet.R, vignettes/dreamlet/inst/doc/errors.R, vignettes/dreamlet/inst/doc/h5ad_on_disk.R, vignettes/dreamlet/inst/doc/non_lin_eff.R suggestsMe: crumblr dependencyCount: 189 Package: DRIMSeq Version: 1.38.0 Depends: R (>= 3.4.0) Imports: utils, stats, MASS, GenomicRanges, IRanges, S4Vectors, BiocGenerics, methods, BiocParallel, limma, edgeR, ggplot2, reshape2 Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid, BiocStyle, knitr, testthat License: GPL (>= 3) MD5sum: 4c9d9a642f09ff6a07b0fb242b57c097 NeedsCompilation: no Title: Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq Description: The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. biocViews: ImmunoOncology, SNP, AlternativeSplicing, DifferentialSplicing, Genetics, RNASeq, Sequencing, WorkflowStep, MultipleComparison, GeneExpression, DifferentialExpression Author: Malgorzata Nowicka [aut, cre] Maintainer: Malgorzata Nowicka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DRIMSeq git_branch: RELEASE_3_22 git_last_commit: 7fdbd09 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DRIMSeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DRIMSeq_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DRIMSeq_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DRIMSeq_1.38.0.tgz vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf vignetteTitles: Differential transcript usage and transcript usage QTL analyses in RNA-seq with the DRIMSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R dependsOnMe: rnaseqDTU importsMe: BANDITS dependencyCount: 52 Package: DriverNet Version: 1.50.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: d7da5285b2e2d8a1c6b97928743be533 NeedsCompilation: no Title: Drivernet: uncovering somatic driver mutations modulating transcriptional networks in cancer Description: DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values. biocViews: Network Author: Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah Maintainer: Jiarui Ding git_url: https://git.bioconductor.org/packages/DriverNet git_branch: RELEASE_3_22 git_last_commit: 45e670a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DriverNet_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DriverNet_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DriverNet_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DriverNet_1.50.0.tgz vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf vignetteTitles: An introduction to DriverNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DriverNet/inst/doc/DriverNet-Overview.R dependencyCount: 1 Package: DropletUtils Version: 1.30.0 Depends: SingleCellExperiment Imports: utils, stats, methods, Matrix, Rcpp, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, BiocParallel, SparseArray (>= 1.5.18), DelayedArray (>= 0.31.9), DelayedMatrixStats, HDF5Array, rhdf5, edgeR, R.utils, dqrng, beachmat, scuttle LinkingTo: Rcpp, beachmat, assorthead, Rhdf5lib, BH, dqrng, scuttle Suggests: testthat, knitr, BiocStyle, rmarkdown, jsonlite, DropletTestFiles License: GPL-3 Archs: x64 MD5sum: d6200555e7a9359ef9a0f60bbcbdd725 NeedsCompilation: yes Title: Utilities for Handling Single-Cell Droplet Data Description: Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix. biocViews: ImmunoOncology, SingleCell, Sequencing, RNASeq, GeneExpression, Transcriptomics, DataImport, Coverage Author: Aaron Lun [aut], Jonathan Griffiths [ctb, cre], Davis McCarthy [ctb], Dongze He [ctb], Rob Patro [ctb] Maintainer: Jonathan Griffiths SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DropletUtils git_branch: RELEASE_3_22 git_last_commit: 6ef5eaa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DropletUtils_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DropletUtils_1.29.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DropletUtils_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DropletUtils_1.30.0.tgz vignettes: vignettes/DropletUtils/inst/doc/DropletUtils.html vignetteTitles: Utilities for handling droplet-based single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DropletUtils/inst/doc/DropletUtils.R dependsOnMe: OSCA.advanced, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: DOtools, scCB2, scPipe, singleCellTK, SpaceTrooper, Spaniel, SpatialExperimentIO, SpatialFeatureExperiment, stPipe, visiumStitched, OSTA suggestsMe: alabaster.spatial, demuxmix, GEOquery, mumosa, Nebulosa, OSTA.data, SingleCellAlleleExperiment, SpatialExperiment, SPOTlight, SVP, tidySpatialExperiment, DropletTestFiles, MerfishData, muscData, spatialLIBD, scCustomize, SoupX dependencyCount: 56 Package: drugTargetInteractions Version: 1.18.0 Depends: methods, R (>= 4.1) Imports: utils, RSQLite, UniProt.ws, biomaRt,ensembldb, BiocFileCache,dplyr,rappdirs, AnnotationFilter, S4Vectors Suggests: RUnit, BiocStyle, knitr, rmarkdown, ggplot2, reshape2, DT, EnsDb.Hsapiens.v86 License: Artistic-2.0 Archs: x64 MD5sum: bb09a904ea50936e88a6c84a9b337f6d NeedsCompilation: no Title: Drug-Target Interactions Description: Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, Proteomics, Metabolomics Author: Thomas Girke [cre, aut] Maintainer: Thomas Girke URL: https://github.com/girke-lab/drugTargetInteractions VignetteBuilder: knitr BugReports: https://github.com/girke-lab/drugTargetInteractions git_url: https://git.bioconductor.org/packages/drugTargetInteractions git_branch: RELEASE_3_22 git_last_commit: 06ba39c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/drugTargetInteractions_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/drugTargetInteractions_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/drugTargetInteractions_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/drugTargetInteractions_1.18.0.tgz vignettes: vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.html vignetteTitles: Drug-Target Interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.R dependencyCount: 106 Package: DrugVsDisease Version: 2.52.0 Depends: R (>= 2.10), affy, limma, biomaRt, ArrayExpress, GEOquery, DrugVsDiseasedata, cMap2data, qvalue Imports: annotate, hgu133a.db, hgu133a2.db, hgu133plus2.db, RUnit, BiocGenerics, xtable License: GPL-3 MD5sum: 7b5f851f18ab0d0c91a9b29f9e32025d NeedsCompilation: no Title: Comparison of disease and drug profiles using Gene set Enrichment Analysis Description: This package generates ranked lists of differential gene expression for either disease or drug profiles. Input data can be downloaded from Array Express or GEO, or from local CEL files. Ranked lists of differential expression and associated p-values are calculated using Limma. Enrichment scores (Subramanian et al. PNAS 2005) are calculated to a reference set of default drug or disease profiles, or a set of custom data supplied by the user. Network visualisation of significant scores are output in Cytoscape format. biocViews: Microarray, GeneExpression, Clustering Author: C. Pacini Maintainer: j. Saez-Rodriguez git_url: https://git.bioconductor.org/packages/DrugVsDisease git_branch: RELEASE_3_22 git_last_commit: 661b563 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DrugVsDisease_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DrugVsDisease_2.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DrugVsDisease_2.52.0.tgz vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf vignetteTitles: DrugVsDisease hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.R dependencyCount: 125 Package: DspikeIn Version: 0.99.29 Depends: R (>= 4.1.0) Imports: ape, Biostrings, data.table, DECIPHER, DESeq2, dplyr, edgeR, flextable, ggalluvial, ggnewscale, ggplot2, ggpubr, ggraph, ggrepel, ggridges, ggtree, ggtreeExtra, graphics, grDevices, igraph, limma, matrixStats, methods, microbiome, officer, grid, reshape2, patchwork, phangorn, phyloseq, randomForest, RColorBrewer, rlang, S4Vectors, scales, stats, tibble, tidyr, SummarizedExperiment, TreeSummarizedExperiment, utils, msa, xml2, ggstar Suggests: Biobase, mia, BiocGenerics, magrittr, BiocManager, cluster, devtools, DT, e1071, foreach, ggtext, intergraph, knitr, optparse, plyr, preprocessCore, qpdf, remotes, rmarkdown, ShortRead, testthat (>= 3.0.0), vegan, viridis License: MIT + file LICENSE MD5sum: ac74ea728f6b511edf25b93af1d68da0 NeedsCompilation: no Title: Estimating Absolute Abundance from Microbial Spike-in Controls Description: Provides a reproducible and modular workflow for absolute microbial quantification using spike-in controls. Supports both single spike-in taxa and synthetic microbial communities with user-defined spike-in volumes and genome copy numbers. Compatible with 'phyloseq' and 'TreeSummarizedExperiment' (TSE) data structures. The package implements methods for spike-in validation, preprocessing, scaling factor estimation, absolute abundance conversion, bias correction, and normalization. Facilitates downstream statistical analyses with 'DESeq2', 'edgeR', and other Bioconductor-compatible methods. Visualization tools are provided via 'ggplot2', 'ggtree', and related packages. Includes detailed vignettes, case studies, and function-level documentation to guide users through experimental design, quantification, and interpretation. biocViews: Microbiome, Preprocessing, QualityControl, DifferentialExpression, Normalization, Sequencing, Visualization, Phylogenetics, ExperimentalDesign, DataImport, Software Author: Mitra Ghotbi [aut, cre] (ORCID: ), Marjan Ghotbi [ctb] (ORCID: ) Maintainer: Mitra Ghotbi URL: https://github.com/mghotbi/DspikeIn VignetteBuilder: knitr BugReports: https://github.com/mghotbi/DspikeIn/issues git_url: https://git.bioconductor.org/packages/DspikeIn git_branch: devel git_last_commit: 27573e4 git_last_commit_date: 2025-10-22 Date/Publication: 2025-10-23 source.ver: src/contrib/DspikeIn_0.99.29.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DspikeIn_0.99.29.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DspikeIn_0.99.29.tgz vignettes: vignettes/DspikeIn/inst/doc/DspikeIn-with-TSE.html vignetteTitles: DspikeIn with TSE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DspikeIn/inst/doc/DspikeIn-with-TSE.R dependencyCount: 203 Package: DSS Version: 2.58.0 Depends: R (>= 3.5.0), methods, Biobase, BiocParallel, bsseq, parallel Imports: utils, graphics, stats, splines Suggests: BiocStyle, knitr, rmarkdown, edgeR License: GPL Archs: x64 MD5sum: 526ba3c1bc0decd34928d0c5d4037a5e NeedsCompilation: yes Title: Dispersion shrinkage for sequencing data Description: DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions. biocViews: Sequencing, RNASeq, DNAMethylation,GeneExpression, DifferentialExpression,DifferentialMethylation Author: Hao Wu, Hao Feng Maintainer: Hao Wu , Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DSS git_branch: RELEASE_3_22 git_last_commit: 11b2949 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DSS_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DSS_2.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DSS_2.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DSS_2.58.0.tgz vignettes: vignettes/DSS/inst/doc/DSS.html vignetteTitles: The DSS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DSS/inst/doc/DSS.R dependsOnMe: DeMixT importsMe: borealis, kissDE, metaseqR2, methylSig suggestsMe: biscuiteer, methrix, NanoMethViz dependencyCount: 88 Package: dStruct Version: 1.16.0 Depends: R (>= 4.1) Imports: zoo, ggplot2, purrr, reshape2, parallel, IRanges, S4Vectors, rlang, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, tidyverse, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: 484bdb7c3bc99bdf6d6ed3d7191eed0e NeedsCompilation: no Title: Identifying differentially reactive regions from RNA structurome profiling data Description: dStruct identifies differentially reactive regions from RNA structurome profiling data. dStruct is compatible with a broad range of structurome profiling technologies, e.g., SHAPE-MaP, DMS-MaPseq, Structure-Seq, SHAPE-Seq, etc. See Choudhary et al., Genome Biology, 2019 for the underlying method. biocViews: StatisticalMethod, StructuralPrediction, Sequencing, Software Author: Krishna Choudhary [aut, cre] (ORCID: ), Sharon Aviran [aut] (ORCID: ) Maintainer: Krishna Choudhary URL: https://github.com/dataMaster-Kris/dStruct VignetteBuilder: knitr BugReports: https://github.com/dataMaster-Kris/dStruct/issues git_url: https://git.bioconductor.org/packages/dStruct git_branch: RELEASE_3_22 git_last_commit: 059be9a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dStruct_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dStruct_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dStruct_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dStruct_1.16.0.tgz vignettes: vignettes/dStruct/inst/doc/dStruct.html vignetteTitles: Differential RNA structurome analysis using `dStruct` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dStruct/inst/doc/dStruct.R dependencyCount: 38 Package: DTA Version: 2.56.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: cc6d4e1176b55830b0ae352349c7e98c NeedsCompilation: no Title: Dynamic Transcriptome Analysis Description: Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements. biocViews: Microarray, DifferentialExpression, GeneExpression, Transcription Author: Bjoern Schwalb, Benedikt Zacher, Sebastian Duemcke, Achim Tresch Maintainer: Bjoern Schwalb git_url: https://git.bioconductor.org/packages/DTA git_branch: RELEASE_3_22 git_last_commit: ac8f312 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DTA_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DTA_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DTA_2.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DTA_2.56.0.tgz vignettes: vignettes/DTA/inst/doc/DTA.pdf vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DTA/inst/doc/DTA.R importsMe: rifiComparative dependencyCount: 5 Package: Dune Version: 1.22.0 Depends: R (>= 3.6) Imports: BiocParallel, SummarizedExperiment, utils, ggplot2, dplyr, tidyr, RColorBrewer, magrittr, gganimate, purrr, aricode Suggests: knitr, rmarkdown, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: aab6e7b6df6db3cb2a8496c469262cdb NeedsCompilation: no Title: Improving replicability in single-cell RNA-Seq cell type discovery Description: Given a set of clustering labels, Dune merges pairs of clusters to increase mean ARI between labels, improving replicability. biocViews: Clustering, GeneExpression, RNASeq, Software, SingleCell, Transcriptomics, Visualization Author: Hector Roux de Bezieux [aut, cre] (ORCID: ), Kelly Street [aut] Maintainer: Hector Roux de Bezieux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Dune git_branch: RELEASE_3_22 git_last_commit: a0ee144 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Dune_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Dune_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Dune_1.22.0.tgz vignettes: vignettes/Dune/inst/doc/Dune.html vignetteTitles: Dune Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Dune/inst/doc/Dune.R dependencyCount: 83 Package: DuplexDiscovereR Version: 1.4.0 Depends: R (>= 4.5), InteractionSet Imports: Gviz, Biostrings, rtracklayer, GenomicAlignments, GenomicRanges, ggsci, igraph, rlang, scales, stringr, dplyr, tibble, tidyr, purrr, methods, grDevices, stats, utils, vctrs Suggests: knitr, UpSetR, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: c302581e7491e0db558bcd7adb5e0de5 NeedsCompilation: no Title: Analysis of the data from RNA duplex probing experiments Description: DuplexDiscovereR is a package designed for analyzing data from RNA cross-linking and proximity ligation protocols such as SPLASH, PARIS, LIGR-seq, and others. DuplexDiscovereR accepts input in the form of chimerically or split-aligned reads. It includes procedures for alignment classification, filtering, and efficient clustering of individual chimeric reads into duplex groups (DGs). Once DGs are identified, the package predicts RNA duplex formation and their hybridization energies. Additional metrics, such as p-values for random ligation hypothesis or mean DG alignment scores, can be calculated to rank final set of RNA duplexes. Data from multiple experiments or replicates can be processed separately and further compared to check the reproducibility of the experimental method. biocViews: Sequencing, Transcriptomics, StructuralPrediction, Clustering, SplicedAlignment Author: Egor Semenchenko [aut, cre, cph] (ORCID: ), Volodymyr Tsybulskyi [ctb] (ORCID: ), Irmtraud M. Meyer [aut, cph] (ORCID: ) Maintainer: Egor Semenchenko URL: https://github.com/Egors01/DuplexDiscovereR/ VignetteBuilder: knitr BugReports: https://github.com/Egors01/DuplexDiscovereR/issues/ git_url: https://git.bioconductor.org/packages/DuplexDiscovereR git_branch: RELEASE_3_22 git_last_commit: 38c4e52 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DuplexDiscovereR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DuplexDiscovereR_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DuplexDiscovereR_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DuplexDiscovereR_1.4.0.tgz vignettes: vignettes/DuplexDiscovereR/inst/doc/DuplexDiscovereR.html vignetteTitles: DuplexDiscovereR tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DuplexDiscovereR/inst/doc/DuplexDiscovereR.R dependencyCount: 155 Package: dupRadar Version: 1.40.0 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1), KernSmooth Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: ed5e7db3fbf619526709986fae3fd251 NeedsCompilation: no Title: Assessment of duplication rates in RNA-Seq datasets Description: Duplication rate quality control for RNA-Seq datasets. biocViews: Technology, Sequencing, RNASeq, QualityControl, ImmunoOncology Author: Sergi Sayols , Holger Klein Maintainer: Sergi Sayols , Holger Klein URL: https://www.bioconductor.org/packages/dupRadar, https://ssayols.github.io/dupRadar/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dupRadar git_branch: RELEASE_3_22 git_last_commit: 25b25ba git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dupRadar_1.40.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dupRadar_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dupRadar_1.40.0.tgz vignettes: vignettes/dupRadar/inst/doc/dupRadar.html vignetteTitles: Using dupRadar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R dependencyCount: 10 Package: dyebias Version: 1.70.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: 1ade344295ee75bb372a76a21dc3955d NeedsCompilation: no Title: The GASSCO method for correcting for slide-dependent gene-specific dye bias Description: Many two-colour hybridizations suffer from a dye bias that is both gene-specific and slide-specific. The former depends on the content of the nucleotide used for labeling; the latter depends on the labeling percentage. The slide-dependency was hitherto not recognized, and made addressing the artefact impossible. Given a reasonable number of dye-swapped pairs of hybridizations, or of same vs. same hybridizations, both the gene- and slide-biases can be estimated and corrected using the GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009), doi:10.1038/msb.2009.21) biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Philip Lijnzaad and Thanasis Margaritis Maintainer: Philip Lijnzaad URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad git_url: https://git.bioconductor.org/packages/dyebias git_branch: RELEASE_3_22 git_last_commit: 7a5438a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/dyebias_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dyebias_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dyebias_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dyebias_1.70.0.tgz vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf vignetteTitles: dye bias correction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dyebias/inst/doc/dyebias-vignette.R suggestsMe: dyebiasexamples dependencyCount: 11 Package: DynDoc Version: 1.88.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: 9261437d03e5f20eb32119508ad4a8c4 NeedsCompilation: no Title: Dynamic document tools Description: A set of functions to create and interact with dynamic documents and vignettes. biocViews: ReportWriting, Infrastructure Author: R. Gentleman, Jeff Gentry Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/DynDoc git_branch: RELEASE_3_22 git_last_commit: 6e6791b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/DynDoc_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DynDoc_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DynDoc_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DynDoc_1.88.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets dependencyCount: 2 Package: easier Version: 1.16.0 Depends: R (>= 4.2.0) Imports: progeny, easierData, dorothea (>= 1.6.0), decoupleR, quantiseqr, ROCR, grDevices, stats, graphics, ggplot2, ggpubr, DESeq2, utils, dplyr, tidyr, tibble, matrixStats, rlang, BiocParallel, reshape2, rstatix, ggrepel, magrittr, coin Suggests: knitr, rmarkdown, BiocStyle, testthat, SummarizedExperiment, viper License: MIT + file LICENSE MD5sum: 2c900adb000d971fd1b3115bd761af0c NeedsCompilation: no Title: Estimate Systems Immune Response from RNA-seq data Description: This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in TCGA data to identify how these descriptors can simultaneously predict several state-of-the-art hallmarks of anti-cancer immune response. In this way we derive cancer-specific models and identify cancer-specific systems biomarkers of immune response. These biomarkers have been experimentally validated in the literature and the performance of EaSIeR predictions has been validated using independent datasets form four different cancer types with patients treated with anti-PD1 or anti-PDL1 therapy. biocViews: GeneExpression, Software, Transcription, SystemsBiology, Pathways, GeneSetEnrichment, ImmunoOncology, Epigenetics, Classification, BiomedicalInformatics, Regression, ExperimentHubSoftware Author: Oscar Lapuente-Santana [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ), Arsenij Ustjanzew [aut] (ORCID: ), Francesca Finotello [aut] (ORCID: ), Federica Eduati [aut] (ORCID: ) Maintainer: Oscar Lapuente-Santana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easier git_branch: RELEASE_3_22 git_last_commit: 7e059da git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/easier_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/easier_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/easier_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/easier_1.16.0.tgz vignettes: vignettes/easier/inst/doc/easier_user_manual.html vignetteTitles: easier User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/easier/inst/doc/easier_user_manual.R dependencyCount: 162 Package: EasyCellType Version: 1.12.0 Depends: R (>= 4.2.0) Imports: clusterProfiler, dplyr, forcats, ggplot2, magrittr, rlang, stats, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, vctrs (>= 0.6.4), BiocStyle Suggests: knitr, rmarkdown, testthat (>= 3.0.0), Seurat, BiocManager, devtools, BiocStyle License: Artistic-2.0 MD5sum: 331ce3bb2fe7ca2bfdb66ac1835f981c NeedsCompilation: no Title: Annotate cell types for scRNA-seq data Description: We developed EasyCellType which can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA) and a modified versio of Fisher's exact test. The function presents annotation recommendations in graphical outcomes: bar plots for each cluster showing candidate cell types, as well as a dot plot summarizing the top 5 significant annotations for each cluster. biocViews: SingleCell, Software, GeneExpression, GeneSetEnrichment Author: Ruoxing Li [aut, cre, ctb], Ziyi Li [ctb] Maintainer: Ruoxing Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EasyCellType git_branch: RELEASE_3_22 git_last_commit: 27aa7b2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EasyCellType_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EasyCellType_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EasyCellType_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EasyCellType_1.12.0.tgz vignettes: vignettes/EasyCellType/inst/doc/my-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EasyCellType/inst/doc/my-vignette.R dependencyCount: 143 Package: easylift Version: 1.8.0 Depends: R (>= 4.1.0), GenomicRanges, BiocFileCache Imports: rtracklayer, GenomeInfoDb, R.utils, tools, methods Suggests: testthat (>= 3.0.0), IRanges, knitr, BiocStyle, rmarkdown License: MIT + file LICENSE MD5sum: 63d38ecee9ffd90963b25cdedf1a53df NeedsCompilation: no Title: An R package to perform genomic liftover Description: The easylift package provides a convenient tool for genomic liftover operations between different genome assemblies. It seamlessly works with Bioconductor's GRanges objects and chain files from the UCSC Genome Browser, allowing for straightforward handling of genomic ranges across various genome versions. One noteworthy feature of easylift is its integration with the BiocFileCache package. This integration automates the management and caching of chain files necessary for liftover operations. Users no longer need to manually specify chain file paths in their function calls, reducing the complexity of the liftover process. biocViews: Software, WorkflowStep, Sequencing, Coverage, GenomeAssembly, DataImport Author: Abdullah Al Nahid [aut, cre] (ORCID: ), Hervé Pagès [aut, rev], Michael Love [aut, rev] (ORCID: ) Maintainer: Abdullah Al Nahid URL: https://github.com/nahid18/easylift, https://nahid18.github.io/easylift VignetteBuilder: knitr BugReports: https://github.com/nahid18/easylift/issues git_url: https://git.bioconductor.org/packages/easylift git_branch: RELEASE_3_22 git_last_commit: 9d97c21 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/easylift_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/easylift_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/easylift_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/easylift_1.8.0.tgz vignettes: vignettes/easylift/inst/doc/easylift.html vignetteTitles: Perform Genomic Liftover hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/easylift/inst/doc/easylift.R dependencyCount: 93 Package: easyreporting Version: 1.22.0 Depends: R (>= 3.5.0) Imports: rmarkdown, methods, tools, shiny, rlang Suggests: distill, BiocStyle, knitr, readxl, edgeR, limma, EDASeq, statmod License: Artistic-2.0 MD5sum: 00b9a66aaa68e5aaab32fcc1c0c4593b NeedsCompilation: no Title: Helps creating report for improving Reproducible Computational Research Description: An S4 class for facilitating the automated creation of rmarkdown files inside other packages/software even without knowing rmarkdown language. Best if implemented in functions as "recursive" style programming. biocViews: ReportWriting Author: Dario Righelli [cre, aut] Maintainer: Dario Righelli VignetteBuilder: knitr BugReports: https://github.com/drighelli/easyreporting/issues git_url: https://git.bioconductor.org/packages/easyreporting git_branch: RELEASE_3_22 git_last_commit: b445d4e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/easyreporting_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/easyreporting_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/easyreporting_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/easyreporting_1.22.0.tgz vignettes: vignettes/easyreporting/inst/doc/bio_usage.html, vignettes/easyreporting/inst/doc/standard_usage.html vignetteTitles: bio_usage.html, standard_usage.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyreporting/inst/doc/bio_usage.R, vignettes/easyreporting/inst/doc/standard_usage.R dependencyCount: 43 Package: easyRNASeq Version: 2.46.0 Imports: Biobase (>= 2.64.0), BiocFileCache (>= 2.12.0), BiocGenerics (>= 0.50.0), BiocParallel (>= 1.38.0), biomaRt (>= 2.60.1), Biostrings (>= 2.77.2), edgeR (>= 4.2.1), Seqinfo, genomeIntervals (>= 1.60.0), GenomicAlignments (>= 1.45.1), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), graphics, IRanges (>= 2.38.1), LSD (>= 4.1-0), methods, parallel, rappdirs (>= 0.3.3), Rsamtools (>= 2.25.1), S4Vectors (>= 0.42.1), ShortRead (>= 1.62.0), utils Suggests: BiocStyle (>= 2.32.1), BSgenome (>= 1.72.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, knitr, rmarkdown, RUnit (>= 0.4.33) License: Artistic-2.0 Archs: x64 MD5sum: 8b4d679513ff0bb958f07e64f6c3c022 NeedsCompilation: no Title: Count summarization and normalization for RNA-Seq data Description: Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package. biocViews: GeneExpression, RNASeq, Genetics, Preprocessing, ImmunoOncology Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler, Niklas Maehler Maintainer: Nicolas Delhomme VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easyRNASeq git_branch: RELEASE_3_22 git_last_commit: e268573 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/easyRNASeq_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/easyRNASeq_2.45.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/easyRNASeq_2.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/easyRNASeq_2.46.0.tgz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf, vignettes/easyRNASeq/inst/doc/simpleRNASeq.html vignetteTitles: R / Bioconductor for High Throughput Sequence Analysis, geneNetworkR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R, vignettes/easyRNASeq/inst/doc/simpleRNASeq.R importsMe: msgbsR dependencyCount: 107 Package: EBarrays Version: 2.74.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) Archs: x64 MD5sum: f372dcca9e7b41430805b44320d98ae9 NeedsCompilation: yes Title: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification Description: EBarrays provides tools for the analysis of replicated/unreplicated microarray data. biocViews: Clustering, DifferentialExpression Author: Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski Maintainer: Ming Yuan git_url: https://git.bioconductor.org/packages/EBarrays git_branch: RELEASE_3_22 git_last_commit: 6b0ecd3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EBarrays_2.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EBarrays_2.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EBarrays_2.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EBarrays_2.74.0.tgz vignettes: vignettes/EBarrays/inst/doc/vignette.pdf vignetteTitles: Introduction to EBarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBarrays/inst/doc/vignette.R dependsOnMe: EBcoexpress, gaga, geNetClassifier importsMe: casper suggestsMe: Category, dcanr dependencyCount: 11 Package: EBcoexpress Version: 1.54.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: x64 MD5sum: 9858f826ab3b31f1e7d78d9019cfab3a NeedsCompilation: yes Title: EBcoexpress for Differential Co-Expression Analysis Description: An Empirical Bayesian Approach to Differential Co-Expression Analysis at the Gene-Pair Level biocViews: Bayesian Author: John A. Dawson Maintainer: John A. Dawson git_url: https://git.bioconductor.org/packages/EBcoexpress git_branch: RELEASE_3_22 git_last_commit: 14b096c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EBcoexpress_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EBcoexpress_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EBcoexpress_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EBcoexpress_1.54.0.tgz vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf vignetteTitles: EBcoexpress Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R suggestsMe: dcanr dependencyCount: 15 Package: EBImage Version: 4.52.0 Depends: methods Imports: BiocGenerics (>= 0.7.1), graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils, htmltools, htmlwidgets, RCurl Suggests: BiocStyle, digest, knitr, rmarkdown, shiny License: LGPL MD5sum: 368566b0a254d05cb5c0f4920301357a NeedsCompilation: yes Title: Image processing and analysis toolbox for R Description: EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data. biocViews: Visualization Author: Andrzej Oleś, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang Huber, with contributions from Joseph Barry and Philip A. Marais Maintainer: Andrzej Oleś URL: https://github.com/aoles/EBImage VignetteBuilder: knitr BugReports: https://github.com/aoles/EBImage/issues git_url: https://git.bioconductor.org/packages/EBImage git_branch: RELEASE_3_22 git_last_commit: 6f9e0ab git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EBImage_4.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EBImage_4.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EBImage_4.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EBImage_4.52.0.tgz vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.html vignetteTitles: Introduction to EBImage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R dependsOnMe: CRImage, cytomapper, flowcatchR, DonaPLLP2013, furrowSeg, MerfishData, nucim importsMe: alabaster.sfe, bnbc, Cardinal, CatsCradle, cytoviewer, flowCHIC, heatmaps, imcRtools, MoleculeExperiment, RBioFormats, simpleSeg, sosta, SpatialFeatureExperiment, SpatialOmicsOverlay, synapsis, xenLite, yamss, BioImageDbs, OSTA, AiES, bioimagetools, GoogleImage2Array, LFApp, LOMAR, ProxReg, RockFab, SAFARI, spatialGE suggestsMe: HilbertVis, Voyager, spicyWorkflow, aroma.core, cooltools, glow, ijtiff, juicr, lidR, metagear, pliman, rcaiman dependencyCount: 45 Package: EBSEA Version: 1.38.0 Depends: R (>= 4.0.0) Imports: DESeq2, graphics, stats, EmpiricalBrownsMethod Suggests: knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: cb1471524e2827ad3408488c736642e0 NeedsCompilation: no Title: Exon Based Strategy for Expression Analysis of genes Description: Calculates differential expression of genes based on exon counts of genes obtained from RNA-seq sequencing data. biocViews: Software, DifferentialExpression, GeneExpression, Sequencing Author: Arfa Mehmood, Asta Laiho, Laura L. Elo Maintainer: Arfa Mehmood VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EBSEA git_branch: RELEASE_3_22 git_last_commit: f76c741 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EBSEA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EBSEA_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EBSEA_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EBSEA_1.38.0.tgz vignettes: vignettes/EBSEA/inst/doc/EBSEA.html vignetteTitles: EBSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSEA/inst/doc/EBSEA.R dependencyCount: 56 Package: EBSeq Version: 2.8.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) Imports: Rcpp (>= 0.12.11), RcppEigen (>= 0.3.2.9.0), BH (<= 1.87.0-1) LinkingTo: Rcpp,RcppEigen,BH License: Artistic-2.0 MD5sum: bd6ab24167f208a3add4691a34c01cb6 NeedsCompilation: yes Title: An R package for gene and isoform differential expression analysis of RNA-seq data Description: Differential Expression analysis at both gene and isoform level using RNA-seq data biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing Author: Xiuyu Ma [cre, aut], Ning Leng [aut], Christina Kendziorski [ctb], Michael A. Newton [ctb] Maintainer: Xiuyu Ma SystemRequirements: c++14 git_url: https://git.bioconductor.org/packages/EBSeq git_branch: RELEASE_3_22 git_last_commit: f20617a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EBSeq_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EBSeq_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EBSeq_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EBSeq_2.8.0.tgz vignettes: vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf vignetteTitles: EBSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeq/inst/doc/EBSeq_Vignette.R dependsOnMe: Oscope importsMe: BatchQC, DEsubs, scDD suggestsMe: compcodeR dependencyCount: 43 Package: ecolitk Version: 1.82.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: c0d88ac32e30a95e7cb9595fd1481c58 NeedsCompilation: no Title: Meta-data and tools for E. coli Description: Meta-data and tools to work with E. coli. The tools are mostly plotting functions to work with circular genomes. They can used with other genomes/plasmids. biocViews: Annotation, Visualization Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/ecolitk git_branch: RELEASE_3_22 git_last_commit: 9d1c40f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ecolitk_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ecolitk_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ecolitk_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ecolitk_1.82.0.tgz vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf vignetteTitles: ecolitk hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ecolitk/inst/doc/ecolitk.R dependencyCount: 7 Package: EDASeq Version: 2.44.0 Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42) Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9), aroma.light, Rsamtools (>= 1.5.75), biomaRt, Biostrings, AnnotationDbi, GenomicFeatures, GenomicRanges, BiocManager Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR, KernSmooth, testthat, DESeq2, rmarkdown License: Artistic-2.0 MD5sum: f8b5b251f689aeea553a5e1728a7b9b3 NeedsCompilation: no Title: Exploratory Data Analysis and Normalization for RNA-Seq Description: Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). biocViews: ImmunoOncology, Sequencing, RNASeq, Preprocessing, QualityControl, DifferentialExpression Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Ludwig Geistlinger [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/EDASeq VignetteBuilder: knitr BugReports: https://github.com/drisso/EDASeq/issues git_url: https://git.bioconductor.org/packages/EDASeq git_branch: RELEASE_3_22 git_last_commit: e7a4a13 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EDASeq_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EDASeq_2.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EDASeq_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EDASeq_2.44.0.tgz vignettes: vignettes/EDASeq/inst/doc/EDASeq.html vignetteTitles: EDASeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R dependsOnMe: RUVSeq importsMe: consensusDE, DaMiRseq, metaseqR2, octad, ribosomeProfilingQC suggestsMe: awst, DEScan2, easyreporting, GRaNIE, HTSFilter, MOSClip, TCGAbiolinks dependencyCount: 113 Package: edge Version: 2.42.0 Depends: R(>= 3.1.0), Biobase Imports: methods, splines, sva, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE MD5sum: a286f29196575af15fa2b5fa8953c6f9 NeedsCompilation: yes Title: Extraction of Differential Gene Expression Description: The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as sva and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis. biocViews: MultipleComparison, DifferentialExpression, TimeCourse, Regression, GeneExpression, DataImport Author: John D. Storey, Jeffrey T. Leek and Andrew J. Bass Maintainer: John D. Storey , Andrew J. Bass URL: https://github.com/jdstorey/edge VignetteBuilder: knitr BugReports: https://github.com/jdstorey/edge/issues git_url: https://git.bioconductor.org/packages/edge git_branch: RELEASE_3_22 git_last_commit: fe1a757 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/edge_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/edge_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/edge_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/edge_2.42.0.tgz vignettes: vignettes/edge/inst/doc/edge.pdf vignetteTitles: edge Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/edge/inst/doc/edge.R dependencyCount: 88 Package: edgeR Version: 4.8.0 Depends: R (>= 3.6.0), limma (>= 3.63.6) Imports: methods, graphics, stats, utils, locfit Suggests: arrow, jsonlite, knitr, Matrix, readr, rhdf5, SeuratObject, splines, AnnotationDbi, Biobase, BiocStyle, org.Hs.eg.db, SummarizedExperiment License: GPL (>=2) MD5sum: 44c27cacf9b9ad91d2805ec2703bb98e NeedsCompilation: yes Title: Empirical Analysis of Digital Gene Expression Data in R Description: Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage. biocViews: AlternativeSplicing, BatchEffect, Bayesian, BiomedicalInformatics, CellBiology, ChIPSeq, Clustering, Coverage, DifferentialExpression, DifferentialMethylation, DifferentialSplicing, DNAMethylation, Epigenetics, FunctionalGenomics, GeneExpression, GeneSetEnrichment, Genetics, Genetics, ImmunoOncology, MultipleComparison, Normalization, Pathways, Proteomics, QualityControl, Regression, RNASeq, SAGE, Sequencing, SingleCell, SystemsBiology, TimeCourse, Transcription, Transcriptomics Author: Yunshun Chen, Lizhong Chen, Aaron TL Lun, Davis J McCarthy, Pedro Baldoni, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth Maintainer: Yunshun Chen , Gordon Smyth , Aaron Lun , Mark Robinson URL: https://bioinf.wehi.edu.au/edgeR/, https://bioconductor.org/packages/edgeR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/edgeR git_branch: RELEASE_3_22 git_last_commit: d547d7e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/edgeR_4.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/edgeR_4.7.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/edgeR_4.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/edgeR_4.8.0.tgz vignettes: vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf, vignettes/edgeR/inst/doc/intro.html vignetteTitles: edgeR User's Guide, A brief introduction to edgeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/edgeR/inst/doc/intro.R dependsOnMe: ASpli, IntEREst, methylMnM, miloR, octad, RUVSeq, TCC, tRanslatome, ReactomeGSA.data, EGSEA123, RNAseq123, rnaseqDTU, RnaSeqGeneEdgeRQL, csawBook, OSCA.advanced, OSCA.multisample, OSCA.workflows, babel, BALLI, BioInsight, SCdeconR importsMe: affycoretools, ATACseqQC, autonomics, AWFisher, BatchQC, baySeq, beer, BioQC, BreastSubtypeR, censcyt, ChromSCape, circRNAprofiler, CleanUpRNAseq, clusterExperiment, CNVRanger, compcodeR, consensusDE, coseq, countsimQC, csaw, cypress, DaMiRseq, Damsel, debrowser, DeeDeeExperiment, DEFormats, DEGreport, DESpace, DEsubs, diffcyt, diffHic, diffUTR, dinoR, DMRcate, doseR, dreamlet, DRIMSeq, DropletUtils, DspikeIn, easyRNASeq, EGSEA, eisaR, EnrichmentBrowser, erccdashboard, ERSSA, extraChIPs, GDCRNATools, GenomicPlot, gg4way, Glimma, GSEABenchmarkeR, hermes, HTSFilter, icetea, infercnv, iSEEde, IsoformSwitchAnalyzeR, KnowSeq, markeR, mastR, MEB, MEDIPS, MetaDICT, metaseqR2, MIRit, MLSeq, moanin, mobileRNA, MOSim, Motif2Site, msgbsR, msmsTests, multiHiCcompare, muscat, mutscan, PathoStat, phantasus, PhIPData, ppcseq, PRONE, PROPER, psichomics, RCM, regsplice, RFLOMICS, RNAseqCovarImpute, ROSeq, Rvisdiff, saseR, scCB2, scde, scone, scran, SEtools, shinyDSP, SIMD, simPIC, singscore, SpaNorm, sparrow, speckle, splatter, SPsimSeq, srnadiff, sSNAPPY, standR, STATegRa, Statial, SurfR, sva, TBSignatureProfiler, TCseq, tradeSeq, treeclimbR, treekoR, tweeDEseq, vidger, xcore, yarn, zinbwave, emtdata, spatialLIBD, ExpHunterSuite, recountWorkflow, aIc, bulkAnalyseR, CAMML, cinaR, CoreMicrobiomeR, cpam, hicream, HTSCluster, idiffomix, microbial, RCPA, RVA, scITD, scRNAtools, ssizeRNA, TransProR, TSGS suggestsMe: ABSSeq, biobroom, ClassifyR, cqn, cydar, dcanr, dearseq, DEScan2, DiffBind, dittoSeq, DSS, easyreporting, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, GeoTcgaData, glmGamPoi, goseq, groHMM, GSAR, GSVA, ideal, iSEEpathways, iSEEu, lemur, missMethyl, MoonlightR, multiMiR, raer, recount, regionReport, ribosomeProfilingQC, satuRn, scider, SeqGate, signifinder, SpliceWiz, stageR, subSeq, systemPipeR, TCGAbiolinks, TFEA.ChIP, tidybulk, topconfects, transmogR, tximeta, tximport, variancePartition, weitrix, Wrench, zenith, zFPKM, leeBamViews, CAGEWorkflow, chipseqDB, DGEobj, DGEobj.utils, DiPALM, easybio, ggpicrust2, glmmSeq, inDAGO, MiscMetabar, palasso, pctax, pmartR, seqgendiff, SIBERG, volcano3D dependencyCount: 10 Package: EDIRquery Version: 1.10.0 Depends: R (>= 4.2.0) Imports: tibble (>= 3.1.6), tictoc (>= 1.0.1), utils (>= 4.1.3), stats (>= 4.1.3), readr (>= 2.1.2), InteractionSet (>= 1.22.0), GenomicRanges (>= 1.46.1) Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 32913b51e9c90a050a60ead40f1e30c4 NeedsCompilation: no Title: Query the EDIR Database For Specific Gene Description: EDIRquery provides a tool to search for genes of interest within the Exome Database of Interspersed Repeats (EDIR). A gene name is a required input, and users can additionally specify repeat sequence lengths, minimum and maximum distance between sequences, and whether to allow a 1-bp mismatch. Outputs include a summary of results by repeat length, as well as a dataframe of query results. Example data provided includes a subset of the data for the gene GAA (ENSG00000171298). To query the full database requires providing a path to the downloaded database files as a parameter. biocViews: Genetics, SequenceMatching Author: Laura D.T. Vo Ngoc [aut, cre] (ORCID: ) Maintainer: Laura D.T. Vo Ngoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EDIRquery git_branch: RELEASE_3_22 git_last_commit: 56a72b8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EDIRquery_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EDIRquery_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EDIRquery_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EDIRquery_1.10.0.tgz vignettes: vignettes/EDIRquery/inst/doc/EDIRquery.pdf vignetteTitles: EDIRquery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDIRquery/inst/doc/EDIRquery.R dependencyCount: 52 Package: eds Version: 1.12.0 Depends: Matrix Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, tximportData, testthat (>= 3.0.0) License: GPL-2 MD5sum: c24a075a6dd5c171f827c90b5d507836 NeedsCompilation: yes Title: eds: Low-level reader for Alevin EDS format Description: This packages provides a single function, readEDS. This is a low-level utility for reading in Alevin EDS format into R. This function is not designed for end-users but instead the package is predominantly for simplifying package dependency graph for other Bioconductor packages. biocViews: Sequencing, RNASeq, GeneExpression, SingleCell Author: Avi Srivastava [aut, cre], Michael Love [aut, ctb] Maintainer: Avi Srivastava URL: https://github.com/mikelove/eds SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eds git_branch: RELEASE_3_22 git_last_commit: d2b8448 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/eds_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/eds_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/eds_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/eds_1.12.0.tgz vignettes: vignettes/eds/inst/doc/eds.html vignetteTitles: eds: Low-level reader function for Alevin EDS format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eds/inst/doc/eds.R importsMe: singleCellTK suggestsMe: tximport dependencyCount: 9 Package: EGAD Version: 1.38.0 Depends: R(>= 3.5) Imports: gplots, Biobase, GEOquery, limma, impute, RColorBrewer, zoo, igraph, plyr, MASS, RCurl, methods Suggests: knitr, testthat, rmarkdown, markdown License: GPL-2 MD5sum: 92aa03bfb09e1473588bdc35b23cd565 NeedsCompilation: no Title: Extending guilt by association by degree Description: The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods. biocViews: Software, FunctionalGenomics, SystemsBiology, GenePrediction, FunctionalPrediction, NetworkEnrichment, GraphAndNetwork, Network Author: Sara Ballouz [aut, cre], Melanie Weber [aut, ctb], Paul Pavlidis [aut], Jesse Gillis [aut, ctb] Maintainer: Sara Ballouz VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/EGAD git_branch: RELEASE_3_22 git_last_commit: e4b84b7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EGAD_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EGAD_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EGAD_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EGAD_1.38.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 88 Package: EGSEA Version: 1.38.0 Depends: R (>= 4.3.0), Biobase, gage (>= 2.14.4), AnnotationDbi, topGO (>= 2.16.0), pathview (>= 1.4.2) Imports: PADOG (>= 1.6.0), GSVA (>= 1.12.0), globaltest (>= 5.18.0), limma (>= 3.20.9), edgeR (>= 3.6.8), HTMLUtils (>= 0.1.5), hwriter (>= 1.2.2), gplots (>= 2.14.2), ggplot2 (>= 1.0.0), safe (>= 3.4.0), stringi (>= 0.5.0), parallel, stats, metap, grDevices, graphics, utils, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, RColorBrewer, methods, EGSEAdata (>= 1.3.1), htmlwidgets, plotly, DT Suggests: BiocStyle, knitr, testthat License: GPL-3 Archs: x64 MD5sum: 1bf20939f05f108e62a070e348aa2b37 NeedsCompilation: no Title: Ensemble of Gene Set Enrichment Analyses Description: This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. EGSEA algorithm utilizes the analysis results of twelve prominent GSE algorithms in the literature to calculate collective significance scores for each gene set. biocViews: ImmunoOncology, DifferentialExpression, GO, GeneExpression, GeneSetEnrichment, Genetics, Microarray, MultipleComparison, OneChannel, Pathways, RNASeq, Sequencing, Software, SystemsBiology, TwoChannel,Metabolomics, Proteomics, KEGG, GraphAndNetwork, GeneSignaling, GeneTarget, NetworkEnrichment, Network, Classification Author: Monther Alhamdoosh [aut, cre], Luyi Tian [aut], Milica Ng [aut], Matthew Ritchie [ctb] Maintainer: Monther Alhamdoosh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGSEA git_branch: RELEASE_3_22 git_last_commit: 1a8a161 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EGSEA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EGSEA_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EGSEA_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EGSEA_1.38.0.tgz vignettes: vignettes/EGSEA/inst/doc/EGSEA.pdf vignetteTitles: EGSEA vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGSEA/inst/doc/EGSEA.R dependsOnMe: EGSEA123 suggestsMe: tidybulk, EGSEAdata dependencyCount: 196 Package: eiR Version: 1.50.0 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl, digest, BiocGenerics, RcppAnnoy (>= 0.0.9) Suggests: BiocStyle, knitcitations, knitr, knitrBootstrap,rmarkdown,RSQLite,codetools License: Artistic-2.0 MD5sum: 23defa04c79b3c9765ea41229ff6ed87 NeedsCompilation: yes Title: Accelerated similarity searching of small molecules Description: The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Yiqun Cao and Tyler Backman Maintainer: Thomas Girke URL: https://github.com/girke-lab/eiR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eiR git_branch: RELEASE_3_22 git_last_commit: 6212cd1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/eiR_1.50.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/eiR_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/eiR_1.50.0.tgz vignettes: vignettes/eiR/inst/doc/eiR.html vignetteTitles: eiR: Accelerated Similarity Searching of Small Molecules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R dependencyCount: 69 Package: eisaR Version: 1.22.0 Depends: R (>= 4.1) Imports: graphics, stats, GenomicRanges, S4Vectors, IRanges, limma, edgeR (>= 4.0), methods, SummarizedExperiment, BiocGenerics, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, QuasR, Rbowtie, Rhisat2, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg38, ensembldb, AnnotationDbi, GenomicFeatures, txdbmaker, rtracklayer, withr License: GPL-3 MD5sum: dff8fad6a51decfcdb713565abf5c226 NeedsCompilation: no Title: Exon-Intron Split Analysis (EISA) in R Description: Exon-intron split analysis (EISA) uses ordinary RNA-seq data to measure changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. For details see Gaidatzis et al., Nat Biotechnol 2015. doi: 10.1038/nbt.3269. eisaR implements the major steps of EISA in R. biocViews: Transcription, GeneExpression, GeneRegulation, FunctionalGenomics, Transcriptomics, Regression, RNASeq Author: Michael Stadler [aut, cre], Dimos Gaidatzis [aut], Lukas Burger [aut], Charlotte Soneson [aut] Maintainer: Michael Stadler URL: https://github.com/fmicompbio/eisaR VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/eisaR/issues git_url: https://git.bioconductor.org/packages/eisaR git_branch: RELEASE_3_22 git_last_commit: ade3864 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/eisaR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/eisaR_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/eisaR_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/eisaR_1.22.0.tgz vignettes: vignettes/eisaR/inst/doc/eisaR.html, vignettes/eisaR/inst/doc/rna-velocity.html vignetteTitles: Using eisaR for Exon-Intron Split Analysis (EISA), Generating reference files for spliced and unspliced abundance estimation with alignment-free methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eisaR/inst/doc/eisaR.R, vignettes/eisaR/inst/doc/rna-velocity.R dependencyCount: 29 Package: ELMER Version: 2.33.1 Depends: R (>= 3.4.0), ELMER.data (>= 2.9.3) Imports: GenomicRanges, ggplot2, reshape, grid, grDevices, graphics, methods, parallel, stats, utils, IRanges, Seqinfo, S4Vectors, GenomicFeatures, TCGAbiolinks (>= 2.23.7), plyr, Matrix, dplyr, Gviz, ComplexHeatmap, circlize, MultiAssayExperiment, SummarizedExperiment, biomaRt, doParallel, downloader, ggrepel, lattice, magrittr, readr, scales, rvest, xml2, plotly, gridExtra, rmarkdown, stringr, tibble, tidyr, progress, purrr, reshape2, ggpubr, rtracklayer (>= 1.61.2), DelayedArray Suggests: BiocStyle, AnnotationHub, ExperimentHub, knitr, testthat, data.table, DT, GenomicInteractions, webshot, R.utils, covr, sesameData License: GPL-3 MD5sum: 51760d489ad5fc4fabf210850542bf3a NeedsCompilation: no Title: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes Description: ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue. biocViews: DNAMethylation, GeneExpression, MotifAnnotation, Software, GeneRegulation, Transcription, Network Author: Tiago Chedraoui Silva [aut, cre], Lijing Yao [aut], Simon Coetzee [aut], Nicole Gull [ctb], Hui Shen [ctb], Peter Laird [ctb], Peggy Farnham [aut], Dechen Li [ctb], Benjamin Berman [aut] Maintainer: Tiago Chedraoui Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ELMER git_branch: devel git_last_commit: fa33cdb git_last_commit_date: 2025-07-22 Date/Publication: 2025-10-07 source.ver: src/contrib/ELMER_2.33.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/ELMER_2.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ELMER_2.33.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ELMER_2.33.1.tgz vignettes: vignettes/ELMER/inst/doc/analysis_data_input.html, vignettes/ELMER/inst/doc/analysis_diff_meth.html, vignettes/ELMER/inst/doc/analysis_get_pair.html, vignettes/ELMER/inst/doc/analysis_gui.html, vignettes/ELMER/inst/doc/analysis_motif_enrichment.html, vignettes/ELMER/inst/doc/analysis_regulatory_tf.html, vignettes/ELMER/inst/doc/index.html, vignettes/ELMER/inst/doc/input.html, vignettes/ELMER/inst/doc/pipe.html, vignettes/ELMER/inst/doc/plots_heatmap.html, vignettes/ELMER/inst/doc/plots_motif_enrichment.html, vignettes/ELMER/inst/doc/plots_scatter.html, vignettes/ELMER/inst/doc/plots_schematic.html, vignettes/ELMER/inst/doc/plots_TF.html, vignettes/ELMER/inst/doc/usecase.html vignetteTitles: "3.1 - Data input - Creating MAE object", "3.2 - Identifying differentially methylated probes", "3.3 - Identifying putative probe-gene pairs", 5 - Integrative analysis workshop with TCGAbiolinks and ELMER - Analysis GUI, "3.4 - Motif enrichment analysis on the selected probes", "3.5 - Identifying regulatory TFs", "1 - ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles", "2 - Introduction: Input data", "3.6 - TCGA.pipe: Running ELMER for TCGA data in a compact way", "4.5 - Heatmap plots", "4.3 - Motif enrichment plots", "4.1 - Scatter plots", "4.2 - Schematic plots", "4.4 - Regulatory TF plots", "11 - ELMER: Use case" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ELMER/inst/doc/analysis_data_input.R, vignettes/ELMER/inst/doc/analysis_diff_meth.R, vignettes/ELMER/inst/doc/analysis_get_pair.R, vignettes/ELMER/inst/doc/analysis_gui.R, vignettes/ELMER/inst/doc/analysis_motif_enrichment.R, vignettes/ELMER/inst/doc/analysis_regulatory_tf.R, vignettes/ELMER/inst/doc/index.R, vignettes/ELMER/inst/doc/input.R, vignettes/ELMER/inst/doc/pipe.R, vignettes/ELMER/inst/doc/plots_heatmap.R, vignettes/ELMER/inst/doc/plots_motif_enrichment.R, vignettes/ELMER/inst/doc/plots_scatter.R, vignettes/ELMER/inst/doc/plots_schematic.R, vignettes/ELMER/inst/doc/plots_TF.R, vignettes/ELMER/inst/doc/usecase.R dependencyCount: 215 Package: ELViS Version: 1.2.0 Depends: R (>= 4.5.0) Imports: reticulate, BiocGenerics, circlize, ComplexHeatmap, data.table, dplyr, GenomicFeatures, GenomicRanges, ggplot2, glue, graphics, grDevices, igraph, IRanges, magrittr, memoise, methods, parallel, patchwork, scales, segclust2d, stats, stringr, txdbmaker, utils, uuid, zoo Suggests: Rsamtools, BiocManager, knitr, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 746efe7f518d4cdd53223c0df04b80c7 NeedsCompilation: no Title: An R Package for Estimating Copy Number Levels of Viral Genome Segments Using Base-Resolution Read Depth Profile Description: Base-resolution copy number analysis of viral genome. Utilizes base-resolution read depth data over viral genome to find copy number segments with two-dimensional segmentation approach. Provides publish-ready figures, including histograms of read depths, coverage line plots over viral genome annotated with copy number change events and viral genes, and heatmaps showing multiple types of data with integrative clustering of samples. biocViews: CopyNumberVariation, Coverage, GenomicVariation, BiomedicalInformatics, Sequencing, Normalization, Visualization, Clustering Author: Hyo Young Choi [aut, cph] (ORCID: ), Jin-Young Lee [aut, cre, cph] (ORCID: ), Xiaobei Zhao [ctb] (ORCID: ), Jeremiah R. Holt [ctb] (ORCID: ), Katherine A. Hoadley [aut] (ORCID: ), D. Neil Hayes [aut, fnd, cph] (ORCID: ) Maintainer: Jin-Young Lee URL: https://github.com/hyochoi/ELViS VignetteBuilder: knitr BugReports: https://github.com/hyochoi/ELViS/issues git_url: https://git.bioconductor.org/packages/ELViS git_branch: RELEASE_3_22 git_last_commit: c2a03f6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ELViS_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ELViS_1.1.9.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ELViS_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ELViS_1.2.0.tgz vignettes: vignettes/ELViS/inst/doc/ELViS_Toy_Example.html vignetteTitles: Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ELViS/inst/doc/ELViS_Toy_Example.R dependencyCount: 135 Package: EMDomics Version: 2.40.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: 7fe8e72a58324c828b8e9d3038aeddc0 NeedsCompilation: no Title: Earth Mover's Distance for Differential Analysis of Genomics Data Description: The EMDomics algorithm is used to perform a supervised multi-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between groups. Usually the data will be gene expression values from array-based or sequence-based experiments, but data from other types of experiments can also be analyzed (e.g. copy number variation). Traditional methods like Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA) use significance tests based on summary statistics (mean and standard deviation) of the distributions. This approach lacks power to identify expression differences between groups that show high levels of intra-group heterogeneity. The Earth Mover's Distance (EMD) algorithm instead computes the "work" needed to transform one distribution into another, thus providing a metric of the overall difference in shape between two distributions. Permutation of sample labels is used to generate q-values for the observed EMD scores. This package also incorporates the Komolgorov-Smirnov (K-S) test and the Cramer von Mises test (CVM), which are both common distribution comparison tests. biocViews: Software, DifferentialExpression, GeneExpression, Microarray Author: Sadhika Malladi [aut, cre], Daniel Schmolze [aut, cre], Andrew Beck [aut], Sheida Nabavi [aut] Maintainer: Sadhika Malladi and Daniel Schmolze VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EMDomics git_branch: RELEASE_3_22 git_last_commit: 6076ee9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EMDomics_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EMDomics_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EMDomics_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EMDomics_2.40.0.tgz vignettes: vignettes/EMDomics/inst/doc/EMDomics.html vignetteTitles: EMDomics Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R dependencyCount: 36 Package: EmpiricalBrownsMethod Version: 1.38.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: f4dc6976c496f5c8d2294ff341ececd5 NeedsCompilation: no Title: Uses Brown's method to combine p-values from dependent tests Description: Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments. biocViews: StatisticalMethod, GeneExpression, Pathways Author: William Poole Maintainer: David Gibbs URL: https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EmpiricalBrownsMethod git_branch: RELEASE_3_22 git_last_commit: 5287738 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EmpiricalBrownsMethod_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EmpiricalBrownsMethod_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EmpiricalBrownsMethod_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EmpiricalBrownsMethod_1.38.0.tgz vignettes: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html vignetteTitles: Empirical Browns Method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R dependsOnMe: poolVIM importsMe: EBSEA dependencyCount: 0 Package: enhancerHomologSearch Version: 1.16.0 Depends: R (>= 4.1.0), methods Imports: BiocGenerics, Biostrings, BSgenome, BiocParallel, BiocFileCache, Seqinfo, GenomicRanges, httr, IRanges, jsonlite, motifmatchr, Matrix, pwalign, rtracklayer, Rcpp, S4Vectors, stats, utils LinkingTo: Rcpp Suggests: GenomeInfoDb, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, MotifDb, testthat, TFBSTools License: GPL (>= 2) Archs: x64 MD5sum: a3e312f35077e7e4ac5be19d9f92a77d NeedsCompilation: yes Title: Identification of putative mammalian orthologs to given enhancer Description: Get ENCODE data of enhancer region via H3K4me1 peaks and search homolog regions for given sequences. The candidates of enhancer homolog regions can be filtered by distance to target TSS. The top candidates from human and mouse will be aligned to each other and then exported as multiple alignments with given enhancer. biocViews: Sequencing, GeneRegulation, Alignment Author: Jianhong Ou [aut, cre] (ORCID: ), Valentina Cigliola [dtc], Kenneth Poss [fnd] Maintainer: Jianhong Ou URL: https://jianhong.github.io/enhancerHomologSearch VignetteBuilder: knitr BugReports: https://github.com/jianhong/enhancerHomologSearch/issues git_url: https://git.bioconductor.org/packages/enhancerHomologSearch git_branch: RELEASE_3_22 git_last_commit: a7e3e8d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/enhancerHomologSearch_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/enhancerHomologSearch_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/enhancerHomologSearch_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/enhancerHomologSearch_1.16.0.tgz vignettes: vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.html vignetteTitles: enhancerHomologSearch Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.R dependencyCount: 99 Package: EnMCB Version: 1.22.0 Depends: R (>= 4.0) Imports: survivalROC, glmnet, rms, mboost, Matrix, igraph, methods, survivalsvm, ggplot2, boot, e1071, survival, BiocFileCache Suggests: SummarizedExperiment, testthat, Biobase, survminer, affycoretools, knitr, plotROC, limma, rmarkdown License: GPL-2 MD5sum: 169588cb377dde8b52c5798058b5bba3 NeedsCompilation: no Title: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models Description: Creation of the correlated blocks using DNA methylation profiles. Machine learning models can be constructed to predict differentially methylated blocks and disease progression. biocViews: Normalization, DNAMethylation, MethylationArray, SupportVectorMachine Author: Xin Yu Maintainer: Xin Yu VignetteBuilder: knitr BugReports: https://github.com/whirlsyu/EnMCB/issues git_url: https://git.bioconductor.org/packages/EnMCB git_branch: RELEASE_3_22 git_last_commit: d87a235 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EnMCB_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EnMCB_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EnMCB_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EnMCB_1.22.0.tgz vignettes: vignettes/EnMCB/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnMCB/inst/doc/vignette.R dependencyCount: 125 Package: ENmix Version: 1.46.0 Depends: parallel,doParallel,foreach,SummarizedExperiment,stats,R (>= 3.5.0) Imports: grDevices,graphics,matrixStats,methods,utils,irlba, geneplotter,impute,minfi,RPMM,illuminaio,dynamicTreeCut,IRanges,gtools, Biobase,ExperimentHub,AnnotationHub,genefilter,gplots,quadprog,S4Vectors Suggests: minfiData, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 72dd78d32dbe9c1598fd7b71d8c26390 NeedsCompilation: no Title: Quality control and analysis tools for Illumina DNA methylation BeadChip Description: Tools for quanlity control, analysis and visulization of Illumina DNA methylation array data. biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel, Microarray, OneChannel, MethylationArray, BatchEffect, Normalization, DataImport, Regression, PrincipalComponent,Epigenetics, MultiChannel, DifferentialMethylation, ImmunoOncology Author: Zongli Xu [cre, aut], Liang Niu [aut], Jack Taylor [ctb] Maintainer: Zongli Xu URL: https://github.com/Bioconductor/ENmix VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ENmix/issues git_url: https://git.bioconductor.org/packages/ENmix git_branch: RELEASE_3_22 git_last_commit: 50af132 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ENmix_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ENmix_1.45.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ENmix_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ENmix_1.46.0.tgz vignettes: vignettes/ENmix/inst/doc/ENmix.html vignetteTitles: ENmix User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENmix/inst/doc/ENmix.R dependencyCount: 162 Package: EnrichDO Version: 1.4.0 Depends: R (>= 4.0.0) Imports: BiocGenerics, Rgraphviz, hash, S4Vectors, dplyr, ggplot2, graph, magrittr, methods, pheatmap, graphics, utils, purrr, tidyr, stats Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: MIT + file LICENSE MD5sum: 1cf3640c69475766b04d130478a227f7 NeedsCompilation: no Title: a Global Weighted Model for Disease Ontology Enrichment Analysis Description: To implement disease ontology (DO) enrichment analysis, this package is designed and presents a double weighted model based on the latest annotations of the human genome with DO terms, by integrating the DO graph topology on a global scale. This package exhibits high accuracy that it can identify more specific DO terms, which alleviates the over enriched problem. The package includes various statistical models and visualization schemes for discovering the associations between genes and diseases from biological big data. biocViews: Annotation, Visualization, GeneSetEnrichment, Software Author: Liang Cheng [aut], Haixiu Yang [aut], Hongyu Fu [aut, cre] Maintainer: Hongyu Fu <2287531995@qq.com> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichDO git_branch: RELEASE_3_22 git_last_commit: da942bc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EnrichDO_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EnrichDO_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EnrichDO_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EnrichDO_1.4.0.tgz vignettes: vignettes/EnrichDO/inst/doc/EnrichDO.html vignetteTitles: EnrichDO: Disease Ontology Enrichment Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EnrichDO/inst/doc/EnrichDO.R dependencyCount: 43 Package: EnrichedHeatmap Version: 1.40.0 Depends: R (>= 3.6.0), methods, grid, ComplexHeatmap (>= 2.11.0), GenomicRanges Imports: matrixStats, stats, GetoptLong, Rcpp, utils, locfit, circlize (>= 0.4.5), IRanges LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, markdown, rmarkdown, genefilter, RColorBrewer License: MIT + file LICENSE MD5sum: 2b8b9e6055231c4f1d4cc77d0b711035 NeedsCompilation: yes Title: Making Enriched Heatmaps Description: Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. Here we implement enriched heatmap by ComplexHeatmap package. Since this type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources. biocViews: Software, Visualization, Sequencing, GenomeAnnotation, Coverage Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/EnrichedHeatmap VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichedHeatmap git_branch: RELEASE_3_22 git_last_commit: 07a50cf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EnrichedHeatmap_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EnrichedHeatmap_1.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EnrichedHeatmap_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EnrichedHeatmap_1.40.0.tgz vignettes: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html, vignettes/EnrichedHeatmap/inst/doc/roadmap.html, vignettes/EnrichedHeatmap/inst/doc/row_odering.html, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.html vignetteTitles: 1. Make Enriched Heatmaps, 4. Visualize Comprehensive Associations in Roadmap dataset, 3. Compare row ordering methods, 2. Visualize Categorical Signals hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.R, vignettes/EnrichedHeatmap/inst/doc/roadmap.R, vignettes/EnrichedHeatmap/inst/doc/row_odering.R, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.R suggestsMe: ComplexHeatmap, epistack, extraChIPs, InteractiveComplexHeatmap dependencyCount: 35 Package: EnrichmentBrowser Version: 2.40.0 Depends: SummarizedExperiment, graph Imports: AnnotationDbi, BiocFileCache, BiocManager, GSEABase, GO.db, KEGGREST, KEGGgraph, Rgraphviz, S4Vectors, SPIA, edgeR, graphite, hwriter, limma, methods, pathview, safe Suggests: ALL, BiocStyle, ComplexHeatmap, DESeq2, ReportingTools, airway, biocGraph, hgu95av2.db, geneplotter, knitr, msigdbr, rmarkdown, statmod License: Artistic-2.0 MD5sum: f4fe371ae142ef9ce7b208daace23ab8 NeedsCompilation: no Title: Seamless navigation through combined results of set-based and network-based enrichment analysis Description: The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Mirko Signorelli [ctb], Rohit Satyam [ctb], Marcel Ramos [ctb], Levi Waldron [ctb], Ralf Zimmer [aut] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/lgeistlinger/EnrichmentBrowser/issues git_url: https://git.bioconductor.org/packages/EnrichmentBrowser git_branch: RELEASE_3_22 git_last_commit: 2118f0c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EnrichmentBrowser_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EnrichmentBrowser_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EnrichmentBrowser_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EnrichmentBrowser_2.40.0.tgz vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.html vignetteTitles: Seamless navigation through combined results of set- & network-based enrichment analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R importsMe: GSEABenchmarkeR, zenith suggestsMe: GenomicSuperSignature, roastgsa, bugphyzz dependencyCount: 93 Package: enrichplot Version: 1.30.0 Depends: R (>= 4.1.0) Imports: aplot (>= 0.2.1), DOSE (>= 3.31.2), ggfun (>= 0.1.7), ggnewscale, ggplot2 (>= 3.5.0), ggrepel (>= 0.9.0), ggtangle (>= 0.0.5), graphics, grid, igraph, methods, plyr, purrr, RColorBrewer, reshape2, rlang, stats, utils, scatterpie, GOSemSim (>= 2.31.2), magrittr, ggtree, yulab.utils (>= 0.1.6) Suggests: clusterProfiler, dplyr, europepmc, ggarchery, ggupset, glue, knitr, rmarkdown, org.Hs.eg.db, prettydoc, tibble, tidyr, ggforce, ggHoriPlot, AnnotationDbi, ggplotify, ggridges, grDevices, gridExtra, ggstar, scales, ggtreeExtra, tidydr License: Artistic-2.0 MD5sum: c1712250497ea189a1e53cd83bab9e83 NeedsCompilation: no Title: Visualization of Functional Enrichment Result Description: The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. It is mainly designed to work with the 'clusterProfiler' package suite. All the visualization methods are developed based on 'ggplot2' graphics. biocViews: Annotation, GeneSetEnrichment, GO, KEGG, Pathways, Software, Visualization Author: Guangchuang Yu [aut, cre] (ORCID: ), Chun-Hui Gao [ctb] (ORCID: ) Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/enrichplot/issues git_url: https://git.bioconductor.org/packages/enrichplot git_branch: RELEASE_3_22 git_last_commit: 47185a0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/enrichplot_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/enrichplot_1.29.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/enrichplot_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/enrichplot_1.30.0.tgz vignettes: vignettes/enrichplot/inst/doc/enrichplot.html vignetteTitles: enrichplot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: maEndToEnd importsMe: CBNplot, ChIPseeker, clusterProfiler, debrowser, enrichViewNet, meshes, MicrobiomeProfiler, ReactomePA, RFLOMICS, TDbasedUFEadv, ExpHunterSuite suggestsMe: GeoTcgaData, mastR, methylGSA, scGraphVerse, tidybulk, ggpicrust2, ReporterScore, SCpubr dependencyCount: 134 Package: enrichViewNet Version: 1.8.0 Depends: R (>= 4.2.0) Imports: gprofiler2, strex, RCy3, jsonlite, stringr, enrichplot, DOSE, igraph, reshape2, methods Suggests: BiocStyle, knitr, rmarkdown, ggplot2, scatterpie, ggtangle, ggrepel, testthat, ggnetwork, magick License: Artistic-2.0 MD5sum: 79bb927d6e9a70bb831c7ca492e6f7b1 NeedsCompilation: no Title: From functional enrichment results to biological networks Description: This package enables the visualization of functional enrichment results as network graphs. First the package enables the visualization of enrichment results, in a format corresponding to the one generated by gprofiler2, as a customizable Cytoscape network. In those networks, both gene datasets (GO terms/pathways/protein complexes) and genes associated to the datasets are represented as nodes. While the edges connect each gene to its dataset(s). The package also provides the option to create enrichment maps from functional enrichment results. Enrichment maps enable the visualization of enriched terms into a network with edges connecting overlapping genes. biocViews: BiologicalQuestion, Software, Network, NetworkEnrichment, GO Author: Astrid Deschênes [aut, cre] (ORCID: ), Pascal Belleau [aut] (ORCID: ), Robert L. Faure [aut] (ORCID: ), Maria J. Fernandes [aut] (ORCID: ), Alexander Krasnitz [aut], David A. Tuveson [aut] (ORCID: ) Maintainer: Astrid Deschênes URL: https://github.com/adeschen/enrichViewNet, https://adeschen.github.io/enrichViewNet/ VignetteBuilder: knitr BugReports: https://github.com/adeschen/enrichViewNet/issues git_url: https://git.bioconductor.org/packages/enrichViewNet git_branch: RELEASE_3_22 git_last_commit: 0359c98 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/enrichViewNet_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/enrichViewNet_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/enrichViewNet_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/enrichViewNet_1.8.0.tgz vignettes: vignettes/enrichViewNet/inst/doc/enrichViewNet.html vignetteTitles: From functional enrichment results to biological networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enrichViewNet/inst/doc/enrichViewNet.R dependencyCount: 161 Package: ensembldb Version: 2.34.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.61.1), GenomicFeatures (>= 1.61.4), AnnotationFilter (>= 1.5.2) Imports: methods, RSQLite (>= 1.1), DBI, Biobase, Seqinfo, GenomeInfoDb (>= 1.45.5), AnnotationDbi (>= 1.31.19), rtracklayer (>= 1.69.1), S4Vectors (>= 0.23.10), Rsamtools, IRanges (>= 2.13.24), ProtGenerics, Biostrings (>= 2.77.2), curl Suggests: BiocStyle, knitr, EnsDb.Hsapiens.v86 (>= 0.99.8), testthat, BSgenome.Hsapiens.NCBI.GRCh38, ggbio (>= 1.24.0), Gviz (>= 1.20.0), rmarkdown, AnnotationHub Enhances: RMariaDB, shiny License: LGPL MD5sum: 27b3e902c21a91e002939f83d28c5478 NeedsCompilation: no Title: Utilities to create and use Ensembl-based annotation databases Description: The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates. biocViews: Genetics, AnnotationData, Sequencing, Coverage Author: Johannes Rainer with contributions from Tim Triche, Sebastian Gibb, Laurent Gatto Christian Weichenberger and Boyu Yu. Maintainer: Johannes Rainer URL: https://github.com/jorainer/ensembldb VignetteBuilder: knitr BugReports: https://github.com/jorainer/ensembldb/issues git_url: https://git.bioconductor.org/packages/ensembldb git_branch: RELEASE_3_22 git_last_commit: a13967b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ensembldb_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ensembldb_2.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ensembldb_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ensembldb_2.34.0.tgz vignettes: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.html, vignettes/ensembldb/inst/doc/coordinate-mapping.html, vignettes/ensembldb/inst/doc/ensembldb.html, vignettes/ensembldb/inst/doc/MySQL-backend.html, vignettes/ensembldb/inst/doc/proteins.html vignetteTitles: Use cases for coordinate mapping with ensembldb, Mapping between genome,, transcript and protein coordinates, Generating an using Ensembl based annotation packages, Using a MariaDB/MySQL server backend, Querying protein features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.R, vignettes/ensembldb/inst/doc/coordinate-mapping.R, vignettes/ensembldb/inst/doc/ensembldb.R, vignettes/ensembldb/inst/doc/MySQL-backend.R, vignettes/ensembldb/inst/doc/proteins.R dependsOnMe: chimeraviz, demuxSNP, AHEnsDbs, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v75, EnsDb.Mmusculus.v79, EnsDb.Rnorvegicus.v75, EnsDb.Rnorvegicus.v79 importsMe: biovizBase, BUSpaRse, chevreulProcess, ChIPpeakAnno, CleanUpRNAseq, consensusDE, diffUTR, epimutacions, epivizrData, ggbio, GRaNIE, Gviz, RAIDS, RITAN, scanMiRApp, signifinder, singleCellTK, TVTB, tximeta, GenomicDistributionsData, scRNAseq, cellGeometry, crosstalkr, locuszoomr, RNAseqQC suggestsMe: AlphaMissenseR, AnnotationHub, autonomics, CNVRanger, eisaR, EpiTxDb, fishpond, GenomicFeatures, ldblock, multicrispr, nullranges, satuRn, txdbmaker, wiggleplotr, celldex, gaawr2, GeneSelectR, GRIN2, pQTLdata dependencyCount: 81 Package: epialleleR Version: 1.18.0 Depends: R (>= 4.1) Imports: stats, methods, utils, data.table, BiocGenerics, GenomicRanges, Rcpp LinkingTo: Rcpp, BH, Rhtslib Suggests: GenomeInfoDb, SummarizedExperiment, VariantAnnotation, RUnit, knitr, rmarkdown, ggplot2 License: Artistic-2.0 MD5sum: 3b94682eeb99c794018ffc447413b060 NeedsCompilation: yes Title: Fast, Epiallele-Aware Methylation Caller and Reporter Description: Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls and reports cytosine methylation as well as frequencies of hypermethylated epialleles at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Among other things, this package can also extract and visualise methylation patterns and assess allele specificity of methylation. biocViews: DNAMethylation, Epigenetics, MethylSeq, LongRead Author: Oleksii Nikolaienko [aut, cre] (ORCID: ) Maintainer: Oleksii Nikolaienko URL: https://github.com/BBCG/epialleleR SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/BBCG/epialleleR/issues git_url: https://git.bioconductor.org/packages/epialleleR git_branch: RELEASE_3_22 git_last_commit: e4e0b6f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epialleleR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epialleleR_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epialleleR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epialleleR_1.18.0.tgz vignettes: vignettes/epialleleR/inst/doc/epialleleR.html, vignettes/epialleleR/inst/doc/values.html vignetteTitles: epialleleR, values hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epialleleR/inst/doc/epialleleR.R, vignettes/epialleleR/inst/doc/values.R dependencyCount: 16 Package: EpiCompare Version: 1.14.0 Depends: R (>= 4.2.0) Imports: AnnotationHub, ChIPseeker, data.table, genomation, GenomicRanges, IRanges (>= 2.41.3), Seqinfo (>= 0.99.2), GenomeInfoDb (>= 1.45.7), ggplot2 (>= 3.5.0), htmltools, methods, plotly, reshape2, rmarkdown, rtracklayer, stats, stringr, utils, BiocGenerics, downloadthis, parallel Suggests: rworkflows, BiocFileCache, BiocParallel, BiocStyle, clusterProfiler, GenomicAlignments, grDevices, knitr, org.Hs.eg.db, testthat (>= 3.0.0), tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, ComplexUpset, plyranges, scales, Matrix, consensusSeekeR, heatmaply, viridis License: GPL-3 MD5sum: abf3f7b8d98b34f9e6e39fc385f4b7a0 NeedsCompilation: no Title: Comparison, Benchmarking & QC of Epigenomic Datasets Description: EpiCompare is used to compare and analyse epigenetic datasets for quality control and benchmarking purposes. The package outputs an HTML report consisting of three sections: (1. General metrics) Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples, (2. Peak overlap) Percentage and statistical significance of overlapping and non-overlapping peaks. Also includes upset plot and (3. Functional annotation) functional annotation (ChromHMM, ChIPseeker and enrichment analysis) of peaks. Also includes peak enrichment around TSS. biocViews: Epigenetics, Genetics, QualityControl, ChIPSeq, MultipleComparison, FunctionalGenomics, ATACSeq, DNaseSeq Author: Sera Choi [aut] (ORCID: ), Brian Schilder [aut] (ORCID: ), Leyla Abbasova [aut], Alan Murphy [aut] (ORCID: ), Nathan Skene [aut] (ORCID: ), Thomas Roberts [ctb], Hiranyamaya Dash [cre] (ORCID: ) Maintainer: Hiranyamaya Dash URL: https://github.com/neurogenomics/EpiCompare VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/EpiCompare/issues git_url: https://git.bioconductor.org/packages/EpiCompare git_branch: RELEASE_3_22 git_last_commit: c5d211a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EpiCompare_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EpiCompare_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EpiCompare_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EpiCompare_1.14.0.tgz vignettes: vignettes/EpiCompare/inst/doc/docker.html, vignettes/EpiCompare/inst/doc/EpiCompare.html, vignettes/EpiCompare/inst/doc/example_report.html vignetteTitles: docker, EpiCompare, example_report hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiCompare/inst/doc/docker.R, vignettes/EpiCompare/inst/doc/EpiCompare.R, vignettes/EpiCompare/inst/doc/example_report.R dependencyCount: 196 Package: epidecodeR Version: 1.18.0 Depends: R (>= 3.1.0) Imports: EnvStats, ggplot2, rtracklayer, GenomicRanges, IRanges, rstatix, ggpubr, methods, stats, utils, dplyr Suggests: knitr, rmarkdown License: GPL-3 MD5sum: a7eb0ed76f872de871b143302aa5e068 NeedsCompilation: no Title: epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation Description: epidecodeR is a package capable of analysing impact of degree of DNA/RNA epigenetic chemical modifications on dysregulation of genes or proteins. This package integrates chemical modification data generated from a host of epigenomic or epitranscriptomic techniques such as ChIP-seq, ATAC-seq, m6A-seq, etc. and dysregulated gene lists in the form of differential gene expression, ribosome occupancy or differential protein translation and identify impact of dysregulation of genes caused due to varying degrees of chemical modifications associated with the genes. epidecodeR generates cumulative distribution function (CDF) plots showing shifts in trend of overall log2FC between genes divided into groups based on the degree of modification associated with the genes. The tool also tests for significance of difference in log2FC between groups of genes. biocViews: DifferentialExpression, GeneRegulation, HistoneModification, FunctionalPrediction, Transcription, GeneExpression, Epitranscriptomics, Epigenetics, FunctionalGenomics, SystemsBiology, Transcriptomics, ChipOnChip Author: Kandarp Joshi [aut, cre], Dan Ohtan Wang [aut] Maintainer: Kandarp Joshi URL: https://github.com/kandarpRJ/epidecodeR, https://epidecoder.shinyapps.io/shinyapp VignetteBuilder: knitr BugReports: https://github.com/kandarpRJ/epidecodeR/issues git_url: https://git.bioconductor.org/packages/epidecodeR git_branch: RELEASE_3_22 git_last_commit: df3fe88 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epidecodeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epidecodeR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epidecodeR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epidecodeR_1.18.0.tgz vignettes: vignettes/epidecodeR/inst/doc/epidecodeR.html vignetteTitles: epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epidecodeR/inst/doc/epidecodeR.R dependencyCount: 123 Package: EpiDISH Version: 2.26.0 Depends: R (>= 4.1) Imports: MASS, e1071, quadprog, parallel, stats, matrixStats, stringr, locfdr, Matrix Suggests: roxygen2, GEOquery, BiocStyle, knitr, rmarkdown, Biobase, testthat License: GPL-2 Archs: x64 MD5sum: 1541b4dc5e82c5211fe432570857e721 NeedsCompilation: no Title: Epigenetic Dissection of Intra-Sample-Heterogeneity Description: EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of blood-tissue of any age, from birth to old-age, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies. biocViews: DNAMethylation, MethylationArray, Epigenetics, DifferentialMethylation, ImmunoOncology Author: Andrew E. Teschendorff [aut], Shijie C. Zheng [aut, cre] Maintainer: Shijie C. Zheng URL: https://github.com/sjczheng/EpiDISH VignetteBuilder: knitr BugReports: https://github.com/sjczheng/EpiDISH/issues git_url: https://git.bioconductor.org/packages/EpiDISH git_branch: RELEASE_3_22 git_last_commit: 4f83a1f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EpiDISH_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EpiDISH_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EpiDISH_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EpiDISH_2.26.0.tgz vignettes: vignettes/EpiDISH/inst/doc/EpiDISH.html vignetteTitles: Epigenetic Dissection of Intra-Sample-Heterogeneity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiDISH/inst/doc/EpiDISH.R dependsOnMe: TOAST suggestsMe: planet dependencyCount: 26 Package: epigraHMM Version: 1.18.0 Depends: R (>= 3.5.0) Imports: Rcpp, magrittr, data.table, SummarizedExperiment, methods, Seqinfo, GenomicRanges, rtracklayer, IRanges, Rsamtools, bamsignals, csaw, S4Vectors, limma, stats, Rhdf5lib, rhdf5, Matrix, MASS, scales, ggpubr, ggplot2, GreyListChIP, pheatmap, grDevices LinkingTo: Rcpp, RcppArmadillo, Rhdf5lib Suggests: GenomeInfoDb, testthat, knitr, rmarkdown, BiocStyle, BSgenome.Rnorvegicus.UCSC.rn4, gcapc, chromstaRData License: MIT + file LICENSE Archs: x64 MD5sum: 9e5e9ee3a3c482f872cd6f1714830dc7 NeedsCompilation: yes Title: Epigenomic R-based analysis with hidden Markov models Description: epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions. biocViews: ChIPSeq, ATACSeq, DNaseSeq, HiddenMarkovModel, Epigenetics Author: Pedro Baldoni [aut, cre] Maintainer: Pedro Baldoni SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epigraHMM git_branch: RELEASE_3_22 git_last_commit: d390572 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epigraHMM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epigraHMM_1.17.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epigraHMM_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epigraHMM_1.18.0.tgz vignettes: vignettes/epigraHMM/inst/doc/epigraHMM.html vignetteTitles: Consensus and Differential Peak Calling With epigraHMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epigraHMM/inst/doc/epigraHMM.R dependencyCount: 136 Package: EpiMix Version: 1.12.0 Depends: R (>= 4.2.0), EpiMix.data (>= 1.2.2) Imports: AnnotationHub, AnnotationDbi, Biobase, biomaRt, data.table, doParallel, doSNOW, downloader, dplyr, ELMER.data, ExperimentHub, foreach, Seqinfo, GenomicFeatures, GenomicRanges, ggplot2, graphics, grDevices, impute, IRanges, limma, methods, parallel, plyr, progress, R.matlab, RColorBrewer, RCurl, rlang, RPMM, S4Vectors, stats, SummarizedExperiment, tibble, tidyr, utils Suggests: BiocStyle, clusterProfiler, DT, GEOquery, karyoploteR, knitr, org.Hs.eg.db, regioneR, Seurat, survival, survminer, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, multiMiR, miRBaseConverter License: GPL-3 Archs: x64 MD5sum: a5101d10ba38758f6afdafb2d3f02b04 NeedsCompilation: no Title: EpiMix: an integrative tool for the population-level analysis of DNA methylation Description: EpiMix is a comprehensive tool for the integrative analysis of high-throughput DNA methylation data and gene expression data. EpiMix enables automated data downloading (from TCGA or GEO), preprocessing, methylation modeling, interactive visualization and functional annotation.To identify hypo- or hypermethylated CpG sites across physiological or pathological conditions, EpiMix uses a beta mixture modeling to identify the methylation states of each CpG probe and compares the methylation of the experimental group to the control group.The output from EpiMix is the functional DNA methylation that is predictive of gene expression. EpiMix incorporates specialized algorithms to identify functional DNA methylation at various genetic elements, including proximal cis-regulatory elements of protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs. biocViews: Software, Epigenetics, Preprocessing, DNAMethylation, GeneExpression, DifferentialMethylation Author: Yuanning Zheng [aut, cre], Markus Sujansky [aut], John Jun [aut], Olivier Gevaert [aut] Maintainer: Yuanning Zheng VignetteBuilder: knitr BugReports: https://github.com/gevaertlab/EpiMix/issues git_url: https://git.bioconductor.org/packages/EpiMix git_branch: RELEASE_3_22 git_last_commit: 36341ff git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EpiMix_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EpiMix_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EpiMix_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EpiMix_1.12.0.tgz vignettes: vignettes/EpiMix/inst/doc/Methylation_Modeling.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiMix/inst/doc/Methylation_Modeling.R importsMe: Moonlight2R dependencyCount: 131 Package: epimutacions Version: 1.14.0 Depends: R (>= 4.3.0), epimutacionsData Imports: minfi, bumphunter, isotree, robustbase, ggplot2, GenomicRanges, GenomicFeatures, IRanges, SummarizedExperiment, stats, matrixStats, BiocGenerics, S4Vectors, utils, biomaRt, BiocParallel, GenomeInfoDb, Homo.sapiens, purrr, tibble, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, rtracklayer, AnnotationDbi, AnnotationHub, ExperimentHub, reshape2, grid, ensembldb, gridExtra, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, ggrepel Suggests: testthat, knitr, rmarkdown, BiocStyle, a4Base, kableExtra, methods, grDevices License: MIT + file LICENSE MD5sum: ea0818747e20af7a2ebe7eeb0315d0d5 NeedsCompilation: yes Title: Robust outlier identification for DNA methylation data Description: The package includes some statistical outlier detection methods for epimutations detection in DNA methylation data. The methods included in the package are MANOVA, Multivariate linear models, isolation forest, robust mahalanobis distance, quantile and beta. The methods compare a case sample with a suspected disease against a reference panel (composed of healthy individuals) to identify epimutations in the given case sample. It also contains functions to annotate and visualize the identified epimutations. biocViews: DNAMethylation, BiologicalQuestion, Preprocessing, StatisticalMethod, Normalization Author: Dolors Pelegri-Siso [aut, cre] (ORCID: ), Juan R. Gonzalez [aut] (ORCID: ), Carlos Ruiz-Arenas [aut] (ORCID: ), Carles Hernandez-Ferrer [aut] (ORCID: ), Leire Abarrategui [aut] (ORCID: ) Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/epimutacions VignetteBuilder: knitr BugReports: https://github.com/isglobal-brge/epimutacions/issues git_url: https://git.bioconductor.org/packages/epimutacions git_branch: RELEASE_3_22 git_last_commit: b56f6f9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epimutacions_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epimutacions_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epimutacions_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epimutacions_1.14.0.tgz vignettes: vignettes/epimutacions/inst/doc/epimutacions.html vignetteTitles: Detection of epimutations with state of the art methods in methylation data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epimutacions/inst/doc/epimutacions.R dependencyCount: 222 Package: epiNEM Version: 1.34.0 Depends: R (>= 4.1) Imports: BoutrosLab.plotting.general, BoolNet, e1071, gtools, stats, igraph, utils, lattice, latticeExtra, RColorBrewer, pcalg, minet, grDevices, graph, mnem, latex2exp Suggests: knitr, RUnit, BiocGenerics, STRINGdb, devtools, rmarkdown, GOSemSim, AnnotationHub, org.Sc.sgd.db, BiocStyle License: GPL-3 Archs: x64 MD5sum: ee3c24b367c50f6ef19a21d3d0493db8 NeedsCompilation: no Title: epiNEM Description: epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. It is tailored towards large scale double knock-out screens. biocViews: Pathways, SystemsBiology, NetworkInference, Network Author: Madeline Diekmann & Martin Pirkl Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/epiNEM/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/epiNEM/issues git_url: https://git.bioconductor.org/packages/epiNEM git_branch: RELEASE_3_22 git_last_commit: fb48046 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epiNEM_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epiNEM_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epiNEM_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epiNEM_1.34.0.tgz vignettes: vignettes/epiNEM/inst/doc/epiNEM.html vignetteTitles: epiNEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epiNEM/inst/doc/epiNEM.R importsMe: bnem, nempi suggestsMe: mnem dependencyCount: 109 Package: EpipwR Version: 1.4.0 Depends: R (>= 4.4.0) Imports: EpipwR.data, ExperimentHub (>= 2.10.0), ggplot2 Suggests: knitr, rmarkdown, testthat (>= 3.0.0), sessioninfo License: Artistic-2.0 MD5sum: 57560298abd2c92969343ecd21c4f817 NeedsCompilation: no Title: Efficient Power Analysis for EWAS with Continuous or Binary Outcomes Description: A quasi-simulation based approach to performing power analysis for EWAS (Epigenome-wide association studies) with continuous or binary outcomes. 'EpipwR' relies on empirical EWAS datasets to determine power at specific sample sizes while keeping computational cost low. EpipwR can be run with a variety of standard statistical tests, controlling for either a false discovery rate or a family-wise type I error rate. biocViews: Epigenetics, ExperimentalDesign Author: Jackson Barth [aut, cre] (ORCID: ), Austin Reynolds [aut], Mary Lauren Benton [ctb], Carissa Fong [ctb] Maintainer: Jackson Barth URL: https://github.com/jbarth216/EpipwR VignetteBuilder: knitr BugReports: https://github.com/jbarth216/EpipwR git_url: https://git.bioconductor.org/packages/EpipwR git_branch: RELEASE_3_22 git_last_commit: 223cc40 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EpipwR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EpipwR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EpipwR_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EpipwR_1.4.0.tgz vignettes: vignettes/EpipwR/inst/doc/EpipwR.html vignetteTitles: EpipwR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpipwR/inst/doc/EpipwR.R dependencyCount: 76 Package: epiregulon Version: 2.0.0 Depends: R (>= 4.4), SingleCellExperiment Imports: AnnotationHub, BiocParallel, ExperimentHub, Matrix, Rcpp, S4Vectors, SummarizedExperiment, checkmate, entropy, lifecycle, methods, scran, scuttle, stats, utils, AnnotationHub, GenomeInfoDb, GenomicRanges, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, motifmatchr, IRanges, scrapper LinkingTo: Rcpp Suggests: knitr, rmarkdown, parallel, BiocStyle, testthat (>= 3.0.0), coin, scater, scMultiome License: MIT + file LICENSE MD5sum: b9fdac4911e75c27dc9c3eb4b9ed2e75 NeedsCompilation: yes Title: Gene regulatory network inference from single cell epigenomic data Description: Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions. biocViews: SingleCell, GeneRegulation,NetworkInference,Network, GeneExpression, Transcription, GeneTarget Author: Xiaosai Yao [aut, cre] (ORCID: ), Tomasz Włodarczyk [aut] (ORCID: ), Aaron Lun [aut], Shang-Yang Chen [aut] Maintainer: Xiaosai Yao URL: https://github.com/xiaosaiyao/epiregulon/ VignetteBuilder: knitr BugReports: https://github.com/xiaosaiyao/epiregulon/issues git_url: https://git.bioconductor.org/packages/epiregulon git_branch: RELEASE_3_22 git_last_commit: d431343 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epiregulon_2.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epiregulon_2.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epiregulon_2.0.0.tgz vignettes: vignettes/epiregulon/inst/doc/multiome.mae.html vignetteTitles: Epiregulon tutorial with MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epiregulon/inst/doc/multiome.mae.R suggestsMe: epiregulon.extra dependencyCount: 139 Package: epiregulon.extra Version: 1.6.0 Depends: R (>= 4.4), SingleCellExperiment Imports: scran, ComplexHeatmap, Matrix, SummarizedExperiment, checkmate, circlize, clusterProfiler, ggplot2, ggraph, igraph, patchwork, reshape2, scales, scater Suggests: epiregulon, knitr, rmarkdown, parallel, BiocStyle, testthat (>= 3.0.0), msigdb, GSEABase, dorothea, scMultiome, S4Vectors, scuttle, vdiffr, ggrastr, ggrepel License: MIT + file LICENSE MD5sum: d4e1ea2c620943d76d98ce2e397e913e NeedsCompilation: no Title: Companion package to epiregulon with additional plotting, differential and graph functions Description: Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions. biocViews: GeneRegulation, Network, GeneExpression, Transcription, ChipOnChip, DifferentialExpression, GeneTarget, Normalization, GraphAndNetwork Author: Xiaosai Yao [aut, cre] (ORCID: ), Tomasz Włodarczyk [aut] (ORCID: ), Timothy Keyes [aut], Shang-Yang Chen [aut] Maintainer: Xiaosai Yao URL: https://github.com/xiaosaiyao/epiregulon.extra/ VignetteBuilder: knitr BugReports: https://github.com/xiaosaiyao/epiregulon.extra/issues git_url: https://git.bioconductor.org/packages/epiregulon.extra git_branch: RELEASE_3_22 git_last_commit: d60eef8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epiregulon.extra_1.6.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epiregulon.extra_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epiregulon.extra_1.6.0.tgz vignettes: vignettes/epiregulon.extra/inst/doc/Data_visualization.html vignetteTitles: Data visualization with epiregulon.extra hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epiregulon.extra/inst/doc/Data_visualization.R dependencyCount: 200 Package: epistack Version: 1.16.0 Depends: R (>= 4.1) Imports: GenomicRanges, SummarizedExperiment, BiocGenerics, S4Vectors, IRanges, graphics, plotrix, grDevices, stats, methods Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, EnrichedHeatmap, biomaRt, rtracklayer, covr, vdiffr, magick License: MIT + file LICENSE MD5sum: ff59f13dc752f3fb688f9943e40c87df NeedsCompilation: no Title: Heatmaps of Stack Profiles from Epigenetic Signals Description: The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. epistack needs three different inputs: 1) a genomic score objects, such as ChIP-seq coverage or DNA methylation values, provided as a `GRanges` (easily obtained from `bigwig` or `bam` files). 2) a list of feature of interest, such as peaks or transcription start sites, provided as a `GRanges` (easily obtained from `gtf` or `bed` files). 3) a score to sort the features, such as peak height or gene expression value. biocViews: RNASeq, Preprocessing, ChIPSeq, GeneExpression, Coverage Author: SACI Safia [aut], DEVAILLY Guillaume [cre, aut] Maintainer: DEVAILLY Guillaume URL: https://github.com/GenEpi-GenPhySE/epistack VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epistack git_branch: RELEASE_3_22 git_last_commit: 9ac611d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epistack_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epistack_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epistack_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epistack_1.16.0.tgz vignettes: vignettes/epistack/inst/doc/using_epistack.html vignetteTitles: Using epistack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epistack/inst/doc/using_epistack.R dependencyCount: 26 Package: epistasisGA Version: 1.12.0 Depends: R (>= 4.2) Imports: BiocParallel, data.table, matrixStats, stats, survival, igraph, batchtools, qgraph, grDevices, parallel, ggplot2, grid, bigmemory, graphics, utils LinkingTo: Rcpp, RcppArmadillo, BH, bigmemory Suggests: BiocStyle, knitr, rmarkdown, magrittr, kableExtra, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 09de4e62bcd99b24f0b9abdf302ca0fe NeedsCompilation: yes Title: An R package to identify multi-snp effects in nuclear family studies using the GADGETS method Description: This package runs the GADGETS method to identify epistatic effects in nuclear family studies. It also provides functions for permutation-based inference and graphical visualization of the results. biocViews: Genetics, SNP, GeneticVariability Author: Michael Nodzenski [aut, cre], Juno Krahn [ctb] Maintainer: Michael Nodzenski URL: https://github.com/mnodzenski/epistasisGA VignetteBuilder: knitr BugReports: https://github.com/mnodzenski/epistasisGA/issues git_url: https://git.bioconductor.org/packages/epistasisGA git_branch: RELEASE_3_22 git_last_commit: f898c7a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epistasisGA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epistasisGA_1.11.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epistasisGA_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epistasisGA_1.12.0.tgz vignettes: vignettes/epistasisGA/inst/doc/E_GADGETS.html, vignettes/epistasisGA/inst/doc/GADGETS.html, vignettes/epistasisGA/inst/doc/Including_Maternal_SNPs.html vignetteTitles: E-GADGETS, GADGETS, Detecting Maternal-SNP Interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epistasisGA/inst/doc/E_GADGETS.R, vignettes/epistasisGA/inst/doc/GADGETS.R, vignettes/epistasisGA/inst/doc/Including_Maternal_SNPs.R dependencyCount: 111 Package: EpiTxDb Version: 1.22.0 Depends: R (>= 4.0), AnnotationDbi, Modstrings Imports: methods, utils, httr, xml2, curl, rex, GenomicFeatures, txdbmaker, GenomicRanges, Seqinfo, BiocGenerics, BiocFileCache, S4Vectors, IRanges, RSQLite, DBI, Biostrings, tRNAdbImport Suggests: BiocStyle, knitr, rmarkdown, testthat, httptest, AnnotationHub, ensembldb, ggplot2, EpiTxDb.Hs.hg38, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Scerevisiae.UCSC.sacCer3, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 Archs: x64 MD5sum: aa856d79d87aaac4a5cfa48205a1734a NeedsCompilation: no Title: Storing and accessing epitranscriptomic information using the AnnotationDbi interface Description: EpiTxDb facilitates the storage of epitranscriptomic information. More specifically, it can keep track of modification identity, position, the enzyme for introducing it on the RNA, a specifier which determines the position on the RNA to be modified and the literature references each modification is associated with. biocViews: Software, Epitranscriptomics Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/EpiTxDb VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/EpiTxDb/issues git_url: https://git.bioconductor.org/packages/EpiTxDb git_branch: RELEASE_3_22 git_last_commit: 54a3d69 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EpiTxDb_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EpiTxDb_1.21.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EpiTxDb_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EpiTxDb_1.22.0.tgz vignettes: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.html, vignettes/EpiTxDb/inst/doc/EpiTxDb.html vignetteTitles: EpiTxDb-creation, EpiTxDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.R, vignettes/EpiTxDb/inst/doc/EpiTxDb.R dependsOnMe: EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3 dependencyCount: 115 Package: epivizr Version: 2.40.0 Depends: R (>= 3.5.0), methods Imports: epivizrServer (>= 1.1.1), epivizrData (>= 1.3.4), GenomicRanges, S4Vectors, IRanges, bumphunter, GenomeInfoDb Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, minfi, rmarkdown License: Artistic-2.0 MD5sum: a7805e9cfe3ef3786eed7a5c89d96e3a NeedsCompilation: no Title: R Interface to epiviz web app Description: This package provides connections to the epiviz web app (http://epiviz.cbcb.umd.edu) for interactive visualization of genomic data. Objects in R/bioc interactive sessions can be displayed in genome browser tracks or plots to be explored by navigation through genomic regions. Fundamental Bioconductor data structures are supported (e.g., GenomicRanges and RangedSummarizedExperiment objects), while providing an easy mechanism to support other data structures (through package epivizrData). Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Florin Chelaru, Llewellyn Smith, Naomi Goldstein, Jayaram Kancherla, Morgan Walter, Brian Gottfried Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA git_url: https://git.bioconductor.org/packages/epivizr git_branch: RELEASE_3_22 git_last_commit: 7b064c5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epivizr_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epivizr_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epivizr_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epivizr_2.40.0.tgz vignettes: vignettes/epivizr/inst/doc/IntroToEpivizr.html vignetteTitles: Introduction to epivizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R dependsOnMe: epivizrStandalone, scTreeViz dependencyCount: 103 Package: epivizrChart Version: 1.32.0 Depends: R (>= 3.5.0) Imports: epivizrData (>= 1.5.1), epivizrServer, htmltools, rjson, methods, BiocGenerics Suggests: testthat, roxygen2, knitr, Biobase, GenomicRanges, S4Vectors, IRanges, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, Homo.sapiens, shiny, minfi, Rsamtools, rtracklayer, RColorBrewer, magrittr, AnnotationHub License: Artistic-2.0 Archs: x64 MD5sum: 482e6e9ac49b2ccaa9bbe30ec0dba109 NeedsCompilation: no Title: R interface to epiviz web components Description: This package provides an API for interactive visualization of genomic data using epiviz web components. Objects in R/BioConductor can be used to generate interactive R markdown/notebook documents or can be visualized in the R Studio's default viewer. biocViews: Visualization, GUI Author: Brian Gottfried [aut], Jayaram Kancherla [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrChart git_branch: RELEASE_3_22 git_last_commit: 86e97b3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epivizrChart_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epivizrChart_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epivizrChart_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epivizrChart_1.32.0.tgz vignettes: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.html, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.html, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.html, vignettes/epivizrChart/inst/doc/VisualizeSumExp.html vignetteTitles: Visualizing Files with epivizrChart, Visualizing genomic data in Shiny Apps using epivizrChart, Introduction to epivizrChart, Visualizing `RangeSummarizedExperiment` objects Shiny Apps using epivizrChart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.R, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.R, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.R, vignettes/epivizrChart/inst/doc/VisualizeSumExp.R dependencyCount: 97 Package: epivizrData Version: 1.38.0 Depends: R (>= 3.4), methods, epivizrServer (>= 1.1.1), Biobase Imports: S4Vectors, GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), OrganismDbi, GenomicFeatures (>= 1.61.4), Seqinfo, IRanges, ensembldb (>= 2.33.1) Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus, TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown, BiocStyle, EnsDb.Mmusculus.v79, AnnotationHub, rtracklayer, utils, RMySQL, DBI, matrixStats License: MIT + file LICENSE MD5sum: fdec69f2f113fff9c1aa530a7f4a7b6f NeedsCompilation: no Title: Data Management API for epiviz interactive visualization app Description: Serve data from Bioconductor Objects through a WebSocket connection. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre], Florin Chelaru [aut] Maintainer: Hector Corrada Bravo URL: http://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrData/issues git_url: https://git.bioconductor.org/packages/epivizrData git_branch: RELEASE_3_22 git_last_commit: c56a41a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epivizrData_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epivizrData_1.37.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epivizrData_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epivizrData_1.38.0.tgz vignettes: vignettes/epivizrData/inst/doc/epivizrData.html vignetteTitles: epivizrData Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R importsMe: epivizr, epivizrChart, scTreeViz dependencyCount: 93 Package: epivizrServer Version: 1.38.0 Depends: R (>= 3.2.3), methods Imports: httpuv (>= 1.3.0), R6 (>= 2.0.0), rjson, mime (>= 0.2) Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 3f1a847486aa0e4a757e8bd5cdf5d33f NeedsCompilation: no Title: WebSocket server infrastructure for epivizr apps and packages Description: This package provides objects to manage WebSocket connections to epiviz apps. Other epivizr package use this infrastructure. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrServer git_url: https://git.bioconductor.org/packages/epivizrServer git_branch: RELEASE_3_22 git_last_commit: 3c23516 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epivizrServer_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epivizrServer_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epivizrServer_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epivizrServer_1.38.0.tgz vignettes: vignettes/epivizrServer/inst/doc/epivizrServer.html vignetteTitles: epivizrServer Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: epivizrData importsMe: epivizr, epivizrChart, epivizrStandalone, scTreeViz dependencyCount: 17 Package: epivizrStandalone Version: 1.38.0 Depends: R (>= 3.2.3), epivizr (>= 2.3.6), methods Imports: git2r, epivizrServer, Seqinfo, BiocGenerics, GenomicFeatures, S4Vectors Suggests: testthat, knitr, rmarkdown, OrganismDbi (>= 1.13.9), Mus.musculus, Biobase, BiocStyle License: MIT + file LICENSE MD5sum: 28394c16d77ab9f43ebee83f9b26b1ee NeedsCompilation: no Title: Run Epiviz Interactive Genomic Data Visualization App within R Description: This package imports the epiviz visualization JavaScript app for genomic data interactive visualization. The 'epivizrServer' package is used to provide a web server running completely within R. This standalone version allows to browse arbitrary genomes through genome annotations provided by Bioconductor packages. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Jayaram Kancherla Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrStandalone git_branch: RELEASE_3_22 git_last_commit: 4274fcb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/epivizrStandalone_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epivizrStandalone_1.37.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epivizrStandalone_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epivizrStandalone_1.38.0.tgz vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html vignetteTitles: Introduction to epivizrStandalone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: scTreeViz dependencyCount: 105 Package: erccdashboard Version: 1.44.0 Depends: R (>= 4.0), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0) Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, qvalue, reshape2, ROCR, scales, stringr, knitr Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 1aff569398a1bed760494afc68783459 NeedsCompilation: no Title: Assess Differential Gene Expression Experiments with ERCC Controls Description: Technical performance metrics for differential gene expression experiments using External RNA Controls Consortium (ERCC) spike-in ratio mixtures. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Genetics, Microarray, mRNAMicroarray, RNASeq, BatchEffect, MultipleComparison, QualityControl Author: Sarah Munro, Steve Lund Maintainer: Sarah Munro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/erccdashboard git_branch: RELEASE_3_22 git_last_commit: 76c1882 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/erccdashboard_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/erccdashboard_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/erccdashboard_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/erccdashboard_1.44.0.tgz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.html vignetteTitles: erccdashboard introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R dependencyCount: 49 Package: ERSSA Version: 1.28.0 Depends: R (>= 4.0.0) Imports: edgeR (>= 3.23.3), DESeq2 (>= 1.21.16), ggplot2 (>= 3.0.0), RColorBrewer (>= 1.1-2), plyr (>= 1.8.4), BiocParallel (>= 1.15.8), apeglm (>= 1.4.2), grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 0c829a3ff2bd11f5938d51f99dd65c6a NeedsCompilation: no Title: Empirical RNA-seq Sample Size Analysis Description: The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate levels. This allows the user to determine, without relying on any a priori assumptions, whether sufficient differential detection has been acheived with their RNA-seq dataset. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, RNASeq, MultipleComparison, QualityControl Author: Zixuan Shao [aut, cre] Maintainer: Zixuan Shao URL: https://github.com/zshao1/ERSSA VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ERSSA git_branch: RELEASE_3_22 git_last_commit: d746337 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ERSSA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ERSSA_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ERSSA_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ERSSA_1.28.0.tgz vignettes: vignettes/ERSSA/inst/doc/ERSSA.html vignetteTitles: ERSSA Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ERSSA/inst/doc/ERSSA.R dependencyCount: 69 Package: esATAC Version: 1.32.0 Depends: R (>= 4.0.0), Rsamtools, GenomicRanges, ShortRead, pipeFrame Imports: Rcpp (>= 0.12.11), methods, knitr, Rbowtie2, rtracklayer, ggplot2, Biostrings, ChIPseeker, clusterProfiler, igraph, rJava, magrittr, digest, BSgenome, AnnotationDbi, GenomicAlignments, GenomicFeatures, R.utils, Seqinfo, BiocGenerics, S4Vectors, IRanges, rmarkdown, tools, VennDiagram, grid, JASPAR2018, TFBSTools, grDevices, graphics, stats, utils, parallel, corrplot, BiocManager, motifmatchr LinkingTo: Rcpp Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat, webshot, prettydoc License: GPL-3 | file LICENSE MD5sum: 9959dc0bf4deaf6eebf21d86b538d2a1 NeedsCompilation: yes Title: An Easy-to-use Systematic pipeline for ATACseq data analysis Description: This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly. biocViews: ImmunoOncology, Sequencing, DNASeq, QualityControl, Alignment, Preprocessing, Coverage, ATACSeq, DNaseSeq Author: Zheng Wei, Wei Zhang Maintainer: Zheng Wei URL: https://github.com/wzthu/esATAC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/wzthu/esATAC/issues git_url: https://git.bioconductor.org/packages/esATAC git_branch: RELEASE_3_22 git_last_commit: 6d7ce39 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/esATAC_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/esATAC_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/esATAC_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/esATAC_1.32.0.tgz vignettes: vignettes/esATAC/inst/doc/esATAC-Introduction.html vignetteTitles: esATAC: an Easy-to-use Systematic pipeline for ATAC-seq data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/esATAC/inst/doc/esATAC-Introduction.R dependencyCount: 190 Package: escape Version: 2.5.5 Depends: R (>= 4.1) Imports: ggdist, ggplot2 (>= 3.5.0), grDevices, Matrix, MatrixGenerics, methods, stats, SummarizedExperiment, utils Suggests: AUCell, BiocParallel, BiocStyle, DelayedMatrixStats, dplyr, fgsea, GSEABase, ggraph, ggridges, ggpointdensity, GSVA, hexbin, igraph, irlba, knitr, msigdb, patchwork, rmarkdown, rlang, scran, SeuratObject, Seurat, SingleCellExperiment, spelling, stringr, testthat (>= 3.0.0), UCell License: MIT + file LICENSE MD5sum: 22a3d4cb51a4798477ce91a69e4d3e09 NeedsCompilation: no Title: Easy single cell analysis platform for enrichment Description: A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells. Alternatively, escape supports use of rank-based GSEA, such as the use of differential gene expression via fgsea. biocViews: Software, SingleCell, Classification, Annotation, GeneSetEnrichment, Sequencing, GeneSignaling, Pathways Author: Nick Borcherding [aut, cre], Jared Andrews [aut], Tobias Hoch [ctb], Alexei Martsinkovskiy [ctb] Maintainer: Nick Borcherding VignetteBuilder: knitr BugReports: https://github.com/BorchLab/escape/issues git_url: https://git.bioconductor.org/packages/escape git_branch: devel git_last_commit: 59e0b12 git_last_commit_date: 2025-07-25 Date/Publication: 2025-10-07 source.ver: src/contrib/escape_2.5.5.tar.gz win.binary.ver: bin/windows/contrib/4.5/escape_2.5.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/escape_2.5.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/escape_2.5.5.tgz vignettes: vignettes/escape/inst/doc/escape.html vignetteTitles: Escape-ingToWork hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/escape/inst/doc/escape.R suggestsMe: Cepo dependencyCount: 51 Package: escheR Version: 1.10.0 Depends: ggplot2, R (>= 4.3) Imports: SpatialExperiment (>= 1.6.1), SingleCellExperiment, rlang, SummarizedExperiment Suggests: STexampleData, BumpyMatrix, knitr, rmarkdown, BiocStyle, ggpubr, scran, scater, scuttle, Seurat, hexbin License: MIT + file LICENSE MD5sum: 66e475ed2fe0b1825108cb9924ca2318 NeedsCompilation: no Title: Unified multi-dimensional visualizations with Gestalt principles Description: The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide this open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows. biocViews: Spatial, SingleCell, Transcriptomics, Visualization, Software Author: Boyi Guo [aut, cre] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ), Erik D. Nelson [ctb] (ORCID: ) Maintainer: Boyi Guo URL: https://github.com/boyiguo1/escheR VignetteBuilder: knitr BugReports: https://github.com/boyiguo1/escheR/issues git_url: https://git.bioconductor.org/packages/escheR git_branch: RELEASE_3_22 git_last_commit: 3f79e54 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/escheR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/escheR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/escheR_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/escheR_1.10.0.tgz vignettes: vignettes/escheR/inst/doc/more_than_visium.html, vignettes/escheR/inst/doc/SRT_eg.html vignetteTitles: beyond_visium, Getting Start with `escheR` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/escheR/inst/doc/more_than_visium.R, vignettes/escheR/inst/doc/SRT_eg.R importsMe: SpotSweeper suggestsMe: tpSVG dependencyCount: 76 Package: esetVis Version: 1.36.0 Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils, grDevices, methods Suggests: ggplot2, ggvis, plotly, ggrepel, knitr, rmarkdown, ALL, hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment, GO.db License: GPL-3 Archs: x64 MD5sum: b1c2dfd1b4c7a384e0de9b3f78efabf7 NeedsCompilation: no Title: Visualizations of expressionSet Bioconductor object Description: Utility functions for visualization of expressionSet (or SummarizedExperiment) Bioconductor object, including spectral map, tsne and linear discriminant analysis. Static plot via the ggplot2 package or interactive via the ggvis or rbokeh packages are available. biocViews: Visualization, DataRepresentation, DimensionReduction, PrincipalComponent, Pathways Author: Laure Cougnaud Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/esetVis git_branch: RELEASE_3_22 git_last_commit: 1962752 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/esetVis_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/esetVis_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/esetVis_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/esetVis_1.36.0.tgz vignettes: vignettes/esetVis/inst/doc/esetVis-vignette.html vignetteTitles: esetVis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/esetVis/inst/doc/esetVis-vignette.R dependencyCount: 56 Package: eudysbiome Version: 1.40.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: 76a4fa47a849836686787a4b0ae18def NeedsCompilation: no Title: Cartesian plot and contingency test on 16S Microbial data Description: eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition. Author: Xiaoyuan Zhou, Christine Nardini Maintainer: Xiaoyuan Zhou git_url: https://git.bioconductor.org/packages/eudysbiome git_branch: RELEASE_3_22 git_last_commit: 7979b5b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/eudysbiome_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/eudysbiome_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/eudysbiome_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/eudysbiome_1.40.0.tgz vignettes: vignettes/eudysbiome/inst/doc/eudysbiome.pdf vignetteTitles: eudysbiome User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eudysbiome/inst/doc/eudysbiome.R dependencyCount: 34 Package: evaluomeR Version: 1.26.0 Depends: R (>= 3.6), SummarizedExperiment, MultiAssayExperiment, cluster (>= 2.0.9), fpc (>= 2.2-3), randomForest (>= 4.6.14), flexmix (>= 2.3.15), RSKC (>= 2.4.2), sparcl (>= 1.0.4) Imports: corrplot (>= 0.84), grDevices, graphics, reshape2, ggplot2, ggdendro, plotrix, stats, matrixStats, Rdpack, MASS, class, prabclus, mclust, kableExtra, dplyr, dendextend (>= 1.16.0) Suggests: BiocStyle, knitr, rmarkdown, magrittr License: GPL-3 MD5sum: ce0c5cac79525355d9f15259353a7bf9 NeedsCompilation: no Title: Evaluation of Bioinformatics Metrics Description: Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics. biocViews: Clustering, Classification, FeatureExtraction Author: José Antonio Bernabé-Díaz [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut], Manuel Quesada-Martínez [aut], Astrid Duque-Ramos [aut], Jesualdo Tomás Fernández-Breis [aut] Maintainer: José Antonio Bernabé-Díaz URL: https://github.com/neobernad/evaluomeR VignetteBuilder: knitr BugReports: https://github.com/neobernad/evaluomeR/issues git_url: https://git.bioconductor.org/packages/evaluomeR git_branch: RELEASE_3_22 git_last_commit: ed75340 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/evaluomeR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/evaluomeR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/evaluomeR_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/evaluomeR_1.26.0.tgz vignettes: vignettes/evaluomeR/inst/doc/manual.html vignetteTitles: Evaluation of Bioinformatics Metrics with evaluomeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/evaluomeR/inst/doc/manual.R dependencyCount: 112 Package: EventPointer Version: 3.18.0 Depends: R (>= 3.5.0), SGSeq, Matrix, SummarizedExperiment Imports: GenomicFeatures, stringr, GenomeInfoDb, igraph, MASS, nnls, limma, matrixStats, RBGL, prodlim, graph, methods, utils, stats, doParallel, foreach, affxparser, GenomicRanges, S4Vectors, IRanges, qvalue, cobs, rhdf5, BSgenome, Biostrings, glmnet, abind, iterators, lpSolve, poibin, speedglm, tximport, fgsea Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, dplyr, kableExtra License: Artistic-2.0 MD5sum: 8ef7d7e4e68fd851fdec483bed3bee68 NeedsCompilation: yes Title: An effective identification of alternative splicing events using junction arrays and RNA-Seq data Description: EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation. biocViews: AlternativeSplicing, DifferentialSplicing, mRNAMicroarray, RNASeq, Transcription, Sequencing, TimeCourse, ImmunoOncology Author: Juan Pablo Romero [aut], Juan A. Ferrer-Bonsoms [aut, cre], Pablo Sacristan [aut], Ander Muniategui [aut], Fernando Carazo [aut], Ander Aramburu [aut], Angel Rubio [aut] Maintainer: Juan A. Ferrer-Bonsoms VignetteBuilder: knitr BugReports: https://github.com/jpromeror/EventPointer/issues git_url: https://git.bioconductor.org/packages/EventPointer git_branch: RELEASE_3_22 git_last_commit: 816a09d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EventPointer_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EventPointer_3.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EventPointer_3.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EventPointer_3.18.0.tgz vignettes: vignettes/EventPointer/inst/doc/EventPointer.html vignetteTitles: EventPointer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EventPointer/inst/doc/EventPointer.R dependencyCount: 143 Package: EWCE Version: 1.18.0 Depends: R (>= 4.2), RNOmni (>= 1.0) Imports: stats, utils, methods, ewceData (>= 1.7.1), dplyr, ggplot2, reshape2, limma, stringr, HGNChelper, Matrix, parallel, SingleCellExperiment, SummarizedExperiment, DelayedArray, BiocParallel, orthogene (>= 0.99.8), data.table Suggests: rworkflows, remotes, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), readxl, memoise, markdown, sctransform, DESeq2, MAST, DelayedMatrixStats, ggdendro, scales, patchwork License: GPL-3 Archs: x64 MD5sum: ddc2e6e58119dc17545f6f1639c70262 NeedsCompilation: no Title: Expression Weighted Celltype Enrichment Description: Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses. biocViews: GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Microarray, mRNAMicroarray, OneChannel, RNASeq, BiomedicalInformatics, Proteomics, Visualization, FunctionalGenomics, SingleCell Author: Alan Murphy [aut] (ORCID: ), Brian Schilder [aut] (ORCID: ), Hiranyamaya Dash [cre] (ORCID: ), Nathan Skene [aut] (ORCID: ) Maintainer: Hiranyamaya Dash URL: https://github.com/NathanSkene/EWCE VignetteBuilder: knitr BugReports: https://github.com/NathanSkene/EWCE/issues git_url: https://git.bioconductor.org/packages/EWCE git_branch: RELEASE_3_22 git_last_commit: 413c045 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/EWCE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EWCE_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EWCE_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EWCE_1.18.0.tgz vignettes: vignettes/EWCE/inst/doc/EWCE.html, vignettes/EWCE/inst/doc/extended.html vignetteTitles: Getting started, Extended examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EWCE/inst/doc/EWCE.R, vignettes/EWCE/inst/doc/extended.R dependencyCount: 199 Package: ExCluster Version: 1.28.0 Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges Imports: stats, methods, grDevices, graphics, utils License: GPL-3 MD5sum: b0ebed5ab95e5bf027f0d4f494529e91 NeedsCompilation: no Title: ExCluster robustly detects differentially expressed exons between two conditions of RNA-seq data, requiring at least two independent biological replicates per condition Description: ExCluster flattens Ensembl and GENCODE GTF files into GFF files, which are used to count reads per non-overlapping exon bin from BAM files. This read counting is done using the function featureCounts from the package Rsubread. Library sizes are normalized across all biological replicates, and ExCluster then compares two different conditions to detect signifcantly differentially spliced genes. This process requires at least two independent biological repliates per condition, and ExCluster accepts only exactly two conditions at a time. ExCluster ultimately produces false discovery rates (FDRs) per gene, which are used to detect significance. Exon log2 fold change (log2FC) means and variances may be plotted for each significantly differentially spliced gene, which helps scientists develop hypothesis and target differential splicing events for RT-qPCR validation in the wet lab. biocViews: ImmunoOncology, DifferentialSplicing, RNASeq, Software Author: R. Matthew Tanner, William L. Stanford, and Theodore J. Perkins Maintainer: R. Matthew Tanner git_url: https://git.bioconductor.org/packages/ExCluster git_branch: RELEASE_3_22 git_last_commit: 81bde96 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ExCluster_1.28.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExCluster_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExCluster_1.28.0.tgz vignettes: vignettes/ExCluster/inst/doc/ExCluster.pdf vignetteTitles: ExCluster Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExCluster/inst/doc/ExCluster.R dependencyCount: 58 Package: ExiMiR Version: 2.52.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), affy (>= 1.26.1), limma Imports: affyio(>= 1.13.3), Biobase(>= 2.5.5), preprocessCore(>= 1.10.0) Suggests: mirna10cdf License: GPL-2 MD5sum: 9b0dd7c78a0c5a672f475533f5854241 NeedsCompilation: no Title: R functions for the normalization of Exiqon miRNA array data Description: This package contains functions for reading raw data in ImaGene TXT format obtained from Exiqon miRCURY LNA arrays, annotating them with appropriate GAL files, and normalizing them using a spike-in probe-based method. Other platforms and data formats are also supported. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, GeneExpression, Transcription Author: Sylvain Gubian , Alain Sewer , PMP SA Maintainer: Sylvain Gubian git_url: https://git.bioconductor.org/packages/ExiMiR git_branch: RELEASE_3_22 git_last_commit: f23173f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ExiMiR_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExiMiR_2.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExiMiR_2.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExiMiR_2.52.0.tgz vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf vignetteTitles: Description of ExiMiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.R dependencyCount: 14 Package: ExperimentHub Version: 3.0.0 Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 3.99.3), BiocFileCache (>= 2.99.3) Imports: utils, S4Vectors, BiocManager, rappdirs Suggests: knitr, BiocStyle, rmarkdown, HubPub, GenomicAlignments Enhances: ExperimentHubData License: Artistic-2.0 MD5sum: 71fcb221ab1233d1640d17202105c500 NeedsCompilation: no Title: Client to access ExperimentHub resources Description: This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/ExperimentHub VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ExperimentHub/issues git_url: https://git.bioconductor.org/packages/ExperimentHub git_branch: RELEASE_3_22 git_last_commit: b1013ec git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ExperimentHub_3.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExperimentHub_2.99.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExperimentHub_3.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExperimentHub_3.0.0.tgz vignettes: vignettes/ExperimentHub/inst/doc/ExperimentHub.html vignetteTitles: ExperimentHub: Access the ExperimentHub Web Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHub/inst/doc/ExperimentHub.R dependsOnMe: adductomicsR, iSEEhub, LRcell, octad, SeqSQC, AWAggregatorData, BeadSorted.Saliva.EPIC, biscuiteerData, bodymapRat, CellMapperData, clustifyrdatahub, CoSIAdata, crisprScoreData, curatedAdipoChIP, CytoMethIC, DMRcatedata, eoPredData, EpiMix.data, ewceData, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, HDCytoData, HiContactsData, HighlyReplicatedRNASeq, HumanAffyData, mcsurvdata, MetaGxBreast, MetaGxPancreas, multiWGCNAdata, muscData, muSpaData, NanoporeRNASeq, NestLink, nullrangesData, ObMiTi, octad.db, RNAmodR.Data, scMultiome, scpdata, sesameData, SimBenchData, SpatialDatasets, spatialDmelxsim, STexampleData, tartare, TENxVisiumData, TENxXeniumData, VectraPolarisData, WeberDivechaLCdata importsMe: BiocHubsShiny, BloodGen3Module, CBNplot, coMethDMR, CTdata, DeconvoBuddies, DMRcate, EpiMix, epimutacions, EpipwR, epiregulon, ExperimentHubData, GSEABenchmarkeR, hpar, iModMix, m6Aboost, MACSr, MatrixQCvis, methodical, MethReg, methylclock, Moonlight2R, MsDataHub, orthos, shinyDSP, signatureSearch, singleCellTK, spatialFDA, TENET, TFEA.ChIP, adductData, BioImageDbs, celldex, CENTREprecomputed, cfToolsData, ChIPDBData, chipseqDBData, CLLmethylation, curatedMetagenomicData, curatedPCaData, curatedTBData, curatedTCGAData, depmap, DoReMiTra, DropletTestFiles, DuoClustering2018, easierData, emtdata, EpipwR.data, FieldEffectCrc, gDNAinRNAseqData, GenomicDistributionsData, HarmonizedTCGAData, HCAData, HCATonsilData, HMP16SData, HMP2Data, homosapienDEE2CellScore, humanHippocampus2024, imcdatasets, iModMixData, JohnsonKinaseData, LRcellTypeMarkers, marinerData, MerfishData, methylclockData, MethylSeqData, microbiomeDataSets, MouseAgingData, MouseGastrulationData, MouseThymusAgeing, msigdb, NxtIRFdata, orthosData, PhyloProfileData, preciseTADhub, ProteinGymR, raerdata, scaeData, scRNAseq, SFEData, signatureSearchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialLIBD, TabulaMurisData, TabulaMurisSenisData, TENET.ExperimentHub, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, TumourMethData, xcoredata, OSTA suggestsMe: AlphaMissenseR, ANF, AnnotationHub, AWAggregator, bambu, Banksy, celaref, CellMapper, crumblr, DESpace, dreamlet, ELMER, genomicInstability, h5mread, HDF5Array, HVP, jazzPanda, mariner, missMethyl, MsBackendRawFileReader, multiWGCNA, muscat, nullranges, planet, quantiseqr, rawDiag, rawrr, recountmethylation, SingleMoleculeFootprinting, sosta, SparseArray, SPOTlight, standR, TCGAbiolinks, TENxIO, Voyager, xcore, BioPlex, celarefData, curatedAdipoArray, epimutacionsData, GSE103322, GSE13015, GSE159526, GSE62944, muleaData, smokingMouse, SubcellularSpatialData, tissueTreg, TransOmicsData dependencyCount: 64 Package: ExperimentHubData Version: 1.36.0 Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData (>= 1.21.3) Imports: methods, ExperimentHub, BiocManager, DBI, httr, curl Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle, rmarkdown, HubPub License: Artistic-2.0 MD5sum: 8655aa9b488cd6299b61832673f9ca27 NeedsCompilation: no Title: Add resources to ExperimentHub Description: Functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentHubData git_branch: RELEASE_3_22 git_last_commit: 4c5fbbf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ExperimentHubData_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExperimentHubData_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExperimentHubData_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExperimentHubData_1.36.0.tgz vignettes: vignettes/ExperimentHubData/inst/doc/ExperimentHubData.html vignetteTitles: Introduction to ExperimentHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RNAmodR.Data importsMe: methylclockData suggestsMe: HubPub, MsDataHub, cfToolsData, homosapienDEE2CellScore, humanHippocampus2024, JohnsonKinaseData, marinerData, scMultiome, smokingMouse, TENET.ExperimentHub dependencyCount: 118 Package: ExperimentSubset Version: 1.20.0 Depends: R (>= 4.0.0), SummarizedExperiment, SingleCellExperiment, SpatialExperiment, TreeSummarizedExperiment Imports: methods, Matrix, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, stats, scran, scater, scds, TENxPBMCData, airway License: MIT + file LICENSE MD5sum: 8f3be125c911e25234a6aa94e926aefa NeedsCompilation: no Title: Manages subsets of data with Bioconductor Experiment objects Description: Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for one or more matrix-like assays along with the associated row and column data. Often only a subset of the original data is needed for down-stream analysis. For example, filtering out poor quality samples will require excluding some columns before analysis. The ExperimentSubset object is a container to efficiently manage different subsets of the same data without having to make separate objects for each new subset. biocViews: Infrastructure, Software, DataImport, DataRepresentation Author: Irzam Sarfraz [aut, cre] (ORCID: ), Muhammad Asif [aut, ths] (ORCID: ), Joshua D. Campbell [aut] (ORCID: ) Maintainer: Irzam Sarfraz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentSubset git_branch: RELEASE_3_22 git_last_commit: fe8c338 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ExperimentSubset_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExperimentSubset_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExperimentSubset_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExperimentSubset_1.20.0.tgz vignettes: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.html vignetteTitles: An introduction to ExperimentSubset class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.R dependencyCount: 88 Package: ExploreModelMatrix Version: 1.22.0 Imports: shiny (>= 1.5.0), shinydashboard, DT, cowplot, utils, dplyr, magrittr, tidyr, ggplot2, stats, methods, rintrojs, scales, tibble, MASS, limma, S4Vectors, shinyjs, rlang Suggests: testthat (>= 2.1.0), knitr, rmarkdown, htmltools, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 142519be9dbfe4d745162ab575bb9750 NeedsCompilation: no Title: Graphical Exploration of Design Matrices Description: Given a sample data table and a design formula, ExploreModelMatrix generates an interactive application for exploration of the resulting design matrix. This can be helpful for interpreting model coefficients and constructing appropriate contrasts in (generalized) linear models. Static visualizations can also be generated. biocViews: ExperimentalDesign, Regression, DifferentialExpression, ShinyApps Author: Charlotte Soneson [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ), Michael Love [aut] (ORCID: ), Florian Geier [aut] (ORCID: ), Michael Stadler [aut] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/ExploreModelMatrix VignetteBuilder: knitr BugReports: https://github.com/csoneson/ExploreModelMatrix/issues git_url: https://git.bioconductor.org/packages/ExploreModelMatrix git_branch: RELEASE_3_22 git_last_commit: 66ed086 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ExploreModelMatrix_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExploreModelMatrix_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExploreModelMatrix_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExploreModelMatrix_1.22.0.tgz vignettes: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.html, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.html vignetteTitles: ExploreModelMatrix-deploy, ExploreModelMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.R, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.R dependencyCount: 80 Package: ExpressionAtlas Version: 2.2.0 Depends: R (>= 4.2.0), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2, RCurl, jsonlite, BiocStyle Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 39ed929b2983bbf6ee48c855217f6354 NeedsCompilation: no Title: Download datasets from EMBL-EBI Expression Atlas Description: This package is for searching for datasets in EMBL-EBI Expression Atlas, and downloading them into R for further analysis. Each Expression Atlas dataset is represented as a SimpleList object with one element per platform. Sequencing data is contained in a SummarizedExperiment object, while microarray data is contained in an ExpressionSet or MAList object. biocViews: ExpressionData, ExperimentData, SequencingData, MicroarrayData, ArrayExpress Author: Maria Keays [aut] (ORCID: ), Pedro Madrigal [aut] (ORCID: ), Anil Thanki [cre] (ORCID: ) Maintainer: Anil Thanki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExpressionAtlas git_branch: RELEASE_3_22 git_last_commit: 6dde6ba git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ExpressionAtlas_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExpressionAtlas_2.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExpressionAtlas_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExpressionAtlas_2.2.0.tgz vignettes: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html vignetteTitles: ExpressionAtlas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R dependencyCount: 65 Package: extraChIPs Version: 1.14.0 Depends: BiocParallel, R (>= 4.2.0), GenomicRanges, ggplot2 (>= 4.0.0), ggside (>= 0.4.0), Seqinfo, SummarizedExperiment (>= 1.39.1), tibble Imports: csaw, dplyr (>= 1.1.1), edgeR (>= 4.0), forcats, GenomeInfoDb, glue, ggrepel, InteractionSet, IRanges, matrixStats, methods, patchwork, RColorBrewer, rlang, Rsamtools, rtracklayer, S4Vectors, scales, stats, stringr, tidyr, tidyselect, vctrs Suggests: apeglm, BiocStyle, SimpleUpset, covr, DESeq2, EnrichedHeatmap, GenomicAlignments, GenomicInteractions, Gviz, ggforce, harmonicmeanp, here, knitr, limma, magrittr, plyranges, quantro, rmarkdown, testthat (>= 3.0.0), tidyverse, VennDiagram License: GPL-3 MD5sum: 1837f2738f6ad8cb956ef9c84053a82e NeedsCompilation: yes Title: Additional functions for working with ChIP-Seq data Description: This package builds on existing tools and adds some simple but extremely useful capabilities for working wth ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented. biocViews: ChIPSeq, HiC, Sequencing, Coverage Author: Stevie Pederson [aut, cre] (ORCID: ) Maintainer: Stevie Pederson URL: https://github.com/smped/extraChIPs VignetteBuilder: knitr BugReports: https://github.com/smped/extraChIPs/issues git_url: https://git.bioconductor.org/packages/extraChIPs git_branch: RELEASE_3_22 git_last_commit: 9b0567a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/extraChIPs_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/extraChIPs_1.13.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/extraChIPs_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/extraChIPs_1.14.0.tgz vignettes: vignettes/extraChIPs/inst/doc/differential_signal_fixed.html, vignettes/extraChIPs/inst/doc/differential_signal_sliding.html, vignettes/extraChIPs/inst/doc/range_based_functions.html vignetteTitles: Differential Signal Analysis (Fixed-Width Windows), Differential Signal Analysis (Sliding Windows), Range-Based Operations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/extraChIPs/inst/doc/differential_signal_fixed.R, vignettes/extraChIPs/inst/doc/differential_signal_sliding.R, vignettes/extraChIPs/inst/doc/range_based_functions.R suggestsMe: motifTestR, transmogR dependencyCount: 97 Package: fabia Version: 2.56.0 Depends: R (>= 3.6.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: ec80856950c59c5bdf2678571e9e1336 NeedsCompilation: yes Title: FABIA: Factor Analysis for Bicluster Acquisition Description: Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/fabia/fabia.html git_url: https://git.bioconductor.org/packages/fabia git_branch: RELEASE_3_22 git_last_commit: 3ac43fc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fabia_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fabia_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fabia_2.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fabia_2.56.0.tgz vignettes: vignettes/fabia/inst/doc/fabia.pdf vignetteTitles: FABIA: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fabia/inst/doc/fabia.R dependsOnMe: hapFabia, superbiclust importsMe: miRSM, mosbi, BcDiag suggestsMe: fabiaData, SUMO dependencyCount: 8 Package: factDesign Version: 1.86.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: 0dc430c4c67bdfce7eee75c3f4b263d1 NeedsCompilation: no Title: Factorial designed microarray experiment analysis Description: This package provides a set of tools for analyzing data from a factorial designed microarray experiment, or any microarray experiment for which a linear model is appropriate. The functions can be used to evaluate tests of contrast of biological interest and perform single outlier detection. biocViews: Microarray, DifferentialExpression Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/factDesign git_branch: RELEASE_3_22 git_last_commit: 6e6ee50 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/factDesign_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/factDesign_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/factDesign_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/factDesign_1.86.0.tgz vignettes: vignettes/factDesign/inst/doc/factDesign.pdf vignetteTitles: factDesign hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/factDesign/inst/doc/factDesign.R dependencyCount: 7 Package: faers Version: 1.6.0 Depends: R (>= 3.5.0) Imports: BiocParallel, brio, cli, curl (>= 6.0.0), data.table, httr2 (>= 1.0.0), MCMCpack, methods, openEBGM, rlang, rvest, tools, utils, vroom, xml2 Suggests: BiocStyle, countrycode, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 07035a6313ceaddc409b10600c7e970e NeedsCompilation: no Title: R interface for FDA Adverse Event Reporting System Description: The FDA Adverse Event Reporting System (FAERS) is a database used for the spontaneous reporting of adverse events and medication errors related to human drugs and therapeutic biological products. faers pacakge serves as the interface between the FAERS database and R. Furthermore, faers pacakge offers a standardized approach for performing pharmacovigilance analysis. biocViews: Software, DataImport, BiomedicalInformatics, Pharmacogenomics, Pharmacogenomics Author: Yun Peng [aut, cre] (ORCID: ), YuXuan Song [aut], Caipeng Qin [aut], JiaXing Lin [aut] Maintainer: Yun Peng VignetteBuilder: knitr BugReports: https://github.com/WangLabCSU/faers git_url: https://git.bioconductor.org/packages/faers git_branch: RELEASE_3_22 git_last_commit: c149fa3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/faers_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/faers_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/faers_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/faers_1.6.0.tgz vignettes: vignettes/faers/inst/doc/FAERS-Pharmacovigilance.html vignetteTitles: FAERS-Pharmacovigilance hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/faers/inst/doc/FAERS-Pharmacovigilance.R dependencyCount: 75 Package: FamAgg Version: 1.38.0 Depends: methods, kinship2, igraph Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: 0790d386e2a1368533638ac382db3aab NeedsCompilation: no Title: Pedigree Analysis and Familial Aggregation Description: Framework providing basic pedigree analysis and plotting utilities as well as a variety of methods to evaluate familial aggregation of traits in large pedigrees. biocViews: Genetics Author: J. Rainer, D. Taliun, C.X. Weichenberger Maintainer: Johannes Rainer URL: https://github.com/EuracBiomedicalResearch/FamAgg VignetteBuilder: knitr BugReports: https://github.com/EuracBiomedicalResearch/FamAgg/issues git_url: https://git.bioconductor.org/packages/FamAgg git_branch: RELEASE_3_22 git_last_commit: 0e247bc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FamAgg_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FamAgg_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FamAgg_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FamAgg_1.38.0.tgz vignettes: vignettes/FamAgg/inst/doc/FamAgg.html vignetteTitles: Pedigree Analysis and Familial Aggregation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R dependencyCount: 89 Package: fastLiquidAssociation Version: 1.46.0 Depends: methods, LiquidAssociation, parallel, doParallel, stats, Hmisc, utils Imports: WGCNA, impute, preprocessCore Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 Archs: x64 MD5sum: cb119e6e7447e1dc8b04163c4798da6f NeedsCompilation: no Title: functions for genome-wide application of Liquid Association Description: This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses. biocViews: Software, GeneExpression, Genetics, Pathways, CellBiology Author: Tina Gunderson Maintainer: Tina Gunderson git_url: https://git.bioconductor.org/packages/fastLiquidAssociation git_branch: RELEASE_3_22 git_last_commit: 457ccd1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fastLiquidAssociation_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fastLiquidAssociation_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fastLiquidAssociation_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fastLiquidAssociation_1.46.0.tgz vignettes: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf vignetteTitles: fastLiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.R dependencyCount: 117 Package: FastqCleaner Version: 1.28.0 Imports: methods, shiny, stats, IRanges, Biostrings, ShortRead, DT, S4Vectors, graphics, htmltools, shinyBS, Rcpp (>= 0.12.12) LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: b52e54d2a3e5f34d0434b5e8ad1e7109 NeedsCompilation: yes Title: A Shiny Application for Quality Control, Filtering and Trimming of FASTQ Files Description: An interactive web application for quality control, filtering and trimming of FASTQ files. This user-friendly tool combines a pipeline for data processing based on Biostrings and ShortRead infrastructure, with a cutting-edge visual environment. Single-Read and Paired-End files can be locally processed. Diagnostic interactive plots (CG content, per-base sequence quality, etc.) are provided for both the input and output files. biocViews: QualityControl,Sequencing,Software,SangerSeq,SequenceMatching Author: Leandro Roser [aut, cre], Fernán Agüero [aut], Daniel Sánchez [aut] Maintainer: Leandro Roser VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FastqCleaner git_branch: RELEASE_3_22 git_last_commit: 112c07e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FastqCleaner_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FastqCleaner_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FastqCleaner_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FastqCleaner_1.28.0.tgz vignettes: vignettes/FastqCleaner/inst/doc/Overview.html vignetteTitles: An Introduction to FastqCleaner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FastqCleaner/inst/doc/Overview.R dependencyCount: 95 Package: fastreeR Version: 1.99.10 Depends: R (>= 4.4) Imports: ape, data.table, dynamicTreeCut, methods, R.utils, rJava, stats, stringr, utils Suggests: BiocFileCache, BiocStyle, ggtree, graphics, knitr, memuse, rmarkdown, spelling, testthat (>= 3.0.0) License: GPL-3 MD5sum: 0e81da990d57d234f3770ecb7f185008 NeedsCompilation: no Title: Phylogenetic, Distance and Other Calculations on VCF and Fasta Files Description: Calculate distances, build phylogenetic trees or perform hierarchical clustering between the samples of a VCF or FASTA file. Functions are implemented in Java-11 and called via rJava. Parallel implementation that operates directly on the VCF or FASTA file for fast execution. biocViews: Phylogenetics, Metagenomics, Clustering Author: Anestis Gkanogiannis [aut, cre] (ORCID: ) Maintainer: Anestis Gkanogiannis URL: https://github.com/gkanogiannis/fastreeR, https://github.com/gkanogiannis/BioInfoJava-Utils SystemRequirements: Java (>= 11) VignetteBuilder: knitr BugReports: https://github.com/gkanogiannis/fastreeR/issues git_url: https://git.bioconductor.org/packages/fastreeR git_branch: devel git_last_commit: 611b7d0 git_last_commit_date: 2025-10-26 Date/Publication: 2025-10-27 source.ver: src/contrib/fastreeR_1.99.10.tar.gz win.binary.ver: bin/windows/contrib/4.5/fastreeR_1.99.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fastreeR_1.99.10.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fastreeR_1.99.10.tgz vignettes: vignettes/fastreeR/inst/doc/fastreeR_vignette.html vignetteTitles: fastreeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastreeR/inst/doc/fastreeR_vignette.R dependencyCount: 27 Package: fastseg Version: 1.56.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, grDevices, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, BiocStyle, knitr License: LGPL (>= 2.0) MD5sum: 5fac7ccd67cce51f8a3f9b646a8afce8 NeedsCompilation: yes Title: fastseg - a fast segmentation algorithm Description: fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. biocViews: Classification, CopyNumberVariation Author: Guenter Klambauer [aut], Sonali Kumari [ctb], Alexander Blume [cre] Maintainer: Alexander Blume URL: http://www.bioinf.jku.at/software/fastseg/index.html VignetteBuilder: knitr BugReports: https://github.com/alexg9010/fastseg/issues git_url: https://git.bioconductor.org/packages/fastseg git_branch: RELEASE_3_22 git_last_commit: 15e6704 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fastseg_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fastseg_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fastseg_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fastseg_1.56.0.tgz vignettes: vignettes/fastseg/inst/doc/fastseg.html vignetteTitles: An R Package for fast segmentation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastseg/inst/doc/fastseg.R importsMe: methylKit dependencyCount: 13 Package: fCCAC Version: 1.36.0 Depends: R (>= 4.2.0), S4Vectors, IRanges, GenomicRanges, grid Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: d1dacb037e793d78890d8512d7aa4985 NeedsCompilation: no Title: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets Description: Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomics, as it allows both to evaluate reproducibility of replicates, and to compare different datasets to identify potential correlations. fCCAC applies functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. fCCAC represents a more sophisticated approach that complements Pearson correlation of genomic coverage. biocViews: Epigenetics, Transcription, Sequencing, Coverage, ChIPSeq, FunctionalGenomics, RNASeq, ATACSeq, MNaseSeq Author: Pedro Madrigal [aut, cre] (ORCID: ) Maintainer: Pedro Madrigal URL: https://github.com/pmb59/fCCAC BugReports: https://github.com/pmb59/fCCAC/issues git_url: https://git.bioconductor.org/packages/fCCAC git_branch: RELEASE_3_22 git_last_commit: 17f1036 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fCCAC_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fCCAC_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fCCAC_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fCCAC_1.36.0.tgz vignettes: vignettes/fCCAC/inst/doc/fCCAC.pdf vignetteTitles: fCCAC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCCAC/inst/doc/fCCAC.R dependencyCount: 132 Package: fCI Version: 1.40.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: c750778d3948be2e7bb36e7a52bba97a NeedsCompilation: no Title: f-divergence Cutoff Index for Differential Expression Analysis in Transcriptomics and Proteomics Description: (f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods. biocViews: Proteomics Author: Shaojun Tang Maintainer: Shaojun Tang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fCI git_branch: RELEASE_3_22 git_last_commit: 8ab72e0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fCI_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fCI_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fCI_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fCI_1.40.0.tgz vignettes: vignettes/fCI/inst/doc/fCI.html vignetteTitles: fCI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCI/inst/doc/fCI.R dependencyCount: 50 Package: fcScan Version: 1.24.0 Imports: stats, plyr, VariantAnnotation, SummarizedExperiment, rtracklayer, GenomicRanges, methods, IRanges, foreach, doParallel, parallel Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 9194584252e6a11c369b60f88ee1cedc NeedsCompilation: no Title: fcScan for detecting clusters of coordinates with user defined options Description: This package is used to detect combination of genomic coordinates falling within a user defined window size along with user defined overlap between identified neighboring clusters. It can be used for genomic data where the clusters are built on a specific chromosome or specific strand. Clustering can be performed with a "greedy" option allowing thus the presence of additional sites within the allowed window size. biocViews: GenomeAnnotation, Clustering Author: Abdullah El-Kurdi [aut], Ghiwa khalil [aut], Georges Khazen [ctb], Pierre Khoueiry [aut, cre] Maintainer: Pierre Khoueiry Abdullah El-Kurdi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcScan git_branch: RELEASE_3_22 git_last_commit: 8a15f70 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fcScan_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fcScan_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fcScan_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fcScan_1.24.0.tgz vignettes: vignettes/fcScan/inst/doc/fcScan_vignette.html vignetteTitles: fcScan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcScan/inst/doc/fcScan_vignette.R dependencyCount: 83 Package: fdrame Version: 1.82.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) MD5sum: 6870046d24259cf94750e22f11d6609a NeedsCompilation: yes Title: FDR adjustments of Microarray Experiments (FDR-AME) Description: This package contains two main functions. The first is fdr.ma which takes normalized expression data array, experimental design and computes adjusted p-values It returns the fdr adjusted p-values and plots, according to the methods described in (Reiner, Yekutieli and Benjamini 2002). The second, is fdr.gui() which creates a simple graphic user interface to access fdr.ma biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli Maintainer: Effi Kenigsberg git_url: https://git.bioconductor.org/packages/fdrame git_branch: RELEASE_3_22 git_last_commit: 9f8727b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fdrame_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fdrame_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fdrame_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fdrame_1.82.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 5 Package: FEAST Version: 1.18.0 Depends: R (>= 4.1), mclust, BiocParallel, SummarizedExperiment Imports: SingleCellExperiment, methods, stats, utils, irlba, TSCAN, SC3, matrixStats Suggests: rmarkdown, Seurat, ggpubr, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-2 MD5sum: 7868c28a62c6d15b4d0cfb8706cb546d NeedsCompilation: yes Title: FEAture SelcTion (FEAST) for Single-cell clustering Description: Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as “features”), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have significant impact on the clustering accuracy. FEAST is an R library for selecting most representative features before performing the core of scRNA-seq clustering. It can be used as a plug-in for the etablished clustering algorithms such as SC3, TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm includes three steps: 1. consensus clustering; 2. gene-level significance inference; 3. validation of an optimized feature set. biocViews: Sequencing, SingleCell, Clustering, FeatureExtraction Author: Kenong Su [aut, cre], Hao Wu [aut] Maintainer: Kenong Su VignetteBuilder: knitr BugReports: https://github.com/suke18/FEAST/issues git_url: https://git.bioconductor.org/packages/FEAST git_branch: RELEASE_3_22 git_last_commit: ebbe15f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FEAST_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FEAST_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FEAST_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FEAST_1.18.0.tgz vignettes: vignettes/FEAST/inst/doc/FEAST.html vignetteTitles: The FEAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FEAST/inst/doc/FEAST.R dependencyCount: 113 Package: FeatSeekR Version: 1.10.0 Imports: pheatmap, MASS, pracma, stats, SummarizedExperiment, methods Suggests: rmarkdown, knitr, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: 50cc70a5cbf660ab8dec2c4a59447e8b NeedsCompilation: no Title: FeatSeekR an R package for unsupervised feature selection Description: FeatSeekR performs unsupervised feature selection using replicated measurements. It iteratively selects features with the highest reproducibility across replicates, after projecting out those dimensions from the data that are spanned by the previously selected features. The selected a set of features has a high replicate reproducibility and a high degree of uniqueness. biocViews: Software, StatisticalMethod, FeatureExtraction, MassSpectrometry Author: Tuemay Capraz [cre, aut] (ORCID: ) Maintainer: Tuemay Capraz URL: https://github.com/tcapraz/FeatSeekR VignetteBuilder: knitr BugReports: https://github.com/tcapraz/FeatSeekR/issues git_url: https://git.bioconductor.org/packages/FeatSeekR git_branch: RELEASE_3_22 git_last_commit: 7b2ed7b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FeatSeekR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FeatSeekR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FeatSeekR_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FeatSeekR_1.10.0.tgz vignettes: vignettes/FeatSeekR/inst/doc/FeatSeekR-vignette.html vignetteTitles: `FeatSeekR` user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FeatSeekR/inst/doc/FeatSeekR-vignette.R dependencyCount: 39 Package: fedup Version: 1.18.0 Depends: R (>= 4.1) Imports: openxlsx, tibble, dplyr, data.table, ggplot2, ggthemes, forcats, RColorBrewer, RCy3, utils, stats Suggests: biomaRt, tidyr, testthat, knitr, rmarkdown, devtools, covr License: MIT + file LICENSE MD5sum: d20c63efc167b730cb3834166d200a83 NeedsCompilation: no Title: Fisher's Test for Enrichment and Depletion of User-Defined Pathways Description: An R package that tests for enrichment and depletion of user-defined pathways using a Fisher's exact test. The method is designed for versatile pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run pathway analysis on custom annotations. This package is also integrated with Cytoscape to provide network-based pathway visualization that enhances the interpretability of the results. biocViews: GeneSetEnrichment, Pathways, NetworkEnrichment, Network Author: Catherine Ross [aut, cre] Maintainer: Catherine Ross URL: https://github.com/rosscm/fedup VignetteBuilder: knitr BugReports: https://github.com/rosscm/fedup/issues git_url: https://git.bioconductor.org/packages/fedup git_branch: RELEASE_3_22 git_last_commit: d4b9c03 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fedup_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fedup_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fedup_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fedup_1.18.0.tgz vignettes: vignettes/fedup/inst/doc/fedup_doubleTest.html, vignettes/fedup/inst/doc/fedup_mutliTest.html, vignettes/fedup/inst/doc/fedup_singleTest.html vignetteTitles: fedup_doubleTest.html, fedup_mutliTest.html, fedup_singleTest.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/fedup/inst/doc/fedup_doubleTest.R, vignettes/fedup/inst/doc/fedup_mutliTest.R, vignettes/fedup/inst/doc/fedup_singleTest.R dependencyCount: 73 Package: FELLA Version: 1.30.0 Depends: R (>= 3.5.0) Imports: methods, igraph, Matrix, KEGGREST, plyr, stats, graphics, utils Suggests: shiny, DT, magrittr, visNetwork, knitr, BiocStyle, rmarkdown, testthat, biomaRt, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, GOSemSim License: GPL-3 MD5sum: c372133f622586bad8f9f24a3e2615d5 NeedsCompilation: no Title: Interpretation and enrichment for metabolomics data Description: Enrichment of metabolomics data using KEGG entries. Given a set of affected compounds, FELLA suggests affected reactions, enzymes, modules and pathways using label propagation in a knowledge model network. The resulting subnetwork can be visualised and exported. biocViews: Software, Metabolomics, GraphAndNetwork, KEGG, GO, Pathways, Network, NetworkEnrichment Author: Sergio Picart-Armada [aut, cre], Francesc Fernandez-Albert [aut], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FELLA git_branch: RELEASE_3_22 git_last_commit: 67f362e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FELLA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FELLA_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FELLA_1.30.0.tgz vignettes: vignettes/FELLA/inst/doc/FELLA.pdf, vignettes/FELLA/inst/doc/musmusculus.pdf, vignettes/FELLA/inst/doc/zebrafish.pdf, vignettes/FELLA/inst/doc/quickstart.html vignetteTitles: FELLA, Example: a fatty liver study on Mus musculus, Example: oxybenzone exposition in gilt-head bream, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FELLA/inst/doc/FELLA.R, vignettes/FELLA/inst/doc/musmusculus.R, vignettes/FELLA/inst/doc/quickstart.R, vignettes/FELLA/inst/doc/zebrafish.R dependencyCount: 39 Package: fenr Version: 1.8.0 Depends: R (>= 4.1.0) Imports: tools, methods, assertthat, rlang, dplyr, tidyr, tidyselect, tibble, purrr, readr, stringr, httr2, rvest, progress, BiocFileCache, shiny, ggplot2 Suggests: BiocStyle, testthat, knitr, rmarkdown, topGO License: MIT + file LICENSE MD5sum: e2a0a0e36c64a1edaee18b334096fe3d NeedsCompilation: no Title: Fast functional enrichment for interactive applications Description: Perform fast functional enrichment on feature lists (like genes or proteins) using the hypergeometric distribution. Tailored for speed, this package is ideal for interactive platforms such as Shiny. It supports the retrieval of functional data from sources like GO, KEGG, Reactome, Bioplanet and WikiPathways. By downloading and preparing data first, it allows for rapid successive tests on various feature selections without the need for repetitive, time-consuming preparatory steps typical of other packages. biocViews: FunctionalPrediction, DifferentialExpression, GeneSetEnrichment, GO, KEGG, Reactome, Proteomics Author: Marek Gierlinski [aut, cre] (ORCID: ) Maintainer: Marek Gierlinski URL: https://github.com/bartongroup/fenr VignetteBuilder: knitr BugReports: https://github.com/bartongroup/fenr/issues git_url: https://git.bioconductor.org/packages/fenr git_branch: RELEASE_3_22 git_last_commit: b9086bc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fenr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fenr_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fenr_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fenr_1.8.0.tgz vignettes: vignettes/fenr/inst/doc/fenr.html vignetteTitles: Fast functional enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/fenr/inst/doc/fenr.R dependencyCount: 86 Package: ffpe Version: 1.54.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) Archs: x64 MD5sum: 254b2dd904a17b8bfe17ca4af6e73192 NeedsCompilation: no Title: Quality assessment and control for FFPE microarray expression data Description: Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots). biocViews: Microarray, GeneExpression, QualityControl Author: Levi Waldron Maintainer: Levi Waldron git_url: https://git.bioconductor.org/packages/ffpe git_branch: RELEASE_3_22 git_last_commit: 63c183c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ffpe_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ffpe_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ffpe_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ffpe_1.54.0.tgz vignettes: vignettes/ffpe/inst/doc/ffpe.pdf vignetteTitles: ffpe package user guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ffpe/inst/doc/ffpe.R dependencyCount: 169 Package: fgga Version: 1.18.0 Depends: R (>= 4.3), RBGL Imports: graph, stats, e1071, methods, gRbase, jsonlite, BiocFileCache, curl, igraph Suggests: knitr, rmarkdown, GOstats, GO.db, BiocGenerics, pROC, RUnit, BiocStyle License: GPL-3 MD5sum: db39553841288ba385e730ce51153f2c NeedsCompilation: no Title: Hierarchical ensemble method based on factor graph Description: Package that implements the FGGA algorithm. This package provides a hierarchical ensemble method based ob factor graphs for the consistent cross-ontology annotation of protein coding genes. FGGA embodies elements of predicate logic, communication theory, supervised learning and inference in graphical models. biocViews: Software, StatisticalMethod, Classification, Network, NetworkInference, SupportVectorMachine, GraphAndNetwork, GO Author: Flavio Spetale [aut, cre] Maintainer: Flavio Spetale URL: https://github.com/fspetale/fgga VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fgga git_branch: RELEASE_3_22 git_last_commit: 2621333 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fgga_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fgga_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fgga_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fgga_1.18.0.tgz vignettes: vignettes/fgga/inst/doc/fgga.html vignetteTitles: FGGA: Factor Graph GO Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgga/inst/doc/fgga.R dependencyCount: 62 Package: FGNet Version: 3.44.0 Depends: R (>= 4.2.0) Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png, methods, stats, utils, graphics, grDevices Suggests: RCurl, gage, topGO, GO.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi, BiocManager License: GPL (>= 2) MD5sum: 58af2febec313abee65209fa52399429 NeedsCompilation: no Title: Functional Gene Networks derived from biological enrichment analyses Description: Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO. biocViews: Annotation, GO, Pathways, GeneSetEnrichment, Network, Visualization, FunctionalGenomics, NetworkEnrichment, Clustering Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las Rivas. Maintainer: Sara Aibar URL: http://www.cicancer.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FGNet git_branch: RELEASE_3_22 git_last_commit: 30b9e81 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FGNet_3.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FGNet_3.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FGNet_3.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FGNet_3.44.0.tgz vignettes: vignettes/FGNet/inst/doc/FGNet.html vignetteTitles: FGNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet.R importsMe: IntramiRExploreR dependencyCount: 31 Package: fgsea Version: 1.36.0 Depends: R (>= 4.1) Imports: Rcpp, data.table, BiocParallel, stats, ggplot2 (>= 2.2.0), cowplot, grid, fastmatch, Matrix, scales, utils LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi, parallel, org.Mm.eg.db, limma, GEOquery, msigdbr, aggregation, Seurat License: MIT + file LICENCE MD5sum: ac34457aa5d670d599bfe2734ce8f7ea NeedsCompilation: yes Title: Fast Gene Set Enrichment Analysis Description: The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Pathways Author: Gennady Korotkevich [aut], Vladimir Sukhov [aut], Nikolay Budin [ctb], Nikita Gusak [ctb], Zieman Mark [ctb], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/fgsea/ VignetteBuilder: knitr BugReports: https://github.com/ctlab/fgsea/issues git_url: https://git.bioconductor.org/packages/fgsea git_branch: RELEASE_3_22 git_last_commit: 1adab01 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fgsea_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fgsea_1.35.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fgsea_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fgsea_1.36.0.tgz vignettes: vignettes/fgsea/inst/doc/fgsea-tutorial.html, vignettes/fgsea/inst/doc/geseca-tutorial.html vignetteTitles: Using fgsea package, Gene set co-regulation analysis tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R, vignettes/fgsea/inst/doc/geseca-tutorial.R dependsOnMe: gsean, metapone, PPInfer importsMe: BioNAR, CelliD, CEMiTool, clustifyr, CoGAPS, cTRAP, DeepTarget, DOSE, EventPointer, fobitools, lipidr, markeR, MIRit, MPAC, multiGSEA, NanoTube, nipalsMCIA, omicsViewer, pairedGSEA, pathlinkR, phantasus, piano, POMA, projectR, RegEnrich, RegionalST, signatureSearch, ViSEAGO, cinaR, DTSEA, mulea, scITD suggestsMe: Cepo, decoupleR, escape, gatom, gCrisprTools, iSEEpathways, mdp, pathMED, sparrow, SpliceWiz, TaxSEA, ttgsea, easybio, genekitr, GeneNMF, ggpicrust2, goat, grandR, Platypus, RCPA, rliger dependencyCount: 38 Package: FilterFFPE Version: 1.20.0 Imports: foreach, doParallel, GenomicRanges, IRanges, Rsamtools, parallel, S4Vectors Suggests: BiocStyle License: LGPL-3 MD5sum: 6ee505173a31bd4406d7303059a3e8b8 NeedsCompilation: no Title: FFPE Artificial Chimeric Read Filter for NGS data Description: This package finds and filters artificial chimeric reads specifically generated in next-generation sequencing (NGS) process of formalin-fixed paraffin-embedded (FFPE) tissues. These artificial chimeric reads can lead to a large number of false positive structural variation (SV) calls. The required input is an indexed BAM file of a FFPE sample. biocViews: StructuralVariation, Sequencing, Alignment, QualityControl, Preprocessing Author: Lanying Wei [aut, cre] (ORCID: ) Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/FilterFFPE git_branch: RELEASE_3_22 git_last_commit: 5c7f62f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FilterFFPE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FilterFFPE_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FilterFFPE_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FilterFFPE_1.20.0.tgz vignettes: vignettes/FilterFFPE/inst/doc/FilterFFPE.pdf vignetteTitles: An introduction to FilterFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FilterFFPE/inst/doc/FilterFFPE.R dependencyCount: 32 Package: findIPs Version: 1.6.0 Depends: graphics, R (>= 4.4.0) Imports: Biobase, BiocParallel, parallel, stats, SummarizedExperiment, survival, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 1a65e7ae53876103419cbc5de2d6c56b NeedsCompilation: no Title: Influential Points Detection for Feature Rankings Description: Feature rankings can be distorted by a single case in the context of high-dimensional data. The cases exerts abnormal influence on feature rankings are called influential points (IPs). The package aims at detecting IPs based on case deletion and quantifies their effects by measuring the rank changes (DOI:10.48550/arXiv.2303.10516). The package applies a novel rank comparing measure using the adaptive weights that stress the top-ranked important features and adjust the weights to ranking properties. biocViews: GeneExpression, DifferentialExpression, Regression, Survival Author: Shuo Wang [aut, cre] (ORCID: ), Junyan Lu [aut] Maintainer: Shuo Wang URL: https://github.com/ShuoStat/findIPs VignetteBuilder: knitr BugReports: https://github.com/ShuoStat/findIPs git_url: https://git.bioconductor.org/packages/findIPs git_branch: RELEASE_3_22 git_last_commit: b043ffe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/findIPs_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/findIPs_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/findIPs_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/findIPs_1.6.0.tgz vignettes: vignettes/findIPs/inst/doc/findIPs.html vignetteTitles: Introduction to package findIPs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/findIPs/inst/doc/findIPs.R dependencyCount: 37 Package: FindIT2 Version: 1.16.0 Depends: GenomicRanges, R (>= 3.5.0) Imports: withr, BiocGenerics, Seqinfo, rtracklayer, S4Vectors, GenomicFeatures, dplyr, rlang, patchwork, ggplot2, BiocParallel, qvalue, stringr, utils, stats, ggrepel, tibble, tidyr, SummarizedExperiment, MultiAssayExperiment, IRanges, progress, purrr, glmnet, methods Suggests: BiocStyle, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0), TxDb.Athaliana.BioMart.plantsmart28 License: Artistic-2.0 Archs: x64 MD5sum: 9a87528bfb218eb77f9edfdce6dfd849 NeedsCompilation: no Title: find influential TF and Target based on multi-omics data Description: This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function like integrate different source information, calculate jaccard similarity for your TF. biocViews: Software, Annotation, ChIPSeq, ATACSeq, GeneRegulation, MultipleComparison, GeneTarget Author: Guandong Shang [aut, cre] (ORCID: ) Maintainer: Guandong Shang URL: https://github.com/shangguandong1996/FindIT2 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/FindIT2 git_url: https://git.bioconductor.org/packages/FindIT2 git_branch: RELEASE_3_22 git_last_commit: 4bef48e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FindIT2_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FindIT2_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FindIT2_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FindIT2_1.16.0.tgz vignettes: vignettes/FindIT2/inst/doc/FindIT2.html vignetteTitles: Introduction to FindIT2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FindIT2/inst/doc/FindIT2.R dependencyCount: 114 Package: FinfoMDS Version: 1.0.0 Depends: R (>= 4.4.0) Imports: phyloseq Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: ae520e10bda86c588b5406b82a62ff7b NeedsCompilation: no Title: Multidimensional Scaling with F-ratio for microbiome visualization Description: F-informed MDS is a new multidimensional scaling-based ordination method that configures data distribution based on the F-statistic (i.e., the ratio of dispersion between groups with shared or differing labels). biocViews: DimensionReduction, MultidimensionalScaling, Visualization, Microbiome Author: Soobin Kim [aut, cre], Hyungseok Kim [aut] Maintainer: Soobin Kim URL: https://github.com/soob-kim/FinfoMDS VignetteBuilder: knitr BugReports: https://github.com/soob-kim/FinfoMDS/issues git_url: https://git.bioconductor.org/packages/FinfoMDS git_branch: RELEASE_3_22 git_last_commit: 91ad4bd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FinfoMDS_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FinfoMDS_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FinfoMDS_1.0.0.tgz vignettes: vignettes/FinfoMDS/inst/doc/FinfoMDS.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FinfoMDS/inst/doc/FinfoMDS.R dependencyCount: 70 Package: FISHalyseR Version: 1.44.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: ae1c057773fab2ac74fe0413b159539c NeedsCompilation: no Title: FISHalyseR a package for automated FISH quantification Description: FISHalyseR provides functionality to process and analyse digital cell culture images, in particular to quantify FISH probes within nuclei. Furthermore, it extract the spatial location of each nucleus as well as each probe enabling spatial co-localisation analysis. biocViews: CellBiology Author: Karesh Arunakirinathan , Andreas Heindl Maintainer: Karesh Arunakirinathan , Andreas Heindl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FISHalyseR git_branch: RELEASE_3_22 git_last_commit: 43dab4f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FISHalyseR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FISHalyseR_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FISHalyseR_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FISHalyseR_1.44.0.tgz vignettes: vignettes/FISHalyseR/inst/doc/FISHalyseR.pdf vignetteTitles: FISHAlyseR Automated fluorescence in situ hybridisation quantification in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FISHalyseR/inst/doc/FISHalyseR.R dependencyCount: 46 Package: fishpond Version: 2.16.0 Imports: graphics, stats, utils, methods, abind, gtools, qvalue, S4Vectors, IRanges, SummarizedExperiment, GenomicRanges, matrixStats, svMisc, Matrix, SingleCellExperiment, jsonlite Suggests: testthat, knitr, rmarkdown, macrophage, tximeta, org.Hs.eg.db, samr, DESeq2, apeglm, tximportData, limma, ensembldb, EnsDb.Hsapiens.v86, GenomicFeatures, AnnotationDbi, pheatmap, Gviz, GenomeInfoDb, data.table License: GPL-2 MD5sum: fff73d458b43e063b41403bb17096b4b NeedsCompilation: no Title: Fishpond: downstream methods and tools for expression data Description: Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains a number of utilities for working with Salmon and Alevin quantification files. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Normalization, Regression, MultipleComparison, BatchEffect, Visualization, DifferentialExpression, DifferentialSplicing, AlternativeSplicing, SingleCell Author: Anqi Zhu [aut, ctb], Michael Love [aut, cre], Avi Srivastava [aut, ctb], Rob Patro [aut, ctb], Joseph Ibrahim [aut, ctb], Hirak Sarkar [ctb], Euphy Wu [ctb], Noor Pratap Singh [ctb], Scott Van Buren [ctb], Dongze He [ctb], Steve Lianoglou [ctb], Wes Wilson [ctb], Jeroen Gilis [ctb] Maintainer: Michael Love URL: https://thelovelab.github.io/fishpond, https://thelovelab.com/mikelove/fishpond VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/fishpond git_url: https://git.bioconductor.org/packages/fishpond git_branch: RELEASE_3_22 git_last_commit: f6a8807 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fishpond_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fishpond_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fishpond_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fishpond_2.16.0.tgz vignettes: vignettes/fishpond/inst/doc/allelic.html, vignettes/fishpond/inst/doc/swish.html vignetteTitles: 2. SEESAW - Allelic expression analysis with Salmon and Swish, 1. Swish: DE analysis accounting for inferential uncertainty hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fishpond/inst/doc/allelic.R, vignettes/fishpond/inst/doc/swish.R dependencyCount: 53 Package: FitHiC Version: 1.36.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: 674eb98d94ff5dd1efdee26b7ff654de NeedsCompilation: yes Title: Confidence estimation for intra-chromosomal contact maps Description: Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C. biocViews: DNA3DStructure, Software Author: Ferhat Ay [aut] (Python original, https://noble.gs.washington.edu/proj/fit-hi-c/), Timothy L. Bailey [aut], William S. Noble [aut], Ruyu Tan [aut, cre, trl] (R port) Maintainer: Ruyu Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FitHiC git_branch: RELEASE_3_22 git_last_commit: 86451bb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FitHiC_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FitHiC_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FitHiC_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FitHiC_1.36.0.tgz vignettes: vignettes/FitHiC/inst/doc/fithic.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FitHiC/inst/doc/fithic.R dependencyCount: 8 Package: flagme Version: 1.66.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) MD5sum: f5c76f41ceaea43d66032772fff9c078 NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography-massspectrometry metabolomics data. biocViews: DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli git_url: https://git.bioconductor.org/packages/flagme git_branch: RELEASE_3_22 git_last_commit: cfc1743 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flagme_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flagme_1.65.0.zip vignettes: vignettes/flagme/inst/doc/flagme-knitr.pdf, vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GC-MS-based metabolomics data, \texttt{flagme}: Fragment-level analysis of \\ GC-MS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flagme/inst/doc/flagme-knitr.R, vignettes/flagme/inst/doc/flagme.R dependencyCount: 161 Package: FLAMES Version: 2.4.0 Depends: R (>= 4.2.0) Imports: abind, basilisk, bambu, BiocParallel, Biostrings, BiocGenerics, crew, circlize, ComplexHeatmap, cowplot, cli, dplyr, GenomicRanges, GenomicFeatures, GenomicAlignments, Seqinfo, ggplot2, ggbio, grid, gridExtra, igraph, jsonlite, magrittr, magick, Matrix, MatrixGenerics, readr, reticulate, Rsamtools, rtracklayer, RColorBrewer, R.utils, S4Arrays, ShortRead, SingleCellExperiment, SummarizedExperiment, SpatialExperiment, scater, scatterpie, S4Vectors, scuttle, stats, scran, stringr, tidyr, utils, withr, methods, tibble, tidyselect, IRanges LinkingTo: Rcpp, Rhtslib, testthat Suggests: BiocStyle, GEOquery, ggrastr, knitr, rmarkdown, uwot, testthat (>= 3.0.0), xml2 License: GPL (>= 3) MD5sum: ea1c364e7ac1ac159b79aa5dd9f59765 NeedsCompilation: yes Title: FLAMES: Full Length Analysis of Mutations and Splicing in long read RNA-seq data Description: Semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution. biocViews: RNASeq, SingleCell, Transcriptomics, DataImport, DifferentialSplicing, AlternativeSplicing, GeneExpression, LongRead Author: Luyi Tian [aut], Changqing Wang [aut, cre], Yupei You [aut], Oliver Voogd [aut], Jakob Schuster [aut], Shian Su [aut], Yair D.J. Prawer [aut], Matthew Ritchie [ctb] Maintainer: Changqing Wang URL: https://mritchielab.github.io/FLAMES SystemRequirements: GNU make, C++17 VignetteBuilder: knitr BugReports: https://github.com/mritchielab/FLAMES/issues git_url: https://git.bioconductor.org/packages/FLAMES git_branch: RELEASE_3_22 git_last_commit: 3a128b2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FLAMES_2.4.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FLAMES_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FLAMES_2.4.0.tgz vignettes: vignettes/FLAMES/inst/doc/FLAMES_vignette.html vignetteTitles: FLAMES hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FLAMES/inst/doc/FLAMES_vignette.R dependencyCount: 249 Package: flowAI Version: 1.40.0 Depends: R (>= 4.3.0) Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2, RColorBrewer, scales, methods, graphics, stats, utils, rmarkdown Suggests: testthat, shiny, BiocStyle License: GPL (>= 2) MD5sum: 811065dc14232f75319433b52d75e680 NeedsCompilation: no Title: Automatic and interactive quality control for flow cytometry data Description: The package is able to perform an automatic or interactive quality control on FCS data acquired using flow cytometry instruments. By evaluating three different properties: 1) flow rate, 2) signal acquisition, 3) dynamic range, the quality control enables the detection and removal of anomalies. biocViews: FlowCytometry, QualityControl, BiomedicalInformatics, ImmunoOncology Author: Gianni Monaco [aut], Chen Hao [ctb] Maintainer: Gianni Monaco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowAI git_branch: RELEASE_3_22 git_last_commit: 1a09303 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowAI_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowAI_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowAI_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowAI_1.40.0.tgz vignettes: vignettes/flowAI/inst/doc/flowAI.html vignetteTitles: Automatic and GUI methods to do quality control on Flow cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowAI/inst/doc/flowAI.R importsMe: CytoPipeline dependencyCount: 65 Package: flowBeads Version: 1.48.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: f12eed61c7d203349f1f7884334f5d9b NeedsCompilation: no Title: flowBeads: Analysis of flow bead data Description: This package extends flowCore to provide functionality specific to bead data. One of the goals of this package is to automate analysis of bead data for the purpose of normalisation. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: Nikolas Pontikos Maintainer: Nikolas Pontikos git_url: https://git.bioconductor.org/packages/flowBeads git_branch: RELEASE_3_22 git_last_commit: 233be48 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowBeads_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowBeads_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowBeads_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowBeads_1.48.0.tgz vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf vignetteTitles: Analysis of Flow Cytometry Bead Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBeads/inst/doc/HowTo-flowBeads.R dependencyCount: 32 Package: flowBin Version: 1.46.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: 03f6d9421c21e37c26caa348fe0ccfad NeedsCompilation: no Title: Combining multitube flow cytometry data by binning Description: Software to combine flow cytometry data that has been multiplexed into multiple tubes with common markers between them, by establishing common bins across tubes in terms of the common markers, then determining expression within each tube for each bin in terms of the tube-specific markers. biocViews: ImmunoOncology, CellBasedAssays, FlowCytometry Author: Kieran O'Neill Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/flowBin git_branch: RELEASE_3_22 git_last_commit: f38d785 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowBin_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowBin_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowBin_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowBin_1.46.0.tgz vignettes: vignettes/flowBin/inst/doc/flowBin.pdf vignetteTitles: flowBin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBin/inst/doc/flowBin.R dependencyCount: 37 Package: flowcatchR Version: 1.44.0 Depends: R (>= 2.10), methods, EBImage Imports: colorRamps, abind, BiocParallel, graphics, stats, utils, plotly, shiny Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 613fba7b3d7f56eef6baccbc6696d555 NeedsCompilation: no Title: Tools to analyze in vivo microscopy imaging data focused on tracking flowing blood cells Description: flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of utilities to help checking the quality of the performed operations (e.g. how good the segmentation was). It allows investigating the issue of tracking flowing cells such as in blood vessels, to categorize the particles in flowing, rolling and adherent. This classification is applied in the study of phenomena such as hemostasis and study of thrombosis development. Moreover, flowcatchR presents an integrated workflow solution, based on the integration with a Shiny App and Jupyter notebooks, which is delivered alongside the package, and can enable fully reproducible bioimage analysis in the R environment. biocViews: Software, Visualization, CellBiology, Classification, Infrastructure, GUI, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/flowcatchR, https://federicomarini.github.io/flowcatchR/ SystemRequirements: ImageMagick VignetteBuilder: knitr BugReports: https://github.com/federicomarini/flowcatchR/issues git_url: https://git.bioconductor.org/packages/flowcatchR git_branch: RELEASE_3_22 git_last_commit: d670991 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowcatchR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowcatchR_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowcatchR_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowcatchR_1.44.0.tgz vignettes: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.html vignetteTitles: flowcatchR: tracking and analyzing cells in time lapse microscopy images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.R dependencyCount: 97 Package: flowCHIC Version: 1.44.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 Archs: x64 MD5sum: 3993d8d9c1c1e5193f38815f980ce4fc NeedsCompilation: no Title: Analyze flow cytometric data using histogram information Description: A package to analyze flow cytometric data of complex microbial communities based on histogram images biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Ingo Fetzer , Susann Müller Maintainer: Author: Joachim Schumann URL: http://www.ufz.de/index.php?en=16773 git_url: https://git.bioconductor.org/packages/flowCHIC git_branch: RELEASE_3_22 git_last_commit: bcfdd11 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowCHIC_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowCHIC_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowCHIC_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowCHIC_1.44.0.tgz vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf vignetteTitles: Analyze flow cytometric data using histogram information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCHIC/inst/doc/flowCHICmanual.R dependencyCount: 77 Package: flowClean Version: 1.48.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: 19cbedafd5de6b189dc5d36fd9bb5d87 NeedsCompilation: no Title: flowClean Description: A quality control tool for flow cytometry data based on compositional data analysis. biocViews: FlowCytometry, QualityControl, ImmunoOncology Author: Kipper Fletez-Brant Maintainer: Kipper Fletez-Brant git_url: https://git.bioconductor.org/packages/flowClean git_branch: RELEASE_3_22 git_last_commit: e75696c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowClean_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowClean_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowClean_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowClean_1.48.0.tgz vignettes: vignettes/flowClean/inst/doc/flowClean.pdf vignetteTitles: flowClean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClean/inst/doc/flowClean.R dependencyCount: 25 Package: flowClust Version: 3.48.0 Depends: R(>= 2.5.0) Imports: BiocGenerics, methods, Biobase, graph, flowCore, parallel Suggests: testthat, flowWorkspace, flowWorkspaceData, knitr, rmarkdown, openCyto, flowStats(>= 4.7.1) License: MIT MD5sum: d08e264d9ab3bf759a3eba437423e328 NeedsCompilation: yes Title: Clustering for Flow Cytometry Description: Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. biocViews: ImmunoOncology, Clustering, Visualization, FlowCytometry Author: Raphael Gottardo, Kenneth Lo , Greg Finak Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowClust git_branch: RELEASE_3_22 git_last_commit: b1ac58b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowClust_3.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowClust_3.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowClust_3.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowClust_3.48.0.tgz vignettes: vignettes/flowClust/inst/doc/flowClust.html vignetteTitles: Robust Model-based Clustering of Flow Cytometry Data\\ The flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClust/inst/doc/flowClust.R importsMe: cyanoFilter, flowTrans, openCyto suggestsMe: BiocGenerics, flowTime, segmenTier dependencyCount: 20 Package: flowCore Version: 2.22.0 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics (>= 0.29.2), grDevices, graphics, methods, stats, utils, stats4, Rcpp, matrixStats, cytolib (>= 2.13.1), S4Vectors LinkingTo: cpp11, BH(>= 1.81.0.0), cytolib, RProtoBufLib Suggests: Rgraphviz, flowViz, flowStats (>= 3.43.4), testthat, flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto, gridExtra License: Artistic-2.0 MD5sum: 39cb295639acaeb8d74e0b3b611dbebe NeedsCompilation: yes Title: flowCore: Basic structures for flow cytometry data Description: Provides S4 data structures and basic functions to deal with flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: B Ellis [aut], Perry Haaland [aut], Florian Hahne [aut], Nolwenn Le Meur [aut], Nishant Gopalakrishnan [aut], Josef Spidlen [aut], Mike Jiang [aut, cre], Greg Finak [aut], Samuel Granjeaud [ctb] Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCore git_branch: RELEASE_3_22 git_last_commit: ec29bd6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowCore_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowCore_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowCore_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowCore_2.22.0.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf, vignettes/flowCore/inst/doc/fcs3.html, vignettes/flowCore/inst/doc/hyperlog.notice.html vignetteTitles: Basic Functions for Flow Cytometry Data, fcs3.html, hyperlog.notice.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowCut, flowFP, flowMatch, flowTime, flowTrans, flowViz, flowVS, ggcyto, immunoClust, infinityFlow, ncdfFlow, HDCytoData, healthyFlowData, highthroughputassays importsMe: CATALYST, cmapR, cyanoFilter, cydar, CytoMDS, cytoMEM, CytoML, CytoPipeline, CytoPipelineGUI, ddPCRclust, diffcyt, flowAI, flowBeads, flowCHIC, flowClust, flowDensity, flowGate, flowMeans, flowPloidy, FlowSOM, flowSpecs, flowStats, flowTrans, flowViz, flowWorkspace, GateFinder, MAPFX, MetaCyto, openCyto, PeacoQC, scDataviz, scifer, Sconify, tidyFlowCore suggestsMe: COMPASS, flowPeaks, SuperCellCyto, flowPloidyData, hypergate, MuPETFlow, segmenTier dependencyCount: 17 Package: flowCut Version: 1.20.0 Depends: R (>= 3.4), flowCore Imports: flowDensity (>= 1.13.1), Cairo, e1071, grDevices, graphics, stats,methods Suggests: RUnit, BiocGenerics, knitr, markdown, rmarkdown License: Artistic-2.0 MD5sum: 3b301ec95ccd28463b445625056d2737 NeedsCompilation: no Title: Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis Description: Common techinical complications such as clogging can result in spurious events and fluorescence intensity shifting, flowCut is designed to detect and remove technical artifacts from your data by removing segments that show statistical differences from other segments. biocViews: FlowCytometry, Preprocessing, QualityControl, CellBasedAssays Author: Justin Meskas [cre, aut], Sherrie Wang [aut] Maintainer: Justin Meskas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCut git_branch: RELEASE_3_22 git_last_commit: f67bd40 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowCut_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowCut_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowCut_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowCut_1.20.0.tgz vignettes: vignettes/flowCut/inst/doc/flowCut.html vignetteTitles: _**flowCut**_: Precise and Accurate Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCut/inst/doc/flowCut.R dependencyCount: 99 Package: flowCyBar Version: 1.46.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 3680cf1bec1cdb7b871d211c374657b0 NeedsCompilation: no Title: Analyze flow cytometric data using gate information Description: A package to analyze flow cytometric data using gate information to follow population/community dynamics biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Susanne Günther , Ingo Fetzer , Susann Müller Maintainer: Joachim Schumann URL: http://www.ufz.de/index.php?de=16773 git_url: https://git.bioconductor.org/packages/flowCyBar git_branch: RELEASE_3_22 git_last_commit: 9b7850a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowCyBar_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowCyBar_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowCyBar_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowCyBar_1.46.0.tgz vignettes: vignettes/flowCyBar/inst/doc/flowCyBar-manual.pdf vignetteTitles: Analyze flow cytometric data using gate information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCyBar/inst/doc/flowCyBar-manual.R dependencyCount: 20 Package: flowDensity Version: 1.44.0 Imports: flowCore, graphics, flowViz (>= 1.42), car, polyclip, gplots, methods, stats, grDevices Suggests: knitr,rmarkdown License: Artistic-2.0 MD5sum: a69fc79ebeb2432174e46fa62f772247 NeedsCompilation: no Title: Sequential Flow Cytometry Data Gating Description: This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data. biocViews: Bioinformatics, FlowCytometry, CellBiology, Clustering, Cancer, FlowCytData, DataRepresentation, StemCell, DensityGating Author: Mehrnoush Malek,M. Jafar Taghiyar Maintainer: Mehrnoush Malek SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowDensity git_branch: RELEASE_3_22 git_last_commit: 4e0f8ef git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowDensity_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowDensity_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowDensity_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowDensity_1.44.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R importsMe: cyanoFilter, ddPCRclust, flowCut dependencyCount: 94 Package: flowFP Version: 1.68.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 Archs: x64 MD5sum: 9399a124696bb3a01175dda783a794a2 NeedsCompilation: yes Title: Fingerprinting for Flow Cytometry Description: Fingerprint generation of flow cytometry data, used to facilitate the application of machine learning and datamining tools for flow cytometry. biocViews: FlowCytometry, CellBasedAssays, Clustering, Visualization Author: Herb Holyst , Wade Rogers Maintainer: Herb Holyst , Wade Rogers git_url: https://git.bioconductor.org/packages/flowFP git_branch: RELEASE_3_22 git_last_commit: d48aa8b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowFP_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowFP_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowFP_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowFP_1.68.0.tgz vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf vignetteTitles: Fingerprinting for Flow Cytometry hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFP/inst/doc/flowFP_HowTo.R dependsOnMe: flowBin importsMe: GateFinder dependencyCount: 32 Package: flowGate Version: 1.10.0 Depends: flowWorkspace (>= 4.0.6), ggcyto (>= 1.16.0), R (>= 4.2) Imports: shiny (>= 1.5.0), BiocManager (>= 1.30.10), flowCore (>= 2.0.1), dplyr (>= 1.0.0), ggplot2 (>= 3.3.2), rlang (>= 0.4.7), purrr, tibble, methods Suggests: knitr, rmarkdown, stringr, tidyverse, testthat License: MIT + file LICENSE MD5sum: 6bc373074c0ff734fa2b764fb9a2158c NeedsCompilation: no Title: Interactive Cytometry Gating in R Description: flowGate adds an interactive Shiny app to allow manual GUI-based gating of flow cytometry data in R. Using flowGate, you can draw 1D and 2D span/rectangle gates, quadrant gates, and polygon gates on flow cytometry data by interactively drawing the gates on a plot of your data, rather than by specifying gate coordinates. This package is especially geared toward wet-lab cytometerists looking to take advantage of R for cytometry analysis, without necessarily having a lot of R experience. biocViews: Software, WorkflowStep, FlowCytometry, Preprocessing, ImmunoOncology, DataImport Author: Andrew Wight [aut, cre], Harvey Cantor [aut, ldr] Maintainer: Andrew Wight VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowGate git_branch: RELEASE_3_22 git_last_commit: 8028a04 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowGate_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowGate_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowGate_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowGate_1.10.0.tgz vignettes: vignettes/flowGate/inst/doc/flowGate.html vignetteTitles: flowGate hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowGate/inst/doc/flowGate.R dependencyCount: 87 Package: flowGraph Version: 1.17.0 Depends: R (>= 4.1) Imports: effsize, furrr, future, purrr, ggiraph, ggrepel, ggplot2, igraph, Matrix, matrixStats, stats, utils, visNetwork, htmlwidgets, grDevices, methods, stringr, stringi, Rdpack, data.table (>= 1.9.5), gridExtra, Suggests: BiocStyle, dplyr, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 Archs: x64 MD5sum: aa0c59dd24daa4af9079db1d6d2a005d NeedsCompilation: no Title: Identifying differential cell populations in flow cytometry data accounting for marker frequency Description: Identifies maximal differential cell populations in flow cytometry data taking into account dependencies between cell populations; flowGraph calculates and plots SpecEnr abundance scores given cell population cell counts. biocViews: FlowCytometry, StatisticalMethod, ImmunoOncology, Software, CellBasedAssays, Visualization Author: Alice Yue [aut, cre] Maintainer: Alice Yue URL: https://github.com/aya49/flowGraph VignetteBuilder: knitr BugReports: https://github.com/aya49/flowGraph/issues git_url: https://git.bioconductor.org/packages/flowGraph git_branch: devel git_last_commit: 16b2487 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/flowGraph_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowGraph_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowGraph_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowGraph_1.18.0.tgz vignettes: vignettes/flowGraph/inst/doc/flowGraph.html vignetteTitles: flowGraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowGraph/inst/doc/flowGraph.R dependencyCount: 82 Package: flowMatch Version: 1.46.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 MD5sum: 31bf17a583bf4f76c810fa40467cc97e NeedsCompilation: yes Title: Matching and meta-clustering in flow cytometry Description: Matching cell populations and building meta-clusters and templates from a collection of FC samples. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Ariful Azad Maintainer: Ariful Azad git_url: https://git.bioconductor.org/packages/flowMatch git_branch: RELEASE_3_22 git_last_commit: 217b75f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowMatch_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowMatch_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowMatch_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowMatch_1.46.0.tgz vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf vignetteTitles: flowMatch: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R dependencyCount: 18 Package: flowMeans Version: 1.70.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 MD5sum: 68531dbc1954fc34c0b8fa8205a585ca NeedsCompilation: no Title: Non-parametric Flow Cytometry Data Gating Description: Identifies cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection. Note: R 2.11.0 or newer is required. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/flowMeans git_branch: RELEASE_3_22 git_last_commit: 27e9466 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowMeans_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowMeans_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowMeans_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowMeans_1.70.0.tgz vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R importsMe: optimalFlow dependencyCount: 40 Package: flowMerge Version: 2.58.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Suggests: knitr, rmarkdown Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 7e278de03da148102ddb5c00f6b8678a NeedsCompilation: no Title: Cluster Merging for Flow Cytometry Data Description: Merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework. Note: users should have a working copy of flowClust 2.0 installed. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Greg Finak , Raphael Gottardo Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMerge git_branch: RELEASE_3_22 git_last_commit: 483d2da git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowMerge_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowMerge_2.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowMerge_2.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowMerge_2.58.0.tgz vignettes: vignettes/flowMerge/inst/doc/flowmerge.html vignetteTitles: Merging Mixture Components for Cell Population Identification in Flow Cytometry Data The flowMerge Package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMerge/inst/doc/flowmerge.R suggestsMe: segmenTier dependencyCount: 48 Package: flowPeaks Version: 1.56.0 Depends: R (>= 2.12.0) Suggests: flowCore License: Artistic-1.0 Archs: x64 MD5sum: 46269bf2e9fa1a8eebb4f0b297890ab2 NeedsCompilation: yes Title: An R package for flow data clustering Description: A fast and automatic clustering to classify the cells into subpopulations based on finding the peaks from the overall density function generated by K-means. biocViews: ImmunoOncology, FlowCytometry, Clustering, Gating Author: Yongchao Ge Maintainer: Yongchao Ge SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/flowPeaks git_branch: RELEASE_3_22 git_last_commit: 79669a9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowPeaks_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowPeaks_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowPeaks_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowPeaks_1.56.0.tgz vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf vignetteTitles: Tutorial of flowPeaks package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPeaks/inst/doc/flowPeaks-guide.R importsMe: ddPCRclust, Polytect dependencyCount: 0 Package: flowPloidy Version: 1.36.0 Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData, testthat License: GPL-3 Archs: x64 MD5sum: f3f65c02e90dfca3d556d9a70cc75eac NeedsCompilation: no Title: Analyze flow cytometer data to determine sample ploidy Description: Determine sample ploidy via flow cytometry histogram analysis. Reads Flow Cytometry Standard (FCS) files via the flowCore bioconductor package, and provides functions for determining the DNA ploidy of samples based on internal standards. biocViews: FlowCytometry, GUI, Regression, Visualization Author: Tyler Smith Maintainer: Tyler Smith URL: https://github.com/plantarum/flowPloidy VignetteBuilder: knitr BugReports: https://github.com/plantarum/flowPloidy/issues git_url: https://git.bioconductor.org/packages/flowPloidy git_branch: RELEASE_3_22 git_last_commit: 671b70e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowPloidy_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowPloidy_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowPloidy_1.36.0.tgz vignettes: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.pdf, vignettes/flowPloidy/inst/doc/histogram-tour.pdf vignetteTitles: flowPloidy: Getting Started, flowPloidy: FCM Histograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.R, vignettes/flowPloidy/inst/doc/histogram-tour.R dependencyCount: 112 Package: flowPlots Version: 1.58.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: c16b8ea263068f279a5cee8861e20731 NeedsCompilation: no Title: flowPlots: analysis plots and data class for gated flow cytometry data Description: Graphical displays with embedded statistical tests for gated ICS flow cytometry data, and a data class which stores "stacked" data and has methods for computing summary measures on stacked data, such as marginal and polyfunctional degree data. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Visualization, DataRepresentation Author: N. Hawkins, S. Self Maintainer: N. Hawkins git_url: https://git.bioconductor.org/packages/flowPlots git_branch: RELEASE_3_22 git_last_commit: a394adc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowPlots_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowPlots_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowPlots_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowPlots_1.58.0.tgz vignettes: vignettes/flowPlots/inst/doc/flowPlots.pdf vignetteTitles: Plots with Embedded Tests for Gated Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPlots/inst/doc/flowPlots.R dependencyCount: 1 Package: FlowSOM Version: 2.18.0 Depends: R (>= 4.0), igraph Imports: stats, utils, colorRamps, ConsensusClusterPlus, dplyr, flowCore, ggforce, ggnewscale, ggplot2, ggpubr, grDevices, magrittr, methods, rlang, Rtsne, tidyr, BiocGenerics, XML Suggests: BiocStyle, testthat, CytoML, flowWorkspace, ggrepel, scattermore, pheatmap, ggpointdensity License: GPL (>= 2) MD5sum: f7e7a934f41fff6f6dc6cdd4b2ba09c9 NeedsCompilation: yes Title: Using self-organizing maps for visualization and interpretation of cytometry data Description: FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. biocViews: CellBiology, FlowCytometry, Clustering, Visualization, Software, CellBasedAssays Author: Sofie Van Gassen [aut, cre], Artuur Couckuyt [aut], Katrien Quintelier [aut], Annelies Emmaneel [aut], Britt Callebaut [aut], Yvan Saeys [aut] Maintainer: Sofie Van Gassen URL: http://www.r-project.org, http://dambi.ugent.be git_url: https://git.bioconductor.org/packages/FlowSOM git_branch: RELEASE_3_22 git_last_commit: b942a41 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FlowSOM_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FlowSOM_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FlowSOM_2.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FlowSOM_2.18.0.tgz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: FlowSOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R importsMe: CATALYST, diffcyt suggestsMe: HDCytoData dependencyCount: 102 Package: flowSpecs Version: 1.24.0 Depends: R (>= 4.0) Imports: ggplot2 (>= 3.1.0), BiocGenerics (>= 0.30.0), BiocParallel (>= 1.18.1), Biobase (>= 2.48.0), reshape2 (>= 1.4.3), flowCore (>= 1.50.0), zoo (>= 1.8.6), stats (>= 3.6.0), methods (>= 3.6.0) Suggests: testthat, knitr, rmarkdown, BiocStyle, DepecheR License: MIT + file LICENSE MD5sum: aa6c6b626bdeec48604b8f865d868be8 NeedsCompilation: no Title: Tools for processing of high-dimensional cytometry data Description: This package is intended to fill the role of conventional cytometry pre-processing software, for spectral decomposition, transformation, visualization and cleanup, and to aid further downstream analyses, such as with DepecheR, by enabling transformation of flowFrames and flowSets to dataframes. Functions for flowCore-compliant automatic 1D-gating/filtering are in the pipe line. The package name has been chosen both as it will deal with spectral cytometry and as it will hopefully give the user a nice pair of spectacles through which to view their data. biocViews: Software,CellBasedAssays,DataRepresentation,ImmunoOncology, FlowCytometry,SingleCell,Visualization,Normalization,DataImport Author: Jakob Theorell [aut, cre] Maintainer: Jakob Theorell VignetteBuilder: knitr BugReports: https://github.com/jtheorell/flowSpecs/issues git_url: https://git.bioconductor.org/packages/flowSpecs git_branch: RELEASE_3_22 git_last_commit: 2da6fa3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowSpecs_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowSpecs_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowSpecs_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowSpecs_1.24.0.tgz vignettes: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.html vignetteTitles: Example workflow for processing of raw spectral cytometry files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.R dependencyCount: 51 Package: flowStats Version: 4.22.0 Depends: R (>= 3.0.2) Imports: BiocGenerics, MASS, flowCore (>= 1.99.6), flowWorkspace, ncdfFlow(>= 2.19.5), flowViz, fda (>= 2.2.6), Biobase, methods, grDevices, graphics, stats, cluster, utils, KernSmooth, lattice, ks, RColorBrewer, rrcov, corpcor, mnormt, clue Suggests: xtable, testthat, openCyto, ggcyto, ggridges Enhances: RBGL,graph License: Artistic-2.0 MD5sum: 7d3d0686722860f6758b65aeeef4a355 NeedsCompilation: no Title: Statistical methods for the analysis of flow cytometry data Description: Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi, Chao-Jen Wong, Kyongryun Lee Maintainer: Greg Finak , Mike Jiang URL: http://www.github.com/RGLab/flowStats BugReports: http://www.github.com/RGLab/flowStats/issues git_url: https://git.bioconductor.org/packages/flowStats git_branch: RELEASE_3_22 git_last_commit: 3f49777 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowStats_4.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowStats_4.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowStats_4.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowStats_4.22.0.tgz vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf vignetteTitles: flowStats Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.R dependsOnMe: flowVS, highthroughputassays suggestsMe: cydar, flowClust, flowCore, flowTime, flowViz, ggcyto, openCyto dependencyCount: 98 Package: flowTime Version: 1.34.0 Depends: R (>= 3.4), flowCore Imports: utils, dplyr (>= 1.0.0), tibble, magrittr, plyr, rlang Suggests: knitr, rmarkdown, flowViz, ggplot2, BiocGenerics, stats, flowClust, openCyto, flowStats, ggcyto License: Artistic-2.0 MD5sum: eba4f475ba4499b524ad1f29b7b90de1 NeedsCompilation: no Title: Annotation and analysis of biological dynamical systems using flow cytometry Description: This package facilitates analysis of both timecourse and steady state flow cytometry experiments. This package was originially developed for quantifying the function of gene regulatory networks in yeast (strain W303) expressing fluorescent reporter proteins using BD Accuri C6 and SORP cytometers. However, the functions are for the most part general and may be adapted for analysis of other organisms using other flow cytometers. Functions in this package facilitate the annotation of flow cytometry data with experimental metadata, as often required for publication and general ease-of-reuse. Functions for creating, saving and loading gate sets are also included. In the past, we have typically generated summary statistics for each flowset for each timepoint and then annotated and analyzed these summary statistics. This method loses a great deal of the power that comes from the large amounts of individual cell data generated in flow cytometry, by essentially collapsing this data into a bulk measurement after subsetting. In addition to these summary functions, this package also contains functions to facilitate annotation and analysis of steady-state or time-lapse data utilizing all of the data collected from the thousands of individual cells in each sample. biocViews: FlowCytometry, TimeCourse, Visualization, DataImport, CellBasedAssays, ImmunoOncology Author: R. Clay Wright [aut, cre], Nick Bolten [aut], Edith Pierre-Jerome [aut] Maintainer: R. Clay Wright VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowTime git_branch: RELEASE_3_22 git_last_commit: c91aa9b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowTime_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowTime_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowTime_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowTime_1.34.0.tgz vignettes: vignettes/flowTime/inst/doc/gating-vignette.html, vignettes/flowTime/inst/doc/steady-state-vignette.html, vignettes/flowTime/inst/doc/time-course-vignette.html vignetteTitles: Yeast gating, Steady-state analysis of flow cytometry data, Time course analysis of flow cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTime/inst/doc/gating-vignette.R, vignettes/flowTime/inst/doc/steady-state-vignette.R, vignettes/flowTime/inst/doc/time-course-vignette.R dependencyCount: 33 Package: flowTrans Version: 1.62.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: ee3107630f652a70ca562c3f6571d6de NeedsCompilation: no Title: Parameter Optimization for Flow Cytometry Data Transformation Description: Profile maximum likelihood estimation of parameters for flow cytometry data transformations. biocViews: ImmunoOncology, FlowCytometry Author: Greg Finak , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak git_url: https://git.bioconductor.org/packages/flowTrans git_branch: RELEASE_3_22 git_last_commit: 0a2fdd0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowTrans_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowTrans_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowTrans_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowTrans_1.62.0.tgz vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf vignetteTitles: flowTrans package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTrans/inst/doc/flowTrans.R dependencyCount: 35 Package: flowViz Version: 1.74.0 Depends: R (>= 2.7.0), flowCore(>= 1.41.9), lattice Imports: stats4, Biobase, flowCore, graphics, grDevices, grid, KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer, stats, utils, hexbin,IDPmisc Suggests: colorspace, flowStats, knitr, rmarkdown, markdown, testthat License: Artistic-2.0 MD5sum: 67412502fcf54698276b0b04ca6a8b95 NeedsCompilation: no Title: Visualization for flow cytometry Description: Provides visualization tools for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, Visualization Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar, M. Jiang Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowViz git_branch: RELEASE_3_22 git_last_commit: e737e04 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowViz_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowViz_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowViz_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowViz_1.74.0.tgz vignettes: vignettes/flowViz/inst/doc/filters.html vignetteTitles: Visualizing Gates with Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R dependsOnMe: flowFP, flowVS importsMe: flowDensity, flowStats, flowTrans, openCyto suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto dependencyCount: 31 Package: flowVS Version: 1.42.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 Archs: x64 MD5sum: 45d821c5c5db51e63f5fa51f8de2d6d6 NeedsCompilation: no Title: Variance stabilization in flow cytometry (and microarrays) Description: Per-channel variance stabilization from a collection of flow cytometry samples by Bertlett test for homogeneity of variances. The approach is applicable to microarrays data as well. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Microarray Author: Ariful Azad Maintainer: Ariful Azad VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowVS git_branch: RELEASE_3_22 git_last_commit: 9e2d194 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowVS_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowVS_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowVS_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowVS_1.42.0.tgz vignettes: vignettes/flowVS/inst/doc/flowVS.pdf vignetteTitles: flowVS: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowVS/inst/doc/flowVS.R dependencyCount: 99 Package: flowWorkspace Version: 4.22.0 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics, cytolib (>= 2.13.1), XML, ggplot2, graph, graphics, grDevices, methods, stats, stats4, utils, RBGL, tools, Rgraphviz, data.table, dplyr, scales(>= 1.3.0), matrixStats, RProtoBufLib, flowCore(>= 2.1.1), ncdfFlow(>= 2.25.4), DelayedArray, S4Vectors LinkingTo: cpp11, BH(>= 1.62.0-1), RProtoBufLib(>= 1.99.4), cytolib (>= 2.3.7),Rhdf5lib Suggests: testthat, flowWorkspaceData (>= 2.23.2), knitr, rmarkdown, ggcyto, parallel, CytoML, openCyto License: AGPL-3.0-only License_restricts_use: no MD5sum: 5aa364135108add2446b48a8b91a3f47 NeedsCompilation: yes Title: Infrastructure for representing and interacting with gated and ungated cytometry data sets. Description: This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Greg Finak, Mike Jiang Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowWorkspace git_branch: RELEASE_3_22 git_last_commit: e1edb9f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/flowWorkspace_4.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowWorkspace_4.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowWorkspace_4.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowWorkspace_4.22.0.tgz vignettes: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.html, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html vignetteTitles: flowWorkspace Introduction: A Package to store and maninpulate gated flow data, How to merge GatingSets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.R, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R dependsOnMe: flowGate, ggcyto, highthroughputassays importsMe: CytoML, flowStats, openCyto, PeacoQC suggestsMe: CATALYST, COMPASS, flowClust, flowCore, FlowSOM linksToMe: CytoML dependencyCount: 58 Package: fmcsR Version: 1.52.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap,rmarkdown, codetools License: Artistic-2.0 MD5sum: f8c2581bab2d95997bab05c2e6c95b00 NeedsCompilation: yes Title: Mismatch Tolerant Maximum Common Substructure Searching Description: The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/fmcsR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fmcsR git_branch: RELEASE_3_22 git_last_commit: de8b882 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fmcsR_1.52.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fmcsR_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fmcsR_1.52.0.tgz vignettes: vignettes/fmcsR/inst/doc/fmcsR.html vignetteTitles: fmcsR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R importsMe: chemodiv suggestsMe: ChemmineR, xnet dependencyCount: 67 Package: fmrs Version: 1.20.0 Depends: R (>= 4.3.0) Imports: methods, survival, stats Suggests: BiocGenerics, testthat, knitr, utils License: GPL-3 MD5sum: 8f3867cc9b54ab80e3ad14fff604985e NeedsCompilation: yes Title: Variable Selection in Finite Mixture of AFT Regression and FMR Models Description: The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net. biocViews: Survival, Regression, DimensionReduction Author: Farhad Shokoohi [aut, cre] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/fmrs/issues git_url: https://git.bioconductor.org/packages/fmrs git_branch: RELEASE_3_22 git_last_commit: f742da6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fmrs_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fmrs_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fmrs_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fmrs_1.20.0.tgz vignettes: vignettes/fmrs/inst/doc/usingfmrs.html vignetteTitles: Using fmrs package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmrs/inst/doc/usingfmrs.R importsMe: HhP dependencyCount: 10 Package: fobitools Version: 1.18.0 Depends: R (>= 4.1) Imports: clisymbols, crayon, dplyr, fgsea, ggplot2, ggraph, magrittr, ontologyIndex, purrr, RecordLinkage, stringr, textclean, tictoc, tidygraph, tidyr, vroom Suggests: BiocStyle, covr, ggrepel, kableExtra, knitr, metabolomicsWorkbenchR, POMA, rmarkdown, rvest, SummarizedExperiment, testthat (>= 2.3.2), tidyverse License: GPL-3 MD5sum: 4868a8cdcd6754728d1150b208c3e6ee NeedsCompilation: no Title: Tools for Manipulating the FOBI Ontology Description: A set of tools for interacting with the Food-Biomarker Ontology (FOBI). A collection of basic manipulation tools for biological significance analysis, graphs, and text mining strategies for annotating nutritional data. biocViews: MassSpectrometry, Metabolomics, Software, Visualization, BiomedicalInformatics, GraphAndNetwork, Annotation, Cheminformatics, Pathways, GeneSetEnrichment Author: Pol Castellano-Escuder [aut, cre] (ORCID: ), Alex Sánchez-Pla [aut] (ORCID: ) Maintainer: Pol Castellano-Escuder URL: https://github.com/pcastellanoescuder/fobitools/ VignetteBuilder: knitr BugReports: https://github.com/pcastellanoescuder/fobitools/issues git_url: https://git.bioconductor.org/packages/fobitools git_branch: RELEASE_3_22 git_last_commit: 03ca5ea git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/fobitools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fobitools_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fobitools_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fobitools_1.18.0.tgz vignettes: vignettes/fobitools/inst/doc/Dietary_data_annotation.html, vignettes/fobitools/inst/doc/food_enrichment_analysis.html, vignettes/fobitools/inst/doc/MW_ST000291_enrichment.html, vignettes/fobitools/inst/doc/MW_ST000629_enrichment.html vignetteTitles: Dietary text annotation, Simple food ORA, Use case ST000291, Use case ST000629 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fobitools/inst/doc/Dietary_data_annotation.R, vignettes/fobitools/inst/doc/food_enrichment_analysis.R, vignettes/fobitools/inst/doc/MW_ST000291_enrichment.R, vignettes/fobitools/inst/doc/MW_ST000629_enrichment.R dependencyCount: 123 Package: FRASER Version: 2.6.0 Depends: BiocParallel, Rsamtools, SummarizedExperiment Imports: AnnotationDbi, BBmisc, Biobase, BiocGenerics, biomaRt, BSgenome, cowplot, data.table, DelayedArray (>= 0.5.11), DelayedMatrixStats, extraDistr, generics, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges, grDevices, ggplot2, ggrepel, HDF5Array, matrixStats, methods, OUTRIDER, pcaMethods, pheatmap, plotly, PRROC, RColorBrewer, rhdf5, Rsubread, R.utils, S4Vectors, stats, tibble, tools, utils, VGAM, RMTstat, pracma LinkingTo: RcppArmadillo, Rcpp Suggests: magick, BiocStyle, knitr, rmarkdown, testthat, covr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, rtracklayer, SGSeq, ggbio, biovizBase, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.UCSC.hg19 License: file LICENSE MD5sum: 4248f47ab3edd42ae35bde79b2d2ec19 NeedsCompilation: yes Title: Find RAre Splicing Events in RNA-Seq Data Description: Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects. biocViews: RNASeq, AlternativeSplicing, Sequencing, Software, Genetics, Coverage Author: Christian Mertes [aut, cre] (ORCID: ), Ines Scheller [aut] (ORCID: ), Karoline Lutz [ctb], Ata Jadid Ahari [ctb] (ORCID: ), Vicente Yepez [aut] (ORCID: ), Julien Gagneur [aut] (ORCID: ) Maintainer: Christian Mertes URL: https://github.com/gagneurlab/FRASER VignetteBuilder: knitr BugReports: https://github.com/gagneurlab/FRASER/issues git_url: https://git.bioconductor.org/packages/FRASER git_branch: RELEASE_3_22 git_last_commit: b8faec2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FRASER_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FRASER_2.5.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FRASER_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FRASER_2.6.0.tgz vignettes: vignettes/FRASER/inst/doc/FRASER.pdf vignetteTitles: FRASER: Find RAre Splicing Events in RNA-seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FRASER/inst/doc/FRASER.R dependencyCount: 189 Package: frenchFISH Version: 1.22.0 Imports: utils, MCMCpack, NHPoisson Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: b73bd975a10c52d0603c3c4729594ee0 NeedsCompilation: no Title: Poisson Models for Quantifying DNA Copy-number from FISH Images of Tissue Sections Description: FrenchFISH comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting. biocViews: Software, BiomedicalInformatics, CellBiology, Genetics, HiddenMarkovModel, Preprocessing Author: Adam Berman, Geoff Macintyre Maintainer: Adam Berman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/frenchFISH git_branch: RELEASE_3_22 git_last_commit: 2393f63 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/frenchFISH_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/frenchFISH_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/frenchFISH_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/frenchFISH_1.22.0.tgz vignettes: vignettes/frenchFISH/inst/doc/frenchFISH.html vignetteTitles: Correcting FISH probe counts with frenchFISH hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frenchFISH/inst/doc/frenchFISH.R dependencyCount: 75 Package: FRGEpistasis Version: 1.46.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 Archs: x64 MD5sum: 9b9dbdf123c309ffb369d20266c6ec36 NeedsCompilation: no Title: Epistasis Analysis for Quantitative Traits by Functional Regression Model Description: A Tool for Epistasis Analysis Based on Functional Regression Model biocViews: Genetics, NetworkInference, GeneticVariability, Software Author: Futao Zhang Maintainer: Futao Zhang git_url: https://git.bioconductor.org/packages/FRGEpistasis git_branch: RELEASE_3_22 git_last_commit: 797194c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FRGEpistasis_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FRGEpistasis_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FRGEpistasis_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FRGEpistasis_1.46.0.tgz vignettes: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.pdf vignetteTitles: FRGEpistasis: A Tool for Epistasis Analysis Based on Functional Regression Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.R dependencyCount: 49 Package: frma Version: 1.62.0 Depends: R (>= 2.10.0), Biobase (>= 2.6.0) Imports: Biobase, MASS, DBI, affy, methods, oligo, oligoClasses, preprocessCore, utils, BiocGenerics Suggests: hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: cb2d731c4fc1edac3957c43ec4c55fcc NeedsCompilation: no Title: Frozen RMA and Barcode Description: Preprocessing and analysis for single microarrays and microarray batches. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry , with contributions from Terry Therneau Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frma git_branch: RELEASE_3_22 git_last_commit: 166b917 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/frma_1.62.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/frma_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/frma_1.62.0.tgz vignettes: vignettes/frma/inst/doc/frma.pdf vignetteTitles: frma: Preprocessing for single arrays and array batches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frma/inst/doc/frma.R importsMe: ChIPXpress, rat2302frmavecs, DeSousa2013 suggestsMe: frmaTools, ath1121501frmavecs, antiProfilesData dependencyCount: 56 Package: frmaTools Version: 1.62.0 Depends: R (>= 2.10.0), affy Imports: Biobase, DBI, methods, preprocessCore, stats, utils Suggests: oligo, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, frma, affyPLM, hgu133aprobe, hgu133atagprobe, hgu133plus2probe, hgu133acdf, hgu133atagcdf, hgu133plus2cdf, hgu133afrmavecs, frmaExampleData License: GPL (>= 2) Archs: x64 MD5sum: 36ed6ec85d4bf11d8aad2871982cd594 NeedsCompilation: no Title: Frozen RMA Tools Description: Tools for advanced use of the frma package. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frmaTools git_branch: RELEASE_3_22 git_last_commit: b25f55e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/frmaTools_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/frmaTools_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/frmaTools_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/frmaTools_1.62.0.tgz vignettes: vignettes/frmaTools/inst/doc/frmaTools.pdf vignetteTitles: frmaTools: Create packages containing the vectors used by frma. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frmaTools/inst/doc/frmaTools.R importsMe: DeSousa2013 dependencyCount: 13 Package: funOmics Version: 1.4.0 Depends: R (>= 4.4.0), NMF Imports: NMF, pathifier, stats, KEGGREST, AnnotationDbi, org.Hs.eg.db, dplyr, stringr Suggests: knitr, rmarkdown, testthat (>= 3.0.0), MultiAssayExperiment, SummarizedExperiment, airway License: MIT + file LICENSE MD5sum: e67a811452b2ab0f64ab857609fe457a NeedsCompilation: no Title: Aggregating Omics Data into Higher-Level Functional Representations Description: The 'funOmics' package ggregates or summarizes omics data into higher level functional representations such as GO terms gene sets or KEGG metabolic pathways. The aggregated data matrix represents functional activity scores that facilitate the analysis of functional molecular sets while allowing to reduce dimensionality and provide easier and faster biological interpretations. Coordinated functional activity scores can be as informative as single molecules! biocViews: Software, Transcriptomics, Metabolomics, Proteomics, Pathways, GO, KEGG Author: Elisa Gomez de Lope [aut, cre] (ORCID: ), Enrico Glaab [ctb] (ORCID: ) Maintainer: Elisa Gomez de Lope URL: https://github.com/elisagdelope/funomics VignetteBuilder: knitr BugReports: https://github.com/elisagdelope/funomics/issues git_url: https://git.bioconductor.org/packages/funOmics git_branch: RELEASE_3_22 git_last_commit: 534915d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/funOmics_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/funOmics_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/funOmics_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/funOmics_1.4.0.tgz vignettes: vignettes/funOmics/inst/doc/funomics_vignette.html vignetteTitles: funOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/funOmics/inst/doc/funomics_vignette.R dependencyCount: 83 Package: funtooNorm Version: 1.34.0 Depends: R(>= 3.4) Imports: pls, matrixStats, minfi, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomeInfoDb, grDevices, graphics, stats Suggests: prettydoc, minfiData, knitr, rmarkdown License: GPL-3 MD5sum: c721c34db45a1260cd42bcd34476f015 NeedsCompilation: no Title: Normalization Procedure for Infinium HumanMethylation450 BeadChip Kit Description: Provides a function to normalize Illumina Infinium Human Methylation 450 BeadChip (Illumina 450K), correcting for tissue and/or cell type. biocViews: DNAMethylation, Preprocessing, Normalization Author: Celia Greenwood ,Stepan Grinek , Maxime Turgeon , Kathleen Klein Maintainer: Kathleen Klein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/funtooNorm git_branch: RELEASE_3_22 git_last_commit: c99650b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/funtooNorm_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/funtooNorm_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/funtooNorm_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/funtooNorm_1.34.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 148 Package: FuseSOM Version: 1.12.0 Depends: R (>= 4.2.0) Imports: psych, FCPS, analogue, coop, pheatmap, ggplotify, fastcluster, fpc, ggplot2, stringr, ggpubr, proxy, cluster, diptest, methods, SummarizedExperiment, stats, S4Vectors LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, SingleCellExperiment License: GPL-2 MD5sum: 8341598fc512b07f7f947bd21b7ba360 NeedsCompilation: yes Title: A Correlation Based Multiview Self Organizing Maps Clustering For IMC Datasets Description: A correlation-based multiview self-organizing map for the characterization of cell types in highly multiplexed in situ imaging cytometry assays (`FuseSOM`) is a tool for unsupervised clustering. `FuseSOM` is robust and achieves high accuracy by combining a `Self Organizing Map` architecture and a `Multiview` integration of correlation based metrics. This allows FuseSOM to cluster highly multiplexed in situ imaging cytometry assays. biocViews: SingleCell, CellBasedAssays, Clustering, Spatial Author: Elijah Willie [aut, cre] Maintainer: Elijah Willie VignetteBuilder: knitr BugReports: https://github.com/ecool50/FuseSOM/issues git_url: https://git.bioconductor.org/packages/FuseSOM git_branch: RELEASE_3_22 git_last_commit: c3271db git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/FuseSOM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FuseSOM_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FuseSOM_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FuseSOM_1.12.0.tgz vignettes: vignettes/FuseSOM/inst/doc/Introduction.html vignetteTitles: FuseSOM package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FuseSOM/inst/doc/Introduction.R suggestsMe: spicyWorkflow dependencyCount: 129 Package: G4SNVHunter Version: 1.2.0 Depends: R (>= 4.3.0) Imports: Biostrings, stats, GenomicRanges, IRanges, Rcpp, RcppRoll, data.table, Seqinfo, S4Vectors, ggplot2, cowplot, progress, ggseqlogo, viridis, ggpointdensity, tools, SummarizedExperiment, VariantAnnotation, dplyr, openxlsx, tidyr, magrittr, ggdensity LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, BiocManager, BSgenome.Hsapiens.UCSC.hg19, DT, rtracklayer, testthat (>= 3.0.0), RBGL License: MIT + file LICENSE MD5sum: 1729175077e69b9e5e9f564a919d8ab8 NeedsCompilation: yes Title: Evaluating SNV-Induced Disruption of G-Quadruplex Structures Description: G-quadruplexes (G4s) are unique nucleic acid secondary structures predominantly found in guanine-rich regions and have been shown to be involved in various biological regulatory processes. G4SNVHunter is an R package designed to rapidly identify genomic sequences with G4-forming propensity and to accurately screen user-provided single nucleotide variants—as well as other small-scale variants such as indels and MNVs—for their potential to destabilize these structures. This allows researchers to then screen these critical variants for deeper study, digging into how they might influence biological functions—think gene regulation, for instance—by impairing G4 formation propensity. biocViews: Epigenetics, SNP Author: Rongxin Zhang [cre, aut] (ORCID: ) Maintainer: Rongxin Zhang URL: https://github.com/rongxinzh/G4SNVHunter VignetteBuilder: knitr BugReports: https://github.com/rongxinzh/G4SNVHunter/issues git_url: https://git.bioconductor.org/packages/G4SNVHunter git_branch: RELEASE_3_22 git_last_commit: 68a2680 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/G4SNVHunter_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/G4SNVHunter_1.1.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/G4SNVHunter_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/G4SNVHunter_1.2.0.tgz vignettes: vignettes/G4SNVHunter/inst/doc/G4SNVHunter.html vignetteTitles: Introduction to G4SNVHunter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/G4SNVHunter/inst/doc/G4SNVHunter.R dependencyCount: 113 Package: GA4GHclient Version: 1.34.0 Depends: R (>= 3.5.0), S4Vectors Imports: BiocGenerics, Biostrings, dplyr, Seqinfo, GenomicRanges, httr, IRanges, jsonlite, methods, VariantAnnotation Suggests: GenomeInfoDb, AnnotationDbi, BiocStyle, DT, knitr, org.Hs.eg.db, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: c59a4d6849903668c3bc58007e39b1ec NeedsCompilation: no Title: A Bioconductor package for accessing GA4GH API data servers Description: GA4GHclient provides an easy way to access public data servers through Global Alliance for Genomics and Health (GA4GH) genomics API. It provides low-level access to GA4GH API and translates response data into Bioconductor-based class objects. biocViews: DataRepresentation, ThirdPartyClient Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHclient VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHclient/issues git_url: https://git.bioconductor.org/packages/GA4GHclient git_branch: RELEASE_3_22 git_last_commit: 7e604d2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GA4GHclient_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GA4GHclient_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GA4GHclient_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GA4GHclient_1.34.0.tgz vignettes: vignettes/GA4GHclient/inst/doc/GA4GHclient.html vignetteTitles: GA4GHclient hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHclient/inst/doc/GA4GHclient.R dependsOnMe: GA4GHshiny dependencyCount: 85 Package: GA4GHshiny Version: 1.32.0 Depends: GA4GHclient Imports: AnnotationDbi, BiocGenerics, dplyr, DT, Seqinfo, GenomeInfoDb, openxlsx, GenomicFeatures, methods, purrr, S4Vectors, shiny, shinyjs, tidyr, shinythemes Suggests: BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 246077362991c8e4f7106bb812303919 NeedsCompilation: no Title: Shiny application for interacting with GA4GH-based data servers Description: GA4GHshiny package provides an easy way to interact with data servers based on Global Alliance for Genomics and Health (GA4GH) genomics API through a Shiny application. It also integrates with Beacon Network. biocViews: GUI Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb], Elizabeth Borgognoni [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHshiny VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHshiny/issues git_url: https://git.bioconductor.org/packages/GA4GHshiny git_branch: RELEASE_3_22 git_last_commit: 8463ac1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GA4GHshiny_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GA4GHshiny_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GA4GHshiny_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GA4GHshiny_1.32.0.tgz vignettes: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.html vignetteTitles: GA4GHshiny hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.R dependencyCount: 124 Package: gaga Version: 2.56.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) Archs: x64 MD5sum: c81756dd021445a937ea601c30c56157 NeedsCompilation: yes Title: GaGa hierarchical model for high-throughput data analysis Description: Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package). biocViews: ImmunoOncology, OneChannel, MassSpectrometry, MultipleComparison, DifferentialExpression, Classification Author: David Rossell . Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/gaga git_branch: RELEASE_3_22 git_last_commit: 5054c05 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gaga_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gaga_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gaga_2.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gaga_2.56.0.tgz vignettes: vignettes/gaga/inst/doc/gagamanual.pdf vignetteTitles: Manual for the gaga library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaga/inst/doc/gagamanual.R importsMe: casper dependencyCount: 17 Package: gage Version: 2.60.0 Depends: R (>= 3.5.0) Imports: graph, KEGGREST, AnnotationDbi, GO.db Suggests: pathview, gageData, org.Hs.eg.db, hgu133a.db, GSEABase, Rsamtools, GenomicAlignments, TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq2, edgeR, limma License: GPL (>=2.0) Archs: x64 MD5sum: c359ca084de605ac9347cf36c1697528 NeedsCompilation: no Title: Generally Applicable Gene-set Enrichment for Pathway Analysis Description: GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods. biocViews: Pathways, GO, DifferentialExpression, Microarray, OneChannel, TwoChannel, RNASeq, Genetics, MultipleComparison, GeneSetEnrichment, GeneExpression, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/gage, http://www.biomedcentral.com/1471-2105/10/161 git_url: https://git.bioconductor.org/packages/gage git_branch: RELEASE_3_22 git_last_commit: 9b2c4d0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gage_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gage_2.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gage_2.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gage_2.60.0.tgz vignettes: vignettes/gage/inst/doc/dataPrep.pdf, vignettes/gage/inst/doc/gage.pdf, vignettes/gage/inst/doc/RNA-seqWorkflow.pdf vignetteTitles: Gene set and data preparation, Generally Applicable Gene-set/Pathway Analysis, RNA-Seq Data Pathway and Gene-set Analysis Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gage/inst/doc/dataPrep.R, vignettes/gage/inst/doc/gage.R, vignettes/gage/inst/doc/RNA-seqWorkflow.R dependsOnMe: EGSEA suggestsMe: FGNet, pathview, SBGNview, gageData dependencyCount: 45 Package: GAprediction Version: 1.36.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 60229a972cdd66ab37b89b4bef59d1a2 NeedsCompilation: no Title: Prediction of gestational age with Illumina HumanMethylation450 data Description: [GAprediction] predicts gestational age using Illumina HumanMethylation450 CpG data. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, Regression, BiomedicalInformatics Author: Jon Bohlin Maintainer: Jon Bohlin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GAprediction git_branch: RELEASE_3_22 git_last_commit: 57760f1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GAprediction_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GAprediction_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GAprediction_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GAprediction_1.36.0.tgz vignettes: vignettes/GAprediction/inst/doc/GAprediction.html vignetteTitles: GAprediction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GAprediction/inst/doc/GAprediction.R dependencyCount: 17 Package: garfield Version: 1.38.0 Suggests: knitr License: GPL-3 MD5sum: 85d053b4ee493af6ae7d903c96fea93c NeedsCompilation: yes Title: GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction Description: GARFIELD is a non-parametric functional enrichment analysis approach described in the paper GARFIELD: GWAS analysis of regulatory or functional information enrichment with LD correction. Briefly, it is a method that leverages GWAS findings with regulatory or functional annotations (primarily from ENCODE and Roadmap epigenomics data) to find features relevant to a phenotype of interest. It performs greedy pruning of GWAS SNPs (LD r2 > 0.1) and then annotates them based on functional information overlap. Next, it quantifies Fold Enrichment (FE) at various GWAS significance cutoffs and assesses them by permutation testing, while matching for minor allele frequency, distance to nearest transcription start site and number of LD proxies (r2 > 0.8). biocViews: Software, StatisticalMethod, Annotation, FunctionalPrediction, GenomeAnnotation Author: Sandro Morganella Maintainer: Valentina Iotchkova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/garfield git_branch: RELEASE_3_22 git_last_commit: 28dced9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/garfield_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/garfield_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/garfield_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/garfield_1.38.0.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 0 Package: GARS Version: 1.30.0 Depends: R (>= 3.5), ggplot2, cluster Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 47d4b726dc22c82c6eb6830ed0dc4c8c NeedsCompilation: no Title: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets Description: Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets. biocViews: Classification, FeatureExtraction, Clustering Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GARS git_branch: RELEASE_3_22 git_last_commit: c28c6e6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GARS_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GARS_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GARS_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GARS_1.30.0.tgz vignettes: vignettes/GARS/inst/doc/GARS.pdf vignetteTitles: GARS: a Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GARS/inst/doc/GARS.R dependencyCount: 269 Package: GateFinder Version: 1.30.0 Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: 53700f66c6c0377f0dc1f1bc85156404 NeedsCompilation: no Title: Projection-based Gating Strategy Optimization for Flow and Mass Cytometry Description: Given a vector of cluster memberships for a cell population, identifies a sequence of gates (polygon filters on 2D scatter plots) for isolation of that cell type. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour , Erin F. Simonds Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/GateFinder git_branch: RELEASE_3_22 git_last_commit: 2102af4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GateFinder_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GateFinder_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GateFinder_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GateFinder_1.30.0.tgz vignettes: vignettes/GateFinder/inst/doc/GateFinder.pdf vignetteTitles: GateFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GateFinder/inst/doc/GateFinder.R dependencyCount: 40 Package: gatom Version: 1.8.0 Depends: R (>= 4.3.0) Imports: data.table, igraph, BioNet, plyr, methods, XML, sna, intergraph, network, GGally, grid, ggplot2, mwcsr, pryr, htmlwidgets, htmltools, shinyCyJS (>= 1.0.0) Suggests: testthat, knitr, rmarkdown, KEGGREST, AnnotationDbi, org.Mm.eg.db, reactome.db, fgsea, readr, BiocStyle, R.utils License: MIT + file LICENCE MD5sum: 8a71fb667407e3b13940d81ca495b2ca NeedsCompilation: no Title: Finding an Active Metabolic Module in Atom Transition Network Description: This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization. biocViews: GeneExpression, DifferentialExpression, Pathways, Network Author: Anastasiia Gainullina [aut], Mariia Emelianova [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/gatom/ VignetteBuilder: knitr BugReports: https://github.com/ctlab/gatom/issues git_url: https://git.bioconductor.org/packages/gatom git_branch: RELEASE_3_22 git_last_commit: 2555401 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gatom_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gatom_1.7.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gatom_1.7.0.tgz vignettes: vignettes/gatom/inst/doc/gatom-tutorial.html vignetteTitles: Using gatom package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gatom/inst/doc/gatom-tutorial.R dependencyCount: 110 Package: GBScleanR Version: 2.3.2 Depends: SeqArray Imports: stats, utils, methods, ggplot2, tidyr, expm, Rcpp, RcppParallel, gdsfmt LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 5ea5704b2be23cf5a14ac10a2aa69248 NeedsCompilation: yes Title: Error correction tool for noisy genotyping by sequencing (GBS) data Description: GBScleanR is a package for quality check, filtering, and error correction of genotype data derived from next generation sequcener (NGS) based genotyping platforms. GBScleanR takes Variant Call Format (VCF) file as input. The main function of this package is `estGeno()` which estimates the true genotypes of samples from given read counts for genotype markers using a hidden Markov model with incorporating uneven observation ratio of allelic reads. This implementation gives robust genotype estimation even in noisy genotype data usually observed in Genotyping-By-Sequnencing (GBS) and similar methods, e.g. RADseq. The current implementation accepts genotype data of a diploid population at any generation of multi-parental cross, e.g. biparental F2 from inbred parents, biparental F2 from outbred parents, and 8-way recombinant inbred lines (8-way RILs) which can be refered to as MAGIC population. biocViews: GeneticVariability, SNP, Genetics, HiddenMarkovModel, Sequencing, QualityControl Author: Tomoyuki Furuta [aut, cre] (ORCID: ) Maintainer: Tomoyuki Furuta URL: https://github.com/tomoyukif/GBScleanR SystemRequirements: GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/tomoyukif/GBScleanR/issues git_url: https://git.bioconductor.org/packages/GBScleanR git_branch: devel git_last_commit: d1bad5d git_last_commit_date: 2025-06-17 Date/Publication: 2025-10-07 source.ver: src/contrib/GBScleanR_2.3.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/GBScleanR_2.3.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GBScleanR_2.3.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GBScleanR_2.3.2.tgz vignettes: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.html vignetteTitles: BasicUsageOfGBScleanR.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.R dependencyCount: 54 Package: gcapc Version: 1.34.0 Depends: R (>= 3.4) Imports: BiocGenerics, Seqinfo, S4Vectors, IRanges, Biostrings, BSgenome, GenomicRanges, Rsamtools, GenomicAlignments, matrixStats, MASS, splines, grDevices, graphics, stats, methods Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: GPL-3 Archs: x64 MD5sum: 44a1ea488159f9c9959f5c6b6f1b5b6b NeedsCompilation: no Title: GC Aware Peak Caller Description: Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation. biocViews: Sequencing, ChIPSeq, BatchEffect, PeakDetection Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/gcapc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gcapc git_branch: RELEASE_3_22 git_last_commit: 689a02e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gcapc_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gcapc_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gcapc_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gcapc_1.34.0.tgz vignettes: vignettes/gcapc/inst/doc/gcapc.html vignetteTitles: The gcapc user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcapc/inst/doc/gcapc.R suggestsMe: epigraHMM dependencyCount: 60 Package: gcatest Version: 2.10.0 Depends: R (>= 4.0) Imports: methods, lfa Suggests: knitr, ggplot2, testthat, BEDMatrix, genio License: GPL (>= 3) MD5sum: 967584f0c568a8749909183bac0b0730 NeedsCompilation: no Title: Genotype Conditional Association TEST Description: GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait models. This test conditions on the trait, which makes it immune to confounding by unmodeled environmental factors. Population structure is modeled via logistic factors, which are estimated using the `lfa` package. biocViews: SNP, DimensionReduction, PrincipalComponent, GenomeWideAssociation Author: Wei Hao [aut], Minsun Song [aut], Alejandro Ochoa [aut, cre] (ORCID: ), John D. Storey [aut] (ORCID: ) Maintainer: Alejandro Ochoa URL: https://github.com/StoreyLab/gcatest VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/gcatest/issues git_url: https://git.bioconductor.org/packages/gcatest git_branch: RELEASE_3_22 git_last_commit: 99d0349 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gcatest_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gcatest_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gcatest_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gcatest_2.10.0.tgz vignettes: vignettes/gcatest/inst/doc/gcatest.pdf vignetteTitles: gcat Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcatest/inst/doc/gcatest.R suggestsMe: jackstraw dependencyCount: 13 Package: GCPtools Version: 1.0.0 Depends: R (>= 4.5.0) Imports: AnVILBase, BiocBaseUtils, dplyr, httr, rlang, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: d86fc427955c2b6fd444afae2503d500 NeedsCompilation: no Title: Tools for working with gcloud and gsutil Description: Lower-level functionality to interface with Google Cloud Platform tools. 'gcloud' and 'gsutil' are both supported. The functionality provided centers around utilities for the AnVIL platform. biocViews: Software, Infrastructure, ThirdPartyClient, DataImport Author: Marcel Ramos [aut, cre] (ORCID: ), Nitesh Turaga [aut], Martin Morgan [aut] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/GCPtools SystemRequirements: gsutil, gcloud VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GCPtools/issues git_url: https://git.bioconductor.org/packages/GCPtools git_branch: RELEASE_3_22 git_last_commit: f9b794c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GCPtools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GCPtools_0.99.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GCPtools_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GCPtools_1.0.0.tgz vignettes: vignettes/GCPtools/inst/doc/GCPtools.html vignetteTitles: GCPtools Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GCPtools/inst/doc/GCPtools.R importsMe: AnVIL, AnVILGCP suggestsMe: AnVILPublish dependencyCount: 37 Package: gCrisprTools Version: 2.16.0 Depends: R (>= 4.1) Imports: Biobase, limma, RobustRankAggreg, ggplot2, SummarizedExperiment, grid, rmarkdown, grDevices, graphics, methods, ComplexHeatmap, stats, utils, parallel Suggests: edgeR, knitr, AnnotationDbi, org.Mm.eg.db, org.Hs.eg.db, BiocGenerics, markdown, RUnit, sparrow, msigdbr, fgsea License: Artistic-2.0 Archs: x64 MD5sum: af9129b457349235ca97d1d84d6a96c7 NeedsCompilation: no Title: Suite of Functions for Pooled Crispr Screen QC and Analysis Description: Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. Version 2.0 extends these applications to include a variety of tools for contextualizing and integrating signals across many experiments, incorporates extended signal enrichment methodologies via the "sparrow" package, and streamlines many formal requirements to aid in interpretablity. biocViews: ImmunoOncology, CRISPR, PooledScreens, ExperimentalDesign, BiomedicalInformatics, CellBiology, FunctionalGenomics, Pharmacogenomics, Pharmacogenetics, SystemsBiology, DifferentialExpression, GeneSetEnrichment, Genetics, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Software, Visualization Author: Russell Bainer, Dariusz Ratman, Steve Lianoglou, Peter Haverty Maintainer: Russell Bainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gCrisprTools git_branch: RELEASE_3_22 git_last_commit: 7b3b1fd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gCrisprTools_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gCrisprTools_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gCrisprTools_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gCrisprTools_2.16.0.tgz vignettes: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.html, vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html vignetteTitles: Contrast_Comparisons_gCrisprTools, Example_Workflow_gCrisprTools, gCrisprTools_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.R, vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R dependencyCount: 81 Package: gcrma Version: 2.82.0 Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats, utils Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector, Biostrings (>= 2.11.32), splines, BiocManager Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL MD5sum: 98e65da32e8843dc3c1eed09551920c2 NeedsCompilation: yes Title: Background Adjustment Using Sequence Information Description: Background adjustment using sequence information biocViews: Microarray, OneChannel, Preprocessing Author: Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald Jeff Gentry Maintainer: Z. Wu git_url: https://git.bioconductor.org/packages/gcrma git_branch: RELEASE_3_22 git_last_commit: 3e3e0ce git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gcrma_2.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gcrma_2.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gcrma_2.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gcrma_2.82.0.tgz vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf vignetteTitles: gcrma1.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyILM, affyPLM, maskBAD, webbioc importsMe: affycoretools, affylmGUI suggestsMe: panp, aroma.affymetrix dependencyCount: 21 Package: GDCRNATools Version: 1.29.0 Depends: R (>= 3.5.0) Imports: shiny, jsonlite, rjson, XML, limma, edgeR, DESeq2, clusterProfiler, DOSE, org.Hs.eg.db, biomaRt, survival, survminer, pathview, ggplot2, gplots, DT, GenomicDataCommons, BiocParallel Suggests: knitr, testthat, prettydoc, rmarkdown License: Artistic-2.0 MD5sum: 1fdf22e566e15c9ca635600208d75518 NeedsCompilation: no Title: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC Description: This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, GeneRegulation, GeneTarget, NetworkInference, Survival, Visualization, GeneSetEnrichment, NetworkEnrichment, Network, RNASeq, GO, KEGG Author: Ruidong Li, Han Qu, Shibo Wang, Julong Wei, Le Zhang, Renyuan Ma, Jianming Lu, Jianguo Zhu, Wei-De Zhong, Zhenyu Jia Maintainer: Ruidong Li , Han Qu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GDCRNATools git_branch: devel git_last_commit: c4da7e4 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/GDCRNATools_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GDCRNATools_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GDCRNATools_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GDCRNATools_1.30.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 233 Package: gDNAx Version: 1.8.0 Depends: R (>= 4.3) Imports: methods, BiocGenerics, BiocParallel, matrixStats, Biostrings, S4Vectors, IRanges, Seqinfo, GenomeInfoDb, GenomicRanges, GenomicFiles, GenomicAlignments, GenomicFeatures, Rsamtools, AnnotationHub, RColorBrewer, AnnotationDbi, bitops, plotrix, SummarizedExperiment, grDevices, graphics, stats, utils, cli Suggests: BiocStyle, knitr, rmarkdown, RUnit, TxDb.Hsapiens.UCSC.hg38.knownGene, gDNAinRNAseqData License: Artistic-2.0 Archs: x64 MD5sum: ad1ab2842997b5e5faaca5147aeba7b1 NeedsCompilation: no Title: Diagnostics for assessing genomic DNA contamination in RNA-seq data Description: Provides diagnostics for assessing genomic DNA contamination in RNA-seq data, as well as plots representing these diagnostics. Moreover, the package can be used to get an insight into the strand library protocol used and, in case of strand-specific libraries, the strandedness of the data. Furthermore, it provides functionality to filter out reads of potential gDNA origin. biocViews: Transcription, Transcriptomics, RNASeq, Sequencing, Preprocessing, Software, GeneExpression, Coverage, DifferentialExpression, FunctionalGenomics, SplicedAlignment, Alignment Author: Beatriz Calvo-Serra [aut], Robert Castelo [aut, cre] Maintainer: Robert Castelo URL: https://github.com/functionalgenomics/gDNAx VignetteBuilder: knitr BugReports: https://github.com/functionalgenomics/gDNAx/issues git_url: https://git.bioconductor.org/packages/gDNAx git_branch: RELEASE_3_22 git_last_commit: b09db45 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gDNAx_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDNAx_1.7.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDNAx_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDNAx_1.8.0.tgz vignettes: vignettes/gDNAx/inst/doc/gDNAx.html vignetteTitles: The gDNAx package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDNAx/inst/doc/gDNAx.R dependencyCount: 103 Package: gDR Version: 1.8.0 Depends: R (>= 4.2), gDRcore (>= 1.7.1), gDRimport (>= 1.7.1), gDRutils (>= 1.7.1) Suggests: BiocStyle, BumpyMatrix, futile.logger, gDRstyle (>= 1.7.1), gDRtestData (>= 1.7.1), kableExtra, knitr, markdown, purrr, rmarkdown, SummarizedExperiment, testthat, yaml License: Artistic-2.0 MD5sum: 036d6e85480c85cc0ac69cd810ecb020 NeedsCompilation: no Title: Umbrella package for R packages in the gDR suite Description: Package is a part of the gDR suite. It reexports functions from other packages in the gDR suite that contain critical processing functions and utilities. The vignette walks through the full processing pipeline for drug response analyses that the gDR suite offers. biocViews: Software, DataImport, ShinyApps Author: Allison Vuong [aut], Bartosz Czech [aut] (ORCID: ), Arkadiusz Gladki [cre, aut] (ORCID: ), Marc Hafner [aut] (ORCID: ), Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDR, https://gdrplatform.github.io/gDR/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDR/issues git_url: https://git.bioconductor.org/packages/gDR git_branch: RELEASE_3_22 git_last_commit: e524eb6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gDR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDR_1.7.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDR_1.8.0.tgz vignettes: vignettes/gDR/inst/doc/gDR.html vignetteTitles: Running the drug response processing pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDR/inst/doc/gDR.R dependencyCount: 209 Package: gDRcore Version: 1.8.0 Depends: R (>= 4.2) Imports: BumpyMatrix, BiocParallel, checkmate, futile.logger, gDRutils (>= 1.7.1), MultiAssayExperiment, purrr, stringr, S4Vectors, SummarizedExperiment, data.table Suggests: BiocStyle, gDRstyle (>= 1.7.1), gDRimport (>= 1.7.1), gDRtestData (>= 1.7.1), IRanges, knitr, pkgbuild, qs, testthat, yaml License: Artistic-2.0 MD5sum: 592d5e7ffade43ab1ff4f6106e77eec4 NeedsCompilation: yes Title: Processing functions and interface to process and analyze drug dose-response data Description: This package contains core functions to process and analyze drug response data. The package provides tools for normalizing, averaging, and calculation of gDR metrics data. All core functions are wrapped into the pipeline function allowing analyzing the data in a straightforward way. biocViews: Software, ShinyApps Author: Bartosz Czech [aut] (ORCID: ), Arkadiusz Gladki [cre, aut] (ORCID: ), Marc Hafner [aut] (ORCID: ), Pawel Piatkowski [aut], Natalia Potocka [aut], Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut], Marcin Kamianowski [aut], Allison Vuong [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRcore, https://gdrplatform.github.io/gDRcore/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRcore/issues git_url: https://git.bioconductor.org/packages/gDRcore git_branch: RELEASE_3_22 git_last_commit: 632e500 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gDRcore_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDRcore_1.7.7.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDRcore_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDRcore_1.8.0.tgz vignettes: vignettes/gDRcore/inst/doc/gDR-annotation.html, vignettes/gDRcore/inst/doc/gDR-data-model.html, vignettes/gDRcore/inst/doc/gDRcore.html vignetteTitles: gDRcore, Vignette Title, gDRcore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDRcore/inst/doc/gDR-annotation.R, vignettes/gDRcore/inst/doc/gDR-data-model.R, vignettes/gDRcore/inst/doc/gDRcore.R dependsOnMe: gDR dependencyCount: 115 Package: gDRimport Version: 1.8.0 Depends: R (>= 4.2) Imports: assertthat, BumpyMatrix, checkmate, CoreGx, PharmacoGx, data.table, futile.logger, gDRutils (>= 1.7.1), magrittr, methods, MultiAssayExperiment, readxl, rio, S4Vectors, stats, stringi, SummarizedExperiment, tibble, tools, utils, XML, yaml, openxlsx Suggests: BiocStyle, gDRtestData (>= 1.7.1), gDRstyle (>= 1.7.1), knitr, purrr, qs, testthat License: Artistic-2.0 MD5sum: e72c3de265ca42ec384e000e6f348774 NeedsCompilation: no Title: Package for handling the import of dose-response data Description: The package is a part of the gDR suite. It helps to prepare raw drug response data for downstream processing. It mainly contains helper functions for importing/loading/validating dose-response data provided in different file formats. biocViews: Software, Infrastructure, DataImport Author: Arkadiusz Gladki [aut, cre] (ORCID: ), Bartosz Czech [aut] (ORCID: ), Marc Hafner [aut] (ORCID: ), Sergiu Mocanu [aut], Dariusz Scigocki [aut], Allison Vuong [aut], Luca Gerosa [aut] (ORCID: ), Janina Smola [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRimport, https://gdrplatform.github.io/gDRimport/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRimport/issues git_url: https://git.bioconductor.org/packages/gDRimport git_branch: RELEASE_3_22 git_last_commit: 45e2207 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gDRimport_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDRimport_1.7.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDRimport_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDRimport_1.8.0.tgz vignettes: vignettes/gDRimport/inst/doc/ConvertingMAEtoPharmacoSet.html, vignettes/gDRimport/inst/doc/ConvertingPharmacoSetToGDR.html, vignettes/gDRimport/inst/doc/gDRimport.html vignetteTitles: Converting a gDR-generated MultiAssayExperiment object into a PharmacoSet, Converting PharmacoSet Drug Response Data into gDR object, gDRimport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDRimport/inst/doc/ConvertingMAEtoPharmacoSet.R, vignettes/gDRimport/inst/doc/ConvertingPharmacoSetToGDR.R, vignettes/gDRimport/inst/doc/gDRimport.R dependsOnMe: gDR suggestsMe: gDRcore dependencyCount: 207 Package: gDRstyle Version: 1.8.0 Depends: R (>= 4.2) Imports: BiocCheck, BiocManager, BiocStyle, checkmate, desc, git2r, lintr (>= 3.0.0), rcmdcheck, remotes, yaml, rjson, pkgbuild, withr Suggests: knitr, pkgdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: d28303a0f35b3120651abc1d890235bd NeedsCompilation: no Title: A package with style requirements for the gDR suite Description: Package fills a helper package role for whole gDR suite. It helps to support good development practices by keeping style requirements and style tests for other packages. It also contains build helpers to make all package requirements met. biocViews: Software, Infrastructure Author: Allison Vuong [aut], Dariusz Scigocki [aut], Marcin Kamianowski [aut], Aleksander Chlebowski [ctb], Janina Smola [aut], Arkadiusz Gladki [cre, aut] (ORCID: ), Bartosz Czech [aut] (ORCID: ) Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRstyle, https://gdrplatform.github.io/gDRstyle/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRstyle/issues git_url: https://git.bioconductor.org/packages/gDRstyle git_branch: RELEASE_3_22 git_last_commit: 72b76e8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gDRstyle_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDRstyle_1.7.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDRstyle_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDRstyle_1.8.0.tgz vignettes: vignettes/gDRstyle/inst/doc/gDRstyle.html, vignettes/gDRstyle/inst/doc/style_guide.html vignetteTitles: gDRstyle-package, gDR-style-guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDRstyle/inst/doc/gDRstyle.R, vignettes/gDRstyle/inst/doc/style_guide.R suggestsMe: gDR, gDRcore, gDRimport, gDRutils, gDRtestData dependencyCount: 103 Package: gDRutils Version: 1.8.0 Depends: R (>= 4.2) Imports: BiocParallel, BumpyMatrix, checkmate, data.table, digest, drc, jsonlite, jsonvalidate, methods, MultiAssayExperiment, S4Vectors, stats, stringr, SummarizedExperiment, qs, utils Suggests: BiocManager, BiocStyle, futile.logger, gDRstyle (>= 1.7.1), gDRtestData (>= 1.7.1), IRanges, knitr, lintr, mockery, purrr, rcmdcheck, rmarkdown, scales, testthat, tools, withr, yaml License: Artistic-2.0 MD5sum: 737814a6f8ad54ca9fcca40b67ae334c NeedsCompilation: no Title: A package with helper functions for processing drug response data Description: This package contains utility functions used throughout the gDR platform to fit data, manipulate data, and convert and validate data structures. This package also has the necessary default constants for gDR platform. Many of the functions are utilized by the gDRcore package. biocViews: Software, Infrastructure Author: Bartosz Czech [aut] (ORCID: ), Arkadiusz Gladki [cre, aut] (ORCID: ), Aleksander Chlebowski [aut], Marc Hafner [aut] (ORCID: ), Pawel Piatkowski [aut], Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut], Allison Vuong [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRutils, https://gdrplatform.github.io/gDRutils/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRutils/issues git_url: https://git.bioconductor.org/packages/gDRutils git_branch: RELEASE_3_22 git_last_commit: b7dd34b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gDRutils_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDRutils_1.7.16.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDRutils_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDRutils_1.8.0.tgz vignettes: vignettes/gDRutils/inst/doc/gDRutils.html vignetteTitles: gDRutils hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDRutils/inst/doc/gDRutils.R dependsOnMe: gDR importsMe: gDRcore, gDRimport dependencyCount: 114 Package: GDSArray Version: 1.30.0 Depends: R (>= 3.5), gdsfmt, methods, BiocGenerics, DelayedArray (>= 0.5.32) Imports: tools, S4Vectors (>= 0.17.34), SNPRelate, SeqArray Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, BiocManager License: GPL-3 MD5sum: ffd842e2addc63cbdc0de0f29eaa8b2c NeedsCompilation: no Title: Representing GDS files as array-like objects Description: GDS files are widely used to represent genotyping or sequence data. The GDSArray package implements the `GDSArray` class to represent nodes in GDS files in a matrix-like representation that allows easy manipulation (e.g., subsetting, mathematical transformation) in _R_. The data remains on disk until needed, so that very large files can be processed. biocViews: Infrastructure, DataRepresentation, Sequencing, GenotypingArray Author: Qian Liu [aut], Martin Morgan [aut], Hervé Pagès [aut], Xiuwen Zheng [aut, cre] Maintainer: Xiuwen Zheng URL: https://github.com/Bioconductor/GDSArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GDSArray/issues git_url: https://git.bioconductor.org/packages/GDSArray git_branch: RELEASE_3_22 git_last_commit: 421dd04 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GDSArray_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GDSArray_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GDSArray_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GDSArray_1.30.0.tgz vignettes: vignettes/GDSArray/inst/doc/GDSArray.html vignetteTitles: GDSArray: Representing GDS files as array-like objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GDSArray/inst/doc/GDSArray.R importsMe: CNVRanger, VariantExperiment suggestsMe: DelayedDataFrame dependencyCount: 31 Package: gdsfmt Version: 1.46.0 Depends: R (>= 2.15.0), methods Suggests: parallel, digest, Matrix, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics License: LGPL-3 Archs: x64 MD5sum: b536a1f4c66649c64501a52435be62cc NeedsCompilation: yes Title: R Interface to CoreArray Genomic Data Structure (GDS) Files Description: Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. biocViews: Infrastructure, DataImport Author: Xiuwen Zheng [aut, cre] (), Stephanie Gogarten [ctb], Jean-loup Gailly and Mark Adler [ctb] (for the included zlib sources), Yann Collet [ctb] (for the included LZ4 sources), xz contributors [ctb] (for the included liblzma sources) Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/gdsfmt VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/gdsfmt/issues git_url: https://git.bioconductor.org/packages/gdsfmt git_branch: RELEASE_3_22 git_last_commit: 36ad4a4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gdsfmt_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gdsfmt_1.45.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gdsfmt_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gdsfmt_1.46.0.tgz vignettes: vignettes/gdsfmt/inst/doc/gdsfmt.html vignetteTitles: Introduction to GDS Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt.R dependsOnMe: bigmelon, GDSArray, RAIDS, SAIGEgds, SCArray, SeqArray, SNPRelate importsMe: CNVRanger, GBScleanR, GENESIS, ggmanh, GWASTools, SCArray.sat, SeqSQC, SeqVarTools, VariantExperiment, CoxMK, EthSEQ, gwid, simplePHENOTYPES, snplinkage suggestsMe: AnnotationHub, HIBAG linksToMe: SeqArray, SNPRelate dependencyCount: 1 Package: GEM Version: 1.36.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics, rmarkdown, markdown License: Artistic-2.0 Archs: x64 MD5sum: ad1e26956680b87bcaf2d0058eb1dd77 NeedsCompilation: no Title: GEM: fast association study for the interplay of Gene, Environment and Methylation Description: Tools for analyzing EWAS, methQTL and GxE genome widely. biocViews: MethylSeq, MethylationArray, GenomeWideAssociation, Regression, DNAMethylation, SNP, GeneExpression, GUI Author: Hong Pan, Joanna D Holbrook, Neerja Karnani, Chee-Keong Kwoh Maintainer: Hong Pan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEM git_branch: RELEASE_3_22 git_last_commit: 6183985 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GEM_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEM_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEM_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEM_1.36.0.tgz vignettes: vignettes/GEM/inst/doc/user_guide.html vignetteTitles: The GEM User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEM/inst/doc/user_guide.R dependencyCount: 23 Package: gemini Version: 1.24.0 Depends: R (>= 4.1.0) Imports: dplyr, grDevices, ggplot2, magrittr, mixtools, scales, pbmcapply, parallel, stats, utils Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: 692e3dff29c0e350e9f8d64fbe17aad9 NeedsCompilation: no Title: GEMINI: Variational inference approach to infer genetic interactions from pairwise CRISPR screens Description: GEMINI uses log-fold changes to model sample-dependent and independent effects, and uses a variational Bayes approach to infer these effects. The inferred effects are used to score and identify genetic interactions, such as lethality and recovery. More details can be found in Zamanighomi et al. 2019 (in press). biocViews: Software, CRISPR, Bayesian, DataImport Author: Mahdi Zamanighomi [aut], Sidharth Jain [aut, cre] Maintainer: Sidharth Jain VignetteBuilder: knitr BugReports: https://github.com/sellerslab/gemini/issues git_url: https://git.bioconductor.org/packages/gemini git_branch: RELEASE_3_22 git_last_commit: 3da3898 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gemini_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gemini_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gemini_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gemini_1.24.0.tgz vignettes: vignettes/gemini/inst/doc/gemini-quickstart.html vignetteTitles: QuickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gemini/inst/doc/gemini-quickstart.R dependencyCount: 82 Package: gemma.R Version: 3.6.0 Imports: magrittr, glue, memoise, jsonlite, data.table, rlang, lubridate, utils, stringr, SummarizedExperiment, Biobase, tibble, tidyr, S4Vectors, httr, rappdirs, bit64, assertthat, digest, R.utils, kableExtra, base64enc Suggests: testthat (>= 2.0.0), rmarkdown, knitr, dplyr, covr, ggplot2, ggrepel, BiocStyle, microbenchmark, magick, purrr, pheatmap, viridis, poolr, listviewer, shiny License: Apache License (>= 2) Archs: x64 MD5sum: 589579ea5ba23d9a979a9c924c12d541 NeedsCompilation: no Title: A wrapper for Gemma's Restful API to access curated gene expression data and differential expression analyses Description: Low- and high-level wrappers for Gemma's RESTful API. They enable access to curated expression and differential expression data from over 10,000 published studies. Gemma is a web site, database and a set of tools for the meta-analysis, re-use and sharing of genomics data, currently primarily targeted at the analysis of gene expression profiles. biocViews: Software, DataImport, Microarray, SingleCell, ThirdPartyClient, DifferentialExpression, GeneExpression, Bayesian, Annotation, ExperimentalDesign, Normalization, BatchEffect, Preprocessing Author: Javier Castillo-Arnemann [aut] (ORCID: ), Jordan Sicherman [aut] (ORCID: ), Ogan Mancarci [cre, aut] (ORCID: ), Guillaume Poirier-Morency [aut] (ORCID: ) Maintainer: Ogan Mancarci URL: https://pavlidislab.github.io/gemma.R/, https://github.com/PavlidisLab/gemma.R VignetteBuilder: knitr BugReports: https://github.com/PavlidisLab/gemma.R/issues git_url: https://git.bioconductor.org/packages/gemma.R git_branch: RELEASE_3_22 git_last_commit: 11548e5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gemma.R_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gemma.R_3.5.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gemma.R_3.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gemma.R_3.6.0.tgz vignettes: vignettes/gemma.R/inst/doc/gemma.R.html, vignettes/gemma.R/inst/doc/metadata.html, vignettes/gemma.R/inst/doc/metanalysis.html vignetteTitles: Accessing curated gene expression data with gemma.R, A guide to metadata for samples and differential expression analyses, A meta analysis on effects of Parkinson's Disease using Gemma.R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gemma.R/inst/doc/gemma.R.R, vignettes/gemma.R/inst/doc/metadata.R, vignettes/gemma.R/inst/doc/metanalysis.R dependencyCount: 90 Package: genArise Version: 1.86.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 0385af05ee75aac3a13f35ea364ef91e NeedsCompilation: no Title: Microarray Analysis tool Description: genArise is an easy to use tool for dual color microarray data. Its GUI-Tk based environment let any non-experienced user performs a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer. biocViews: Microarray, TwoChannel, Preprocessing Author: Ana Patricia Gomez Mayen ,\\ Gustavo Corral Guille , \\ Lina Riego Ruiz ,\\ Gerardo Coello Coutino Maintainer: IFC Development Team URL: http://www.ifc.unam.mx/genarise git_url: https://git.bioconductor.org/packages/genArise git_branch: RELEASE_3_22 git_last_commit: bdf6506 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/genArise_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genArise_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genArise_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genArise_1.86.0.tgz vignettes: vignettes/genArise/inst/doc/genArise.pdf vignetteTitles: genAriseGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genArise/inst/doc/genArise.R dependencyCount: 11 Package: geneAttribution Version: 1.36.0 Imports: utils, GenomicRanges, org.Hs.eg.db, BiocGenerics, Seqinfo, GenomicFeatures, IRanges, rtracklayer Suggests: TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 7383b2cdf86b79f90be1800380385451 NeedsCompilation: no Title: Identification of candidate genes associated with genetic variation Description: Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format. biocViews: SNP, GenePrediction, GenomeWideAssociation, VariantAnnotation, GenomicVariation Author: Arthur Wuster Maintainer: Arthur Wuster VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneAttribution git_branch: RELEASE_3_22 git_last_commit: 3e52d75 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/geneAttribution_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneAttribution_1.35.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneAttribution_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneAttribution_1.36.0.tgz vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 77 Package: GeneBreak Version: 1.40.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: bb8be698a43a6be455a6f03be9567845 NeedsCompilation: no Title: Gene Break Detection Description: Recurrent breakpoint gene detection on copy number aberration profiles. biocViews: aCGH, CopyNumberVariation, DNASeq, Genetics, Sequencing, WholeGenome, Visualization Author: Evert van den Broek, Stef van Lieshout Maintainer: Evert van den Broek URL: https://github.com/stefvanlieshout/GeneBreak git_url: https://git.bioconductor.org/packages/GeneBreak git_branch: RELEASE_3_22 git_last_commit: ba80da7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeneBreak_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneBreak_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneBreak_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneBreak_1.40.0.tgz vignettes: vignettes/GeneBreak/inst/doc/GeneBreak.pdf vignetteTitles: GeneBreak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneBreak/inst/doc/GeneBreak.R dependencyCount: 49 Package: geneClassifiers Version: 1.34.0 Depends: R (>= 3.6.0) Imports: utils, methods, stats, Biobase, BiocGenerics Suggests: testthat License: GPL-2 MD5sum: b948829b5a445d143fe4b022cc2b76b4 NeedsCompilation: no Title: Application of gene classifiers Description: This packages aims for easy accessible application of classifiers which have been published in literature using an ExpressionSet as input. biocViews: GeneExpression, BiomedicalInformatics, Classification, Survival, Microarray Author: R Kuiper [cre, aut] (ORCID: ) Maintainer: R Kuiper URL: https://doi.org/doi:10.18129/B9.bioc.geneClassifiers BugReports: https://github.com/rkuiper/geneClassifiers/issues git_url: https://git.bioconductor.org/packages/geneClassifiers git_branch: RELEASE_3_22 git_last_commit: ae60fa5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/geneClassifiers_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneClassifiers_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneClassifiers_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneClassifiers_1.34.0.tgz vignettes: vignettes/geneClassifiers/inst/doc/geneClassifiers.pdf, vignettes/geneClassifiers/inst/doc/MissingCovariates.pdf vignetteTitles: geneClassifiers introduction, geneClassifiers and missing probesets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneClassifiers/inst/doc/geneClassifiers.R dependencyCount: 7 Package: GeneExpressionSignature Version: 1.56.0 Depends: R (>= 4.0) Imports: Biobase, stats, methods Suggests: apcluster, GEOquery, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 06fc71ca3845a17dd8a94afe9016a787 NeedsCompilation: no Title: Gene Expression Signature based Similarity Metric Description: This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest. biocViews: GeneExpression Author: Yang Cao [aut, cre], Fei Li [ctb], Lu Han [ctb] Maintainer: Yang Cao URL: https://github.com/yiluheihei/GeneExpressionSignature VignetteBuilder: knitr BugReports: https://github.com/yiluheihei/GeneExpressionSignature/issues/ git_url: https://git.bioconductor.org/packages/GeneExpressionSignature git_branch: RELEASE_3_22 git_last_commit: 804fd06 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeneExpressionSignature_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneExpressionSignature_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneExpressionSignature_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneExpressionSignature_1.56.0.tgz vignettes: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.html vignetteTitles: GeneExpressionSignature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.R dependencyCount: 7 Package: genefilter Version: 1.92.0 Imports: MatrixGenerics (>= 1.11.1), AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival, grDevices Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, RColorBrewer, BiocStyle, knitr License: Artistic-2.0 Archs: x64 MD5sum: 06c2d7996767eac12666bb4e6e3cd584 NeedsCompilation: yes Title: genefilter: methods for filtering genes from high-throughput experiments Description: Some basic functions for filtering genes. biocViews: Microarray Author: Robert Gentleman [aut], Vincent J. Carey [aut], Wolfgang Huber [aut], Florian Hahne [aut], Emmanuel Taiwo [ctb] ('howtogenefinder' vignette translation from Sweave to RMarkdown / HTML.), Khadijah Amusat [ctb] (Converted genefilter vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefilter git_branch: RELEASE_3_22 git_last_commit: b24c1ba git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/genefilter_1.92.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genefilter_1.91.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genefilter_1.92.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genefilter_1.92.0.tgz vignettes: vignettes/genefilter/inst/doc/independent_filtering_plots.pdf, vignettes/genefilter/inst/doc/howtogenefilter.html, vignettes/genefilter/inst/doc/howtogenefinder.html vignetteTitles: 03 - Additional plots for: Independent filtering increases power for detecting differentially expressed genes,, Bourgon et al.,, PNAS (2010), Using the genefilter function to filter genes from a microarray, howtogenefinder.knit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefilter/inst/doc/howtogenefilter.R, vignettes/genefilter/inst/doc/howtogenefinder.R, vignettes/genefilter/inst/doc/independent_filtering_plots.R dependsOnMe: CNTools, GeneMeta, sva, Hiiragi2013, maEndToEnd, rnaseqGene, lmQCM importsMe: a4Base, annmap, Category, cbaf, ClassifyR, countsimQC, covRNA, DEXSeq, GSRI, metaseqR2, methylCC, methylumi, minfi, MLInterfaces, mogsa, NBAMSeq, pcaExplorer, PECA, phenoTest, protGear, SpliceWiz, tilingArray, XDE, zinbwave, FlowSorted.Blood.EPIC, IHWpaper, causalBatch, CoNI, dGAselID, netgsa suggestsMe: annotate, BioNet, categoryCompare, clusterStab, codelink, cola, compcodeR, DelayedArray, EnrichedHeatmap, factDesign, ffpe, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, HDF5Array, logicFS, lumi, npGSEA, oligo, phyloseq, pvac, qpgraph, rtracklayer, siggenes, simplifyEnrichment, TCGAbiolinks, topGO, BloodCancerMultiOmics2017, curatedBladderData, curatedOvarianData, estrogen, ffpeExampleData, gageData, MAQCsubset, RforProteomics, rheumaticConditionWOLLBOLD, Single.mTEC.Transcriptomes, maGUI, SuperLearner dependencyCount: 53 Package: genefu Version: 2.42.0 Depends: R (>= 4.1), survcomp, biomaRt, iC10, AIMS Imports: amap, impute, mclust, limma, graphics, stats, utils, iC10TrainingData Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable, knitr, caret, survival, BiocStyle, magick, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: b3f2ab629b4520d19e5640a94e7663b1 NeedsCompilation: no Title: Computation of Gene Expression-Based Signatures in Breast Cancer Description: This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis. biocViews: DifferentialExpression, GeneExpression, Visualization, Clustering, Classification Author: Deena M.A. Gendoo [aut], Natchar Ratanasirigulchai [aut], Markus S. Schroeder [aut], Laia Pare [aut], Joel S Parker [aut], Aleix Prat [aut], Nikta Feizi [ctb], Christopher Eeles [ctb], Jermiah Joseph [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/genefu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefu git_branch: RELEASE_3_22 git_last_commit: 8925b07 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/genefu_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genefu_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genefu_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genefu_2.42.0.tgz vignettes: vignettes/genefu/inst/doc/genefu.html vignetteTitles: genefu: A Package For Breast Cancer Gene Expression Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefu/inst/doc/genefu.R importsMe: consensusOV, PDATK suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 118 Package: GeneGA Version: 1.60.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: c1615287e01cd78bf12c406588c051a3 NeedsCompilation: no Title: Design gene based on both mRNA secondary structure and codon usage bias using Genetic algorithm Description: R based Genetic algorithm for gene expression optimization by considering both mRNA secondary structure and codon usage bias, GeneGA includes the information of highly expressed genes of almost 200 genomes. Meanwhile, Vienna RNA Package is needed to ensure GeneGA to function properly. biocViews: GeneExpression Author: Zhenpeng Li and Haixiu Huang Maintainer: Zhenpeng Li URL: http://www.tbi.univie.ac.at/~ivo/RNA/ git_url: https://git.bioconductor.org/packages/GeneGA git_branch: RELEASE_3_22 git_last_commit: ab92f39 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeneGA_1.60.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneGA_1.60.0.tgz vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf vignetteTitles: GeneGA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGA/inst/doc/GeneGA.R dependencyCount: 17 Package: GeneMeta Version: 1.82.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: ac0e1cfc2525ca937f30d0ff3b0999eb NeedsCompilation: no Title: MetaAnalysis for High Throughput Experiments Description: A collection of meta-analysis tools for analysing high throughput experimental data biocViews: Sequencing, GeneExpression, Microarray Author: Lara Lusa , R. Gentleman, M. Ruschhaupt Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GeneMeta git_branch: RELEASE_3_22 git_last_commit: a64e7aa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeneMeta_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneMeta_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneMeta_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneMeta_1.82.0.tgz vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf vignetteTitles: GeneMeta Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneMeta/inst/doc/GeneMeta.R importsMe: XDE suggestsMe: genefu dependencyCount: 54 Package: GeneNetworkBuilder Version: 1.52.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13) Imports: plyr, graph, htmlwidgets, Rgraphviz, RCy3, rjson, XML, methods, grDevices, stats, graphics LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, RBGL, knitr, shiny, STRINGdb, BiocStyle, magick, rmarkdown, org.Hs.eg.db License: GPL (>= 2) MD5sum: 41d3e8d9929f63d5e8deb446e4575a64 NeedsCompilation: yes Title: GeneNetworkBuilder: a bioconductor package for building regulatory network using ChIP-chip/ChIP-seq data and Gene Expression Data Description: Appliation for discovering direct or indirect targets of transcription factors using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, GeneNetworkBuilder generates a regulatory network of the TF. biocViews: Sequencing, Microarray, GraphAndNetwork Author: Jianhong Ou, Haibo Liu, Heidi A Tissenbaum and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneNetworkBuilder git_branch: RELEASE_3_22 git_last_commit: f2c4229 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeneNetworkBuilder_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneNetworkBuilder_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneNetworkBuilder_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneNetworkBuilder_1.52.0.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.html vignetteTitles: GeneNetworkBuilder Vignette, Generate Network from a list of gene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.R, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.R dependencyCount: 69 Package: GeneOverlap Version: 1.46.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: x64 MD5sum: 81712006f0593296edc53a05745985ca NeedsCompilation: no Title: Test and visualize gene overlaps Description: Test two sets of gene lists and visualize the results. biocViews: MultipleComparison, Visualization Author: Li Shen, Icahn School of Medicine at Mount Sinai Maintainer: Antnio Miguel de Jesus Domingues, Max-Planck Institute for Cell Biology and Genetics URL: http://shenlab-sinai.github.io/shenlab-sinai/ git_url: https://git.bioconductor.org/packages/GeneOverlap git_branch: RELEASE_3_22 git_last_commit: afeb1c4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeneOverlap_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneOverlap_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneOverlap_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneOverlap_1.46.0.tgz vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf vignetteTitles: Testing and visualizing gene overlaps with the "GeneOverlap" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R dependencyCount: 9 Package: geneplast Version: 1.36.0 Depends: R (>= 4.0), methods Imports: igraph, snow, ape, grDevices, graphics, stats, utils, data.table Suggests: RTN, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown, Fletcher2013b, geneplast.data, geneplast.data.string.v91, ggplot2, ggpubr, plyr License: GPL (>= 2) MD5sum: c69ee0a5044890d152b15fd45ebac65b NeedsCompilation: no Title: Evolutionary and plasticity analysis of orthologous groups Description: Geneplast is designed for evolutionary and plasticity analysis based on orthologous groups distribution in a given species tree. It uses Shannon information theory and orthologs abundance to estimate the Evolutionary Plasticity Index. Additionally, it implements the Bridge algorithm to determine the evolutionary root of a given gene based on its orthologs distribution. biocViews: Genetics, GeneRegulation, SystemsBiology Author: Rodrigo Dalmolin, Mauro Castro Maintainer: Mauro Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplast git_branch: RELEASE_3_22 git_last_commit: 9e89d38 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/geneplast_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneplast_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneplast_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneplast_1.36.0.tgz vignettes: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.html, vignettes/geneplast/inst/doc/geneplast.html vignetteTitles: "Supporting Material for Trefflich2019.", "Geneplast: evolutionary analysis of orthologous groups." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.R, vignettes/geneplast/inst/doc/geneplast.R importsMe: geneplast.data suggestsMe: TreeAndLeaf, geneplast.data dependencyCount: 24 Package: geneplotter Version: 1.88.0 Depends: R (>= 2.10), methods, Biobase, BiocGenerics, lattice, annotate Imports: AnnotationDbi, graphics, grDevices, grid, RColorBrewer, stats, utils Suggests: Rgraphviz, fibroEset, hgu95av2.db, hu6800.db, hgu133a.db, BiocStyle, knitr License: Artistic-2.0 MD5sum: 4b8fc2ee134935254e5e2a725c9047eb NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: Robert Gentleman [aut], Rohit Satyam [ctb] (Converted geneplotter vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplotter git_branch: RELEASE_3_22 git_last_commit: 35e1fe9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/geneplotter_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneplotter_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneplotter_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneplotter_1.88.0.tgz vignettes: vignettes/geneplotter/inst/doc/visualize.pdf, vignettes/geneplotter/inst/doc/byChroms.html vignetteTitles: Visualization of Microarray Data, How to Assemble a chromLocation Object hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplotter/inst/doc/byChroms.R, vignettes/geneplotter/inst/doc/visualize.R dependsOnMe: HD2013SGI, Hiiragi2013, maEndToEnd importsMe: biocGraph, DEXSeq, MethylSeekR suggestsMe: biocGraph, Category, EnrichmentBrowser, GOstats, Single.mTEC.Transcriptomes dependencyCount: 49 Package: geneRecommender Version: 1.82.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) Archs: x64 MD5sum: 665f1b62d88011a175399e6ecb080293 NeedsCompilation: no Title: A gene recommender algorithm to identify genes coexpressed with a query set of genes Description: This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions. biocViews: Microarray, Clustering Author: Gregory J. Hather , with contributions from Art B. Owen and Terence P. Speed Maintainer: Greg Hather git_url: https://git.bioconductor.org/packages/geneRecommender git_branch: RELEASE_3_22 git_last_commit: d498b6a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/geneRecommender_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneRecommender_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneRecommender_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneRecommender_1.82.0.tgz vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf vignetteTitles: Using the geneRecommender Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRecommender/inst/doc/geneRecommender.R dependencyCount: 7 Package: GeneRegionScan Version: 1.66.0 Depends: methods, Biobase (>= 2.5.5), Biostrings Imports: S4Vectors (>= 0.9.25), Biobase (>= 2.5.5), affxparser, RColorBrewer, Biostrings Suggests: BSgenome, affy, AnnotationDbi License: GPL (>= 2) MD5sum: 00a9abf08f4451fcd6dd428d515a7bf4 NeedsCompilation: no Title: GeneRegionScan Description: A package with focus on analysis of discrete regions of the genome. This package is useful for investigation of one or a few genes using Affymetrix data, since it will extract probe level data using the Affymetrix Power Tools application and wrap these data into a ProbeLevelSet. A ProbeLevelSet directly extends the expressionSet, but includes additional information about the sequence of each probe and the probe set it is derived from. The package includes a number of functions used for plotting these probe level data as a function of location along sequences of mRNA-strands. This can be used for analysis of variable splicing, and is especially well suited for use with exon-array data. biocViews: Microarray, DataImport, SNP, OneChannel, Visualization Author: Lasse Folkersen, Diego Diez Maintainer: Lasse Folkersen git_url: https://git.bioconductor.org/packages/GeneRegionScan git_branch: RELEASE_3_22 git_last_commit: 3505f1d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeneRegionScan_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneRegionScan_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneRegionScan_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneRegionScan_1.66.0.tgz vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf vignetteTitles: GeneRegionScan hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.R dependencyCount: 18 Package: geneRxCluster Version: 1.46.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 26587c2331807c8c0e685f160fb1f236 NeedsCompilation: yes Title: gRx Differential Clustering Description: Detect Differential Clustering of Genomic Sites such as gene therapy integrations. The package provides some functions for exploring genomic insertion sites originating from two different sources. Possibly, the two sources are two different gene therapy vectors. Vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of insertions and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of False Discovery Rates (FDRs) providing statistical methods for comparing retroviral vectors. A scan statistic for comparing two vectors using multiple window widths to detect clustering differentials and compute FDRs is implemented here. biocViews: Sequencing, Clustering, Genetics Author: Charles Berry Maintainer: Charles Berry git_url: https://git.bioconductor.org/packages/geneRxCluster git_branch: RELEASE_3_22 git_last_commit: bb7d55c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/geneRxCluster_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneRxCluster_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneRxCluster_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneRxCluster_1.46.0.tgz vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf vignetteTitles: Using geneRxCluster hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRxCluster/inst/doc/tutorial.R dependencyCount: 11 Package: GeneSelectMMD Version: 2.54.0 Depends: R (>= 2.13.2), Biobase Imports: MASS, graphics, stats, limma Suggests: ALL License: GPL (>= 2) MD5sum: fdf42c68f1aff4b11d195cd46cdb43ff NeedsCompilation: yes Title: Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions Description: Gene selection based on a mixture of marginal distributions. biocViews: DifferentialExpression Author: Jarrett Morrow , Weiliang Qiu , Wenqing He , Xiaogang Wang , Ross Lazarus . Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/GeneSelectMMD git_branch: RELEASE_3_22 git_last_commit: bc4828d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeneSelectMMD_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneSelectMMD_2.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneSelectMMD_2.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneSelectMMD_2.54.0.tgz vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf vignetteTitles: Gene Selection based on a mixture of marginal distributions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R importsMe: iCheck dependencyCount: 11 Package: GENESIS Version: 2.40.0 Imports: Biobase, BiocGenerics, BiocParallel, GWASTools, gdsfmt, GenomicRanges, IRanges, S4Vectors, SeqArray, SeqVarTools, SNPRelate, data.table, graphics, grDevices, igraph, Matrix, methods, reshape2, stats, utils Suggests: CompQuadForm, COMPoissonReg, poibin, SPAtest, survey, testthat, BiocStyle, knitr, rmarkdown, GWASdata, dplyr, ggplot2, GGally, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, GenomeInfoDb License: GPL-3 Archs: x64 MD5sum: 3e14628dc23f68047377b0c66191e3fa NeedsCompilation: yes Title: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness Description: The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes. biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod, DimensionReduction, PrincipalComponent, GenomeWideAssociation, QualityControl, BiocViews Author: Matthew P. Conomos, Stephanie M. Gogarten, Lisa Brown, Han Chen, Thomas Lumley, Kenneth Rice, Tamar Sofer, Adrienne Stilp, Timothy Thornton, Chaoyu Yu Maintainer: Stephanie M. Gogarten URL: https://github.com/UW-GAC/GENESIS VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENESIS git_branch: RELEASE_3_22 git_last_commit: 3637b1b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GENESIS_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GENESIS_2.39.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GENESIS_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GENESIS_2.40.0.tgz vignettes: vignettes/GENESIS/inst/doc/assoc_test_seq.html, vignettes/GENESIS/inst/doc/assoc_test.html, vignettes/GENESIS/inst/doc/pcair.html vignetteTitles: Analyzing Sequence Data using the GENESIS Package, Genetic Association Testing using the GENESIS Package, Population Structure and Relatedness Inference using the GENESIS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENESIS/inst/doc/assoc_test_seq.R, vignettes/GENESIS/inst/doc/assoc_test.R, vignettes/GENESIS/inst/doc/pcair.R dependsOnMe: RAIDS dependencyCount: 118 Package: GeneStructureTools Version: 1.30.0 Imports: Biostrings,GenomicRanges,IRanges,data.table,plyr,stringdist,stringr,S4Vectors,BSgenome.Mmusculus.UCSC.mm10,stats,utils,Gviz,rtracklayer,methods Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 59c627ac7e89bf1dcdd975dafc7be89f NeedsCompilation: no Title: Tools for spliced gene structure manipulation and analysis Description: GeneStructureTools can be used to create in silico alternative splicing events, and analyse potential effects this has on functional gene products. biocViews: ImmunoOncology, Software, DifferentialSplicing, FunctionalPrediction, Transcriptomics, AlternativeSplicing, RNASeq Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneStructureTools git_branch: RELEASE_3_22 git_last_commit: 3b1f07b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeneStructureTools_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneStructureTools_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneStructureTools_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneStructureTools_1.30.0.tgz vignettes: vignettes/GeneStructureTools/inst/doc/Vignette.html vignetteTitles: Introduction to GeneStructureTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneStructureTools/inst/doc/Vignette.R dependencyCount: 155 Package: geNetClassifier Version: 1.50.0 Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods Imports: e1071, graphics, grDevices Suggests: leukemiasEset, RUnit, BiocGenerics Enhances: RColorBrewer, igraph, infotheo License: GPL (>= 2) MD5sum: f5880c2195d67edd07bf500e64dcf9ce NeedsCompilation: no Title: Classify diseases and build associated gene networks using gene expression profiles Description: Comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier. biocViews: Classification, DifferentialExpression, Microarray Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Sara Aibar URL: http://www.cicancer.org git_url: https://git.bioconductor.org/packages/geNetClassifier git_branch: RELEASE_3_22 git_last_commit: e2584c3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/geNetClassifier_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geNetClassifier_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geNetClassifier_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geNetClassifier_1.50.0.tgz vignettes: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf vignetteTitles: geNetClassifier-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.R importsMe: bioCancer, canceR dependencyCount: 18 Package: GeneticsPed Version: 1.72.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE MD5sum: 3d159145962f34fb27c9a18ef136cb57 NeedsCompilation: yes Title: Pedigree and genetic relationship functions Description: Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care! biocViews: Genetics Author: Gregor Gorjanc and David A. Henderson , with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING) Maintainer: David Henderson URL: http://rgenetics.org git_url: https://git.bioconductor.org/packages/GeneticsPed git_branch: RELEASE_3_22 git_last_commit: 1c0fb83 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeneticsPed_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneticsPed_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneticsPed_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneticsPed_1.72.0.tgz vignettes: vignettes/GeneticsPed/inst/doc/geneticRelatedness.pdf, vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf vignetteTitles: Calculation of genetic relatedness/relationship between individuals in the pedigree, Pedigree handling, Quantitative genetic (animal) model example in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneticsPed/inst/doc/geneticRelatedness.R, vignettes/GeneticsPed/inst/doc/pedigreeHandling.R, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.R dependencyCount: 11 Package: geneXtendeR Version: 1.36.0 Depends: rtracklayer, GO.db, R (>= 3.5.0) Imports: data.table, dplyr, graphics, networkD3, RColorBrewer, SnowballC, tm, utils, wordcloud, AnnotationDbi, BiocStyle, org.Rn.eg.db Suggests: knitr, rmarkdown, testthat, org.Ag.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Pt.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, rtracklayer License: GPL (>= 3) MD5sum: fea01159b69c88a9d3778a9885a0b331 NeedsCompilation: yes Title: Optimized Functional Annotation Of ChIP-seq Data Description: geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR. biocViews: ChIPSeq, Genetics, Annotation, GenomeAnnotation, DifferentialPeakCalling, Coverage, PeakDetection, ChipOnChip, HistoneModification, DataImport, NaturalLanguageProcessing, Visualization, GO, Software Author: Bohdan Khomtchouk [aut, cre], William Koehler [aut] Maintainer: Bohdan Khomtchouk URL: https://github.com/Bohdan-Khomtchouk/geneXtendeR VignetteBuilder: knitr BugReports: https://github.com/Bohdan-Khomtchouk/geneXtendeR/issues git_url: https://git.bioconductor.org/packages/geneXtendeR git_branch: RELEASE_3_22 git_last_commit: d65d2af git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/geneXtendeR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneXtendeR_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneXtendeR_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneXtendeR_1.36.0.tgz vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf vignetteTitles: geneXtendeR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 116 Package: GENIE3 Version: 1.32.0 Imports: stats, reshape2, dplyr Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase, SummarizedExperiment, testthat, methods, BiocStyle License: GPL (>= 2) Archs: x64 MD5sum: f92fe8d0fa845875695dc39f181cf35b NeedsCompilation: yes Title: GEne Network Inference with Ensemble of trees Description: This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data. biocViews: NetworkInference, SystemsBiology, DecisionTree, Regression, Network, GraphAndNetwork, GeneExpression Author: Van Anh Huynh-Thu, Sara Aibar, Pierre Geurts Maintainer: Van Anh Huynh-Thu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENIE3 git_branch: RELEASE_3_22 git_last_commit: 620284f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GENIE3_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GENIE3_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GENIE3_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GENIE3_1.32.0.tgz vignettes: vignettes/GENIE3/inst/doc/GENIE3.html vignetteTitles: GENIE3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENIE3/inst/doc/GENIE3.R importsMe: BioNERO, MetNet, scGraphVerse, bulkAnalyseR dependencyCount: 26 Package: genomation Version: 1.42.0 Depends: R (>= 3.5.0), grid Imports: Biostrings (>= 2.47.6), BSgenome (>= 1.47.3), data.table, Seqinfo, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), S4Vectors (>= 0.17.25), ggplot2, gridBase, impute, IRanges (>= 2.13.12), matrixStats, methods, parallel, plotrix, plyr, readr, reshape2, Rsamtools (>= 1.31.2), seqPattern, rtracklayer (>= 1.39.7), Rcpp (>= 0.12.14) LinkingTo: Rcpp Suggests: BiocGenerics, genomationData, knitr, RColorBrewer, rmarkdown, RUnit License: Artistic-2.0 MD5sum: c6bde9282b375b75d178281a0b77185e NeedsCompilation: yes Title: Summary, annotation and visualization of genomic data Description: A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input. biocViews: Annotation, Sequencing, Visualization, CpGIsland Author: Altuna Akalin [aut, cre], Vedran Franke [aut, cre], Katarzyna Wreczycka [aut], Alexander Gosdschan [ctb], Liz Ing-Simmons [ctb], Bozena Mika-Gospodorz [ctb] Maintainer: Altuna Akalin , Vedran Franke , Katarzyna Wreczycka URL: http://bioinformatics.mdc-berlin.de/genomation/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/genomation/issues git_url: https://git.bioconductor.org/packages/genomation git_branch: RELEASE_3_22 git_last_commit: 01d23ac git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/genomation_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genomation_1.41.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genomation_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genomation_1.42.0.tgz vignettes: vignettes/genomation/inst/doc/GenomationManual.html vignetteTitles: genomation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomation/inst/doc/GenomationManual.R importsMe: CexoR, EpiCompare, fCCAC, GenomicPlot, RCAS suggestsMe: methylKit dependencyCount: 99 Package: GenomAutomorphism Version: 1.12.0 Depends: R (>= 4.4.0), Imports: Biostrings, BiocGenerics, BiocParallel, Seqinfo, GenomicRanges, IRanges, matrixStats, XVector, dplyr, data.table, parallel, doParallel, foreach, methods, S4Vectors, stats, numbers, utils Suggests: spelling, rmarkdown, BiocStyle, testthat (>= 3.0.0), knitr License: Artistic-2.0 MD5sum: 6f1b80288e15875a18a9c0572f01a66e NeedsCompilation: no Title: Compute the automorphisms between DNA's Abelian group representations Description: This is a R package to compute the automorphisms between pairwise aligned DNA sequences represented as elements from a Genomic Abelian group. In a general scenario, from genomic regions till the whole genomes from a given population (from any species or close related species) can be algebraically represented as a direct sum of cyclic groups or more specifically Abelian p-groups. Basically, we propose the representation of multiple sequence alignments of length N bp as element of a finite Abelian group created by the direct sum of homocyclic Abelian group of prime-power order. biocViews: MathematicalBiology, ComparativeGenomics, FunctionalGenomics, MultipleSequenceAlignment, WholeGenome Author: Robersy Sanchez [aut, cre] (ORCID: ) Maintainer: Robersy Sanchez URL: https://github.com/genomaths/GenomAutomorphism VignetteBuilder: knitr BugReports: https://github.com/genomaths/GenomAutomorphism/issues git_url: https://git.bioconductor.org/packages/GenomAutomorphism git_branch: RELEASE_3_22 git_last_commit: bb13ab8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomAutomorphism_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomAutomorphism_1.11.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomAutomorphism_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomAutomorphism_1.12.0.tgz vignettes: vignettes/GenomAutomorphism/inst/doc/GenomAutomorphism.html vignetteTitles: Get started-with GenomAutomorphism hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomAutomorphism/inst/doc/GenomAutomorphism.R dependencyCount: 46 Package: GenomeInfoDb Version: 1.46.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.53.2), S4Vectors (>= 0.47.6), IRanges (>= 2.41.1), Seqinfo (>= 0.99.2) Imports: stats, utils, UCSC.utils Suggests: GenomeInfoDbData, R.utils, data.table, GenomicRanges, Rsamtools, GenomicAlignments, BSgenome, GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: a8294ee538627cf2b3f74a3a0deb9f1c NeedsCompilation: no Title: Utilities for manipulating chromosome names, including modifying them to follow a particular naming style Description: Contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order. biocViews: Genetics, DataRepresentation, Annotation, GenomeAnnotation Author: Sonali Arora [aut], Martin Morgan [aut], Marc Carlson [aut], Hervé Pagès [aut, cre], Prisca Chidimma Maduka [ctb], Atuhurira Kirabo Kakopo [ctb], Haleema Khan [ctb] (vignette translation from Sweave to Rmarkdown / HTML), Emmanuel Chigozie Elendu [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomeInfoDb VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w BugReports: https://github.com/Bioconductor/GenomeInfoDb/issues git_url: https://git.bioconductor.org/packages/GenomeInfoDb git_branch: RELEASE_3_22 git_last_commit: 121e4ed git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomeInfoDb_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomeInfoDb_1.45.9.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomeInfoDb_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomeInfoDb_1.46.0.tgz vignettes: vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf, vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.html vignetteTitles: GenomeInfoDb: Introduction to GenomeInfoDb, Submitting your organism to GenomeInfoDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.R, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R dependsOnMe: BSgenomeForge, CODEX, IdeoViz, SCOPE, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, UCSCRepeatMasker, annotation, liftOver, variants, RTIGER importsMe: AllelicImbalance, AnnotationHubData, ATACseqQC, atena, BaalChIP, bambu, Banksy, bedbaser, BindingSiteFinder, biovizBase, biscuiteer, breakpointR, BUSpaRse, cageminer, cardelino, cfdnakit, cfDNAPro, chimeraviz, ChIPpeakAnno, ChIPseeker, circRNAprofiler, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, CopyNumberPlots, crisprDesign, CrispRVariants, Damsel, derfinder, derfinderPlot, DEScan2, diffHic, diffUTR, DMRcaller, easylift, ensembldb, EpiCompare, epimutacions, epiregulon, epivizr, EventPointer, extraChIPs, FRASER, funtooNorm, GA4GHshiny, gDNAx, GenomicDistributions, GenomicFiles, GenomicScores, ggbio, GRaNIE, GUIDEseq, Gviz, gwascat, h5vc, HiCaptuRe, HiContacts, idr2d, igblastr, karyoploteR, katdetectr, linkSet, mariner, metagene2, metaseqR2, methimpute, methodical, MethylSeekR, methylumi, missMethyl, Motif2Site, motifbreakR, multiHiCcompare, MungeSumstats, musicatk, MutationalPatterns, myvariant, NADfinder, normr, OGRE, ORFik, plotgardener, proActiv, ProteoDisco, PureCN, R3CPET, raer, RareVariantVis, RCAS, RcisTarget, recount, regioneR, regionReport, RESOLVE, rGREAT, ribosomeProfilingQC, roar, scanMiRApp, scDblFinder, scmeth, scRNAseqApp, scruff, seqCAT, SGSeq, signeR, SigsPack, Site2Target, SNPhood, SOMNiBUS, SparseSignatures, SPICEY, spiky, SpliceWiz, STADyUM, svaNUMT, svaRetro, TAPseq, TCGAutils, tidyCoverage, TnT, trackViewer, transcriptR, txdbmaker, Ularcirc, UMI4Cats, UPDhmm, VanillaICE, VariantFiltering, VariantTools, VaSP, VplotR, wiggleplotr, fitCons.UCSC.hg19, grasp2db, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BioPlex, chipenrich.data, GenomicDistributionsData, MethylSeqData, OSTA, crispRdesignR, driveR, hicream, ICAMS, locuszoomr, MAAPER, Signac, tepr suggestsMe: AlphaMissenseR, AnnotationForge, AnnotationHub, annotatr, BgeeCall, BSgenome, bumphunter, Chicago, crupR, dar, DEXSeq, DFplyr, DiffBind, DMRcate, enhancerHomologSearch, epialleleR, epigraHMM, ExperimentHubData, fishpond, GA4GHclient, GENESIS, GenomicFeatures, GenomicPlot, GenomicRanges, GenomicTuples, gmapR, gmoviz, HelloRanges, HicAggR, icetea, jazzPanda, ldblock, megadepth, methrix, multicrispr, nullranges, Organism.dplyr, OUTRIDER, parglms, peakCombiner, PICB, plyinteractions, QDNAseq, RaggedExperiment, recoup, regioneReloaded, rtracklayer, scGraphVerse, scLANE, scTreeViz, Seqinfo, seqsetvis, sesame, sitadela, SomaticSignatures, splatter, SummarizedExperiment, systemPipeR, TEKRABber, treeclimbR, UCSC.utils, VariantAnnotation, BioMartGOGeneSets, CTCF, excluderanges, sesameData, xcoredata, seqpac, gkmSVM, GRIN2, polyRAD, Seurat dependencyCount: 20 Package: genomeIntervals Version: 1.66.0 Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics (>= 0.15.2) Imports: Seqinfo, GenomicRanges (>= 1.21.16), IRanges(>= 2.3.14), S4Vectors (>= 0.7.10) License: Artistic-2.0 Archs: x64 MD5sum: 25fe3812eaa74604093e86796842adbf NeedsCompilation: no Title: Operations on genomic intervals Description: This package defines classes for representing genomic intervals and provides functions and methods for working with these. Note: The package provides the basic infrastructure for and is enhanced by the package 'girafe'. biocViews: DataImport, Infrastructure, Genetics Author: Julien Gagneur , Joern Toedling, Richard Bourgon, Nicolas Delhomme Maintainer: Julien Gagneur git_url: https://git.bioconductor.org/packages/genomeIntervals git_branch: RELEASE_3_22 git_last_commit: 70caf1e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/genomeIntervals_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genomeIntervals_1.65.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genomeIntervals_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genomeIntervals_1.66.0.tgz vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf vignetteTitles: Overview of the genomeIntervals package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomeIntervals/inst/doc/genomeIntervals.R importsMe: easyRNASeq dependencyCount: 12 Package: genomes Version: 3.40.0 Depends: readr, curl License: GPL-3 MD5sum: 70986a5d06dd64284d292f304d793753 NeedsCompilation: no Title: Genome sequencing project metadata Description: Download genome and assembly reports from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben git_url: https://git.bioconductor.org/packages/genomes git_branch: RELEASE_3_22 git_last_commit: f14d52f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/genomes_3.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genomes_3.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genomes_3.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genomes_3.40.0.tgz vignettes: vignettes/genomes/inst/doc/genomes.pdf vignetteTitles: Genome metadata hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomes/inst/doc/genomes.R dependencyCount: 30 Package: GenomicAlignments Version: 1.46.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.47.6), IRanges (>= 2.23.9), Seqinfo, GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), Biostrings (>= 2.77.2), Rsamtools (>= 2.25.1) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel, cigarillo (>= 0.99.2) LinkingTo: S4Vectors, IRanges Suggests: ShortRead, rtracklayer, BSgenome, GenomicFeatures, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19, DESeq2, edgeR, RUnit, knitr, BiocStyle License: Artistic-2.0 MD5sum: 889ee5bcd354094e9eec84e4a1669dd7 NeedsCompilation: yes Title: Representation and manipulation of short genomic alignments Description: Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. biocViews: Infrastructure, DataImport, Genetics, Sequencing, RNASeq, SNP, Coverage, Alignment, ImmunoOncology Author: Hervé Pagès [aut, cre], Valerie Obenchain [aut], Martin Morgan [aut], Fedor Bezrukov [ctb], Robert Castelo [ctb], Halimat C. Atanda [ctb] (Translated 'WorkingWithAlignedNucleotides' vignette from Sweave to RMarkdown / HTML.) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomicAlignments VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs BugReports: https://github.com/Bioconductor/GenomicAlignments/issues git_url: https://git.bioconductor.org/packages/GenomicAlignments git_branch: RELEASE_3_22 git_last_commit: 4bd0167 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicAlignments_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicAlignments_1.45.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicAlignments_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicAlignments_1.46.0.tgz vignettes: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.pdf, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.html vignetteTitles: An Introduction to the GenomicAlignments Package, Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides (WORK-IN-PROGRESS!) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.R, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R dependsOnMe: AllelicImbalance, Basic4Cseq, ChIPexoQual, groHMM, HelloRanges, igvR, ORFik, prebs, recoup, RiboDiPA, ShortRead, SplicingGraphs, sequencing importsMe: ASpli, ATACseqQC, ATACseqTFEA, atena, BaalChIP, bambu, biovizBase, breakpointR, CAGEfightR, CAGEr, cfDNAPro, chimeraviz, ChIPpeakAnno, CNEr, consensusDE, CoverageView, CrispRVariants, crupR, CSSQ, DAMEfinder, DegNorm, derfinder, DEScan2, DiffBind, DMRcaller, DNAfusion, DuplexDiscovereR, easyRNASeq, esATAC, FLAMES, FRASER, gcapc, gDNAx, genomation, GenomicFiles, GenomicPlot, ggbio, gmapR, gmoviz, GreyListChIP, GUIDEseq, Gviz, icetea, INSPEcT, IntEREst, MDTS, metagene2, metaseqR2, methylPipe, mosaics, Motif2Site, MotifPeeker, msgbsR, NADfinder, PICB, plyranges, pram, proActiv, raer, ramwas, RiboProfiling, ribosomeProfilingQC, RNAmodR, roar, Rqc, rtracklayer, saseR, scPipe, scruff, seqsetvis, SGSeq, spiky, SPLINTER, srnadiff, strandCheckR, TAPseq, TCseq, trackViewer, transcriptR, Ularcirc, UMI4Cats, VaSP, VplotR, ZygosityPredictor, leeBamViews, alakazam, iimi, MAAPER, PACVr, VALERIE suggestsMe: BindingSiteFinder, BiocParallel, cigarillo, csaw, DEXSeq, EpiCompare, ExperimentHub, extraChIPs, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, GenomicTuples, igblastr, igvShiny, IRanges, QuasR, Rsamtools, similaRpeak, Streamer, systemPipeR, NanoporeRNASeq, RNAseqData.HNRNPC.bam.chr14, seqmagick dependencyCount: 41 Package: GenomicDataCommons Version: 1.33.1 Depends: R (>= 4.1.0) Imports: stats, httr, xml2, jsonlite, utils, rlang, readr, GenomicRanges, IRanges, dplyr, rappdirs, tibble, tidyr Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer, ggplot2, GenomicAlignments, Rsamtools, BiocParallel, TxDb.Hsapiens.UCSC.hg38.knownGene, VariantAnnotation, maftools, R.utils, data.table License: Artistic-2.0 MD5sum: 31d4d5c24009506684748505e2e9e25b NeedsCompilation: no Title: NIH / NCI Genomic Data Commons Access Description: Programmatically access the NIH / NCI Genomic Data Commons RESTful service. biocViews: DataImport, Sequencing Author: Martin Morgan [aut], Sean Davis [aut, cre], Marcel Ramos [ctb] Maintainer: Sean Davis URL: https://bioconductor.org/packages/GenomicDataCommons, http://github.com/Bioconductor/GenomicDataCommons, http://bioconductor.github.io/GenomicDataCommons/ VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicDataCommons/issues/new git_url: https://git.bioconductor.org/packages/GenomicDataCommons git_branch: devel git_last_commit: b59db7b git_last_commit_date: 2025-05-12 Date/Publication: 2025-10-07 source.ver: src/contrib/GenomicDataCommons_1.33.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicDataCommons_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicDataCommons_1.33.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicDataCommons_1.33.1.tgz vignettes: vignettes/GenomicDataCommons/inst/doc/overview.html, vignettes/GenomicDataCommons/inst/doc/questions-and-answers.html, vignettes/GenomicDataCommons/inst/doc/somatic_mutations.html vignetteTitles: Introduction to Accessing the NCI Genomic Data Commons, Questions and answers from over the years, Somatic Mutation Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicDataCommons/inst/doc/overview.R, vignettes/GenomicDataCommons/inst/doc/questions-and-answers.R, vignettes/GenomicDataCommons/inst/doc/somatic_mutations.R importsMe: GDCRNATools, TCGAutils suggestsMe: autonomics, imageTCGA dependencyCount: 51 Package: GenomicDistributions Version: 1.18.0 Depends: R (>= 4.0), IRanges, GenomicRanges Imports: data.table, ggplot2, reshape2, methods, utils, Biostrings, plyr, dplyr, scales, broom, GenomeInfoDb, stats Suggests: AnnotationFilter, rtracklayer, testthat, knitr, BiocStyle, rmarkdown, GenomicDistributionsData Enhances: BSgenome, extrafont, ensembldb, GenomicFeatures License: BSD_2_clause + file LICENSE MD5sum: 408307439ef4aaac7614ec9101354af5 NeedsCompilation: no Title: GenomicDistributions: fast analysis of genomic intervals with Bioconductor Description: If you have a set of genomic ranges, this package can help you with visualization and comparison. It produces several kinds of plots, for example: Chromosome distribution plots, which visualize how your regions are distributed over chromosomes; feature distance distribution plots, which visualizes how your regions are distributed relative to a feature of interest, like Transcription Start Sites (TSSs); genomic partition plots, which visualize how your regions overlap given genomic features such as promoters, introns, exons, or intergenic regions. It also makes it easy to compare one set of ranges to another. biocViews: Software, GenomeAnnotation, GenomeAssembly, DataRepresentation, Sequencing, Coverage, FunctionalGenomics, Visualization Author: Kristyna Kupkova [aut, cre], Jose Verdezoto [aut], Tessa Danehy [aut], John Lawson [aut], Jose Verdezoto [aut], Michal Stolarczyk [aut], Jason Smith [aut], Bingjie Xue [aut], Sophia Rogers [aut], John Stubbs [aut], Nathan C. Sheffield [aut] Maintainer: Kristyna Kupkova URL: http://code.databio.org/GenomicDistributions VignetteBuilder: knitr BugReports: http://github.com/databio/GenomicDistributions git_url: https://git.bioconductor.org/packages/GenomicDistributions git_branch: RELEASE_3_22 git_last_commit: 7d87c4e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicDistributions_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicDistributions_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicDistributions_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicDistributions_1.18.0.tgz vignettes: vignettes/GenomicDistributions/inst/doc/full-power.html, vignettes/GenomicDistributions/inst/doc/intro.html vignetteTitles: 2. Full power GenomicDistributions, 1. Getting started with GenomicDistributions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenomicDistributions/inst/doc/intro.R dependencyCount: 60 Package: GenomicFeatures Version: 1.62.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.51.2), S4Vectors (>= 0.47.6), IRanges (>= 2.37.1), Seqinfo (>= 0.99.2), GenomicRanges (>= 1.61.1), AnnotationDbi (>= 1.41.4) Imports: methods, utils, stats, DBI, XVector, Biostrings (>= 2.77.2), rtracklayer (>= 1.69.1) Suggests: GenomeInfoDb, txdbmaker, org.Mm.eg.db, org.Hs.eg.db, BSgenome, BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17), BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.17), FDb.UCSC.tRNAs, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1), TxDb.Mmusculus.UCSC.mm10.knownGene (>= 3.4.7), TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.4.6), SNPlocs.Hsapiens.dbSNP144.GRCh38, Rsamtools, pasillaBamSubset (>= 0.0.5), GenomicAlignments (>= 1.15.7), ensembldb, AnnotationFilter, RUnit, BiocStyle, knitr, markdown License: Artistic-2.0 Archs: x64 MD5sum: a4be7ce617184bbdb12b57928b481c30 NeedsCompilation: no Title: Query the gene models of a given organism/assembly Description: Extract the genomic locations of genes, transcripts, exons, introns, and CDS, for the gene models stored in a TxDb object. A TxDb object is a small database that contains the gene models of a given organism/assembly. Bioconductor provides a small collection of TxDb objects in the form of ready-to-install TxDb packages for the most commonly studied organisms. Additionally, the user can easily make a TxDb object (or package) for the organism/assembly of their choice by using the tools from the txdbmaker package. biocViews: Genetics, Infrastructure, Annotation, Sequencing, GenomeAnnotation Author: H. Pagès [aut, cre], M. Carlson [aut], P. Aboyoun [aut], S. Falcon [aut], M. Morgan [aut], D. Sarkar [aut], M. Lawrence [aut], V. Obenchain [aut], S. Arora [ctb], J. MacDonald [ctb], M. Ramos [ctb], S. Saini [ctb], P. Shannon [ctb], L. Shepherd [ctb], D. Tenenbaum [ctb], D. Van Twisk [ctb] Maintainer: H. Pagès URL: https://bioconductor.org/packages/GenomicFeatures VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicFeatures/issues git_url: https://git.bioconductor.org/packages/GenomicFeatures git_branch: RELEASE_3_22 git_last_commit: f4dfd41 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicFeatures_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicFeatures_1.61.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicFeatures_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicFeatures_1.62.0.tgz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.html vignetteTitles: Obtaining and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R dependsOnMe: Cogito, cpvSNP, CRISPRseek, ensembldb, GSReg, Guitar, HelloRanges, mygene, OrganismDbi, OUTRIDER, RareVariantVis, RiboDiPA, SplicingGraphs, txdbmaker, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, Homo.sapiens, Mus.musculus, Rattus.norvegicus, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Cfamiliaris.UCSC.canFam4.refGene, TxDb.Cfamiliaris.UCSC.canFam5.refGene, TxDb.Cfamiliaris.UCSC.canFam6.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg38.refGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, generegulation importsMe: AllelicImbalance, AnnotationHubData, annotatr, appreci8R, ASpli, atena, bambu, BgeeCall, BindingSiteFinder, biovizBase, bumphunter, BUSpaRse, CAGEfightR, CAGEr, casper, chevreulProcess, ChIPpeakAnno, ChIPseeker, compEpiTools, consensusDE, crisprDesign, crisprViz, crupR, CSSQ, Damsel, decompTumor2Sig, derfinder, derfinderPlot, DNAfusion, doubletrouble, EDASeq, ELMER, ELViS, EpiMix, epimutacions, EpiTxDb, epivizrData, epivizrStandalone, esATAC, EventPointer, FindIT2, FLAMES, FRASER, GA4GHshiny, gDNAx, geneAttribution, GenomicInteractionNodes, GenomicPlot, ggbio, gmapR, gmoviz, goseq, GUIDEseq, Gviz, gwascat, HiLDA, icetea, InPAS, INSPEcT, IntEREst, karyoploteR, lumi, magpie, metaseqR2, methylumi, msgbsR, multicrispr, musicatk, ORFik, Organism.dplyr, OutSplice, proActiv, proBAMr, ProteoDisco, PureCN, qpgraph, QuasR, raer, RCAS, recoup, RgnTX, rGREAT, RiboCrypt, RiboProfiling, ribosomeProfilingQC, RITAN, RNAmodR, saseR, scanMiRApp, scruff, SGSeq, sitadela, SPICEY, SPLINTER, srnadiff, StructuralVariantAnnotation, svaNUMT, 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rtracklayer, scPipe, Seqinfo, ShortRead, SummarizedExperiment, systemPipeR, TFutils, tidyCoverage, TnT, VplotR, wiggleplotr, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer7, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Rnorvegicus.UCSC.rn6, BioPlex, curatedAdipoChIP, ObMiTi, Single.mTEC.Transcriptomes, systemPipeRdata, CAGEWorkflow, polyRAD dependencyCount: 75 Package: GenomicFiles Version: 1.46.0 Depends: BiocGenerics, BiocParallel, GenomicRanges, MatrixGenerics, methods, Rsamtools (>= 2.25.1), rtracklayer (>= 1.69.1), SummarizedExperiment (>= 1.39.1) Imports: BiocBaseUtils, GenomeInfoDb (>= 1.45.7), GenomicAlignments (>= 1.45.1), IRanges, S4Vectors, Seqinfo, VariantAnnotation (>= 1.55.1) Suggests: BiocStyle, Biostrings, deepSNV, genefilter, Homo.sapiens, knitr, RNAseqData.HNRNPC.bam.chr14, RUnit, snpStats License: Artistic-2.0 MD5sum: f86c18a1e0f995acb1c0b64ab3581d4c NeedsCompilation: no Title: Distributed computing by file or by range Description: This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation. biocViews: Genetics, Infrastructure, DataImport, Sequencing, Coverage Author: Bioconductor Package Maintainer [aut, cre], Valerie Obenchain [aut], Michael Love [aut], Lori Shepherd [aut], Martin Morgan [aut], Sonali Kumari [ctb] (Converted 'GenomicFiles' vignettes from Sweave to RMarkdown / HTML.) Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/GenomicFiles VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=3PK_jx44QTs BugReports: https://github.com/Bioconductor/GenomicFiles/issues git_url: https://git.bioconductor.org/packages/GenomicFiles git_branch: RELEASE_3_22 git_last_commit: a7f3143 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicFiles_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicFiles_1.45.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicFiles_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicFiles_1.46.0.tgz vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.html vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R dependsOnMe: IntEREst importsMe: CAGEfightR, derfinder, gDNAx, QuasR, Rqc, TFutils, VCFArray suggestsMe: ldblock, MungeSumstats dependencyCount: 81 Package: genomicInstability Version: 1.16.0 Depends: R (>= 4.1.0), checkmate Imports: mixtools, SummarizedExperiment Suggests: SingleCellExperiment, ExperimentHub, pROC License: file LICENSE MD5sum: e6a8ca74a608da74a66a420ab3618d93 NeedsCompilation: no Title: Genomic Instability estimation for scRNA-Seq Description: This package contain functions to run genomic instability analysis (GIA) from scRNA-Seq data. GIA estimates the association between gene expression and genomic location of the coding genes. It uses the aREA algorithm to quantify the enrichment of sets of contiguous genes (loci-blocks) on the gene expression profiles and estimates the Genomic Instability Score (GIS) for each analyzed cell. biocViews: SystemsBiology, GeneExpression, SingleCell Author: Mariano Alvarez [aut, cre], Pasquale Laise [aut], DarwinHealth [cph] Maintainer: Mariano Alvarez URL: https://github.com/DarwinHealth/genomicInstability BugReports: https://github.com/DarwinHealth/genomicInstability git_url: https://git.bioconductor.org/packages/genomicInstability git_branch: RELEASE_3_22 git_last_commit: ababbae git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/genomicInstability_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genomicInstability_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genomicInstability_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genomicInstability_1.16.0.tgz vignettes: vignettes/genomicInstability/inst/doc/genomicInstability.pdf vignetteTitles: Using genomicInstability hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genomicInstability/inst/doc/genomicInstability.R dependencyCount: 97 Package: GenomicInteractionNodes Version: 1.14.0 Depends: R (>= 4.2.0), stats Imports: AnnotationDbi, graph, GO.db, GenomicRanges, GenomicFeatures, Seqinfo, methods, IRanges, RBGL, S4Vectors Suggests: RUnit, BiocStyle, knitr, rmarkdown, rtracklayer, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: file LICENSE MD5sum: f27cb25d23fd7be49876501c5b8cb28b NeedsCompilation: no Title: A R/Bioconductor package to detect the interaction nodes from HiC/HiChIP/HiCAR data Description: The GenomicInteractionNodes package can import interactions from bedpe file and define the interaction nodes, the genomic interaction sites with multiple interaction loops. The interaction nodes is a binding platform regulates one or multiple genes. The detected interaction nodes will be annotated for downstream validation. biocViews: HiC, Sequencing, Software Author: Jianhong Ou [aut, cre] (ORCID: ), Yarui Diao [fnd] Maintainer: Jianhong Ou URL: https://github.com/jianhong/GenomicInteractionNodes VignetteBuilder: knitr BugReports: https://github.com/jianhong/GenomicInteractionNodes/issues git_url: https://git.bioconductor.org/packages/GenomicInteractionNodes git_branch: RELEASE_3_22 git_last_commit: d3ee518 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicInteractionNodes_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicInteractionNodes_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicInteractionNodes_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicInteractionNodes_1.14.0.tgz vignettes: vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.html vignetteTitles: GenomicInteractionNodes Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.R dependencyCount: 79 Package: GenomicInteractions Version: 1.44.0 Depends: R (>= 3.5), InteractionSet Imports: Rsamtools, rtracklayer, GenomicRanges (>= 1.29.6), IRanges, BiocGenerics (>= 0.15.3), data.table, stringr, Seqinfo, ggplot2, grid, gridExtra, methods, igraph, S4Vectors (>= 0.13.13), dplyr, Gviz, Biobase, graphics, stats, utils, grDevices Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 3a1fb0c195837d5860e8c6be1ab94fdc NeedsCompilation: no Title: Utilities for handling genomic interaction data Description: Utilities for handling genomic interaction data such as ChIA-PET or Hi-C, annotating genomic features with interaction information, and producing plots and summary statistics. biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard, B. Maintainer: Liz Ing-Simmons URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicInteractions git_branch: RELEASE_3_22 git_last_commit: 7901852 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicInteractions_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicInteractions_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicInteractions_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicInteractions_1.44.0.tgz vignettes: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html vignetteTitles: chiapet_vignette.html, GenomicInteractions-HiC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R importsMe: CAGEfightR, HiCaptuRe, OHCA suggestsMe: Chicago, ELMER, extraChIPs, linkSet, sevenC, chicane dependencyCount: 154 Package: GenomicOZone Version: 1.24.0 Depends: R (>= 4.0.0), Ckmeans.1d.dp (>= 4.3.0), GenomicRanges, biomaRt, ggplot2 Imports: grDevices, stats, utils, plyr, gridExtra, lsr, parallel, ggbio, S4Vectors, IRanges, Seqinfo, Rdpack Suggests: readxl, GEOquery, knitr, rmarkdown License: LGPL (>=3) MD5sum: e36edda19f8b7f0f4385919c4c47250a NeedsCompilation: no Title: Delineate outstanding genomic zones of differential gene activity Description: The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. It enables characterization of effects on multiple genes within adaptive genomic neighborhoods, which could arise from genome reorganization, structural variation, or epigenome alteration. It guarantees cluster optimality, linear runtime to sample size, and reproducibility. One can apply it on genome-wide activity measurements such as copy number, transcriptomic, proteomic, and methylation data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, FunctionalPrediction, GeneRegulation, BiomedicalInformatics, CellBiology, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Clustering, Regression, RNASeq, Annotation, Visualization, Sequencing, Coverage, DifferentialMethylation, GenomicVariation, StructuralVariation, CopyNumberVariation Author: Hua Zhong, Mingzhou Song Maintainer: Hua Zhong, Mingzhou Song VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicOZone git_branch: RELEASE_3_22 git_last_commit: 0fee919 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicOZone_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicOZone_1.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicOZone_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicOZone_1.24.0.tgz vignettes: vignettes/GenomicOZone/inst/doc/GenomicOZone.html vignetteTitles: GenomicOZone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicOZone/inst/doc/GenomicOZone.R dependencyCount: 156 Package: GenomicPlot Version: 1.8.0 Depends: R (>= 4.4.0), GenomicRanges (>= 1.46.1) Imports: methods, Rsamtools, parallel, tidyr, rtracklayer (>= 1.54.0), plyranges (>= 1.14.0), cowplot (>= 1.1.1), VennDiagram, ggplotify, Seqinfo, IRanges, ComplexHeatmap, RCAS (>= 1.20.0), scales (>= 1.2.0), GenomicAlignments (>= 1.30.0), edgeR, circlize, viridis, ggsignif (>= 0.6.3), ggsci (>= 2.9), ggpubr, grDevices, graphics, stats, utils, GenomicFeatures, genomation (>= 1.36.0), txdbmaker, ggplot2 (>= 3.3.5), BiocGenerics, dplyr, grid Suggests: knitr, rmarkdown, R.utils, Biobase, BiocStyle, testthat, AnnotationDbi, GenomeInfoDb License: GPL-2 MD5sum: 862f0e6d7eb34f32885841a4ba776072 NeedsCompilation: no Title: Plot profiles of next generation sequencing data in genomic features Description: Visualization of next generation sequencing (NGS) data is essential for interpreting high-throughput genomics experiment results. 'GenomicPlot' facilitates plotting of NGS data in various formats (bam, bed, wig and bigwig); both coverage and enrichment over input can be computed and displayed with respect to genomic features (such as UTR, CDS, enhancer), and user defined genomic loci or regions. Statistical tests on signal intensity within user defined regions of interest can be performed and represented as boxplots or bar graphs. Parallel processing is used to speed up computation on multicore platforms. In addition to genomic plots which is suitable for displaying of coverage of genomic DNA (such as ChIPseq data), metagenomic (without introns) plots can also be made for RNAseq or CLIPseq data as well. biocViews: AlternativeSplicing, ChIPSeq, Coverage, GeneExpression, RNASeq, Sequencing, Software, Transcription, Visualization, Annotation Author: Shuye Pu [aut, cre] (ORCID: ) Maintainer: Shuye Pu URL: https://github.com/shuye2009/GenomicPlot VignetteBuilder: knitr BugReports: https://github.com/shuye2009/GenomicPlot/issues git_url: https://git.bioconductor.org/packages/GenomicPlot git_branch: RELEASE_3_22 git_last_commit: e9ce4a7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicPlot_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicPlot_1.7.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicPlot_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicPlot_1.8.0.tgz vignettes: vignettes/GenomicPlot/inst/doc/GenomicPlot_vignettes.html vignetteTitles: GenomicPlot_vignettes.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicPlot/inst/doc/GenomicPlot_vignettes.R dependencyCount: 212 Package: GenomicRanges Version: 1.62.0 Depends: R (>= 4.0.0), methods, stats4, BiocGenerics (>= 0.53.2), S4Vectors (>= 0.45.2), IRanges (>= 2.43.6), Seqinfo (>= 0.99.3) Imports: utils, stats LinkingTo: S4Vectors, IRanges Suggests: GenomeInfoDb, Biobase, AnnotationDbi, annotate, Biostrings (>= 2.77.2), SummarizedExperiment (>= 1.39.1), Rsamtools, GenomicAlignments, rtracklayer, BSgenome, GenomicFeatures, txdbmaker, Gviz, VariantAnnotation, AnnotationHub, DESeq2, DEXSeq, edgeR, KEGGgraph, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, KEGGREST, hgu95av2.db, hgu95av2probe, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, RUnit, digest, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: a092719dd4b8e30d756ea0471325d0e0 NeedsCompilation: yes Title: Representation and manipulation of genomic intervals Description: The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. biocViews: Genetics, Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, Coverage Author: Patrick Aboyoun [aut], Hervé Pagès [aut, cre], Michael Lawrence [aut], Sonali Arora [ctb], Martin Morgan [ctb], Kayla Morrell [ctb], Valerie Obenchain [ctb], Marcel Ramos [ctb], Lori Shepherd [ctb], Dan Tenenbaum [ctb], Daniel van Twisk [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomicRanges VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicRanges/issues git_url: https://git.bioconductor.org/packages/GenomicRanges git_branch: RELEASE_3_22 git_last_commit: 69b49ef git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicRanges_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicRanges_1.61.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicRanges_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicRanges_1.62.0.tgz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf, vignettes/GenomicRanges/inst/doc/Ten_things_slides.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html vignetteTitles: 5. Extending GenomicRanges, 2. GenomicRanges HOWTOs, 3. A quick introduction to GRanges and GRangesList objects (slides), 4. Ten Things You Didn't Know (slides from BioC 2016), 1. An Introduction to the GenomicRanges Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.R, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.R, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.R, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.R, vignettes/GenomicRanges/inst/doc/Ten_things_slides.R dependsOnMe: alabaster.ranges, AllelicImbalance, annmap, AnnotationHubData, BaalChIP, Basic4Cseq, betaHMM, BindingSiteFinder, biomvRCNS, BiSeq, bnbc, breakpointR, BSgenome, bsseq, bumphunter, CAFE, CAGEfightR, casper, chimeraviz, ChIPpeakAnno, chipseq, chromPlot, cn.mops, cnvGSA, CNVPanelizer, CNVRanger, COCOA, Cogito, compEpiTools, consensusSeekeR, CSAR, csaw, CSSQ, deepSNV, DEScan2, DESeq2, DEXSeq, DiffBind, diffHic, DMCFB, DMCHMM, DMRcaller, DNAshapeR, easylift, EnrichedHeatmap, ensembldb, esATAC, ExCluster, extraChIPs, fastseg, fCCAC, FindIT2, GeneBreak, GenomicAlignments, 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XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BioPlex, biscuiteerData, chipenrich.data, COSMIC.67, ELMER.data, fourDNData, GenomicDistributionsData, leeBamViews, mCSEAdata, MethylSeqData, pepDat, scMultiome, scRNAseq, sesameData, SomaticCancerAlterations, spatialLIBD, TENET.ExperimentHub, TumourMethData, VariantToolsData, ExpHunterSuite, recountWorkflow, seqpac, cinaR, cpp11bigwig, crispRdesignR, driveR, GencoDymo2, geneHapR, geno2proteo, GenoPop, hahmmr, HiCociety, hicream, hoardeR, ICAMS, karyotapR, locuszoomr, lolliplot, LoopRig, MAAPER, MitoHEAR, noisyr, numbat, PACVr, RapidoPGS, revert, SATS, scPloidy, Signac, tepr, VALERIE suggestsMe: AlphaMissenseR, AnnotationHub, autonomics, biobroom, BiocGenerics, BiocParallel, CCAFE, Chicago, ComplexHeatmap, DFplyr, epivizrChart, GenomeInfoDb, ggmanh, Glimma, GSReg, GWASTools, HDF5Array, InteractiveComplexHeatmap, interactiveDisplay, IRanges, iscream, iSEE, maftools, MiRaGE, MIRit, omicsPrint, parglms, recountmethylation, RTCGA, S4Vectors, SeqGSEA, Seqinfo, splatter, TFutils, universalmotif, updateObject, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, CTCF, GenomicState, BeadArrayUseCases, GeuvadisTranscriptExpr, MEDIPSData, MetaScope, nanotubes, RNAmodR.Data, Single.mTEC.Transcriptomes, systemPipeRdata, xcoredata, CAGEWorkflow, chicane, DGEobj, gkmSVM, MoBPS, polyRAD, Rgff, rliger, seqmagick, Seurat, sigminer, smer, SNPassoc, updog, valr dependencyCount: 10 Package: GenomicScores Version: 2.22.0 Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges, methods, BiocGenerics (>= 0.13.8) Imports: stats, utils, XML, httr, Biobase, BiocManager, BiocFileCache, IRanges (>= 2.3.23), Biostrings, Seqinfo, GenomeInfoDb (>= 1.45.5), AnnotationHub, rhdf5, DelayedArray, HDF5Array Suggests: RUnit, BiocStyle, knitr, rmarkdown, VariantAnnotation, gwascat, RColorBrewer, shiny, shinyjs, shinycustomloader, data.table, DT, magrittr, shinydashboard, BSgenome.Hsapiens.UCSC.hg38, phastCons100way.UCSC.hg38, MafDb.1Kgenomes.phase1.hs37d5, MafH5.gnomAD.v4.0.GRCh38, SNPlocs.Hsapiens.dbSNP144.GRCh37, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 MD5sum: 7cf7f934377a6958ac12db583bef5b10 NeedsCompilation: no Title: Infrastructure to work with genomewide position-specific scores Description: Provide infrastructure to store and access genomewide position-specific scores within R and Bioconductor. biocViews: Infrastructure, Genetics, Annotation, Sequencing, Coverage, AnnotationHubSoftware Author: Robert Castelo [aut, cre], Pau Puigdevall [ctb], Pablo Rodríguez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GenomicScores VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GenomicScores/issues git_url: https://git.bioconductor.org/packages/GenomicScores git_branch: RELEASE_3_22 git_last_commit: 8f6570c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicScores_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicScores_2.21.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicScores_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicScores_2.22.0.tgz vignettes: vignettes/GenomicScores/inst/doc/GenomicScores.html vignetteTitles: An introduction to the GenomicScores package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicScores/inst/doc/GenomicScores.R dependsOnMe: AlphaMissense.v2023.hg19, AlphaMissense.v2023.hg38, cadd.v1.6.hg19, cadd.v1.6.hg38, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons30way.UCSC.hg38, phastCons35way.UCSC.mm39, phastCons7way.UCSC.hg38, phyloP35way.UCSC.mm39 importsMe: appreci8R, ATACseqQC, primirTSS, RareVariantVis, VariantFiltering suggestsMe: methrix dependencyCount: 82 Package: GenomicSuperSignature Version: 1.18.0 Depends: R (>= 4.1.0), SummarizedExperiment Imports: ComplexHeatmap, ggplot2, methods, S4Vectors, Biobase, ggpubr, dplyr, plotly, BiocFileCache, grid, flextable, irlba Suggests: knitr, rmarkdown, devtools, roxygen2, pkgdown, usethis, BiocStyle, testthat, forcats, stats, wordcloud, circlize, EnrichmentBrowser, clusterProfiler, msigdbr, cluster, RColorBrewer, reshape2, tibble, BiocManager, bcellViper, readr, utils License: Artistic-2.0 MD5sum: a6de768265cf498d3b9f8b9066791448 NeedsCompilation: no Title: Interpretation of RNA-seq experiments through robust, efficient comparison to public databases Description: This package provides a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. Through the pre-computed index, users can identify public resources associated with their dataset such as gene sets, MeSH term, and publication. Functions to identify interpretable annotations and intuitive visualization options are implemented in this package. biocViews: Transcriptomics, SystemsBiology, PrincipalComponent, RNASeq, Sequencing, Pathways, Clustering Author: Sehyun Oh [aut, cre], Levi Waldron [aut], Sean Davis [aut] Maintainer: Sehyun Oh URL: https://github.com/shbrief/GenomicSuperSignature VignetteBuilder: knitr BugReports: https://github.com/shbrief/GenomicSuperSignature/issues git_url: https://git.bioconductor.org/packages/GenomicSuperSignature git_branch: RELEASE_3_22 git_last_commit: e2b134e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicSuperSignature_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicSuperSignature_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicSuperSignature_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicSuperSignature_1.18.0.tgz vignettes: vignettes/GenomicSuperSignature/inst/doc/Contents.html, vignettes/GenomicSuperSignature/inst/doc/Quickstart.html vignetteTitles: Introduction on RAVmodel, Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicSuperSignature/inst/doc/Contents.R, vignettes/GenomicSuperSignature/inst/doc/Quickstart.R dependencyCount: 164 Package: GenomicTuples Version: 1.44.0 Depends: R (>= 4.0), GenomicRanges (>= 1.37.4), Seqinfo, S4Vectors (>= 0.17.25) Imports: methods, BiocGenerics (>= 0.21.2), Rcpp (>= 0.11.2), IRanges (>= 2.19.13), data.table, stats4, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, covr, GenomicAlignments, Biostrings, GenomeInfoDb License: Artistic-2.0 Archs: x64 MD5sum: 1ec83e12bce045f2926ad1ac3b114691 NeedsCompilation: yes Title: Representation and Manipulation of Genomic Tuples Description: GenomicTuples defines general purpose containers for storing genomic tuples. It aims to provide functionality for tuples of genomic co-ordinates that are analogous to those available for genomic ranges in the GenomicRanges Bioconductor package. biocViews: Infrastructure, DataRepresentation, Sequencing Author: Peter Hickey [aut, cre], Marcin Cieslik [ctb], Hervé Pagès [ctb] Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/GenomicTuples/issues git_url: https://git.bioconductor.org/packages/GenomicTuples git_branch: RELEASE_3_22 git_last_commit: dcc8d1f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenomicTuples_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicTuples_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicTuples_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicTuples_1.44.0.tgz vignettes: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html vignetteTitles: GenomicTuplesIntroduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R dependencyCount: 13 Package: GenProSeq Version: 1.14.0 Depends: keras, mclust, R (>= 4.2) Imports: tensorflow, word2vec, DeepPINCS, ttgsea, CatEncoders, reticulate, stats Suggests: VAExprs, stringdist, knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: 786b395c2419fc9aa506062d02e0de76 NeedsCompilation: no Title: Generating Protein Sequences with Deep Generative Models Description: Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. Machine learning has enabled us to generate useful protein sequences on a variety of scales. Generative models are machine learning methods which seek to model the distribution underlying the data, allowing for the generation of novel samples with similar properties to those on which the model was trained. Generative models of proteins can learn biologically meaningful representations helpful for a variety of downstream tasks. Furthermore, they can learn to generate protein sequences that have not been observed before and to assign higher probability to protein sequences that satisfy desired criteria. In this package, common deep generative models for protein sequences, such as variational autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer encoder is applied to protein sequences for the autoregressive model. biocViews: Software, Proteomics Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenProSeq git_branch: RELEASE_3_22 git_last_commit: e047101 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GenProSeq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenProSeq_1.13.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenProSeq_1.14.0.tgz vignettes: vignettes/GenProSeq/inst/doc/GenProSeq.html vignetteTitles: GenProSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenProSeq/inst/doc/GenProSeq.R dependencyCount: 146 Package: GeoDiff Version: 1.16.0 Depends: R (>= 4.1.0), Biobase Imports: Matrix, robust, plyr, lme4, Rcpp (>= 1.0.4.6), withr, methods, graphics, stats, testthat, GeomxTools, NanoStringNCTools LinkingTo: Rcpp, RcppArmadillo, roptim Suggests: knitr, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: 1a595683794e47e8516fdaeba61a4479 NeedsCompilation: yes Title: Count model based differential expression and normalization on GeoMx RNA data Description: A series of statistical models using count generating distributions for background modelling, feature and sample QC, normalization and differential expression analysis on GeoMx RNA data. The application of these methods are demonstrated by example data analysis vignette. biocViews: GeneExpression, DifferentialExpression, Normalization Author: Nicole Ortogero [cre], Lei Yang [aut], Zhi Yang [aut] Maintainer: Nicole Ortogero URL: https://github.com/Nanostring-Biostats/GeoDiff VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/GeoDiff git_url: https://git.bioconductor.org/packages/GeoDiff git_branch: RELEASE_3_22 git_last_commit: 0185ce6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeoDiff_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeoDiff_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeoDiff_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeoDiff_1.16.0.tgz vignettes: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.html vignetteTitles: Workflow_WTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.R dependencyCount: 145 Package: GEOfastq Version: 1.18.0 Imports: xml2, rvest, stringr, RCurl, doParallel, foreach, plyr Suggests: BiocCheck, roxygen2, knitr, rmarkdown, testthat License: MIT + file LICENSE Archs: x64 MD5sum: d86d5f84080b0ea1ee58ff6d56c11303 NeedsCompilation: no Title: Downloads ENA Fastqs With GEO Accessions Description: GEOfastq is used to download fastq files from the European Nucleotide Archive (ENA) starting with an accession from the Gene Expression Omnibus (GEO). To do this, sample metadata is retrieved from GEO and the Sequence Read Archive (SRA). SRA run accessions are then used to construct FTP and aspera download links for fastq files generated by the ENA. biocViews: RNASeq, DataImport Author: Alex Pickering [cre, aut] (ORCID: ) Maintainer: Alex Pickering VignetteBuilder: knitr BugReports: https://github.com/alexvpickering/GEOfastq/issues git_url: https://git.bioconductor.org/packages/GEOfastq git_branch: RELEASE_3_22 git_last_commit: fa24ecf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GEOfastq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEOfastq_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEOfastq_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEOfastq_1.18.0.tgz vignettes: vignettes/GEOfastq/inst/doc/GEOfastq.html vignetteTitles: Using the GEOfastq Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GEOfastq/inst/doc/GEOfastq.R dependencyCount: 36 Package: GEOmetadb Version: 1.72.0 Depends: R.utils,RSQLite Suggests: knitr, rmarkdown, dplyr, dbplyr, tm, wordcloud License: Artistic-2.0 MD5sum: 4c1115e0e3a2baca6831a60eff8de9a8 NeedsCompilation: no Title: A compilation of metadata from NCBI GEO Description: The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data of interest can be challenging using current tools. GEOmetadb is an attempt to make access to the metadata associated with samples, platforms, and datasets much more feasible. This is accomplished by parsing all the NCBI GEO metadata into a SQLite database that can be stored and queried locally. GEOmetadb is simply a thin wrapper around the SQLite database along with associated documentation. Finally, the SQLite database is updated regularly as new data is added to GEO and can be downloaded at will for the most up-to-date metadata. GEOmetadb paper: http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798 . biocViews: Infrastructure Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEOmetadb git_branch: RELEASE_3_22 git_last_commit: b851bf6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GEOmetadb_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEOmetadb_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEOmetadb_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEOmetadb_1.72.0.tgz vignettes: vignettes/GEOmetadb/inst/doc/GEOmetadb.html vignetteTitles: GEOmetadb hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R suggestsMe: antiProfilesData, maGUI dependencyCount: 24 Package: geomeTriD Version: 1.4.0 Depends: R (>= 4.4.0) Imports: aricode, BiocGenerics, Biostrings, clue, cluster, dbscan, future.apply, Seqinfo, GenomicRanges, graphics, grDevices, grid, htmlwidgets, igraph, InteractionSet, IRanges, MASS, Matrix, methods, plotrix, progressr, RANN, rgl, rjson, S4Vectors, scales, stats, trackViewer Suggests: RUnit, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, manipulateWidget, shiny, BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 533e52084db0668a9747e07bced4ea1c NeedsCompilation: no Title: A R/Bioconductor package for interactive 3D plot of epigenetic data or single cell data Description: The geomeTriD (Three-Dimensional Geometry) Package provides interactive 3D visualization of chromatin structures using the WebGL-based 'three.js' (https://threejs.org/) or the rgl rendering library. It is designed to identify and explore spatial chromatin patterns within genomic regions. The package generates dynamic 3D plots and HTML widgets that integrate seamlessly with Shiny applications, enabling researchers to visualize chromatin organization, detect spatial features, and compare structural dynamics across different conditions and data types. biocViews: Visualization Author: Jianhong Ou [aut, cre] (ORCID: ), Kenneth Poss [aut, fnd] Maintainer: Jianhong Ou URL: https://github.com/jianhong/geomeTriD VignetteBuilder: knitr BugReports: https://github.com/jianhong/geomeTriD/issues git_url: https://git.bioconductor.org/packages/geomeTriD git_branch: RELEASE_3_22 git_last_commit: 6d6e43f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/geomeTriD_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geomeTriD_1.3.16.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geomeTriD_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geomeTriD_1.4.0.tgz vignettes: vignettes/geomeTriD/inst/doc/geomeTriD.html vignetteTitles: geomeTriD Vignette: Plot data in 3D hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/geomeTriD/inst/doc/geomeTriD.R dependencyCount: 173 Package: GeomxTools Version: 3.14.0 Depends: R (>= 3.6), Biobase, NanoStringNCTools, S4Vectors Imports: BiocGenerics, rjson, readxl, EnvStats, reshape2, methods, utils, stats, data.table, lmerTest, dplyr, stringr, grDevices, graphics, GGally, rlang, ggplot2, SeuratObject Suggests: rmarkdown, knitr, testthat (>= 3.0.0), parallel, ggiraph, Seurat, SpatialExperiment (>= 1.4.0), SpatialDecon, patchwork License: MIT MD5sum: 21a95be2e11385b686873a7993141c2c NeedsCompilation: no Title: NanoString GeoMx Tools Description: Tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, mRNAMicroarray, ProprietaryPlatforms, RNASeq, Sequencing, ExperimentalDesign, Normalization, Spatial Author: Maddy Griswold [cre, aut], Nicole Ortogero [aut], Zhi Yang [aut], Ronalyn Vitancol [aut], David Henderson [aut] Maintainer: Maddy Griswold VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeomxTools git_branch: RELEASE_3_22 git_last_commit: 5afc17d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeomxTools_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeomxTools_3.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeomxTools_3.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeomxTools_3.14.0.tgz vignettes: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.html, vignettes/GeomxTools/inst/doc/GeomxSet_coercions.html, vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.html vignetteTitles: Developer Introduction to the NanoStringGeoMxSet, Coercion of GeoMxSet to Seurat and SpatialExperiment Objects, Protein data using GeomxTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.R, vignettes/GeomxTools/inst/doc/GeomxSet_coercions.R, vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.R dependsOnMe: GeoMxWorkflows importsMe: GeoDiff, SpatialDecon, SpatialOmicsOverlay dependencyCount: 123 Package: GEOquery Version: 2.78.0 Depends: R (>= 4.1.0), methods, Biobase Imports: readr (>= 1.3.1), xml2, dplyr, data.table, tidyr, magrittr, limma, curl, rentrez, R.utils, stringr, SummarizedExperiment, S4Vectors, rvest, httr2 Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr, markdown, quarto, DropletUtils, SingleCellExperiment License: MIT + file LICENSE MD5sum: 16b5a6c7ed0d5dae355d8ff857688917 NeedsCompilation: no Title: Get data from NCBI Gene Expression Omnibus (GEO) Description: The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor. biocViews: Microarray, DataImport, OneChannel, TwoChannel, SAGE Author: Sean Davis [aut, cre] (ORCID: ) Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery, http://seandavi.github.io/GEOquery, http://seandavi.github.io/GEOquery/ VignetteBuilder: quarto BugReports: https://github.com/seandavi/GEOquery/issues/new git_url: https://git.bioconductor.org/packages/GEOquery git_branch: RELEASE_3_22 git_last_commit: 3a4b52d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GEOquery_2.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEOquery_2.77.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEOquery_2.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEOquery_2.78.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: DrugVsDisease, SCAN.UPC, dyebiasexamples, GSE103322, GSE13015, GSE62944 importsMe: bigmelon, ChIPXpress, DExMA, EGAD, minfi, Moonlight2R, MoonlightR, phantasus, recount, BeadArrayUseCases, BioPlex, GSE13015, healthyControlsPresenceChecker, geneExpressionFromGEO, RCPA suggestsMe: AUCell, autonomics, COTAN, ctsGE, dearseq, diffcoexp, dyebias, EpiDISH, EpiMix, fgsea, FLAMES, GeneExpressionSignature, GenomicOZone, GeoTcgaData, methylclock, multiClust, MultiDataSet, omicsPrint, phantasusLite, quantiseqr, RegEnrich, RGSEA, Rnits, runibic, skewr, TargetScore, zFPKM, ath1121501frmavecs, airway, antiProfilesData, muscData, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, RegParallel, AnnoProbe, BED, easybio, evanverse, fdrci, maGUI, metaMA, MLML2R, NACHO, TcGSA, tinyarray dependencyCount: 74 Package: GEOsubmission Version: 1.62.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: 059b1c6199288af11d9e5da613f25060 NeedsCompilation: no Title: Prepares microarray data for submission to GEO Description: Helps to easily submit a microarray dataset and the associated sample information to GEO by preparing a single file for upload (direct deposit). biocViews: Microarray Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/GEOsubmission git_branch: RELEASE_3_22 git_last_commit: ee0c478 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GEOsubmission_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEOsubmission_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEOsubmission_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEOsubmission_1.62.0.tgz vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf vignetteTitles: GEOsubmission Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOsubmission/inst/doc/GEOsubmission.R dependencyCount: 12 Package: GeoTcgaData Version: 2.10.0 Depends: R (>= 4.2.0) Imports: utils, data.table, plyr, cqn, topconfects, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, DESeq2, S4Vectors, ChAMP, impute, tidyr, clusterProfiler, org.Hs.eg.db, edgeR, limma, quantreg, minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, dearseq, NOISeq, testthat (>= 3.0.0), CATT, TCGAbiolinks, enrichplot, GEOquery, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: b64f0ee984ac175a09cae181fa27122c NeedsCompilation: no Title: Processing Various Types of Data on GEO and TCGA Description: Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data. biocViews: GeneExpression, DifferentialExpression, RNASeq, CopyNumberVariation, Microarray, Software, DNAMethylation, DifferentialMethylation, SNP, ATACSeq, MethylationArray Author: Erqiang Hu [aut, cre] (ORCID: ) Maintainer: Erqiang Hu <13766876214@163.com> URL: https://github.com/YuLab-SMU/GeoTcgaData VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/GeoTcgaData/issues git_url: https://git.bioconductor.org/packages/GeoTcgaData git_branch: RELEASE_3_22 git_last_commit: 30ba4f4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GeoTcgaData_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeoTcgaData_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeoTcgaData_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeoTcgaData_2.10.0.tgz vignettes: vignettes/GeoTcgaData/inst/doc/GeoTcgaData.html vignetteTitles: GeoTcgaData hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeoTcgaData/inst/doc/GeoTcgaData.R dependencyCount: 55 Package: gep2pep Version: 1.30.0 Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods, Biobase, XML, rhdf5, digest, iterators Suggests: WriteXLS, testthat, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: e4522868042cf9f9f0472adeffdaec3c NeedsCompilation: no Title: Creation and Analysis of Pathway Expression Profiles (PEPs) Description: Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "drug set enrichment analysis" and "gene2drug" drug discovery analysis respectively. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DimensionReduction, Pathways, GO Author: Francesco Napolitano Maintainer: Francesco Napolitano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gep2pep git_branch: RELEASE_3_22 git_last_commit: d902b27 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gep2pep_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gep2pep_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gep2pep_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gep2pep_1.30.0.tgz vignettes: vignettes/gep2pep/inst/doc/vignette.html vignetteTitles: Introduction to gep2pep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gep2pep/inst/doc/vignette.R dependencyCount: 56 Package: getDEE2 Version: 1.20.0 Depends: R (>= 4.4) Imports: stats, utils, SummarizedExperiment, htm2txt Suggests: knitr, testthat, rmarkdown License: GPL-3 Archs: x64 MD5sum: 4bb409aa7c2d569535cf817b274ac29e NeedsCompilation: no Title: Programmatic access to the DEE2 RNA expression dataset Description: Digital Expression Explorer 2 (or DEE2 for short) is a repository of processed RNA-seq data in the form of counts. It was designed so that researchers could undertake re-analysis and meta-analysis of published RNA-seq studies quickly and easily. As of April 2020, over 1 million SRA datasets have been processed. This package provides an R interface to access these expression data. More information about the DEE2 project can be found at the project homepage (http://dee2.io) and main publication (https://doi.org/10.1093/gigascience/giz022). biocViews: GeneExpression, Transcriptomics, Sequencing Author: Mark Ziemann [aut, cre], Antony Kaspi [aut] Maintainer: Mark 0000-0002-7688-6974 Ziemann URL: https://github.com/markziemann/getDEE2 VignetteBuilder: knitr BugReports: https://github.com/markziemann/getDEE2 git_url: https://git.bioconductor.org/packages/getDEE2 git_branch: RELEASE_3_22 git_last_commit: f082615 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/getDEE2_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/getDEE2_1.19.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/getDEE2_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/getDEE2_1.20.0.tgz vignettes: vignettes/getDEE2/inst/doc/getDEE2.html vignetteTitles: getDEE2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/getDEE2/inst/doc/getDEE2.R importsMe: homosapienDEE2CellScore dependencyCount: 26 Package: geva Version: 1.18.0 Depends: R (>= 4.1) Imports: grDevices, graphics, methods, stats, utils, dbscan, fastcluster, matrixStats Suggests: devtools, knitr, rmarkdown, roxygen2, limma, topGO, testthat (>= 3.0.0) License: LGPL-3 MD5sum: e3b74795e29c7d124c15d7c35710092a NeedsCompilation: no Title: Gene Expression Variation Analysis (GEVA) Description: Statistic methods to evaluate variations of differential expression (DE) between multiple biological conditions. It takes into account the fold-changes and p-values from previous differential expression (DE) results that use large-scale data (*e.g.*, microarray and RNA-seq) and evaluates which genes would react in response to the distinct experiments. This evaluation involves an unique pipeline of statistical methods, including weighted summarization, quantile detection, cluster analysis, and ANOVA tests, in order to classify a subset of relevant genes whose DE is similar or dependent to certain biological factors. biocViews: Classification, DifferentialExpression, GeneExpression, Microarray, MultipleComparison, RNASeq, SystemsBiology, Transcriptomics Author: Itamar José Guimarães Nunes [aut, cre] (ORCID: ), Murilo Zanini David [ctb], Bruno César Feltes [ctb] (ORCID: ), Marcio Dorn [ctb] (ORCID: ) Maintainer: Itamar José Guimarães Nunes URL: https://github.com/sbcblab/geva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geva git_branch: RELEASE_3_22 git_last_commit: c291ceb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/geva_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geva_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geva_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geva_1.18.0.tgz vignettes: vignettes/geva/inst/doc/geva.pdf vignetteTitles: GEVA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geva/inst/doc/geva.R dependencyCount: 10 Package: GEWIST Version: 1.54.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 79627554afcb18909e4f6cfb35783a0e NeedsCompilation: no Title: Gene Environment Wide Interaction Search Threshold Description: This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions. biocViews: MultipleComparison, Genetics Author: Wei Q. Deng, Guillaume Pare Maintainer: Wei Q. Deng git_url: https://git.bioconductor.org/packages/GEWIST git_branch: RELEASE_3_22 git_last_commit: 344e010 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GEWIST_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEWIST_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEWIST_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEWIST_1.54.0.tgz vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf vignetteTitles: GEWIST.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEWIST/inst/doc/GEWIST.R dependencyCount: 70 Package: geyser Version: 1.2.0 Depends: R (>= 3.5.0) Imports: bslib (>= 0.6.0), BiocStyle, ComplexHeatmap, dplyr, DT, ggbeeswarm, ggplot2, htmltools, magrittr, shiny, SummarizedExperiment, tibble, tidyselect, tidyr Suggests: airway, knitr, DESeq2, recount3, rmarkdown, stringr, testthat (>= 3.0.0) License: CC0 Archs: x64 MD5sum: 0dd4f8e8fac29e34d5799daca791e26c NeedsCompilation: no Title: Gene Expression displaYer of SummarizedExperiment in R Description: Lightweight Expression displaYer (plotter / viewer) of SummarizedExperiment object in R. This package provides a quick and easy Shiny-based GUI to empower a user to use a SummarizedExperiment object to view (gene) expression grouped from the sample metadata columns (in the `colData` slot). Feature expression can either be viewed with a box plot or a heatmap. biocViews: Software, ShinyApps, GUI, GeneExpression Author: David McGaughey [aut, cre] (ORCID: ) Maintainer: David McGaughey URL: https://github.com/davemcg/geyser VignetteBuilder: knitr BugReports: https://github.com/davemcg/geyser/issues git_url: https://git.bioconductor.org/packages/geyser git_branch: RELEASE_3_22 git_last_commit: 5cbf44a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/geyser_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geyser_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geyser_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geyser_1.2.0.tgz vignettes: vignettes/geyser/inst/doc/Gene_Expression_Plotting_GUI.html vignetteTitles: Gene_Expression_Plotting_GUI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geyser/inst/doc/Gene_Expression_Plotting_GUI.R dependencyCount: 109 Package: gg4way Version: 1.8.0 Depends: R (>= 4.3.0), ggplot2 Imports: DESeq2, dplyr, edgeR, ggrepel, glue, janitor, limma, magrittr, methods, purrr, rlang, scales, stats, stringr, tibble, tidyr Suggests: airway, BiocStyle, knitr, org.Hs.eg.db, rmarkdown, testthat, vdiffr License: MIT + file LICENSE MD5sum: 3e3a03d682651a4fdd8687eccf6fc7d7 NeedsCompilation: no Title: 4way Plots of Differential Expression Description: 4way plots enable a comparison of the logFC values from two contrasts of differential gene expression. The gg4way package creates 4way plots using the ggplot2 framework and supports popular Bioconductor objects. The package also provides information about the correlation between contrasts and significant genes of interest. biocViews: Software, Visualization, DifferentialExpression, GeneExpression, Transcription, RNASeq, SingleCell, Sequencing Author: Benjamin I Laufer [aut, cre], Brad A Friedman [aut] Maintainer: Benjamin I Laufer URL: https://github.com/ben-laufer/gg4way VignetteBuilder: knitr BugReports: https://github.com/ben-laufer/gg4way/issues git_url: https://git.bioconductor.org/packages/gg4way git_branch: RELEASE_3_22 git_last_commit: 768ce2f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gg4way_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gg4way_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gg4way_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gg4way_1.8.0.tgz vignettes: vignettes/gg4way/inst/doc/gg4way.html vignetteTitles: gg4way hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gg4way/inst/doc/gg4way.R dependencyCount: 75 Package: ggbio Version: 1.58.0 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales, reshape2, gtable, Hmisc, biovizBase (>= 1.29.2), Biobase, S4Vectors (>= 0.13.13), IRanges (>= 2.11.16), Seqinfo, GenomeInfoDb (>= 1.45.5), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), Biostrings (>= 2.77.2), Rsamtools (>= 2.25.1), GenomicAlignments (>= 1.45.1), BSgenome (>= 1.77.1), VariantAnnotation (>= 1.55.1), rtracklayer (>= 1.69.1), GenomicFeatures (>= 1.61.4), OrganismDbi, ensembldb (>= 2.33.1), AnnotationDbi, AnnotationFilter, rlang Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat, EnsDb.Hsapiens.v75, tinytex License: Artistic-2.0 Archs: x64 MD5sum: a2c59172e33fec52ea1b5378dec2c4f4 NeedsCompilation: no Title: Visualization tools for genomic data Description: The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries. biocViews: Infrastructure, Visualization Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence URL: https://lawremi.github.io/ggbio/ VignetteBuilder: knitr BugReports: https://github.com/lawremi/ggbio/issues git_url: https://git.bioconductor.org/packages/ggbio git_branch: RELEASE_3_22 git_last_commit: 678f10e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ggbio_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggbio_1.57.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggbio_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggbio_1.58.0.tgz vignettes: vignettes/ggbio/inst/doc/ggbio.pdf vignetteTitles: Part 0: Introduction and quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CAFE, intansv importsMe: BOBaFIT, cageminer, Damsel, derfinderPlot, FLAMES, GenomicOZone, msgbsR, R3CPET, ReportingTools, RiboProfiling, scafari, scruff, SomaticSignatures, OHCA suggestsMe: bambu, ensembldb, FRASER, gwascat, interactiveDisplay, NanoStringNCTools, OUTRIDER, regionReport, RnBeads, StructuralVariantAnnotation, universalmotif, NanoporeRNASeq, Single.mTEC.Transcriptomes, SomaticCancerAlterations dependencyCount: 135 Package: ggcyto Version: 1.38.0 Depends: methods, ggplot2(>= 3.5.0), flowCore(>= 1.41.5), ncdfFlow(>= 2.17.1), flowWorkspace(>= 4.3.1) Imports: plyr, scales, hexbin, data.table, RColorBrewer, gridExtra, rlang Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats, openCyto, flowViz, ggridges, vdiffr License: file LICENSE Archs: x64 MD5sum: 3c2675a7ed6e8e839500af92f407f6b7 NeedsCompilation: no Title: Visualize Cytometry data with ggplot Description: With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Infrastructure, Visualization Author: Mike Jiang Maintainer: Mike Jiang URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggcyto git_branch: RELEASE_3_22 git_last_commit: af58dd0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ggcyto_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggcyto_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggcyto_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggcyto_1.38.0.tgz vignettes: vignettes/ggcyto/inst/doc/autoplot.html, vignettes/ggcyto/inst/doc/ggcyto.flowSet.html, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.html, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.html vignetteTitles: Quick plot for cytometry data, Visualize flowSet with ggcyto, Visualize GatingSet with ggcyto, Feature summary of ggcyto hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggcyto/inst/doc/autoplot.R, vignettes/ggcyto/inst/doc/ggcyto.flowSet.R, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.R, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.R dependsOnMe: flowGate importsMe: CytoML, CytoPipeline suggestsMe: CATALYST, flowCore, flowStats, flowTime, flowWorkspace, openCyto dependencyCount: 62 Package: ggkegg Version: 1.8.0 Depends: R (>= 4.3.0), ggplot2, ggraph, XML, igraph, tidygraph Imports: BiocFileCache, data.table, dplyr, magick, patchwork, shadowtext, stringr, tibble, methods, utils, stats, grDevices, gtable Suggests: knitr, clusterProfiler, bnlearn, rmarkdown, BiocStyle, AnnotationDbi, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 151b740f08ad91cf519d89fa7e2e3be9 NeedsCompilation: no Title: Analyzing and visualizing KEGG information using the grammar of graphics Description: This package aims to import, parse, and analyze KEGG data such as KEGG PATHWAY and KEGG MODULE. The package supports visualizing KEGG information using ggplot2 and ggraph through using the grammar of graphics. The package enables the direct visualization of the results from various omics analysis packages. biocViews: Pathways, DataImport, KEGG Author: Noriaki Sato [cre, aut] Maintainer: Noriaki Sato URL: https://github.com/noriakis/ggkegg VignetteBuilder: knitr BugReports: https://github.com/noriakis/ggkegg/issues git_url: https://git.bioconductor.org/packages/ggkegg git_branch: RELEASE_3_22 git_last_commit: c13dd11 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ggkegg_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggkegg_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggkegg_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggkegg_1.8.0.tgz vignettes: vignettes/ggkegg/inst/doc/usage_of_ggkegg.html vignetteTitles: ggkegg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggkegg/inst/doc/usage_of_ggkegg.R importsMe: pathfindR dependencyCount: 98 Package: ggmanh Version: 1.14.0 Depends: methods, ggplot2 Imports: gdsfmt, ggrepel, grDevices, paletteer, RColorBrewer, rlang, scales, SeqArray (>= 1.32.0), stats, tidyr, dplyr, pals, magrittr Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0), GenomicRanges License: MIT + file LICENSE MD5sum: a1af9a081c2597ad8813bbdb635e0d46 NeedsCompilation: no Title: Visualization Tool for GWAS Result Description: Manhattan plot and QQ Plot are commonly used to visualize the end result of Genome Wide Association Study. The "ggmanh" package aims to keep the generation of these plots simple while maintaining customizability. Main functions include manhattan_plot, qqunif, and thinPoints. biocViews: Visualization, GenomeWideAssociation, Genetics Author: John Lee [aut, cre], John Lee [aut] (AbbVie), Xiuwen Zheng [ctb, dtc] Maintainer: John Lee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggmanh git_branch: RELEASE_3_22 git_last_commit: 6b069ef git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ggmanh_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggmanh_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggmanh_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggmanh_1.14.0.tgz vignettes: vignettes/ggmanh/inst/doc/ggmanh.html vignetteTitles: Guide to ggmanh Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggmanh/inst/doc/ggmanh.R suggestsMe: SAIGEgds, plotthis dependencyCount: 60 Package: ggmsa Version: 1.16.0 Depends: R (>= 4.1.0) Imports: Biostrings, ggplot2, magrittr, tidyr, utils, stats, aplot, RColorBrewer, ggfun (>= 0.2.0), ggforce, dplyr, R4RNA, grDevices, seqmagick, grid, methods, ggtree (>= 1.17.1) Suggests: ggtreeExtra, ape, cowplot, knitr, rmarkdown, readxl, ggnewscale, kableExtra, gggenes, statebins, prettydoc, testthat (>= 3.0.0), yulab.utils License: Artistic-2.0 MD5sum: a671703a361dcb947f97efed938439cb NeedsCompilation: no Title: Plot Multiple Sequence Alignment using 'ggplot2' Description: A visual exploration tool for multiple sequence alignment and associated data. Supports MSA of DNA, RNA, and protein sequences using 'ggplot2'. Multiple sequence alignment can easily be combined with other 'ggplot2' plots, such as phylogenetic tree Visualized by 'ggtree', boxplot, genome map and so on. More features: visualization of sequence logos, sequence bundles, RNA secondary structures and detection of sequence recombinations. biocViews: Software, Visualization, Alignment, Annotation, MultipleSequenceAlignment Author: Guangchuang Yu [aut, cre, ths] (ORCID: ), Lang Zhou [aut], Shuangbin Xu [ctb], Huina Huang [ctb] Maintainer: Guangchuang Yu URL: https://doi.org/10.1093/bib/bbac222(paper), https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggmsa/issues git_url: https://git.bioconductor.org/packages/ggmsa git_branch: RELEASE_3_22 git_last_commit: c81d424 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ggmsa_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggmsa_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggmsa_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggmsa_1.16.0.tgz vignettes: vignettes/ggmsa/inst/doc/ggmsa.html vignetteTitles: ggmsa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggmsa/inst/doc/ggmsa.R importsMe: ggaligner dependencyCount: 93 Package: GGPA Version: 1.22.0 Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats Imports: Rcpp (>= 0.11.3) LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle License: GPL (>= 2) MD5sum: 3065b37764878555d60ac56111024e52 NeedsCompilation: yes Title: graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture Description: Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Hang J. Kim, Carter Allen Maintainer: Dongjun Chung URL: https://github.com/dongjunchung/GGPA/ SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/GGPA git_branch: RELEASE_3_22 git_last_commit: bcc94da git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GGPA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GGPA_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GGPA_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GGPA_1.22.0.tgz vignettes: vignettes/GGPA/inst/doc/GGPA-example.pdf vignetteTitles: GGPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGPA/inst/doc/GGPA-example.R dependencyCount: 54 Package: ggsc Version: 1.8.0 Depends: R (>= 4.1.0) Imports: Rcpp, RcppParallel, cli, dplyr, ggfun (>= 0.1.5), ggplot2, grDevices, grid, methods, rlang, scattermore, stats, Seurat, SingleCellExperiment, SummarizedExperiment, tidydr, tidyr, tibble, utils, RColorBrewer, yulab.utils, scales LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: aplot, BiocParallel, forcats, ggforce, ggnewscale, igraph, knitr, ks, Matrix, prettydoc, rmarkdown, scran, scater, scatterpie (>= 0.2.4), scuttle, shadowtext, sf, SeuratObject, SpatialExperiment, STexampleData, testthat (>= 3.0.0), MASS License: Artistic-2.0 MD5sum: ccd827598ccdf90d5d804f4055bd6745 NeedsCompilation: yes Title: Visualizing Single Cell and Spatial Transcriptomics Description: Useful functions to visualize single cell and spatial data. It supports visualizing 'Seurat', 'SingleCellExperiment' and 'SpatialExperiment' objects through grammar of graphics syntax implemented in 'ggplot2'. biocViews: DimensionReduction, GeneExpression, SingleCell, Software, Spatial, Transcriptomics,Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Shuangbin Xu [aut] (ORCID: ), Noriaki Sato [ctb] Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/ggsc (devel), https://yulab-smu.top/ggsc/ (docs) SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggsc/issues git_url: https://git.bioconductor.org/packages/ggsc git_branch: RELEASE_3_22 git_last_commit: 8d55cf2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ggsc_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggsc_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggsc_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggsc_1.8.0.tgz vignettes: vignettes/ggsc/inst/doc/ggsc.html vignetteTitles: Visualizing single cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggsc/inst/doc/ggsc.R suggestsMe: SVP dependencyCount: 170 Package: ggseqalign Version: 1.4.0 Depends: R (>= 4.4.0) Imports: pwalign, dplyr, ggplot2 Suggests: Biostrings, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 Archs: x64 MD5sum: 7b494cc419b5d8d2f699ca6bbc185e8f NeedsCompilation: no Title: Minimal Visualization of Sequence Alignments Description: Simple visualizations of alignments of DNA or AA sequences as well as arbitrary strings. Compatible with Biostrings and ggplot2. The plots are fully customizable using ggplot2 modifiers such as theme(). biocViews: Alignment, MultipleSequenceAlignment, Software, Visualization Author: Simeon Lim Rossmann [aut, cre] (ORCID: ) Maintainer: Simeon Lim Rossmann URL: https://github.com/simeross/ggseqalign VignetteBuilder: knitr BugReports: https://github.com/simeross/ggseqalign/issues git_url: https://git.bioconductor.org/packages/ggseqalign git_branch: RELEASE_3_22 git_last_commit: 90288f7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ggseqalign_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggseqalign_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggseqalign_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggseqalign_1.4.0.tgz vignettes: vignettes/ggseqalign/inst/doc/ggseqalign.html vignetteTitles: Quickstart Guide to ggseqalign hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggseqalign/inst/doc/ggseqalign.R dependencyCount: 40 Package: ggspavis Version: 1.16.0 Depends: ggplot2 Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, ggside, grid, ggrepel, RColorBrewer, scales, grDevices, methods, stats Suggests: BiocStyle, rmarkdown, knitr, OSTA.data, VisiumIO, arrow, STexampleData, BumpyMatrix, scater, scran, uwot, testthat, patchwork License: MIT + file LICENSE MD5sum: d6ea27807708df89d65e117fa4d43bbe NeedsCompilation: no Title: Visualization functions for spatial transcriptomics data Description: Visualization functions for spatial transcriptomics data. Includes functions to generate several types of plots, including spot plots, feature (molecule) plots, reduced dimension plots, spot-level quality control (QC) plots, and feature-level QC plots, for datasets from the 10x Genomics Visium and other technological platforms. Datasets are assumed to be in either SpatialExperiment or SingleCellExperiment format. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, QualityControl, DimensionReduction Author: Lukas M. Weber [aut, cre] (ORCID: ), Helena L. Crowell [aut] (ORCID: ), Yixing E. Dong [aut] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/ggspavis VignetteBuilder: knitr BugReports: https://github.com/lmweber/ggspavis/issues git_url: https://git.bioconductor.org/packages/ggspavis git_branch: RELEASE_3_22 git_last_commit: 9ea2f70 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ggspavis_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggspavis_1.15.7.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggspavis_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggspavis_1.16.0.tgz vignettes: vignettes/ggspavis/inst/doc/ggspavis_overview.html vignetteTitles: ggspavis overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggspavis/inst/doc/ggspavis_overview.R importsMe: OSTA suggestsMe: smoothclust, HCATonsilData dependencyCount: 78 Package: ggtree Version: 3.99.2 Depends: R (>= 3.5.0) Imports: ape, aplot, dplyr, ggplot2 (>= 4.0.0), grid, magrittr, methods, purrr, rlang, ggfun (>= 0.1.7), yulab.utils (>= 0.1.6), tidyr, tidytree (>= 0.4.5), treeio (>= 1.8.0), utils, scales, stats, cli, ggiraph (>= 0.9.1) Suggests: emojifont, ggimage, ggplotify, shadowtext, grDevices, knitr, prettydoc, rmarkdown, testthat, tibble, glue, Biostrings License: Artistic-2.0 MD5sum: 6d5a272159216c8ea936bd190ac171a6 NeedsCompilation: no Title: an R package for visualization of tree and annotation data Description: 'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data. biocViews: Alignment, Annotation, Clustering, DataImport, MultipleSequenceAlignment, Phylogenetics, ReproducibleResearch, Software, Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Tommy Tsan-Yuk Lam [aut, ths], Shuangbin Xu [aut] (ORCID: ), Lin Li [ctb], Bradley Jones [ctb], Justin Silverman [ctb], Watal M. Iwasaki [ctb], Yonghe Xia [ctb], Ruizhu Huang [ctb] Maintainer: Guangchuang Yu URL: https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book), http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12628 (paper) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtree/issues git_url: https://git.bioconductor.org/packages/ggtree git_branch: devel git_last_commit: 75aa3fc git_last_commit_date: 2025-10-19 Date/Publication: 2025-10-20 source.ver: src/contrib/ggtree_3.99.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggtree_3.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggtree_3.99.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggtree_3.99.2.tgz vignettes: vignettes/ggtree/inst/doc/ggtree.html vignetteTitles: ggtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtree/inst/doc/ggtree.R dependsOnMe: ggtreeDendro, tanggle importsMe: cardelino, cellmig, cogeqc, crumblr, DspikeIn, enrichplot, ggmsa, ggtreeExtra, ggtreeSpace, iSEEtree, lefser, LymphoSeq, MetaboDynamics, miaViz, MicrobiotaProcess, mitology, orthogene, philr, scBubbletree, scDotPlot, singleCellTK, sitePath, SVP, systemPipeTools, treeclimbR, treekoR, BioVizSeq, DAISIEprep, ddtlcm, delimtools, dowser, EvoPhylo, FossilSim, genBaRcode, harrietr, mycolorsTB, numbat, Platypus, RevGadgets, scistreer, shinyTempSignal, STraTUS, Sysrecon, TransProR suggestsMe: compcodeR, fastreeR, syntenet, TreeAndLeaf, treeio, TreeSummarizedExperiment, universalmotif, aplot, aplotExtra, CoOL, DAISIE, deeptime, gggenomes, ggimage, ggtangle, idiogramFISH, MetaNet, nosoi, oppr, PCMBase, pctax, RAINBOWR, rhierbaps, rphylopic, treestructure dependencyCount: 78 Package: ggtreeDendro Version: 1.11.0 Depends: ggtree (>= 3.5.3) Imports: ggplot2, stats, tidytree, utils Suggests: aplot, cluster, knitr, MASS, mdendro, prettydoc, pvclust, rmarkdown, testthat (>= 3.0.0), treeio, yulab.utils License: Artistic-2.0 MD5sum: d1bc2f96f77aac86d1fea55c2d4609bc NeedsCompilation: no Title: Drawing 'dendrogram' using 'ggtree' Description: Offers a set of 'autoplot' methods to visualize tree-like structures (e.g., hierarchical clustering and classification/regression trees) using 'ggtree'. You can adjust graphical parameters using grammar of graphic syntax and integrate external data to the tree. biocViews: Clustering, Classification, DecisionTree, Phylogenetics, Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Shuangbin Xu [ctb] (ORCID: ), Chuanjie Zhang [ctb] Maintainer: Guangchuang Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggtreeDendro git_branch: devel git_last_commit: 35768c1 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/ggtreeDendro_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggtreeDendro_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggtreeDendro_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggtreeDendro_1.11.0.tgz vignettes: vignettes/ggtreeDendro/inst/doc/ggtreeDendro.html vignetteTitles: Visualizing Dendrogram using ggtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeDendro/inst/doc/ggtreeDendro.R dependencyCount: 79 Package: ggtreeExtra Version: 1.19.1 Imports: ggplot2 (>= 4.0.0), utils, rlang, ggnewscale, stats, ggtree, tidytree (>= 0.3.9), cli, magrittr Suggests: treeio, ggstar, patchwork, knitr, rmarkdown, prettydoc, markdown, testthat (>= 3.0.0), pillar License: GPL (>= 3) MD5sum: 9232ad726a5884887959236f11c8bc9f NeedsCompilation: no Title: An R Package To Add Geometric Layers On Circular Or Other Layout Tree Of "ggtree" Description: 'ggtreeExtra' extends the method for mapping and visualizing associated data on phylogenetic tree using 'ggtree'. These associated data can be presented on the external panels to circular layout, fan layout, or other rectangular layout tree built by 'ggtree' with the grammar of 'ggplot2'. biocViews: Software, Visualization, Phylogenetics, Annotation Author: Shuangbin Xu [aut, cre] (ORCID: ), Guangchuang Yu [aut, ctb] (ORCID: ) Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/ggtreeExtra/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtreeExtra/issues git_url: https://git.bioconductor.org/packages/ggtreeExtra git_branch: devel git_last_commit: e44a79f git_last_commit_date: 2025-09-16 Date/Publication: 2025-10-07 source.ver: src/contrib/ggtreeExtra_1.19.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggtreeExtra_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggtreeExtra_1.19.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggtreeExtra_1.19.1.tgz vignettes: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html vignetteTitles: ggtreeExtra hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.R importsMe: DspikeIn, MicrobiotaProcess suggestsMe: enrichplot, ggmsa, pctax, TransProR dependencyCount: 80 Package: ggtreeSpace Version: 1.5.0 Depends: R (>= 4.1.0) Imports: interp, ape, dplyr, GGally, ggplot2, grid, ggtree, phytools, rlang, tibble, tidyr, tidyselect, stats Suggests: knitr, prettydoc, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: fc67f041c8bd091188003c4032ed75a1 NeedsCompilation: no Title: Visualizing Phylomorphospaces using 'ggtree' Description: This package is a comprehensive visualization tool specifically designed for exploring phylomorphospace. It not only simplifies the process of generating phylomorphospace, but also enhances it with the capability to add graphic layers to the plot with grammar of graphics to create fully annotated phylomorphospaces. It also provide some utilities to help interpret evolutionary patterns. biocViews: Annotation, Visualization, Phylogenetics, Software Author: Guangchuang Yu [aut, cre, ths, cph] (ORCID: ), Li Lin [ctb] Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/ggtreeSpace VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtreeSpace/issues git_url: https://git.bioconductor.org/packages/ggtreeSpace git_branch: devel git_last_commit: 44249a1 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/ggtreeSpace_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggtreeSpace_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggtreeSpace_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggtreeSpace_1.5.0.tgz vignettes: vignettes/ggtreeSpace/inst/doc/ggtreeSpace.html vignetteTitles: Introduction to ggtreeSpace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeSpace/inst/doc/ggtreeSpace.R dependencyCount: 110 Package: GIGSEA Version: 1.28.0 Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: e3ee70ae0dfb103328c92a153ddc634c NeedsCompilation: no Title: Genotype Imputed Gene Set Enrichment Analysis Description: We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal. biocViews: GeneSetEnrichment,SNP,VariantAnnotation,GeneExpression,GeneRegulation,Regression,DifferentialExpression Author: Shijia Zhu Maintainer: Shijia Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GIGSEA git_branch: RELEASE_3_22 git_last_commit: 7623ec8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GIGSEA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GIGSEA_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GIGSEA_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GIGSEA_1.28.0.tgz vignettes: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.pdf vignetteTitles: GIGSEA: Genotype Imputed Gene Set Enrichment Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.R suggestsMe: GIGSEAdata dependencyCount: 11 Package: ginmappeR Version: 1.6.0 Imports: KEGGREST, UniProt.ws, XML, rentrez, httr, utils, memoise, cachem, jsonlite, rvest Suggests: RUnit, BiocGenerics, markdown, knitr License: GPL-3 + file LICENSE MD5sum: 289ece20c2494ac2e04900e2e3eb26df NeedsCompilation: no Title: Gene Identifier Mapper Description: Provides functionalities to translate gene or protein identifiers between state-of-art biological databases: CARD (), NCBI Protein, Nucleotide and Gene (), UniProt () and KEGG (). Also offers complementary functionality like NCBI identical proteins or UniProt similar genes clusters retrieval. biocViews: Annotation, KEGG, Genetics, ThirdPartyClient, Software Author: Fernando Sola [aut, cre] (ORCID: ), Daniel Ayala [aut] (ORCID: ), Marina Pulido [aut] (ORCID: ), Rafael Ayala [aut] (ORCID: ), Lorena López-Cerero [aut] (ORCID: ), Inma Hernández [aut] (ORCID: ), David Ruiz [aut] (ORCID: ) Maintainer: Fernando Sola VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ginmappeR git_branch: RELEASE_3_22 git_last_commit: f5ef284 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ginmappeR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ginmappeR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ginmappeR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ginmappeR_1.6.0.tgz vignettes: vignettes/ginmappeR/inst/doc/ginmappeR.html vignetteTitles: ginmappeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ginmappeR/inst/doc/ginmappeR.R dependencyCount: 70 Package: GLAD Version: 2.74.0 Depends: R (>= 2.10) Imports: aws License: GPL-2 MD5sum: dbc9d44470644125121ccfdd2ca9e45a NeedsCompilation: yes Title: Gain and Loss Analysis of DNA Description: Analysis of array CGH data : detection of breakpoints in genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified. biocViews: Microarray, CopyNumberVariation Author: Philippe Hupe Maintainer: Philippe Hupe URL: http://bioinfo.curie.fr SystemRequirements: gsl. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. git_url: https://git.bioconductor.org/packages/GLAD git_branch: RELEASE_3_22 git_last_commit: 9a31255 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GLAD_2.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GLAD_2.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GLAD_2.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GLAD_2.74.0.tgz vignettes: vignettes/GLAD/inst/doc/GLAD.pdf vignetteTitles: GLAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GLAD/inst/doc/GLAD.R dependsOnMe: ITALICS importsMe: ITALICS, MANOR suggestsMe: aroma.cn, aroma.core dependencyCount: 4 Package: GladiaTOX Version: 1.26.0 Depends: R (>= 3.6.0), data.table (>= 1.9.4) Imports: DBI, RMariaDB, RSQLite, numDeriv, RColorBrewer, parallel, stats, methods, graphics, grDevices, xtable, tools, brew, stringr, RJSONIO, ggplot2, ggrepel, tidyr, utils, RCurl, XML Suggests: roxygen2, knitr, rmarkdown, testthat, BiocStyle License: GPL-2 MD5sum: d97235b1ee01137cd035a5e1f6a052ff NeedsCompilation: no Title: R Package for Processing High Content Screening data Description: GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the tcpl core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate pdf reports for quality control and data processing. biocViews: Software, WorkflowStep, Normalization, Preprocessing, QualityControl Author: Vincenzo Belcastro [aut, cre], Dayne L Filer [aut], Stephane Cano [aut] Maintainer: PMP S.A. R Support URL: https://github.com/philipmorrisintl/GladiaTOX VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GladiaTOX git_branch: RELEASE_3_22 git_last_commit: 5a81b1c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GladiaTOX_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GladiaTOX_1.25.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GladiaTOX_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GladiaTOX_1.26.0.tgz vignettes: vignettes/GladiaTOX/inst/doc/GladiaTOX.html vignetteTitles: GladiaTOX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GladiaTOX/inst/doc/GladiaTOX.R dependencyCount: 60 Package: Glimma Version: 2.20.0 Depends: R (>= 4.0.0) Imports: htmlwidgets, edgeR, DESeq2, limma, SummarizedExperiment, stats, jsonlite, methods, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle, IRanges, GenomicRanges, pryr, AnnotationHub, scRNAseq, scater, scran, scRNAseq, License: GPL-3 Archs: x64 MD5sum: 002a0fcf3c8b7e1a84dbbd2a3bc04c7b NeedsCompilation: no Title: Interactive visualizations for gene expression analysis Description: This package produces interactive visualizations for RNA-seq data analysis, utilizing output from limma, edgeR, or DESeq2. It produces interactive htmlwidgets versions of popular RNA-seq analysis plots to enhance the exploration of analysis results by overlaying interactive features. The plots can be viewed in a web browser or embedded in notebook documents. biocViews: DifferentialExpression, GeneExpression, Microarray, ReportWriting, RNASeq, Sequencing, Visualization Author: Shian Su [aut, cre], Hasaru Kariyawasam [aut], Oliver Voogd [aut], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee [ctb], Isaac Virshup [ctb] Maintainer: Shian Su URL: https://github.com/hasaru-k/GlimmaV2 VignetteBuilder: knitr BugReports: https://github.com/hasaru-k/GlimmaV2/issues git_url: https://git.bioconductor.org/packages/Glimma git_branch: RELEASE_3_22 git_last_commit: 73c6c44 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Glimma_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Glimma_2.19.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Glimma_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Glimma_2.20.0.tgz vignettes: vignettes/Glimma/inst/doc/DESeq2.html, vignettes/Glimma/inst/doc/limma_edger.html, vignettes/Glimma/inst/doc/single_cell_edger.html vignetteTitles: DESeq2, Introduction using limma or edgeR, Single Cells with edgeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Glimma/inst/doc/DESeq2.R, vignettes/Glimma/inst/doc/limma_edger.R, vignettes/Glimma/inst/doc/single_cell_edger.R dependsOnMe: RNAseq123 importsMe: affycoretools dependencyCount: 80 Package: glmGamPoi Version: 1.22.0 Depends: R (>= 4.1.0) Imports: Rcpp, beachmat, DelayedMatrixStats, matrixStats, MatrixGenerics, SparseArray (>= 1.5.21), S4Vectors, DelayedArray, HDF5Array, Matrix, SummarizedExperiment, SingleCellExperiment, BiocGenerics, methods, stats, utils, splines, rlang, vctrs LinkingTo: Rcpp, RcppArmadillo, beachmat, assorthead Suggests: testthat (>= 2.1.0), zoo, DESeq2, edgeR, limma, MASS, statmod, ggplot2, bench, BiocParallel, knitr, rmarkdown, BiocStyle, TENxPBMCData, muscData, scran, dplyr License: GPL-3 MD5sum: 18e48614cc222291ead18ac8c4679035 NeedsCompilation: yes Title: Fit a Gamma-Poisson Generalized Linear Model Description: Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments. biocViews: Regression, RNASeq, Software, SingleCell Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ), Nathan Lubock [ctb] (ORCID: ), Michael Love [ctb] Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/glmGamPoi SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/const-ae/glmGamPoi/issues git_url: https://git.bioconductor.org/packages/glmGamPoi git_branch: RELEASE_3_22 git_last_commit: 908cde9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/glmGamPoi_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/glmGamPoi_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/glmGamPoi_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/glmGamPoi_1.22.0.tgz vignettes: vignettes/glmGamPoi/inst/doc/glmGamPoi.html, vignettes/glmGamPoi/inst/doc/pseudobulk.html vignetteTitles: glmGamPoi Quickstart, Pseudobulk and differential expression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmGamPoi/inst/doc/glmGamPoi.R, vignettes/glmGamPoi/inst/doc/pseudobulk.R importsMe: BASiCStan, lemur, transformGamPoi, SCdeconR suggestsMe: DESeq2, DEXSeq, Seurat dependencyCount: 43 Package: glmSparseNet Version: 1.28.0 Depends: R (>= 4.3.0) Imports: biomaRt, checkmate, dplyr, forcats, futile.logger, ggplot2, glue, httr, lifecycle, methods, parallel, readr, rlang, glmnet, Matrix, MultiAssayExperiment, SummarizedExperiment, survminer, TCGAutils, utils Suggests: BiocStyle, curatedTCGAData, knitr, magrittr, reshape2, pROC, rmarkdown, survival, testthat, VennDiagram, withr License: GPL-3 MD5sum: a42e158b729f9d868b62c879a93db4d8 NeedsCompilation: no Title: Network Centrality Metrics for Elastic-Net Regularized Models Description: glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian". biocViews: Software, StatisticalMethod, DimensionReduction, Regression, Classification, Survival, Network, GraphAndNetwork Author: André Veríssimo [aut, cre] (ORCID: ), Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb] Maintainer: André Veríssimo URL: https://www.github.com/sysbiomed/glmSparseNet VignetteBuilder: knitr BugReports: https://www.github.com/sysbiomed/glmSparseNet/issues git_url: https://git.bioconductor.org/packages/glmSparseNet git_branch: RELEASE_3_22 git_last_commit: b51637e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/glmSparseNet_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/glmSparseNet_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/glmSparseNet_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/glmSparseNet_1.28.0.tgz vignettes: vignettes/glmSparseNet/inst/doc/example_brca_logistic.html, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.html, vignettes/glmSparseNet/inst/doc/example_brca_survival.html, vignettes/glmSparseNet/inst/doc/example_prad_survival.html, vignettes/glmSparseNet/inst/doc/example_skcm_survival.html, vignettes/glmSparseNet/inst/doc/separate2GroupsCox.html vignetteTitles: Example for Classification -- Breast Invasive Carcinoma, Breast survival dataset using network from STRING DB, Example for Survival Data -- Breast Invasive Carcinoma, Example for Survival Data -- Prostate Adenocarcinoma, Example for Survival Data -- Skin Melanoma, Separate 2 groups in Cox regression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmSparseNet/inst/doc/example_brca_logistic.R, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.R, vignettes/glmSparseNet/inst/doc/example_brca_survival.R, vignettes/glmSparseNet/inst/doc/example_prad_survival.R, vignettes/glmSparseNet/inst/doc/example_skcm_survival.R, vignettes/glmSparseNet/inst/doc/separate2GroupsCox.R importsMe: priorityelasticnet dependencyCount: 183 Package: GlobalAncova Version: 4.28.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi, Biobase, dendextend, GSEABase, VGAM Suggests: GO.db, golubEsets, hu6800.db, vsn, Rgraphviz License: GPL (>= 2) Archs: x64 MD5sum: 929403cd8e04a5c0ad0391eac2898fc5 NeedsCompilation: yes Title: Global test for groups of variables via model comparisons Description: The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. biocViews: Microarray, OneChannel, DifferentialExpression, Pathways, Regression Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel Maintainer: Manuela Hummel git_url: https://git.bioconductor.org/packages/GlobalAncova git_branch: RELEASE_3_22 git_last_commit: 99efd2a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GlobalAncova_4.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GlobalAncova_4.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GlobalAncova_4.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GlobalAncova_4.28.0.tgz vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R importsMe: miRtest suggestsMe: GiANT dependencyCount: 71 Package: globalSeq Version: 1.38.0 Depends: R (>= 3.0.0) Suggests: knitr, testthat, SummarizedExperiment, S4Vectors License: GPL-3 MD5sum: 80cec8059cb02c48a2a8459ab69d8fa1 NeedsCompilation: no Title: Global Test for Counts Description: The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data. biocViews: GeneExpression, ExonArray, DifferentialExpression, GenomeWideAssociation, Transcriptomics, DimensionReduction, Regression, Sequencing, WholeGenome, RNASeq, ExomeSeq, miRNA, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/globalSeq VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/globalSeq/issues git_url: https://git.bioconductor.org/packages/globalSeq git_branch: RELEASE_3_22 git_last_commit: 659d1e4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/globalSeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/globalSeq_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/globalSeq_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/globalSeq_1.38.0.tgz vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf, vignettes/globalSeq/inst/doc/article.html, vignettes/globalSeq/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R dependencyCount: 0 Package: globaltest Version: 5.64.0 Depends: methods, survival Imports: Biobase, AnnotationDbi, annotate, graphics Suggests: vsn, golubEsets, KEGGREST, hu6800.db, Rgraphviz, GO.db, lungExpression, org.Hs.eg.db, GSEABase, penalized, gss, MASS, boot, rpart, mstate License: GPL (>= 2) MD5sum: 8588aa89f95c6d24254ef8820253d0d6 NeedsCompilation: no Title: Testing Groups of Covariates/Features for Association with a Response Variable, with Applications to Gene Set Testing Description: The global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms. biocViews: Microarray, OneChannel, Bioinformatics, DifferentialExpression, GO, Pathways Author: Jelle Goeman and Jan Oosting, with contributions by Livio Finos, Aldo Solari, Dominic Edelmann Maintainer: Jelle Goeman git_url: https://git.bioconductor.org/packages/globaltest git_branch: RELEASE_3_22 git_last_commit: 81b15c8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/globaltest_5.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/globaltest_5.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/globaltest_5.64.0.tgz vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf vignetteTitles: Global Test hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globaltest/inst/doc/GlobalTest.R dependsOnMe: GlobalAncova importsMe: BiSeq, EGSEA, SIM, miRtest suggestsMe: topGO, GiANT, penalized dependencyCount: 51 Package: GloScope Version: 2.0.0 Depends: R (>= 4.4.0) Imports: utils, stats, MASS, mclust, ggplot2, RANN, FNN, BiocParallel, mvnfast, SingleCellExperiment, rlang, RColorBrewer, pheatmap, vegan, cluster, boot, permute Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown, zellkonverter License: Artistic-2.0 Archs: x64 MD5sum: 68308f784b491e9903aea9088e137b7c NeedsCompilation: no Title: Population-level Representation on scRNA-Seq data Description: This package aims at representing and summarizing the entire single-cell profile of a sample. It allows researchers to perform important bioinformatic analyses at the sample-level such as visualization and quality control. The main functions Estimate sample distribution and calculate statistical divergence among samples, and visualize the distance matrix through MDS plots. biocViews: DataRepresentation, QualityControl, RNASeq, Sequencing, Software, SingleCell Author: William Torous [aut, cre] (ORCID: ), Hao Wang [aut] (ORCID: ), Elizabeth Purdom [aut], Boying Gong [aut] Maintainer: William Torous VignetteBuilder: knitr BugReports: https://github.com/epurdom/GloScope/issues git_url: https://git.bioconductor.org/packages/GloScope git_branch: RELEASE_3_22 git_last_commit: 81cd950 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GloScope_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GloScope_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GloScope_2.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GloScope_2.0.0.tgz vignettes: vignettes/GloScope/inst/doc/GloScopeTutorial.html vignetteTitles: GloScope hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GloScope/inst/doc/GloScopeTutorial.R dependencyCount: 67 Package: gmapR Version: 1.51.1 Depends: R (>= 2.15.0), methods, Seqinfo, GenomicRanges (>= 1.61.1), Rsamtools (>= 1.31.2) Imports: S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), BiocGenerics (>= 0.25.1), rtracklayer (>= 1.39.7), GenomicFeatures (>= 1.31.3), Biostrings, VariantAnnotation (>= 1.25.11), tools, Biobase, BSgenome, GenomicAlignments (>= 1.15.6), BiocParallel, BiocIO Suggests: GenomeInfoDb, RUnit, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Scerevisiae.UCSC.sacCer3, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, LungCancerLines License: Artistic-2.0 MD5sum: 8ab87d5e234b621a079f3fad297c4e8e NeedsCompilation: yes Title: An R interface to the GMAP/GSNAP/GSTRUCT suite Description: GSNAP and GMAP are a pair of tools to align short-read data written by Tom Wu. This package provides convenience methods to work with GMAP and GSNAP from within R. In addition, it provides methods to tally alignment results on a per-nucleotide basis using the bam_tally tool. biocViews: Alignment Author: Cory Barr, Thomas Wu, Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/gmapR git_branch: devel git_last_commit: 29973df git_last_commit_date: 2025-07-31 Date/Publication: 2025-10-07 source.ver: src/contrib/gmapR_1.51.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gmapR_1.51.1.tgz vignettes: vignettes/gmapR/inst/doc/gmapR.pdf vignetteTitles: gmapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmapR/inst/doc/gmapR.R suggestsMe: VariantTools, VariantToolsData dependencyCount: 78 Package: GmicR Version: 1.24.0 Imports: AnnotationDbi, ape, bnlearn, Category, DT, doParallel, foreach, gRbase, GSEABase, gRain, GOstats, org.Hs.eg.db, org.Mm.eg.db, shiny, WGCNA, data.table, grDevices, graphics, reshape2, stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 + file LICENSE MD5sum: eb36e1cef0a999e668173bd70e3fe417 NeedsCompilation: no Title: Combines WGCNA and xCell readouts with bayesian network learrning to generate a Gene-Module Immune-Cell network (GMIC) Description: This package uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell. It is a hypothesis generating tool. biocViews: Software, SystemsBiology, GraphAndNetwork, Network, NetworkInference, GUI, ImmunoOncology, GeneExpression, QualityControl, Bayesian, Clustering Author: Richard Virgen-Slane Maintainer: Richard Virgen-Slane VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GmicR git_branch: RELEASE_3_22 git_last_commit: a9a4b5a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GmicR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GmicR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GmicR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GmicR_1.24.0.tgz vignettes: vignettes/GmicR/inst/doc/GmicR_vignette.html vignetteTitles: GmicR_vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GmicR/inst/doc/GmicR_vignette.R dependencyCount: 149 Package: gmoviz Version: 1.22.0 Depends: circlize, GenomicRanges, graphics, R (>= 4.0) Imports: grid, gridBase, Rsamtools, ComplexHeatmap, BiocGenerics, Biostrings, Seqinfo, methods, GenomicAlignments, GenomicFeatures, IRanges, rtracklayer, pracma, colorspace, S4Vectors Suggests: testthat, knitr, rmarkdown, pasillaBamSubset, BiocStyle, BiocManager, GenomeInfoDb License: GPL-3 MD5sum: e0487af3140d55badf3439976323afed NeedsCompilation: no Title: Seamless visualization of complex genomic variations in GMOs and edited cell lines Description: Genetically modified organisms (GMOs) and cell lines are widely used models in all kinds of biological research. As part of characterising these models, DNA sequencing technology and bioinformatics analyses are used systematically to study their genomes. Therefore, large volumes of data are generated and various algorithms are applied to analyse this data, which introduces a challenge on representing all findings in an informative and concise manner. `gmoviz` provides users with an easy way to visualise and facilitate the explanation of complex genomic editing events on a larger, biologically-relevant scale. biocViews: Visualization, Sequencing, GeneticVariability, GenomicVariation, Coverage Author: Kathleen Zeglinski [cre, aut], Arthur Hsu [aut], Monther Alhamdoosh [aut] (ORCID: ), Constantinos Koutsakis [aut] Maintainer: Kathleen Zeglinski VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gmoviz git_branch: RELEASE_3_22 git_last_commit: 64c2454 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gmoviz_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gmoviz_1.21.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gmoviz_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gmoviz_1.22.0.tgz vignettes: vignettes/gmoviz/inst/doc/gmoviz_advanced.html, vignettes/gmoviz/inst/doc/gmoviz_overview.html vignetteTitles: Advanced usage of gmoviz, Introduction to gmoviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmoviz/inst/doc/gmoviz_advanced.R, vignettes/gmoviz/inst/doc/gmoviz_overview.R dependencyCount: 91 Package: GMRP Version: 1.38.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics License: GPL (>= 2) MD5sum: 758f33fe40e18700b24c8efdc7301e6f NeedsCompilation: no Title: GWAS-based Mendelian Randomization and Path Analyses Description: Perform Mendelian randomization analysis of multiple SNPs to determine risk factors causing disease of study and to exclude confounding variabels and perform path analysis to construct path of risk factors to the disease. biocViews: Sequencing, Regression, SNP Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/GMRP git_branch: RELEASE_3_22 git_last_commit: 293f58c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GMRP_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GMRP_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GMRP_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GMRP_1.38.0.tgz vignettes: vignettes/GMRP/inst/doc/GMRP-manual.pdf, vignettes/GMRP/inst/doc/GMRP.pdf vignetteTitles: GMRP-manual.pdf, Causal Effect Analysis of Risk Factors for Disease with the "GMRP" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GMRP/inst/doc/GMRP.R dependencyCount: 16 Package: GNET2 Version: 1.26.0 Depends: R (>= 3.6) Imports: ggplot2,xgboost,Rcpp,reshape2,grid,DiagrammeR,methods,stats,matrixStats,graphics,SummarizedExperiment,dplyr,igraph, grDevices, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: Apache License 2.0 MD5sum: 5605d6c87e4979f44d50cc212f217d39 NeedsCompilation: yes Title: Constructing gene regulatory networks from expression data through functional module inference Description: Cluster genes to functional groups with E-M process. Iteratively perform TF assigning and Gene assigning, until the assignment of genes did not change, or max number of iterations is reached. biocViews: GeneExpression, Regression, Network, NetworkInference, Software Author: Chen Chen, Jie Hou and Jianlin Cheng Maintainer: Chen Chen URL: https://github.com/chrischen1/GNET2 VignetteBuilder: knitr BugReports: https://github.com/chrischen1/GNET2/issues git_url: https://git.bioconductor.org/packages/GNET2 git_branch: RELEASE_3_22 git_last_commit: 90fdc52 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GNET2_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GNET2_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GNET2_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GNET2_1.26.0.tgz vignettes: vignettes/GNET2/inst/doc/run_gnet2.html vignetteTitles: Build functional gene modules with GNET2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GNET2/inst/doc/run_gnet2.R dependencyCount: 94 Package: GNOSIS Version: 1.8.0 Depends: R (>= 4.3.0), shiny, shinydashboard, shinydashboardPlus, dashboardthemes, shinyWidgets, shinymeta, tidyverse, operator.tools, maftools Imports: DT, fontawesome, shinycssloaders, cBioPortalData, shinyjs, reshape2, RColorBrewer, survival, survminer, stats, compareGroups, rpart, partykit, DescTools, car, rstatix, fabricatr, shinylogs, magrittr Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: f86d2e55cb17ef1e70151b660cc6f2ec NeedsCompilation: no Title: Genomics explorer using statistical and survival analysis in R Description: GNOSIS incorporates a range of R packages enabling users to efficiently explore and visualise clinical and genomic data obtained from cBioPortal. GNOSIS uses an intuitive GUI and multiple tab panels supporting a range of functionalities. These include data upload and initial exploration, data recoding and subsetting, multiple visualisations, survival analysis, statistical analysis and mutation analysis, in addition to facilitating reproducible research. biocViews: Software, ShinyApps, Survival, GUI Author: Lydia King [aut, cre] (ORCID: ), Marcel Ramos [ctb] Maintainer: Lydia King URL: https://github.com/Lydia-King/GNOSIS/ VignetteBuilder: knitr Video: https://doi.org/10.5281/zenodo.5788544 BugReports: https://github.com/Lydia-King/GNOSIS/issues git_url: https://git.bioconductor.org/packages/GNOSIS git_branch: RELEASE_3_22 git_last_commit: 6153684 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GNOSIS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GNOSIS_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GNOSIS_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GNOSIS_1.8.0.tgz vignettes: vignettes/GNOSIS/inst/doc/GNOSIS.html vignetteTitles: GNOSIS Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GNOSIS/inst/doc/GNOSIS.R dependencyCount: 309 Package: GOexpress Version: 1.44.0 Depends: R (>= 3.4), grid, stats, graphics, Biobase (>= 2.22.0) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6), RCurl (>= 1.95) Suggests: BiocStyle License: GPL (>= 3) MD5sum: c72e9f9302a3d33045bd18c8bfd1d324 NeedsCompilation: no Title: Visualise microarray and RNAseq data using gene ontology annotations Description: The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Clustering, TimeCourse, Microarray, Sequencing, RNASeq, Annotation, MultipleComparison, Pathways, GO, Visualization, ImmunoOncology Author: Kevin Rue-Albrecht [aut, cre], Tharvesh M.L. Ali [ctb], Paul A. McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee [ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen V. Gordon [ths], David E. MacHugh [ths], Hugo Gruson [ctb] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/GOexpress git_url: https://git.bioconductor.org/packages/GOexpress git_branch: RELEASE_3_22 git_last_commit: a785251 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GOexpress_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GOexpress_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GOexpress_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GOexpress_1.44.0.tgz vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf vignetteTitles: UsersGuide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.R suggestsMe: InteractiveComplexHeatmap dependencyCount: 81 Package: GOfuncR Version: 1.30.0 Depends: R (>= 3.4), vioplot (>= 0.2), Imports: Rcpp (>= 0.11.5), mapplots (>= 1.5), gtools (>= 3.5.0), GenomicRanges (>= 1.28.4), IRanges, AnnotationDbi, utils, grDevices, graphics, stats, LinkingTo: Rcpp Suggests: Homo.sapiens, BiocStyle, knitr, markdown, rmarkdown, testthat License: GPL (>= 2) MD5sum: d490fb9349d23105bfa8b7a0e434ca56 NeedsCompilation: yes Title: Gene ontology enrichment using FUNC Description: GOfuncR performs a gene ontology enrichment analysis based on the ontology enrichment software FUNC. GO-annotations are obtained from OrganismDb or OrgDb packages ('Homo.sapiens' by default); the GO-graph is included in the package and updated regularly (01-May-2021). GOfuncR provides the standard candidate vs. background enrichment analysis using the hypergeometric test, as well as three additional tests: (i) the Wilcoxon rank-sum test that is used when genes are ranked, (ii) a binomial test that is used when genes are associated with two counts and (iii) a Chi-square or Fisher's exact test that is used in cases when genes are associated with four counts. To correct for multiple testing and interdependency of the tests, family-wise error rates are computed based on random permutations of the gene-associated variables. GOfuncR also provides tools for exploring the ontology graph and the annotations, and options to take gene-length or spatial clustering of genes into account. It is also possible to provide custom gene coordinates, annotations and ontologies. biocViews: GeneSetEnrichment, GO Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GOfuncR git_branch: RELEASE_3_22 git_last_commit: e9544dc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GOfuncR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GOfuncR_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GOfuncR_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GOfuncR_1.30.0.tgz vignettes: vignettes/GOfuncR/inst/doc/GOfuncR.html vignetteTitles: Introduction to GOfuncR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOfuncR/inst/doc/GOfuncR.R dependencyCount: 52 Package: GOpro Version: 1.36.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, dendextend, doParallel, foreach, parallel, org.Hs.eg.db, GO.db, Rcpp, stats, graphics, MultiAssayExperiment, IRanges, S4Vectors LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, RTCGA.PANCAN12, BiocStyle, testthat License: GPL-3 MD5sum: e87f83a9a9bd8c6706601982f0c00ae0 NeedsCompilation: yes Title: Find the most characteristic gene ontology terms for groups of human genes Description: Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng). biocViews: Annotation, Clustering, GO, GeneExpression, GeneSetEnrichment, MultipleComparison Author: Lidia Chrabaszcz Maintainer: Lidia Chrabaszcz URL: https://github.com/mi2-warsaw/GOpro VignetteBuilder: knitr BugReports: https://github.com/mi2-warsaw/GOpro/issues git_url: https://git.bioconductor.org/packages/GOpro git_branch: RELEASE_3_22 git_last_commit: e5ba5d5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GOpro_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GOpro_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GOpro_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GOpro_1.36.0.tgz vignettes: vignettes/GOpro/inst/doc/GOpro_vignette.html vignetteTitles: GOpro: Determine groups of genes and find their characteristic GO term hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOpro/inst/doc/GOpro_vignette.R dependencyCount: 89 Package: goProfiles Version: 1.72.0 Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr Suggests: org.Hs.eg.db License: GPL-2 MD5sum: eb9f25ba492b2c381202138d175b97e7 NeedsCompilation: no Title: goProfiles: an R package for the statistical analysis of functional profiles Description: The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'. biocViews: Annotation, GO, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Microarray, MultipleComparison, Pathways, Software Author: Alex Sanchez, Jordi Ocana and Miquel Salicru Maintainer: Alex Sanchez git_url: https://git.bioconductor.org/packages/goProfiles git_branch: RELEASE_3_22 git_last_commit: 88f7e33 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/goProfiles_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/goProfiles_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/goProfiles_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/goProfiles_1.72.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles-comparevisual.pdf, vignettes/goProfiles/inst/doc/goProfiles-plotProfileMF.pdf, vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles-comparevisual.pdf, goProfiles-plotProfileMF.pdf, goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R importsMe: goSorensen dependencyCount: 48 Package: GOSemSim Version: 2.36.0 Depends: R (>= 4.2.0) Imports: AnnotationDbi, DBI, digest, GO.db, methods, rlang, R.utils, stats, utils, yulab.utils (>= 0.2.1) LinkingTo: Rcpp Suggests: AnnotationHub, BiocManager, clusterProfiler, DOSE, knitr, org.Hs.eg.db, prettydoc, readr, rmarkdown, testthat, tidyr, tidyselect, ROCR License: Artistic-2.0 MD5sum: 3d050b825e7c1de00cafe96c99f523d3 NeedsCompilation: yes Title: GO-terms Semantic Similarity Measures Description: The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively. biocViews: Annotation, GO, Clustering, Pathways, Network, Software Author: Guangchuang Yu [aut, cre], Alexey Stukalov [ctb], Pingfan Guo [ctb], Chuanle Xiao [ctb], Lluís Revilla Sancho [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/GOSemSim/issues git_url: https://git.bioconductor.org/packages/GOSemSim git_branch: RELEASE_3_22 git_last_commit: c1bf5c5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GOSemSim_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GOSemSim_2.35.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GOSemSim_2.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GOSemSim_2.36.0.tgz vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.html vignetteTitles: GOSemSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R dependsOnMe: tRanslatome importsMe: clusterProfiler, DOSE, enrichplot, meshes, Rcpi, rrvgo, ViSEAGO, BiSEp suggestsMe: BioCor, epiNEM, FELLA, SemDist, genekitr, protr, scDiffCom dependencyCount: 52 Package: goseq Version: 1.62.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase (>= 1.9.2) Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db, BiocGenerics, methods, rtracklayer, GenomicFeatures, Seqinfo Suggests: edgeR, org.Hs.eg.db License: LGPL (>= 2) MD5sum: a44a291d31ca93419a7bfc64bd8be0a7 NeedsCompilation: no Title: Gene Ontology analyser for RNA-seq and other length biased data Description: Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data. biocViews: ImmunoOncology, Sequencing, GO, GeneExpression, Transcription, RNASeq, DifferentialExpression, Annotation, GeneSetEnrichment, KEGG, Pathways, Software Author: Matthew Young [aut], Nadia Davidson [aut], Federico Marini [ctb, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/goseq BugReports: https://github.com/federicomarini/goseq/issues git_url: https://git.bioconductor.org/packages/goseq git_branch: RELEASE_3_22 git_last_commit: e3ed9b9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/goseq_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/goseq_1.61.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/goseq_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/goseq_1.62.0.tgz vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goseq/inst/doc/goseq.R dependsOnMe: rgsepd importsMe: Damsel, ideal, mosdef, SMITE suggestsMe: sparrow dependencyCount: 106 Package: goSorensen Version: 1.12.0 Depends: R (>= 4.4) Imports: clusterProfiler, goProfiles, org.Hs.eg.db, parallel, stats, stringr Suggests: BiocManager, BiocStyle, knitr, rmarkdown, org.At.tair.db, org.Ag.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcSakai.eg.db, org.EcK12.eg.db, org.Gg.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Pt.eg.db, org.Xl.eg.db, GO.db, ggplot2, ggrepel, DT, magick License: GPL-3 MD5sum: d75d9449f63c51e164e577157ce37c5f NeedsCompilation: no Title: Statistical inference based on the Sorensen-Dice dissimilarity and the Gene Ontology (GO) Description: This package implements inferential methods to compare gene lists in terms of their biological meaning as expressed in the GO. The compared gene lists are characterized by cross-tabulation frequency tables of enriched GO items. Dissimilarity between gene lists is evaluated using the Sorensen-Dice index. The fundamental guiding principle is that two gene lists are taken as similar if they share a great proportion of common enriched GO items. biocViews: Annotation, GO, GeneSetEnrichment, Software, Microarray, Pathways, GeneExpression, MultipleComparison, GraphAndNetwork, Reactome, Clustering, KEGG Author: Pablo Flores [aut, cre] (), Jordi Ocana [aut, ctb] (0000-0002-4736-699), Alexandre Sanchez-Pla [ctb] (), Miquel Salicru [ctb] () Maintainer: Pablo Flores VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSorensen git_branch: RELEASE_3_22 git_last_commit: 2cbafaa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/goSorensen_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/goSorensen_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/goSorensen_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/goSorensen_1.12.0.tgz vignettes: vignettes/goSorensen/inst/doc/Dissimilarities_Matrix.html, vignettes/goSorensen/inst/doc/goSorensen_Introduction.html vignetteTitles: Working with te Irrelevance-threshold Matrix of Dissimilarities., An Introduction to goSorensen R-Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSorensen/inst/doc/Dissimilarities_Matrix.R, vignettes/goSorensen/inst/doc/goSorensen_Introduction.R dependencyCount: 140 Package: goSTAG Version: 1.34.0 Depends: R (>= 3.4) Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: de137157dd243765130a85d7d910e5fc NeedsCompilation: no Title: A tool to use GO Subtrees to Tag and Annotate Genes within a set Description: Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Clustering, Microarray, mRNAMicroarray, RNASeq, Visualization, GO, ImmunoOncology Author: Brian D. Bennett and Pierre R. Bushel Maintainer: Brian D. Bennett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSTAG git_branch: RELEASE_3_22 git_last_commit: 805c363 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/goSTAG_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/goSTAG_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/goSTAG_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/goSTAG_1.34.0.tgz vignettes: vignettes/goSTAG/inst/doc/goSTAG.html vignetteTitles: The goSTAG User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSTAG/inst/doc/goSTAG.R dependencyCount: 65 Package: GOstats Version: 2.76.0 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.43.2), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), AnnotationForge, Rgraphviz Suggests: hgu95av2.db (>= 1.13.0), ALL, multtest, genefilter, RColorBrewer, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 MD5sum: cabe1874fe2d01711c5e87bd1f9bfcf6 NeedsCompilation: no Title: Tools for manipulating GO and microarrays Description: A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations. biocViews: Annotation, GO, MultipleComparison, GeneExpression, Microarray, Pathways, GeneSetEnrichment, GraphAndNetwork Author: Robert Gentleman [aut], Seth Falcon [ctb], Robert Castelo [ctb], Sonali Kumari [ctb] (Converted vignettes from Sweave to R Markdown / HTML.), Dennis Ndubi [ctb] (Converted GOstatsHyperG vignette from Sweave to R Markdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GOstats git_branch: RELEASE_3_22 git_last_commit: 7c7bec8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GOstats_2.76.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GOstats_2.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GOstats_2.76.0.tgz vignettes: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.html, vignettes/GOstats/inst/doc/GOstatsHyperG.html, vignettes/GOstats/inst/doc/GOvis.html vignetteTitles: How To Use GOstats and Category to do Hypergeometric testing with unsupported model organisms, Hypergeometric Tests Using GOstats, Visualizing and Distances Using GO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.R, vignettes/GOstats/inst/doc/GOstatsHyperG.R, vignettes/GOstats/inst/doc/GOvis.R dependsOnMe: MineICA importsMe: affycoretools, attract, categoryCompare, GmicR, ideal, miRLAB, pcaExplorer, scTensor suggestsMe: a4, Category, fastLiquidAssociation, fgga, GSEAlm, interactiveDisplay, MineICA, MLP, qpgraph, RnBeads, safe, maGUI, sand dependencyCount: 64 Package: GOTHiC Version: 1.46.0 Depends: R (>= 3.5.0), methods, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors (>= 0.9.38), IRanges, Rsamtools, ShortRead, rtracklayer, ggplot2, BiocManager, grDevices, utils, stats, Seqinfo Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 MD5sum: 14cd5d8d4a417d14c5379c7532a0935f NeedsCompilation: no Title: Binomial test for Hi-C data analysis Description: This is a Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. It takes mapped paired NGS reads as input and gives back the list of significant interactions for a given bin size in the genome. biocViews: ImmunoOncology, Sequencing, Preprocessing, Epigenetics, HiC Author: Borbala Mifsud and Robert Sugar Maintainer: Borbala Mifsud git_url: https://git.bioconductor.org/packages/GOTHiC git_branch: RELEASE_3_22 git_last_commit: 9a54459 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GOTHiC_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GOTHiC_1.45.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GOTHiC_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GOTHiC_1.46.0.tgz vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf vignetteTitles: package_vignettes.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOTHiC/inst/doc/package_vignettes.R importsMe: OHCA dependencyCount: 86 Package: goTools Version: 1.84.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: e17e6b393ca0b240e8e76e2ba8da4f48 NeedsCompilation: no Title: Functions for Gene Ontology database Description: Wraper functions for description/comparison of oligo ID list using Gene Ontology database biocViews: Microarray,GO,Visualization Author: Yee Hwa (Jean) Yang , Agnes Paquet Maintainer: Agnes Paquet git_url: https://git.bioconductor.org/packages/goTools git_branch: RELEASE_3_22 git_last_commit: 93f27cc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/goTools_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/goTools_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/goTools_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/goTools_1.84.0.tgz vignettes: vignettes/goTools/inst/doc/goTools.pdf vignetteTitles: goTools overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/goTools/inst/doc/goTools.R dependencyCount: 44 Package: GPA Version: 1.22.0 Depends: R (>= 4.0.0), methods, graphics, Rcpp Imports: parallel, ggplot2, ggrepel, plyr, vegan, DT, shiny, shinyBS, stats, utils, grDevices LinkingTo: Rcpp Suggests: gpaExample License: GPL (>= 2) MD5sum: 2349d01d6b0a09ce976ccb87fd9d5286 NeedsCompilation: yes Title: GPA (Genetic analysis incorporating Pleiotropy and Annotation) Description: This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Emma Kortemeier, Carter Allen Maintainer: Dongjun Chung URL: http://dongjunchung.github.io/GPA/ SystemRequirements: GNU make BugReports: https://github.com/dongjunchung/GPA/issues git_url: https://git.bioconductor.org/packages/GPA git_branch: RELEASE_3_22 git_last_commit: 51e16b3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GPA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GPA_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GPA_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GPA_1.22.0.tgz vignettes: vignettes/GPA/inst/doc/GPA-example.pdf vignetteTitles: GPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GPA/inst/doc/GPA-example.R dependencyCount: 71 Package: gpls Version: 1.82.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: fea8734398d134751c3116bf29fd6eb6 NeedsCompilation: no Title: Classification using generalized partial least squares Description: Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification. biocViews: Classification, Microarray, Regression Author: Beiying Ding Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/gpls git_branch: RELEASE_3_22 git_last_commit: afaad7c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gpls_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gpls_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gpls_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gpls_1.82.0.tgz vignettes: vignettes/gpls/inst/doc/gpls.pdf vignetteTitles: gpls Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpls/inst/doc/gpls.R suggestsMe: MLInterfaces dependencyCount: 1 Package: GrafGen Version: 1.6.0 Depends: R (>= 4.3.0) Imports: stats, utils, graphics, ggplot2, plotly, scales, RColorBrewer, dplyr, grDevices, GenomicRanges, shiny, cowplot, ggpubr, stringr, rlang Suggests: knitr, rmarkdown, RUnit, BiocManager, BiocGenerics, BiocStyle, devtools License: GPL-2 MD5sum: 4a66029a1b18914cbed6b211cebaeecf NeedsCompilation: yes Title: Classification of Helicobacter Pylori Genomes Description: To classify Helicobacter pylori genomes according to genetic distance from nine reference populations. The nine reference populations are hpgpAfrica, hpgpAfrica-distant, hpgpAfroamerica, hpgpEuroamerica, hpgpMediterranea, hpgpEurope, hpgpEurasia, hpgpAsia, and hpgpAklavik86-like. The vertex populations are Africa, Europe and Asia. biocViews: Genetics, Software, GenomeAnnotation, Classification Author: William Wheeler [aut, cre], Difei Wang [aut], Isaac Zhao [aut], Yumi Jin [aut], Charles Rabkin [aut] Maintainer: William Wheeler VignetteBuilder: knitr BugReports: https://github.com/wheelerb/GrafGen/issues git_url: https://git.bioconductor.org/packages/GrafGen git_branch: RELEASE_3_22 git_last_commit: 9333d5e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GrafGen_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GrafGen_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GrafGen_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GrafGen_1.6.0.tgz vignettes: vignettes/GrafGen/inst/doc/vignette.html vignetteTitles: GrafGen: Classifying H. pylori genomes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GrafGen/inst/doc/vignette.R dependencyCount: 123 Package: GRaNIE Version: 1.14.0 Depends: R (>= 4.2.0) Imports: futile.logger, checkmate, patchwork (>= 1.2.0), reshape2, data.table, matrixStats, Matrix, GenomicRanges, RColorBrewer, ComplexHeatmap, DESeq2, circlize, progress, utils, methods, stringr, tools, scales, igraph, S4Vectors, ggplot2, rlang, Biostrings, GenomeInfoDb (>= 1.34.8), SummarizedExperiment, forcats, gridExtra, limma, tidyselect, readr, grid, tidyr (>= 1.3.0), dplyr, stats, grDevices, graphics, magrittr, tibble, viridis, colorspace, biomaRt, topGO, AnnotationHub, ensembldb Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Mmulatta.UCSC.rheMac10, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm39.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Dm.eg.db, org.Mmu.eg.db, IHW, clusterProfiler, ReactomePA, DOSE, BiocFileCache, ChIPseeker, testthat (>= 3.0.0), BiocStyle, csaw, BiocParallel, WGCNA, variancePartition, purrr, EDASeq, JASPAR2022, JASPAR2024, RSQLite, TFBSTools, motifmatchr, rbioapi, LDlinkR License: Artistic-2.0 MD5sum: 4f0e29e3b97c744c07a3ae4f29b51447 NeedsCompilation: no Title: GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using single-cell or bulk chromatin accessibility and RNA-seq data Description: Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using single-cell or bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally (Capture) Hi-C data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach. biocViews: Software, GeneExpression, GeneRegulation, NetworkInference, GeneSetEnrichment, BiomedicalInformatics, Genetics, Transcriptomics, ATACSeq, RNASeq, GraphAndNetwork, Regression, Transcription, ChIPSeq Author: Christian Arnold [cre, aut], Judith Zaugg [aut], Rim Moussa [aut], Armando Reyes-Palomares [ctb], Giovanni Palla [ctb], Maksim Kholmatov [ctb] Maintainer: Christian Arnold URL: https://grp-zaugg.embl-community.io/GRaNIE VignetteBuilder: knitr BugReports: https://git.embl.de/grp-zaugg/GRaNIE/issues git_url: https://git.bioconductor.org/packages/GRaNIE git_branch: RELEASE_3_22 git_last_commit: 7d55acc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GRaNIE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GRaNIE_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GRaNIE_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GRaNIE_1.14.0.tgz vignettes: vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.html, vignettes/GRaNIE/inst/doc/GRaNIE_singleCell_eGRNs.html, vignettes/GRaNIE/inst/doc/GRaNIE_workflow.html vignetteTitles: Package Details, Single-cell eGRN inference, Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.R, vignettes/GRaNIE/inst/doc/GRaNIE_singleCell_eGRNs.R, vignettes/GRaNIE/inst/doc/GRaNIE_workflow.R dependencyCount: 152 Package: graper Version: 1.26.0 Depends: R (>= 3.6) Imports: Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) Archs: x64 MD5sum: be760c45fad6b28475b7b1b01a5b3d0e NeedsCompilation: yes Title: Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes Description: This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach. biocViews: Regression, Bayesian, Classification Author: Britta Velten [aut, cre], Wolfgang Huber [aut] Maintainer: Britta Velten VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graper git_branch: RELEASE_3_22 git_last_commit: 76fc31b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/graper_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/graper_1.25.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/graper_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/graper_1.26.0.tgz vignettes: vignettes/graper/inst/doc/example_linear.html, vignettes/graper/inst/doc/example_logistic.html vignetteTitles: example_linear, example_logistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graper/inst/doc/example_linear.R, vignettes/graper/inst/doc/example_logistic.R dependencyCount: 29 Package: graph Version: 1.88.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11) Imports: stats, stats4, utils Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster, BiocStyle, knitr Enhances: Rgraphviz License: Artistic-2.0 Archs: x64 MD5sum: c6dcb5fc44a32acad2acf8fb09ff675a NeedsCompilation: yes Title: graph: A package to handle graph data structures Description: A package that implements some simple graph handling capabilities. biocViews: GraphAndNetwork Author: R Gentleman [aut], Elizabeth Whalen [aut], W Huber [aut], S Falcon [aut], Jeff Gentry [aut], Paul Shannon [aut], Halimat C. Atanda [ctb] (Converted 'MultiGraphClass' and 'GraphClass' vignettes from Sweave to RMarkdown / HTML.), Paul Villafuerte [ctb] (Converted vignettes from Sweave to RMarkdown / HTML.), Aliyu Atiku Mustapha [ctb] (Converted 'Graph' vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graph git_branch: RELEASE_3_22 git_last_commit: 424ddf6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/graph_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/graph_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/graph_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/graph_1.88.0.tgz vignettes: vignettes/graph/inst/doc/clusterGraph.html, vignettes/graph/inst/doc/graph.html, vignettes/graph/inst/doc/graphAttributes.html, vignettes/graph/inst/doc/GraphClass.html, vignettes/graph/inst/doc/MultiGraphClass.html vignetteTitles: clusterGraph and distGraph, How to use the graph package, Attributes for Graph Objects, Graph Design, graphBAM and MultiGraph Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graph/inst/doc/clusterGraph.R, vignettes/graph/inst/doc/graph.R, vignettes/graph/inst/doc/graphAttributes.R, vignettes/graph/inst/doc/GraphClass.R, vignettes/graph/inst/doc/MultiGraphClass.R dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, BLMA, CellNOptR, clipper, CNORfeeder, EnrichmentBrowser, GOstats, GraphAT, GSEABase, hypergraph, keggorthology, MineICA, pathRender, RbcBook1, RBGL, RCyjs, Rgraphviz, ROntoTools, SRAdb, topGO, vtpnet, DLBCL, SNAData, yeastExpData, cyjShiny, dlsem, graphpcor, gridGraphviz, PairViz, PerfMeas importsMe: AnnotationHubData, BgeeDB, BiocCheck, BiocFHIR, biocGraph, BiocPkgTools, biocViews, bnem, CAMERA, Category, categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, consICA, CytoML, DEGraph, DEsubs, EnrichDO, epiNEM, EventPointer, fgga, flowClust, flowWorkspace, gage, GeneNetworkBuilder, GenomicInteractionNodes, GraphAT, graphite, hyperdraw, KEGGgraph, MIRit, mnem, MOSClip, NCIgraph, netresponse, OncoSimulR, ontoProc, openCyto, oposSOM, OrganismDbi, pathview, qpgraph, RCy3, RGraph2js, rsbml, scGraphVerse, SplicingGraphs, Streamer, VariantFiltering, BioPlex, abn, BayesNetBP, BCDAG, BiDAG, BNrich, ceg, CePa, classGraph, clustNet, CodeDepends, cogmapr, ggm, gridDebug, HEMDAG, net4pg, netgsa, NetPreProc, pcalg, pcgen, rags2ridges, RANKS, RCPA, rsolr, rSpectral, SEMgraph, stablespec, topologyGSA, tpc, unifDAG suggestsMe: anansi, AnnotationDbi, DAPAR, DEGraph, EBcoexpress, ecolitk, gwascat, KEGGlincs, MLP, NetPathMiner, omXplore, rBiopaxParser, RCX, rTRM, S4Vectors, SPIA, VariantTools, arulesViz, bnlearn, bnstruct, bsub, ChoR, gbutils, GeneNet, gMCP, lava, loon, maGUI, netmeta, psych, rEMM, rPref, sisal, textplot, tidygraph, zenplots dependencyCount: 7 Package: GraphAlignment Version: 1.74.0 License: file LICENSE License_restricts_use: yes MD5sum: 648901bc2117e51bab5217a7cd73eaef NeedsCompilation: yes Title: GraphAlignment Description: Graph alignment is an extension package for the R programming environment which provides functions for finding an alignment between two networks based on link and node similarity scores. (J. Berg and M. Laessig, "Cross-species analysis of biological networks by Bayesian alignment", PNAS 103 (29), 10967-10972 (2006)) biocViews: GraphAndNetwork, Network Author: Joern P. Meier , Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg. Maintainer: Joern P. Meier URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/ git_url: https://git.bioconductor.org/packages/GraphAlignment git_branch: RELEASE_3_22 git_last_commit: 61750ff git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GraphAlignment_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GraphAlignment_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GraphAlignment_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GraphAlignment_1.74.0.tgz vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf vignetteTitles: GraphAlignment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GraphAlignment/inst/doc/GraphAlignment.R dependencyCount: 0 Package: GraphAT Version: 1.82.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL MD5sum: de9e456e29829e09d4d988e1b89523cc NeedsCompilation: no Title: Graph Theoretic Association Tests Description: Functions and data used in Balasubramanian, et al. (2004) biocViews: Network, GraphAndNetwork Author: R. Balasubramanian, T. LaFramboise, D. Scholtens Maintainer: Thomas LaFramboise git_url: https://git.bioconductor.org/packages/GraphAT git_branch: RELEASE_3_22 git_last_commit: 711f8ad git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GraphAT_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GraphAT_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GraphAT_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GraphAT_1.82.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 21 Package: graphite Version: 1.56.0 Depends: R (>= 4.2), methods Imports: AnnotationDbi, graph (>= 1.67.1), httr, rappdirs, stats, utils, graphics, rlang, lifecycle, purrr, dir.expiry Suggests: checkmate, a4Preproc, ALL, BiocStyle, clipper, codetools, hgu133plus2.db, hgu95av2.db, impute, knitr, org.Hs.eg.db, parallel, R.rsp, RCy3, rmarkdown, SPIA (>= 2.2), testthat, topologyGSA (>= 1.4.0) License: AGPL-3 MD5sum: 63ec56235b9ee8daed35574c85136088 NeedsCompilation: no Title: GRAPH Interaction from pathway Topological Environment Description: Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases. biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network, Reactome, KEGG, Metabolomics Author: Gabriele Sales [cre] (ORCID: ), Enrica Calura [aut], Chiara Romualdi [aut] Maintainer: Gabriele Sales URL: https://github.com/sales-lab/graphite VignetteBuilder: R.rsp BugReports: https://github.com/sales-lab/graphite/issues git_url: https://git.bioconductor.org/packages/graphite git_branch: RELEASE_3_22 git_last_commit: d5cf402 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/graphite_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/graphite_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/graphite_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/graphite_1.56.0.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf, vignettes/graphite/inst/doc/metabolites.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment, metabolites.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graphite/inst/doc/graphite.R importsMe: CBNplot, EnrichmentBrowser, MIRit, mogsa, MOSClip, multiGSEA, ReactomePA, sSNAPPY, ICDS, netgsa suggestsMe: clipper, InterCellar, metaboliteIDmapping dependencyCount: 49 Package: GRENITS Version: 1.62.0 Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8), ggplot2 (>= 0.9.0) Imports: graphics, grDevices, reshape2, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: network License: GPL (>= 2) MD5sum: 0adf487b9a857c10ff386265cd75fc62 NeedsCompilation: yes Title: Gene Regulatory Network Inference Using Time Series Description: The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model. biocViews: NetworkInference, GeneRegulation, TimeCourse, GraphAndNetwork, GeneExpression, Network, Bayesian Author: Edward Morrissey Maintainer: Edward Morrissey git_url: https://git.bioconductor.org/packages/GRENITS git_branch: RELEASE_3_22 git_last_commit: b6bf2b7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GRENITS_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GRENITS_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GRENITS_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GRENITS_1.62.0.tgz vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf vignetteTitles: GRENITS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRENITS/inst/doc/GRENITS_package.R dependencyCount: 30 Package: GreyListChIP Version: 1.42.0 Depends: R (>= 4.0), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, Seqinfo, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, RUnit, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 8b17d18a4ec0d8a9bd921af871c82bdd NeedsCompilation: no Title: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs Description: Identify regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling. Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis. biocViews: ChIPSeq, Alignment, Preprocessing, DifferentialPeakCalling, Sequencing, GenomeAnnotation, Coverage Author: Matt Eldridge [cre], Gord Brown [aut] Maintainer: Matt Eldridge git_url: https://git.bioconductor.org/packages/GreyListChIP git_branch: RELEASE_3_22 git_last_commit: df5cbdf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GreyListChIP_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GreyListChIP_1.41.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GreyListChIP_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GreyListChIP_1.42.0.tgz vignettes: vignettes/GreyListChIP/inst/doc/GreyList-demo.pdf vignetteTitles: Generating Grey Lists from Input Libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GreyListChIP/inst/doc/GreyList-demo.R importsMe: DiffBind, epigraHMM dependencyCount: 59 Package: GRmetrics Version: 1.36.0 Depends: R (>= 4.0), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors, stats Suggests: knitr, rmarkdown, BiocStyle, tinytex License: GPL-3 MD5sum: 4cc3c3b71b873738d5066cb370653046 NeedsCompilation: no Title: Calculate growth-rate inhibition (GR) metrics Description: Functions for calculating and visualizing growth-rate inhibition (GR) metrics. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Software, TimeCourse, Visualization Author: Nicholas Clark Maintainer: Nicholas Clark , Mario Medvedovic URL: https://github.com/uc-bd2k/GRmetrics VignetteBuilder: knitr BugReports: https://github.com/uc-bd2k/GRmetrics/issues git_url: https://git.bioconductor.org/packages/GRmetrics git_branch: RELEASE_3_22 git_last_commit: 2a3df60 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GRmetrics_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GRmetrics_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GRmetrics_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GRmetrics_1.36.0.tgz vignettes: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html vignetteTitles: GRmetrics: an R package for calculation and visualization of dose-response metrics based on growth rate inhibition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.R dependencyCount: 127 Package: groHMM Version: 1.44.0 Depends: R (>= 4.1.0), MASS, parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Seqinfo, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), rtracklayer (>= 1.39.7) Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 8c9a090d6cb476f71121f4ff34443142 NeedsCompilation: yes Title: GRO-seq Analysis Pipeline Description: A pipeline for the analysis of GRO-seq data. biocViews: Sequencing, Software Author: Charles G. Danko [aut], Minho Chae [aut], Andre Martins [ctb], W. Lee Kraus [aut, fnd], Anusha Nagari [ctb], Tulip Nandu [cre, ctb], Pariksheet Nanda [ctb] (ORCID: ) Maintainer: Tulip Nandu URL: https://github.com/Kraus-Lab/groHMM BugReports: https://github.com/Kraus-Lab/groHMM/issues git_url: https://git.bioconductor.org/packages/groHMM git_branch: RELEASE_3_22 git_last_commit: 28ddd37 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/groHMM_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/groHMM_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/groHMM_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/groHMM_1.44.0.tgz vignettes: vignettes/groHMM/inst/doc/groHMM.pdf vignetteTitles: groHMM tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/groHMM/inst/doc/groHMM.R dependencyCount: 58 Package: GSALightning Version: 1.38.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 2d33103076f3e69e527af27eaf2595a0 NeedsCompilation: no Title: Fast Permutation-based Gene Set Analysis Description: GSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation. biocViews: Software, BiologicalQuestion, GeneSetEnrichment, DifferentialExpression, GeneExpression, Transcription Author: Billy Heung Wing Chang Maintainer: Billy Heung Wing Chang URL: https://github.com/billyhw/GSALightning VignetteBuilder: knitr BugReports: https://github.com/billyhw/GSALightning/issues git_url: https://git.bioconductor.org/packages/GSALightning git_branch: RELEASE_3_22 git_last_commit: 504932d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GSALightning_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSALightning_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSALightning_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSALightning_1.38.0.tgz vignettes: vignettes/GSALightning/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSALightning/inst/doc/vignette.R dependencyCount: 9 Package: GSAR Version: 1.44.0 Depends: R (>= 3.0.1), igraph (>= 0.7.1) Imports: stats, graphics Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate, org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR, BiocStyle License: GPL (>=2) MD5sum: 0b779a1b97bec3e4386c209923ef75e7 NeedsCompilation: no Title: Gene Set Analysis in R Description: Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure. biocViews: Software, StatisticalMethod, DifferentialExpression Author: Yasir Rahmatallah , Galina Glazko Maintainer: Yasir Rahmatallah , Galina Glazko git_url: https://git.bioconductor.org/packages/GSAR git_branch: RELEASE_3_22 git_last_commit: cf683e2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GSAR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSAR_1.43.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSAR_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSAR_1.44.0.tgz vignettes: vignettes/GSAR/inst/doc/GSAR.pdf vignetteTitles: Gene Set Analysis in R -- the GSAR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSAR/inst/doc/GSAR.R dependencyCount: 17 Package: GSCA Version: 2.40.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) Archs: x64 MD5sum: 6169159acc98b9dcd47b2256559dd5ab NeedsCompilation: no Title: GSCA: Gene Set Context Analysis Description: GSCA takes as input several lists of activated and repressed genes. GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression. GSCA provides both traditional R functions and interactive, user-friendly user interface. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji git_url: https://git.bioconductor.org/packages/GSCA git_branch: RELEASE_3_22 git_last_commit: 0731439 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GSCA_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSCA_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSCA_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSCA_2.40.0.tgz vignettes: vignettes/GSCA/inst/doc/GSCA.pdf vignetteTitles: GSCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSCA/inst/doc/GSCA.R dependencyCount: 61 Package: gscreend Version: 1.24.0 Depends: R (>= 3.6) Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel, graphics Suggests: knitr, testthat, rmarkdown, BiocStyle License: GPL-3 MD5sum: 0864b8024f0267881b9ab5d79215e5ba NeedsCompilation: no Title: Analysis of pooled genetic screens Description: Package for the analysis of pooled genetic screens (e.g. CRISPR-KO). The analysis of such screens is based on the comparison of gRNA abundances before and after a cell proliferation phase. The gscreend packages takes gRNA counts as input and allows detection of genes whose knockout decreases or increases cell proliferation. biocViews: Software, StatisticalMethod, PooledScreens, CRISPR Author: Katharina Imkeller [cre, aut], Wolfgang Huber [aut] Maintainer: Katharina Imkeller URL: https://github.com/imkeller/gscreend VignetteBuilder: knitr BugReports: https://github.com/imkeller/gscreend/issues git_url: https://git.bioconductor.org/packages/gscreend git_branch: RELEASE_3_22 git_last_commit: 3d8d358 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gscreend_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gscreend_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gscreend_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gscreend_1.24.0.tgz vignettes: vignettes/gscreend/inst/doc/gscreend_simulated_data.html vignetteTitles: Example_simulated hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gscreend/inst/doc/gscreend_simulated_data.R dependencyCount: 49 Package: GSEABase Version: 1.72.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.13.8), Biobase (>= 2.17.8), annotate (>= 1.45.3), methods, graph (>= 1.37.2) Imports: AnnotationDbi, XML Suggests: hgu95av2.db, GO.db, org.Hs.eg.db, Rgraphviz, ReportingTools, testthat, BiocStyle, knitr, RUnit License: Artistic-2.0 MD5sum: 73b757a1f0ccd87eb276ed0e62a2796f NeedsCompilation: no Title: Gene set enrichment data structures and methods Description: This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA). biocViews: GeneExpression, GeneSetEnrichment, GraphAndNetwork, GO, KEGG Author: Martin Morgan [aut], Seth Falcon [aut], Robert Gentleman [aut], Paul Villafuerte [ctb] ('GSEABase' vignette translation from Sweave to Rmarkdown / HTML), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEABase git_branch: RELEASE_3_22 git_last_commit: 8f4e176 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GSEABase_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSEABase_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSEABase_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSEABase_1.72.0.tgz vignettes: vignettes/GSEABase/inst/doc/GSEABase.html vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R dependsOnMe: AGDEX, BicARE, CCPROMISE, Cepo, cpvSNP, npGSEA, PROMISE, splineTimeR, TissueEnrich, GSVAdata, OSCA.basic importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cosmosR, dreamlet, EnrichmentBrowser, gep2pep, GlobalAncova, GmicR, GSRI, GSVA, mastR, miRSM, mogsa, oppar, PanomiR, phenoTest, PROMISE, RcisTarget, ReportingTools, scTGIF, signatureSearch, singleCellTK, singscore, slalom, sparrow, TFutils, TMSig, vissE, zenith, msigdb, clustermole, RVA suggestsMe: BiocSet, epiregulon.extra, escape, gage, globaltest, GOstats, GSAR, MAST, pathMED, phenoTest, BaseSet, evanverse dependencyCount: 47 Package: GSEABenchmarkeR Version: 1.30.0 Depends: R (>= 4.5.0), Biobase, SummarizedExperiment Imports: AnnotationDbi, AnnotationHub, BiocFileCache, BiocParallel, edgeR, EnrichmentBrowser, ExperimentHub, grDevices, graphics, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, methods, S4Vectors, stats, utils Suggests: BiocStyle, GSE62944, knitr, rappdirs, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 769aefc9c5f7b9c4798f78a006bf6805 NeedsCompilation: no Title: Reproducible GSEA Benchmarking Description: The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Lucas Schiffer [ctb], Marcel Ramos [ctb], Ralf Zimmer [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/GSEABenchmarkeR VignetteBuilder: knitr BugReports: https://github.com/waldronlab/GSEABenchmarkeR/issues git_url: https://git.bioconductor.org/packages/GSEABenchmarkeR git_branch: RELEASE_3_22 git_last_commit: 7ab14b8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GSEABenchmarkeR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSEABenchmarkeR_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSEABenchmarkeR_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSEABenchmarkeR_1.30.0.tgz vignettes: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.html vignetteTitles: Reproducible GSEA Benchmarking hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.R suggestsMe: roastgsa dependencyCount: 110 Package: GSEAlm Version: 1.70.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 4d72e3a70dd24f893bfbc4a3109459c8 NeedsCompilation: no Title: Linear Model Toolset for Gene Set Enrichment Analysis Description: Models and methods for fitting linear models to gene expression data, together with tools for computing and using various regression diagnostics. biocViews: Microarray Author: Assaf Oron, Robert Gentleman (with contributions from S. Falcon and Z. Jiang) Maintainer: Assaf Oron git_url: https://git.bioconductor.org/packages/GSEAlm git_branch: RELEASE_3_22 git_last_commit: 4a18e2c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GSEAlm_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSEAlm_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSEAlm_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSEAlm_1.70.0.tgz vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf vignetteTitles: Linear models in GSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEAlm/inst/doc/GSEAlm.R dependencyCount: 7 Package: GSEAmining Version: 1.20.0 Depends: R (>= 4.0) Imports: dplyr, tidytext, dendextend, tibble, ggplot2, ggwordcloud, stringr, gridExtra, rlang, grDevices, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle, clusterProfiler, testthat, tm License: GPL-3 | file LICENSE Archs: x64 MD5sum: aed72c65f28d5b0e7b079db63d431b57 NeedsCompilation: no Title: Make Biological Sense of Gene Set Enrichment Analysis Outputs Description: Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes. Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology (GO) classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging. Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. For these reasons, GSEAmining was born to be an easy tool to create reproducible reports to help researchers make biological sense of GSEA outputs. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge (core) subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets. For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets (as wordclouds) - The most enriched genes in the leading edge subsets (as bar plots). In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study. biocViews: GeneSetEnrichment, Clustering, Visualization Author: Oriol Arqués [aut, cre] Maintainer: Oriol Arqués VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEAmining git_branch: RELEASE_3_22 git_last_commit: b5d7a90 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GSEAmining_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSEAmining_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSEAmining_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSEAmining_1.20.0.tgz vignettes: vignettes/GSEAmining/inst/doc/GSEAmining.html vignetteTitles: GSEAmining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSEAmining/inst/doc/GSEAmining.R dependencyCount: 56 Package: gsean Version: 1.30.0 Depends: R (>= 3.5), fgsea, PPInfer Suggests: SummarizedExperiment, pasilla, org.Dm.eg.db, AnnotationDbi, knitr, plotly, WGCNA, rmarkdown License: Artistic-2.0 MD5sum: c0827198e6d5ee7753a7d93e557ed72a NeedsCompilation: yes Title: Gene Set Enrichment Analysis with Networks Description: Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, GeneExpression, NetworkEnrichment, Pathways, DifferentialExpression Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gsean git_branch: RELEASE_3_22 git_last_commit: cb6d153 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gsean_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gsean_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gsean_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gsean_1.30.0.tgz vignettes: vignettes/gsean/inst/doc/gsean.html vignetteTitles: gsean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gsean/inst/doc/gsean.R dependencyCount: 108 Package: GSgalgoR Version: 1.20.0 Imports: cluster, doParallel, foreach, matchingR, nsga2R, survival, proxy, stats, methods, Suggests: knitr, rmarkdown, ggplot2, BiocStyle, genefu, survcomp, Biobase, survminer, breastCancerTRANSBIG, breastCancerUPP, iC10TrainingData, pamr, testthat License: MIT + file LICENSE MD5sum: 9eee0af0784e4098f1d504e15dc7dfd3 NeedsCompilation: no Title: An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer Description: A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The 'Galgo' framework combines the advantages of clustering algorithms for grouping heterogeneous 'omics' data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high. biocViews: GeneExpression, Transcription, Clustering, Classification, Survival Author: Martin Guerrero [aut], Carlos Catania [cre] Maintainer: Carlos Catania URL: https://github.com/harpomaxx/GSgalgoR VignetteBuilder: knitr BugReports: https://github.com/harpomaxx/GSgalgoR/issues git_url: https://git.bioconductor.org/packages/GSgalgoR git_branch: RELEASE_3_22 git_last_commit: fc892d4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GSgalgoR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSgalgoR_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSgalgoR_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSgalgoR_1.20.0.tgz vignettes: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.html, vignettes/GSgalgoR/inst/doc/GSgalgoR.html vignetteTitles: GSgalgoR_callbacks.html, GSgalgoR.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.R, vignettes/GSgalgoR/inst/doc/GSgalgoR.R dependencyCount: 22 Package: GSReg Version: 1.44.0 Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures, AnnotationDbi Suggests: GenomicRanges, GSBenchMark License: GPL-2 MD5sum: d3b9f8868f59e19830b461c5632cbfc0 NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA). biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment, AlternativeSplicing Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari , Elana J. Fertig git_url: https://git.bioconductor.org/packages/GSReg git_branch: RELEASE_3_22 git_last_commit: 142159e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GSReg_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSReg_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSReg_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSReg_1.44.0.tgz vignettes: vignettes/GSReg/inst/doc/GSReg.pdf vignetteTitles: Working with the GSReg package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSReg/inst/doc/GSReg.R dependencyCount: 84 Package: GSRI Version: 2.58.0 Depends: R (>= 2.14.2), fdrtool Imports: methods, graphics, stats, utils, genefilter, Biobase, GSEABase, les (>= 1.1.6) Suggests: limma, hgu95av2.db Enhances: parallel License: GPL-3 MD5sum: 88b01f21e89a9f4d46a28335ca1d4dc5 NeedsCompilation: no Title: Gene Set Regulation Index Description: The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI). biocViews: Microarray, Transcription, DifferentialExpression, GeneSetEnrichment, GeneRegulation Author: Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/GSRI git_branch: RELEASE_3_22 git_last_commit: 9282321 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GSRI_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSRI_2.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSRI_2.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSRI_2.58.0.tgz vignettes: vignettes/GSRI/inst/doc/gsri.pdf vignetteTitles: Introduction to the GSRI package: Estimating Regulatory Effects utilizing the Gene Set Regulation Index hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSRI/inst/doc/gsri.R dependencyCount: 65 Package: GSVA Version: 2.4.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, graphics, BiocGenerics, MatrixGenerics, S4Vectors, S4Arrays, HDF5Array, SparseArray, DelayedArray, IRanges, Biobase, SummarizedExperiment, GSEABase, Matrix (>= 1.5-0), parallel, BiocParallel, SingleCellExperiment, SpatialExperiment, sparseMatrixStats, DelayedMatrixStats, BiocSingular, cli LinkingTo: cli Suggests: RUnit, BiocStyle, knitr, rmarkdown, limma, RColorBrewer, org.Hs.eg.db, genefilter, edgeR, GSVAdata, sva, ggplot2, TENxPBMCData, scuttle, scran, igraph, shiny, shinydashboard, ggplot2, data.table, plotly, future, promises, shinybusy, shinyjs License: Artistic-2.0 MD5sum: 1d56ebaf88b7d11f072bf2a6eb519a51 NeedsCompilation: yes Title: Gene Set Variation Analysis for Microarray and RNA-Seq Data Description: Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. biocViews: FunctionalGenomics, Microarray, RNASeq, Pathways, GeneSetEnrichment Author: Robert Castelo [aut, cre], Justin Guinney [aut], Alexey Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb], Axel Klenk [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GSVA VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GSVA/issues git_url: https://git.bioconductor.org/packages/GSVA git_branch: RELEASE_3_22 git_last_commit: ab6d8f1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GSVA_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSVA_2.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSVA_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSVA_2.4.0.tgz vignettes: vignettes/GSVA/inst/doc/GSVA_scRNAseq.html, vignettes/GSVA/inst/doc/GSVA.html vignetteTitles: GSVA on single-cell RNA-seq data, Gene set variation analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSVA/inst/doc/GSVA_scRNAseq.R, vignettes/GSVA/inst/doc/GSVA.R dependsOnMe: SMDIC importsMe: consensusOV, EGSEA, octad, oppar, pathMED, signifinder, singleCellTK, TBSignatureProfiler, autoGO, clustermole, DRviaSPCN, GSEMA, psSubpathway, scMappR, SIGN, sigQC, spatialGE, ssdGSA suggestsMe: decoupleR, escape, MCbiclust, mitology, sparrow, SPONGE, ReporterScore dependencyCount: 102 Package: gtrellis Version: 1.42.0 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: circlize (>= 0.4.8), GetoptLong, grDevices, utils Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, rmarkdown, ComplexHeatmap (>= 1.99.0), Cairo, png, jpeg, tiff License: MIT + file LICENSE MD5sum: a477918ad7e12c811a1874d1e5f54e1e NeedsCompilation: no Title: Genome Level Trellis Layout Description: Genome level Trellis graph visualizes genomic data conditioned by genomic categories (e.g. chromosomes). For each genomic category, multiple dimensional data which are represented as tracks describe different features from different aspects. This package provides high flexibility to arrange genomic categories and to add self-defined graphics in the plot. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/gtrellis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gtrellis git_branch: RELEASE_3_22 git_last_commit: e192227 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gtrellis_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gtrellis_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gtrellis_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gtrellis_1.42.0.tgz vignettes: vignettes/gtrellis/inst/doc/gtrellis.html vignetteTitles: Make Genome-level Trellis Graph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R importsMe: YAPSA dependencyCount: 20 Package: GUIDEseq Version: 1.40.0 Depends: R (>= 3.5.0), GenomicRanges, BiocGenerics Imports: Biostrings, pwalign, CRISPRseek, ChIPpeakAnno, data.table, matrixStats, BSgenome, parallel, IRanges (>= 2.5.5), S4Vectors (>= 0.9.6), stringr, multtest, GenomicAlignments (>= 1.7.3), GenomeInfoDb, Rsamtools, hash, limma,dplyr, GenomicFeatures, rio, tidyr, tools, methods, purrr, ggplot2, openxlsx, patchwork, rlang Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat (>= 3.0.0) License: GPL (>= 2) Archs: x64 MD5sum: 0e43165a3113e19ccd630ba263f2120a NeedsCompilation: no Title: GUIDE-seq and PEtag-seq analysis pipeline Description: The package implements GUIDE-seq and PEtag-seq analysis workflow including functions for filtering UMI and reads with low coverage, obtaining unique insertion sites (proxy of cleavage sites), estimating the locations of the insertion sites, aka, peaks, merging estimated insertion sites from plus and minus strand, and performing off target search of the extended regions around insertion sites with mismatches and indels. biocViews: ImmunoOncology, GeneRegulation, Sequencing, WorkflowStep, CRISPR Author: Lihua Julie Zhu, Michael Lawrence, Ankit Gupta, Hervé Pagès , Alper Kucukural, Manuel Garber, Scot A. Wolfe Maintainer: Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GUIDEseq git_branch: RELEASE_3_22 git_last_commit: 45a0042 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GUIDEseq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GUIDEseq_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GUIDEseq_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GUIDEseq_1.40.0.tgz vignettes: vignettes/GUIDEseq/inst/doc/GUIDEseq.pdf vignetteTitles: GUIDEseq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GUIDEseq/inst/doc/GUIDEseq.R dependencyCount: 178 Package: Guitar Version: 2.26.0 Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges, magrittr, ggplot2, methods, stats,utils ,knitr,dplyr License: GPL-2 MD5sum: 288235911662385f228a01bd4e0d1598 NeedsCompilation: no Title: Guitar Description: The package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site. biocViews: Sequencing, SplicedAlignment, Alignment, DataImport, RNASeq, MethylSeq, QualityControl, Transcription Author: Xiao Du, Hui Liu, Lin Zhang, Jia Meng Maintainer: Jia Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Guitar git_branch: RELEASE_3_22 git_last_commit: 4c03ea8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Guitar_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Guitar_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Guitar_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Guitar_2.26.0.tgz vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf vignetteTitles: Guitar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Guitar/inst/doc/Guitar-Overview.R dependencyCount: 96 Package: gVenn Version: 1.0.0 Depends: R (>= 4.5.0) Imports: ComplexHeatmap, eulerr, GenomicRanges, IRanges, lubridate, methods, rtracklayer, stringr, writexl Suggests: testthat (>= 3.0.0), ggplot2, withr, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 21158cee1032dc950663a8d2acbc3fc0 NeedsCompilation: no Title: Proportional Venn and UpSet Diagrams for Gene Sets and Genomic Regions Description: Tools to compute and visualize overlaps between gene sets or genomic regions. Venn diagrams with proportional areas are provided, while UpSet plots are recommended for larger numbers of sets. The package supports GRanges and GRangesList inputs, and integrates with analysis workflows for ChIP-seq, ATAC-seq, and other genomic interval data. It generates clean, interpretable, and publication-ready figures. biocViews: Software, Visualization, ChIPSeq, ATACSeq, Epigenetics, DataRepresentation, Sequencing Author: Christophe Tav [aut, cre] (ORCID: ) Maintainer: Christophe Tav URL: https://github.com/ckntav/gVenn, https://ckntav.github.io/gVenn/ VignetteBuilder: knitr BugReports: https://github.com/ckntav/gVenn/issues git_url: https://git.bioconductor.org/packages/gVenn git_branch: RELEASE_3_22 git_last_commit: 27b5dfc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gVenn_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gVenn_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gVenn_1.0.0.tgz vignettes: vignettes/gVenn/inst/doc/gVenn.html vignetteTitles: gVenn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gVenn/inst/doc/gVenn.R dependencyCount: 88 Package: Gviz Version: 1.54.0 Depends: R (>= 4.3), methods, S4Vectors (>= 0.9.25), IRanges (>= 1.99.18), GenomicRanges (>= 1.61.1), grid Imports: XVector (>= 0.5.7), rtracklayer (>= 1.69.1), lattice, RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5), Biobase (>= 2.15.3), GenomicFeatures (>= 1.61.4), ensembldb (>= 2.11.3), BSgenome (>= 1.77.1), Biostrings (>= 2.77.2), biovizBase (>= 1.13.8), Rsamtools (>= 2.25.1), latticeExtra (>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>= 1.45.1), Seqinfo, GenomeInfoDb, BiocGenerics (>= 0.11.3), digest(>= 0.6.8), graphics, grDevices, stats, utils Suggests: BSgenome.Hsapiens.UCSC.hg19, xml2, BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: cd0fa7b7ace17f2d8343b3caba113207 NeedsCompilation: no Title: Plotting data and annotation information along genomic coordinates Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. biocViews: Visualization, Microarray, Sequencing Author: Florian Hahne [aut], Steffen Durinck [aut], Robert Ivanek [aut, cre] (ORCID: ), Arne Mueller [aut], Steve Lianoglou [aut], Ge Tan [aut], Lance Parsons [aut], Shraddha Pai [aut], Thomas McCarthy [ctb], Felix Ernst [ctb], Mike Smith [ctb] Maintainer: Robert Ivanek URL: https://github.com/ivanek/Gviz VignetteBuilder: knitr BugReports: https://github.com/ivanek/Gviz/issues git_url: https://git.bioconductor.org/packages/Gviz git_branch: RELEASE_3_22 git_last_commit: 05ebfa6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Gviz_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Gviz_1.53.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Gviz_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Gviz_1.54.0.tgz vignettes: vignettes/Gviz/inst/doc/Gviz.html vignetteTitles: The Gviz User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Gviz/inst/doc/Gviz.R dependsOnMe: biomvRCNS, chimeraviz, cicero, Pviz, methylationArrayAnalysis, rnaseqGene, csawBook importsMe: AllelicImbalance, ASpli, CAGEfightR, crisprViz, DMRcate, DuplexDiscovereR, ELMER, epimutacions, GenomicInteractions, maser, MEAL, methylPipe, motifbreakR, OGRE, primirTSS, regutools, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, SPLINTER, srnadiff, tadar, trackViewer, TVTB, uncoverappLib, VariantFiltering, DMRcatedata suggestsMe: annmap, BindingSiteFinder, cellbaseR, CNEr, CNVRanger, ensembldb, extraChIPs, fishpond, GenomicRanges, gwascat, interactiveDisplay, MIRit, pqsfinder, QuasR, RnBeads, segmenter, SplicingGraphs, TFutils, Single.mTEC.Transcriptomes, CAGEWorkflow, chipseqDB, chicane, GRIN2, RTIGER dependencyCount: 151 Package: GWAS.BAYES Version: 1.20.0 Depends: R (>= 4.3.0) Imports: GA (>= 3.2), caret (>= 6.0-86), memoise (>= 1.1.0), Matrix (>= 1.2-18), limma (>= 3.54.0), stats (>= 4.2.2), MASS (>= 7.3-58.1) Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP License: GPL-3 + file LICENSE Archs: x64 MD5sum: 484d3351243a309b2601dfbb66a6563c NeedsCompilation: no Title: Bayesian analysis of Gaussian GWAS data Description: This package is built to perform GWAS analysis using Bayesian techniques. Currently, GWAS.BAYES has functionality for the implementation of BICOSS (Williams, J., Ferreira, M. A., and Ji, T. (2022). BICOSS: Bayesian iterative conditional stochastic search for GWAS. BMC Bioinformatics), BGWAS (Williams, J., Xu, S., Ferreira, M. A.. (2023) "BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies." BMC Bioinformatics), and GINA. All methods currently are for the analysis of Gaussian phenotypes The research related to this package was supported in part by National Science Foundation awards DMS 1853549, DMS 1853556, and DMS 2054173. biocViews: Bayesian, AssayDomain, SNP, GenomeWideAssociation Author: Jacob Williams [aut, cre] (ORCID: ), Marco Ferreira [aut] (ORCID: ), Tieming Ji [aut] Maintainer: Jacob Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWAS.BAYES git_branch: RELEASE_3_22 git_last_commit: 2463904 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GWAS.BAYES_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GWAS.BAYES_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GWAS.BAYES_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GWAS.BAYES_1.20.0.tgz vignettes: vignettes/GWAS.BAYES/inst/doc/Vignette_BICOSS.html, vignettes/GWAS.BAYES/inst/doc/Vignette_GINA.html vignetteTitles: BICOSS, GINA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GWAS.BAYES/inst/doc/Vignette_BICOSS.R, vignettes/GWAS.BAYES/inst/doc/Vignette_GINA.R dependencyCount: 91 Package: gwascat Version: 2.42.0 Depends: R (>= 4.1.0), methods Imports: S4Vectors (>= 0.9.25), IRanges, Seqinfo, GenomeInfoDb, GenomicRanges (>= 1.29.6), GenomicFeatures, readr, Biostrings, AnnotationDbi, BiocFileCache, snpStats, VariantAnnotation, AnnotationHub, data.table, tibble Suggests: DO.db, DT, knitr, RBGL, testthat, rmarkdown, dplyr, Gviz, Rsamtools, rtracklayer, graph, ggbio, DelayedArray, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BiocStyle Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37 License: Artistic-2.0 Archs: x64 MD5sum: 010bd335e62cb286729522b777db5578 NeedsCompilation: no Title: representing and modeling data in the EMBL-EBI GWAS catalog Description: Represent and model data in the EMBL-EBI GWAS catalog. biocViews: Genetics Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwascat git_branch: RELEASE_3_22 git_last_commit: 5ae3745 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gwascat_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gwascat_2.41.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gwascat_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gwascat_2.42.0.tgz vignettes: vignettes/gwascat/inst/doc/gwascat.html, vignettes/gwascat/inst/doc/gwascatOnt.html vignetteTitles: gwascat: structuring and querying the NHGRI GWAS catalog, gwascat -- GRanges for GWAS hits in EBI catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R, vignettes/gwascat/inst/doc/gwascatOnt.R dependsOnMe: vtpnet, liftOver importsMe: circRNAprofiler suggestsMe: GenomicScores, hmdbQuery, ldblock, parglms, TFutils, grasp2db dependencyCount: 111 Package: GWASTools Version: 1.56.0 Depends: Biobase Imports: graphics, stats, utils, methods, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth, data.table Suggests: ncdf4, GWASdata, BiocGenerics, RUnit, Biostrings, GenomicRanges, IRanges, SNPRelate, snpStats, S4Vectors, VariantAnnotation, parallel, BiocStyle, knitr License: Artistic-2.0 MD5sum: eb140ae8c8410f604a2e28e32ae1fc51 NeedsCompilation: no Title: Tools for Genome Wide Association Studies Description: Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis. biocViews: SNP, GeneticVariability, QualityControl, Microarray Author: Stephanie M. Gogarten [aut], Cathy Laurie [aut], Tushar Bhangale [aut], Matthew P. Conomos [aut], Cecelia Laurie [aut], Michael Lawrence [aut], Caitlin McHugh [aut], Ian Painter [aut], Xiuwen Zheng [aut], Jess Shen [aut], Rohit Swarnkar [aut], Adrienne Stilp [aut], Sarah Nelson [aut], David Levine [aut], Sonali Kumari [ctb] (Converted vignettes from Sweave to RMarkdown / HTML.), Stephanie M. Gogarten [cre] Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/GWASTools VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWASTools git_branch: RELEASE_3_22 git_last_commit: 121c763 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GWASTools_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GWASTools_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GWASTools_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GWASTools_1.56.0.tgz vignettes: vignettes/GWASTools/inst/doc/DataCleaning.pdf, vignettes/GWASTools/inst/doc/Formats.pdf, vignettes/GWASTools/inst/doc/Affymetrix.html vignetteTitles: GWAS Data Cleaning, Data formats in GWASTools, Preparing Affymetrix Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWASTools/inst/doc/Affymetrix.R, vignettes/GWASTools/inst/doc/DataCleaning.R, vignettes/GWASTools/inst/doc/Formats.R dependsOnMe: mBPCR, GWASdata, snplinkage importsMe: GENESIS, gwasurvivr suggestsMe: podkat dependencyCount: 95 Package: gwasurvivr Version: 1.28.0 Depends: R (>= 3.4.0) Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats, SummarizedExperiment, stats, utils, SNPRelate Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 3aa7fc13337d742fe06d8fc932291c4f NeedsCompilation: no Title: gwasurvivr: an R package for genome wide survival analysis Description: gwasurvivr is a package to perform survival analysis using Cox proportional hazard models on imputed genetic data. biocViews: GenomeWideAssociation, Survival, Regression, Genetics, SNP, GeneticVariability, Pharmacogenomics, BiomedicalInformatics Author: Abbas Rizvi, Ezgi Karaesmen, Martin Morgan, Lara Sucheston-Campbell Maintainer: Abbas Rizvi URL: https://github.com/suchestoncampbelllab/gwasurvivr VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwasurvivr git_branch: RELEASE_3_22 git_last_commit: ac1c829 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gwasurvivr_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gwasurvivr_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gwasurvivr_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gwasurvivr_1.28.0.tgz vignettes: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.html vignetteTitles: gwasurvivr Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.R dependencyCount: 144 Package: GWENA Version: 1.20.0 Depends: R (>= 4.1) Imports: WGCNA (>= 1.67), dplyr (>= 0.8.3), dynamicTreeCut (>= 1.63-1), ggplot2 (>= 3.1.1), gprofiler2 (>= 0.1.6), magrittr (>= 1.5), tibble (>= 2.1.1), tidyr (>= 1.0.0), NetRep (>= 1.2.1), igraph (>= 1.2.4.1), RColorBrewer (>= 1.1-2), purrr (>= 0.3.3), rlist (>= 0.4.6.1), matrixStats (>= 0.55.0), SummarizedExperiment (>= 1.14.1), stringr (>= 1.4.0), cluster (>= 2.1.0), grDevices (>= 4.0.4), methods, graphics, stats, utils Suggests: testthat (>= 2.1.0), knitr (>= 1.25), rmarkdown (>= 1.16), prettydoc (>= 0.3.0), httr (>= 1.4.1), S4Vectors (>= 0.22.1), BiocStyle (>= 2.15.8) License: GPL-3 MD5sum: 67fa6acc611a80e7218c9ed66b09d107 NeedsCompilation: no Title: Pipeline for augmented co-expression analysis Description: The development of high-throughput sequencing led to increased use of co-expression analysis to go beyong single feature (i.e. gene) focus. We propose GWENA (Gene Whole co-Expression Network Analysis) , a tool designed to perform gene co-expression network analysis and explore the results in a single pipeline. It includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparison of networks configuration between conditions. biocViews: Software, GeneExpression, Network, Clustering, GraphAndNetwork, GeneSetEnrichment, Pathways, Visualization, RNASeq, Transcriptomics, mRNAMicroarray, Microarray, NetworkEnrichment, Sequencing, GO Author: Gwenaëlle Lemoine [aut, cre] (ORCID: ), Marie-Pier Scott-Boyer [ths], Arnaud Droit [fnd] Maintainer: Gwenaëlle Lemoine VignetteBuilder: knitr BugReports: https://github.com/Kumquatum/GWENA/issues git_url: https://git.bioconductor.org/packages/GWENA git_branch: RELEASE_3_22 git_last_commit: 9621d77 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/GWENA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GWENA_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GWENA_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GWENA_1.20.0.tgz vignettes: vignettes/GWENA/inst/doc/GWENA_guide.html vignetteTitles: GWENA-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWENA/inst/doc/GWENA_guide.R dependencyCount: 135 Package: gypsum Version: 1.6.0 Imports: utils, httr2, jsonlite, parallel, filelock, rappdirs Suggests: knitr, rmarkdown, testthat, BiocStyle, digest, jsonvalidate, DBI, RSQLite, S4Vectors, methods License: MIT + file LICENSE MD5sum: f3dd8f91eaf009e2d554e953522c77c4 NeedsCompilation: no Title: Interface to the gypsum REST API Description: Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various adminstrative and management tasks. Check out the documentation at https://github.com/ArtifactDB/gypsum-worker for more details. biocViews: DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/ArtifactDB/gypsum-R VignetteBuilder: knitr BugReports: https://github.com/ArtifactDB/gypsum-R/issues git_url: https://git.bioconductor.org/packages/gypsum git_branch: RELEASE_3_22 git_last_commit: ff6acdc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/gypsum_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gypsum_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gypsum_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gypsum_1.6.0.tgz vignettes: vignettes/gypsum/inst/doc/userguide.html vignetteTitles: Hitting the gypsum API hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gypsum/inst/doc/userguide.R importsMe: celldex, scRNAseq dependencyCount: 21 Package: h5mread Version: 1.2.0 Depends: R (>= 4.5), methods, rhdf5, BiocGenerics, SparseArray Imports: stats, tools, rhdf5filters, S4Vectors, IRanges, S4Arrays LinkingTo: Rhdf5lib, S4Vectors Suggests: BiocParallel, ExperimentHub, TENxBrainData, HDF5Array, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: b39adb72729ff85b4d673c39e4de28e9 NeedsCompilation: yes Title: A fast HDF5 reader Description: The main function in the h5mread package is h5mread(), which allows reading arbitrary data from an HDF5 dataset into R, similarly to what the h5read() function from the rhdf5 package does. In the case of h5mread(), the implementation has been optimized to make it as fast and memory-efficient as possible. biocViews: Infrastructure, DataRepresentation, DataImport Author: Hervé Pagès [aut, cre] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/h5mread SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/h5mread/issues git_url: https://git.bioconductor.org/packages/h5mread git_branch: RELEASE_3_22 git_last_commit: e2dfd60 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/h5mread_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/h5mread_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/h5mread_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/h5mread_1.2.0.tgz vignettes: vignettes/h5mread/inst/doc/h5mread.html vignetteTitles: The h5mread package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/h5mread/inst/doc/h5mread.R dependsOnMe: HDF5Array importsMe: SpliceWiz suggestsMe: MultiAssayExperiment dependencyCount: 23 Package: h5vc Version: 2.44.0 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>= 2.13.1), methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb LinkingTo: Rhtslib (>= 1.99.1) Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics, rmarkdown License: GPL (>= 3) MD5sum: 4c41b73e27aa27959a32c4b25f51855d NeedsCompilation: yes Title: Managing alignment tallies using a hdf5 backend Description: This package contains functions to interact with tally data from NGS experiments that is stored in HDF5 files. Author: Paul Theodor Pyl Maintainer: Paul Theodor Pyl SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/h5vc git_branch: RELEASE_3_22 git_last_commit: 159497f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/h5vc_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/h5vc_2.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/h5vc_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/h5vc_2.44.0.tgz vignettes: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html vignetteTitles: Building a minimal genome browser with h5vc and shiny, h5vc -- Tour hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.R, vignettes/h5vc/inst/doc/h5vc.tour.R suggestsMe: h5vcData dependencyCount: 84 Package: hapFabia Version: 1.52.0 Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: 385f4ba5f559ba136ecff321aeb6903b NeedsCompilation: yes Title: hapFabia: Identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data Description: A package to identify very short IBD segments in large sequencing data by FABIA biclustering. Two haplotypes are identical by descent (IBD) if they share a segment that both inherited from a common ancestor. Current IBD methods reliably detect long IBD segments because many minor alleles in the segment are concordant between the two haplotypes. However, many cohort studies contain unrelated individuals which share only short IBD segments. This package provides software to identify short IBD segments in sequencing data. Knowledge of short IBD segments are relevant for phasing of genotyping data, association studies, and for population genetics, where they shed light on the evolutionary history of humans. The package supports VCF formats, is based on sparse matrix operations, and provides visualization of haplotype clusters in different formats. biocViews: Genetics, GeneticVariability, SNP, Sequencing, Sequencing, Visualization, Clustering, SequenceMatching, Software Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html git_url: https://git.bioconductor.org/packages/hapFabia git_branch: RELEASE_3_22 git_last_commit: e495caf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hapFabia_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hapFabia_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hapFabia_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hapFabia_1.52.0.tgz vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf vignetteTitles: hapFabia: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hapFabia/inst/doc/hapfabia.R dependencyCount: 9 Package: Harman Version: 1.38.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.11.2), graphics, stats, Ckmeans.1d.dp, parallel, methods, matrixStats LinkingTo: Rcpp Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit, RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA, affydata, minfiData, sva License: GPL-3 + file LICENCE Archs: x64 MD5sum: 44c149167e5981cc0527b9db7fd30e23 NeedsCompilation: yes Title: The removal of batch effects from datasets using a PCA and constrained optimisation based technique Description: Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user. biocViews: BatchEffect, Microarray, MultipleComparison, PrincipalComponent, Normalization, Preprocessing, DNAMethylation, Transcription, Software, StatisticalMethod Author: Yalchin Oytam [aut], Josh Bowden [aut], Jason Ross [aut, cre] Maintainer: Jason Ross URL: http://www.bioinformatics.csiro.au/harman/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Harman git_branch: RELEASE_3_22 git_last_commit: c60055e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Harman_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Harman_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Harman_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Harman_1.38.0.tgz vignettes: vignettes/Harman/inst/doc/IntroductionToHarman.html vignetteTitles: IntroductionToHarman hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R importsMe: debrowser suggestsMe: HarmanData dependencyCount: 11 Package: HarmonizR Version: 1.8.0 Depends: R (>= 4.2.0) Imports: doParallel (>= 1.0.16), foreach (>= 1.5.1), janitor (>= 2.1.0), plyr (>= 1.8.6), sva (>= 3.36.0), seriation (>= 1.3.5), limma (>= 3.46.0), SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: c52bf95bd0cadbe1fc3189ea2bf1f6e1 NeedsCompilation: no Title: Handles missing values and makes more data available Description: An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss. biocViews: BatchEffect Author: Simon Schlumbohm [aut, cre], Julia Neumann [aut], Philipp Neumann [aut] Maintainer: Simon Schlumbohm VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HarmonizR git_branch: RELEASE_3_22 git_last_commit: 8828598 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HarmonizR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HarmonizR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HarmonizR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HarmonizR_1.8.0.tgz vignettes: vignettes/HarmonizR/inst/doc/HarmonizR_Vignette.html vignetteTitles: HarmonizR_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HarmonizR/inst/doc/HarmonizR_Vignette.R dependencyCount: 108 Package: HDF5Array Version: 1.38.0 Depends: R (>= 3.4), methods, SparseArray (>= 1.7.5), DelayedArray (>= 0.33.5), h5mread (>= 0.99.4) Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.51.2), S4Vectors, IRanges, S4Arrays (>= 1.1.1), rhdf5 Suggests: BiocParallel, GenomicRanges, SummarizedExperiment (>= 1.15.1), h5vcData, ExperimentHub, TENxBrainData, zellkonverter, GenomicFeatures, SingleCellExperiment, DelayedMatrixStats, genefilter, RSpectra, RUnit, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: c712de799df6ff39106a33db555e41fd NeedsCompilation: no Title: HDF5 datasets as array-like objects in R Description: The HDF5Array package is an HDF5 backend for DelayedArray objects. It implements the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects. biocViews: Infrastructure, DataRepresentation, DataImport, Sequencing, RNASeq, Coverage, Annotation, GenomeAnnotation, SingleCell, ImmunoOncology Author: Hervé Pagès [aut, cre] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/HDF5Array VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HDF5Array/issues git_url: https://git.bioconductor.org/packages/HDF5Array git_branch: RELEASE_3_22 git_last_commit: 9bca08f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HDF5Array_1.38.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HDF5Array_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HDF5Array_1.37.0.tgz vignettes: vignettes/HDF5Array/inst/doc/HDF5Array_performance.html vignetteTitles: HDF5Array performance hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDF5Array/inst/doc/HDF5Array_performance.R dependsOnMe: MAGAR, TENxBrainData, TENxPBMCData importsMe: alabaster.matrix, beachmat.hdf5, BgeeDB, biscuiteer, bsseq, Cepo, chihaya, clusterExperiment, CuratedAtlasQueryR, cytomapper, DelayedTensor, DropletUtils, FRASER, GenomicScores, glmGamPoi, GSVA, lemur, LoomExperiment, mariner, methodical, methrix, minfi, MOFA2, netSmooth, orthos, recountmethylation, scmeth, signatureSearch, SpliceWiz, TENxIO, transformGamPoi, vmrseq, xenLite, MafH5.gnomAD.v4.0.GRCh38, curatedTCGAData, HCAData, HCATonsilData, imcdatasets, MerfishData, MethylSeqData, orthosData, scMultiome, SingleCellMultiModal, TabulaMurisSenisData, TumourMethData, ebvcube, rliger suggestsMe: beachmat, BiocGenerics, BiocSklearn, cellxgenedp, DeconvoBuddies, DelayedArray, DelayedMatrixStats, h5mread, iSEE, MAST, mbkmeans, MuData, MultiAssayExperiment, PDATK, QFeatures, S4Arrays, SCArray, scMerge, scran, scry, SparseArray, SummarizedExperiment, zellkonverter, STexampleData, spicyWorkflow, SeuratObject, SpatialDDLS dependencyCount: 25 Package: HDTD Version: 1.44.0 Depends: R (>= 4.1) Imports: stats, Rcpp (>= 1.0.1) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 48e823ead58763b261ecb51e43e47193 NeedsCompilation: yes Title: Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD) Description: Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables. biocViews: DifferentialExpression, Genetics, GeneExpression, Microarray, Sequencing, StatisticalMethod, Software Author: Anestis Touloumis [cre, aut] (ORCID: ), John C. Marioni [aut] (ORCID: ), Simon Tavar\'{e} [aut] (ORCID: ) Maintainer: Anestis Touloumis URL: http://github.com/AnestisTouloumis/HDTD VignetteBuilder: knitr BugReports: http://github.com/AnestisTouloumis/HDTD/issues git_url: https://git.bioconductor.org/packages/HDTD git_branch: RELEASE_3_22 git_last_commit: b83dbae git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HDTD_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HDTD_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HDTD_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HDTD_1.44.0.tgz vignettes: vignettes/HDTD/inst/doc/HDTD.html vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/HDTD.R dependencyCount: 5 Package: hdxmsqc Version: 1.6.0 Depends: R(>= 4.3), QFeatures, S4Vectors, Spectra Imports: dplyr, tidyr, ggplot2, BiocStyle, knitr, methods, grDevices, stats, MsCoreUtils Suggests: RColorBrewer, pheatmap, MASS, patchwork, testthat License: file LICENSE Archs: x64 MD5sum: adef46a1710adc7ce4fc8b4ca9fff202 NeedsCompilation: no Title: An R package for quality Control for hydrogen deuterium exchange mass spectrometry experiments Description: The hdxmsqc package enables us to analyse and visualise the quality of HDX-MS experiments. Either as a final quality check before downstream analysis and publication or as part of a interative procedure to determine the quality of the data. The package builds on the QFeatures and Spectra packages to integrate with other mass-spectrometry data. biocViews: QualityControl,DataImport, Proteomics, MassSpectrometry, Metabolomics Author: Oliver M. Crook [aut, cre] (ORCID: ) Maintainer: Oliver M. Crook URL: http://github.com/ococrook/hdxmsqc VignetteBuilder: knitr BugReports: https://github.com/ococrook/hdxmsqc/issues git_url: https://git.bioconductor.org/packages/hdxmsqc git_branch: RELEASE_3_22 git_last_commit: 5bbfc95 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hdxmsqc_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hdxmsqc_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hdxmsqc_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hdxmsqc_1.6.0.tgz vignettes: vignettes/hdxmsqc/inst/doc/qc-streamlined.html vignetteTitles: Qualityt control for differential hydrogen deuterium exchange mass spectrometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hdxmsqc/inst/doc/qc-streamlined.R dependencyCount: 114 Package: heatmaps Version: 1.34.0 Depends: R (>= 3.5.0) Imports: methods, grDevices, graphics, stats, Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix, Matrix, EBImage, RColorBrewer, BiocGenerics, Seqinfo Suggests: BSgenome.Drerio.UCSC.danRer7, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: bc299657fed4cec55e33ea156f9094a1 NeedsCompilation: no Title: Flexible Heatmaps for Functional Genomics and Sequence Features Description: This package provides functions for plotting heatmaps of genome-wide data across genomic intervals, such as ChIP-seq signals at peaks or across promoters. Many functions are also provided for investigating sequence features. biocViews: Visualization, SequenceMatching, FunctionalGenomics Author: Malcolm Perry Maintainer: Malcolm Perry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/heatmaps git_branch: RELEASE_3_22 git_last_commit: 1fbdefe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/heatmaps_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/heatmaps_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/heatmaps_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/heatmaps_1.34.0.tgz vignettes: vignettes/heatmaps/inst/doc/heatmaps.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/heatmaps/inst/doc/heatmaps.R dependencyCount: 58 Package: Heatplus Version: 3.18.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: 2fc31223abae7097ed60fe4a2a61e26f NeedsCompilation: no Title: Heatmaps with row and/or column covariates and colored clusters Description: Display a rectangular heatmap (intensity plot) of a data matrix. By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. biocViews: Microarray, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: https://github.com/alexploner/Heatplus BugReports: https://github.com/alexploner/Heatplus/issues git_url: https://git.bioconductor.org/packages/Heatplus git_branch: RELEASE_3_22 git_last_commit: d75c2dd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Heatplus_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Heatplus_3.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Heatplus_3.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Heatplus_3.18.0.tgz vignettes: vignettes/Heatplus/inst/doc/annHeatmap.pdf, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf, vignettes/Heatplus/inst/doc/oldHeatplus.pdf vignetteTitles: Annotated and regular heatmaps, Commented package source, Old functions (deprecated) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Heatplus/inst/doc/annHeatmap.R, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R, vignettes/Heatplus/inst/doc/oldHeatplus.R dependsOnMe: phenoTest, tRanslatome, heatmapFlex suggestsMe: mtbls2, RforProteomics dependencyCount: 4 Package: HelloRanges Version: 1.36.0 Depends: methods, BiocGenerics, S4Vectors (>= 0.17.39), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.10), Biostrings (>= 2.41.3), BSgenome, GenomicFeatures (>= 1.31.5), VariantAnnotation (>= 1.19.3), Rsamtools, GenomicAlignments (>= 1.15.7), rtracklayer (>= 1.33.8), Seqinfo, SummarizedExperiment, BiocIO Imports: docopt, stats, tools, utils Suggests: GenomeInfoDb, HelloRangesData, BiocStyle, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: x64 MD5sum: c90a70d43d00c65499d30ff103beaede NeedsCompilation: no Title: Introduce *Ranges to bedtools users Description: Translates bedtools command-line invocations to R code calling functions from the Bioconductor *Ranges infrastructure. This is intended to educate novice Bioconductor users and to compare the syntax and semantics of the two frameworks. biocViews: Sequencing, Annotation, Coverage, GenomeAnnotation, DataImport, SequenceMatching, VariantAnnotation Author: Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/HelloRanges git_branch: RELEASE_3_22 git_last_commit: 8035909 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HelloRanges_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HelloRanges_1.35.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HelloRanges_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HelloRanges_1.36.0.tgz vignettes: vignettes/HelloRanges/inst/doc/tutorial.pdf vignetteTitles: HelloRanges Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HelloRanges/inst/doc/tutorial.R importsMe: OMICsPCA suggestsMe: plyranges dependencyCount: 79 Package: HELP Version: 1.68.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) Archs: x64 MD5sum: b535df2e5b7788524fb47eaad43da1f8 NeedsCompilation: no Title: Tools for HELP data analysis Description: The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications. biocViews: CpGIsland, DNAMethylation, Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, Visualization Author: Reid F. Thompson , John M. Greally , with contributions from Mark Reimers Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/HELP git_branch: RELEASE_3_22 git_last_commit: e5034bf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HELP_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HELP_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HELP_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HELP_1.68.0.tgz vignettes: vignettes/HELP/inst/doc/HELP.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HELP/inst/doc/HELP.R dependencyCount: 8 Package: HEM Version: 1.82.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) Archs: x64 MD5sum: 72ff71e5cae74145120758b61835852c NeedsCompilation: yes Title: Heterogeneous error model for identification of differentially expressed genes under multiple conditions Description: This package fits heterogeneous error models for analysis of microarray data biocViews: Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: HyungJun Cho URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/HEM git_branch: RELEASE_3_22 git_last_commit: 112d4da git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HEM_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HEM_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HEM_1.82.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: hermes Version: 1.14.0 Depends: ggfortify, R (>= 4.1), SummarizedExperiment (>= 1.16) Imports: assertthat, Biobase, BiocGenerics, biomaRt, checkmate (>= 2.1), circlize, ComplexHeatmap, DESeq2, dplyr, edgeR, EnvStats, forcats (>= 1.0.0), GenomicRanges, ggplot2, ggrepel (>= 0.9), IRanges, limma, magrittr, matrixStats (>= 1.5.0), methods, MultiAssayExperiment, purrr, R6, Rdpack (>= 2.6.2), rlang, S4Vectors, stats, tidyr, utils Suggests: BiocStyle, DelayedArray, DT, grid, httr, knitr, rmarkdown, statmod, testthat (>= 3.2.2), vdiffr (>= 1.0.8) License: Apache License 2.0 MD5sum: 0abd0bc1ceb0dfed7cec74febaffa617 NeedsCompilation: no Title: Preprocessing, analyzing, and reporting of RNA-seq data Description: Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed `RNA-seq` data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from `BioMart`. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including cpm, rpkm and tpm can be used, and 'DESeq2` as well as voom differential expression analyses are available. biocViews: RNASeq, DifferentialExpression, Normalization, Preprocessing, QualityControl Author: Daniel Sabanés Bové [aut, cre], Namrata Bhatia [aut], Stefanie Bienert [aut], Benoit Falquet [aut], Haocheng Li [aut], Jeff Luong [aut], Lyndsee Midori Zhang [aut], Alex Richardson [aut], Simona Rossomanno [aut], Tim Treis [aut], Mark Yan [aut], Naomi Chang [aut], Chendi Liao [aut], Carolyn Zhang [aut], Joseph N. Paulson [aut], F. Hoffmann-La Roche AG [cph, fnd] Maintainer: Daniel Sabanés Bové URL: https://insightsengineering.github.io/hermes/ VignetteBuilder: knitr BugReports: https://github.com/insightsengineering/hermes/issues git_url: https://git.bioconductor.org/packages/hermes git_branch: RELEASE_3_22 git_last_commit: b415d55 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hermes_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hermes_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hermes_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hermes_1.14.0.tgz vignettes: vignettes/hermes/inst/doc/hermes.html vignetteTitles: Introduction to `hermes` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hermes/inst/doc/hermes.R dependencyCount: 127 Package: HERON Version: 1.8.0 Depends: R (>= 4.4.0), SummarizedExperiment (>= 1.1.6), GenomicRanges, IRanges, S4Vectors Imports: matrixStats, stats, data.table, harmonicmeanp, metap, cluster, spdep, Matrix, limma, methods Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: 344f8f82fadc70fd2f8a9bb2046e9f63 NeedsCompilation: no Title: Hierarchical Epitope pROtein biNding Description: HERON is a software package for analyzing peptide binding array data. In addition to identifying significant binding probes, HERON also provides functions for finding epitopes (string of consecutive peptides within a protein). HERON also calculates significance on the probe, epitope, and protein level by employing meta p-value methods. HERON is designed for obtaining calls on the sample level and calculates fractions of hits for different conditions. biocViews: Microarray, Software Author: Sean McIlwain [aut, cre] (ORCID: ), Irene Ong [aut] (ORCID: ) Maintainer: Sean McIlwain URL: https://github.com/Ong-Research/HERON VignetteBuilder: knitr BugReports: https://github.com/Ong-Research/HERON/issues git_url: https://git.bioconductor.org/packages/HERON git_branch: RELEASE_3_22 git_last_commit: 571b710 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HERON_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HERON_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HERON_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HERON_1.8.0.tgz vignettes: vignettes/HERON/inst/doc/full_analysis.html vignetteTitles: Analyzing High Density Peptide Array Data using HERON hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HERON/inst/doc/full_analysis.R dependencyCount: 74 Package: Herper Version: 1.20.0 Depends: R (>= 4.0), reticulate Imports: utils, rjson, withr, stats Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: a5041c4e3ff53e8c7fa4d961f63545cc NeedsCompilation: no Title: The Herper package is a simple toolset to install and manage conda packages and environments from R Description: Many tools for data analysis are not available in R, but are present in public repositories like conda. The Herper package provides a comprehensive set of functions to interact with the conda package managament system. With Herper users can install, manage and run conda packages from the comfort of their R session. Herper also provides an ad-hoc approach to handling external system requirements for R packages. For people developing packages with python conda dependencies we recommend using basilisk (https://bioconductor.org/packages/release/bioc/html/basilisk.html) to internally support these system requirments pre-hoc. biocViews: Infrastructure, Software Author: Matt Paul [aut] (ORCID: ), Thomas Carroll [aut, cre] (ORCID: ), Doug Barrows [aut], Kathryn Rozen-Gagnon [ctb] Maintainer: Thomas Carroll URL: https://github.com/RockefellerUniversity/Herper VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Herper git_branch: RELEASE_3_22 git_last_commit: b9032f3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Herper_1.20.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Herper_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Herper_1.20.0.tgz vignettes: vignettes/Herper/inst/doc/QuickStart.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Herper/inst/doc/QuickStart.R dependencyCount: 19 Package: HGC Version: 1.18.0 Depends: R (>= 4.1.0) Imports: Rcpp (>= 1.0.0), RcppEigen(>= 0.3.2.0), Matrix, RANN, ape, dendextend, ggplot2, mclust, patchwork, dplyr, grDevices, methods, stats LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: b84ba71aae97c3d2b8cdaf13cc54192b NeedsCompilation: yes Title: A fast hierarchical graph-based clustering method Description: HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6. biocViews: SingleCell, Software, Clustering, RNASeq, GraphAndNetwork, DNASeq Author: Zou Ziheng [aut], Hua Kui [aut], XGlab [cre, cph] Maintainer: XGlab SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HGC git_branch: RELEASE_3_22 git_last_commit: e944a08 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HGC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HGC_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HGC_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HGC_1.18.0.tgz vignettes: vignettes/HGC/inst/doc/HGC.html vignetteTitles: HGC package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HGC/inst/doc/HGC.R dependencyCount: 45 Package: HIBAG Version: 1.46.0 Depends: R (>= 3.2.0) Imports: methods, RcppParallel LinkingTo: RcppParallel (>= 5.0.0) Suggests: parallel, ggplot2, reshape2, gdsfmt, SNPRelate, SeqArray, knitr, markdown, rmarkdown, Rsamtools License: GPL-3 MD5sum: e3dca1f2ac222aeb104fbe628bdda6f9 NeedsCompilation: yes Title: HLA Genotype Imputation with Attribute Bagging Description: Imputes HLA classical alleles using GWAS SNP data, and it relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection. biocViews: Genetics, StatisticalMethod Author: Xiuwen Zheng [aut, cre, cph] (ORCID: ), Bruce Weir [ctb, ths] (ORCID: ) Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/HIBAG, https://hibag.s3.amazonaws.com/index.html SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIBAG git_branch: RELEASE_3_22 git_last_commit: 2750cec git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HIBAG_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HIBAG_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HIBAG_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HIBAG_1.46.0.tgz vignettes: vignettes/HIBAG/inst/doc/HIBAG.html, vignettes/HIBAG/inst/doc/HLA_Association.html, vignettes/HIBAG/inst/doc/Implementation.html vignetteTitles: HIBAG vignette html, HLA association vignette html, HIBAG algorithm implementation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIBAG/inst/doc/HIBAG.R, vignettes/HIBAG/inst/doc/HLA_Association.R, vignettes/HIBAG/inst/doc/Implementation.R dependencyCount: 2 Package: HicAggR Version: 1.6.0 Depends: R (>= 4.2.0) Imports: InteractionSet, BiocGenerics, BiocParallel, dplyr, Seqinfo, GenomicRanges, ggplot2, grDevices, IRanges, Matrix, methods, rhdf5, rlang, rtracklayer, S4Vectors, stats, utils, strawr, tibble, stringr, tidyr, gridExtra, data.table, reshape, checkmate, purrr, withr Suggests: GenomeInfoDb, covr, tools, kableExtra (>= 1.3.4), knitr (>= 1.45), rmarkdown, testthat (>= 3.0.0), BiocFileCache (>= 2.6.1) License: MIT + file LICENSE MD5sum: 375e6f14a0d16d464b7dcfba388da8d9 NeedsCompilation: no Title: Set of 3D genomic interaction analysis tools Description: This package provides a set of functions useful in the analysis of 3D genomic interactions. It includes the import of standard HiC data formats into R and HiC normalisation procedures. The main objective of this package is to improve the visualization and quantification of the analysis of HiC contacts through aggregation. The package allows to import 1D genomics data, such as peaks from ATACSeq, ChIPSeq, to create potential couples between features of interest under user-defined parameters such as distance between pairs of features of interest. It allows then the extraction of contact values from the HiC data for these couples and to perform Aggregated Peak Analysis (APA) for visualization, but also to compare normalized contact values between conditions. Overall the package allows to integrate 1D genomics data with 3D genomics data, providing an easy access to HiC contact values. biocViews: Software, HiC, DataImport, DataRepresentation, Normalization, Visualization, DNA3DStructure, ATACSeq, ChIPSeq, DNaseSeq, RNASeq Author: Robel Tesfaye [aut, ctb] (ORCID: ), David Depierre [aut], Naomi Schickele [ctb], Nicolas Chanard [aut], Refka Askri [ctb], Stéphane Schaak [aut, ctb], Pascal Martin [ctb], Olivier Cuvier [cre, ctb] (ORCID: ) Maintainer: Olivier Cuvier URL: https://bioconductor.org/packages/HicAggR, https://cuvierlab.github.io/HicAggR/, https://github.com/CuvierLab/HicAggR VignetteBuilder: knitr BugReports: https://github.com/CuvierLab/HicAggR/issues git_url: https://git.bioconductor.org/packages/HicAggR git_branch: RELEASE_3_22 git_last_commit: 8551a70 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HicAggR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HicAggR_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HicAggR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HicAggR_1.6.0.tgz vignettes: vignettes/HicAggR/inst/doc/HicAggR.html vignetteTitles: HicAggR - In depth tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HicAggR/inst/doc/HicAggR.R dependencyCount: 95 Package: HiCaptuRe Version: 1.0.0 Depends: R (>= 4.5.0) Imports: Biostrings, BSgenome, cli, data.table, dplyr, GenomeInfoDb, GenomicInteractions, GenomicRanges, InteractionSet, ggplot2, ggpubr, ggVennDiagram, gplots, igraph, IRanges, memoise, methods, S4Vectors, stringr, tibble, tidyr, UpSetR, utils Suggests: BSgenome.Hsapiens.NCBI.GRCh38, knitr, rmarkdown, DT, testthat, BiocStyle, kableExtra License: GPL-3 MD5sum: 767ca9948f155ff49a0d9eed05e1d392 NeedsCompilation: no Title: HiCaptuRe: Manipulating and integrating Capture Hi-C data Description: Capture Hi-C is a set of techniques that enable the detection of genomic interactions involving regions of interest, known as baits. By focusing on selected loci, these approaches reduce sequencing costs while maintaining high resolution at the level of restriction fragments. HiCaptuRe provides tools to import, annotate, manipulate, and export Capture Hi-C data. The package accounts for the specific structure of bait–otherEnd interactions, facilitates integration with other omics datasets, and enables comparison across samples and conditions. biocViews: Epigenetics, HiC, Sequencing, DataImport, Software Author: Laureano Tomas-Daza [aut, cre] (ORCID: ) Maintainer: Laureano Tomas-Daza URL: https://github.com/LaureTomas/HiCaptuRe VignetteBuilder: knitr BugReports: https://github.com/LaureTomas/HiCaptuRe/issues git_url: https://git.bioconductor.org/packages/HiCaptuRe git_branch: RELEASE_3_22 git_last_commit: 0a37211 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HiCaptuRe_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCaptuRe_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCaptuRe_1.0.0.tgz vignettes: vignettes/HiCaptuRe/inst/doc/vignette_functions.html, vignettes/HiCaptuRe/inst/doc/vignetteIntroduction.html vignetteTitles: Functions, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCaptuRe/inst/doc/vignette_functions.R, vignettes/HiCaptuRe/inst/doc/vignetteIntroduction.R dependencyCount: 202 Package: HiCBricks Version: 1.28.0 Depends: R (>= 3.6), utils, curl, rhdf5, R6, grid Imports: ggplot2, viridis, RColorBrewer, scales, reshape2, stringr, data.table, Seqinfo, GenomicRanges, stats, IRanges, grDevices, S4Vectors, digest, tibble, jsonlite, BiocParallel, R.utils, readr, methods Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 7efb13f2568fbc2fbeca0cdb1bb54a83 NeedsCompilation: no Title: Framework for Storing and Accessing Hi-C Data Through HDF Files Description: HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization. biocViews: DataImport, Infrastructure, Software, Technology, Sequencing, HiC Author: Koustav Pal [aut, cre], Carmen Livi [ctb], Ilario Tagliaferri [ctb] Maintainer: Koustav Pal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCBricks git_branch: RELEASE_3_22 git_last_commit: 3296656 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HiCBricks_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCBricks_1.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCBricks_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCBricks_1.28.0.tgz vignettes: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.html vignetteTitles: IntroductionToHiCBricks.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.R importsMe: bnbc dependencyCount: 73 Package: HiCcompare Version: 1.32.0 Depends: R (>= 3.5.0), dplyr Imports: data.table, ggplot2, gridExtra, mgcv, stats, InteractionSet, GenomicRanges, IRanges, S4Vectors, BiocParallel, KernSmooth, methods, utils, graphics, pheatmap, gtools, rhdf5 Suggests: knitr, rmarkdown, testthat, multiHiCcompare License: MIT + file LICENSE MD5sum: ab752c6d14325ca2516759965853c068 NeedsCompilation: no Title: HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets Description: HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets. biocViews: Software, HiC, Sequencing, Normalization Author: Mikhail Dozmorov [aut, cre] (ORCID: ), Kellen Cresswell [aut], John Stansfield [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/HiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/HiCcompare/issues git_url: https://git.bioconductor.org/packages/HiCcompare git_branch: RELEASE_3_22 git_last_commit: 9add140 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HiCcompare_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCcompare_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCcompare_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCcompare_1.32.0.tgz vignettes: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.html vignetteTitles: HiCcompare Usage Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.R importsMe: multiHiCcompare, scHiCcompare, SpectralTAD, TADCompare dependencyCount: 71 Package: HiCDCPlus Version: 1.18.0 Imports: Rcpp,InteractionSet,GenomicInteractions,bbmle,pscl,BSgenome,data.table,dplyr,tidyr,GenomeInfoDb,rlang,splines,MASS,GenomicRanges,IRanges,tibble,R.utils,Biostrings,rtracklayer,methods,S4Vectors LinkingTo: Rcpp Suggests: BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, RUnit, BiocGenerics, knitr, rmarkdown, HiTC, DESeq2, Matrix, BiocFileCache, rappdirs Enhances: parallel License: GPL-3 MD5sum: e815dda7bdce4f1f1e04e7539ca682f9 NeedsCompilation: yes Title: Hi-C Direct Caller Plus Description: Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw contact matrix for each chromosome up to a specified genomic distance, binned by uniform genomic intervals or restriction enzyme fragments, by training a background model to account for random polymer ligation and systematic sources of read count variation. biocViews: HiC, DNA3DStructure, Software, Normalization Author: Merve Sahin [cre, aut] (ORCID: ) Maintainer: Merve Sahin SystemRequirements: JRE 8+ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCDCPlus git_branch: RELEASE_3_22 git_last_commit: 2c8bd39 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HiCDCPlus_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCDCPlus_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCDCPlus_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCDCPlus_1.18.0.tgz vignettes: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.html vignetteTitles: Analyzing Hi-C and HiChIP data with HiCDCPlus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.R dependencyCount: 165 Package: HiCDOC Version: 1.12.0 Depends: InteractionSet, GenomicRanges, SummarizedExperiment, R (>= 4.1.0) Imports: methods, ggplot2, Rcpp (>= 0.12.8), stats, S4Vectors, gtools, pbapply, BiocParallel, BiocGenerics, grid, cowplot, gridExtra, data.table, multiHiCcompare, Seqinfo LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, BiocManager, rhdf5 License: LGPL-3 MD5sum: cfcb2314dd201a51996b729bf42e95b9 NeedsCompilation: yes Title: A/B compartment detection and differential analysis Description: HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular `.tsv` files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files. biocViews: HiC, DNA3DStructure, Normalization, Sequencing, Software, Clustering Author: Kurylo Cyril [aut], Zytnicki Matthias [aut], Foissac Sylvain [aut], Maigné Élise [aut, cre] Maintainer: Maigné Élise URL: https://github.com/mzytnicki/HiCDOC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/mzytnicki/HiCDOC/issues git_url: https://git.bioconductor.org/packages/HiCDOC git_branch: RELEASE_3_22 git_last_commit: 814d8c7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HiCDOC_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCDOC_1.11.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCDOC_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCDOC_1.12.0.tgz vignettes: vignettes/HiCDOC/inst/doc/HiCDOC.html vignetteTitles: HiCDOC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCDOC/inst/doc/HiCDOC.R importsMe: treediff dependencyCount: 93 Package: HiCExperiment Version: 1.10.0 Depends: R (>= 4.2) Imports: InteractionSet, strawr, Seqinfo, GenomicRanges, IRanges, S4Vectors, BiocGenerics, BiocIO, BiocParallel, methods, rhdf5, Matrix, vroom, dplyr, stats Suggests: HiContacts, HiContactsData, BiocFileCache, rtracklayer, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: cb041cdc5e77b16c4b801286ba504936 NeedsCompilation: no Title: Bioconductor class for interacting with Hi-C files in R Description: R generic interface to Hi-C contact matrices in `.(m)cool`, `.hic` or HiC-Pro derived formats, as well as other Hi-C processed file formats. Contact matrices can be partially parsed using a random access method, allowing a memory-efficient representation of Hi-C data in R. The `HiCExperiment` class stores the Hi-C contacts parsed from local contact matrix files. `HiCExperiment` instances can be further investigated in R using the `HiContacts` analysis package. biocViews: HiC, DNA3DStructure, DataImport Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/HiCExperiment VignetteBuilder: knitr BugReports: https://github.com/js2264/HiCExperiment/issues git_url: https://git.bioconductor.org/packages/HiCExperiment git_branch: RELEASE_3_22 git_last_commit: 7a770a4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HiCExperiment_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCExperiment_1.9.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCExperiment_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCExperiment_1.10.0.tgz vignettes: vignettes/HiCExperiment/inst/doc/HiCExperiment.html vignetteTitles: Introduction to HiCExperiment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCExperiment/inst/doc/HiCExperiment.R dependsOnMe: HiContacts, DNAZooData importsMe: fourDNData, OHCA dependencyCount: 64 Package: HiContacts Version: 1.12.0 Depends: R (>= 4.2), HiCExperiment Imports: InteractionSet, SummarizedExperiment, GenomicRanges, IRanges, GenomeInfoDb, S4Vectors, methods, BiocGenerics, BiocIO, BiocParallel, RSpectra, Matrix, tibble, tidyr, dplyr, readr, stringr, ggplot2, ggrastr, scales, stats, utils Suggests: HiContactsData, rtracklayer, GenomicFeatures, Biostrings, BSgenome.Scerevisiae.UCSC.sacCer3, WGCNA, Rfast, terra, patchwork, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 7d4443c991d50015c9beef48ec6230ee NeedsCompilation: no Title: Analysing cool files in R with HiContacts Description: HiContacts provides a collection of tools to analyse and visualize Hi-C datasets imported in R by HiCExperiment. biocViews: HiC, DNA3DStructure Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/HiContacts VignetteBuilder: knitr BugReports: https://github.com/js2264/HiContacts/issues git_url: https://git.bioconductor.org/packages/HiContacts git_branch: RELEASE_3_22 git_last_commit: 16d8499 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HiContacts_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiContacts_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiContacts_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiContacts_1.12.0.tgz vignettes: vignettes/HiContacts/inst/doc/HiContacts.html vignetteTitles: Introduction to HiContacts hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiContacts/inst/doc/HiContacts.R importsMe: OHCA suggestsMe: HiCExperiment dependencyCount: 101 Package: HiCParser Version: 1.2.0 Imports: data.table, InteractionSet, GenomicRanges, SummarizedExperiment, Rcpp (>= 1.0.12), S4Vectors, gtools, pbapply, BiocGenerics, Seqinfo LinkingTo: Rcpp Suggests: rhdf5, BiocStyle, knitr, sessioninfo, testthat (>= 3.0.0) License: LGPL MD5sum: dd813eb182ef5b62d5537d1b7829ede4 NeedsCompilation: yes Title: Parser for HiC data in R Description: This package is a parser to import HiC data into R. It accepts several type of data: tabular files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files. The HiC data can be several files, for several replicates and conditions. The data is formated in an InteractionSet object. biocViews: Software, HiC, DataImport Author: Zytnicki Matthias [aut], Maigné Élise [aut, cre] Maintainer: Maigné Élise URL: https://github.com/emaigne/HiCParser VignetteBuilder: knitr BugReports: https://github.com/emaigne/HiCParser/issues git_url: https://git.bioconductor.org/packages/HiCParser git_branch: RELEASE_3_22 git_last_commit: c089341 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HiCParser_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCParser_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCParser_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCParser_1.2.0.tgz vignettes: vignettes/HiCParser/inst/doc/HiCParser.html vignetteTitles: Introduction to HiCParser hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCParser/inst/doc/HiCParser.R dependencyCount: 31 Package: HiCPotts Version: 1.0.0 Depends: R (>= 4.5) Imports: Rcpp(>= 0.11.0), parallel, stats, Biostrings, GenomicRanges, rtracklayer, strawr, rhdf5, BSgenome,IRanges, S4Vectors, BSgenome.Dmelanogaster.UCSC.dm6 LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr (>= 1.30), rmarkdown (>= 2.10), ggplot2 (>= 3.5.0), reshape2 (>= 1.4.4), testthat (>= 3.0.0), BiocManager License: GPL-3 MD5sum: 7456c94b3d76cfd65b6a907d11d61c3d NeedsCompilation: yes Title: HiCPotts: Hierarchical Modeling to Identify and Correct Genomic Biases in Hi-C Description: The HiCPotts package provides a comprehensive Bayesian framework for analyzing Hi-C interaction data, integrating both spatial and genomic biases within a probabilistic modeling framework. At its core, HiCPotts leverages the Potts model (Wu, 1982)—a well-established graphical model—to capture and quantify spatial dependencies across interaction loci arranged on a genomic lattice. By treating each interaction as a spatially correlated random variable, the Potts model enables robust segmentation of the genomic landscape into meaningful components, such as noise, true signals, and false signals. To model the influence of various genomic biases, HiCPotts employs a regression-based approach incorporating multiple covariates: Genomic distance (D): The distance between interacting loci, recognized as a fundamental driver of contact frequency. GC-content (GC): The local GC composition around the interacting loci, which can influence chromatin structure and interaction patterns. Transposable elements (TEs): The presence and abundance of repetitive elements that may shape contact probability through chromatin organization. Accessibility score (Acc): A measure of chromatin openness, informing how accessible certain genomic regions are to interaction. By embedding these covariates into a hierarchical mixture model, HiCPotts characterizes each interaction’s probability of belonging to one of several latent components. The model parameters, including regression coefficients, zero-inflation parameters (for ZIP/ZINB distributions), and dispersion terms (for NB/ZINB distributions), are inferred via a MCMC sampler. This algorithm draws samples from the joint posterior distribution, allowing for flexible posterior inference on model parameters and hidden states. From these posterior samples, HiCPotts computes posterior means of regression parameters and other quantities of interest. These posterior estimates are then used to calculate the posterior probabilities that assign each interaction to a specific component. The resulting classification sheds light on the underlying structure: distinguishing genuine high-confidence interactions (signal) from background noise and potential false signals, while simultaneously quantifying the impact of genomic biases on observed interaction frequencies. In summary, HiCPotts seamlessly integrates spatial modeling, bias correction, and probabilistic classification into a unified Bayesian inference framework. It provides rich posterior summaries and interpretable, model-based assignments of interaction states, enabling researchers to better understand the interplay between genomic organization, biases, and spatial correlation in Hi-C data. biocViews: StatisticalMethod, FunctionalGenomics, GenomeAnnotation, GenomeWideAssociation, PeakDetection, DataImport, Spatial, Bayesian, Classification, HiddenMarkovModel, Regression Author: Itunu. Godwin Osuntoki [aut, cre] (ORCID: ), Nicolae. Radu Zabet [aut] Maintainer: Itunu. Godwin Osuntoki URL: https://github.com/igosungithub/HiCPotts VignetteBuilder: knitr BugReports: https://github.com/igosungithub/HiCPotts/issues git_url: https://git.bioconductor.org/packages/HiCPotts git_branch: RELEASE_3_22 git_last_commit: d5f20b8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HiCPotts_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCPotts_0.99.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCPotts_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCPotts_1.0.0.tgz vignettes: vignettes/HiCPotts/inst/doc/HiCPotts_vignette.html vignetteTitles: Bayesian Analysis of Hi-C Interactions with HiCPotts hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCPotts/inst/doc/HiCPotts_vignette.R dependencyCount: 65 Package: hicVennDiagram Version: 1.8.0 Depends: R (>= 4.3.0) Imports: Seqinfo, GenomicRanges, IRanges, InteractionSet, rtracklayer, ggplot2, ComplexUpset, reshape2, eulerr, S4Vectors, methods, utils, htmlwidgets, svglite Suggests: BiocStyle, knitr, rmarkdown, testthat, ChIPpeakAnno, grid, TxDb.Hsapiens.UCSC.hg38.knownGene License: GPL-3 MD5sum: d32b8e8dbbdcc7f68e03d2f811f559b8 NeedsCompilation: no Title: Venn Diagram for genomic interaction data Description: A package to generate high-resolution Venn and Upset plots for genomic interaction data from HiC, ChIA-PET, HiChIP, PLAC-Seq, Hi-TrAC, HiCAR and etc. The package generates plots specifically crafted to eliminate the deceptive visual representation caused by the counts method. biocViews: DNA3DStructure, HiC, Visualization Author: Jianhong Ou [aut, cre] (ORCID: ) Maintainer: Jianhong Ou URL: https://github.com/jianhong/hicVennDiagram VignetteBuilder: knitr BugReports: https://github.com/jianhong/hicVennDiagram/issues git_url: https://git.bioconductor.org/packages/hicVennDiagram git_branch: RELEASE_3_22 git_last_commit: 33c3f4f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hicVennDiagram_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hicVennDiagram_1.7.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hicVennDiagram_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hicVennDiagram_1.8.0.tgz vignettes: vignettes/hicVennDiagram/inst/doc/hicVennDiagram.html vignetteTitles: hicVennDiagram Vignette: overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hicVennDiagram/inst/doc/hicVennDiagram.R dependencyCount: 109 Package: hierGWAS Version: 1.40.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 Archs: x64 MD5sum: 819038784f8485b6f64542e88358b047 NeedsCompilation: no Title: Asessing statistical significance in predictive GWA studies Description: Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers. biocViews: SNP, LinkageDisequilibrium, Clustering Author: Laura Buzdugan Maintainer: Laura Buzdugan git_url: https://git.bioconductor.org/packages/hierGWAS git_branch: RELEASE_3_22 git_last_commit: b0d3d26 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hierGWAS_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hierGWAS_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hierGWAS_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hierGWAS_1.40.0.tgz vignettes: vignettes/hierGWAS/inst/doc/hierGWAS.pdf vignetteTitles: User manual for R-Package hierGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hierGWAS/inst/doc/hierGWAS.R dependencyCount: 19 Package: hierinf Version: 1.28.0 Depends: R (>= 3.6.0) Imports: fmsb, glmnet, methods, parallel, stats Suggests: knitr, MASS, testthat License: GPL-3 | file LICENSE MD5sum: f6cd088b69b7b48de408d64d069cb46c NeedsCompilation: no Title: Hierarchical Inference Description: Tools to perform hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised) linear models. A possible application is to perform hierarchical inference for GWA studies to find significant groups or single SNPs (if the signal is strong) in a data-driven and automated procedure. The method is based on an efficient hierarchical multiple testing correction and controls the FWER. The functions can easily be run in parallel. biocViews: Clustering, GenomeWideAssociation, LinkageDisequilibrium, Regression, SNP Author: Claude Renaux [aut, cre], Laura Buzdugan [aut], Markus Kalisch [aut], Peter Bühlmann [aut] Maintainer: Claude Renaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hierinf git_branch: RELEASE_3_22 git_last_commit: 2e6b0a4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hierinf_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hierinf_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hierinf_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hierinf_1.28.0.tgz vignettes: vignettes/hierinf/inst/doc/vignette-hierinf.pdf vignetteTitles: vignette-hierinf.Rnw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hierinf/inst/doc/vignette-hierinf.R dependencyCount: 19 Package: HilbertCurve Version: 2.4.0 Depends: R (>= 4.0.0), grid Imports: methods, utils, png, grDevices, circlize (>= 0.3.3), IRanges, GenomicRanges, polylabelr, Rcpp LinkingTo: Rcpp Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.99.0), markdown, RColorBrewer, RCurl, GetoptLong, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 12a06586b94b075aea9d2a51d69163c0 NeedsCompilation: yes Title: Making 2D Hilbert Curve Description: Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve. biocViews: Software, Visualization, Sequencing, Coverage, GenomeAnnotation Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/HilbertCurve, https://jokergoo.github.io/HilbertCurve/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HilbertCurve git_branch: RELEASE_3_22 git_last_commit: eb5f665 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HilbertCurve_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HilbertCurve_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HilbertCurve_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HilbertCurve_2.4.0.tgz vignettes: vignettes/HilbertCurve/inst/doc/HilbertCurve.html vignetteTitles: The HilbertCurve package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: InteractiveComplexHeatmap dependencyCount: 20 Package: HilbertVis Version: 1.68.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) MD5sum: c191531d73c5eff175ec8a552f554f8d NeedsCompilation: yes Title: Hilbert curve visualization Description: Functions to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert git_url: https://git.bioconductor.org/packages/HilbertVis git_branch: RELEASE_3_22 git_last_commit: 64d4b47 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HilbertVis_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HilbertVis_1.67.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HilbertVis_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HilbertVis_1.68.0.tgz vignettes: vignettes/HilbertVis/inst/doc/HilbertVis.pdf vignetteTitles: Visualising very long data vectors with the Hilbert curve hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HilbertVis/inst/doc/HilbertVis.R dependsOnMe: HilbertVisGUI importsMe: ChIPseqR dependencyCount: 6 Package: HilbertVisGUI Version: 1.68.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) MD5sum: 13d308972f2c8a5952c560c10fb22fc2 NeedsCompilation: yes Title: HilbertVisGUI Description: An interactive tool to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert SystemRequirements: gtkmm-2.4, GNU make git_url: https://git.bioconductor.org/packages/HilbertVisGUI git_branch: RELEASE_3_22 git_last_commit: 242ca58 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HilbertVisGUI_1.68.0.tar.gz vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE dependencyCount: 7 Package: HiLDA Version: 1.24.0 Depends: R(>= 4.1), ggplot2 Imports: R2jags, abind, cowplot, grid, forcats, stringr, GenomicRanges, S4Vectors, XVector, Biostrings, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, BiocGenerics, tidyr, grDevices, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: x64 MD5sum: 7db8a2e955c57c70b689f074ad0249ae NeedsCompilation: yes Title: Conducting statistical inference on comparing the mutational exposures of mutational signatures by using hierarchical latent Dirichlet allocation Description: A package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation. It statistically tests whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. The package also provides inference and visualization. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Bayesian Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/HiLDA, https://doi.org/10.1101/577452 SystemRequirements: JAGS 4.0.0 VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/issues git_url: https://git.bioconductor.org/packages/HiLDA git_branch: RELEASE_3_22 git_last_commit: 6a179d6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HiLDA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiLDA_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiLDA_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiLDA_1.24.0.tgz vignettes: vignettes/HiLDA/inst/doc/HiLDA.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/HiLDA/inst/doc/HiLDA.R importsMe: selectKSigs dependencyCount: 107 Package: HIPPO Version: 1.22.0 Depends: R (>= 3.6.0) Imports: ggplot2, graphics, stats, reshape2, gridExtra, Rtsne, umap, dplyr, rlang, magrittr, irlba, Matrix, SingleCellExperiment, ggrepel Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 0366c2a123a92985c7b5d62e26fff280 NeedsCompilation: no Title: Heterogeneity-Induced Pre-Processing tOol Description: For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering. biocViews: Sequencing, SingleCell, GeneExpression, DifferentialExpression, Clustering Author: Tae Kim [aut, cre], Mengjie Chen [aut] Maintainer: Tae Kim URL: https://github.com/tk382/HIPPO VignetteBuilder: knitr BugReports: https://github.com/tk382/HIPPO/issues git_url: https://git.bioconductor.org/packages/HIPPO git_branch: RELEASE_3_22 git_last_commit: d76a6fb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HIPPO_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HIPPO_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HIPPO_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HIPPO_1.22.0.tgz vignettes: vignettes/HIPPO/inst/doc/example.html vignetteTitles: Example analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIPPO/inst/doc/example.R dependencyCount: 71 Package: HIREewas Version: 1.28.0 Depends: R (>= 3.5.0) Imports: quadprog, gplots, grDevices, stats Suggests: BiocStyle, knitr, BiocGenerics License: GPL (>= 2) MD5sum: dcd23984637e45a1d423e4301c972aef NeedsCompilation: yes Title: Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies Description: In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. The "HIREewas" R package is to implement HIRE model in R. biocViews: DNAMethylation, DifferentialMethylation, FeatureExtraction Author: Xiangyu Luo , Can Yang , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIREewas git_branch: RELEASE_3_22 git_last_commit: 97c294e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HIREewas_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HIREewas_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HIREewas_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HIREewas_1.28.0.tgz vignettes: vignettes/HIREewas/inst/doc/HIREewas.pdf vignetteTitles: HIREewas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIREewas/inst/doc/HIREewas.R dependencyCount: 10 Package: HiTC Version: 1.54.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix, parallel, Seqinfo Suggests: BiocStyle, HiCDataHumanIMR90, BSgenome.Hsapiens.UCSC.hg18 License: Artistic-2.0 MD5sum: 26cee0a596dc171e10fd423f0b475124 NeedsCompilation: no Title: High Throughput Chromosome Conformation Capture analysis Description: The HiTC package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided. biocViews: Sequencing, HighThroughputSequencing, HiC Author: Nicolas Servant Maintainer: Nicolas Servant git_url: https://git.bioconductor.org/packages/HiTC git_branch: RELEASE_3_22 git_last_commit: f8056e1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HiTC_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiTC_1.53.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiTC_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiTC_1.54.0.tgz vignettes: vignettes/HiTC/inst/doc/HiC_analysis.pdf, vignettes/HiTC/inst/doc/HiTC.pdf vignetteTitles: Hi-C data analysis using HiTC, Introduction to HiTC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiTC/inst/doc/HiC_analysis.R, vignettes/HiTC/inst/doc/HiTC.R suggestsMe: HiCDCPlus, HiCDataHumanIMR90, adjclust dependencyCount: 58 Package: hmdbQuery Version: 1.29.0 Depends: R (>= 3.5), XML Imports: S4Vectors, methods, utils Suggests: knitr, annotate, gwascat, testthat, rmarkdown License: Artistic-2.0 MD5sum: 479de2174310c608d557638bfe8d03a1 NeedsCompilation: no Title: utilities for exploration of human metabolome database Description: Define utilities for exploration of human metabolome database, including functions to retrieve specific metabolite entries and data snapshots with pairwise associations (metabolite-gene,-protein,-disease). biocViews: Metabolomics, Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hmdbQuery git_branch: devel git_last_commit: 51dfaa9 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-08 source.ver: src/contrib/hmdbQuery_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hmdbQuery_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hmdbQuery_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hmdbQuery_1.30.0.tgz vignettes: vignettes/hmdbQuery/inst/doc/hmdbQuery.html vignetteTitles: hmdbQuery: working with Human Metabolome Database (hmdb.ca) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hmdbQuery/inst/doc/hmdbQuery.R dependencyCount: 9 Package: HMMcopy Version: 1.52.0 Depends: R (>= 2.10.0), data.table (>= 1.11.8) License: GPL-3 MD5sum: 3e4f34694f1cbcee404682b7da942d1e NeedsCompilation: yes Title: Copy number prediction with correction for GC and mappability bias for HTS data Description: Corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for furthur analysis. Designed for rapid correction of high coverage whole genome tumour and normal samples. biocViews: Sequencing, Preprocessing, Visualization, CopyNumberVariation, Microarray Author: Daniel Lai, Gavin Ha, Sohrab Shah Maintainer: Daniel Lai git_url: https://git.bioconductor.org/packages/HMMcopy git_branch: RELEASE_3_22 git_last_commit: 6e596f0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HMMcopy_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HMMcopy_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HMMcopy_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HMMcopy_1.52.0.tgz vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf vignetteTitles: HMMcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HMMcopy/inst/doc/HMMcopy.R importsMe: qsea dependencyCount: 2 Package: HoloFoodR Version: 1.4.0 Depends: R(>= 4.4.0), MultiAssayExperiment, TreeSummarizedExperiment Imports: dplyr, httr2, jsonlite, S4Vectors, stringi, stats, SummarizedExperiment, utils Suggests: BiocStyle, DT, ggh4x, ggsignif, knitr, MGnifyR, mia, miaViz, MOFA2, patchwork, reticulate, rmarkdown, scater, shadowtext, testthat, UpSetR License: Artistic-2.0 | file LICENSE MD5sum: ec0c1ad1a80bf662696cb324fe0d1306 NeedsCompilation: no Title: R interface to EBI HoloFood resource Description: Utility package to facilitate integration and analysis of EBI HoloFood data in R. This package streamlines access to the resource, allowing for direct loading of data into formats optimized for downstream analytics. biocViews: Software, Infrastructure, DataImport, Microbiome, MicrobiomeData Author: Tuomas Borman [aut, cre] (ORCID: ), Artur Sannikov [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ) Maintainer: Tuomas Borman URL: https://github.com/EBI-Metagenomics/HoloFoodR VignetteBuilder: knitr BugReports: https://github.com/EBI-Metagenomics/HoloFoodR/issues git_url: https://git.bioconductor.org/packages/HoloFoodR git_branch: RELEASE_3_22 git_last_commit: e2cd9d0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HoloFoodR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HoloFoodR_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HoloFoodR_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HoloFoodR_1.4.0.tgz vignettes: vignettes/HoloFoodR/inst/doc/HoloFoodR.html vignetteTitles: HoloFoodR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HoloFoodR/inst/doc/HoloFoodR.R dependencyCount: 75 Package: hoodscanR Version: 1.8.0 Depends: R (>= 4.3) Imports: knitr, rmarkdown, SpatialExperiment, SummarizedExperiment, circlize, ComplexHeatmap, scico, rlang, utils, ggplot2, grid, methods, stats, RANN, Rcpp (>= 1.0.9) LinkingTo: Rcpp Suggests: testthat (>= 3.0.0), BiocStyle License: GPL-3 + file LICENSE MD5sum: a51a2b94b137be9a5aa8be22bbf271fb NeedsCompilation: yes Title: Spatial cellular neighbourhood scanning in R Description: hoodscanR is an user-friendly R package providing functions to assist cellular neighborhood analysis of any spatial transcriptomics data with single-cell resolution. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. The package can result in cell-level neighborhood annotation output, along with funtions to perform neighborhood colocalization analysis and neighborhood-based cell clustering. biocViews: Spatial, Transcriptomics, SingleCell, Clustering Author: Ning Liu [aut, cre] (ORCID: ), Jarryd Martin [aut] Maintainer: Ning Liu URL: https://github.com/DavisLaboratory/hoodscanR, https://davislaboratory.github.io/hoodscanR/ VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/hoodscanR/issues git_url: https://git.bioconductor.org/packages/hoodscanR git_branch: RELEASE_3_22 git_last_commit: 7fcc1c2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hoodscanR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hoodscanR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hoodscanR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hoodscanR_1.8.0.tgz vignettes: vignettes/hoodscanR/inst/doc/Quick_start.html vignetteTitles: hoodscanR_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hoodscanR/inst/doc/Quick_start.R importsMe: OSTA dependencyCount: 110 Package: hopach Version: 2.70.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) MD5sum: fb6d52fa9f317fd683364b7acfaffcb2 NeedsCompilation: yes Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH) Description: The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). biocViews: Clustering Author: Katherine S. Pollard, with Mark J. van der Laan and Greg Wall Maintainer: Katherine S. Pollard URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/ git_url: https://git.bioconductor.org/packages/hopach git_branch: RELEASE_3_22 git_last_commit: 6f2b34d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hopach_2.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hopach_2.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hopach_2.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hopach_2.70.0.tgz vignettes: vignettes/hopach/inst/doc/hopach.pdf vignetteTitles: hopach hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hopach/inst/doc/hopach.R importsMe: phenoTest, scClassify, treekoR suggestsMe: MicrobiotaProcess dependencyCount: 9 Package: HPAanalyze Version: 1.28.0 Depends: R (>= 3.5.0) Imports: dplyr, openxlsx, ggplot2, tibble, xml2, stats, utils, gridExtra Suggests: knitr, rmarkdown, devtools, BiocStyle License: GPL-3 + file LICENSE MD5sum: 5f0f10f26a05aef2d803eba41178e09a NeedsCompilation: no Title: Retrieve and analyze data from the Human Protein Atlas Description: Provide functions for retrieving, exploratory analyzing and visualizing the Human Protein Atlas data. HPAanalyze is designed to fullfill 3 main tasks: (1) Import, subsetting and export downloadable datasets; (2) Visualization of downloadable datasets for exploratory analysis; and (3) Working with the individual XML files. This package aims to serve researchers with little programming experience, but also allow power users to use the imported data as desired. biocViews: Proteomics, CellBiology, Visualization, Software Author: Anh Nhat Tran [aut, cre] Maintainer: Anh Nhat Tran URL: https://github.com/anhtr/HPAanalyze VignetteBuilder: knitr BugReports: https://github.com/anhtr/HPAanalyze/issues git_url: https://git.bioconductor.org/packages/HPAanalyze git_branch: RELEASE_3_22 git_last_commit: 6d7d09e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HPAanalyze_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HPAanalyze_1.27.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HPAanalyze_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HPAanalyze_1.28.0.tgz vignettes: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.html, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.html, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.html, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.html, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.html, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.html, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.html vignetteTitles: "1. Quick-start guide: Acquire and visualize the Human Protein Atlas (HPA) data in one function with HPAanalyze", "2. In-depth: Working with Human Protein Atlas (HPA) data in R with HPAanalyze", "3. Tutorial: Combine HPAanalyze with your Human Protein Atlas (HPA) queries", "4. Tutorial: Working with Human Protein Atlas (HPA) xml files offline", "5. Tutorial: Export Human Protein Atlas (HPA) data as JSON", "6. Tutorial: Download histology images from the Human Protein Atlas", "99. Code for figures from HPAanalyze paper" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.R, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.R, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.R, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.R, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.R, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.R, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.R dependencyCount: 37 Package: hpar Version: 1.52.0 Depends: R (>= 3.5.0) Imports: utils, ExperimentHub Suggests: org.Hs.eg.db, GO.db, AnnotationDbi, knitr, BiocStyle, testthat, rmarkdown, dplyr, DT License: Artistic-2.0 MD5sum: 5c2f733a152c83667e4aab3e743803fe NeedsCompilation: no Title: Human Protein Atlas in R Description: The hpar package provides a simple R interface to and data from the Human Protein Atlas project. biocViews: Proteomics, CellBiology, DataImport, FunctionalGenomics, SystemsBiology, ExperimentHubSoftware Author: Laurent Gatto [cre, aut] (ORCID: ), Manon Martin [aut] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hpar git_branch: RELEASE_3_22 git_last_commit: 7bb1503 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hpar_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hpar_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hpar_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hpar_1.52.0.tgz vignettes: vignettes/hpar/inst/doc/hpar.html vignetteTitles: Human Protein Atlas in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hpar/inst/doc/hpar.R importsMe: MetaboSignal suggestsMe: pRoloc, RforProteomics dependencyCount: 65 Package: HTqPCR Version: 1.64.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 Archs: x64 MD5sum: 57a5a6bd4ac4adfc6389ac5346c197ca NeedsCompilation: no Title: Automated analysis of high-throughput qPCR data Description: Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs). biocViews: MicrotitrePlateAssay, DifferentialExpression, GeneExpression, DataImport, QualityControl, Preprocessing, Visualization, MultipleComparison, qPCR Author: Heidi Dvinge, Paul Bertone Maintainer: Matthew N. McCall URL: http://www.ebi.ac.uk/bertone/software git_url: https://git.bioconductor.org/packages/HTqPCR git_branch: RELEASE_3_22 git_last_commit: c55e513 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HTqPCR_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HTqPCR_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HTqPCR_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HTqPCR_1.64.0.tgz vignettes: vignettes/HTqPCR/inst/doc/HTqPCR.pdf vignetteTitles: qPCR analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTqPCR/inst/doc/HTqPCR.R importsMe: nondetects dependencyCount: 21 Package: HTSFilter Version: 1.50.0 Depends: R (>= 4.0.0) Imports: edgeR, DESeq2, BiocParallel, Biobase, utils, stats, grDevices, graphics, methods Suggests: EDASeq, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: cc2a8bc72b020b6548f38041c8c5604c NeedsCompilation: no Title: Filter replicated high-throughput transcriptome sequencing data Description: This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions. biocViews: Sequencing, RNASeq, Preprocessing, DifferentialExpression, GeneExpression, Normalization, ImmunoOncology Author: Andrea Rau [cre, aut] (ORCID: ), Melina Gallopin [ctb], Gilles Celeux [ctb], Florence Jaffrézic [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HTSFilter git_branch: RELEASE_3_22 git_last_commit: bc11c10 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HTSFilter_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HTSFilter_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HTSFilter_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HTSFilter_1.50.0.tgz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.html vignetteTitles: HTSFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R importsMe: coseq suggestsMe: HTSCluster, inDAGO dependencyCount: 58 Package: HuBMAPR Version: 1.4.0 Depends: R (>= 4.4.0) Imports: httr2, dplyr, tidyr, tibble, rjsoncons, utils, stringr, whisker, purrr, rlang Suggests: testthat (>= 3.0.0), knitr, ggplot2, rmarkdown, BiocStyle, pryr License: Artistic-2.0 Archs: x64 MD5sum: 1a0b47903bebc2badc27383957dead85 NeedsCompilation: no Title: Interface to 'HuBMAP' Description: 'HuBMAP' provides an open, global bio-molecular atlas of the human body at the cellular level. The `datasets()`, `samples()`, `donors()`, `publications()`, and `collections()` functions retrieves the information for each of these entity types. `*_details()` are available for individual entries of each entity type. `*_derived()` are available for retrieving derived datasets or samples for individual entries of each entity type. Data files can be accessed using `bulk_data_transfer()`. biocViews: Software, SingleCell, DataImport, ThirdPartyClient, Spatial, Infrastructure Author: Christine Hou [aut, cre] (ORCID: ), Martin Morgan [aut] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Christine Hou URL: https://christinehou11.github.io/HuBMAPR/, https://github.com/christinehou11/HuBMAPR VignetteBuilder: knitr BugReports: https://github.com/christinehou11/HuBMAPR/issues git_url: https://git.bioconductor.org/packages/HuBMAPR git_branch: RELEASE_3_22 git_last_commit: e815208 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HuBMAPR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HuBMAPR_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HuBMAPR_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HuBMAPR_1.4.0.tgz vignettes: vignettes/HuBMAPR/inst/doc/hubmapr_vignettes.html vignetteTitles: Accessing Human Cell Atlas Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HuBMAPR/inst/doc/hubmapr_vignettes.R dependencyCount: 34 Package: HubPub Version: 1.18.0 Imports: available, usethis, biocthis, dplyr, aws.s3, fs, BiocManager, utils Suggests: AnnotationHubData, ExperimentHubData, GenomeInfoDbData, testthat, knitr, rmarkdown, BiocStyle, License: Artistic-2.0 Archs: x64 MD5sum: baffe2ba5ed5f1bc11cb9b82ddc97bb1 NeedsCompilation: no Title: Utilities to create and use Bioconductor Hubs Description: HubPub provides users with functionality to help with the Bioconductor Hub structures. The package provides the ability to create a skeleton of a Hub style package that the user can then populate with the necessary information. There are also functions to help add resources to the Hub package metadata files as well as publish data to the Bioconductor S3 bucket. biocViews: DataImport, Infrastructure, Software, ThirdPartyClient Author: Kayla Interdonato [aut, cre], Martin Morgan [aut], Lori Shepherd [ctb] Maintainer: Kayla Interdonato VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HubPub/issues git_url: https://git.bioconductor.org/packages/HubPub git_branch: RELEASE_3_22 git_last_commit: b71f04d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HubPub_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HubPub_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HubPub_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HubPub_1.18.0.tgz vignettes: vignettes/HubPub/inst/doc/CreateAHubPackage.html, vignettes/HubPub/inst/doc/HubPub.html vignetteTitles: Creating A Hub Package: ExperimentHub or AnnotationHub, HubPub: Help with publication of Hub packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HubPub/inst/doc/CreateAHubPackage.R, vignettes/HubPub/inst/doc/HubPub.R suggestsMe: AnnotationHub, AnnotationHubData, ExperimentHub, ExperimentHubData dependencyCount: 76 Package: hummingbird Version: 1.20.0 Depends: R (>= 4.0) Imports: Rcpp, graphics, GenomicRanges, SummarizedExperiment, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=2) MD5sum: c6886390856b917fb18a3a7fa346ea9e NeedsCompilation: yes Title: Bayesian Hidden Markov Model for the detection of differentially methylated regions Description: A package for detecting differential methylation. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations. Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance. At last, our methodology does not require replication in either or both of the two comparison groups. biocViews: HiddenMarkovModel, Bayesian, DNAMethylation, BiomedicalInformatics, Sequencing, GeneExpression, DifferentialExpression, DifferentialMethylation Author: Eleni Adam [aut, cre], Tieming Ji [aut], Desh Ranjan [aut] Maintainer: Eleni Adam VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hummingbird git_branch: RELEASE_3_22 git_last_commit: 88acb85 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hummingbird_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hummingbird_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hummingbird_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hummingbird_1.20.0.tgz vignettes: vignettes/hummingbird/inst/doc/hummingbird.html vignetteTitles: hummingbird hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hummingbird/inst/doc/hummingbird.R dependencyCount: 26 Package: HVP Version: 1.0.0 Imports: Matrix, methods, stats Suggests: SingleCellExperiment, SummarizedExperiment, Seurat, SeuratObject, ggplot2, progress, testthat, splatter, scater, devtools, knitr, rmarkdown, BiocStyle, ExperimentHub License: MIT + file LICENSE MD5sum: 589de48530ad0708aedd5baffe0a6f50 NeedsCompilation: no Title: Hierarchical Variance Partitioning Description: HVP is a quantitative batch effect metric that estimates the proportion of variance associated with batch effects in a data set. biocViews: SingleCell, Transcriptomics, GeneExpression, BatchEffect Author: Wei Xin Chan [aut, cre] (ORCID: ) Maintainer: Wei Xin Chan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HVP git_branch: RELEASE_3_22 git_last_commit: 2fefafe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HVP_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HVP_0.99.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HVP_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HVP_1.0.0.tgz vignettes: vignettes/HVP/inst/doc/HVP.html vignetteTitles: Quantifying batch effects with HVP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HVP/inst/doc/HVP.R dependencyCount: 8 Package: HybridExpress Version: 1.6.0 Depends: R (>= 4.3.0) Imports: ggplot2, patchwork, rlang, DESeq2, SummarizedExperiment, stats, methods, RColorBrewer, ComplexHeatmap, grDevices, BiocParallel Suggests: BiocStyle, knitr, sessioninfo, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 05d506c86128b0467711ef99a5e8de54 NeedsCompilation: no Title: Comparative analysis of RNA-seq data for hybrids and their progenitors Description: HybridExpress can be used to perform comparative transcriptomics analysis of hybrids (or allopolyploids) relative to their progenitor species. The package features functions to perform exploratory analyses of sample grouping, identify differentially expressed genes in hybrids relative to their progenitors, classify genes in expression categories (N = 12) and classes (N = 5), and perform functional analyses. We also provide users with graphical functions for the seamless creation of publication-ready figures that are commonly used in the literature. biocViews: Software, FunctionalGenomics, GeneExpression, Transcriptomics, RNASeq, Classification, DifferentialExpression Author: Fabricio Almeida-Silva [aut, cre] (ORCID: ), Lucas Prost-Boxoen [aut] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabricio Almeida-Silva URL: https://github.com/almeidasilvaf/HybridExpress VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/HybridExpress git_url: https://git.bioconductor.org/packages/HybridExpress git_branch: RELEASE_3_22 git_last_commit: b3f2227 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HybridExpress_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HybridExpress_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HybridExpress_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HybridExpress_1.6.0.tgz vignettes: vignettes/HybridExpress/inst/doc/HybridExpress.html vignetteTitles: Comparative transcriptomic analysis of hybrids and their progenitors hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridExpress/inst/doc/HybridExpress.R dependencyCount: 71 Package: HybridMTest Version: 1.54.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: 9be715d9ba87eeadb7b22e3dac2a86ea NeedsCompilation: no Title: Hybrid Multiple Testing Description: Performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using empirical Bayes probability (EBP) estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weigth. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs. biocViews: GeneExpression, Genetics, Microarray Author: Stan Pounds , Demba Fofana Maintainer: Demba Fofana git_url: https://git.bioconductor.org/packages/HybridMTest git_branch: RELEASE_3_22 git_last_commit: 4dada28 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/HybridMTest_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HybridMTest_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HybridMTest_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HybridMTest_1.54.0.tgz vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf vignetteTitles: Hybrid Multiple Testing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridMTest/inst/doc/HybridMTest.R dependencyCount: 15 Package: hyperdraw Version: 1.62.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: 161e5ccedc20d03aeac5b87ac137205a NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz git_url: https://git.bioconductor.org/packages/hyperdraw git_branch: RELEASE_3_22 git_last_commit: d222272 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hyperdraw_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hyperdraw_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hyperdraw_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hyperdraw_1.62.0.tgz vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf vignetteTitles: Hyperdraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hyperdraw/inst/doc/hyperdraw.R dependencyCount: 12 Package: hypergraph Version: 1.82.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: a57b27694959050d343b081363931c7e NeedsCompilation: no Title: A package providing hypergraph data structures Description: A package that implements some simple capabilities for representing and manipulating hypergraphs. biocViews: GraphAndNetwork Author: Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/hypergraph git_branch: RELEASE_3_22 git_last_commit: 640ba9c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/hypergraph_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hypergraph_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hypergraph_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hypergraph_1.82.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs importsMe: hyperdraw dependencyCount: 8 Package: iASeq Version: 1.54.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 46dab4e90f0ec409a636e27775ce87ad NeedsCompilation: no Title: iASeq: integrating multiple sequencing datasets for detecting allele-specific events Description: It fits correlation motif model to multiple RNAseq or ChIPseq studies to improve detection of allele-specific events and describe correlation patterns across studies. biocViews: ImmunoOncology, SNP, RNASeq, ChIPSeq Author: Yingying Wei, Hongkai Ji Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/iASeq git_branch: RELEASE_3_22 git_last_commit: fd57a35 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iASeq_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iASeq_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iASeq_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iASeq_1.54.0.tgz vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf vignetteTitles: iASeq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iASeq/inst/doc/iASeqVignette.R dependencyCount: 2 Package: iasva Version: 1.28.0 Depends: R (>= 3.5), Imports: irlba, stats, cluster, graphics, SummarizedExperiment, BiocParallel Suggests: knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot, DescTools, RColorBrewer License: GPL-2 MD5sum: 747c5360f7131f0d016761f51c532444 NeedsCompilation: no Title: Iteratively Adjusted Surrogate Variable Analysis Description: Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors. biocViews: Preprocessing, QualityControl, BatchEffect, RNASeq, Software, StatisticalMethod, FeatureExtraction, ImmunoOncology Author: Donghyung Lee [aut, cre], Anthony Cheng [aut], Nathan Lawlor [aut], Duygu Ucar [aut] Maintainer: Donghyung Lee , Anthony Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iasva git_branch: RELEASE_3_22 git_last_commit: 2a8ca9c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iasva_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iasva_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iasva_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iasva_1.28.0.tgz vignettes: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.html vignetteTitles: "Detecting hidden heterogeneity in single cell RNA-Seq data" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.R dependencyCount: 37 Package: iBBiG Version: 1.54.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 Archs: x64 MD5sum: 160f3796d7c2a58024f1684850703490 NeedsCompilation: yes Title: Iterative Binary Biclustering of Genesets Description: iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes biocViews: Clustering, Annotation, GeneSetEnrichment Author: Daniel Gusenleitner, Aedin Culhane Maintainer: Aedin Culhane URL: http://bcb.dfci.harvard.edu/~aedin/publications/ git_url: https://git.bioconductor.org/packages/iBBiG git_branch: RELEASE_3_22 git_last_commit: 077f41c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iBBiG_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iBBiG_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iBBiG_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iBBiG_1.54.0.tgz vignettes: vignettes/iBBiG/inst/doc/tutorial.pdf vignetteTitles: iBBiG User Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBBiG/inst/doc/tutorial.R importsMe: miRSM dependencyCount: 52 Package: Ibex Version: 0.99.34 Depends: R (>= 4.5.0) Imports: basilisk, immApex (>= 1.3.2), methods, Matrix, reticulate (>= 1.43.0), rlang, SeuratObject, scRepertoire, SingleCellExperiment, stats, SummarizedExperiment, tensorflow, tools Suggests: basilisk.utils, BiocStyle, bluster, dplyr, ggplot2, kableExtra, knitr, markdown, mumosa, patchwork, Peptides, rmarkdown, scater, spelling, testthat (>= 3.0.0), utils, viridis License: MIT + file LICENSE MD5sum: 2d81f350ac79984616a885225b4042dd NeedsCompilation: no Title: Methods for BCR single-cell embedding Description: Implementation of the Ibex algorithm for single-cell embedding based on BCR sequences. The package includes a standalone function to encode BCR sequence information by amino acid properties or sequence order using tensorflow-based autoencoder. In addition, the package interacts with SingleCellExperiment or Seurat data objects. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing Author: Nick Borcherding [aut, cre] Maintainer: Nick Borcherding URL: https://github.com/BorchLab/Ibex/ SystemRequirements: Python (via basilisk) VignetteBuilder: knitr BugReports: https://github.com/BorchLab/Ibex/issues git_url: https://git.bioconductor.org/packages/Ibex git_branch: devel git_last_commit: 124a6b3 git_last_commit_date: 2025-10-16 Date/Publication: 2025-10-17 source.ver: src/contrib/Ibex_0.99.34.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Ibex_0.99.34.tgz vignettes: vignettes/Ibex/inst/doc/Ibex.html vignetteTitles: Charging through Ibex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Ibex/inst/doc/Ibex.R dependencyCount: 132 Package: ibh Version: 1.58.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: 02a9f5f43219c04f87a84f6ace3b8663 NeedsCompilation: no Title: Interaction Based Homogeneity for Evaluating Gene Lists Description: This package contains methods for calculating Interaction Based Homogeneity to evaluate fitness of gene lists to an interaction network which is useful for evaluation of clustering results and gene list analysis. BioGRID interactions are used in the calculation. The user can also provide their own interactions. biocViews: QualityControl, DataImport, GraphAndNetwork, NetworkEnrichment Author: Kircicegi Korkmaz, Volkan Atalay, Rengul Cetin Atalay. Maintainer: Kircicegi Korkmaz git_url: https://git.bioconductor.org/packages/ibh git_branch: RELEASE_3_22 git_last_commit: 224881d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ibh_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ibh_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ibh_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ibh_1.58.0.tgz vignettes: vignettes/ibh/inst/doc/ibh.pdf vignetteTitles: ibh hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ibh/inst/doc/ibh.R dependencyCount: 1 Package: iBMQ Version: 1.50.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 MD5sum: 04c411ab1f81aa819085ab3cf1ddf340 NeedsCompilation: yes Title: integrated Bayesian Modeling of eQTL data Description: integrated Bayesian Modeling of eQTL data biocViews: Microarray, Preprocessing, GeneExpression, SNP Author: Marie-Pier Scott-Boyer and Greg Imholte Maintainer: Greg Imholte URL: http://www.rglab.org SystemRequirements: GSL and OpenMP git_url: https://git.bioconductor.org/packages/iBMQ git_branch: RELEASE_3_22 git_last_commit: aff5dfa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iBMQ_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iBMQ_1.49.0.zip vignettes: vignettes/iBMQ/inst/doc/iBMQ.pdf vignetteTitles: iBMQ: An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBMQ/inst/doc/iBMQ.R dependencyCount: 25 Package: iCARE Version: 1.38.0 Depends: R (>= 3.3.0), plotrix, gtools, Hmisc Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE Archs: x64 MD5sum: 9ef7beb4e7244aa8f53b5900e011c038 NeedsCompilation: yes Title: Individualized Coherent Absolute Risk Estimation (iCARE) Description: An R package to build, validate and apply absolute risk models biocViews: Software, StatisticalMethod, GenomeWideAssociation Author: Parichoy Pal Choudhury, Paige Maas, William Wheeler, Nilanjan Chatterjee Maintainer: Parichoy Pal Choudhury git_url: https://git.bioconductor.org/packages/iCARE git_branch: RELEASE_3_22 git_last_commit: 38314cd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iCARE_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iCARE_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iCARE_1.38.0.tgz vignettes: vignettes/iCARE/inst/doc/vignette_model_validation.pdf, vignettes/iCARE/inst/doc/vignette.pdf vignetteTitles: iCARE Vignette Model Validation, iCARE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iCARE/inst/doc/vignette_model_validation.R, vignettes/iCARE/inst/doc/vignette.R dependencyCount: 63 Package: Icens Version: 1.82.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: 265fcc9fa33b10fad738bcaea0e0dd4d NeedsCompilation: no Title: NPMLE for Censored and Truncated Data Description: Many functions for computing the NPMLE for censored and truncated data. biocViews: Infrastructure Author: R. Gentleman and Alain Vandal Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Icens git_branch: RELEASE_3_22 git_last_commit: 5f951fe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Icens_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Icens_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Icens_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Icens_1.82.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess, icensBKL, interval importsMe: PROcess suggestsMe: LTRCtrees, ReIns dependencyCount: 10 Package: icetea Version: 1.28.0 Depends: R (>= 4.0) Imports: stats, utils, methods, graphics, grDevices, ggplot2, GenomicFeatures, ShortRead, BiocParallel, Biostrings, S4Vectors, Rsamtools, BiocGenerics, IRanges, GenomicAlignments, GenomicRanges, rtracklayer, SummarizedExperiment, VariantAnnotation, limma, edgeR, csaw, DESeq2, TxDb.Dmelanogaster.UCSC.dm6.ensGene Suggests: GenomeInfoDb, knitr, rmarkdown, Rsubread (>= 1.29.0), testthat License: GPL-3 + file LICENSE Archs: x64 MD5sum: 454c21e576443538342c19afb3118bb5 NeedsCompilation: no Title: Integrating Cap Enrichment with Transcript Expression Analysis Description: icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis. biocViews: ImmunoOncology, Transcription, GeneExpression, Sequencing, RNASeq, Transcriptomics, DifferentialExpression Author: Vivek Bhardwaj [aut, cre] Maintainer: Vivek Bhardwaj URL: https://github.com/vivekbhr/icetea VignetteBuilder: knitr BugReports: https://github.com/vivekbhr/icetea/issues git_url: https://git.bioconductor.org/packages/icetea git_branch: RELEASE_3_22 git_last_commit: f7f280d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/icetea_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/icetea_1.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/icetea_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/icetea_1.28.0.tgz vignettes: vignettes/icetea/inst/doc/mapcap_analysis.html vignetteTitles: Analysing transcript 5'-profiling data using icetea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/icetea/inst/doc/mapcap_analysis.R dependencyCount: 107 Package: iCheck Version: 1.40.0 Depends: R (>= 3.2.0), Biobase, lumi, gplots Imports: stats, graphics, preprocessCore, grDevices, randomForest, affy, limma, parallel, GeneSelectMMD, rgl, MASS, lmtest, scatterplot3d, utils License: GPL (>= 2) MD5sum: 8ba43d53e0a16b907c94977ac7df22e5 NeedsCompilation: no Title: QC Pipeline and Data Analysis Tools for High-Dimensional Illumina mRNA Expression Data Description: QC pipeline and data analysis tools for high-dimensional Illumina mRNA expression data. biocViews: GeneExpression, DifferentialExpression, Microarray, Preprocessing, DNAMethylation, OneChannel, TwoChannel, QualityControl Author: Weiliang Qiu [aut, cre], Brandon Guo [aut, ctb], Christopher Anderson [aut, ctb], Barbara Klanderman [aut, ctb], Vincent Carey [aut, ctb], Benjamin Raby [aut, ctb] Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/iCheck git_branch: RELEASE_3_22 git_last_commit: 109cb15 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iCheck_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iCheck_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iCheck_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iCheck_1.40.0.tgz vignettes: vignettes/iCheck/inst/doc/iCheck.pdf vignetteTitles: iCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCheck/inst/doc/iCheck.R dependencyCount: 188 Package: iChip Version: 1.64.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) MD5sum: c0281d1937b8ff031c223971af2f741d NeedsCompilation: yes Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models Description: Hidden Ising models are implemented to identify enriched genomic regions in ChIP-chip data. They can be used to analyze the data from multiple platforms (e.g., Affymetrix, Agilent, and NimbleGen), and the data with single to multiple replicates. biocViews: ChIPchip, OneChannel, AgilentChip, Microarray Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iChip git_branch: RELEASE_3_22 git_last_commit: ab53168 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iChip_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iChip_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iChip_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iChip_1.64.0.tgz vignettes: vignettes/iChip/inst/doc/iChip.pdf vignetteTitles: iChip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iChip/inst/doc/iChip.R dependencyCount: 7 Package: iClusterPlus Version: 1.46.0 Depends: R (>= 4.1.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 5224ba028f4c480d5abc5f3d769658b8 NeedsCompilation: yes Title: Integrative clustering of multi-type genomic data Description: Integrative clustering of multiple genomic data using a joint latent variable model. biocViews: Multi-omics, Clustering Author: Qianxing Mo, Ronglai Shen Maintainer: Qianxing Mo , Ronglai Shen git_url: https://git.bioconductor.org/packages/iClusterPlus git_branch: RELEASE_3_22 git_last_commit: 35314e5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iClusterPlus_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iClusterPlus_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iClusterPlus_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iClusterPlus_1.46.0.tgz vignettes: vignettes/iClusterPlus/inst/doc/iClusterPlus.pdf, vignettes/iClusterPlus/inst/doc/iManual.pdf vignetteTitles: iClusterPlus, iManual.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MultiDataSet dependencyCount: 1 Package: iCNV Version: 1.30.0 Depends: R (>= 3.3.1), CODEX Imports: fields, ggplot2, truncnorm, tidyr, data.table, dplyr, grDevices, graphics, stats, utils, rlang Suggests: knitr, rmarkdown, WES.1KG.WUGSC License: GPL-2 Archs: x64 MD5sum: f73cecc88e5f45207f99fb884009b39c NeedsCompilation: no Title: Integrated Copy Number Variation detection Description: Integrative copy number variation (CNV) detection from multiple platform and experimental design. biocViews: ImmunoOncology, ExomeSeq, WholeGenome, SNP, CopyNumberVariation, HiddenMarkovModel Author: Zilu Zhou, Nancy Zhang Maintainer: Zilu Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iCNV git_branch: RELEASE_3_22 git_last_commit: 5531587 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iCNV_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iCNV_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iCNV_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iCNV_1.30.0.tgz vignettes: vignettes/iCNV/inst/doc/iCNV-vignette.html vignetteTitles: iCNV Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCNV/inst/doc/iCNV-vignette.R dependencyCount: 95 Package: iCOBRA Version: 1.38.0 Depends: R (>= 4.4.0) Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2, ggplot2 (>= 3.4.0), scales, ROCR, dplyr, DT, limma, methods, UpSetR, utils, rlang Suggests: knitr, markdown, rmarkdown, testthat License: GPL (>=2) Archs: x64 MD5sum: 5b0d178a97e6ffff86405ba5b3448ea9 NeedsCompilation: no Title: Comparison and Visualization of Ranking and Assignment Methods Description: This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. Various types of performance plots can be generated programmatically. The package also contains a shiny application for interactive exploration of results. biocViews: Classification, Visualization Author: Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/iCOBRA VignetteBuilder: knitr BugReports: https://github.com/csoneson/iCOBRA/issues git_url: https://git.bioconductor.org/packages/iCOBRA git_branch: RELEASE_3_22 git_last_commit: 60e06fc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iCOBRA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iCOBRA_1.37.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iCOBRA_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iCOBRA_1.38.0.tgz vignettes: vignettes/iCOBRA/inst/doc/iCOBRA.html vignetteTitles: iCOBRA User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R suggestsMe: muscat dependencyCount: 81 Package: ideal Version: 2.4.0 Depends: topGO Imports: DESeq2, SummarizedExperiment, mosdef (>= 1.1.0), GenomicRanges, IRanges, S4Vectors, ggplot2 (>= 2.0.0), heatmaply, plotly, pheatmap, IHW, gplots, UpSetR, goseq, stringr, dplyr, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, DT, rentrez, rintrojs, rlang, ggrepel, knitr, rmarkdown, shinyAce, BiocParallel, grDevices, graphics, base64enc, methods, utils, stats Suggests: testthat, BiocStyle, markdown, airway, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, htmltools, edgeR License: MIT + file LICENSE MD5sum: f7300503c31aacefc55fab15cecaa4f4 NeedsCompilation: no Title: Interactive Differential Expression AnaLysis Description: This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. Support for reproducibility of the whole analysis is provided by means of a template report which gets automatically compiled and can be stored/shared. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, RNASeq, Sequencing, Visualization, QualityControl, GUI, GeneSetEnrichment, ReportWriting, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/ideal, https://federicomarini.github.io/ideal/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/ideal/issues git_url: https://git.bioconductor.org/packages/ideal git_branch: RELEASE_3_22 git_last_commit: ed2684d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ideal_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ideal_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ideal_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ideal_2.4.0.tgz vignettes: vignettes/ideal/inst/doc/ideal-usersguide.html vignetteTitles: ideal User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ideal/inst/doc/ideal-usersguide.R dependencyCount: 240 Package: IdeoViz Version: 1.46.0 Depends: R (>= 3.5.0), Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer, graphics, GenomeInfoDb License: GPL-2 MD5sum: a4d1d7256f5815eec87758a1d1078449 NeedsCompilation: no Title: Plots data (continuous/discrete) along chromosomal ideogram Description: Plots data associated with arbitrary genomic intervals along chromosomal ideogram. biocViews: Visualization,Microarray Author: Shraddha Pai , Jingliang Ren Maintainer: Shraddha Pai git_url: https://git.bioconductor.org/packages/IdeoViz git_branch: RELEASE_3_22 git_last_commit: 917a5d0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IdeoViz_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IdeoViz_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IdeoViz_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IdeoViz_1.46.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 60 Package: idiogram Version: 1.86.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 Archs: x64 MD5sum: 005c88c240cf22d59e63681bfe6dfe91 NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema git_url: https://git.bioconductor.org/packages/idiogram git_branch: RELEASE_3_22 git_last_commit: 6bd2a2e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/idiogram_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/idiogram_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/idiogram_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/idiogram_1.86.0.tgz vignettes: vignettes/idiogram/inst/doc/idiogram.pdf vignetteTitles: HOWTO: idiogram hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idiogram/inst/doc/idiogram.R dependencyCount: 47 Package: idpr Version: 1.20.0 Depends: R (>= 4.1.0) Imports: ggplot2 (>= 3.3.0), magrittr (>= 1.5), dplyr (>= 0.8.5), plyr (>= 1.8.6), jsonlite (>= 1.6.1), rlang (>= 0.4.6), Biostrings (>= 2.56.0), methods (>= 4.0.0) Suggests: knitr, rmarkdown, pwalign, msa, ape, testthat, seqinr License: LGPL (>= 3) Archs: x64 MD5sum: 0e09690d0bb0a6f55e812f031b91872f NeedsCompilation: no Title: Profiling and Analyzing Intrinsically Disordered Proteins in R Description: ‘idpr’ aims to integrate tools for the computational analysis of intrinsically disordered proteins (IDPs) within R. This package is used to identify known characteristics of IDPs for a sequence of interest with easily reported and dynamic results. Additionally, this package includes tools for IDP-based sequence analysis to be used in conjunction with other R packages. Described in McFadden WM & Yanowitz JL (2022). "idpr: A package for profiling and analyzing Intrinsically Disordered Proteins in R." PloS one, 17(4), e0266929. . biocViews: StructuralPrediction, Proteomics, CellBiology Author: William M. McFadden [cre, aut], Judith L. Yanowitz [aut, fnd], Michael Buszczak [ctb, fnd] Maintainer: William M. McFadden VignetteBuilder: knitr BugReports: https://github.com/wmm27/idpr/issues git_url: https://git.bioconductor.org/packages/idpr git_branch: RELEASE_3_22 git_last_commit: 47a2bb1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/idpr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/idpr_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/idpr_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/idpr_1.20.0.tgz vignettes: vignettes/idpr/inst/doc/chargeHydropathy-vignette.html, vignettes/idpr/inst/doc/disorderedMatrices-vignette.html, vignettes/idpr/inst/doc/idpr-vignette.html, vignettes/idpr/inst/doc/iupred-vignette.html, vignettes/idpr/inst/doc/sequenceMAP-vignette.html, vignettes/idpr/inst/doc/structuralTendency-vignette.html vignetteTitles: Charge and Hydropathy Vignette, Disordered Matrices Vignette, idpr Package Overview Vignette, IUPred Vignette, Sequence Map Vignette, Structural Tendency Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idpr/inst/doc/chargeHydropathy-vignette.R, vignettes/idpr/inst/doc/disorderedMatrices-vignette.R, vignettes/idpr/inst/doc/idpr-vignette.R, vignettes/idpr/inst/doc/iupred-vignette.R, vignettes/idpr/inst/doc/sequenceMAP-vignette.R, vignettes/idpr/inst/doc/structuralTendency-vignette.R dependencyCount: 42 Package: idr2d Version: 1.24.0 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.6), futile.logger (>= 1.4.3), GenomeInfoDb (>= 1.14.0), GenomicRanges (>= 1.30), ggplot2 (>= 3.1.1), grDevices, grid, idr (>= 1.2), IRanges (>= 2.18.0), magrittr (>= 1.5), methods, reticulate (>= 1.13), scales (>= 1.0.0), stats, stringr (>= 1.3.1), utils Suggests: DT (>= 0.4), htmltools (>= 0.3.6), knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 15fea78ae6d5d18914b3969c19eb32de NeedsCompilation: no Title: Irreproducible Discovery Rate for Genomic Interactions Data Description: A tool to measure reproducibility between genomic experiments that produce two-dimensional peaks (interactions between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an extension of the original idr package, which is intended for (one-dimensional) ChIP-seq peaks. biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics, FunctionalGenomics, Classification, HiC Author: Konstantin Krismer [aut, cre, cph] (ORCID: ), David Gifford [ths, cph] (ORCID: ) Maintainer: Konstantin Krismer URL: https://idr2d.mit.edu SystemRequirements: Python (>= 3.5.0), hic-straw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/idr2d git_branch: RELEASE_3_22 git_last_commit: 19958df git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/idr2d_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/idr2d_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/idr2d_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/idr2d_1.24.0.tgz vignettes: vignettes/idr2d/inst/doc/idr1d.html, vignettes/idr2d/inst/doc/idr2d.html vignetteTitles: Identify reproducible genomic peaks from replicate ChIP-seq experiments, Identify reproducible genomic interactions from replicate ChIA-PET experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/idr2d/inst/doc/idr1d.R, vignettes/idr2d/inst/doc/idr2d.R dependencyCount: 62 Package: IFAA Version: 1.12.0 Depends: R (>= 4.2.0), Imports: mathjaxr, doRNG, foreach (>= 1.4.3), Matrix (>= 1.4-0), HDCI (>= 1.0-2), parallel (>= 3.3.0), doParallel (>= 1.0.11), parallelly , glmnet, stats, utils, SummarizedExperiment, stringr, S4Vectors, DescTools, MatrixExtra, methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: c739619740dc0f92af8538bdab78eb71 NeedsCompilation: no Title: Robust Inference for Absolute Abundance in Microbiome Analysis Description: This package offers a robust approach to make inference on the association of covariates with the absolute abundance (AA) of microbiome in an ecosystem. It can be also directly applied to relative abundance (RA) data to make inference on AA because the ratio of two RA is equal to the ratio of their AA. This algorithm can estimate and test the associations of interest while adjusting for potential confounders. The estimates of this method have easy interpretation like a typical regression analysis. High-dimensional covariates are handled with regularization and it is implemented by parallel computing. False discovery rate is automatically controlled by this approach. Zeros do not need to be imputed by a positive value for the analysis. The IFAA package also offers the 'MZILN' function for estimating and testing associations of abundance ratios with covariates. biocViews: Software, Technology, Sequencing, Microbiome, Regression Author: Quran Wu [aut], Zhigang Li [aut, cre] Maintainer: Zhigang Li URL: https://pubmed.ncbi.nlm.nih.gov/35241863/, https://pubmed.ncbi.nlm.nih.gov/30923584/, https://github.com/quranwu/IFAA VignetteBuilder: knitr BugReports: https://github.com/quranwu/IFAA/issues git_url: https://git.bioconductor.org/packages/IFAA git_branch: RELEASE_3_22 git_last_commit: 025aaf4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IFAA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IFAA_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IFAA_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IFAA_1.12.0.tgz vignettes: vignettes/IFAA/inst/doc/IFAA.pdf vignetteTitles: IFAA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IFAA/inst/doc/IFAA.R dependencyCount: 100 Package: igblastr Version: 1.0.0 Depends: R (>= 4.2.0), tibble, Biostrings Imports: methods, utils, stats, tools, R.utils, curl, httr, xml2, rvest, xtable, jsonlite, S4Vectors, IRanges, GenomeInfoDb Suggests: GenomicAlignments, parallel, testthat, knitr, rmarkdown, BiocStyle, ggplot2, dplyr, scales, ggseqlogo License: Artistic-2.0 MD5sum: 3ec52a7186aebd3d623489672b5e2113 NeedsCompilation: no Title: User-friendly R Wrapper to IgBLAST Description: The igblastr package provides functions to conveniently install and use a local IgBLAST installation from within R. IgBLAST is described at . Online IgBLAST: . biocViews: Immunology, Immunogenetics, ImmunoOncology, CellBiology Author: Hervé Pagès [aut, cre] (ORCID: ), Ollivier Hyrien [aut, fnd] (ORCID: ), Kellie MacPhee [ctb] (ORCID: ), Michael Duff [ctb] (ORCID: ), Jason Taylor [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/igblastr VignetteBuilder: knitr BugReports: https://github.com/HyrienLab/igblastr/issues git_url: https://git.bioconductor.org/packages/igblastr git_branch: RELEASE_3_22 git_last_commit: 1435b82 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/igblastr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/igblastr_0.99.12.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/igblastr_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/igblastr_1.0.0.tgz vignettes: vignettes/igblastr/inst/doc/igblastr_overview.html vignetteTitles: igblastr overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/igblastr/inst/doc/igblastr_overview.R dependencyCount: 44 Package: iGC Version: 1.40.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 Archs: x64 MD5sum: 7fd0b2e7bd51da32a038a1cbbe0bd3e8 NeedsCompilation: no Title: An integrated analysis package of Gene expression and Copy number alteration Description: This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data. biocViews: Software, Biological Question, DifferentialExpression, GenomicVariation, AssayDomain, CopyNumberVariation, GeneExpression, ResearchField, Genetics, Technology, Microarray, Sequencing, WorkflowStep, MultipleComparison Author: Yi-Pin Lai [aut], Liang-Bo Wang [aut, cre], Tzu-Pin Lu [aut], Eric Y. Chuang [aut] Maintainer: Liang-Bo Wang URL: http://github.com/ccwang002/iGC VignetteBuilder: knitr BugReports: http://github.com/ccwang002/iGC/issues git_url: https://git.bioconductor.org/packages/iGC git_branch: RELEASE_3_22 git_last_commit: 2d90bc5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iGC_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iGC_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iGC_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iGC_1.40.0.tgz vignettes: vignettes/iGC/inst/doc/Introduction.html vignetteTitles: Introduction to iGC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iGC/inst/doc/Introduction.R dependencyCount: 5 Package: IgGeneUsage Version: 1.24.0 Depends: R (>= 4.2.0) Imports: methods, reshape2 (>= 1.4.3), Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), rstantools (>= 2.2.0), SummarizedExperiment, tidyr LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), ggplot2, ggforce, ggrepel, patchwork License: MIT + file LICENSE Archs: x64 MD5sum: 2fc36edc7beb539e4830c1ee2b88dfea NeedsCompilation: yes Title: Differential gene usage in immune repertoires Description: Detection of biases in the usage of immunoglobulin (Ig) genes is an important task in immune repertoire profiling. IgGeneUsage detects aberrant Ig gene usage between biological conditions using a probabilistic model which is analyzed computationally by Bayes inference. With this IgGeneUsage also avoids some common problems related to the current practice of null-hypothesis significance testing. biocViews: DifferentialExpression, Regression, Genetics, Bayesian, BiomedicalInformatics, ImmunoOncology, MathematicalBiology Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski URL: https://github.com/snaketron/IgGeneUsage SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/snaketron/IgGeneUsage/issues git_url: https://git.bioconductor.org/packages/IgGeneUsage git_branch: RELEASE_3_22 git_last_commit: 823c8a0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IgGeneUsage_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IgGeneUsage_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IgGeneUsage_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IgGeneUsage_1.24.0.tgz vignettes: vignettes/IgGeneUsage/inst/doc/User_Manual.html vignetteTitles: User Manual: IgGeneUsage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IgGeneUsage/inst/doc/User_Manual.R dependencyCount: 78 Package: igvR Version: 1.30.0 Depends: R (>= 3.5.0), GenomicRanges, GenomicAlignments, BrowserViz (>= 2.17.1) Imports: methods, BiocGenerics, httpuv, utils, rtracklayer, VariantAnnotation, RColorBrewer, httr Suggests: RUnit, BiocStyle, knitr, rmarkdown, MotifDb, seqLogo License: MIT + file LICENSE Archs: x64 MD5sum: de90dd5b8ad6f34f98b650ab005a9f44 NeedsCompilation: no Title: igvR: integrative genomics viewer Description: Access to igv.js, the Integrative Genomics Viewer running in a web browser. biocViews: Visualization, ThirdPartyClient, GenomeBrowsers Author: Paul Shannon Maintainer: Arkadiusz Gladki URL: https://gladkia.github.io/igvR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/igvR git_branch: RELEASE_3_22 git_last_commit: a0c5ecf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/igvR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/igvR_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/igvR_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/igvR_1.30.0.tgz vignettes: vignettes/igvR/inst/doc/v00.basicIntro.html, vignettes/igvR/inst/doc/v01.stockGenome.html, vignettes/igvR/inst/doc/v02.customGenome.html, vignettes/igvR/inst/doc/v03.ctcfChIP.html, vignettes/igvR/inst/doc/v04.pairedEnd.html, vignettes/igvR/inst/doc/v05.ucscTableBrowser.html, vignettes/igvR/inst/doc/v06.annotationHub.html, vignettes/igvR/inst/doc/v07.gwas.html vignetteTitles: "Introduction: a simple demo", "Use a Stock Genome", "Use a Custom Genome", "Explore CTCF ChIP-seq alignments,, MACS2 narrowPeaks,, Motif Matching and H3K4me3 methylation", "Paired-end Interaction Tracks", "Obtain and Display H3K4Me3 K562 track from UCSC table browser", "Obtain and Display H3K27ac K562 track from the AnnotationHub", "GWAS Tracks" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvR/inst/doc/v00.basicIntro.R, vignettes/igvR/inst/doc/v01.stockGenome.R, vignettes/igvR/inst/doc/v02.customGenome.R, vignettes/igvR/inst/doc/v03.ctcfChIP.R, vignettes/igvR/inst/doc/v04.pairedEnd.R, vignettes/igvR/inst/doc/v05.ucscTableBrowser.R, vignettes/igvR/inst/doc/v06.annotationHub.R, vignettes/igvR/inst/doc/v07.gwas.R dependencyCount: 86 Package: igvShiny Version: 1.6.0 Depends: R (>= 3.5.0), GenomicRanges, methods, shiny Imports: BiocGenerics, checkmate, futile.logger, GenomeInfoDbData, htmlwidgets, httr, jsonlite, randomcoloR, utils Suggests: BiocStyle, GenomicAlignments, knitr, Rsamtools, rtracklayer, RUnit, shinytest2, VariantAnnotation License: MIT + file LICENSE MD5sum: 94ef96a96b60c4a12a702de7e1f8abf9 NeedsCompilation: no Title: igvShiny: a wrapper of Integrative Genomics Viewer (IGV - an interactive tool for visualization and exploration integrated genomic data) Description: This package is a wrapper of Integrative Genomics Viewer (IGV). It comprises an htmlwidget version of IGV. It can be used as a module in Shiny apps. biocViews: Software, ShinyApps, Sequencing, Coverage Author: Paul Shannon [aut], Arkadiusz Gladki [aut, cre] (ORCID: ), Karolina Scigocka [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gladkia/igvShiny, https://gladkia.github.io/igvShiny/ VignetteBuilder: knitr BugReports: https://github.com/gladkia/igvShiny/issues git_url: https://git.bioconductor.org/packages/igvShiny git_branch: RELEASE_3_22 git_last_commit: b68e926 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/igvShiny_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/igvShiny_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/igvShiny_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/igvShiny_1.6.0.tgz vignettes: vignettes/igvShiny/inst/doc/igvShiny.html vignetteTitles: igvShiny Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvShiny/inst/doc/igvShiny.R dependencyCount: 76 Package: IHW Version: 1.38.0 Depends: R (>= 3.3.0) Imports: methods, slam, lpsymphony, fdrtool, BiocGenerics Suggests: ggplot2, dplyr, gridExtra, scales, DESeq2, airway, testthat, Matrix, BiocStyle, knitr, rmarkdown, devtools License: Artistic-2.0 MD5sum: ccb08a6b6886e8dc90fc05b9428b436f NeedsCompilation: no Title: Independent Hypothesis Weighting Description: Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis. biocViews: ImmunoOncology, MultipleComparison, RNASeq Author: Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut] Maintainer: Nikos Ignatiadis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IHW git_branch: RELEASE_3_22 git_last_commit: 68e5257 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IHW_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IHW_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IHW_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IHW_1.38.0.tgz vignettes: vignettes/IHW/inst/doc/introduction_to_ihw.html vignetteTitles: "Introduction to IHW" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R dependsOnMe: IHWpaper importsMe: ideal, muscat, scp suggestsMe: DEWSeq, GRaNIE, metagenomeSeq, BisRNA, DGEobj.utils, readyomics dependencyCount: 10 Package: illuminaio Version: 0.52.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 Archs: x64 MD5sum: 6eb3d2972cf2a013c184136f668d15a5 NeedsCompilation: yes Title: Parsing Illumina Microarray Output Files Description: Tools for parsing Illumina's microarray output files, including IDAT. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms Author: Keith Baggerly [aut], Henrik Bengtsson [aut], Kasper Daniel Hansen [aut, cre], Matt Ritchie [aut], Mike L. Smith [aut], Tim Triche Jr. [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/illuminaio BugReports: https://github.com/HenrikBengtsson/illuminaio/issues git_url: https://git.bioconductor.org/packages/illuminaio git_branch: RELEASE_3_22 git_last_commit: ba74243 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/illuminaio_0.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/illuminaio_0.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/illuminaio_0.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/illuminaio_0.52.0.tgz vignettes: vignettes/illuminaio/inst/doc/EncryptedFormat.pdf, vignettes/illuminaio/inst/doc/illuminaio.pdf vignetteTitles: Description of Encrypted IDAT Format, Introduction to illuminaio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/illuminaio/inst/doc/illuminaio.R dependsOnMe: normalize450K, RnBeads, wateRmelon, EGSEA123 importsMe: bigmelon, crlmm, methylumi, minfi suggestsMe: limma dependencyCount: 4 Package: ILoReg Version: 1.20.0 Depends: R (>= 4.0.0) Imports: Matrix, parallel, foreach, aricode, LiblineaR, SparseM, ggplot2, cowplot, RSpectra, umap, Rtsne, fastcluster, parallelDist, cluster, dendextend, DescTools, plyr, scales, pheatmap, reshape2, dplyr, doRNG, SingleCellExperiment, SummarizedExperiment, S4Vectors, methods, stats, doSNOW, utils Suggests: knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: c46c24e97a0fb3ecb595220d5ddad385 NeedsCompilation: no Title: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data Description: ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided. biocViews: SingleCell, Software, Clustering, DimensionReduction, RNASeq, Visualization, Transcriptomics, DataRepresentation, DifferentialExpression, Transcription, GeneExpression Author: Johannes Smolander [cre, aut], Sini Junttila [aut], Mikko S Venäläinen [aut], Laura L Elo [aut] Maintainer: Johannes Smolander URL: https://github.com/elolab/ILoReg VignetteBuilder: knitr BugReports: https://github.com/elolab/ILoReg/issues git_url: https://git.bioconductor.org/packages/ILoReg git_branch: RELEASE_3_22 git_last_commit: 50b509c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ILoReg_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ILoReg_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ILoReg_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ILoReg_1.20.0.tgz vignettes: vignettes/ILoReg/inst/doc/ILoReg.html vignetteTitles: ILoReg package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ILoReg/inst/doc/ILoReg.R dependencyCount: 126 Package: imageTCGA Version: 1.2.0 Depends: R (>= 3.5.0), shiny Imports: DT, dplyr, bslib, bsicons, ggplot2, viridis, tidyr, leaflet, clipr, rlang Suggests: BiocManager, BiocStyle, knitr, curl, glue, rmarkdown, sessioninfo, testthat, tibble, GenomicDataCommons License: Artistic-2.0 MD5sum: d86a4b92932c29a3e2bfdec4bedc1df8 NeedsCompilation: no Title: TCGA Diagnostic Image Database Explorer Description: A Shiny application to explore the TCGA Diagnostic Image Database. biocViews: ShinyApps Author: Ilaria Billato [aut, cre] (ORCID: , affiliation: Department of Biology, University of Padova), Marcel Ramos [ctb] (affiliation: CUNY Graduate School of Public Health and Health Policy, New York, NY USA), Mohamed Omar [ctb] (affiliation: Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California), Sehyun Oh [ctb] (affiliation: CUNY Graduate School of Public Health and Health Policy, New York, NY USA), Levi Waldron [ctb] (affiliation: CUNY Graduate School of Public Health and Health Policy, New York, NY USA), Davide Risso [ctb] (affiliation: Department of Statistical Sciences, University of Padova), Chiara Romualdi [ctb] (affiliation: Department of Biology, University of Padova) Maintainer: Ilaria Billato URL: https://github.com/billila/imageTCGA VignetteBuilder: knitr BugReports: https://github.com/billila/imageTCGA/issues git_url: https://git.bioconductor.org/packages/imageTCGA git_branch: RELEASE_3_22 git_last_commit: 4f8b7fe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/imageTCGA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/imageTCGA_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/imageTCGA_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/imageTCGA_1.2.0.tgz vignettes: vignettes/imageTCGA/inst/doc/imageTCGA.html vignetteTitles: imageTCGA: A Shiny application to explore the TCGA Diagnostic Images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imageTCGA/inst/doc/imageTCGA.R dependencyCount: 92 Package: imcRtools Version: 1.16.0 Depends: R (>= 4.1), SpatialExperiment Imports: S4Vectors, stats, utils, SummarizedExperiment, methods, pheatmap, scuttle, stringr, readr, EBImage, cytomapper, abind, BiocParallel, viridis, dplyr, magrittr, DT, igraph, SingleCellExperiment, vroom, BiocNeighbors, RTriangle, ggraph, tidygraph, ggplot2, data.table, sf, concaveman, tidyselect, distances, MatrixGenerics, rlang, grDevices Suggests: CATALYST, grid, tidyr, BiocStyle, knitr, rmarkdown, markdown, testthat License: GPL-3 Archs: x64 MD5sum: aa23464e1f7456419d2aa0abd624608e NeedsCompilation: no Title: Methods for imaging mass cytometry data analysis Description: This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected. biocViews: ImmunoOncology, SingleCell, Spatial, DataImport, Clustering Author: Nils Eling [aut], Tobias Hoch [ctb], Vito Zanotelli [ctb], Jana Fischer [ctb], Daniel Schulz [ctb, cre] (ORCID: ), Lasse Meyer [ctb], Lutz Marlene [ctb], Schiller Chiara [ctb], Ibañez Victor [ctb] Maintainer: Daniel Schulz URL: https://github.com/BodenmillerGroup/imcRtools VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/imcRtools/issues git_url: https://git.bioconductor.org/packages/imcRtools git_branch: RELEASE_3_22 git_last_commit: 13fa3df git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/imcRtools_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/imcRtools_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/imcRtools_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/imcRtools_1.16.0.tgz vignettes: vignettes/imcRtools/inst/doc/imcRtools.html vignetteTitles: "Tools for IMC data analysis" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imcRtools/inst/doc/imcRtools.R importsMe: OSTA suggestsMe: spicyR dependencyCount: 179 Package: IMMAN Version: 1.30.0 Imports: STRINGdb, pwalign, igraph, graphics, utils, seqinr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: efe5f8663ec0ab8000c9c4e3b04ac0fe NeedsCompilation: no Title: Interlog protein network reconstruction by Mapping and Mining ANalysis Description: Reconstructing Interlog Protein Network (IPN) integrated from several Protein protein Interaction Networks (PPINs). Using this package, overlaying different PPINs to mine conserved common networks between diverse species will be applicable. biocViews: SequenceMatching, Alignment, SystemsBiology, GraphAndNetwork, Network, Proteomics Author: Minoo Ashtiani, Payman Nickchi, Abdollah Safari, Mehdi Mirzaie, Mohieddin Jafari Maintainer: Minoo Ashtiani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IMMAN git_branch: RELEASE_3_22 git_last_commit: 84fecb6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IMMAN_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IMMAN_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IMMAN_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IMMAN_1.30.0.tgz vignettes: vignettes/IMMAN/inst/doc/IMMAN.html vignetteTitles: IMMAN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMMAN/inst/doc/IMMAN.R dependencyCount: 69 Package: immApex Version: 1.4.0 Depends: R (>= 4.3.0) Imports: hash, httr, Matrix, matrixStats, methods, Rcpp, rvest, SingleCellExperiment, stats, stringr, utils LinkingTo: Rcpp Suggests: BiocStyle, dplyr, ggraph, ggplot2, igraph, knitr, markdown, Peptides, randomForest, rmarkdown, scRepertoire, spelling, testthat, tidygraph, viridis License: MIT + file LICENSE Archs: x64 MD5sum: 59da7305028218c9cfdbfd929b8cbc6a NeedsCompilation: yes Title: Tools for Adaptive Immune Receptor Sequence-Based Machine and Deep Learning Description: A set of tools to for machine and deep learning in R from amino acid and nucleotide sequences focusing on adaptive immune receptors. The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing, MotifAnnotation Author: Nick Borcherding [aut, cre] Maintainer: Nick Borcherding URL: https://github.com/BorchLab/immApex/ SystemRequirements: Python (via basilisk) VignetteBuilder: knitr BugReports: https://github.com/BorchLab/immApex/issues git_url: https://git.bioconductor.org/packages/immApex git_branch: RELEASE_3_22 git_last_commit: b2a3afb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/immApex_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/immApex_1.3.7.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/immApex_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/immApex_1.4.0.tgz vignettes: vignettes/immApex/inst/doc/immApex.html vignetteTitles: Machine and Deep Learning Models with immApex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/immApex/inst/doc/immApex.R importsMe: Ibex, scRepertoire dependencyCount: 51 Package: immunoClust Version: 1.42.0 Depends: R(>= 4.2), flowCore Imports: methods, stats, graphics, grid, lattice, grDevices Suggests: BiocStyle, utils, testthat License: Artistic-2.0 Archs: x64 MD5sum: e80f4c5716e257c086abed223d4688ab NeedsCompilation: yes Title: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry Description: immunoClust is a model based clustering approach for Flow Cytometry samples. The cell-events of single Flow Cytometry samples are modelled by a mixture of multinominal normal- or t-distributions. The cell-event clusters of several samples are modelled by a mixture of multinominal normal-distributions aiming stable co-clusters across these samples. biocViews: Clustering, FlowCytometry, SingleCell, CellBasedAssays, ImmunoOncology Author: Till Soerensen [aut, cre] Maintainer: Till Soerensen git_url: https://git.bioconductor.org/packages/immunoClust git_branch: RELEASE_3_22 git_last_commit: 3c4237c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/immunoClust_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/immunoClust_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/immunoClust_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/immunoClust_1.42.0.tgz vignettes: vignettes/immunoClust/inst/doc/immunoClust.pdf vignetteTitles: immunoClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunoClust/inst/doc/immunoClust.R dependencyCount: 20 Package: immunogenViewer Version: 1.4.0 Depends: R (>= 4.0) Imports: ggplot2, httr, jsonlite, patchwork, UniProt.ws Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), DT License: Apache License (>= 2) MD5sum: 6f1298c0a8a97ce263a2d3cc6900af84 NeedsCompilation: no Title: Visualization and evaluation of protein immunogens Description: Plots protein properties and visualizes position of peptide immunogens within protein sequence. Allows evaluation of immunogens based on structural and functional annotations to infer suitability for antibody-based methods aiming to detect native proteins. biocViews: FeatureExtraction, Proteomics, Software, Visualization Author: Katharina Waury [aut, cre] (ORCID: ) Maintainer: Katharina Waury URL: https://github.com/kathiwaury/immunogenViewer VignetteBuilder: knitr BugReports: https://github.com/kathiwaury/immunogenViewer/issues git_url: https://git.bioconductor.org/packages/immunogenViewer git_branch: RELEASE_3_22 git_last_commit: bf34fd3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/immunogenViewer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/immunogenViewer_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/immunogenViewer_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/immunogenViewer_1.4.0.tgz vignettes: vignettes/immunogenViewer/inst/doc/immunogenViewer_vignette.html vignetteTitles: Using immunogenViewer to evaluate and choose antibodies hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunogenViewer/inst/doc/immunogenViewer_vignette.R dependencyCount: 76 Package: immunotation Version: 1.18.0 Depends: R (>= 4.1) Imports: stringr, ontologyIndex, curl, ggplot2, readr, rvest, tidyr, xml2, maps, rlang Suggests: BiocGenerics, rmarkdown, BiocStyle, knitr, testthat, DT License: GPL-3 MD5sum: 7260134bfd7d2ade6ab1b21bd828f125 NeedsCompilation: no Title: Tools for working with diverse immune genes Description: MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus. More than 28,000 different HLA alleles have been reported, with significant differences in allele frequencies between human populations worldwide. Reproducible and consistent annotation of HLA alleles in large-scale bioinformatics workflows remains challenging, because the available reference databases and software tools often use different HLA naming schemes. The package immunotation provides tools for consistent annotation of HLA genes in typical immunoinformatics workflows such as for example the prediction of MHC-presented peptides in different human donors. Converter functions that provide mappings between different HLA naming schemes are based on the MHC restriction ontology (MRO). The package also provides automated access to HLA alleles frequencies in worldwide human reference populations stored in the Allele Frequency Net Database. biocViews: Software, ImmunoOncology, BiomedicalInformatics, Genetics, Annotation Author: Katharina Imkeller [cre, aut] Maintainer: Katharina Imkeller VignetteBuilder: knitr BugReports: https://github.com/imkeller/immunotation/issues git_url: https://git.bioconductor.org/packages/immunotation git_branch: RELEASE_3_22 git_last_commit: d568f5f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/immunotation_1.18.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/immunotation_1.17.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/immunotation_1.18.0.tgz vignettes: vignettes/immunotation/inst/doc/immunotation_vignette.html vignetteTitles: User guide immunotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunotation/inst/doc/immunotation_vignette.R dependencyCount: 58 Package: iModMix Version: 1.0.0 Depends: R (>= 4.5.0) Imports: config (>= 0.3.2), golem (>= 0.4.1), shiny (>= 1.7.5), ComplexHeatmap, DT, RColorBrewer, WGCNA, corrplot, cowplot, dynamicTreeCut, ggplot2, glassoFast, impute, purrr, stringr, tidyr, visNetwork, shinyBS, httr, dplyr, stats, iModMixData, SummarizedExperiment, ExperimentHub (>= 2.99.0) Suggests: testthat (>= 3.0.0), ggfortify, shinyWidgets, pROC, tuneR, knitr, curl, readxl, reshape2, vroom, here, enrichR, rmarkdown License: GPL-3 MD5sum: d6bedfcacd57c5bf2eea5816a319ff48 NeedsCompilation: no Title: Integrative Modules for Multi-Omics Data Description: The iModMix network-based method offers an integrated framework for analyzing multi-omics data, including metabolomics, proteomics, and transcriptomics data, enabling the exploration of intricate molecular associations within heterogeneous biological systems. biocViews: Software, Network, Clustering, Visualization, Transcriptomics, Proteomics, Metabolomics, GeneExpression, PrincipalComponent Author: Isis Narvaez-Bandera [aut, cre] (ORCID: ) Maintainer: Isis Narvaez-Bandera URL: https://github.com/biodatalab/iModMix VignetteBuilder: knitr BugReports: https://github.com/biodatalab/iModMix/issues git_url: https://git.bioconductor.org/packages/iModMix git_branch: RELEASE_3_22 git_last_commit: f1207f8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iModMix_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iModMix_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iModMix_1.0.0.tgz vignettes: vignettes/iModMix/inst/doc/iModMixTutorial.html vignetteTitles: Publication-ready integration omics data including unidentified metabolites hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iModMix/inst/doc/iModMixTutorial.R dependencyCount: 156 Package: IMPCdata Version: 1.46.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: 1cb38c3610f5fa83120e6e76a17e99d8 NeedsCompilation: no Title: Retrieves data from IMPC database Description: Package contains methods for data retrieval from IMPC Database. biocViews: ExperimentData Author: Natalja Kurbatova, Jeremy Mason Maintainer: Jeremy Mason git_url: https://git.bioconductor.org/packages/IMPCdata git_branch: RELEASE_3_22 git_last_commit: ae84de7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IMPCdata_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IMPCdata_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IMPCdata_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IMPCdata_1.46.0.tgz vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf vignetteTitles: IMPCdata Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IMPCdata/inst/doc/IMPCdata.R dependencyCount: 1 Package: impute Version: 1.84.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: ee7140a3554580cc68febaeb4612935c NeedsCompilation: yes Title: impute: Imputation for microarray data Description: Imputation for microarray data (currently KNN only) biocViews: Microarray Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu Maintainer: Balasubramanian Narasimhan git_url: https://git.bioconductor.org/packages/impute git_branch: RELEASE_3_22 git_last_commit: 9873337 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/impute_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/impute_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/impute_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/impute_1.84.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AMARETTO, CGHcall, TIN, curatedBreastData, imputeLCMD, moduleColor, swamp importsMe: biscuiteer, BreastSubtypeR, cola, DExMA, doppelgangR, EGAD, EpiMix, fastLiquidAssociation, genefu, genomation, iModMix, MAGAR, MatrixQCvis, MEAT, methylclock, MethylMix, miRLAB, MSnbase, netboost, pmp, POMA, REMP, RNAAgeCalc, Rnits, MetaGxBreast, MetaGxPancreas, DIscBIO, ePCR, FAMT, GSEMA, iC10, lilikoi, metamorphr, mi4p, PCAPAM50, PINSPlus, Rnmr1D, samr, speaq, WGCNA suggestsMe: BioNet, DAPAR, GeoTcgaData, graphite, MethPed, MsCoreUtils, QFeatures, qmtools, RnBeads, scp, TBSignatureProfiler, TCGAutils, corrselect, DDPNA, GSA, maGUI, metabodecon, MetChem, romic dependencyCount: 0 Package: INDEED Version: 2.24.0 Depends: glasso (>= 1.8), R (>= 3.5) Imports: devtools (>= 1.13.0), graphics (>= 3.3.1), stats (>= 3.3.1), utils (>= 3.3.1), igraph (>= 1.2.4), visNetwork(>= 2.0.6) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8), testthat (>= 2.0.0) License: Artistic-2.0 MD5sum: eb8bccc0e3d3a0059c6ee67ceb1ec45e NeedsCompilation: no Title: Interactive Visualization of Integrated Differential Expression and Differential Network Analysis for Biomarker Candidate Selection Package Description: An R package for integrated differential expression and differential network analysis based on omic data for cancer biomarker discovery. Both correlation and partial correlation can be used to generate differential network to aid the traditional differential expression analysis to identify changes between biomolecules on both their expression and pairwise association levels. A detailed description of the methodology has been published in Methods journal (PMID: 27592383). An interactive visualization feature allows for the exploration and selection of candidate biomarkers. biocViews: ImmunoOncology, Software, ResearchField, BiologicalQuestion, StatisticalMethod, DifferentialExpression, MassSpectrometry, Metabolomics Author: Yiming Zuo , Kian Ghaffari , Zhenzhi Li Maintainer: Ressom group , Yiming Zuo URL: http://github.com/ressomlab/INDEED VignetteBuilder: knitr BugReports: http://github.com/ressomlab/INDEED/issues git_url: https://git.bioconductor.org/packages/INDEED git_branch: RELEASE_3_22 git_last_commit: 00316e6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/INDEED_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/INDEED_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/INDEED_2.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/INDEED_2.24.0.tgz vignettes: vignettes/INDEED/inst/doc/Introduction_to_INDEED.pdf vignetteTitles: INDEED R package for cancer biomarker discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INDEED/inst/doc/Introduction_to_INDEED.R dependencyCount: 107 Package: infercnv Version: 1.26.0 Depends: R(>= 4.0) Imports: graphics, grDevices, RColorBrewer, gplots, futile.logger, stats, utils, methods, ape, phyclust, Matrix, fastcluster, parallelDist, dplyr, HiddenMarkov, ggplot2, edgeR, coin, caTools, digest, RANN, igraph, reshape2, rjags, fitdistrplus, future, foreach, doParallel, Seurat, BiocGenerics, SummarizedExperiment, SingleCellExperiment, tidyr, parallel, coda, gridExtra, argparse Suggests: BiocStyle, knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: 60d1ca0f9e02c4fe493b7f0948e2fee2 NeedsCompilation: no Title: Infer Copy Number Variation from Single-Cell RNA-Seq Data Description: Using single-cell RNA-Seq expression to visualize CNV in cells. biocViews: Software, CopyNumberVariation, VariantDetection, StructuralVariation, GenomicVariation, Genetics, Transcriptomics, StatisticalMethod, Bayesian, HiddenMarkovModel, SingleCell Author: Timothy Tickle [aut], Itay Tirosh [aut], Christophe Georgescu [aut, cre], Maxwell Brown [aut], Brian Haas [aut] Maintainer: Christophe Georgescu URL: https://github.com/broadinstitute/inferCNV/wiki SystemRequirements: JAGS 4.x.y VignetteBuilder: knitr BugReports: https://github.com/broadinstitute/inferCNV/issues git_url: https://git.bioconductor.org/packages/infercnv git_branch: RELEASE_3_22 git_last_commit: 334e1db git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/infercnv_1.26.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/infercnv_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/infercnv_1.26.0.tgz vignettes: vignettes/infercnv/inst/doc/inferCNV.html vignetteTitles: Visualizing Large-scale Copy Number Variation in Single-Cell RNA-Seq Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/infercnv/inst/doc/inferCNV.R suggestsMe: SCpubr dependencyCount: 194 Package: infinityFlow Version: 1.20.0 Depends: R (>= 4.0.0), flowCore Imports: stats, grDevices, utils, graphics, pbapply, matlab, png, raster, grid, uwot, gtools, Biobase, generics, parallel, methods, xgboost Suggests: knitr, rmarkdown, keras, tensorflow, glmnetUtils, e1071 License: GPL-3 MD5sum: c86d72864759f2122568b944ea9622d5 NeedsCompilation: no Title: Augmenting Massively Parallel Cytometry Experiments Using Multivariate Non-Linear Regressions Description: Pipeline to analyze and merge data files produced by BioLegend's LEGENDScreen or BD Human Cell Surface Marker Screening Panel (BD Lyoplates). biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell, Proteomics Author: Etienne Becht [cre, aut] Maintainer: Etienne Becht VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/infinityFlow git_branch: RELEASE_3_22 git_last_commit: 0cce204 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/infinityFlow_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/infinityFlow_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/infinityFlow_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/infinityFlow_1.20.0.tgz vignettes: vignettes/infinityFlow/inst/doc/basic_usage.html, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.html vignetteTitles: Basic usage of the infinityFlow package, Training non default regression models hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/infinityFlow/inst/doc/basic_usage.R, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.R dependencyCount: 41 Package: Informeasure Version: 1.20.0 Depends: R (>= 4.0) Imports: entropy Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), SummarizedExperiment License: Artistic-2.0 MD5sum: f9686837e00954c1447fe7d0fac0bac3 NeedsCompilation: no Title: R implementation of information measures Description: This package consolidates a comprehensive set of information measurements, encompassing mutual information, conditional mutual information, interaction information, partial information decomposition, and part mutual information. biocViews: GeneExpression, NetworkInference, Network, Software Author: Chu Pan [aut, cre] Maintainer: Chu Pan URL: https://github.com/chupan1218/Informeasure VignetteBuilder: knitr BugReports: https://github.com/chupan1218/Informeasure/issues git_url: https://git.bioconductor.org/packages/Informeasure git_branch: RELEASE_3_22 git_last_commit: 12cc3ed git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Informeasure_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Informeasure_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Informeasure_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Informeasure_1.20.0.tgz vignettes: vignettes/Informeasure/inst/doc/Informeasure.html vignetteTitles: Informeasure: a tool to quantify nonlinear dependence between variables in biological regulatory networks from an information theory perspective hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Informeasure/inst/doc/Informeasure.R dependencyCount: 1 Package: InPAS Version: 2.18.0 Depends: R (>= 3.5) Imports: AnnotationDbi,batchtools,Biobase,Biostrings,BSgenome,cleanUpdTSeq, depmixS4,dplyr,flock,future,future.apply,GenomeInfoDb,GenomicRanges, GenomicFeatures, ggplot2, IRanges, limma, magrittr,methods,parallelly, plyranges, preprocessCore, readr,reshape2, RSQLite, Seqinfo, stats, S4Vectors, utils Suggests: BiocGenerics,BiocManager, BiocStyle, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.UCSC.hg19, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v79, knitr, markdown, rmarkdown, rtracklayer, RUnit, grDevices, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL (>= 2) MD5sum: 8de499814a4b1ef296a485476897ec3a NeedsCompilation: no Title: Identify Novel Alternative PolyAdenylation Sites (PAS) from RNA-seq data Description: Alternative polyadenylation (APA) is one of the important post- transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites and the differential usage of APA sites from RNA-Seq data. It leverages cleanUpdTSeq to fine tune identified APA sites by removing false sites. biocViews: Alternative Polyadenylation, Differential Polyadenylation Site Usage, RNA-seq, Gene Regulation, Transcription Author: Jianhong Ou [aut, cre], Haibo Liu [aut], Lihua Julie Zhu [aut], Sungmi M. Park [aut], Michael R. Green [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InPAS git_branch: RELEASE_3_22 git_last_commit: b015763 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/InPAS_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/InPAS_2.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/InPAS_2.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/InPAS_2.18.0.tgz vignettes: vignettes/InPAS/inst/doc/InPAS.html vignetteTitles: InPAS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InPAS/inst/doc/InPAS.R dependencyCount: 144 Package: INPower Version: 1.46.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE Archs: x64 MD5sum: fb6609549052104c95cbf2113e6d8692 NeedsCompilation: no Title: An R package for computing the number of susceptibility SNPs Description: An R package for computing the number of susceptibility SNPs and power of future studies biocViews: SNP Author: Ju-Hyun Park Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/INPower git_branch: RELEASE_3_22 git_last_commit: 8fe327f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/INPower_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/INPower_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/INPower_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/INPower_1.46.0.tgz vignettes: vignettes/INPower/inst/doc/vignette.pdf vignetteTitles: INPower Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INPower/inst/doc/vignette.R dependencyCount: 2 Package: INSPEcT Version: 1.40.0 Depends: R (>= 3.6), methods, Biobase, BiocParallel Imports: pROC, deSolve, rootSolve, KernSmooth, readxl, GenomicFeatures, GenomicRanges, IRanges, BiocGenerics, GenomicAlignments, Rsamtools, S4Vectors, Seqinfo, DESeq2, plgem, rtracklayer, SummarizedExperiment, TxDb.Mmusculus.UCSC.mm9.knownGene, shiny Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: adf7198090b95abb8f667b66c1676eee NeedsCompilation: no Title: Modeling RNA synthesis, processing and degradation with RNA-seq data Description: INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data. biocViews: Sequencing, RNASeq, GeneRegulation, TimeCourse, SystemsBiology Author: Stefano de Pretis Maintainer: Stefano de Pretis , Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/INSPEcT git_branch: RELEASE_3_22 git_last_commit: c9f5ce1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/INSPEcT_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/INSPEcT_1.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/INSPEcT_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/INSPEcT_1.40.0.tgz vignettes: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html, vignettes/INSPEcT/inst/doc/INSPEcT.html vignetteTitles: INSPEcT_GUI.html, INSPEcT - INference of Synthesis,, Processing and dEgradation rates from Transcriptomic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.R, vignettes/INSPEcT/inst/doc/INSPEcT.R dependencyCount: 124 Package: INTACT Version: 1.10.0 Depends: R (>= 4.3.0) Imports: SQUAREM, bdsmatrix, numDeriv, stats, tidyr, ggplot2 Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 + file LICENSE MD5sum: c9d4e0c99fb15ff8e6b792fa2def3085 NeedsCompilation: no Title: Integrate TWAS and Colocalization Analysis for Gene Set Enrichment Analysis Description: This package integrates colocalization probabilities from colocalization analysis with transcriptome-wide association study (TWAS) scan summary statistics to implicate genes that may be biologically relevant to a complex trait. The probabilistic framework implemented in this package constrains the TWAS scan z-score-based likelihood using a gene-level colocalization probability. Given gene set annotations, this package can estimate gene set enrichment using posterior probabilities from the TWAS-colocalization integration step. biocViews: Bayesian, GeneSetEnrichment Author: Jeffrey Okamoto [aut, cre] (ORCID: ), Xiaoquan Wen [aut] (ORCID: ) Maintainer: Jeffrey Okamoto URL: https://github.com/jokamoto97/INTACT VignetteBuilder: knitr BugReports: https://github.com/jokamoto97/INTACT/issues git_url: https://git.bioconductor.org/packages/INTACT git_branch: RELEASE_3_22 git_last_commit: 3b057f6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/INTACT_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/INTACT_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/INTACT_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/INTACT_1.10.0.tgz vignettes: vignettes/INTACT/inst/doc/INTACT.html vignetteTitles: INTACT: Integrate TWAS and Colocalization Analysis for Gene Set Enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INTACT/inst/doc/INTACT.R dependencyCount: 39 Package: InTAD Version: 1.30.0 Depends: R (>= 3.5), methods, S4Vectors, IRanges, GenomicRanges, MultiAssayExperiment, SummarizedExperiment,stats Imports: BiocGenerics,Biobase,rtracklayer,parallel,graphics,mclust,qvalue, ggplot2,utils,ggpubr Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 351200bf6d33991b2933b716068cae4a NeedsCompilation: no Title: Search for correlation between epigenetic signals and gene expression in TADs Description: The package is focused on the detection of correlation between expressed genes and selected epigenomic signals (i.e. enhancers obtained from ChIP-seq data) either within topologically associated domains (TADs) or between chromatin contact loop anchors. Various parameters can be controlled to investigate the influence of external factors and visualization plots are available for each analysis step. biocViews: Epigenetics, Sequencing, ChIPSeq, RNASeq, HiC, GeneExpression,ImmunoOncology Author: Konstantin Okonechnikov, Serap Erkek, Lukas Chavez Maintainer: Konstantin Okonechnikov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InTAD git_branch: RELEASE_3_22 git_last_commit: a8e25c4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/InTAD_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/InTAD_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/InTAD_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/InTAD_1.30.0.tgz vignettes: vignettes/InTAD/inst/doc/InTAD.html vignetteTitles: Correlation of epigenetic signals and genes in TADs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InTAD/inst/doc/InTAD.R dependencyCount: 127 Package: intansv Version: 1.50.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: MIT + file LICENSE MD5sum: 288eb766a7b8fd9ae2d5f0adefb20490 NeedsCompilation: no Title: Integrative analysis of structural variations Description: This package provides efficient tools to read and integrate structural variations predicted by popular softwares. Annotation and visulation of structural variations are also implemented in the package. biocViews: Genetics, Annotation, Sequencing, Software Author: Wen Yao Maintainer: Wen Yao git_url: https://git.bioconductor.org/packages/intansv git_branch: RELEASE_3_22 git_last_commit: 0bb1a74 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/intansv_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/intansv_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/intansv_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/intansv_1.50.0.tgz vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf vignetteTitles: An Introduction to intansv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/intansv/inst/doc/intansvOverview.R dependencyCount: 136 Package: interacCircos Version: 1.20.0 Depends: R (>= 4.1) Imports: RColorBrewer, htmlwidgets, plyr, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: d21a145d086d5d954184668b40489fdb NeedsCompilation: no Title: The Generation of Interactive Circos Plot Description: Implement in an efficient approach to display the genomic data, relationship, information in an interactive circular genome(Circos) plot. 'interacCircos' are inspired by 'circosJS', 'BioCircos.js' and 'NG-Circos' and we integrate the modules of 'circosJS', 'BioCircos.js' and 'NG-Circos' into this R package, based on 'htmlwidgets' framework. biocViews: Visualization Author: Zhe Cui [aut, cre] Maintainer: Zhe Cui VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interacCircos git_branch: RELEASE_3_22 git_last_commit: 62873b8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/interacCircos_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/interacCircos_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/interacCircos_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/interacCircos_1.20.0.tgz vignettes: vignettes/interacCircos/inst/doc/interacCircos.html vignetteTitles: interacCircos hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interacCircos/inst/doc/interacCircos.R dependencyCount: 35 Package: InteractionSet Version: 1.38.0 Depends: GenomicRanges, SummarizedExperiment Imports: methods, Matrix, Rcpp, BiocGenerics, S4Vectors (>= 0.27.12), IRanges, Seqinfo LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: c62ca62246ce81ecaf497f38973db3e9 NeedsCompilation: yes Title: Base Classes for Storing Genomic Interaction Data Description: Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments. biocViews: Infrastructure, DataRepresentation, Software, HiC Author: Aaron Lun [aut, cre], Malcolm Perry [aut], Elizabeth Ing-Simmons [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InteractionSet git_branch: RELEASE_3_22 git_last_commit: b784a04 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/InteractionSet_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/InteractionSet_1.37.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/InteractionSet_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/InteractionSet_1.38.0.tgz vignettes: vignettes/InteractionSet/inst/doc/interactions.html vignetteTitles: Genomic interaction classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InteractionSet/inst/doc/interactions.R dependsOnMe: diffHic, DuplexDiscovereR, GenomicInteractions, HiCDOC, sevenC, nullrangesData importsMe: CAGEfightR, ChIPpeakAnno, DegCre, DMRcaller, EDIRquery, extraChIPs, geomeTriD, HicAggR, HiCaptuRe, HiCcompare, HiCExperiment, HiContacts, HiCParser, hicVennDiagram, linkSet, mariner, nullranges, plyinteractions, trackViewer, hicream, treediff suggestsMe: plotgardener, transmogR, updateObject, CAGEWorkflow dependencyCount: 26 Package: InteractiveComplexHeatmap Version: 1.18.0 Depends: R (>= 4.0.0), ComplexHeatmap (>= 2.11.0) Imports: grDevices, stats, shiny, grid, GetoptLong, S4Vectors (>= 0.26.1), digest, IRanges, kableExtra (>= 1.3.1), utils, svglite, htmltools, clisymbols, jsonlite, RColorBrewer, fontawesome Suggests: knitr, rmarkdown, testthat, EnrichedHeatmap, GenomicRanges, data.table, circlize, GenomicFeatures, tidyverse, tidyHeatmap, cluster, org.Hs.eg.db, simplifyEnrichment, GO.db, SC3, GOexpress, SingleCellExperiment, scater, gplots, pheatmap, airway, DESeq2, DT, cola, BiocManager, gridtext, HilbertCurve (>= 1.21.1), shinydashboard, SummarizedExperiment, pkgndep, ks License: MIT + file LICENSE MD5sum: 3c0f2b956852a18414ac2e3e7eb1f8fe NeedsCompilation: no Title: Make Interactive Complex Heatmaps Description: This package can easily make heatmaps which are produced by the ComplexHeatmap package into interactive applications. It provides two types of interactivities: 1. on the interactive graphics device, and 2. on a Shiny app. It also provides functions for integrating the interactive heatmap widgets for more complex Shiny app development. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/InteractiveComplexHeatmap VignetteBuilder: knitr BugReports: https://github.com/jokergoo/InteractiveComplexHeatmap/issues git_url: https://git.bioconductor.org/packages/InteractiveComplexHeatmap git_branch: RELEASE_3_22 git_last_commit: 592d2bb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/InteractiveComplexHeatmap_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/InteractiveComplexHeatmap_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/InteractiveComplexHeatmap_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/InteractiveComplexHeatmap_1.18.0.tgz vignettes: vignettes/InteractiveComplexHeatmap/inst/doc/decoration.html, vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.html, vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.html, vignettes/InteractiveComplexHeatmap/inst/doc/implementation.html, vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.html, vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.html, vignettes/InteractiveComplexHeatmap/inst/doc/share.html, vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.html vignetteTitles: 4. Decorations on heatmaps, 6. A Shiny app for visualizing DESeq2 results, 7. Implement interactive heatmap from scratch, 2. How interactive complex heatmap is implemented, 5. Interactivate heatmaps indirectly generated by pheatmap(),, heatmap.2() and heatmap(), 1. How to visualize heatmaps interactively, 8. Share interactive heatmaps to collaborators, 3. Functions for Shiny app development hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InteractiveComplexHeatmap/inst/doc/decoration.R, vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.R, vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.R, vignettes/InteractiveComplexHeatmap/inst/doc/implementation.R, vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.R, vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.R, vignettes/InteractiveComplexHeatmap/inst/doc/share.R, vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.R importsMe: mineSweepR suggestsMe: CTexploreR, simona, simplifyEnrichment, metasnf dependencyCount: 82 Package: interactiveDisplay Version: 1.48.0 Depends: R (>= 3.5.0), methods, BiocGenerics, grid Imports: interactiveDisplayBase (>= 1.7.3), shiny, RColorBrewer, ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi Suggests: RUnit, hgu95av2.db, knitr, GenomicRanges, SummarizedExperiment, GOstats, ggbio, GO.db, Gviz, rtracklayer, metagenomeSeq, gplots, vegan, Biobase Enhances: rstudio License: Artistic-2.0 Archs: x64 MD5sum: cc2fe5f87a037ab97d2825b38e911c21 NeedsCompilation: no Title: Package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplay package contains the methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData, ShinyApps Author: Bioconductor Package Maintainer [cre], Shawn Balcome [aut], Marc Carlson [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/interactiveDisplay git_branch: RELEASE_3_22 git_last_commit: a68c9ad git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/interactiveDisplay_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/interactiveDisplay_1.47.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/interactiveDisplay_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/interactiveDisplay_1.48.0.tgz vignettes: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.pdf vignetteTitles: interactiveDisplay: A package for enabling interactive visualization of Bioconductor objects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.R suggestsMe: metagenomeSeq dependencyCount: 104 Package: interactiveDisplayBase Version: 1.48.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny, DT Suggests: knitr, markdown Enhances: rstudioapi License: Artistic-2.0 MD5sum: 58e76ec75e5783fac3eea30fdca66bd1 NeedsCompilation: no Title: Base package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplayBase package contains the the basic methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData, ShinyApps Author: Bioconductor Package Maintainer [cre], Shawn Balcome [aut], Marc Carlson [ctb], Marcel Ramos [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/interactiveDisplayBase git_branch: RELEASE_3_22 git_last_commit: fca65ea git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/interactiveDisplayBase_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/interactiveDisplayBase_1.47.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/interactiveDisplayBase_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/interactiveDisplayBase_1.48.0.tgz vignettes: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html vignetteTitles: Using interactiveDisplayBase for Bioconductor object visualization and modification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R importsMe: interactiveDisplay dependencyCount: 49 Package: InterCellar Version: 2.16.0 Depends: R (>= 4.1) Imports: config, golem, shiny, DT, shinydashboard, shinyFiles, shinycssloaders, data.table, fs, dplyr, tidyr, circlize, colourpicker, dendextend, factoextra, ggplot2, plotly, plyr, shinyFeedback, shinyalert, tibble, umap, visNetwork, wordcloud2, readxl, htmlwidgets, colorspace, signal, scales, htmltools, ComplexHeatmap, grDevices, stats, tools, utils, biomaRt, rlang, fmsb, igraph Suggests: testthat (>= 3.0.0), knitr, rmarkdown, glue, graphite, processx, attempt, BiocStyle, httr License: MIT + file LICENSE Archs: x64 MD5sum: 82e85b8cafc125d1c924d980b004ce5e NeedsCompilation: no Title: InterCellar: an R-Shiny app for interactive analysis and exploration of cell-cell communication in single-cell transcriptomics Description: InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell communication in one or multiple conditions. biocViews: Software, SingleCell, Visualization, GO, Transcriptomics Author: Marta Interlandi [cre, aut] (ORCID: ) Maintainer: Marta Interlandi URL: https://github.com/martaint/InterCellar VignetteBuilder: knitr BugReports: https://github.com/martaint/InterCellar/issues git_url: https://git.bioconductor.org/packages/InterCellar git_branch: RELEASE_3_22 git_last_commit: 7306a93 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/InterCellar_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/InterCellar_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/InterCellar_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/InterCellar_2.16.0.tgz vignettes: vignettes/InterCellar/inst/doc/user_guide.html vignetteTitles: InterCellar User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InterCellar/inst/doc/user_guide.R dependencyCount: 200 Package: IntEREst Version: 1.34.0 Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, SummarizedExperiment, edgeR, S4Vectors, GenomicFiles Imports: seqLogo, Biostrings, GenomicFeatures (>= 1.39.4), txdbmaker, IRanges, seqinr, graphics, grDevices, stats, utils, grid, methods, DBI, RMariaDB, GenomicAlignments, BiocParallel, BiocGenerics, DEXSeq, DESeq2 Suggests: clinfun, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-2 Archs: x64 MD5sum: ce3d20b0f29c495b9e2b761e8a69241d NeedsCompilation: no Title: Intron-Exon Retention Estimator Description: This package performs Intron-Exon Retention analysis on RNA-seq data (.bam files). biocViews: Software, AlternativeSplicing, Coverage, DifferentialSplicing, Sequencing, RNASeq, Alignment, Normalization, DifferentialExpression, ImmunoOncology Author: Ali Oghabian , Dario Greco , Mikko Frilander Maintainer: Ali Oghabian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IntEREst git_branch: RELEASE_3_22 git_last_commit: 8a4b0c3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IntEREst_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IntEREst_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IntEREst_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IntEREst_1.34.0.tgz vignettes: vignettes/IntEREst/inst/doc/IntEREst.html vignetteTitles: IntEREst,, Intron Exon Retention Estimator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntEREst/inst/doc/IntEREst.R dependencyCount: 139 Package: IntramiRExploreR Version: 1.32.0 Depends: R (>= 3.4) Imports: igraph (>= 1.0.1), FGNet (>= 3.0.7), knitr (>= 1.12.3), stats, utils, grDevices, graphics Suggests: gProfileR, topGO, org.Dm.eg.db, rmarkdown, testthat License: GPL-2 MD5sum: 6f0c33d9f4a47e61b9882124bae4e7b7 NeedsCompilation: no Title: Predicting Targets for Drosophila Intragenic miRNAs Description: Intra-miR-ExploreR, an integrative miRNA target prediction bioinformatics tool, identifies targets combining expression and biophysical interactions of a given microRNA (miR). Using the tool, we have identified targets for 92 intragenic miRs in D. melanogaster, using available microarray expression data, from Affymetrix 1 and Affymetrix2 microarray array platforms, providing a global perspective of intragenic miR targets in Drosophila. Predicted targets are grouped according to biological functions using the DAVID Gene Ontology tool and are ranked based on a biologically relevant scoring system, enabling the user to identify functionally relevant targets for a given miR. biocViews: Software, Microarray, GeneTarget, StatisticalMethod, GeneExpression, GenePrediction Author: Surajit Bhattacharya and Daniel Cox Maintainer: Surajit Bhattacharya URL: https://github.com/VilainLab/IntramiRExploreR VignetteBuilder: knitr BugReports: https://github.com/VilainLab/IntramiRExploreR git_url: https://git.bioconductor.org/packages/IntramiRExploreR git_branch: RELEASE_3_22 git_last_commit: f12ae09 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IntramiRExploreR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IntramiRExploreR_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IntramiRExploreR_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IntramiRExploreR_1.32.0.tgz vignettes: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR.pdf, vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.html vignetteTitles: IntramiRExploreR.pdf, IntramiRExploreR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.R dependencyCount: 37 Package: IONiseR Version: 2.34.0 Depends: R (>= 3.4) Imports: rhdf5, dplyr, magrittr, tidyr, ShortRead, Biostrings, ggplot2, methods, BiocGenerics, XVector, tibble, stats, BiocParallel, bit64, stringr, utils Suggests: BiocStyle, knitr, rmarkdown, gridExtra, testthat, minionSummaryData License: MIT + file LICENSE MD5sum: 599c1cb03fa8f114972c379049f782df NeedsCompilation: no Title: Quality Assessment Tools for Oxford Nanopore MinION data Description: IONiseR provides tools for the quality assessment of Oxford Nanopore MinION data. It extracts summary statistics from a set of fast5 files and can be used either before or after base calling. In addition to standard summaries of the read-types produced, it provides a number of plots for visualising metrics relative to experiment run time or spatially over the surface of a flowcell. biocViews: QualityControl, DataImport, Sequencing Author: Mike Smith [aut, cre] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IONiseR git_branch: RELEASE_3_22 git_last_commit: 25723cd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IONiseR_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IONiseR_2.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IONiseR_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IONiseR_2.34.0.tgz vignettes: vignettes/IONiseR/inst/doc/IONiseR.html vignetteTitles: Quality assessment tools for nanopore data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IONiseR/inst/doc/IONiseR.R dependencyCount: 85 Package: ipdDb Version: 1.28.0 Depends: R (>= 3.5.0), methods, AnnotationDbi (>= 1.43.1), AnnotationHub Imports: Biostrings, GenomicRanges, RSQLite, DBI, IRanges, stats, assertthat Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 61104f31a176b1acf51daaf1e9e7bc65 NeedsCompilation: no Title: IPD IMGT/HLA and IPD KIR database for Homo sapiens Description: All alleles from the IPD IMGT/HLA and IPD KIR database for Homo sapiens. Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA KIR Nomenclature in non-human species Immunogenetics (2018), in preparation. biocViews: GenomicVariation, SequenceMatching, VariantAnnotation, DataRepresentation,AnnotationHubSoftware Author: Steffen Klasberg Maintainer: Steffen Klasberg URL: https://github.com/DKMS-LSL/ipdDb organism: Homo sapiens VignetteBuilder: knitr BugReports: https://github.com/DKMS-LSL/ipdDb/issues/new git_url: https://git.bioconductor.org/packages/ipdDb git_branch: RELEASE_3_22 git_last_commit: 3758ef6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ipdDb_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ipdDb_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ipdDb_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ipdDb_1.28.0.tgz vignettes: vignettes/ipdDb/inst/doc/Readme.html vignetteTitles: ipdDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ipdDb/inst/doc/Readme.R dependencyCount: 66 Package: IPO Version: 1.36.0 Depends: xcms (>= 1.50.0), rsm, CAMERA, grDevices, graphics, stats, utils Imports: BiocParallel Suggests: RUnit, BiocGenerics, msdata, mtbls2, faahKO, knitr Enhances: parallel License: GPL (>= 2) + file LICENSE MD5sum: bfae6c7f29691d4869a33c4a72f9a443 NeedsCompilation: no Title: Automated Optimization of XCMS Data Processing parameters Description: The outcome of XCMS data processing strongly depends on the parameter settings. IPO (`Isotopologue Parameter Optimization`) is a parameter optimization tool that is applicable for different kinds of samples and liquid chromatography coupled to high resolution mass spectrometry devices, fast and free of labeling steps. IPO uses natural, stable 13C isotopes to calculate a peak picking score. Retention time correction is optimized by minimizing the relative retention time differences within features and grouping parameters are optimized by maximizing the number of features showing exactly one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiment. The resulting scores are evaluated using response surface models. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Gunnar Libiseller , Christoph Magnes , Thomas Lieb Maintainer: Thomas Lieb URL: https://github.com/rietho/IPO VignetteBuilder: knitr BugReports: https://github.com/rietho/IPO/issues/new git_url: https://git.bioconductor.org/packages/IPO git_branch: RELEASE_3_22 git_last_commit: 94c7d49 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IPO_1.36.0.tar.gz vignettes: vignettes/IPO/inst/doc/IPO.html vignetteTitles: XCMS Parameter Optimization with IPO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IPO/inst/doc/IPO.R dependencyCount: 156 Package: IRanges Version: 2.44.0 Depends: R (>= 4.0.0), methods, utils, stats, BiocGenerics (>= 0.53.2), S4Vectors (>= 0.47.6) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: a169fe4941da3987cebbd383aa9b30f3 NeedsCompilation: yes Title: Foundation of integer range manipulation in Bioconductor Description: Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. 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Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre], Patrick Aboyoun [aut], Michael Lawrence [aut] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/IRanges BugReports: https://github.com/Bioconductor/IRanges/issues git_url: https://git.bioconductor.org/packages/IRanges git_branch: RELEASE_3_22 git_last_commit: 964a290 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IRanges_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IRanges_2.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IRanges_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IRanges_2.44.0.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Overview of the IRanges package hasREADME: FALSE hasNEWS: TRUE 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pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, harbChIP, LiebermanAidenHiC2009 importsMe: alabaster.bumpy, alabaster.ranges, alabaster.se, ALDEx2, AllelicImbalance, annmap, annotatr, appreci8R, ASpli, AssessORF, ATACseqQC, ATACseqTFEA, atena, ballgown, bamsignals, BBCAnalyzer, BgeeCall, BindingSiteFinder, Bioc.gff, biovizBase, biscuiteer, BiSeq, bnbc, breakpointR, bsseq, BUMHMM, BumpyMatrix, BUSpaRse, CAGEfightR, cageminer, CAGEr, cBioPortalData, cfdnakit, cfDNAPro, ChIPanalyser, chipenrich, ChIPexoQual, ChIPseeker, chipseq, ChIPseqR, ChIPsim, ChromHeatMap, ChromSCape, chromVAR, cicero, circRNAprofiler, CircSeqAlignTk, cleanUpdTSeq, cleaver, cn.mops, CNEr, CNVfilteR, CNVMetrics, CNVPanelizer, CNVRanger, CNVrd2, COCOA, coMethDMR, compEpiTools, ComplexHeatmap, CompoundDb, conumee, CopyNumberPlots, CoverageView, crisprBase, crisprBowtie, crisprDesign, crisprScore, CRISPRseek, CrispRVariants, crisprViz, crupR, csaw, CTexploreR, dada2, DAMEfinder, debrowser, DECIPHER, deconvR, DegCre, DegNorm, DelayedMatrixStats, deltaCaptureC, demuxSNP, derfinder, derfinderHelper, derfinderPlot, DEScan2, DiffBind, diffHic, diffUTR, DMRcaller, DMRcate, DMRScan, dmrseq, DNAfusion, DominoEffect, dreamlet, DRIMSeq, DropletUtils, dStruct, easyRNASeq, EDASeq, eisaR, ELMER, ELViS, enhancerHomologSearch, EnrichedHeatmap, ensembldb, EpiCompare, epidecodeR, epigraHMM, EpiMix, epimutacions, epiregulon, epistack, EpiTxDb, epivizr, epivizrData, esATAC, EventPointer, extraChIPs, FastqCleaner, fastseg, fcScan, FilterFFPE, FindIT2, fishpond, FLAMES, FRASER, G4SNVHunter, GA4GHclient, gcapc, gDNAx, geneAttribution, GENESIS, genomation, GenomAutomorphism, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicOZone, GenomicPlot, GenomicScores, GenomicTuples, geomeTriD, ggbio, gmapR, gmoviz, GOfuncR, GOpro, GOTHiC, GSVA, GUIDEseq, gVenn, gwascat, h5mread, h5vc, HDF5Array, heatmaps, hermes, HicAggR, HiCaptuRe, HiCBricks, HiCcompare, HiCExperiment, HiContacts, hicVennDiagram, HilbertCurve, hummingbird, icetea, ideal, idr2d, igblastr, InPAS, INSPEcT, intansv, InteractionSet, InteractiveComplexHeatmap, IntEREst, ipdDb, iSEEu, IsoformSwitchAnalyzeR, IVAS, karyoploteR, katdetectr, knowYourCG, linkSet, LOLA, m6Aboost, magpie, mariner, maser, MatrixRider, MDTS, MEAL, MEDIPS, MesKit, metagene2, metaseqR2, methimpute, methInheritSim, methodical, MethReg, methrix, methylCC, methylInheritance, methylKit, methylPipe, MethylSeekR, methylSig, methylumi, mia, minfi, MinimumDistance, MIRA, missMethyl, mobileRNA, Modstrings, monaLisa, mosaics, MOSim, Motif2Site, motifbreakR, motifmatchr, MotifPeeker, motifTestR, MouseFM, msa, MSA2dist, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MsExperiment, msgbsR, MSnbase, MultiAssayExperiment, MultiDataSet, mumosa, MungeSumstats, musicatk, MutationalPatterns, mutscan, NanoMethViz, NanoStringNCTools, ncRNAtools, normr, nucleoSim, nucleR, nullranges, OGRE, oligoClasses, OmaDB, OMICsPCA, openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, OutSplice, packFinder, panelcn.mops, pcaExplorer, pdInfoBuilder, peakCombiner, PhIPData, PICB, plotgardener, plyinteractions, podkat, pqsfinder, pram, prebs, preciseTAD, primirTSS, proActiv, ProteoDisco, PSMatch, PureCN, Pviz, QDNAseq, QFeatures, qpgraph, qPLEXanalyzer, qsea, QuasR, R3CPET, r3Cseq, raer, RaggedExperiment, RAIDS, ramr, RareVariantVis, RCAS, recount, recoup, REDseq, regioneR, regutools, REMP, ReportingTools, RESOLVE, rfaRm, rfPred, RgnTX, RiboCrypt, RiboDiPA, RiboProfiling, riboSeqR, ribosomeProfilingQC, rigvf, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, roar, rprimer, Rqc, Rsamtools, RSVSim, RTN, rtracklayer, sarks, saseR, SCAN.UPC, scanMiR, scanMiRApp, scDblFinder, scHOT, scPipe, scRNAseqApp, segmenter, segmentSeq, SeqArray, seqCAT, Seqinfo, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, ShortRead, signeR, signifinder, SimFFPE, SingleMoleculeFootprinting, sitadela, Site2Target, SMITE, snapcount, SNPhood, SomaticSignatures, SOMNiBUS, SparseArray, SparseSignatures, Spectra, SpectriPy, spiky, SpliceWiz, SplicingGraphs, SPLINTER, srnadiff, STADyUM, strandCheckR, StructuralVariantAnnotation, SummarizedExperiment, SynExtend, tadar, TAPseq, target, TCGAbiolinks, TCGAutils, TCseq, TENET, TFBSTools, TFEA.ChIP, TFHAZ, tidyCoverage, TnT, tracktables, trackViewer, transcriptR, transmogR, TreeSummarizedExperiment, TRESS, tricycle, tRNA, tRNAdbImport, tRNAscanImport, TVTB, txcutr, txdbmaker, tximeta, UMI4Cats, Uniquorn, universalmotif, UPDhmm, VanillaICE, VarCon, VariantAnnotation, VariantExperiment, VariantFiltering, VaSP, VDJdive, vmrseq, wavClusteR, wiggleplotr, xcms, xcore, XVector, yamss, ZygosityPredictor, fitCons.UCSC.hg19, GenomicState, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.charm.hg18.example, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.mirna.3.1, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, fourDNData, leeBamViews, MethylSeqData, pd.atdschip.tiling, sesameData, SomaticCancerAlterations, spatialLIBD, seqpac, alakazam, cpp11bigwig, crispRdesignR, cubar, GencoDymo2, geneHapR, geno2proteo, GenoPop, hahmmr, HiCociety, hoardeR, ICAMS, iimi, karyotapR, locuszoomr, lolliplot, longreadvqs, LoopRig, MAAPER, MitoHEAR, noisyr, numbat, PACVr, RapidoPGS, refseqR, revert, rnaCrosslinkOO, RTIGER, SATS, Signac, tidygenomics, VALERIE suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, BREW3R.r, CCAFE, Chicago, ClassifyR, DFplyr, easylift, epivizrChart, gDRcore, gDRutils, Glimma, GWASTools, HilbertVis, HilbertVisGUI, iscream, maftools, martini, MiRaGE, multicrispr, partCNV, plyxp, regionReport, RTCGA, S4Vectors, SigsPack, splatter, svaNUMT, svaRetro, systemPipeR, TFutils, tidybulk, MetaScope, scMultiome, systemPipeRdata, xcoredata, yeastRNASeq, fuzzyjoin, gkmSVM, MiscMetabar, MoBPS, polyRAD, pQTLdata, rliger, scPloidy, seqmagick, Seurat, sigminer, SNPassoc, updog, valr linksToMe: Bioc.gff, Biostrings, cigarillo, CNEr, DECIPHER, GenomicAlignments, GenomicRanges, kebabs, MatrixRider, pwalign, Rsamtools, rtracklayer, ShortRead, SparseArray, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 8 Package: ISAnalytics Version: 1.20.0 Depends: R (>= 4.5) Imports: utils, dplyr, readr, tidyr, purrr, rlang, tibble, stringr, fs, lubridate, lifecycle, ggplot2, ggrepel, stats, readxl, tools, grDevices, forcats, glue, shiny, shinyWidgets, datamods, bslib, vegan, data.table, DT Suggests: testthat, covr, knitr, BiocStyle, sessioninfo, rmarkdown, roxygen2, withr, extraDistr, ggalluvial, scales, gridExtra, R.utils, RefManageR, flexdashboard, circlize, plotly, gtools, eulerr, openxlsx, jsonlite, pheatmap, BiocParallel, progressr, future, doFuture, foreach, psych, Rcapture License: CC BY 4.0 Archs: x64 MD5sum: ea519838d8689d5a8b69398cb1709c8d NeedsCompilation: no Title: Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies Description: In gene therapy, stem cells are modified using viral vectors to deliver the therapeutic transgene and replace functional properties since the genetic modification is stable and inherited in all cell progeny. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites (IS), essential for monitoring the evolution of genetically modified cells in vivo. A comprehensive toolkit for the analysis of IS is required to foster clonal trackign studies and supporting the assessment of safety and long term efficacy in vivo. This package is aimed at (1) supporting automation of IS workflow, (2) performing base and advance analysis for IS tracking (clonal abundance, clonal expansions and statistics for insertional mutagenesis, etc.), (3) providing basic biology insights of transduced stem cells in vivo. biocViews: BiomedicalInformatics, Sequencing, SingleCell, CellBiology, FunctionalGenomics, DataImport Author: Francesco Gazzo [cre] (ORCID: ), Giulia Pais [aut] (ORCID: ), Andrea Calabria [aut], Giulio Spinozzi [aut] Maintainer: Francesco Gazzo URL: https://calabrialab.github.io/ISAnalytics, https://github.com//calabrialab/isanalytics, https://calabrialab.github.io/ISAnalytics/ VignetteBuilder: knitr BugReports: https://github.com/calabrialab/ISAnalytics/issues git_url: https://git.bioconductor.org/packages/ISAnalytics git_branch: RELEASE_3_22 git_last_commit: 72d44b9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ISAnalytics_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ISAnalytics_1.19.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ISAnalytics_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ISAnalytics_1.20.0.tgz vignettes: vignettes/ISAnalytics/inst/doc/ISAnalytics.html, vignettes/ISAnalytics/inst/doc/workflow_start.html vignetteTitles: ISAnalytics, workflow_start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ISAnalytics/inst/doc/ISAnalytics.R, vignettes/ISAnalytics/inst/doc/workflow_start.R dependencyCount: 117 Package: iscream Version: 1.0.0 Depends: R (>= 4.5) Imports: Rcpp, Matrix, data.table, methods, pbapply, parallelly, stringfish, LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppSpdlog, Rhtslib, stringfish Suggests: BiocFileCache, BiocStyle, bsseq, ggplot2, ggridges, knitr, microbenchmark, rmarkdown, GenomicRanges, IRanges, Rsamtools, SummarizedExperiment, S4Vectors, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: b9614328b66fb1830adb3c99d26f8f9c NeedsCompilation: yes Title: Make fast and memory efficient BED file queries, summaries and matrices Description: BED files store ranged genomic data that can be queried even when the files are compressed. iscream can query data from BED files and return them in muliple formats: parsed records or their summary statistics as data frames or GenomicRanges objects, and matrices as matrix, GenomicRanges, or SummarizedExperiment objects. iscream also provides specialized support for importing methylation data. biocViews: DataImport, Software, Sequencing, SingleCell, DNAMethylation Author: James Eapen [aut, cre] (ORCID: ), Jacob Morrison [aut] (ORCID: ), Nathan Spix [ctb], Hui Shen [aut, ths, fnd] (ORCID: ) Maintainer: James Eapen URL: https://huishenlab.github.io/iscream/, https://github.com/huishenlab/iscream/ SystemRequirements: htslib: htslib-devel (rpm) or libhts-dev (deb) & tabix: htslib-tools (rpm) or tabix (deb) & GNU make VignetteBuilder: knitr BugReports: https://github.com/huishenlab/iscream/issues/ git_url: https://git.bioconductor.org/packages/iscream git_branch: RELEASE_3_22 git_last_commit: faf115f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iscream_1.0.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iscream_1.0.0.tgz vignettes: vignettes/iscream/inst/doc/data_structures.html, vignettes/iscream/inst/doc/htslib.html, vignettes/iscream/inst/doc/iscream.html, vignettes/iscream/inst/doc/manuscript_data.html, vignettes/iscream/inst/doc/performance.html, vignettes/iscream/inst/doc/tabix.html, vignettes/iscream/inst/doc/TSS.html vignetteTitles: iscream compatible data structures, htslib.html, An introduction to iscream, Manuscript data availabiliy, Improving iscream performance, iscream vs Rsamtools::scanTabix, Plotting TSS methylation profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/iscream/inst/doc/data_structures.R, vignettes/iscream/inst/doc/htslib.R, vignettes/iscream/inst/doc/iscream.R dependencyCount: 20 Package: iSEE Version: 2.22.0 Depends: SummarizedExperiment, SingleCellExperiment Imports: methods, BiocGenerics, S4Vectors, utils, stats, shiny, shinydashboard, shinyAce, shinyjs, DT, rintrojs, ggplot2, ggrepel, colourpicker, igraph, vipor, mgcv, graphics, grDevices, viridisLite, shinyWidgets, listviewer, ComplexHeatmap, circlize, grid Suggests: testthat, covr, BiocStyle, knitr, rmarkdown, scRNAseq, TENxPBMCData, scater, DelayedArray, HDF5Array, RColorBrewer, viridis, htmltools, GenomicRanges License: MIT + file LICENSE MD5sum: 3b3222c2e76934d14cee4d17f9dba5d9 NeedsCompilation: no Title: Interactive SummarizedExperiment Explorer Description: Create an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects, including row- and column-level metadata. The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results. biocViews: CellBasedAssays, Clustering, DimensionReduction, FeatureExtraction, GeneExpression, GUI, ImmunoOncology, ShinyApps, SingleCell, Transcription, Transcriptomics, Visualization Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Aaron Lun [aut] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://isee.github.io/iSEE/ VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEE/issues git_url: https://git.bioconductor.org/packages/iSEE git_branch: RELEASE_3_22 git_last_commit: 3931647 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iSEE_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEE_2.21.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEE_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEE_2.22.0.tgz vignettes: vignettes/iSEE/inst/doc/basic.html, vignettes/iSEE/inst/doc/bigdata.html, vignettes/iSEE/inst/doc/configure.html, vignettes/iSEE/inst/doc/custom.html, vignettes/iSEE/inst/doc/ecm.html, vignettes/iSEE/inst/doc/links.html, vignettes/iSEE/inst/doc/voice.html vignetteTitles: 1. The iSEE User's Guide, 6. Using iSEE with big data, 3. Configuring iSEE apps, 5. Deploying custom panels, 4. The ExperimentColorMap Class, 2. Sharing information across panels, 7. Speech recognition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEE/inst/doc/basic.R, vignettes/iSEE/inst/doc/bigdata.R, vignettes/iSEE/inst/doc/configure.R, vignettes/iSEE/inst/doc/custom.R, vignettes/iSEE/inst/doc/ecm.R, vignettes/iSEE/inst/doc/links.R, vignettes/iSEE/inst/doc/voice.R dependsOnMe: iSEEde, iSEEhex, iSEEpathways, iSEEtree, iSEEu, miaDash, OSCA.advanced importsMe: iSEEfier, iSEEhub, iSEEindex suggestsMe: schex, DuoClustering2018, HCAData, HCATonsilData, TabulaMurisData, TabulaMurisSenisData dependencyCount: 109 Package: iSEEde Version: 1.8.0 Depends: iSEE Imports: DESeq2, edgeR, methods, S4Vectors, shiny, SummarizedExperiment Suggests: airway, BiocStyle, covr, knitr, limma, org.Hs.eg.db, RefManageR, rmarkdown, scuttle, sessioninfo, statmod, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: f0972b0465b4499f3baea32dd73f6244 NeedsCompilation: no Title: iSEE extension for panels related to differential expression analysis Description: This package contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels or modes facilitating the analysis of differential expression results. This package does not perform differential expression. Instead, it provides methods to embed precomputed differential expression results in a SummarizedExperiment object, in a manner that is compatible with interactive visualisation in iSEE applications. biocViews: Software, Infrastructure, DifferentialExpression Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Thomas Sandmann [ctb] (ORCID: ), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEde VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEde git_url: https://git.bioconductor.org/packages/iSEEde git_branch: RELEASE_3_22 git_last_commit: 71c233e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iSEEde_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEde_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEde_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEde_1.8.0.tgz vignettes: vignettes/iSEEde/inst/doc/annotations.html, vignettes/iSEEde/inst/doc/iSEEde.html, vignettes/iSEEde/inst/doc/methods.html, vignettes/iSEEde/inst/doc/rounding.html vignetteTitles: Using annotations to facilitate interactive exploration, Introduction to iSEEde, Supported differential expression methods, Rounding numeric values hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEde/inst/doc/annotations.R, vignettes/iSEEde/inst/doc/iSEEde.R, vignettes/iSEEde/inst/doc/methods.R, vignettes/iSEEde/inst/doc/rounding.R suggestsMe: iSEEpathways dependencyCount: 123 Package: iSEEfier Version: 1.6.0 Depends: R (>= 4.1.0) Imports: iSEE, iSEEu, methods, ggplot2, igraph, rlang, stats, SummarizedExperiment, SingleCellExperiment, visNetwork, BiocBaseUtils Suggests: knitr, rmarkdown, scater, scRNAseq, org.Mm.eg.db, scuttle, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: ff91df3cd69033002f45241c761da4ff NeedsCompilation: no Title: Streamlining the creation of initial states for starting an iSEE instance Description: iSEEfier provides a set of functionality to quickly and intuitively create, inspect, and combine initial configuration objects. These can be conveniently passed in a straightforward manner to the function call to launch iSEE() with the specified configuration. This package currently works seamlessly with the sets of panels provided by the iSEE and iSEEu packages, but can be extended to accommodate the usage of any custom panel (e.g. from iSEEde, iSEEpathways, or any panel developed independently by the user). biocViews: CellBasedAssays, Clustering, DimensionReduction, FeatureExtraction, GUI, GeneExpression, ImmunoOncology, ShinyApps, SingleCell, Software, Transcription, Transcriptomics, Visualization Author: Najla Abassi [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Najla Abassi URL: https://github.com/NajlaAbassi/iSEEfier VignetteBuilder: knitr BugReports: https://github.com/NajlaAbassi/iSEEfier/issues git_url: https://git.bioconductor.org/packages/iSEEfier git_branch: RELEASE_3_22 git_last_commit: 164f3c4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iSEEfier_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEfier_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEfier_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEfier_1.6.0.tgz vignettes: vignettes/iSEEfier/inst/doc/iSEEfier_userguide.html vignetteTitles: iSEEfier_userguide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEEfier/inst/doc/iSEEfier_userguide.R dependencyCount: 115 Package: iSEEhex Version: 1.12.0 Depends: SummarizedExperiment, iSEE Imports: ggplot2, hexbin, methods, shiny Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), scRNAseq, scater License: Artistic-2.0 MD5sum: 36bc9398f00ed2459e4f7ccc068bb87d NeedsCompilation: no Title: iSEE extension for summarising data points in hexagonal bins Description: This package provides panels summarising data points in hexagonal bins for `iSEE`. It is part of `iSEEu`, the iSEE universe of panels that extend the `iSEE` package. biocViews: Software, Infrastructure Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Federico Marini [aut] (ORCID: ), Aaron Lun [aut] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEhex VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEhex git_url: https://git.bioconductor.org/packages/iSEEhex git_branch: RELEASE_3_22 git_last_commit: 4898c30 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iSEEhex_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEhex_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEhex_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEhex_1.12.0.tgz vignettes: vignettes/iSEEhex/inst/doc/iSEEhex.html vignetteTitles: The iSEEhex package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEhex/inst/doc/iSEEhex.R dependsOnMe: iSEEu dependencyCount: 111 Package: iSEEhub Version: 1.12.0 Depends: SummarizedExperiment, SingleCellExperiment, ExperimentHub Imports: AnnotationHub, BiocManager, DT, iSEE, methods, rintrojs, S4Vectors, shiny, shinydashboard, shinyjs, utils Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), nullrangesData Enhances: BioPlex, biscuiteerData, bodymapRat, CLLmethylation, CopyNeutralIMA, curatedAdipoArray, curatedAdipoChIP, curatedMetagenomicData, curatedTCGAData, DMRcatedata, DuoClustering2018, easierData, emtdata, epimutacionsData, FieldEffectCrc, GenomicDistributionsData, GSE103322, GSE13015, GSE62944, HDCytoData, HMP16SData, HumanAffyData, imcdatasets, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, MethylSeqData, muscData, NxtIRFdata, ObMiTi, quantiseqr, restfulSEData, RLHub, sesameData, SimBenchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialDmelxsim, STexampleData, TabulaMurisData, TabulaMurisSenisData, TENxVisiumData, tissueTreg, VectraPolarisData, xcoredata License: Artistic-2.0 MD5sum: e5b7aae3140c4cd5a600921bb1f1a53c NeedsCompilation: no Title: iSEE for the Bioconductor ExperimentHub Description: This package defines a custom landing page for an iSEE app interfacing with the Bioconductor ExperimentHub. The landing page allows users to browse the ExperimentHub, select a data set, download and cache it, and import it directly into a Bioconductor iSEE app. biocViews: DataImport, ImmunoOncology Infrastructure, ShinyApps, SingleCell, Software Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEhub VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEhub git_url: https://git.bioconductor.org/packages/iSEEhub git_branch: RELEASE_3_22 git_last_commit: b3f4559 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iSEEhub_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEhub_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEhub_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEhub_1.12.0.tgz vignettes: vignettes/iSEEhub/inst/doc/contributing.html, vignettes/iSEEhub/inst/doc/iSEEhub.html vignetteTitles: Contributing to iSEEhub, Introduction to iSEEhub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEhub/inst/doc/contributing.R, vignettes/iSEEhub/inst/doc/iSEEhub.R dependencyCount: 142 Package: iSEEindex Version: 1.8.0 Depends: SummarizedExperiment, SingleCellExperiment Imports: BiocFileCache, DT, iSEE, methods, paws.storage, rintrojs, shiny, shinydashboard, shinyjs, stringr, urltools, utils Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, markdown, scRNAseq, sessioninfo, testthat (>= 3.0.0), yaml License: Artistic-2.0 MD5sum: 1eebe6478ce84cd14d9ee12be9d17968 NeedsCompilation: no Title: iSEE extension for a landing page to a custom collection of data sets Description: This package provides an interface to any collection of data sets within a single iSEE web-application. The main functionality of this package is to define a custom landing page allowing app maintainers to list a custom collection of data sets that users can selected from and directly load objects into an iSEE web-application. biocViews: Software, Infrastructure Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Thomas Sandmann [ctb] (ORCID: ), Federico Marini [aut] (ORCID: ), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEindex VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEindex git_url: https://git.bioconductor.org/packages/iSEEindex git_branch: RELEASE_3_22 git_last_commit: 2160c20 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iSEEindex_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEindex_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEindex_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEindex_1.8.0.tgz vignettes: vignettes/iSEEindex/inst/doc/header.html, vignettes/iSEEindex/inst/doc/iSEEindex.html, vignettes/iSEEindex/inst/doc/resources.html vignetteTitles: Adding custom header and footer to the landing page, Introduction to iSEEindex, Implementing custom iSEEindex resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEindex/inst/doc/header.R, vignettes/iSEEindex/inst/doc/iSEEindex.R, vignettes/iSEEindex/inst/doc/resources.R dependencyCount: 138 Package: iSEEpathways Version: 1.8.0 Depends: iSEE Imports: ggplot2, methods, S4Vectors, shiny, shinyWidgets, stats, SummarizedExperiment Suggests: airway, BiocStyle, covr, edgeR, fgsea, GO.db, iSEEde, knitr, org.Hs.eg.db, RefManageR, rmarkdown, scater, scuttle, sessioninfo, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: c3eed062de31d9401d562179ac815862 NeedsCompilation: no Title: iSEE extension for panels related to pathway analysis Description: This package contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels or modes facilitating the analysis of pathway analysis results. This package does not perform pathway analysis. Instead, it provides methods to embed precomputed pathway analysis results in a SummarizedExperiment object, in a manner that is compatible with interactive visualisation in iSEE applications. biocViews: Software, Infrastructure, DifferentialExpression, GeneExpression, GUI, Visualization, Pathways, GeneSetEnrichment, GO, ShinyApps Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Thomas Sandmann [ctb] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Federico Marini [ctb] (ORCID: ), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEpathways VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEpathways git_url: https://git.bioconductor.org/packages/iSEEpathways git_branch: RELEASE_3_22 git_last_commit: 8fca83e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iSEEpathways_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEpathways_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEpathways_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEpathways_1.8.0.tgz vignettes: vignettes/iSEEpathways/inst/doc/gene-ontology.html, vignettes/iSEEpathways/inst/doc/integration.html, vignettes/iSEEpathways/inst/doc/iSEEpathways.html vignetteTitles: Working with the Gene Ontology, Integration with other panels, Introduction to iSEEpathways hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEpathways/inst/doc/gene-ontology.R, vignettes/iSEEpathways/inst/doc/integration.R, vignettes/iSEEpathways/inst/doc/iSEEpathways.R dependencyCount: 110 Package: iSEEtree Version: 1.3.2 Depends: R (>= 4.4.0), iSEE (>= 2.19.4) Imports: ape, ggplot2, ggtree, grDevices, methods, miaViz, purrr, S4Vectors, shiny, mia, shinyWidgets, SingleCellExperiment, SummarizedExperiment, tidygraph, TreeSummarizedExperiment, utils Suggests: biomformat, BiocStyle, knitr, RefManageR, remotes, rmarkdown, scater, testthat (>= 3.0.0), vegan License: Artistic-2.0 MD5sum: 137533334ab5e5ae6a848eb5212d3ed3 NeedsCompilation: no Title: Interactive visualisation for microbiome data Description: iSEEtree is an extension of iSEE for the TreeSummarizedExperiment data container. It provides interactive panel designs to explore hierarchical datasets, such as the microbiome and cell lines. biocViews: Software, Visualization, Microbiome, GUI, ShinyApps, DataImport Author: Giulio Benedetti [aut, cre] (ORCID: ), Ely Seraidarian [ctb] (ORCID: ), Leo Lahti [aut] (ORCID: ) Maintainer: Giulio Benedetti URL: https://github.com/microbiome/iSEEtree VignetteBuilder: knitr BugReports: https://github.com/microbiome/iSEEtree/issues git_url: https://git.bioconductor.org/packages/iSEEtree git_branch: devel git_last_commit: b1b4d6e git_last_commit_date: 2025-08-29 Date/Publication: 2025-10-15 source.ver: src/contrib/iSEEtree_1.3.2.tar.gz vignettes: vignettes/iSEEtree/inst/doc/iSEEtree.html, vignettes/iSEEtree/inst/doc/panels.html vignetteTitles: iSEEtree, iSEEtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEtree/inst/doc/iSEEtree.R, vignettes/iSEEtree/inst/doc/panels.R importsMe: miaDash dependencyCount: 227 Package: iSEEu Version: 1.22.0 Depends: iSEE, iSEEhex Imports: methods, S4Vectors, IRanges, shiny, SummarizedExperiment, SingleCellExperiment, ggplot2 (>= 3.4.0), DT, stats, colourpicker, shinyAce Suggests: scRNAseq, scater, scran, airway, edgeR, AnnotationDbi, org.Hs.eg.db, GO.db, KEGGREST, knitr, igraph, rmarkdown, BiocStyle, htmltools, Rtsne, uwot, testthat (>= 2.1.0), covr License: MIT + file LICENSE MD5sum: 9adb46c4aff10f6fc29f60de2aba915e NeedsCompilation: no Title: iSEE Universe Description: iSEEu (the iSEE universe) contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels, or modes allowing easy configuration of iSEE applications. biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Federico Marini [aut] (ORCID: ), Aaron Lun [aut] (ORCID: ), Michael Stadler [ctb] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEu VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEEu/issues git_url: https://git.bioconductor.org/packages/iSEEu git_branch: RELEASE_3_22 git_last_commit: 9b9f27f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iSEEu_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEu_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEu_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEu_1.22.0.tgz vignettes: vignettes/iSEEu/inst/doc/iSEEu.html vignetteTitles: Panel universe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEEu/inst/doc/iSEEu.R importsMe: iSEEfier dependencyCount: 112 Package: iSeq Version: 1.62.0 Depends: R (>= 2.10.0) License: GPL (>= 2) MD5sum: 6481c80648649279a6d158ac2946e61a NeedsCompilation: yes Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models Description: Bayesian hidden Ising models are implemented to identify IP-enriched genomic regions from ChIP-seq data. They can be used to analyze ChIP-seq data with and without controls and replicates. biocViews: ChIPSeq, Sequencing Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iSeq git_branch: RELEASE_3_22 git_last_commit: 693b8cc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iSeq_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSeq_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSeq_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSeq_1.62.0.tgz vignettes: vignettes/iSeq/inst/doc/iSeq.pdf vignetteTitles: iSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSeq/inst/doc/iSeq.R dependencyCount: 0 Package: ISLET Version: 1.12.0 Depends: R(>= 4.2.0), Matrix, parallel, BiocParallel, SummarizedExperiment, BiocGenerics, lme4, nnls Imports: stats, methods, purrr, abind Suggests: BiocStyle, knitr, rmarkdown, htmltools, RUnit, dplyr License: GPL-2 MD5sum: a5741e99b18b36c570a9cf5e3e0f40c1 NeedsCompilation: no Title: Individual-Specific ceLl typE referencing Tool Description: ISLET is a method to conduct signal deconvolution for general -omics data. It can estimate the individual-specific and cell-type-specific reference panels, when there are multiple samples observed from each subject. It takes the input of the observed mixture data (feature by sample matrix), and the cell type mixture proportions (sample by cell type matrix), and the sample-to-subject information. It can solve for the reference panel on the individual-basis and conduct test to identify cell-type-specific differential expression (csDE) genes. It also improves estimated cell type mixture proportions by integrating personalized reference panels. biocViews: Software, RNASeq, Transcriptomics, Transcription, Sequencing, GeneExpression, DifferentialExpression, DifferentialMethylation Author: Hao Feng [aut, cre] (ORCID: ), Qian Li [aut], Guanqun Meng [aut] Maintainer: Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ISLET git_branch: RELEASE_3_22 git_last_commit: ab9b785 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ISLET_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ISLET_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ISLET_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ISLET_1.12.0.tgz vignettes: vignettes/ISLET/inst/doc/ISLET.html vignetteTitles: Individual-specific and cell-type-specific deconvolution using ISLET hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ISLET/inst/doc/ISLET.R dependencyCount: 55 Package: islify Version: 1.2.0 Depends: R (>= 4.5) Imports: autothresholdr (>= 1.4.2), Matrix (>= 1.6.1), RBioFormats (>= 1.0.0), tiff (>= 0.1.12), png (>= 0.1.8), dbscan (>= 1.1.12), abind (>= 1.4.8), methods (>= 4.3.3), stats (>= 4.3.3) Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: x64 MD5sum: 4cd70e972329c4b14b3a6b78509373b2 NeedsCompilation: no Title: Automatic scoring and classification of cell-based assay images Description: This software is meant to be used for classification of images of cell-based assays for neuronal surface autoantibody detection or similar techniques. It takes imaging files as input and creates a composite score from these, that for example can be used to classify samples as negative or positive for a certain antibody-specificity. The reason for its name is that I during its creation have thought about the individual picture as an archielago where we with different filters control the water level as well as ground characteristica, thereby finding islands of interest. biocViews: Software,CellBasedAssays,BiomedicalInformatics,FeatureExtraction, Visualization,Pathways,Classification Author: Jakob Theorell [aut, cre, fnd] (ORCID: , Funding provided by the Swedish Wenner-Gren Foundations) Maintainer: Jakob Theorell URL: https://github.com/Bioconductor/islify VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/islify/issues git_url: https://git.bioconductor.org/packages/islify git_branch: RELEASE_3_22 git_last_commit: 8fdf523 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/islify_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/islify_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/islify_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/islify_1.2.0.tgz vignettes: vignettes/islify/inst/doc/islify_usage.html vignetteTitles: Typical usage of islify hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/islify/inst/doc/islify_usage.R dependencyCount: 83 Package: isobar Version: 1.56.0 Depends: R (>= 2.10.0), Biobase, stats, methods Imports: distr, plyr, biomaRt, ggplot2 Suggests: MSnbase, OrgMassSpecR, XML, RJSONIO, Hmisc, gplots, RColorBrewer, gridExtra, limma, boot, DBI, MASS License: LGPL-2 MD5sum: d70986d5bebc9b08feefbd12e09a79f1 NeedsCompilation: no Title: Analysis and quantitation of isobarically tagged MSMS proteomics data Description: isobar provides methods for preprocessing, normalization, and report generation for the analysis of quantitative mass spectrometry proteomics data labeled with isobaric tags, such as iTRAQ and TMT. Features modules for integrating and validating PTM-centric datasets (isobar-PTM). More information on http://www.ms-isobar.org. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Bioinformatics, MultipleComparisons, QualityControl Author: Florian P Breitwieser and Jacques Colinge , with contributions from Alexey Stukalov , Xavier Robin and Florent Gluck Maintainer: Florian P Breitwieser URL: https://github.com/fbreitwieser/isobar BugReports: https://github.com/fbreitwieser/isobar/issues git_url: https://git.bioconductor.org/packages/isobar git_branch: RELEASE_3_22 git_last_commit: c9c34f7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/isobar_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/isobar_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/isobar_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/isobar_1.56.0.tgz vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf, vignettes/isobar/inst/doc/isobar-ptm.pdf, vignettes/isobar/inst/doc/isobar-usecases.pdf, vignettes/isobar/inst/doc/isobar.pdf vignetteTitles: isobar for developers, isobar for quantification of PTM datasets, Usecases for isobar package, isobar package for iTRAQ and TMT protein quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/isobar/inst/doc/isobar-devel.R, vignettes/isobar/inst/doc/isobar-ptm.R, vignettes/isobar/inst/doc/isobar-usecases.R, vignettes/isobar/inst/doc/isobar.R dependencyCount: 80 Package: IsoBayes Version: 1.8.0 Depends: R (>= 4.3.0) Imports: methods, Rcpp, data.table, glue, stats, doParallel, parallel, doRNG, foreach, iterators, ggplot2, HDInterval, SummarizedExperiment, S4Vectors LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 05ad35f4077d4179b487a56b2666d546 NeedsCompilation: yes Title: IsoBayes: Single Isoform protein inference Method via Bayesian Analyses Description: IsoBayes is a Bayesian method to perform inference on single protein isoforms. Our approach infers the presence/absence of protein isoforms, and also estimates their abundance; additionally, it provides a measure of the uncertainty of these estimates, via: i) the posterior probability that a protein isoform is present in the sample; ii) a posterior credible interval of its abundance. IsoBayes inputs liquid cromatography mass spectrometry (MS) data, and can work with both PSM counts, and intensities. When available, trascript isoform abundances (i.e., TPMs) are also incorporated: TPMs are used to formulate an informative prior for the respective protein isoform relative abundance. We further identify isoforms where the relative abundance of proteins and transcripts significantly differ. We use a two-layer latent variable approach to model two sources of uncertainty typical of MS data: i) peptides may be erroneously detected (even when absent); ii) many peptides are compatible with multiple protein isoforms. In the first layer, we sample the presence/absence of each peptide based on its estimated probability of being mistakenly detected, also known as PEP (i.e., posterior error probability). In the second layer, for peptides that were estimated as being present, we allocate their abundance across the protein isoforms they map to. These two steps allow us to recover the presence and abundance of each protein isoform. biocViews: StatisticalMethod, Bayesian, Proteomics, MassSpectrometry, AlternativeSplicing, Sequencing, RNASeq, GeneExpression, Genetics, Visualization, Software Author: Jordy Bollon [aut], Simone Tiberi [aut, cre] (ORCID: ) Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/IsoBayes SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/IsoBayes/issues git_url: https://git.bioconductor.org/packages/IsoBayes git_branch: RELEASE_3_22 git_last_commit: 31036f8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IsoBayes_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IsoBayes_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IsoBayes_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IsoBayes_1.8.0.tgz vignettes: vignettes/IsoBayes/inst/doc/IsoBayes.html vignetteTitles: IsoBayes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IsoBayes/inst/doc/IsoBayes.R dependencyCount: 53 Package: IsoCorrectoR Version: 1.28.0 Depends: R (>= 3.5) Imports: dplyr, magrittr, methods, quadprog, readr, readxl, stringr, tibble, tools, utils, pracma, WriteXLS Suggests: IsoCorrectoRGUI, knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 34020758120253164a25ec64c8e5b8e6 NeedsCompilation: no Title: Correction for natural isotope abundance and tracer purity in MS and MS/MS data from stable isotope labeling experiments Description: IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as ultra-high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). See the Bioconductor package IsoCorrectoRGUI for a graphical user interface to IsoCorrectoR. NOTE: With R version 4.0.0, writing correction results to Excel files may currently not work on Windows. However, writing results to csv works as before. biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoR git_branch: RELEASE_3_22 git_last_commit: 43b4678 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IsoCorrectoR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IsoCorrectoR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IsoCorrectoR_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IsoCorrectoR_1.28.0.tgz vignettes: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.R importsMe: IsoCorrectoRGUI dependencyCount: 40 Package: IsoCorrectoRGUI Version: 1.26.0 Depends: R (>= 3.6) Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: x64 MD5sum: 1186ccd37c8124053049803bfc7b6272 NeedsCompilation: no Title: Graphical User Interface for IsoCorrectoR Description: IsoCorrectoRGUI is a Graphical User Interface for the IsoCorrectoR package. IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, GUI, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Kuerner [aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoRGUI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoRGUI git_branch: RELEASE_3_22 git_last_commit: 9c84760 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IsoCorrectoRGUI_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IsoCorrectoRGUI_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IsoCorrectoRGUI_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IsoCorrectoRGUI_1.26.0.tgz vignettes: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.R suggestsMe: IsoCorrectoR dependencyCount: 43 Package: IsoformSwitchAnalyzeR Version: 2.10.0 Depends: R (>= 4.2), limma, DEXSeq, satuRn (>= 1.7.0), sva, ggplot2 (>= 3.3.5), pfamAnalyzeR Imports: methods, BSgenome, plyr, reshape2, gridExtra, Biostrings (>= 2.50.0), IRanges, GenomicRanges, RColorBrewer, rtracklayer, VennDiagram, DBI, grDevices, graphics, stats, utils, Seqinfo, grid, tximport (>= 1.7.1), tximeta (>= 1.7.12), edgeR, futile.logger, stringr, dplyr, magrittr, readr, tibble, XVector, BiocGenerics, RCurl, Biobase, SummarizedExperiment, tidyr, S4Vectors, BiocParallel, pwalign Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, rmarkdown License: GPL (>= 2) MD5sum: 821d5d087eb963d970f057c2c6ee0064 NeedsCompilation: yes Title: Identify, Annotate and Visualize Isoform Switches with Functional Consequences from both short- and long-read RNA-seq data Description: Analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff etc. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Visualization, StatisticalMethod, TranscriptomeVariant, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, Transcriptomics, RNASeq, Annotation, FunctionalPrediction, GenePrediction, DataImport, MultipleComparison, BatchEffect, ImmunoOncology Author: Kristoffer Vitting-Seerup [cre, aut] (ORCID: ), Jeroen Gilis [ctb] (ORCID: ) Maintainer: Kristoffer Vitting-Seerup URL: http://bioconductor.org/packages/IsoformSwitchAnalyzeR/ VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/IsoformSwitchAnalyzeR/issues git_url: https://git.bioconductor.org/packages/IsoformSwitchAnalyzeR git_branch: RELEASE_3_22 git_last_commit: 34e7c83 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IsoformSwitchAnalyzeR_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IsoformSwitchAnalyzeR_2.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IsoformSwitchAnalyzeR_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IsoformSwitchAnalyzeR_2.10.0.tgz vignettes: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html vignetteTitles: IsoformSwitchAnalyzeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.R dependencyCount: 152 Package: ISoLDE Version: 1.38.0 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) MD5sum: dcbddf50c2557607c8493629e64fc75f NeedsCompilation: yes Title: Integrative Statistics of alleLe Dependent Expression Description: This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below. biocViews: ImmunoOncology, GeneExpression, Transcription, GeneSetEnrichment, Genetics, Sequencing, RNASeq, MultipleComparison, SNP, GeneticVariability, Epigenetics, MathematicalBiology, GeneRegulation Author: Christelle Reynès [aut, cre], Marine Rohmer [aut], Guilhem Kister [aut] Maintainer: Christelle Reynès URL: www.r-project.org git_url: https://git.bioconductor.org/packages/ISoLDE git_branch: RELEASE_3_22 git_last_commit: 2d795af git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ISoLDE_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ISoLDE_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ISoLDE_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ISoLDE_1.38.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: ITALICS Version: 2.70.0 Depends: R (>= 2.0.0), GLAD, ITALICSData, oligo, affxparser, pd.mapping50k.xba240 Imports: affxparser, DBI, GLAD, oligo, oligoClasses, stats Suggests: pd.mapping50k.hind240, pd.mapping250k.sty, pd.mapping250k.nsp License: GPL-2 MD5sum: 8aff6c044bc9a90309b571a1c5951882 NeedsCompilation: no Title: ITALICS Description: A Method to normalize of Affymetrix GeneChip Human Mapping 100K and 500K set biocViews: Microarray, CopyNumberVariation Author: Guillem Rigaill, Philippe Hupe Maintainer: Guillem Rigaill URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/ITALICS git_branch: RELEASE_3_22 git_last_commit: 4a9bedd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ITALICS_2.70.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ITALICS_2.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ITALICS_2.70.0.tgz vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf vignetteTitles: ITALICS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ITALICS/inst/doc/ITALICS.R dependencyCount: 60 Package: iterativeBMA Version: 1.68.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: 901271baced9d5f0eff684dc082bd208 NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) algorithm Description: The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402). biocViews: Microarray, Classification Author: Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter Maintainer: Ka Yee Yeung URL: http://faculty.washington.edu/kayee/research.html git_url: https://git.bioconductor.org/packages/iterativeBMA git_branch: RELEASE_3_22 git_last_commit: 03b19fe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iterativeBMA_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iterativeBMA_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iterativeBMA_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iterativeBMA_1.68.0.tgz vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMA/inst/doc/iterativeBMA.R dependencyCount: 22 Package: iterativeBMAsurv Version: 1.68.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: 3176b2ad79390fa560cc2934c6592560 NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis Description: The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data. biocViews: Microarray Author: Amalia Annest, University of Washington, Tacoma, WA Ka Yee Yeung, University of Washington, Seattle, WA Maintainer: Ka Yee Yeung URL: http://expression.washington.edu/ibmasurv/protected git_url: https://git.bioconductor.org/packages/iterativeBMAsurv git_branch: RELEASE_3_22 git_last_commit: 42729d3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/iterativeBMAsurv_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iterativeBMAsurv_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iterativeBMAsurv_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iterativeBMAsurv_1.68.0.tgz vignettes: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm For Survival Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.R dependencyCount: 19 Package: IVAS Version: 2.30.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, Biobase Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, Seqinfo, ggfortify, grDevices, methods, Matrix, BiocParallel,utils, stats Suggests: BiocStyle License: GPL-2 MD5sum: 16f5e1b818ccd590cc5ecc0c671fba1c NeedsCompilation: no Title: Identification of genetic Variants affecting Alternative Splicing Description: Identification of genetic variants affecting alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Sangsoo Kim Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IVAS git_branch: RELEASE_3_22 git_last_commit: 5aaa72a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/IVAS_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IVAS_2.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IVAS_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IVAS_2.30.0.tgz vignettes: vignettes/IVAS/inst/doc/IVAS.pdf vignetteTitles: IVAS : Identification of genetic Variants affecting Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IVAS/inst/doc/IVAS.R dependencyCount: 113 Package: ivygapSE Version: 1.32.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: shiny, survival, survminer, hwriter, plotly, ggplot2, S4Vectors, graphics, stats, utils, UpSetR Suggests: knitr, png, limma, grid, DT, randomForest, digest, testthat, rmarkdown, BiocStyle, magick, statmod, codetools License: Artistic-2.0 Archs: x64 MD5sum: 63fd9c4b7dbcb8c2447231f637fb0877 NeedsCompilation: no Title: A SummarizedExperiment for Ivy-GAP data Description: Define a SummarizedExperiment and exploratory app for Ivy-GAP glioblastoma image, expression, and clinical data. biocViews: Transcription, Software, Visualization, Survival, GeneExpression, Sequencing Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ivygapSE git_branch: RELEASE_3_22 git_last_commit: 97f6008 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ivygapSE_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ivygapSE_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ivygapSE_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ivygapSE_1.32.0.tgz vignettes: vignettes/ivygapSE/inst/doc/ivygapSE.html vignetteTitles: ivygapSE -- SummarizedExperiment for Ivy-GAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ivygapSE/inst/doc/ivygapSE.R dependencyCount: 149 Package: IWTomics Version: 1.33.0 Depends: R (>= 3.5.0), GenomicRanges Imports: parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools Suggests: knitr License: GPL (>=2) Archs: x64 MD5sum: 5e0a9ebaea24008c48aa8c4f7a09ca01 NeedsCompilation: no Title: Interval-Wise Testing for Omics Data Description: Implementation of the Interval-Wise Testing (IWT) for omics data. This inferential procedure tests for differences in "Omics" data between two groups of genomic regions (or between a group of genomic regions and a reference center of symmetry), and does not require fixing location and scale at the outset. biocViews: StatisticalMethod, MultipleComparison, DifferentialExpression, DifferentialMethylation, DifferentialPeakCalling, GenomeAnnotation, DataImport Author: Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini Maintainer: Marzia A Cremona VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IWTomics git_branch: devel git_last_commit: 841c61c git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/IWTomics_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IWTomics_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IWTomics_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IWTomics_1.34.0.tgz vignettes: vignettes/IWTomics/inst/doc/IWTomics.pdf vignetteTitles: Introduction to IWTomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IWTomics/inst/doc/IWTomics.R dependencyCount: 58 Package: jazzPanda Version: 1.2.0 Depends: R (>= 4.5.0) Imports: spatstat.geom, dplyr, glmnet, caret, foreach, stats, magrittr, doParallel, BiocParallel, methods, BumpyMatrix,SpatialExperiment Suggests: BiocStyle, knitr, rmarkdown, spatstat, Seurat, statmod, corrplot, ggplot2, ggraph, ggrepel, gridExtra, reshape2, igraph, jsonlite, vdiffr, patchwork, ggpubr, tidyr, SpatialFeatureExperiment, ExperimentHub, TENxXeniumData, SingleCellExperiment, SFEData, Matrix, data.table, scran, scater, grid, GenomeInfoDb, testthat (>= 3.0.0) License: GPL-3 MD5sum: 072476d55dd7b0c3c30d4d2b94fe6ace NeedsCompilation: no Title: Finding spatially relevant marker genes in image based spatial transcriptomics data Description: This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise. biocViews: Spatial, GeneExpression, DifferentialExpression, StatisticalMethod, Transcriptomics Author: Melody Jin [aut, cre] (ORCID: ) Maintainer: Melody Jin URL: https://github.com/phipsonlab/jazzPanda, https://bhuvad.github.io/jazzPanda/ VignetteBuilder: knitr BugReports: https://github.com/phipsonlab/jazzPanda/issues git_url: https://git.bioconductor.org/packages/jazzPanda git_branch: RELEASE_3_22 git_last_commit: 8e3c90a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/jazzPanda_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/jazzPanda_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/jazzPanda_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/jazzPanda_1.2.0.tgz vignettes: vignettes/jazzPanda/inst/doc/jazzPanda.html vignetteTitles: jazzPanda example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/jazzPanda/inst/doc/jazzPanda.R dependencyCount: 136 Package: karyoploteR Version: 1.36.0 Depends: R (>= 3.4), regioneR, GenomicRanges, methods Imports: regioneR, GenomicRanges, IRanges, Rsamtools, stats, graphics, memoise, rtracklayer, Seqinfo, GenomeInfoDb, S4Vectors, biovizBase, digest, bezier, GenomicFeatures, bamsignals, AnnotationDbi, grDevices, VariantAnnotation Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, magrittr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Hs.eg.db, org.Mm.eg.db, pasillaBamSubset License: Artistic-2.0 MD5sum: 72c4e734047ddcbf490286773be38cee NeedsCompilation: no Title: Plot customizable linear genomes displaying arbitrary data Description: karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones. biocViews: Visualization, CopyNumberVariation, Sequencing, Coverage, DNASeq, ChIPSeq, MethylSeq, DataImport, OneChannel Author: Bernat Gel [aut, cre] (ORCID: ) Maintainer: Bernat Gel URL: https://github.com/bernatgel/karyoploteR VignetteBuilder: knitr BugReports: https://github.com/bernatgel/karyoploteR/issues git_url: https://git.bioconductor.org/packages/karyoploteR git_branch: RELEASE_3_22 git_last_commit: 50652ef git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/karyoploteR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/karyoploteR_1.35.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/karyoploteR_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/karyoploteR_1.36.0.tgz vignettes: vignettes/karyoploteR/inst/doc/karyoploteR.html vignetteTitles: karyoploteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/karyoploteR/inst/doc/karyoploteR.R dependsOnMe: CopyNumberPlots importsMe: CNVfilteR, CNViz, multicrispr suggestsMe: Category, EpiMix, UPDhmm, MitoHEAR dependencyCount: 132 Package: katdetectr Version: 1.12.0 Depends: R (>= 4.2) Imports: Biobase (>= 2.54.0), BiocParallel (>= 1.26.2), BSgenome (>= 1.62.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.3), BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.4), changepoint (>= 2.2.3), changepoint.np (>= 1.0.3), checkmate (>= 2.0.0), dplyr (>= 1.0.8), GenomeInfoDb (>= 1.28.4), GenomicRanges (>= 1.44.0), ggplot2 (>= 3.3.5), ggtext (>= 0.1.1), IRanges (>= 2.26.0), maftools (>= 2.10.5), methods (>= 4.1.3), plyranges (>= 1.17.0), Rdpack (>= 2.3.1), rlang (>= 1.0.2), S4Vectors (>= 0.30.2), scales (>= 1.2.0), tibble (>= 3.1.6), tidyr (>= 1.2.0), tools, utils, VariantAnnotation (>= 1.38.0) Suggests: BiocStyle (>= 2.26.0), knitr (>= 1.37), rmarkdown (>= 2.13), stats, testthat (>= 3.0.0) License: GPL-3 + file LICENSE Archs: x64 MD5sum: 9b08da96de7617a3becaced8691c1a1c NeedsCompilation: no Title: Detection, Characterization and Visualization of Kataegis in Sequencing Data Description: Kataegis refers to the occurrence of regional hypermutation and is a phenomenon observed in a wide range of malignancies. Using changepoint detection katdetectr aims to identify putative kataegis foci from common data-formats housing genomic variants. Katdetectr has shown to be a robust package for the detection, characterization and visualization of kataegis. biocViews: WholeGenome, Software, SNP, Sequencing, Classification, VariantAnnotation Author: Daan Hazelaar [aut, cre] (ORCID: ), Job van Riet [aut] (ORCID: ), Harmen van de Werken [ths] (ORCID: ) Maintainer: Daan Hazelaar URL: https://doi.org/doi:10.18129/B9.bioc.katdetectr VignetteBuilder: knitr BugReports: https://github.com/ErasmusMC-CCBC/katdetectr/issues git_url: https://git.bioconductor.org/packages/katdetectr git_branch: RELEASE_3_22 git_last_commit: 3e318d9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/katdetectr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/katdetectr_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/katdetectr_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/katdetectr_1.12.0.tgz vignettes: vignettes/katdetectr/inst/doc/General_overview.html vignetteTitles: Overview_katdetectr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/katdetectr/inst/doc/General_overview.R dependencyCount: 125 Package: KBoost Version: 1.18.0 Depends: R (>= 4.1), stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 | GPL-3 MD5sum: 2890fdc97332a57a35f46ff59a9b7918 NeedsCompilation: no Title: Inference of gene regulatory networks from gene expression data Description: Reconstructing gene regulatory networks and transcription factor activity is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-art algorithm are often not able to handle large amounts of data. Furthermore, many of the present methods predict numerous false positives and are unable to integrate other sources of information such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. KBoost can also use a prior network built on previously known transcription factor targets. We have benchmarked KBoost using three different datasets against other high performing algorithms. The results show that our method compares favourably to other methods across datasets. biocViews: Network, GraphAndNetwork, Bayesian, NetworkInference, GeneRegulation, Transcriptomics, SystemsBiology, Transcription, GeneExpression, Regression, PrincipalComponent Author: Luis F. Iglesias-Martinez [aut, cre] (ORCID: ), Barbara de Kegel [aut], Walter Kolch [aut] Maintainer: Luis F. Iglesias-Martinez URL: https://github.com/Luisiglm/KBoost VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KBoost git_branch: RELEASE_3_22 git_last_commit: c7af30f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/KBoost_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KBoost_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KBoost_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KBoost_1.18.0.tgz vignettes: vignettes/KBoost/inst/doc/KBoost.html vignetteTitles: KBoost hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KBoost/inst/doc/KBoost.R dependencyCount: 2 Package: KCsmart Version: 2.68.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: 1e3e4ae9a01b4a9887f2ad2a29be9c27 NeedsCompilation: no Title: Multi sample aCGH analysis package using kernel convolution Description: Multi sample aCGH analysis package using kernel convolution biocViews: CopyNumberVariation, Visualization, aCGH, Microarray Author: Jorma de Ronde, Christiaan Klijn, Arno Velds Maintainer: Jorma de Ronde git_url: https://git.bioconductor.org/packages/KCsmart git_branch: RELEASE_3_22 git_last_commit: 2e7c515 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/KCsmart_2.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KCsmart_2.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KCsmart_2.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KCsmart_2.68.0.tgz vignettes: vignettes/KCsmart/inst/doc/KCS.pdf vignetteTitles: KCsmart example session hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KCsmart/inst/doc/KCS.R dependencyCount: 19 Package: kebabs Version: 1.44.0 Depends: R (>= 3.3.0), Biostrings (>= 2.35.5), kernlab Imports: methods, stats, Rcpp (>= 0.11.2), Matrix (>= 1.5-0), XVector (>= 0.7.3), S4Vectors (>= 0.27.3), e1071, LiblineaR, graphics, grDevices, utils, apcluster LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, Biobase, BiocGenerics, knitr License: GPL (>= 2.1) MD5sum: 085aed39643f9c682043ae2754a959ee NeedsCompilation: yes Title: Kernel-Based Analysis of Biological Sequences Description: The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions. biocViews: SupportVectorMachine, Classification, Clustering, Regression Author: Johannes Palme [aut], Ulrich Bodenhofer [aut, cre, ths] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/kebabs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kebabs git_branch: RELEASE_3_22 git_last_commit: db39eb7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/kebabs_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/kebabs_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/kebabs_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/kebabs_1.44.0.tgz vignettes: vignettes/kebabs/inst/doc/kebabs.pdf vignetteTitles: KeBABS - An R Package for Kernel Based Analysis of Biological Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kebabs/inst/doc/kebabs.R dependsOnMe: procoil importsMe: odseq dependencyCount: 26 Package: KEGGgraph Version: 1.70.0 Depends: R (>= 3.5.0) Imports: methods, XML (>= 2.3-0), graph, utils, RCurl, Rgraphviz Suggests: RBGL, testthat, RColorBrewer, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) MD5sum: e2a546b76f932eb47aadb7b876ab63c4 NeedsCompilation: no Title: KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor Description: KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: Jitao David Zhang, with inputs from Paul Shannon and Hervé Pagès Maintainer: Jitao David Zhang URL: https://accio.github.io/research/#software git_url: https://git.bioconductor.org/packages/KEGGgraph git_branch: RELEASE_3_22 git_last_commit: f90447d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/KEGGgraph_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KEGGgraph_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KEGGgraph_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KEGGgraph_1.70.0.tgz vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf vignetteTitles: KEGGgraph: graph approach to KEGG PATHWAY, KEGGgraph: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraph.R, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R dependsOnMe: lpNet, ROntoTools, SPIA importsMe: clipper, DEGraph, EnrichmentBrowser, MetaboSignal, MWASTools, NCIgraph, pathview, iCARH suggestsMe: DEGraph, GenomicRanges, kangar00, maGUI, rags2ridges dependencyCount: 14 Package: KEGGlincs Version: 1.36.0 Depends: R (>= 3.3), KOdata, hgu133a.db, org.Hs.eg.db (>= 3.3.0) Imports: AnnotationDbi,KEGGgraph,igraph,plyr,gtools,httr,RJSONIO,KEGGREST, methods,graphics,stats,utils, XML, grDevices Suggests: BiocManager (>= 1.20.3), knitr, graph License: GPL-3 MD5sum: 2f7a030b261b306c30ca9a3f85d84ad1 NeedsCompilation: no Title: Visualize all edges within a KEGG pathway and overlay LINCS data Description: See what is going on 'under the hood' of KEGG pathways by explicitly re-creating the pathway maps from information obtained from KGML files. biocViews: NetworkInference, GeneExpression, DataRepresentation, ThirdPartyClient,CellBiology,GraphAndNetwork,Pathways,KEGG,Network Author: Shana White Maintainer: Shana White , Mario Medvedovic SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGlincs git_branch: RELEASE_3_22 git_last_commit: d01fd07 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/KEGGlincs_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KEGGlincs_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KEGGlincs_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KEGGlincs_1.36.0.tgz vignettes: vignettes/KEGGlincs/inst/doc/Example-workflow.html vignetteTitles: KEGGlincs Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGlincs/inst/doc/Example-workflow.R dependencyCount: 61 Package: keggorthology Version: 2.62.0 Depends: R (>= 2.5.0), hgu95av2.db, graph Imports: AnnotationDbi, DBI, grDevices, methods, tools, utils Suggests: RBGL,ALL License: Artistic-2.0 MD5sum: 9b0071ce888ba3de0177c6cbd85fa760 NeedsCompilation: no Title: graph support for KO, KEGG Orthology Description: graphical representation of the Feb 2010 KEGG Orthology. The KEGG orthology is a set of pathway IDs that are not to be confused with the KEGG ortholog IDs. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/keggorthology git_branch: RELEASE_3_22 git_last_commit: 5b39ffd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/keggorthology_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/keggorthology_2.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/keggorthology_2.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/keggorthology_2.62.0.tgz vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf vignetteTitles: keggorthology overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/keggorthology/inst/doc/keggorth.R suggestsMe: MLInterfaces dependencyCount: 46 Package: KEGGREST Version: 1.50.0 Depends: R (>= 3.5.0) Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, BiocStyle, knitr, markdown License: Artistic-2.0 MD5sum: 719627205317fc510d64d84158fdce8f NeedsCompilation: no Title: Client-side REST access to the Kyoto Encyclopedia of Genes and Genomes (KEGG) Description: A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see ). Note that KEGGREST is based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. biocViews: Annotation, Pathways, ThirdPartyClient, KEGG Author: Dan Tenenbaum [aut], Bioconductor Package Maintainer [aut, cre], Martin Morgan [ctb], Kozo Nishida [ctb], Marcel Ramos [ctb], Kristina Riemer [ctb], Lori Shepherd [ctb], Jeremy Volkening [ctb] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/KEGGREST VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/KEGGREST/issues git_url: https://git.bioconductor.org/packages/KEGGREST git_branch: RELEASE_3_22 git_last_commit: bb924dc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/KEGGREST_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KEGGREST_1.49.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KEGGREST_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KEGGREST_1.50.0.tgz vignettes: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html vignetteTitles: Accessing the KEGG REST API hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R dependsOnMe: ROntoTools, Hiiragi2013 importsMe: ADAM, adSplit, AnnotationDbi, attract, BiocSet, ChIPpeakAnno, CNEr, EnrichmentBrowser, FELLA, funOmics, gage, ginmappeR, MetaboDynamics, MetaboSignal, MWASTools, PADOG, pairkat, pathview, SBGNview, SMITE, terapadog, transomics2cytoscape, YAPSA, WayFindR suggestsMe: anansi, Category, categoryCompare, dmGsea, gatom, GenomicRanges, globaltest, iSEEu, MLP, padma, rGREAT, RTopper, SomaScan.db, CALANGO, ggpicrust2, maGUI, phoenics, ReporterScore, scDiffCom dependencyCount: 24 Package: KinSwingR Version: 1.28.0 Depends: R (>= 3.5) Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 4f8534fe668a2f37c365779fecda3b92 NeedsCompilation: no Title: KinSwingR: network-based kinase activity prediction Description: KinSwingR integrates phosphosite data derived from mass-spectrometry data and kinase-substrate predictions to predict kinase activity. Several functions allow the user to build PWM models of kinase-subtrates, statistically infer PWM:substrate matches, and integrate these data to infer kinase activity. biocViews: Proteomics, SequenceMatching, Network Author: Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KinSwingR git_branch: RELEASE_3_22 git_last_commit: 5de1986 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/KinSwingR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KinSwingR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KinSwingR_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KinSwingR_1.28.0.tgz vignettes: vignettes/KinSwingR/inst/doc/KinSwingR.html vignetteTitles: KinSwingR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KinSwingR/inst/doc/KinSwingR.R dependencyCount: 36 Package: kissDE Version: 1.30.0 Imports: aods3, Biobase, DESeq2, DSS, ggplot2, gplots, graphics, grDevices, matrixStats, stats, utils, foreach, doParallel, parallel, shiny, shinycssloaders, ade4, factoextra, DT Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: b2a3d9836a5a84052f59f60ebf5899d3 NeedsCompilation: no Title: Retrieves Condition-Specific Variants in RNA-Seq Data Description: Retrieves condition-specific variants in RNA-seq data (SNVs, alternative-splicings, indels). It has been developed as a post-treatment of 'KisSplice' but can also be used with user's own data. biocViews: AlternativeSplicing, DifferentialSplicing, ExperimentalDesign, GenomicVariation, RNASeq, Transcriptomics Author: Clara Benoit-Pilven [aut], Camille Marchet [aut], Janice Kielbassa [aut], Lilia Brinza [aut], Audric Cologne [aut], Aurélie Siberchicot [aut, cre], Vincent Lacroix [aut], Frank Picard [ctb], Laurent Jacob [ctb], Vincent Miele [ctb] Maintainer: Aurélie Siberchicot URL: https://github.com/lbbe-software/kissDE git_url: https://git.bioconductor.org/packages/kissDE git_branch: RELEASE_3_22 git_last_commit: ffd8523 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/kissDE_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/kissDE_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/kissDE_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/kissDE_1.30.0.tgz vignettes: vignettes/kissDE/inst/doc/kissDE.pdf vignetteTitles: kissDE.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kissDE/inst/doc/kissDE.R dependencyCount: 201 Package: kmcut Version: 1.4.0 Imports: survival, tools, methods, pracma, doParallel, foreach, parallel, SummarizedExperiment, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, License: Artistic-2.0 MD5sum: 2880eab06b23ee2afd302af3affafb32 NeedsCompilation: no Title: Optimized Kaplan Meier analysis and identification and validation of prognostic biomarkers Description: The purpose of the package is to identify prognostic biomarkers and an optimal numeric cutoff for each biomarker that can be used to stratify a group of test subjects (samples) into two sub-groups with significantly different survival (better vs. worse). The package was developed for the analysis of gene expression data, such as RNA-seq. However, it can be used with any quantitative variable that has a sufficiently large proportion of unique values. biocViews: Software, StatisticalMethod, GeneExpression, Survival Author: Igor Kuznetsov [aut, cre], Javed Khan [aut] Maintainer: Igor Kuznetsov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kmcut git_branch: RELEASE_3_22 git_last_commit: 1fb44d1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/kmcut_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/kmcut_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/kmcut_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/kmcut_1.4.0.tgz vignettes: vignettes/kmcut/inst/doc/kmcut_intro.html vignetteTitles: kmcut_intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kmcut/inst/doc/kmcut_intro.R dependencyCount: 33 Package: KnowSeq Version: 1.24.0 Depends: R (>= 4.0), cqn (>= 1.28.1) Imports: stringr, methods, ggplot2 (>= 3.3.0), jsonlite, kernlab, rlist, rmarkdown, reshape2, e1071, randomForest, caret, XML, praznik, R.utils, httr, sva (>= 3.30.1), edgeR (>= 3.24.3), limma (>= 3.38.3), grDevices, graphics, stats, utils, Hmisc (>= 4.4.0), gridExtra Suggests: knitr License: GPL (>=2) MD5sum: 4b572a60fcc00d0a277e6eea1c7d92e3 NeedsCompilation: no Title: KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline Description: KnowSeq proposes a novel methodology that comprises the most relevant steps in the Transcriptomic gene expression analysis. KnowSeq expects to serve as an integrative tool that allows to process and extract relevant biomarkers, as well as to assess them through a Machine Learning approaches. Finally, the last objective of KnowSeq is the biological knowledge extraction from the biomarkers (Gene Ontology enrichment, Pathway listing and Visualization and Evidences related to the addressed disease). Although the package allows analyzing all the data manually, the main strenght of KnowSeq is the possibilty of carrying out an automatic and intelligent HTML report that collect all the involved steps in one document. It is important to highligh that the pipeline is totally modular and flexible, hence it can be started from whichever of the different steps. KnowSeq expects to serve as a novel tool to help to the experts in the field to acquire robust knowledge and conclusions for the data and diseases to study. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataImport, Classification, FeatureExtraction, Sequencing, RNASeq, BatchEffect, Normalization, Preprocessing, QualityControl, Genetics, Transcriptomics, Microarray, Alignment, Pathways, SystemsBiology, GO, ImmunoOncology Author: Daniel Castillo-Secilla [aut, cre], Juan Manuel Galvez [ctb], Francisco Carrillo-Perez [ctb], Marta Verona-Almeida [ctb], Daniel Redondo-Sanchez [ctb], Francisco Manuel Ortuno [ctb], Luis Javier Herrera [ctb], Ignacio Rojas [ctb] Maintainer: Daniel Castillo-Secilla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KnowSeq git_branch: RELEASE_3_22 git_last_commit: 803fc01 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/KnowSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KnowSeq_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KnowSeq_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KnowSeq_1.24.0.tgz vignettes: vignettes/KnowSeq/inst/doc/KnowSeq.html vignetteTitles: The KnowSeq users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KnowSeq/inst/doc/KnowSeq.R dependencyCount: 168 Package: knowYourCG Version: 1.6.0 Depends: R (>= 4.4.0) Imports: sesameData, dplyr, methods, rlang, GenomicRanges, IRanges, reshape2, S4Vectors, stats, stringr, utils, ggplot2, ggrepel, tibble, wheatmap, magrittr Suggests: testthat (>= 3.0.0), SummarizedExperiment, rmarkdown, knitr, sesame, gprofiler2, ggrastr License: MIT + file LICENSE Archs: x64 MD5sum: 0275ac6349721ed1c62ca98e1d1bf7d1 NeedsCompilation: yes Title: Functional analysis of DNA methylome datasets Description: KnowYourCG (KYCG) is a supervised learning framework designed for the functional analysis of DNA methylation data. Unlike existing tools that focus on genes or genomic intervals, KnowYourCG directly targets CpG dinucleotides, featuring automated supervised screenings of diverse biological and technical influences, including sequence motifs, transcription factor binding, histone modifications, replication timing, cell-type-specific methylation, and trait-epigenome associations. KnowYourCG addresses the challenges of data sparsity in various methylation datasets, including low-pass Nanopore sequencing, single-cell DNA methylomes, 5-hydroxymethylation profiles, spatial DNA methylation maps, and array-based datasets for epigenome-wide association studies and epigenetic clocks. biocViews: Epigenetics, DNAMethylation, Sequencing, SingleCell, Spatial, MethylationArray Author: Zhou Wanding [aut] (ORCID: ), Goldberg David [aut, cre] (ORCID: ), Fu Hongxiang [ctb] Maintainer: Goldberg David URL: https://github.com/zhou-lab/knowYourCG VignetteBuilder: knitr BugReports: https://github.com/zhou-lab/knowYourCG/issues git_url: https://git.bioconductor.org/packages/knowYourCG git_branch: RELEASE_3_22 git_last_commit: b6973cd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/knowYourCG_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/knowYourCG_1.5.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/knowYourCG_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/knowYourCG_1.6.0.tgz vignettes: vignettes/knowYourCG/inst/doc/Array.html, vignettes/knowYourCG/inst/doc/Continuous.html, vignettes/knowYourCG/inst/doc/Sequencing.html vignetteTitles: "2. Array Data Analysis", "3. Continuous Variable Enrichment Analysis", "1. Sequencing Data Analysis" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/knowYourCG/inst/doc/Array.R, vignettes/knowYourCG/inst/doc/Continuous.R, vignettes/knowYourCG/inst/doc/Sequencing.R dependencyCount: 90 Package: koinar Version: 1.4.0 Depends: R (>= 4.3) Imports: httr, jsonlite, methods, utils Suggests: BiocManager, BiocStyle (>= 2.26), httptest, knitr, lattice, msdata, OrgMassSpecR, protViz, S4Vectors, Spectra, testthat, mzR License: Apache License 2.0 MD5sum: 55d1867bb5e5a715643ebcb252813b1b NeedsCompilation: no Title: KoinaR - Remote machine learning inference using Koina Description: A client to simplify fetching predictions from the Koina web service. Koina is a model repository enabling the remote execution of models. Predictions are generated as a response to HTTP/S requests, the standard protocol used for nearly all web traffic. biocViews: MassSpectrometry, Proteomics, Infrastructure, Software Author: Ludwig Lautenbacher [aut, cre] (ORCID: ), Christian Panse [aut] (ORCID: ) Maintainer: Ludwig Lautenbacher URL: https://github.com/wilhelm-lab/koina VignetteBuilder: knitr BugReports: https://github.com/wilhelm-lab/koina/issues git_url: https://git.bioconductor.org/packages/koinar git_branch: RELEASE_3_22 git_last_commit: ce41812 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/koinar_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/koinar_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/koinar_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/koinar_1.4.0.tgz vignettes: vignettes/koinar/inst/doc/koina.html vignetteTitles: On using the R lang client for koina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/koinar/inst/doc/koina.R dependencyCount: 11 Package: LACE Version: 2.14.0 Depends: R (>= 4.2.0) Imports: curl, igraph, foreach, doParallel, sortable, dplyr, forcats, data.tree, graphics, grDevices, parallel, RColorBrewer, Rfast, stats, SummarizedExperiment, utils, purrr, stringi, stringr, Matrix, tidyr, jsonlite, readr, configr, DT, tools, fs, data.table, htmltools, htmlwidgets, bsplus, shinyvalidate, shiny, shinythemes, shinyFiles, shinyjs, shinyBS, shinydashboard, biomaRt, callr, logr, ggplot2, svglite Suggests: BiocGenerics, BiocStyle, testthat, knitr, rmarkdown License: file LICENSE MD5sum: 91b0868c642f45ba20c671cee5c35512 NeedsCompilation: no Title: Longitudinal Analysis of Cancer Evolution (LACE) Description: LACE is an algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a weighed likelihood function computed on multiple time points. biocViews: BiomedicalInformatics, SingleCell, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] (ORCID: ), Gianluca Ascolani [aut] Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/LACE VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/LACE git_url: https://git.bioconductor.org/packages/LACE git_branch: RELEASE_3_22 git_last_commit: 8399e5f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LACE_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LACE_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LACE_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LACE_2.14.0.tgz vignettes: vignettes/LACE/inst/doc/v1_introduction.html, vignettes/LACE/inst/doc/v2_running_LACE.html, vignettes/LACE/inst/doc/v3_LACE_interface.html vignetteTitles: Introduction, Running LACE, LACE-interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LACE/inst/doc/v1_introduction.R, vignettes/LACE/inst/doc/v2_running_LACE.R, vignettes/LACE/inst/doc/v3_LACE_interface.R dependencyCount: 159 Package: LBE Version: 1.78.0 Depends: stats Imports: graphics, stats, utils Suggests: qvalue License: GPL-2 MD5sum: 8759cb212ff13108aa4a0a8d9d4b45b0 NeedsCompilation: no Title: Estimation of the false discovery rate Description: LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. biocViews: MultipleComparison Author: Cyril Dalmasso Maintainer: Cyril Dalmasso git_url: https://git.bioconductor.org/packages/LBE git_branch: RELEASE_3_22 git_last_commit: 0a3adb8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LBE_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LBE_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LBE_1.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LBE_1.78.0.tgz vignettes: vignettes/LBE/inst/doc/LBE.pdf vignetteTitles: LBE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LBE/inst/doc/LBE.R dependencyCount: 3 Package: ldblock Version: 1.40.0 Depends: R (>= 3.5), methods, rlang Imports: BiocGenerics (>= 0.25.1), Seqinfo, httr, Matrix Suggests: RUnit, knitr, BiocStyle, gwascat, rmarkdown, snpStats, VariantAnnotation, GenomeInfoDb, ensembldb, EnsDb.Hsapiens.v75, Rsamtools, GenomicFiles (>= 1.13.6) License: Artistic-2.0 MD5sum: 5ec0e4684cb1dd381e967013c12142ef NeedsCompilation: no Title: data structures for linkage disequilibrium measures in populations Description: Define data structures for linkage disequilibrium measures in populations. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ldblock git_branch: RELEASE_3_22 git_last_commit: debfb00 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ldblock_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ldblock_1.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ldblock_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ldblock_1.40.0.tgz vignettes: vignettes/ldblock/inst/doc/ldblock.html vignetteTitles: ldblock package: linkage disequilibrium data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ldblock/inst/doc/ldblock.R dependencyCount: 24 Package: LEA Version: 3.22.0 Depends: R (>= 3.3.0), methods, stats, utils, graphics Suggests: knitr License: GPL-3 MD5sum: e16ae35cdcd2a62fa049e9d54e8fc3ec NeedsCompilation: yes Title: LEA: an R package for Landscape and Ecological Association Studies Description: LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset statistics based on new or predicted environments (genetic.gap, genetic.offset). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets. biocViews: Software, Statistical Method, Clustering, Regression Author: Eric Frichot , Olivier Francois , Clement Gain Maintainer: Olivier Francois URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LEA git_branch: RELEASE_3_22 git_last_commit: ab2d86d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LEA_3.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LEA_3.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LEA_3.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LEA_3.22.0.tgz vignettes: vignettes/LEA/inst/doc/LEA.pdf vignetteTitles: LEA: An R Package for Landscape and Ecological Association Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LEA/inst/doc/LEA.R suggestsMe: tidypopgen dependencyCount: 4 Package: LedPred Version: 1.44.0 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: 1e00d5f1951600f5b5f885018b4fa11e NeedsCompilation: no Title: Learning from DNA to Predict Enhancers Description: This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences. biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq, Sequencing, Classification Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez Maintainer: Aitor Gonzalez BugReports: https://github.com/aitgon/LedPred/issues git_url: https://git.bioconductor.org/packages/LedPred git_branch: RELEASE_3_22 git_last_commit: 087da83 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LedPred_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LedPred_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LedPred_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LedPred_1.44.0.tgz vignettes: vignettes/LedPred/inst/doc/LedPred.pdf vignetteTitles: LedPred Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LedPred/inst/doc/LedPred.R dependencyCount: 63 Package: lefser Version: 1.19.2 Depends: SummarizedExperiment, R (>= 4.5.0) Imports: coin, MASS, ggplot2, S4Vectors, stats, methods, utils, dplyr, testthat, tibble, tidyr, forcats, stringr, ggtree, BiocGenerics, ape, ggrepel, mia, purrr, tidyselect, treeio Suggests: knitr, rmarkdown, curatedMetagenomicData, BiocStyle, phyloseq, pkgdown, covr, withr License: Artistic-2.0 MD5sum: 45f5d5a761a8cf4d08e2dca5960fba44 NeedsCompilation: no Title: R implementation of the LEfSE method for microbiome biomarker discovery Description: lefser is the R implementation of the popular microbiome biomarker discovery too, LEfSe. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers from two-level classes (and optional sub-classes). biocViews: Software, Sequencing, DifferentialExpression, Microbiome, StatisticalMethod, Classification Author: Sehyun Oh [cre, ctb] (ORCID: ), Asya Khleborodova [aut], Samuel Gamboa-Tuz [ctb], Marcel Ramos [ctb] (ORCID: ), Ludwig Geistlinger [ctb] (ORCID: ), Levi Waldron [ctb] (ORCID: ) Maintainer: Sehyun Oh URL: https://github.com/waldronlab/lefser VignetteBuilder: knitr BugReports: https://github.com/waldronlab/lefser/issues git_url: https://git.bioconductor.org/packages/lefser git_branch: devel git_last_commit: 6430a61 git_last_commit_date: 2025-10-24 Date/Publication: 2025-10-27 source.ver: src/contrib/lefser_1.19.2.tar.gz vignettes: vignettes/lefser/inst/doc/lefser.html vignetteTitles: Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lefser/inst/doc/lefser.R suggestsMe: dar, curatedMetagenomicData, ggpicrust2 dependencyCount: 207 Package: lemur Version: 1.8.0 Depends: R (>= 4.1) Imports: stats, utils, irlba, methods, SingleCellExperiment, SummarizedExperiment, rlang (>= 1.1.0), vctrs (>= 0.6.0), glmGamPoi (>= 1.12.0), BiocGenerics, S4Vectors, Matrix, DelayedMatrixStats, HDF5Array, MatrixGenerics, matrixStats, Rcpp, harmony (>= 1.2.0), limma, BiocNeighbors LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), tidyverse, uwot, dplyr, edgeR, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: d56bb4ca489678faa4717a3bdbe9d79d NeedsCompilation: yes Title: Latent Embedding Multivariate Regression Description: Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed. biocViews: Transcriptomics, DifferentialExpression, SingleCell, DimensionReduction, Regression Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/lemur VignetteBuilder: knitr BugReports: https://github.com/const-ae/lemur/issues git_url: https://git.bioconductor.org/packages/lemur git_branch: RELEASE_3_22 git_last_commit: c361bc9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/lemur_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lemur_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lemur_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lemur_1.8.0.tgz vignettes: vignettes/lemur/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lemur/inst/doc/Introduction.R dependencyCount: 70 Package: les Version: 1.60.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 52305ab8113a2ec8512d89317c39f830 NeedsCompilation: no Title: Identifying Differential Effects in Tiling Microarray Data Description: The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes. biocViews: Microarray, DifferentialExpression, ChIPchip, DNAMethylation, Transcription Author: Julian Gehring, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/les git_branch: RELEASE_3_22 git_last_commit: b8e7a70 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/les_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/les_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/les_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/les_1.60.0.tgz vignettes: vignettes/les/inst/doc/les.pdf vignetteTitles: Introduction to the les package: Identifying Differential Effects in Tiling Microarray Data with the Loci of Enhanced Significance Framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/les/inst/doc/les.R importsMe: GSRI dependencyCount: 13 Package: levi Version: 1.28.0 Imports: DT(>= 0.4), RColorBrewer(>= 1.1-2), colorspace(>= 1.3-2), dplyr(>= 0.7.4), ggplot2(>= 2.2.1), httr(>= 1.3.1), igraph(>= 1.2.1), reshape2(>= 1.4.3), shiny(>= 1.0.5), shinydashboard(>= 0.7.0), shinyjs(>= 1.0), xml2(>= 1.2.0), knitr, Rcpp (>= 0.12.18), grid, grDevices, stats, utils, testthat, methods, rmarkdown LinkingTo: Rcpp Suggests: rmarkdown, BiocStyle License: GPL (>= 2) Archs: x64 MD5sum: 74d8db9331c8cdce37ab042625ba064d NeedsCompilation: yes Title: Landscape Expression Visualization Interface Description: The tool integrates data from biological networks with transcriptomes, displaying a heatmap with surface curves to evidence the altered regions. biocViews: GeneExpression, Sequencing, Network, Software Author: Rafael Pilan [aut], Isabelle Silva [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/levi git_branch: RELEASE_3_22 git_last_commit: 3d30784 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/levi_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/levi_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/levi_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/levi_1.28.0.tgz vignettes: vignettes/levi/inst/doc/levi.html vignetteTitles: "Using levi" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/levi/inst/doc/levi.R dependencyCount: 95 Package: lfa Version: 2.10.0 Depends: R (>= 4.0) Imports: utils, methods, corpcor, RSpectra Suggests: knitr, ggplot2, testthat, BEDMatrix, genio License: GPL (>= 3) MD5sum: 14ea97249d98544df7a8b8452c14f94d NeedsCompilation: yes Title: Logistic Factor Analysis for Categorical Data Description: Logistic Factor Analysis is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. The main method estimates genetic population structure from genotype data. There are also methods for estimating individual-specific allele frequencies using the population structure. Lastly, a structured Hardy-Weinberg equilibrium (HWE) test is developed, which quantifies the goodness of fit of the genotype data to the estimated population structure, via the estimated individual-specific allele frequencies (all of which generalizes traditional HWE tests). biocViews: SNP, DimensionReduction, PrincipalComponent, Regression Author: Wei Hao [aut], Minsun Song [aut], Alejandro Ochoa [aut, cre] (ORCID: ), John D. Storey [aut] (ORCID: ) Maintainer: Alejandro Ochoa URL: https://github.com/StoreyLab/lfa VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/lfa/issues git_url: https://git.bioconductor.org/packages/lfa git_branch: RELEASE_3_22 git_last_commit: 9604f60 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/lfa_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lfa_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lfa_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lfa_2.10.0.tgz vignettes: vignettes/lfa/inst/doc/lfa.pdf vignetteTitles: lfa Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lfa/inst/doc/lfa.R importsMe: gcatest suggestsMe: jackstraw dependencyCount: 12 Package: Lheuristic Version: 1.2.0 Depends: R (>= 4.4.0) Imports: Hmisc, stats, energy, grDevices, graphics, utils, MultiAssayExperiment, ggplot2, ggpubr Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: bf908a53747bc1b79e6577ef99f2553c NeedsCompilation: no Title: Detection of scatterplots with L-shaped pattern Description: The Lheuristic package identifies scatterpots that follow and L-shaped, negative distribution. It can be used to identify genes regulated by methylation by integration of an expression and a methylation array. The package uses two different methods to detect expression and methyaltion L- shapped scatterplots. The parameters can be changed to detect other scatterplot patterns. biocViews: DNAMethylation, StatisticalMethod, MethylationArray Author: Sanchez Pla Alex [aut, cre] (ORCID: ), Miro Cau Berta [aut] (ORCID: ) Maintainer: Sanchez Pla Alex URL: https://github.com/ASPresearch/Lheuristic VignetteBuilder: knitr BugReports: https://github.com/ASPresearch/Lheuristic/issues git_url: https://git.bioconductor.org/packages/Lheuristic git_branch: RELEASE_3_22 git_last_commit: ef97be8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Lheuristic_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Lheuristic_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Lheuristic_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Lheuristic_1.2.0.tgz vignettes: vignettes/Lheuristic/inst/doc/vignette.html vignetteTitles: L-shaped selection from methylation and expression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/Lheuristic/inst/doc/vignette.R dependencyCount: 127 Package: limma Version: 3.66.0 Depends: R (>= 3.6.0) Imports: grDevices, graphics, stats, utils, methods, statmod Suggests: BiasedUrn, ellipse, gplots, knitr, locfit, MASS, splines, affy, AnnotationDbi, Biobase, BiocStyle, GO.db, illuminaio, org.Hs.eg.db, vsn License: GPL (>=2) MD5sum: 2942e95892caca27e60800ec303e3d79 NeedsCompilation: yes Title: Linear Models for Microarray and Omics Data Description: Data analysis, linear models and differential expression for omics data. biocViews: ExonArray, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, DataImport, Bayesian, Clustering, Regression, TimeCourse, Microarray, MicroRNAArray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel, Sequencing, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl, BiomedicalInformatics, CellBiology, Cheminformatics, Epigenetics, FunctionalGenomics, Genetics, ImmunoOncology, Metabolomics, Proteomics, SystemsBiology, Transcriptomics Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Goknur Giner [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb], Charity Law [ctb], Mengbo Li [ctb], Lizhong Chen [ctb] Maintainer: Gordon Smyth URL: https://bioinf.wehi.edu.au/limma/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/limma git_branch: RELEASE_3_22 git_last_commit: 1c4b971 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/limma_3.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/limma_3.65.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/limma_3.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/limma_3.66.0.tgz vignettes: vignettes/limma/inst/doc/usersguide.pdf, vignettes/limma/inst/doc/intro.html vignetteTitles: limma User's Guide, A brief introduction to limma hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limma/inst/doc/intro.R dependsOnMe: ASpli, BLMA, cghMCR, codelink, convert, Cormotif, DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, HTqPCR, IsoformSwitchAnalyzeR, limpa, marray, metagenomeSeq, metaseqR2, mpra, NanoTube, octad, protGear, qpcrNorm, qusage, RBM, RnBeads, Rnits, splineTimeR, TMSig, TOAST, tRanslatome, TurboNorm, variancePartition, wateRmelon, zenith, CCl4, Fletcher2013a, HD2013SGI, ReactomeGSA.data, EGSEA123, maEndToEnd, methylationArrayAnalysis, RNAseq123, OSCA.advanced, OSCA.basic, OSCA.workflows, BALLI, BioInsight, CEDA, cp4p, DAAGbio, DRomics, fmt, PerfMeas importsMe: a4Base, ABSSeq, affycoretools, affylmGUI, AMARETTO, animalcules, ArrayExpress, arrayQuality, artMS, ATACseqQC, ATACseqTFEA, attract, autonomics, AWFisher, ballgown, barbieQ, BatchQC, BERT, biotmle, BloodGen3Module, bnem, bsseq, bumphunter, casper, ChAMP, CleanUpRNAseq, clusterExperiment, CNVRanger, combi, compcodeR, consensusDE, consensusOV, crlmm, csaw, cTRAP, ctsGE, DAMEfinder, DaMiRseq, debrowser, DeeDeeExperiment, DEP, derfinderPlot, DESpace, DEsubs, DExMA, DiffBind, diffcyt, diffHic, diffUTR, distinct, DMRcate, Doscheda, dreamlet, DRIMSeq, DspikeIn, EGAD, EGSEA, eisaR, EnrichmentBrowser, epigraHMM, EpiMix, erccdashboard, EventPointer, EWCE, ExploreModelMatrix, flowBin, gCrisprTools, GDCRNATools, genefu, GeneSelectMMD, GEOquery, gg4way, Glimma, GRaNIE, GWAS.BAYES, HarmonizR, hermes, HERON, HTqPCR, icetea, iCheck, iChip, iCOBRA, ideal, InPAS, KnowSeq, lemur, limmaGUI, LimROTS, Linnorm, lipidr, lmdme, markeR, mastR, MatrixQCvis, MBECS, MBQN, MEAL, methylKit, MethylMix, microbiomeExplorer, miloR, minfi, MIRit, miRLAB, missMethyl, MLSeq, moanin, monocle, MoonlightR, msImpute, mspms, msqrob2, MSstats, MSstatsTMT, MultiDataSet, muscat, mutscan, NADfinder, NanoMethViz, nethet, nondetects, NormalyzerDE, OLIN, omicRexposome, OVESEG, PAA, PADOG, pairedGSEA, PanomiR, PathoStat, pcaExplorer, PECA, PepSetTest, pepStat, phantasus, phenomis, phenoTest, PhosR, PolySTest, POMA, POWSC, projectR, PRONE, psichomics, qmtools, qPLEXanalyzer, qsea, RegEnrich, regsplice, RFLOMICS, RNAseqCovarImpute, roastgsa, ROSeq, RTN, RTopper, saseR, satuRn, scClassify, scone, scran, scviR, seqsetvis, shinyDSP, shinyepico, singleCellTK, SmartPhos, sparrow, speckle, SPsimSeq, standR, STATegRa, Statial, sva, timecourse, TOP, ToxicoGx, TPP, TPP2D, transcriptogramer, TVTB, tweeDEseq, unifiedWMWqPCR, vsclust, vsn, weitrix, Wrench, XAItest, yamss, yarn, BeadArrayUseCases, signatureSearchData, spatialLIBD, ExpHunterSuite, ExpressionNormalizationWorkflow, recountWorkflow, aliases2entrez, batchtma, BPM, Cascade, cinaR, DiPALM, dsb, eLNNpairedCov, Grouphmap, GSEMA, GWASbyCluster, hicream, lfproQC, lilikoi, limorhyde2, lipidomeR, MetAlyzer, metaMA, mi4p, miRtest, MKmisc, MKomics, MSclassifR, newIMVC, nlcv, OncoSubtype, Patterns, plfMA, promor, RANKS, RCPA, RPPanalyzer, scBio, scGOclust, scRNAtools, scROSHI, ssizeRNA, tinyarray, TransProR, treediff, wrProteo suggestsMe: ABarray, ADaCGH2, Biobase, biobroom, BiocSet, BioNet, BioQC, blase, Category, categoryCompare, celaref, CellBench, CellMixS, ChIPpeakAnno, ClassifyR, CMA, coGPS, CONSTANd, cydar, Damsel, DAPAR, dar, dearseq, DEGreport, derfinder, DEScan2, dyebias, easyreporting, EnMCB, extraChIPs, fgsea, fishpond, gage, GeoTcgaData, geva, glmGamPoi, GSRI, GSVA, Harman, Heatplus, iSEEde, isobar, ivygapSE, les, lumi, MAST, methylumi, MLP, npGSEA, oligo, oppar, piano, PREDA, proDA, puma, QFeatures, qsvaR, raer, randRotation, recountmethylation, ribosomeProfilingQC, rtracklayer, Rvisdiff, signifinder, SpliceWiz, stageR, subSeq, systemPipeR, tadar, TCGAbiolinks, TFEA.ChIP, tidybulk, topconfects, tximeta, tximport, ViSEAGO, zFPKM, BloodCancerMultiOmics2017, bugphyzz, GeuvadisTranscriptExpr, mammaPrintData, msigdb, seventyGeneData, arrays, CAGEWorkflow, fluentGenomics, simpleSingleCell, AnnoProbe, aroma.affymetrix, canvasXpress, corncob, DGEobj.utils, easybio, ggpicrust2, GiANT, hexbin, inDAGO, limorhyde, maGUI, NACHO, pctax, Platypus, pmartR, protti, RepeatedHighDim, SCdeconR, seqgendiff, Seurat, simphony, st, volcano3D, wrGraph, wrMisc dependencyCount: 6 Package: limmaGUI Version: 1.86.0 Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot, xtable, utils License: GPL (>=2) MD5sum: a56abcfc6fdcafc1045d1bf656e699a2 NeedsCompilation: no Title: GUI for limma Package With Two Color Microarrays Description: A Graphical User Interface for differential expression analysis of two-color microarray data using the limma package. biocViews: GUI, GeneExpression, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, TwoChannel, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [aut], Gordon Smyth [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limmaGUI/ git_url: https://git.bioconductor.org/packages/limmaGUI git_branch: RELEASE_3_22 git_last_commit: dd885bc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/limmaGUI_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/limmaGUI_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/limmaGUI_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/limmaGUI_1.86.0.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf, vignettes/limmaGUI/inst/doc/LinModIntro.pdf, vignettes/limmaGUI/inst/doc/about.html, vignettes/limmaGUI/inst/doc/CustMenu.html, vignettes/limmaGUI/inst/doc/import.html, vignettes/limmaGUI/inst/doc/index.html, vignettes/limmaGUI/inst/doc/InputFiles.html, vignettes/limmaGUI/inst/doc/lgDevel.html, vignettes/limmaGUI/inst/doc/windowsFocus.html vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette, LinModIntro.pdf, about.html, CustMenu.html, import.html, index.html, InputFiles.html, lgDevel.html, windowsFocus.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R dependencyCount: 11 Package: limpa Version: 1.2.0 Depends: limma Imports: methods, stats, data.table, statmod Suggests: arrow, knitr, BiocStyle License: GPL (>=2) MD5sum: e473a26d70bbefba6cd0f850fe35865a NeedsCompilation: no Title: Quantification and Differential Analysis of Proteomics Data Description: Quantification and differential analysis of mass-spectrometry proteomics data, with probabilistic recovery of information from missing values. Estimates the detection probability curve (DPC), which relates the probability of successful detection to the underlying expression level of each peptide, and uses it to incorporate peptide missing values into protein quantification and into subsequent differential expression analyses. The package produces objects suitable for downstream analysis in limma. The package accepts peptide-level data with missing values and produces complete protein quantifications without missing values. The uncertainty introduced by missing value imputation is propagated through to the limma analyses using variance modeling and precision weights. The package name "limpa" is an acronym for "Linear Models for Proteomics Data". biocViews: Bayesian, BiologicalQuestion, DataImport, DifferentialExpression, GeneExpression, MassSpectrometry, Preprocessing, Proteomics, Regression, Software Author: Mengbo Li [aut] (ORCID: ), Pedro Baldoni [ctb] (ORCID: ), Gordon Smyth [cre, aut] (ORCID: ) Maintainer: Gordon Smyth VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/limpa git_branch: RELEASE_3_22 git_last_commit: 8349fa0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/limpa_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/limpa_1.1.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/limpa_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/limpa_1.2.0.tgz vignettes: vignettes/limpa/inst/doc/limpa.html vignetteTitles: Analyzing mass spectrometry data with limpa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limpa/inst/doc/limpa.R dependencyCount: 8 Package: limpca Version: 1.6.0 Depends: R (>= 3.5.0) Imports: ggplot2, stringr, plyr, ggrepel, reshape2, grDevices, graphics, doParallel, parallel, dplyr, tibble, tidyr, ggsci, tidyverse, methods, stats, SummarizedExperiment, S4Vectors Suggests: BiocStyle, pander, rmarkdown, car, gridExtra, knitr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 3b989006f2ac5e4a2c5a4d4536b73a17 NeedsCompilation: no Title: An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods Description: This package has for objectives to provide a method to make Linear Models for high-dimensional designed data. limpca applies a GLM (General Linear Model) version of ASCA and APCA to analyse multivariate sample profiles generated by an experimental design. ASCA/APCA provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design and contrarily to MANOVA, it can deal with mutlivariate datasets having more variables than observations. This method can handle unbalanced design. biocViews: StatisticalMethod, PrincipalComponent, Regression, Visualization, ExperimentalDesign, MultipleComparison, GeneExpression, Metabolomics Author: Bernadette Govaerts [aut, ths], Sebastien Franceschini [ctb], Robin van Oirbeek [ctb], Michel Thiel [aut], Pascal de Tullio [dtc], Manon Martin [aut, cre] (ORCID: ), Nadia Benaiche [ctb] Maintainer: Manon Martin URL: https://github.com/ManonMartin/limpca, https://manonmartin.github.io/limpca/ VignetteBuilder: knitr BugReports: https://github.com/ManonMartin/limpca/issues git_url: https://git.bioconductor.org/packages/limpca git_branch: RELEASE_3_22 git_last_commit: 2c1edbe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/limpca_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/limpca_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/limpca_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/limpca_1.6.0.tgz vignettes: vignettes/limpca/inst/doc/limpca.html, vignettes/limpca/inst/doc/Trout.html, vignettes/limpca/inst/doc/UCH.html vignetteTitles: Get started with limpca, Analysis of the Trout dataset with limpca, Analysis of the UCH dataset with limpca hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limpca/inst/doc/limpca.R, vignettes/limpca/inst/doc/Trout.R, vignettes/limpca/inst/doc/UCH.R dependencyCount: 133 Package: LimROTS Version: 1.2.0 Depends: R (>= 4.5.0), SummarizedExperiment Imports: limma, stringr, qvalue, utils, stats, BiocParallel, S4Vectors, dplyr Suggests: BiocStyle, ggplot2, magick, testthat (>= 3.0.0), knitr, rmarkdown, caret, ROTS License: Artistic-2.0 MD5sum: 1c2ac0ca950e198d889ef54d5735761f NeedsCompilation: no Title: LimROTS: A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Differential Expression Analysis Description: Differential expression analysis is a prevalent method utilised in the examination of diverse biological data. The reproducibility-optimized test statistic (ROTS) modifies a t-statistic based on the data's intrinsic characteristics and ranks features according to their statistical significance for differential expression between two or more groups (f-statistic). Focussing on proteomics and metabolomics, the current ROTS implementation cannot account for technical or biological covariates such as MS batches or gender differences among the samples. Consequently, we developed LimROTS, which employs a reproducibility-optimized test statistic utilising the limma methodology to simulate complex experimental designs. LimROTS is a hybrid method integrating empirical bayes and reproducibility-optimized statistics for robust analysis of proteomics and metabolomics data. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics, ImmunoOncology, Metabolomics, mRNAMicroarray Author: Ali Mostafa Anwar [aut, cre] (ORCID: ), Leo Lahti [aut, ths] (ORCID: ), Akewak Jeba [aut, ctb] (ORCID: ), Eleanor Coffey [aut, ths] (ORCID: ) Maintainer: Ali Mostafa Anwar URL: https://github.com/AliYoussef96/LimROTS, https://aliyoussef96.github.io/LimROTS/ VignetteBuilder: knitr BugReports: https://github.com/AliYoussef96/LimROTS/issues git_url: https://git.bioconductor.org/packages/LimROTS git_branch: RELEASE_3_22 git_last_commit: a9ce432 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LimROTS_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LimROTS_1.1.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LimROTS_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LimROTS_1.2.0.tgz vignettes: vignettes/LimROTS/inst/doc/LimROTS.html vignetteTitles: LimROTS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LimROTS/inst/doc/LimROTS.R dependencyCount: 67 Package: lineagespot Version: 1.14.0 Imports: VariantAnnotation, MatrixGenerics, SummarizedExperiment, data.table, stringr, httr, utils Suggests: BiocStyle, RefManageR, rmarkdown, knitr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 134bdf2b54730e020987fed027bdc5d6 NeedsCompilation: no Title: Detection of SARS-CoV-2 lineages in wastewater samples using next-generation sequencing Description: Lineagespot is a framework written in R, and aims to identify SARS-CoV-2 related mutations based on a single (or a list) of variant(s) file(s) (i.e., variant calling format). The method can facilitate the detection of SARS-CoV-2 lineages in wastewater samples using next generation sequencing, and attempts to infer the potential distribution of the SARS-CoV-2 lineages. biocViews: VariantDetection, VariantAnnotation, Sequencing Author: Nikolaos Pechlivanis [aut, cre] (ORCID: ), Maria Tsagiopoulou [aut], Maria Christina Maniou [aut], Anastasis Togkousidis [aut], Evangelia Mouchtaropoulou [aut], Taxiarchis Chassalevris [aut], Serafeim Chaintoutis [aut], Chrysostomos Dovas [aut], Maria Petala [aut], Margaritis Kostoglou [aut], Thodoris Karapantsios [aut], Stamatia Laidou [aut], Elisavet Vlachonikola [aut], Aspasia Orfanou [aut], Styliani-Christina Fragkouli [aut], Sofoklis Keisaris [aut], Anastasia Chatzidimitriou [aut], Agis Papadopoulos [aut], Nikolaos Papaioannou [aut], Anagnostis Argiriou [aut], Fotis E. Psomopoulos [aut] Maintainer: Nikolaos Pechlivanis URL: https://github.com/BiodataAnalysisGroup/lineagespot VignetteBuilder: knitr BugReports: https://github.com/BiodataAnalysisGroup/lineagespot/issues git_url: https://git.bioconductor.org/packages/lineagespot git_branch: RELEASE_3_22 git_last_commit: c41b331 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/lineagespot_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lineagespot_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lineagespot_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lineagespot_1.14.0.tgz vignettes: vignettes/lineagespot/inst/doc/lineagespot.html vignetteTitles: lineagespot User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lineagespot/inst/doc/lineagespot.R dependencyCount: 82 Package: LinkHD Version: 1.24.0 Depends: R(>= 3.6.0), methods, ggplot2, stats Imports: scales, cluster, graphics, ggpubr, gridExtra, vegan, rio, MultiAssayExperiment, emmeans, reshape2, data.table Suggests: MASS (>= 7.3.0), knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: ef018a0ea1b4180082f8d977a95d7f8e NeedsCompilation: no Title: LinkHD: a versatile framework to explore and integrate heterogeneous data Description: Here we present Link-HD, an approach to integrate heterogeneous datasets, as a generalization of STATIS-ACT (“Structuration des Tableaux A Trois Indices de la Statistique–Analyse Conjointe de Tableaux”), a family of methods to join and compare information from multiple subspaces. However, STATIS-ACT has some drawbacks since it only allows continuous data and it is unable to establish relationships between samples and features. In order to tackle these constraints, we incorporate multiple distance options and a linear regression based Biplot model in order to stablish relationships between observations and variable and perform variable selection. biocViews: Classification,MultipleComparison,Regression,Software Author: Laura M. Zingaretti [aut, cre] Maintainer: "Laura M Zingaretti" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinkHD git_branch: RELEASE_3_22 git_last_commit: ad5025e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LinkHD_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LinkHD_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LinkHD_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LinkHD_1.24.0.tgz vignettes: vignettes/LinkHD/inst/doc/LinkHD.html vignetteTitles: Annotating Genomic Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LinkHD/inst/doc/LinkHD.R dependencyCount: 125 Package: linkSet Version: 1.0.0 Depends: GenomicRanges, S4Vectors, R (>= 4.5.0) Imports: methods, IRanges, GenomeInfoDb, BiocGenerics, Organism.dplyr, InteractionSet, ggplot2, patchwork, scales, foreach, iterators, stats, rlang, MASS, data.table, DBI, doParallel, AnnotationDbi Suggests: knitr, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Mm.eg.db, org.Hs.eg.db, GenomicFeatures, GenomicInteractions, gamlss, gamlss.tr, BiocStyle, rtracklayer License: MIT + file LICENSE MD5sum: 2261788bcaf120e994dc1f299112a8b5 NeedsCompilation: no Title: Base Classes for Storing Genomic Link Data Description: Provides a comprehensive framework for representing, analyzing, and visualizing genomic interactions, particularly focusing on gene-enhancer relationships. The package extends the GenomicRanges infrastructure to handle paired genomic regions with specialized methods for chromatin interaction data from Hi-C, Promoter Capture Hi-C (PCHi-C), and single-cell ATAC-seq experiments. Key features include conversion from common interaction formats, annotation of promoters and enhancers, distance-based analyses, interaction strength metrics, statistical modeling using CHiCANE methodology, and tailored visualization tools. The package aims to standardize the representation of genomic interaction data while providing domain-specific functions not available in general genomic interaction packages. biocViews: Software, HiC, DataRepresentation, Sequencing, SingleCell, Coverage Author: Gilbert Han [aut, cre] (ORCID: ) Maintainer: Gilbert Han URL: https://github.com/GilbertHan1011/linkSet, https://gilberthan1011.github.io/linkSet VignetteBuilder: knitr BugReports: https://github.com/GilbertHan1011/linkSet/issues/new git_url: https://git.bioconductor.org/packages/linkSet git_branch: RELEASE_3_22 git_last_commit: e983433 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/linkSet_1.0.0.tar.gz vignettes: vignettes/linkSet/inst/doc/hic_workthrough.html, vignettes/linkSet/inst/doc/linkSet.html vignetteTitles: Hi-C Workflow with linkSet, linkSet: Base Classes for Storing Genomic Link Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/linkSet/inst/doc/hic_workthrough.R, vignettes/linkSet/inst/doc/linkSet.R dependencyCount: 114 Package: Linnorm Version: 2.34.0 Depends: R(>= 4.1.0) Imports: Rcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap, Rtsne, gmodels LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, markdown, gplots, RColorBrewer, moments, testthat, matrixStats License: MIT + file LICENSE Archs: x64 MD5sum: 7109dc0437da067bad8983a345223bbb NeedsCompilation: yes Title: Linear model and normality based normalization and transformation method (Linnorm) Description: Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. biocViews: ImmunoOncology, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Genetics, Normalization, Software, Transcription, BatchEffect, PeakDetection, Clustering, Network, SingleCell Author: Shun Hang Yip Maintainer: Shun Hang Yip URL: https://doi.org/10.1093/nar/gkx828 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Linnorm git_branch: RELEASE_3_22 git_last_commit: 05a328b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Linnorm_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Linnorm_2.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Linnorm_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Linnorm_2.34.0.tgz vignettes: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.pdf vignetteTitles: Linnorm User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.R importsMe: mnem suggestsMe: SCdeconR dependencyCount: 62 Package: lionessR Version: 1.24.0 Depends: R (>= 3.6.0) Imports: stats, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, igraph, reshape2, limma, License: MIT + file LICENSE MD5sum: 112723ae0a83149b91b6e4cfa0bac4a5 NeedsCompilation: no Title: Modeling networks for individual samples using LIONESS Description: LIONESS, or Linear Interpolation to Obtain Network Estimates for Single Samples, can be used to reconstruct single-sample networks (https://arxiv.org/abs/1505.06440). This code implements the LIONESS equation in the lioness function in R to reconstruct single-sample networks. The default network reconstruction method we use is based on Pearson correlation. However, lionessR can run on any network reconstruction algorithms that returns a complete, weighted adjacency matrix. lionessR works for both unipartite and bipartite networks. biocViews: Network, NetworkInference, GeneExpression Author: Marieke Lydia Kuijjer [aut] (ORCID: ), Ping-Han Hsieh [cre] (ORCID: ) Maintainer: Ping-Han Hsieh URL: https://github.com/mararie/lionessR VignetteBuilder: knitr BugReports: https://github.com/mararie/lionessR/issues git_url: https://git.bioconductor.org/packages/lionessR git_branch: RELEASE_3_22 git_last_commit: ca01a73 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/lionessR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lionessR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lionessR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lionessR_1.24.0.tgz vignettes: vignettes/lionessR/inst/doc/lionessR.html vignetteTitles: lionessR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lionessR/inst/doc/lionessR.R dependencyCount: 25 Package: lipidr Version: 2.24.0 Depends: R (>= 3.6.0), SummarizedExperiment Imports: methods, stats, utils, data.table, S4Vectors, rlang, dplyr, tidyr, forcats, ggplot2, limma, fgsea, ropls, imputeLCMD, magrittr Suggests: knitr, rmarkdown, BiocStyle, ggrepel, plotly, spelling, testthat License: MIT + file LICENSE MD5sum: b3c91727ebf675aa6ef766af0f816d09 NeedsCompilation: no Title: Data Mining and Analysis of Lipidomics Datasets Description: lipidr an easy-to-use R package implementing a complete workflow for downstream analysis of targeted and untargeted lipidomics data. lipidomics results can be imported into lipidr as a numerical matrix or a Skyline export, allowing integration into current analysis frameworks. Data mining of lipidomics datasets is enabled through integration with Metabolomics Workbench API. lipidr allows data inspection, normalization, univariate and multivariate analysis, displaying informative visualizations. lipidr also implements a novel Lipid Set Enrichment Analysis (LSEA), harnessing molecular information such as lipid class, total chain length and unsaturation. biocViews: Lipidomics, MassSpectrometry, Normalization, QualityControl, Visualization Author: Ahmed Mohamed [cre] (ORCID: ), Ahmed Mohamed [aut], Jeffrey Molendijk [aut] Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/lipidr VignetteBuilder: knitr BugReports: https://github.com/ahmohamed/lipidr/issues/ git_url: https://git.bioconductor.org/packages/lipidr git_branch: RELEASE_3_22 git_last_commit: 2e5e4b4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/lipidr_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lipidr_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lipidr_2.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lipidr_2.23.0.tgz vignettes: vignettes/lipidr/inst/doc/workflow.html vignetteTitles: lipidr_workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lipidr/inst/doc/workflow.R suggestsMe: rgoslin dependencyCount: 120 Package: LipidTrend Version: 1.0.0 Depends: R (>= 4.5.0) Imports: dplyr, ggnewscale, ggplot2, magrittr, methods, rlang, SummarizedExperiment, MKmisc, matrixTests Suggests: BiocStyle, devtools, knitr, roxygen2, rmarkdown, testthat (>= 3.0.0), S4Vectors, Enhances: data.table, License: MIT + file LICENSE MD5sum: 9a5bacfad8407804567243b2b6d1bb40 NeedsCompilation: no Title: LipidTrend: Analysis and Visualization of Lipid Feature Tendencies Description: "LipidTrend" is an R package that implements a permutation-based statistical test to identify significant differences in lipidomic features between groups. The test incorporates Gaussian kernel smoothing of region statistics to improve stability and accuracy, particularly when dealing with small sample sizes. This package also includes two plotting functions for visualizing significant tendencies in 1D and 2D feature data, respectively. biocViews: Software, Lipidomics, StatisticalMethod, DifferentialExpression, Visualization Author: Wei-Chung Cheng [aut, cre, cph] (ORCID: ), Chia-Hsin Liu [aut, ctb], Pei-Chun Shen [aut, ctb], Wen-Jen Lin [aut, ctb], Hung-Ching Chang [aut, ctb], Meng-Hsin Tsai [aut, ctb] Maintainer: Wei-Chung Cheng URL: https://github.com/BioinfOMICS/LipidTrend VignetteBuilder: knitr BugReports: https://github.com/BioinfOMICS/LipidTrend/issues git_url: https://git.bioconductor.org/packages/LipidTrend git_branch: RELEASE_3_22 git_last_commit: 85ac973 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LipidTrend_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LipidTrend_0.99.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LipidTrend_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LipidTrend_1.0.0.tgz vignettes: vignettes/LipidTrend/inst/doc/LipidTrend.html vignetteTitles: LipidTrend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LipidTrend/inst/doc/LipidTrend.R dependencyCount: 55 Package: LiquidAssociation Version: 1.64.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) Archs: x64 MD5sum: c090258d90b18b364be77d58461c91a2 NeedsCompilation: no Title: LiquidAssociation Description: The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data. biocViews: Pathways, GeneExpression, CellBiology, Genetics, Network, TimeCourse Author: Yen-Yi Ho Maintainer: Yen-Yi Ho git_url: https://git.bioconductor.org/packages/LiquidAssociation git_branch: RELEASE_3_22 git_last_commit: 0c8ec8b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LiquidAssociation_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LiquidAssociation_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LiquidAssociation_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LiquidAssociation_1.64.0.tgz vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf vignetteTitles: LiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.R dependsOnMe: fastLiquidAssociation dependencyCount: 60 Package: lisaClust Version: 1.18.0 Depends: R (>= 4.1.0) Imports: ggplot2, class, concaveman, grid, BiocParallel, spatstat.explore, spatstat.geom, BiocGenerics, S4Vectors, methods, spicyR, purrr, stats, data.table, dplyr, tidyr, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, pheatmap, spatstat.random, lifecycle, simpleSeg, rlang, Suggests: SpatialDatasets, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>=2) MD5sum: e4c742938dee639b4630abe33c508676 NeedsCompilation: no Title: lisaClust: Clustering of Local Indicators of Spatial Association Description: lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution. biocViews: SingleCell, CellBasedAssays, Spatial Author: Ellis Patrick [aut, cre], Nicolas Canete [aut], Nicholas Robertson [ctb], Alex Qin [ctb], Shreya shreya.rajeshrao@sydney.edu.au Rao [ctb] Maintainer: Ellis Patrick URL: https://ellispatrick.github.io/lisaClust/, https://github.com/ellispatrick/lisaClust VignetteBuilder: knitr BugReports: https://github.com/ellispatrick/lisaClust/issues git_url: https://git.bioconductor.org/packages/lisaClust git_branch: RELEASE_3_22 git_last_commit: 8ecd502 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/lisaClust_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lisaClust_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lisaClust_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lisaClust_1.18.0.tgz vignettes: vignettes/lisaClust/inst/doc/lisaClust.html vignetteTitles: "Inroduction to lisaClust" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lisaClust/inst/doc/lisaClust.R suggestsMe: Statial, spicyWorkflow dependencyCount: 235 Package: lmdme Version: 1.52.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: 701c6c81e6dbe345c11962ee9596ba9f NeedsCompilation: no Title: Linear Model decomposition for Designed Multivariate Experiments Description: linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS. biocViews: Microarray, OneChannel, TwoChannel, Visualization, DifferentialExpression, ExperimentData, Cancer Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar/?page_id=38 git_url: https://git.bioconductor.org/packages/lmdme git_branch: RELEASE_3_22 git_last_commit: a117911 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/lmdme_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lmdme_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lmdme_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lmdme_1.52.0.tgz vignettes: vignettes/lmdme/inst/doc/lmdme-vignette.pdf vignetteTitles: lmdme: linear model framework for PCA/PLS analysis of ANOVA decomposition on Designed Multivariate Experiments in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lmdme/inst/doc/lmdme-vignette.R dependencyCount: 9 Package: LOBSTAHS Version: 1.36.0 Depends: R (>= 3.4), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE Archs: x64 MD5sum: 1b56cfed518e02fb8d797705e7ccc365 NeedsCompilation: no Title: Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences Description: LOBSTAHS is a multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico data for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Lipidomics, DataImport Author: James Collins [aut, cre], Helen Fredricks [aut], Bethanie Edwards [aut], Henry Holm [aut], Benjamin Van Mooy [aut], Daniel Lowenstein [aut] Maintainer: Henry Holm , Daniel Lowenstein , James Collins URL: http://bioconductor.org/packages/LOBSTAHS VignetteBuilder: knitr BugReports: https://github.com/vanmooylipidomics/LOBSTAHS/issues/new git_url: https://git.bioconductor.org/packages/LOBSTAHS git_branch: RELEASE_3_22 git_last_commit: b35ad6c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LOBSTAHS_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LOBSTAHS_1.35.1.zip vignettes: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.html vignetteTitles: Discovery,, Identification,, and Screening of Lipids and Oxylipins in HPLC-MS Datasets Using LOBSTAHS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.R dependsOnMe: PtH2O2lipids dependencyCount: 154 Package: loci2path Version: 1.30.0 Depends: R (>= 3.5) Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods, grDevices, stats, graphics, GenomicRanges, BiocParallel, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 9f8212ab52546f2101aa9fc0778f859c NeedsCompilation: no Title: Loci2path: regulatory annotation of genomic intervals based on tissue-specific expression QTLs Description: loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB. biocViews: FunctionalGenomics, Genetics, GeneSetEnrichment, Software, GeneExpression, Sequencing, Coverage, BioCarta Author: Tianlei Xu Maintainer: Tianlei Xu URL: https://github.com/StanleyXu/loci2path VignetteBuilder: knitr BugReports: https://github.com/StanleyXu/loci2path/issues git_url: https://git.bioconductor.org/packages/loci2path git_branch: RELEASE_3_22 git_last_commit: ce699f6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/loci2path_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/loci2path_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/loci2path_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/loci2path_1.30.0.tgz vignettes: vignettes/loci2path/inst/doc/loci2path-vignette.html vignetteTitles: loci2path hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/loci2path/inst/doc/loci2path-vignette.R dependencyCount: 38 Package: logicFS Version: 2.30.0 Depends: LogicReg, mcbiopi, survival Imports: graphics, methods, stats Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 19c936e15bf37930722b0b570f63b0ee NeedsCompilation: no Title: Identification of SNP Interactions Description: Identification of interactions between binary variables using Logic Regression. Can, e.g., be used to find interesting SNP interactions. Contains also a bagging version of logic regression for classification. biocViews: SNP, Classification, Genetics Author: Holger Schwender, Tobias Tietz Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/logicFS git_branch: RELEASE_3_22 git_last_commit: 9a783bc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/logicFS_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/logicFS_2.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/logicFS_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/logicFS_2.30.0.tgz vignettes: vignettes/logicFS/inst/doc/logicFS.pdf vignetteTitles: logicFS Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logicFS/inst/doc/logicFS.R suggestsMe: trio dependencyCount: 12 Package: LOLA Version: 1.39.1 Depends: R (>= 3.5.0) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, reshape2, utils, stats, methods Suggests: parallel, testthat, knitr, BiocStyle, rmarkdown Enhances: simpleCache, qvalue, ggplot2 License: GPL-3 MD5sum: 6813f8315fa0555954e58c2d88a54157 NeedsCompilation: no Title: Locus overlap analysis for enrichment of genomic ranges Description: Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data. biocViews: GeneSetEnrichment, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing Author: Nathan Sheffield [aut, cre], Christoph Bock [ctb] Maintainer: Nathan Sheffield URL: http://code.databio.org/LOLA VignetteBuilder: knitr BugReports: http://github.com/nsheff/LOLA git_url: https://git.bioconductor.org/packages/LOLA git_branch: devel git_last_commit: 02b32bf git_last_commit_date: 2025-06-27 Date/Publication: 2025-10-07 source.ver: src/contrib/LOLA_1.39.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/LOLA_1.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LOLA_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LOLA_1.40.0.tgz vignettes: vignettes/LOLA/inst/doc/choosingUniverse.html, vignettes/LOLA/inst/doc/gettingStarted.html, vignettes/LOLA/inst/doc/usingLOLACore.html vignetteTitles: 3. Choosing a Universe, 1. Getting Started with LOLA, 2. Using LOLA Core hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LOLA/inst/doc/choosingUniverse.R, vignettes/LOLA/inst/doc/gettingStarted.R, vignettes/LOLA/inst/doc/usingLOLACore.R suggestsMe: COCOA, MAGAR, MIRA, ramr dependencyCount: 24 Package: looking4clusters Version: 1.0.0 Depends: R (>= 4.5.0) Imports: stats, utils, SummarizedExperiment, SingleCellExperiment, BiocBaseUtils, jsonlite Suggests: knitr, rmarkdown, Seurat, parallelDist, uwot, NMF, fpc, dendextend, cluster, Rtsne, scRNAseq, Matrix License: GPL-2 | GPL-3 MD5sum: 3129e93c00d9dbd971de985815cce234 NeedsCompilation: no Title: Interactive Visualization of scRNA-Seq Description: Enables the interactive visualization of dimensional reduction, clustering, and cell properties for scRNA-Seq results. It generates an interactive HTML page using either a numeric matrix, SummarizedExperiment, SingleCellExperiment or Seurat objects as input. The input data can be projected into two-dimensional representations by applying dimensionality reduction methods such as PCA, MDS, t-SNE, UMAP, and NMF. Displaying multiple dimensionality reduction results within the same interface, with interconnected graphs, provides different perspectives that facilitate accurate cell classification. The package also integrates unsupervised clustering techniques, whose results that can be viewed interactively in the graphical interface. In addition to visualization, this interface allows manual selection of groups, labeling of cell entities based on processed meta-information, generation of new graphs displaying gene expression values for each cell, sample identification, and visual comparison of samples and clusters. biocViews: Software, Visualization, DataRepresentation, GeneExpression, MultipleComparison, Classification, Clustering Author: David Barrios [aut, cre] (ORCID: ), Angela Villaverde [aut] (ORCID: ), Carlos Prieto [aut] (ORCID: ) Maintainer: David Barrios URL: https://github.com/BioinfoUSAL/looking4clusters/ VignetteBuilder: knitr BugReports: https://github.com/BioinfoUSAL/looking4clusters/issues/ git_url: https://git.bioconductor.org/packages/looking4clusters git_branch: RELEASE_3_22 git_last_commit: 8c0f82c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/looking4clusters_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/looking4clusters_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/looking4clusters_1.0.0.tgz vignettes: vignettes/looking4clusters/inst/doc/looking4clusters.html vignetteTitles: scRNA-Seq,, Dimensional Reduction,, Clustering and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/looking4clusters/inst/doc/looking4clusters.R dependencyCount: 28 Package: LoomExperiment Version: 1.28.0 Depends: R (>= 3.5.0), S4Vectors, SingleCellExperiment, SummarizedExperiment, methods, rhdf5, BiocIO Imports: DelayedArray, GenomicRanges, HDF5Array, Matrix, stats, stringr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, reticulate License: Artistic-2.0 MD5sum: 62a5aa0972e911108f3982430b65bd91 NeedsCompilation: no Title: LoomExperiment container Description: The LoomExperiment package provide a means to easily convert the Bioconductor "Experiment" classes to loom files and vice versa. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Martin Morgan, Daniel Van Twisk Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LoomExperiment git_branch: RELEASE_3_22 git_last_commit: 4b227ef git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LoomExperiment_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LoomExperiment_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LoomExperiment_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LoomExperiment_1.28.0.tgz vignettes: vignettes/LoomExperiment/inst/doc/LoomExperiment.html vignetteTitles: An introduction to the LoomExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LoomExperiment/inst/doc/LoomExperiment.R dependsOnMe: OSCA.intro suggestsMe: adverSCarial dependencyCount: 40 Package: LPE Version: 1.84.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: f7df619734d8db722e09d75a92098628 NeedsCompilation: no Title: Methods for analyzing microarray data using Local Pooled Error (LPE) method Description: This LPE library is used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional 'BH' or 'BY' procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions. To use it for paired data, see LPEP library. For using LPE in multiple conditions, use HEM library. biocViews: Microarray, DifferentialExpression Author: Nitin Jain , Michael O'Connell , Jae K. Lee . Includes R source code contributed by HyungJun Cho Maintainer: Nitin Jain URL: http://www.r-project.org, http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/, http://sourceforge.net/projects/r-lpe/ git_url: https://git.bioconductor.org/packages/LPE git_branch: RELEASE_3_22 git_last_commit: 6030f88 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LPE_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LPE_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LPE_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LPE_1.84.0.tgz vignettes: vignettes/LPE/inst/doc/LPE.pdf vignetteTitles: LPE test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPE/inst/doc/LPE.R dependsOnMe: PLPE suggestsMe: ABarray dependencyCount: 1 Package: lpNet Version: 2.42.0 Depends: lpSolve, KEGGgraph License: Artistic License 2.0 MD5sum: 734af919b199210108389eb16de145a6 NeedsCompilation: no Title: Linear Programming Model for Network Inference Description: lpNet aims at infering biological networks, in particular signaling and gene networks. For that it takes perturbation data, either steady-state or time-series, as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used. biocViews: NetworkInference Author: Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali Maintainer: Lars Kaderali git_url: https://git.bioconductor.org/packages/lpNet git_branch: RELEASE_3_22 git_last_commit: a938ca3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/lpNet_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lpNet_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lpNet_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lpNet_2.42.0.tgz vignettes: vignettes/lpNet/inst/doc/vignette_lpNet.pdf vignetteTitles: lpNet,, network inference with a linear optimization program. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpNet/inst/doc/vignette_lpNet.R dependencyCount: 16 Package: lpsymphony Version: 1.38.0 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr, testthat Enhances: slam License: EPL MD5sum: b98d6f231cb9a805093cf2384c2858e0 NeedsCompilation: yes Title: Symphony integer linear programming solver in R Description: This package was derived from Rsymphony_0.1-17 from CRAN. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY. For Windows, precompiled DLLs are included in this package. biocViews: Infrastructure, ThirdPartyClient Author: Vladislav Kim [aut, cre], Ted Ralphs [ctb], Menal Guzelsoy [ctb], Ashutosh Mahajan [ctb], Reinhard Harter [ctb], Kurt Hornik [ctb], Cyrille Szymanski [ctb], Stefan Theussl [ctb], Mike Smith [ctb] (ORCID: ) Maintainer: Vladislav Kim URL: http://R-Forge.R-project.org/projects/rsymphony, https://projects.coin-or.org/SYMPHONY, http://www.coin-or.org/download/source/SYMPHONY/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/lpsymphony/issues git_url: https://git.bioconductor.org/packages/lpsymphony git_branch: RELEASE_3_22 git_last_commit: 68cefe9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/lpsymphony_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lpsymphony_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lpsymphony_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lpsymphony_1.38.0.tgz vignettes: vignettes/lpsymphony/inst/doc/lpsymphony.pdf vignetteTitles: Introduction to lpsymphony hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpsymphony/inst/doc/lpsymphony.R importsMe: IHW suggestsMe: oppr, prioritizr dependencyCount: 0 Package: LRBaseDbi Version: 2.20.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: testthat, BiocStyle, AnnotationHub License: Artistic-2.0 MD5sum: 0385f12bcad579be04035bd2da665087 NeedsCompilation: no Title: DBI to construct LRBase-related package Description: Interface to construct LRBase package (LRBase.XXX.eg.db). biocViews: Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki VignetteBuilder: utils git_url: https://git.bioconductor.org/packages/LRBaseDbi git_branch: RELEASE_3_22 git_last_commit: 5edc0aa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LRBaseDbi_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LRBaseDbi_2.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LRBaseDbi_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LRBaseDbi_2.20.0.tgz vignettes: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.pdf vignetteTitles: LRBaseDbi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.R suggestsMe: scTensor dependencyCount: 43 Package: LRcell Version: 1.18.0 Depends: R (>= 4.1), ExperimentHub, AnnotationHub Imports: BiocParallel, dplyr, ggplot2, ggrepel, magrittr, stats, utils Suggests: LRcellTypeMarkers, BiocStyle, knitr, rmarkdown, roxygen2, testthat License: MIT + file LICENSE MD5sum: ad49fb969c87539d79cf31883ae9f6a9 NeedsCompilation: no Title: Differential cell type change analysis using Logistic/linear Regression Description: The goal of LRcell is to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. To achieve this, LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing (scRNA-seq) as indicators for various cell types in the tissue of interest. Next, for each cell type, using its marker genes as indicators, we apply Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. Finally, these p-values are compared to predict which one(s) are likely to be responsible for the differential gene expression pattern observed in the bulk RNA-seq experiments. LRcell is inspired by the LRpath[@sartor2009lrpath] algorithm developed by Sartor et al., originally designed for pathway/gene set enrichment analysis. LRcell contains three major components: LRcell analysis, plot generation and marker gene selection. All modules in this package are written in R. This package also provides marker genes in the Prefrontal Cortex (pFC) human brain region, human PBMC and nine mouse brain regions (Frontal Cortex, Cerebellum, Globus Pallidus, Hippocampus, Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra and Thalamus). biocViews: SingleCell, GeneSetEnrichment, Sequencing, Regression, GeneExpression, DifferentialExpression Author: Wenjing Ma [cre, aut] (ORCID: ) Maintainer: Wenjing Ma VignetteBuilder: knitr BugReports: https://github.com/marvinquiet/LRcell/issues git_url: https://git.bioconductor.org/packages/LRcell git_branch: RELEASE_3_22 git_last_commit: de409ff git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/LRcell_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LRcell_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LRcell_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LRcell_1.18.0.tgz vignettes: vignettes/LRcell/inst/doc/LRcell-vignette.html vignetteTitles: LRcell Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LRcell/inst/doc/LRcell-vignette.R suggestsMe: LRcellTypeMarkers dependencyCount: 86 Package: lumi Version: 2.62.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures, GenomicRanges, annotate, lattice, mgcv (>= 1.4-0), nleqslv, KernSmooth, preprocessCore, RSQLite, DBI, AnnotationDbi, MASS, graphics, stats, stats4, methods Suggests: beadarray, limma, vsn, lumiBarnes, lumiHumanAll.db, lumiHumanIDMapping, genefilter, RColorBrewer License: LGPL (>= 2) MD5sum: a38d15f511df1550d4efecebaa8e5420 NeedsCompilation: no Title: BeadArray Specific Methods for Illumina Methylation and Expression Microarrays Description: The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. biocViews: Microarray, OneChannel, Preprocessing, DNAMethylation, QualityControl, TwoChannel Author: Pan Du, Richard Bourgon, Gang Feng, Simon Lin Maintainer: Lei Huang git_url: https://git.bioconductor.org/packages/lumi git_branch: RELEASE_3_22 git_last_commit: 1123e59 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/lumi_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lumi_2.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lumi_2.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lumi_2.62.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: iCheck, wateRmelon, lumiHumanIDMapping, lumiMouseIDMapping, lumiRatIDMapping, ffpeExampleData, lumiBarnes, MAQCsubset, mvoutData importsMe: arrayMvout, ffpe, MineICA suggestsMe: Harman, methylumi, tigre, maGUI dependencyCount: 164 Package: LymphoSeq Version: 1.37.0 Depends: R (>= 3.3), LymphoSeqDB Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq, RColorBrewer, circlize, grid, utils, stats, ggtree, msa, Biostrings, phangorn, stringdist, UpSetR Suggests: knitr, pheatmap, wordcloud, rmarkdown License: Artistic-2.0 MD5sum: dc76bef013321074e6096e34b67b8ebc NeedsCompilation: no Title: Analyze high-throughput sequencing of T and B cell receptors Description: This R package analyzes high-throughput sequencing of T and B cell receptor complementarity determining region 3 (CDR3) sequences generated by Adaptive Biotechnologies' ImmunoSEQ assay. Its input comes from tab-separated value (.tsv) files exported from the ImmunoSEQ analyzer. biocViews: Software, Technology, Sequencing, TargetedResequencing, Alignment, MultipleSequenceAlignment Author: David Coffey Maintainer: David Coffey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LymphoSeq git_branch: devel git_last_commit: 61ac6ca git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/LymphoSeq_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LymphoSeq_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LymphoSeq_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LymphoSeq_1.37.0.tgz vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.html vignetteTitles: Analysis of high-throughput sequencing of T and B cell receptors with LymphoSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LymphoSeq/inst/doc/LymphoSeq.R dependencyCount: 110 Package: M3C Version: 1.32.0 Depends: R (>= 3.5.0) Imports: ggplot2, Matrix, doSNOW, cluster, parallel, foreach, doParallel, matrixcalc, Rtsne, corpcor, umap Suggests: knitr, rmarkdown License: AGPL-3 MD5sum: bb638f63a6cf6dc4e3b2a128a304c3c9 NeedsCompilation: no Title: Monte Carlo Reference-based Consensus Clustering Description: M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1. biocViews: Clustering, GeneExpression, Transcription, RNASeq, Sequencing, ImmunoOncology Author: Christopher John, David Watson Maintainer: Christopher John VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/M3C git_branch: RELEASE_3_22 git_last_commit: 90d5f05 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/M3C_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/M3C_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/M3C_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/M3C_1.32.0.tgz vignettes: vignettes/M3C/inst/doc/M3Cvignette.pdf vignetteTitles: M3C hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3C/inst/doc/M3Cvignette.R importsMe: lilikoi suggestsMe: parameters dependencyCount: 49 Package: M3Drop Version: 1.36.0 Depends: R (>= 3.4), numDeriv Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics, stats, matrixStats, Matrix, irlba, reldist, Hmisc, methods, scater Suggests: ROCR, knitr, M3DExampleData, SingleCellExperiment, Seurat, Biobase License: GPL (>=2) Archs: x64 MD5sum: 49702ff87af71a9ef5390ce116ea4674 NeedsCompilation: no Title: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq Description: This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model. biocViews: RNASeq, Sequencing, Transcriptomics, GeneExpression, Software, DifferentialExpression, DimensionReduction, FeatureExtraction Author: Tallulah Andrews Maintainer: Tallulah Andrews URL: https://github.com/tallulandrews/M3Drop VignetteBuilder: knitr BugReports: https://github.com/tallulandrews/M3Drop/issues git_url: https://git.bioconductor.org/packages/M3Drop git_branch: RELEASE_3_22 git_last_commit: 27208a6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/M3Drop_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/M3Drop_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/M3Drop_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/M3Drop_1.36.0.tgz vignettes: vignettes/M3Drop/inst/doc/M3Drop_Vignette.pdf vignetteTitles: Introduction to M3Drop hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3Drop/inst/doc/M3Drop_Vignette.R importsMe: scMerge dependencyCount: 158 Package: m6Aboost Version: 1.16.0 Depends: S4Vectors, adabag, GenomicRanges, R (>= 4.1) Imports: dplyr, rtracklayer, BSgenome, Biostrings, utils, methods, IRanges, ExperimentHub Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 MD5sum: 061521e02713a41176e8e868e64e8f81 NeedsCompilation: no Title: m6Aboost Description: This package can help user to run the m6Aboost model on their own miCLIP2 data. The package includes functions to assign the read counts and get the features to run the m6Aboost model. The miCLIP2 data should be stored in a GRanges object. More details can be found in the vignette. biocViews: Sequencing, Epigenetics, Genetics, ExperimentHubSoftware Author: You Zhou [aut, cre] (ORCID: ), Kathi Zarnack [aut] (ORCID: ) Maintainer: You Zhou URL: https://github.com/ZarnackGroup/m6Aboost VignetteBuilder: knitr BugReports: https://github.com/ZarnackGroup/m6Aboost/issues git_url: https://git.bioconductor.org/packages/m6Aboost git_branch: RELEASE_3_22 git_last_commit: 97ed1dd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/m6Aboost_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/m6Aboost_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/m6Aboost_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/m6Aboost_1.16.0.tgz vignettes: vignettes/m6Aboost/inst/doc/m6AboosVignettes.html vignetteTitles: m6Aboost Vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/m6Aboost/inst/doc/m6AboosVignettes.R dependencyCount: 168 Package: maaslin3 Version: 1.2.0 Depends: R (>= 4.4) Imports: dplyr, plyr, pbapply, lmerTest, parallel, lme4, optparse, logging, multcomp, ggplot2, RColorBrewer, patchwork, scales, rlang, tibble, ggnewscale, survival, methods, BiocGenerics, SummarizedExperiment, TreeSummarizedExperiment Suggests: knitr, testthat (>= 2.1.0), rmarkdown, markdown, kableExtra License: MIT + file LICENSE MD5sum: df2ae241ed1bd5ec6919d83dba9d77c8 NeedsCompilation: no Title: "Refining and extending generalized multivariate linear models for meta-omic association discovery" Description: MaAsLin 3 refines and extends generalized multivariate linear models for meta-omicron association discovery. It finds abundance and prevalence associations between microbiome meta-omics features and complex metadata in population-scale epidemiological studies. The software includes multiple analysis methods (including support for multiple covariates, repeated measures, and ordered predictors), filtering, normalization, and transform options to customize analysis for your specific study. biocViews: Metagenomics, Software, Microbiome, Normalization, MultipleComparison Author: William Nickols [aut, cre] (ORCID: ), Jacob Nearing [aut] Maintainer: William Nickols URL: http://huttenhower.sph.harvard.edu/maaslin3 VignetteBuilder: knitr BugReports: https://github.com/biobakery/maaslin3/issues git_url: https://git.bioconductor.org/packages/maaslin3 git_branch: RELEASE_3_22 git_last_commit: 9669f95 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/maaslin3_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maaslin3_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maaslin3_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maaslin3_1.2.0.tgz vignettes: vignettes/maaslin3/inst/doc/maaslin3_manual.html, vignettes/maaslin3/inst/doc/maaslin3_tutorial.html vignetteTitles: Manual, Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maaslin3/inst/doc/maaslin3_manual.R, vignettes/maaslin3/inst/doc/maaslin3_tutorial.R dependencyCount: 102 Package: maCorrPlot Version: 1.80.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: 78eac883474c7d36ca6bd45a6d4300bc NeedsCompilation: no Title: Visualize artificial correlation in microarray data Description: Graphically displays correlation in microarray data that is due to insufficient normalization biocViews: Microarray, Preprocessing, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=15799785 git_url: https://git.bioconductor.org/packages/maCorrPlot git_branch: RELEASE_3_22 git_last_commit: 6ef2043 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/maCorrPlot_1.80.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maCorrPlot_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maCorrPlot_1.80.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maCorrPlot_1.80.0.tgz vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf vignetteTitles: maCorrPlot Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maCorrPlot/inst/doc/maCorrPlot.R dependencyCount: 6 Package: MACSQuantifyR Version: 1.24.0 Imports: readxl, graphics, tools, utils, grDevices, ggplot2, ggrepel, methods, stats, latticeExtra, lattice, rmarkdown, png, grid, gridExtra, prettydoc, rvest, xml2 Suggests: knitr, testthat, R.utils, spelling License: Artistic-2.0 MD5sum: a1e0ab7c5e63225deb91c429ebc986b8 NeedsCompilation: no Title: Fast treatment of MACSQuantify FACS data Description: Automatically process the metadata of MACSQuantify FACS sorter. It runs multiple modules: i) imports of raw file and graphical selection of duplicates in well plate, ii) computes statistics on data and iii) can compute combination index. biocViews: DataImport, Preprocessing, Normalization, FlowCytometry, DataRepresentation, GUI Author: Raphaël Bonnet [aut, cre], Marielle Nebout [dtc],Giulia Biondani [dtc], Jean-François Peyron[aut,ths], Inserm [fnd] Maintainer: Raphaël Bonnet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSQuantifyR git_branch: RELEASE_3_22 git_last_commit: 4afd1ec git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MACSQuantifyR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MACSQuantifyR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MACSQuantifyR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MACSQuantifyR_1.24.0.tgz vignettes: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.html vignetteTitles: MACSQuantifyR_step_by_step_analysis, MACSQuantifyR_simple_pipeline, MACSQuantifyR_quick_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.R dependencyCount: 79 Package: MACSr Version: 1.18.0 Depends: R (>= 4.1.0) Imports: utils, reticulate, S4Vectors, methods, basilisk, ExperimentHub, AnnotationHub Suggests: testthat, knitr, rmarkdown, BiocStyle, MACSdata License: BSD_3_clause + file LICENSE MD5sum: c4e93fc34d9ac3d7b6e86b18f71ee4e9 NeedsCompilation: no Title: MACS: Model-based Analysis for ChIP-Seq Description: The Model-based Analysis of ChIP-Seq (MACS) is a widely used toolkit for identifying transcript factor binding sites. This package is an R wrapper of the lastest MACS3. biocViews: Software, ChIPSeq, ATACSeq, ImmunoOncology Author: Philippa Doherty [aut], Qiang Hu [aut, cre] Maintainer: Qiang Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSr git_branch: RELEASE_3_22 git_last_commit: 9263ac2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MACSr_1.18.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MACSr_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MACSr_1.18.0.tgz vignettes: vignettes/MACSr/inst/doc/MACSr.html vignetteTitles: MACSr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MACSr/inst/doc/MACSr.R dependencyCount: 76 Package: made4 Version: 1.84.0 Depends: RColorBrewer,gplots,scatterplot3d, Biobase, SummarizedExperiment Imports: ade4 Suggests: affy, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: b6d70200649adf8308cd5989fb31f816 NeedsCompilation: no Title: Multivariate analysis of microarray data using ADE4 Description: Multivariate data analysis and graphical display of microarray data. Functions include for supervised dimension reduction (between group analysis) and joint dimension reduction of 2 datasets (coinertia analysis). It contains functions that require R package ade4. biocViews: Clustering, Classification, DimensionReduction, PrincipalComponent,Transcriptomics, MultipleComparison, GeneExpression, Sequencing, Microarray Author: Aedin Culhane Maintainer: Aedin Culhane URL: http://www.hsph.harvard.edu/aedin-culhane/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/made4 git_branch: RELEASE_3_22 git_last_commit: 34e97a7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/made4_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/made4_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/made4_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/made4_1.84.0.tgz vignettes: vignettes/made4/inst/doc/introduction.html vignetteTitles: Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/made4/inst/doc/introduction.R importsMe: omicade4 dependencyCount: 38 Package: maftools Version: 2.26.0 Depends: R (>= 4.1.0) Imports: data.table, grDevices, methods, RColorBrewer, Rhtslib, survival, DNAcopy, pheatmap LinkingTo: Rhtslib Suggests: berryFunctions, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomicRanges, IRanges, knitr, mclust, MultiAssayExperiment, NMF, R.utils, RaggedExperiment, rmarkdown, S4Vectors License: MIT + file LICENSE MD5sum: c9d39777e09f29393454200caebbf9cd NeedsCompilation: yes Title: Summarize, Analyze and Visualize MAF Files Description: Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort. biocViews: DataRepresentation, DNASeq, Visualization, DriverMutation, VariantAnnotation, FeatureExtraction, Classification, SomaticMutation, Sequencing, FunctionalGenomics, Survival Author: Anand Mayakonda [aut, cre] (ORCID: ) Maintainer: Anand Mayakonda URL: https://github.com/PoisonAlien/maftools SystemRequirements: GNU make, curl VignetteBuilder: knitr BugReports: https://github.com/PoisonAlien/maftools/issues git_url: https://git.bioconductor.org/packages/maftools git_branch: RELEASE_3_22 git_last_commit: 9e83363 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/maftools_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maftools_2.25.10.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maftools_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maftools_2.26.0.tgz vignettes: vignettes/maftools/inst/doc/cancer_hotspots.html, vignettes/maftools/inst/doc/cnv_analysis.html, vignettes/maftools/inst/doc/maftools.html, vignettes/maftools/inst/doc/oncoplots.html vignetteTitles: 03: Cancer report, 04: Copy number analysis, 01: Summarize,, Analyze,, and Visualize MAF Files, 02: Customizing oncoplots hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maftools/inst/doc/cancer_hotspots.R, vignettes/maftools/inst/doc/cnv_analysis.R, vignettes/maftools/inst/doc/maftools.R, vignettes/maftools/inst/doc/oncoplots.R dependsOnMe: GNOSIS importsMe: CaMutQC, CIMICE, katdetectr, musicatk, aplotExtra, PMAPscore, Rediscover, sigminer, SMDIC, ssMutPA suggestsMe: GenomicDataCommons, MultiAssayExperiment, survtype, TCGAbiolinks dependencyCount: 26 Package: MAGAR Version: 1.18.0 Depends: R (>= 4.1), HDF5Array, RnBeads, snpStats, crlmm Imports: doParallel, igraph, bigstatsr, rjson, plyr, data.table, UpSetR, reshape2, jsonlite, methods, ff, argparse, impute, RnBeads.hg19, RnBeads.hg38, utils, stats Suggests: gridExtra, VennDiagram, qqman, LOLA, RUnit, rmutil, rmarkdown, JASPAR2018, TFBSTools, seqLogo, knitr, devtools, BiocGenerics, BiocManager License: GPL-3 MD5sum: 57257e0cbc7655c3a231f8b9003182cf NeedsCompilation: no Title: MAGAR: R-package to compute methylation Quantitative Trait Loci (methQTL) from DNA methylation and genotyping data Description: "Methylation-Aware Genotype Association in R" (MAGAR) computes methQTL from DNA methylation and genotyping data from matched samples. MAGAR uses a linear modeling stragety to call CpGs/SNPs that are methQTLs. MAGAR accounts for the local correlation structure of CpGs. biocViews: Regression, Epigenetics, DNAMethylation, SNP, GeneticVariability, MethylationArray, Microarray, CpGIsland, MethylSeq, Sequencing, mRNAMicroarray, Preprocessing, CopyNumberVariation, TwoChannel, ImmunoOncology, DifferentialMethylation, BatchEffect, QualityControl, DataImport, Network, Clustering, GraphAndNetwork Author: Michael Scherer [cre, aut] (ORCID: ) Maintainer: Michael Scherer URL: https://github.com/MPIIComputationalEpigenetics/MAGAR VignetteBuilder: knitr BugReports: https://github.com/MPIIComputationalEpigenetics/MAGAR/issues git_url: https://git.bioconductor.org/packages/MAGAR git_branch: RELEASE_3_22 git_last_commit: 07bf8e4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MAGAR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MAGAR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MAGAR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MAGAR_1.18.0.tgz vignettes: vignettes/MAGAR/inst/doc/MAGAR.html vignetteTitles: MAGAR: Methylation-Aware Genotype Association in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAGAR/inst/doc/MAGAR.R dependencyCount: 198 Package: magpie Version: 1.10.0 Depends: R (>= 4.3.0) Imports: utils, rtracklayer, Matrix, matrixStats, stats, S4Vectors, methods, graphics, GenomicRanges, GenomicFeatures, IRanges, Rsamtools, AnnotationDbi, aod, BiocParallel, DESeq2, openxlsx, RColorBrewer, reshape2, TRESS Suggests: knitr, rmarkdown, kableExtra, RUnit, TBX20BamSubset, BiocGenerics, BiocStyle License: MIT + file LICENSE MD5sum: 5a1dce8c61a0038c108d3d87c7d6f43e NeedsCompilation: no Title: MeRIP-Seq data Analysis for Genomic Power Investigation and Evaluation Description: This package aims to perform power analysis for the MeRIP-seq study. It calculates FDR, FDC, power, and precision under various study design parameters, including but not limited to sample size, sequencing depth, and testing method. It can also output results into .xlsx files or produce corresponding figures of choice. biocViews: Epitranscriptomics, DifferentialMethylation, Sequencing, RNASeq, Software Author: Daoyu Duan [aut, cre], Zhenxing Guo [aut] Maintainer: Daoyu Duan URL: https://github.com/dxd429/magpie VignetteBuilder: knitr BugReports: https://github.com/dxd429/magpie/issues git_url: https://git.bioconductor.org/packages/magpie git_branch: RELEASE_3_22 git_last_commit: 0afc3bf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/magpie_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/magpie_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/magpie_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/magpie_1.10.0.tgz vignettes: vignettes/magpie/inst/doc/magpie.html vignetteTitles: magpie Package User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/magpie/inst/doc/magpie.R dependencyCount: 99 Package: magrene Version: 1.12.0 Depends: R (>= 4.2.0) Imports: utils, stats, BiocParallel Suggests: BiocStyle, covr, knitr, rmarkdown, ggplot2, sessioninfo, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: a5d9fd4a9881a39d5faa96da68cb14fb NeedsCompilation: no Title: Motif Analysis In Gene Regulatory Networks Description: magrene allows the identification and analysis of graph motifs in (duplicated) gene regulatory networks (GRNs), including lambda, V, PPI V, delta, and bifan motifs. GRNs can be tested for motif enrichment by comparing motif frequencies to a null distribution generated from degree-preserving simulated GRNs. Motif frequencies can be analyzed in the context of gene duplications to explore the impact of small-scale and whole-genome duplications on gene regulatory networks. Finally, users can calculate interaction similarity for gene pairs based on the Sorensen-Dice similarity index. biocViews: Software, MotifDiscovery, NetworkEnrichment, SystemsBiology, GraphAndNetwork Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/magrene VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/magrene git_url: https://git.bioconductor.org/packages/magrene git_branch: RELEASE_3_22 git_last_commit: 48a1be6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/magrene_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/magrene_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/magrene_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/magrene_1.12.0.tgz vignettes: vignettes/magrene/inst/doc/magrene.html vignetteTitles: Introduction to magrene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/magrene/inst/doc/magrene.R dependencyCount: 13 Package: MAI Version: 1.16.0 Depends: R (>= 3.5.0) Imports: caret, parallel, doParallel, foreach, e1071, future.apply, future, missForest, pcaMethods, tidyverse, stats, utils, methods, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: 807f612210b643b3c244b4e1e7b6dbc9 NeedsCompilation: no Title: Mechanism-Aware Imputation Description: A two-step approach to imputing missing data in metabolomics. Step 1 uses a random forest classifier to classify missing values as either Missing Completely at Random/Missing At Random (MCAR/MAR) or Missing Not At Random (MNAR). MCAR/MAR are combined because it is often difficult to distinguish these two missing types in metabolomics data. Step 2 imputes the missing values based on the classified missing mechanisms, using the appropriate imputation algorithms. Imputation algorithms tested and available for MCAR/MAR include Bayesian Principal Component Analysis (BPCA), Multiple Imputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and Random Forest. Imputation algorithms tested and available for MNAR include nsKNN and a single imputation approach for imputation of metabolites where left-censoring is present. biocViews: Software, Metabolomics, StatisticalMethod, Classification Author: Jonathan Dekermanjian [aut, cre], Elin Shaddox [aut], Debmalya Nandy [aut], Debashis Ghosh [aut], Katerina Kechris [aut] Maintainer: Jonathan Dekermanjian URL: https://github.com/KechrisLab/MAI VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/MAI/issues git_url: https://git.bioconductor.org/packages/MAI git_branch: RELEASE_3_22 git_last_commit: e7b90f5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MAI_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MAI_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MAI_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MAI_1.16.0.tgz vignettes: vignettes/MAI/inst/doc/UsingMAI.html vignetteTitles: Utilizing Mechanism-Aware Imputation (MAI) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MAI/inst/doc/UsingMAI.R dependencyCount: 173 Package: MAIT Version: 1.44.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Suggests: faahKO Enhances: rgl License: GPL-2 MD5sum: 02b89386d793d74bef3a2f03d89fc3e3 NeedsCompilation: no Title: Statistical Analysis of Metabolomic Data Description: The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Software Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina Andres-LaCueva, Alexandre Perera Maintainer: Pol Sola-Santos git_url: https://git.bioconductor.org/packages/MAIT git_branch: RELEASE_3_22 git_last_commit: cf370fa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MAIT_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MAIT_1.43.0.zip vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf vignetteTitles: \maketitleMAIT Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAIT/inst/doc/MAIT_Vignette.R dependencyCount: 200 Package: makecdfenv Version: 1.86.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils License: GPL (>= 2) MD5sum: 5250c45e35bf6a1fb3798686a2c2f33d NeedsCompilation: yes Title: CDF Environment Maker Description: This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment. biocViews: OneChannel, DataImport, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Wolfgang Huber , Ben Bolstad Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/makecdfenv git_branch: RELEASE_3_22 git_last_commit: 74bd911 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/makecdfenv_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/makecdfenv_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/makecdfenv_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/makecdfenv_1.86.0.tgz vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf vignetteTitles: makecdfenv primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/makecdfenv/inst/doc/makecdfenv.R dependsOnMe: altcdfenvs dependencyCount: 12 Package: MANOR Version: 1.82.0 Depends: R (>= 2.10) Imports: GLAD, graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, bookdown License: GPL-2 MD5sum: 0a7339813e2abfc11d36bcd1cfeb4987 NeedsCompilation: yes Title: CGH Micro-Array NORmalization Description: Importation, normalization, visualization, and quality control functions to correct identified sources of variability in array-CGH experiments. biocViews: Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, CopyNumberVariation, Normalization Author: Pierre Neuvial , Philippe Hupé Maintainer: Pierre Neuvial URL: http://bioinfo.curie.fr/projects/manor/index.html VignetteBuilder: knitr BugReports: https://github.com/pneuvial/MANOR/issues git_url: https://git.bioconductor.org/packages/MANOR git_branch: RELEASE_3_22 git_last_commit: 0f49f4e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MANOR_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MANOR_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MANOR_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MANOR_1.82.0.tgz vignettes: vignettes/MANOR/inst/doc/MANOR.html vignetteTitles: Overview of the MANOR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MANOR/inst/doc/MANOR.R dependencyCount: 9 Package: MantelCorr Version: 1.80.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) Archs: x64 MD5sum: f98fae810a791f6de47a42ce6a7de462 NeedsCompilation: no Title: Compute Mantel Cluster Correlations Description: Computes Mantel cluster correlations from a (p x n) numeric data matrix (e.g. microarray gene-expression data). biocViews: Clustering Author: Brian Steinmeyer and William Shannon Maintainer: Brian Steinmeyer git_url: https://git.bioconductor.org/packages/MantelCorr git_branch: RELEASE_3_22 git_last_commit: 35af70e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MantelCorr_1.80.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MantelCorr_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MantelCorr_1.80.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MantelCorr_1.80.0.tgz vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf vignetteTitles: MantelCorrVignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MantelCorr/inst/doc/MantelCorrVignette.R dependencyCount: 1 Package: MAPFX Version: 1.6.0 Depends: R (>= 4.4.0) Imports: flowCore, Biobase, stringr, uwot, iCellR, igraph, ggplot2, RColorBrewer, Rfast, ComplexHeatmap, circlize, glmnetUtils, e1071, xgboost, parallel, pbapply, reshape2, gtools, utils, stats, cowplot, methods, grDevices, graphics Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 8e3ce09372ecd00ac93bcf74df54e09e NeedsCompilation: no Title: MAssively Parallel Flow cytometry Xplorer (MAPFX): A Toolbox for Analysing Data from the Massively-Parallel Cytometry Experiments Description: MAPFX is an end-to-end toolbox that pre-processes the raw data from MPC experiments (e.g., BioLegend's LEGENDScreen and BD Lyoplates assays), and further imputes the ‘missing’ infinity markers in the wells without those measurements. The pipeline starts by performing background correction on raw intensities to remove the noise from electronic baseline restoration and fluorescence compensation by adapting a normal-exponential convolution model. Unwanted technical variation, from sources such as well effects, is then removed using a log-normal model with plate, column, and row factors, after which infinity markers are imputed using the informative backbone markers as predictors. The completed dataset can then be used for clustering and other statistical analyses. Additionally, MAPFX can be used to normalise data from FFC assays as well. biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell, Proteomics, Clustering Author: Hsiao-Chi Liao [aut, cre] (ORCID: ), Agus Salim [ctb], infinityFlow [ctb] Maintainer: Hsiao-Chi Liao URL: https://github.com/HsiaoChiLiao/MAPFX VignetteBuilder: knitr BugReports: https://github.com/HsiaoChiLiao/MAPFX/issues git_url: https://git.bioconductor.org/packages/MAPFX git_branch: RELEASE_3_22 git_last_commit: ca3ce0f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MAPFX_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MAPFX_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MAPFX_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MAPFX_1.6.0.tgz vignettes: vignettes/MAPFX/inst/doc/MAPFX_Vignette.html vignetteTitles: MAPFX: MAssively Parallel Flow cytometry Xplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAPFX/inst/doc/MAPFX_Vignette.R dependencyCount: 187 Package: maPredictDSC Version: 1.48.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 Archs: x64 MD5sum: eaf5c3f80b8e39b1231bbc523c518a35 NeedsCompilation: no Title: Phenotype prediction using microarray data: approach of the best overall team in the IMPROVER Diagnostic Signature Challenge Description: This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types. biocViews: Microarray, Classification Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC git_url: https://git.bioconductor.org/packages/maPredictDSC git_branch: RELEASE_3_22 git_last_commit: 27c85a2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/maPredictDSC_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maPredictDSC_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maPredictDSC_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maPredictDSC_1.48.0.tgz vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf vignetteTitles: maPredictDSC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maPredictDSC/inst/doc/maPredictDSC.R dependencyCount: 131 Package: mapscape Version: 1.34.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), base64enc (>= 0.1-3), stringr (>= 1.0.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: b20558fde09bc2114308aa565ab57ade NeedsCompilation: no Title: mapscape Description: MapScape integrates clonal prevalence, clonal hierarchy, anatomic and mutational information to provide interactive visualization of spatial clonal evolution. There are four inputs to MapScape: (i) the clonal phylogeny, (ii) clonal prevalences, (iii) an image reference, which may be a medical image or drawing and (iv) pixel locations for each sample on the referenced image. Optionally, MapScape can accept a data table of mutations for each clone and their variant allele frequencies in each sample. The output of MapScape consists of a cropped anatomical image surrounded by two representations of each tumour sample. The first, a cellular aggregate, visually displays the prevalence of each clone. The second shows a skeleton of the clonal phylogeny while highlighting only those clones present in the sample. Together, these representations enable the analyst to visualize the distribution of clones throughout anatomic space. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mapscape git_branch: RELEASE_3_22 git_last_commit: 02dbe2a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mapscape_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mapscape_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mapscape_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mapscape_1.34.0.tgz vignettes: vignettes/mapscape/inst/doc/mapscape_vignette.html vignetteTitles: MapScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mapscape/inst/doc/mapscape_vignette.R dependencyCount: 36 Package: mariner Version: 1.10.0 Depends: R (>= 4.2.0) Imports: methods, S4Vectors, BiocGenerics, BiocManager, GenomicRanges, InteractionSet, data.table, stats, rlang, glue, assertthat, plyranges, magrittr, dbscan, purrr, progress, GenomeInfoDb, strawr (>= 0.0.91), DelayedArray, HDF5Array, abind, BiocParallel, IRanges, SummarizedExperiment, rhdf5, plotgardener, RColorBrewer, colourvalues, utils, grDevices, graphics, grid Suggests: knitr, testthat (>= 3.0.0), dplyr, rmarkdown, ExperimentHub, marinerData, TxDb.Hsapiens.UCSC.hg38.knownGene, fields License: MIT + file LICENSE Archs: x64 MD5sum: f2f6e2667977dc7220162c1cbe2aff5f NeedsCompilation: no Title: Mariner: Explore the Hi-Cs Description: Tools for manipulating paired ranges and working with Hi-C data in R. Functionality includes manipulating/merging paired regions, generating paired ranges, extracting/aggregating interactions from `.hic` files, and visualizing the results. Designed for compatibility with plotgardener for visualization. biocViews: FunctionalGenomics, Visualization, HiC Author: Eric Davis [aut, cre] (ORCID: ), Sarah Parker [aut] (ORCID: ) Maintainer: Eric Davis URL: https://ericscottdavis.com/mariner/, https://github.com/EricSDavis/mariner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mariner git_branch: RELEASE_3_22 git_last_commit: 0e27ac7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mariner_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mariner_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mariner_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mariner_1.10.0.tgz vignettes: vignettes/mariner/inst/doc/mariner.html vignetteTitles: Introduction to mariner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mariner/inst/doc/mariner.R suggestsMe: nullranges dependencyCount: 106 Package: markeR Version: 1.0.0 Depends: R (>= 4.5.0) Imports: circlize, edgeR, ComplexHeatmap, ggh4x, ggplot2, ggpubr, grid, gridExtra, pROC, RColorBrewer, reshape2, rstatix, scales, stats, utils, fgsea, limma, ggrepel, effectsize, msigdbr, tibble Suggests: devtools, markdown, renv, testthat, BiocManager, knitr, rmarkdown, roxygen2, mockery, covr, magick, BiocStyle License: Artistic-2.0 MD5sum: 3c44058ec2e0eefd8696b0b14c176d25 NeedsCompilation: no Title: An R Toolkit for Evaluating Gene Signatures as Phenotypic Markers Description: markeR is an R package that provides a modular and extensible framework for the systematic evaluation of gene sets as phenotypic markers using transcriptomic data. The package is designed to support both quantitative analyses and visual exploration of gene set behaviour across experimental and clinical phenotypes. It implements multiple methods, including score-based and enrichment approaches, and also allows the exploration of expression behaviour of individual genes. In addition, users can assess the similarity of their own gene sets against established collections (e.g., those from MSigDB), facilitating biological interpretation. biocViews: GeneExpression, Transcriptomics, Visualization, Software, GeneSetEnrichment, Classification Author: Rita Martins-Silva [aut, cre] (ORCID: ), Alexandre Kaizeler [aut, ctb] (ORCID: ), Nuno Luís Barbosa-Morais [aut, led, ths] (ORCID: ) Maintainer: Rita Martins-Silva URL: https://diseasetranscriptomicslab.github.io/markeR/, https://github.com/DiseaseTranscriptomicsLab/markeR VignetteBuilder: knitr BugReports: https://github.com/DiseaseTranscriptomicsLab/markeR/issues git_url: https://git.bioconductor.org/packages/markeR git_branch: RELEASE_3_22 git_last_commit: defebad git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/markeR_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/markeR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/markeR_1.0.0.tgz vignettes: vignettes/markeR/inst/doc/markeR.html vignetteTitles: Introduction to markeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/markeR/inst/doc/markeR.R dependencyCount: 127 Package: marr Version: 1.20.0 Depends: R (>= 4.0) Imports: Rcpp, SummarizedExperiment, utils, methods, ggplot2, dplyr, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, covr License: GPL (>= 3) Archs: x64 MD5sum: fac6375fb4cff2bef100a71ff8db61d7 NeedsCompilation: yes Title: Maximum rank reproducibility Description: marr (Maximum Rank Reproducibility) is a nonparametric approach that detects reproducible signals using a maximal rank statistic for high-dimensional biological data. In this R package, we implement functions that measures the reproducibility of features per sample pair and sample pairs per feature in high-dimensional biological replicate experiments. The user-friendly plot functions in this package also plot histograms of the reproducibility of features per sample pair and sample pairs per feature. Furthermore, our approach also allows the users to select optimal filtering threshold values for the identification of reproducible features and sample pairs based on output visualization checks (histograms). This package also provides the subset of data filtered by reproducible features and/or sample pairs. biocViews: QualityControl, Metabolomics, MassSpectrometry, RNASeq, ChIPSeq Author: Tusharkanti Ghosh [aut, cre], Max McGrath [aut], Daisy Philtron [aut], Katerina Kechris [aut], Debashis Ghosh [aut, cph] Maintainer: Tusharkanti Ghosh VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/marr/issues git_url: https://git.bioconductor.org/packages/marr git_branch: RELEASE_3_22 git_last_commit: c204de7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/marr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/marr_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/marr_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/marr_1.20.0.tgz vignettes: vignettes/marr/inst/doc/MarrVignette.html vignetteTitles: The marr user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/marr/inst/doc/MarrVignette.R dependencyCount: 49 Package: marray Version: 1.88.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: 2349218e34a737521c9fc3856cba8520 NeedsCompilation: no Title: Exploratory analysis for two-color spotted microarray data Description: Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking. biocViews: Microarray, TwoChannel, Preprocessing Author: Yee Hwa (Jean) Yang with contributions from Agnes Paquet and Sandrine Dudoit. Maintainer: Yee Hwa (Jean) Yang URL: http://www.maths.usyd.edu.au/u/jeany/ git_url: https://git.bioconductor.org/packages/marray git_branch: RELEASE_3_22 git_last_commit: 5ff7e84 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/marray_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/marray_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/marray_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/marray_1.88.0.tgz vignettes: vignettes/marray/inst/doc/marray.pdf, vignettes/marray/inst/doc/marrayClasses.pdf, vignettes/marray/inst/doc/marrayClassesShort.pdf, vignettes/marray/inst/doc/marrayInput.pdf, vignettes/marray/inst/doc/marrayNorm.pdf, vignettes/marray/inst/doc/marrayPlots.pdf vignetteTitles: marray Overview, marrayClasses Overview, marrayClasses Tutorial (short), marrayInput Introduction, marray Normalization, marrayPlots Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/marray/inst/doc/marray.R, vignettes/marray/inst/doc/marrayClasses.R, vignettes/marray/inst/doc/marrayClassesShort.R, vignettes/marray/inst/doc/marrayInput.R, vignettes/marray/inst/doc/marrayNorm.R, vignettes/marray/inst/doc/marrayPlots.R dependsOnMe: CGHbase, convert, dyebias, MineICA, nnNorm, OLIN, RBM, stepNorm, TurboNorm, beta7, dyebiasexamples importsMe: arrayQuality, ChAMP, methylPipe, MSstats, MSstatsShiny, nnNorm, OLIN, OLINgui, piano, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz, hexbin dependencyCount: 7 Package: martini Version: 1.30.0 Depends: R (>= 4.0) Imports: igraph (>= 1.0.1), Matrix, memoise (>= 2.0.0), methods (>= 3.3.2), Rcpp (>= 0.12.8), snpStats (>= 1.20.0), stats, utils, LinkingTo: Rcpp, RcppEigen (>= 0.3.3.5.0) Suggests: biomaRt (>= 2.34.1), circlize (>= 0.4.11), STRINGdb (>= 2.2.0), httr (>= 1.2.1), IRanges (>= 2.8.2), S4Vectors (>= 0.12.2), knitr, testthat, readr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 8a3c5b65a3acd208cb817a0ee6c8fa82 NeedsCompilation: yes Title: GWAS Incorporating Networks Description: martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre] (ORCID: ), Chloe-Agathe Azencott [aut] (ORCID: ) Maintainer: Hector Climente-Gonzalez URL: https://github.com/hclimente/martini VignetteBuilder: knitr BugReports: https://github.com/hclimente/martini/issues git_url: https://git.bioconductor.org/packages/martini git_branch: RELEASE_3_22 git_last_commit: 989beee git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/martini_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/martini_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/martini_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/martini_1.30.0.tgz vignettes: vignettes/martini/inst/doc/scones_usage.html, vignettes/martini/inst/doc/simulate_phenotype.html vignetteTitles: Running SConES, Simulating SConES-based phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/martini/inst/doc/scones_usage.R, vignettes/martini/inst/doc/simulate_phenotype.R dependencyCount: 27 Package: maser Version: 1.28.0 Depends: R (>= 3.5.0), ggplot2, GenomicRanges Imports: dplyr, rtracklayer, reshape2, Gviz, DT, Seqinfo, stats, utils, IRanges, methods, BiocGenerics, parallel, data.table Suggests: testthat, knitr, rmarkdown, BiocStyle, AnnotationHub License: MIT + file LICENSE MD5sum: eee6b5a12e1b98277f73348b81175721 NeedsCompilation: no Title: Mapping Alternative Splicing Events to pRoteins Description: This package provides functionalities for downstream analysis, annotation and visualizaton of alternative splicing events generated by rMATS. biocViews: AlternativeSplicing, Transcriptomics, Visualization Author: Diogo F.T. Veiga [aut, cre] Maintainer: Diogo F.T. Veiga URL: https://github.com/DiogoVeiga/maser VignetteBuilder: knitr BugReports: https://github.com/DiogoVeiga/maser/issues git_url: https://git.bioconductor.org/packages/maser git_branch: RELEASE_3_22 git_last_commit: f9a04bc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/maser_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maser_1.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maser_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maser_1.28.0.tgz vignettes: vignettes/maser/inst/doc/Introduction.html, vignettes/maser/inst/doc/Protein_mapping.html vignetteTitles: Introduction, Mapping protein features to splicing events hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maser/inst/doc/Introduction.R, vignettes/maser/inst/doc/Protein_mapping.R dependencyCount: 159 Package: maSigPro Version: 1.82.0 Depends: R (>= 2.3.1) Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS License: GPL (>= 2) MD5sum: f1acec6a901886d6d963401e585cff8c NeedsCompilation: no Title: Significant Gene Expression Profile Differences in Time Course Gene Expression Data Description: maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. biocViews: Microarray, RNA-Seq, Differential Expression, TimeCourse Author: Ana Conesa and Maria Jose Nueda Maintainer: Maria Jose Nueda git_url: https://git.bioconductor.org/packages/maSigPro git_branch: RELEASE_3_22 git_last_commit: a7bf8e2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/maSigPro_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maSigPro_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maSigPro_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maSigPro_1.82.0.tgz vignettes: vignettes/maSigPro/inst/doc/maSigPro.pdf, vignettes/maSigPro/inst/doc/maSigProUsersGuide.pdf vignetteTitles: maSigPro Vignette, maSigProUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: maskBAD Version: 1.54.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe, hgu95av2cdf License: GPL (>= 2) MD5sum: 5ed8989d971f530d40226fdbf43545b9 NeedsCompilation: no Title: Masking probes with binding affinity differences Description: Package includes functions to analyze and mask microarray expression data. biocViews: Microarray Author: Michael Dannemann Maintainer: Michael Dannemann git_url: https://git.bioconductor.org/packages/maskBAD git_branch: RELEASE_3_22 git_last_commit: 0b6a244 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/maskBAD_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maskBAD_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maskBAD_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maskBAD_1.54.0.tgz vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf vignetteTitles: Package maskBAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maskBAD/inst/doc/maskBAD.R dependencyCount: 22 Package: MassArray Version: 1.62.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, stats, utils License: GPL (>=2) MD5sum: 7f89ac171230e19d088dc86f7b73b22c NeedsCompilation: no Title: Analytical Tools for MassArray Data Description: This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis. biocViews: ImmunoOncology, DNAMethylation, SNP, MassSpectrometry, Genetics, DataImport, Visualization Author: Reid F. Thompson , John M. Greally Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/MassArray git_branch: RELEASE_3_22 git_last_commit: 2c04299 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MassArray_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MassArray_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MassArray_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MassArray_1.62.0.tgz vignettes: vignettes/MassArray/inst/doc/MassArray.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassArray/inst/doc/MassArray.R dependencyCount: 5 Package: massiR Version: 1.46.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: f13d821e4428002f0d761e637e5baebe NeedsCompilation: no Title: massiR: MicroArray Sample Sex Identifier Description: Predicts the sex of samples in gene expression microarray datasets biocViews: Software, Microarray, GeneExpression, Clustering, Classification, QualityControl Author: Sam Buckberry Maintainer: Sam Buckberry git_url: https://git.bioconductor.org/packages/massiR git_branch: RELEASE_3_22 git_last_commit: 48d7562 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/massiR_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/massiR_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/massiR_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/massiR_1.46.0.tgz vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf vignetteTitles: massiR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/massiR/inst/doc/massiR_Vignette.R dependencyCount: 15 Package: MassSpecWavelet Version: 1.76.0 Suggests: signal, waveslim, BiocStyle, knitr, rmarkdown, RUnit, bench License: LGPL (>= 2) MD5sum: 222930862724b90388337a655785a01d NeedsCompilation: yes Title: Peak Detection for Mass Spectrometry data using wavelet-based algorithms Description: Peak Detection in Mass Spectrometry data is one of the important preprocessing steps. The performance of peak detection affects subsequent processes, including protein identification, profile alignment and biomarker identification. Using Continuous Wavelet Transform (CWT), this package provides a reliable algorithm for peak detection that does not require any type of smoothing or previous baseline correction method, providing more consistent results for different spectra. See ) Maintainer: Sergio Oller Moreno URL: https://github.com/zeehio/MassSpecWavelet VignetteBuilder: knitr BugReports: http://github.com/zeehio/MassSpecWavelet/issues git_url: https://git.bioconductor.org/packages/MassSpecWavelet git_branch: RELEASE_3_22 git_last_commit: 196b6c1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MassSpecWavelet_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MassSpecWavelet_1.75.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MassSpecWavelet_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MassSpecWavelet_1.76.0.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/FindingLocalMaxima.html, vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.html vignetteTitles: Finding local maxima, Using the MassSpecWavelet package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/FindingLocalMaxima.R, vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq, xcms, NMRphasing, Rnmr1D, speaq suggestsMe: downlit, metabodecon dependencyCount: 0 Package: MAST Version: 1.36.0 Depends: SingleCellExperiment (>= 1.2.0), R(>= 3.5) Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr, stringr, abind, methods, parallel, reshape2, stats, stats4, graphics, utils, SummarizedExperiment(>= 1.5.3), progress, Matrix Suggests: knitr, rmarkdown, testthat, lme4(>= 1.0), blme, roxygen2(> 6.0.0), numDeriv, car, gdata, lattice, GGally, GSEABase, NMF, TxDb.Hsapiens.UCSC.hg19.knownGene, rsvd, limma, RColorBrewer, BiocStyle, scater, DelayedArray, HDF5Array, zinbwave, dplyr License: GPL(>= 2) MD5sum: a49b25e6c82267b8d314aeacdc09fee5 NeedsCompilation: no Title: Model-based Analysis of Single Cell Transcriptomics Description: Methods and models for handling zero-inflated single cell assay data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, RNASeq, Transcriptomics, SingleCell Author: Andrew McDavid [aut, cre], Greg Finak [aut], Masanao Yajima [aut] Maintainer: Andrew McDavid URL: https://github.com/RGLab/MAST/ VignetteBuilder: knitr BugReports: https://github.com/RGLab/MAST/issues git_url: https://git.bioconductor.org/packages/MAST git_branch: RELEASE_3_22 git_last_commit: be35999 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MAST_1.36.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MAST_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MAST_1.36.0.tgz vignettes: vignettes/MAST/inst/doc/MAITAnalysis.html, vignettes/MAST/inst/doc/MAST-interoperability.html, vignettes/MAST/inst/doc/MAST-Intro.html vignetteTitles: Using MAST for filtering,, differential expression and gene set enrichment in MAIT cells, Interoptability between MAST and SingleCellExperiment-derived packages, An Introduction to MAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAST/inst/doc/MAITAnalysis.R, vignettes/MAST/inst/doc/MAST-interoperability.R, vignettes/MAST/inst/doc/MAST-Intro.R dependsOnMe: POWSC importsMe: celaref, singleCellTK, DWLS suggestsMe: clusterExperiment, EWCE, Seurat, SeuratExplorer dependencyCount: 55 Package: mastR Version: 1.10.0 Depends: R (>= 4.3.0) Imports: AnnotationDbi, Biobase, dplyr, edgeR, ggplot2, ggpubr, graphics, grDevices, GSEABase, limma, Matrix, methods, msigdb, org.Hs.eg.db, patchwork, SeuratObject, SingleCellExperiment, stats, SummarizedExperiment, tidyr, utils Suggests: BiocManager, BiocStyle, clusterProfiler, ComplexHeatmap, depmap, enrichplot, ggrepel, ggvenn, gridExtra, jsonlite, knitr, rmarkdown, RobustRankAggreg, rvest, scuttle, singscore, splatter, testthat (>= 3.0.0), UpSetR License: MIT + file LICENSE Archs: x64 MD5sum: a3f7b4ee44e5e7e5862c19092544a2a5 NeedsCompilation: no Title: Markers Automated Screening Tool in R Description: mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of group markers in various biological and medical applications, including cancer research and developmental biology. biocViews: Software, GeneExpression, Transcriptomics, DifferentialExpression, Visualization Author: Jinjin Chen [aut, cre] (ORCID: ), Ahmed Mohamed [aut, ctb] (ORCID: ), Chin Wee Tan [ctb] (ORCID: ) Maintainer: Jinjin Chen URL: https://davislaboratory.github.io/mastR VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/mastR/issues git_url: https://git.bioconductor.org/packages/mastR git_branch: RELEASE_3_22 git_last_commit: 2cd82fb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mastR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mastR_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mastR_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mastR_1.10.0.tgz vignettes: vignettes/mastR/inst/doc/mastR_Demo.html vignetteTitles: mastR_Demo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mastR/inst/doc/mastR_Demo.R dependencyCount: 150 Package: matchBox Version: 1.52.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: ddc92c5cb4bb447720b6a4058209ce25 NeedsCompilation: no Title: Utilities to compute, compare, and plot the agreement between ordered vectors of features (ie. distinct genomic experiments). The package includes Correspondence-At-the-TOP (CAT) analysis. Description: The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance. biocViews: Software, Annotation, Microarray, MultipleComparison, Visualization Author: Luigi Marchionni , Anuj Gupta Maintainer: Luigi Marchionni , Anuj Gupta git_url: https://git.bioconductor.org/packages/matchBox git_branch: RELEASE_3_22 git_last_commit: 809d87b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/matchBox_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/matchBox_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/matchBox_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/matchBox_1.52.0.tgz vignettes: vignettes/matchBox/inst/doc/matchBox.pdf vignetteTitles: Working with the matchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matchBox/inst/doc/matchBox.R dependencyCount: 0 Package: MatrixGenerics Version: 1.22.0 Depends: matrixStats (>= 1.4.1) Imports: methods Suggests: Matrix, sparseMatrixStats, SparseArray, DelayedArray, DelayedMatrixStats, SummarizedExperiment, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: f1d0d731b82aed99d77e403280a14deb NeedsCompilation: no Title: S4 Generic Summary Statistic Functions that Operate on Matrix-Like Objects Description: S4 generic functions modeled after the 'matrixStats' API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities. biocViews: Infrastructure, Software Author: Constantin Ahlmann-Eltze [aut] (ORCID: ), Peter Hickey [aut, cre] (ORCID: ), Hervé Pagès [aut] Maintainer: Peter Hickey URL: https://bioconductor.org/packages/MatrixGenerics BugReports: https://github.com/Bioconductor/MatrixGenerics/issues git_url: https://git.bioconductor.org/packages/MatrixGenerics git_branch: RELEASE_3_22 git_last_commit: 75d9a54 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MatrixGenerics_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MatrixGenerics_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MatrixGenerics_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MatrixGenerics_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: DelayedArray, DelayedMatrixStats, GenomicFiles, SparseArray, sparseMatrixStats, SummarizedExperiment, VariantAnnotation importsMe: atena, Banksy, blase, CoreGx, crisprDesign, CTexploreR, DeconvoBuddies, demuxSNP, DESeq2, dreamlet, escape, FLAMES, genefilter, glmGamPoi, GSVA, imcRtools, lemur, lineagespot, methodical, mia, miaSim, miloR, MinimumDistance, MultiAssayExperiment, omicsGMF, PDATK, RaggedExperiment, RAIDS, RCSL, SanityR, saseR, scater, scone, scPCA, scran, scuttle, scviR, shinyMethyl, StabMap, tadar, TENxIO, tLOH, tpSVG, transformGamPoi, universalmotif, VanillaICE, Voyager, zitools, homosapienDEE2CellScore, spatialLIBD suggestsMe: bnem, cypress, MungeSumstats, scrapper, MoBPS dependencyCount: 2 Package: MatrixQCvis Version: 1.18.0 Depends: R (>= 4.1.0), DT (>= 0.33), SummarizedExperiment (>= 1.20.0), plotly (>= 4.9.3), shiny (>= 1.6.0) Imports: ComplexHeatmap (>= 2.7.9), dplyr (>= 1.0.5), ExperimentHub (>= 2.6.0), ggplot2 (>= 3.3.3), grDevices (>= 4.1.0), Hmisc (>= 4.5-0), htmlwidgets (>= 1.5.3), impute (>= 1.65.0), imputeLCMD (>= 2.0), limma (>= 3.47.12), MASS (>= 7.3-58.1), methods (>= 4.1.0), pcaMethods (>= 1.83.0), proDA (>= 1.5.0), rlang (>= 0.4.10), rmarkdown (>= 2.7), Rtsne (>= 0.15), shinydashboard (>= 0.7.1), shinyhelper (>= 0.3.2), shinyjs (>= 2.0.0), stats (>= 4.1.0), sva (>= 3.52.0), tibble (>= 3.1.1), tidyr (>= 1.1.3), umap (>= 0.2.7.0), UpSetR (>= 1.4.0), vsn (>= 3.59.1) Suggests: BiocGenerics (>= 0.37.4), BiocStyle (>= 2.19.2), hexbin (>= 1.28.2), httr (>= 1.4.7), jpeg (>= 0.1-10), knitr (>= 1.33), statmod (>= 1.5.0), testthat (>= 3.0.2) License: GPL-3 MD5sum: 54c05a2a827cdba992f756ce9d3b21ac NeedsCompilation: no Title: Shiny-based interactive data-quality exploration for omics data Description: Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object. biocViews: Visualization, ShinyApps, GUI, QualityControl, DimensionReduction, Metabolomics, Proteomics, Transcriptomics Author: Thomas Naake [aut, cre] (ORCID: ), Wolfgang Huber [aut] (ORCID: ) Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MatrixQCvis git_branch: RELEASE_3_22 git_last_commit: b35fe86 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MatrixQCvis_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MatrixQCvis_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MatrixQCvis_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MatrixQCvis_1.18.0.tgz vignettes: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.html vignetteTitles: Shiny-based interactive data quality exploration of omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.R dependencyCount: 188 Package: MatrixRider Version: 1.42.0 Depends: R (>= 3.1.2) Imports: methods, TFBSTools, IRanges, XVector, Biostrings LinkingTo: IRanges, XVector, Biostrings, S4Vectors Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014 License: GPL-3 MD5sum: 4af73c9fe3341f8f199418e416ce32c6 NeedsCompilation: yes Title: Obtain total affinity and occupancies for binding site matrices on a given sequence Description: Calculates a single number for a whole sequence that reflects the propensity of a DNA binding protein to interact with it. The DNA binding protein has to be described with a PFM matrix, for example gotten from Jaspar. biocViews: GeneRegulation, Genetics, MotifAnnotation Author: Elena Grassi Maintainer: Elena Grassi git_url: https://git.bioconductor.org/packages/MatrixRider git_branch: RELEASE_3_22 git_last_commit: a79a3cc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MatrixRider_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MatrixRider_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MatrixRider_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MatrixRider_1.42.0.tgz vignettes: vignettes/MatrixRider/inst/doc/MatrixRider.pdf vignetteTitles: Total affinity and occupancies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixRider/inst/doc/MatrixRider.R dependencyCount: 81 Package: matter Version: 2.12.0 Depends: R (>= 4.4), BiocParallel, Matrix, methods Imports: BiocGenerics, ProtGenerics, digest, irlba, stats, stats4, graphics, grDevices, parallel, utils LinkingTo: BH Suggests: BiocStyle, knitr, testthat, plotly License: Artistic-2.0 | file LICENSE MD5sum: 5242739d18d1c034c0d0c7a903d1be7f NeedsCompilation: yes Title: Out-of-core statistical computing and signal processing Description: Toolbox for larger-than-memory scientific computing and visualization, providing efficient out-of-core data structures using files or shared memory, for dense and sparse vectors, matrices, and arrays, with applications to nonuniformly sampled signals and images. biocViews: Infrastructure, DataRepresentation, DataImport, DimensionReduction, Preprocessing Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: https://github.com/kuwisdelu/matter VignetteBuilder: knitr BugReports: https://github.com/kuwisdelu/matter/issues git_url: https://git.bioconductor.org/packages/matter git_branch: RELEASE_3_22 git_last_commit: 42b13d8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/matter_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/matter_2.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/matter_2.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/matter_2.12.0.tgz vignettes: vignettes/matter/inst/doc/matter2-guide.html, vignettes/matter/inst/doc/matter2-signal.html vignetteTitles: 1. Matter 2: User guide for flexible out-of-memory data structures, 2. Matter 2: Signal and image processing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/matter/inst/doc/matter2-guide.R, vignettes/matter/inst/doc/matter2-signal.R dependsOnMe: CardinalIO importsMe: Cardinal dependencyCount: 24 Package: MBAmethyl Version: 1.44.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: cb80adc24dfaa9292a9c7c733e36ba02 NeedsCompilation: no Title: Model-based analysis of DNA methylation data Description: This package provides a function for reconstructing DNA methylation values from raw measurements. It iteratively implements the group fused lars to smooth related-by-location methylation values and the constrained least squares to remove probe affinity effect across multiple sequences. biocViews: DNAMethylation, MethylationArray Author: Tao Wang, Mengjie Chen Maintainer: Tao Wang git_url: https://git.bioconductor.org/packages/MBAmethyl git_branch: RELEASE_3_22 git_last_commit: 3f2bd86 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MBAmethyl_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MBAmethyl_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBAmethyl_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MBAmethyl_1.44.0.tgz vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf vignetteTitles: MBAmethyl Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBAmethyl/inst/doc/MBAmethyl.R dependencyCount: 0 Package: MBASED Version: 1.44.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 MD5sum: 9734eebbc1e92a23a0d81370c0b4a11e NeedsCompilation: no Title: Package containing functions for ASE analysis using Meta-analysis Based Allele-Specific Expression Detection Description: The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE. biocViews: Sequencing, GeneExpression, Transcription Author: Oleg Mayba, Houston Gilbert Maintainer: Oleg Mayba git_url: https://git.bioconductor.org/packages/MBASED git_branch: RELEASE_3_22 git_last_commit: 3039beb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MBASED_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MBASED_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBASED_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MBASED_1.44.0.tgz vignettes: vignettes/MBASED/inst/doc/MBASED.pdf vignetteTitles: MBASED hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBASED/inst/doc/MBASED.R dependencyCount: 36 Package: MBCB Version: 1.64.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>=2) MD5sum: deab4c0ae901f340126722e930c2e7cd NeedsCompilation: no Title: MBCB (Model-based Background Correction for Beadarray) Description: This package provides a model-based background correction method, which incorporates the negative control beads to pre-process Illumina BeadArray data. biocViews: Microarray, Preprocessing Author: Yang Xie Maintainer: Bo Yao URL: https://qbrc.swmed.edu/ git_url: https://git.bioconductor.org/packages/MBCB git_branch: RELEASE_3_22 git_last_commit: 926ff14 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MBCB_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MBCB_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBCB_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MBCB_1.64.0.tgz vignettes: vignettes/MBCB/inst/doc/MBCB.pdf vignetteTitles: MBCB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBCB/inst/doc/MBCB.R dependencyCount: 5 Package: MBECS Version: 1.14.0 Depends: R (>= 4.1) Imports: methods, magrittr, phyloseq, limma, lme4, lmerTest, pheatmap, rmarkdown, cluster, dplyr, ggplot2, gridExtra, ruv, sva, tibble, tidyr, vegan, stats, utils, Matrix Suggests: knitr, markdown, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 735762c24cd9f6bc110c93c2bc04afd6 NeedsCompilation: no Title: Evaluation and correction of batch effects in microbiome data-sets Description: The Microbiome Batch Effect Correction Suite (MBECS) provides a set of functions to evaluate and mitigate unwated noise due to processing in batches. To that end it incorporates a host of batch correcting algorithms (BECA) from various packages. In addition it offers a correction and reporting pipeline that provides a preliminary look at the characteristics of a data-set before and after correcting for batch effects. biocViews: BatchEffect, Microbiome, ReportWriting, Visualization, Normalization, QualityControl Author: Michael Olbrich [aut, cre] (ORCID: ) Maintainer: Michael Olbrich URL: https://github.com/rmolbrich/MBECS VignetteBuilder: knitr BugReports: https://github.com/rmolbrich/MBECS/issues/new git_url: https://git.bioconductor.org/packages/MBECS git_branch: RELEASE_3_22 git_last_commit: 32d603f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MBECS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MBECS_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBECS_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MBECS_1.14.0.tgz vignettes: vignettes/MBECS/inst/doc/mbecs_vignette.html vignetteTitles: MBECS introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBECS/inst/doc/mbecs_vignette.R dependencyCount: 141 Package: mbkmeans Version: 1.26.0 Depends: R (>= 3.6) Imports: methods, DelayedArray, Rcpp, S4Vectors, SingleCellExperiment, SummarizedExperiment, ClusterR, benchmarkme, Matrix, BiocParallel LinkingTo: Rcpp, RcppArmadillo (>= 0.7.2), Rhdf5lib, beachmat, ClusterR Suggests: beachmat, HDF5Array, Rhdf5lib, BiocStyle, TENxPBMCData, scater, DelayedMatrixStats, bluster, knitr, testthat, rmarkdown License: MIT + file LICENSE MD5sum: caac6c2a1ef587791b59b673719d0003 NeedsCompilation: yes Title: Mini-batch K-means Clustering for Single-Cell RNA-seq Description: Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation. biocViews: Clustering, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Yuwei Ni [aut, cph], Davide Risso [aut, cre, cph], Stephanie Hicks [aut, cph], Elizabeth Purdom [aut, cph] Maintainer: Davide Risso SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/drisso/mbkmeans/issues git_url: https://git.bioconductor.org/packages/mbkmeans git_branch: RELEASE_3_22 git_last_commit: 4dc9338 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mbkmeans_1.26.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mbkmeans_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mbkmeans_1.26.0.tgz vignettes: vignettes/mbkmeans/inst/doc/Vignette.html vignetteTitles: mbkmeans vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mbkmeans/inst/doc/Vignette.R dependsOnMe: OSCA.basic importsMe: clusterExperiment suggestsMe: bluster, concordexR, scDblFinder dependencyCount: 81 Package: mBPCR Version: 1.64.0 Depends: oligoClasses, GWASTools Imports: Biobase, graphics, methods, utils, grDevices Suggests: xtable License: GPL (>= 2) MD5sum: 4f765fb5e8123551e8c720f98a6a8077 NeedsCompilation: no Title: Bayesian Piecewise Constant Regression for DNA copy number estimation Description: It contains functions for estimating the DNA copy number profile using mBPCR with the aim of detecting regions with copy number changes. biocViews: aCGH, SNP, Microarray, CopyNumberVariation Author: P.M.V. Rancoita , with contributions from M. Hutter Maintainer: P.M.V. Rancoita URL: http://www.idsia.ch/~paola/mBPCR git_url: https://git.bioconductor.org/packages/mBPCR git_branch: RELEASE_3_22 git_last_commit: 26b7f88 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mBPCR_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mBPCR_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mBPCR_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mBPCR_1.64.0.tgz vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf vignetteTitles: mBPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mBPCR/inst/doc/mBPCR.R dependencyCount: 114 Package: MBQN Version: 2.22.0 Depends: R (>= 3.6) Imports: stats, graphics, utils, limma (>= 3.30.13), SummarizedExperiment (>= 1.10.0), preprocessCore (>= 1.36.0), BiocFileCache, rappdirs, xml2, RCurl, ggplot2, PairedData, rmarkdown Suggests: knitr License: GPL-3 + file LICENSE MD5sum: aa889d4b952f546322a3847a0160c52e NeedsCompilation: no Title: Mean/Median-balanced quantile normalization Description: Modified quantile normalization for omics or other matrix-like data distorted in location and scale. biocViews: Normalization, Preprocessing, Proteomics, Software Author: Eva Brombacher [aut, cre] (ORCID: ), Clemens Kreutz [aut, ctb] (ORCID: ), Ariane Schad [aut, ctb] (ORCID: ) Maintainer: Eva Brombacher URL: https://github.com/arianeschad/mbqn VignetteBuilder: knitr BugReports: https://github.com/arianeschad/MBQN/issues git_url: https://git.bioconductor.org/packages/MBQN git_branch: RELEASE_3_22 git_last_commit: a7445d7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MBQN_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MBQN_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBQN_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MBQN_2.22.0.tgz vignettes: vignettes/MBQN/inst/doc/MBQNpackage.html vignetteTitles: MBQN Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MBQN/inst/doc/MBQNpackage.R dependencyCount: 102 Package: mbQTL Version: 1.10.0 Depends: R (>= 4.3.0) Imports: MatrixEQTL, dplyr, ggplot2, readxl, stringr, tidyr, metagenomeSeq, pheatmap, broom, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: f5ad0e2e70da49398bf36bb16efcd56e NeedsCompilation: no Title: mbQTL: A package for SNP-Taxa mGWAS analysis Description: mbQTL is a statistical R package for simultaneous 16srRNA,16srDNA (microbial) and variant, SNP, SNV (host) relationship, correlation, regression studies. We apply linear, logistic and correlation based statistics to identify the relationships of taxa, genus, species and variant, SNP, SNV in the infected host. We produce various statistical significance measures such as P values, FDR, BC and probability estimation to show significance of these relationships. Further we provide various visualization function for ease and clarification of the results of these analysis. The package is compatible with dataframe, MRexperiment and text formats. biocViews: SNP, Microbiome, WholeGenome, Metagenomics, StatisticalMethod, Regression Author: Mercedeh Movassagh [aut, cre] (ORCID: ), Steven Schiff [aut], Joseph N Paulson [aut] Maintainer: Mercedeh Movassagh URL: "https://github.com/Mercedeh66/mbQTL" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mbQTL git_branch: RELEASE_3_22 git_last_commit: 596996d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mbQTL_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mbQTL_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mbQTL_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mbQTL_1.10.0.tgz vignettes: vignettes/mbQTL/inst/doc/mbQTL_Vignette.html vignetteTitles: MbQTL_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mbQTL/inst/doc/mbQTL_Vignette.R dependencyCount: 72 Package: MBttest Version: 1.38.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 36da7f741e329de579536992a16b8795 NeedsCompilation: no Title: Multiple Beta t-Tests Description: MBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE, GeneExpression, Transcription, AlternativeSplicing,Coverage, DifferentialSplicing Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/MBttest git_branch: RELEASE_3_22 git_last_commit: 555f2e5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MBttest_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MBttest_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBttest_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MBttest_1.38.0.tgz vignettes: vignettes/MBttest/inst/doc/MBttest-manual.pdf, vignettes/MBttest/inst/doc/MBttest.pdf vignetteTitles: MBttest-manual.pdf, Analysing RNA-Seq count data with the "MBttest" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBttest/inst/doc/MBttest.R dependencyCount: 11 Package: MCbiclust Version: 1.34.0 Depends: R (>= 3.4) Imports: BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db, org.Hs.eg.db, GGally, ggplot2, scales, cluster, WGCNA Suggests: gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr, pander, devtools, testthat, GSVA License: GPL-2 Archs: x64 MD5sum: 2fb19591e59bdfce1dcf28b4244dfc5a NeedsCompilation: no Title: Massive correlating biclusters for gene expression data and associated methods Description: Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set. biocViews: ImmunoOncology, Clustering, Microarray, StatisticalMethod, Software, RNASeq, GeneExpression Author: Robert Bentham Maintainer: Robert Bentham VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MCbiclust git_branch: RELEASE_3_22 git_last_commit: 68eea3e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MCbiclust_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MCbiclust_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MCbiclust_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MCbiclust_1.34.0.tgz vignettes: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.html vignetteTitles: Introduction to MCbiclust hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.R dependencyCount: 126 Package: mdp Version: 1.30.0 Depends: R (>= 4.0) Imports: ggplot2, gridExtra, grid, stats, utils Suggests: testthat, knitr, rmarkdown, fgsea, BiocManager License: GPL-3 MD5sum: e9962d37b81c054094ff697aafa049d5 NeedsCompilation: no Title: Molecular Degree of Perturbation calculates scores for transcriptome data samples based on their perturbation from controls Description: The Molecular Degree of Perturbation webtool quantifies the heterogeneity of samples. It takes a data.frame of omic data that contains at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. It is based on the Molecular Distance to Health (Pankla et al. 2009), and expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes. biocViews: BiomedicalInformatics, QualityControl, Transcriptomics, SystemsBiology, Microarray, QualityControl Author: Melissa Lever [aut], Pedro Russo [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya URL: https://mdp.sysbio.tools/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mdp git_branch: RELEASE_3_22 git_last_commit: 188c090 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mdp_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mdp_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mdp_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mdp_1.30.0.tgz vignettes: vignettes/mdp/inst/doc/my-vignette.html vignetteTitles: Running the mdp package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdp/inst/doc/my-vignette.R dependencyCount: 23 Package: mdqc Version: 1.72.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 73e5b149f797730798161c6210e16f04 NeedsCompilation: no Title: Mahalanobis Distance Quality Control for microarrays Description: MDQC is a multivariate quality assessment method for microarrays based on quality control (QC) reports. The Mahalanobis distance of an array's quality attributes is used to measure the similarity of the quality of that array against the quality of the other arrays. Then, arrays with unusually high distances can be flagged as potentially low-quality. biocViews: Microarray, QualityControl Author: Justin Harrington Maintainer: Gabriela Cohen-Freue git_url: https://git.bioconductor.org/packages/mdqc git_branch: RELEASE_3_22 git_last_commit: 90cd0da git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mdqc_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mdqc_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mdqc_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mdqc_1.72.0.tgz vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf vignetteTitles: Introduction to MDQC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdqc/inst/doc/mdqcvignette.R importsMe: arrayMvout dependencyCount: 7 Package: MDTS Version: 1.30.0 Depends: R (>= 3.5.0) Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings, DNAcopy, Rsamtools, parallel, stringr Suggests: testthat, knitr License: Artistic-2.0 MD5sum: 2a84e28eb2e9799d1c2d6af003500634 NeedsCompilation: no Title: Detection of de novo deletion in targeted sequencing trios Description: A package for the detection of de novo copy number deletions in targeted sequencing of trios with high sensitivity and positive predictive value. biocViews: StatisticalMethod, Technology, Sequencing, TargetedResequencing, Coverage, DataImport Author: Jack M.. Fu [aut, cre] Maintainer: Jack M.. Fu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MDTS git_branch: RELEASE_3_22 git_last_commit: 2e2c502 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MDTS_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MDTS_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MDTS_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MDTS_1.30.0.tgz vignettes: vignettes/MDTS/inst/doc/mdts.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MDTS/inst/doc/mdts.R dependencyCount: 51 Package: MEAL Version: 1.40.0 Depends: R (>= 3.6.0), Biobase, MultiDataSet Imports: GenomicRanges, limma, vegan, BiocGenerics, minfi, IRanges, S4Vectors, methods, parallel, ggplot2 (>= 2.0.0), permute, Gviz, missMethyl, isva, SummarizedExperiment, SmartSVA, graphics, stats, utils, matrixStats Suggests: testthat, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData, BiocStyle, rmarkdown, brgedata License: Artistic-2.0 MD5sum: 8f940cf916ba94e0b6d1927d966acfdb NeedsCompilation: no Title: Perform methylation analysis Description: Package to integrate methylation and expression data. It can also perform methylation or expression analysis alone. Several plotting functionalities are included as well as a new region analysis based on redundancy analysis. Effect of SNPs on a region can also be estimated. biocViews: DNAMethylation, Microarray, Software, WholeGenome Author: Carlos Ruiz-Arenas [aut, cre], Juan R. Gonzalez [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEAL git_branch: RELEASE_3_22 git_last_commit: 0882535 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MEAL_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEAL_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MEAL_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MEAL_1.40.0.tgz vignettes: vignettes/MEAL/inst/doc/caseExample.html, vignettes/MEAL/inst/doc/MEAL.html vignetteTitles: Expression and Methylation Analysis with MEAL, Methylation Analysis with MEAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAL/inst/doc/caseExample.R, vignettes/MEAL/inst/doc/MEAL.R dependencyCount: 228 Package: MeasurementError.cor Version: 1.82.0 License: LGPL MD5sum: a66df8c188f09c71fe445b5599a46a9f NeedsCompilation: no Title: Measurement Error model estimate for correlation coefficient Description: Two-stage measurement error model for correlation estimation with smaller bias than the usual sample correlation biocViews: StatisticalMethod Author: Beiying Ding Maintainer: Beiying Ding git_url: https://git.bioconductor.org/packages/MeasurementError.cor git_branch: RELEASE_3_22 git_last_commit: 7dda8fd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MeasurementError.cor_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MeasurementError.cor_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MeasurementError.cor_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MeasurementError.cor_1.82.0.tgz vignettes: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf vignetteTitles: MeasurementError.cor Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.R dependencyCount: 0 Package: MEAT Version: 1.22.0 Depends: R (>= 4.0) Imports: impute (>= 1.58), dynamicTreeCut (>= 1.63), glmnet (>= 2.0), grDevices, graphics, stats, utils, stringr, tibble, RPMM (>= 1.25), minfi (>= 1.30), dplyr, SummarizedExperiment, wateRmelon Suggests: knitr, markdown, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 72a91b7372956a03de3ae97fcb92370a NeedsCompilation: no Title: Muscle Epigenetic Age Test Description: This package estimates epigenetic age in skeletal muscle, using DNA methylation data generated with the Illumina Infinium technology (HM27, HM450 and HMEPIC). biocViews: Epigenetics, DNAMethylation, Microarray, Normalization, BiomedicalInformatics, MethylationArray, Preprocessing Author: Sarah Voisin [aut, cre] (), Steve Horvath [ctb] () Maintainer: Sarah Voisin URL: https://github.com/sarah-voisin/MEAT VignetteBuilder: knitr BugReports: https://github.com/sarah-voisin/MEAT/issues git_url: https://git.bioconductor.org/packages/MEAT git_branch: RELEASE_3_22 git_last_commit: 06f9620 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MEAT_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEAT_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MEAT_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MEAT_1.22.0.tgz vignettes: vignettes/MEAT/inst/doc/MEAT.html vignetteTitles: MEAT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAT/inst/doc/MEAT.R dependencyCount: 179 Package: MEB Version: 1.24.0 Depends: R (>= 3.6.0) Imports: e1071, edgeR, scater, stats, wrswoR, SummarizedExperiment, SingleCellExperiment Suggests: knitr,rmarkdown,BiocStyle License: GPL-2 MD5sum: 6561961d0516a3016279c96f4ff6fd63 NeedsCompilation: no Title: A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq and scRNA-seq data Description: This package provides a method to identify differential expression genes in the same or different species. Given that non-DE genes have some similarities in features, a scaling-free minimum enclosing ball (SFMEB) model is built to cover those non-DE genes in feature space, then those DE genes, which are enormously different from non-DE genes, being regarded as outliers and rejected outside the ball. The method on this package is described in the article 'A minimum enclosing ball method to detect differential expression genes for RNA-seq data'. The SFMEB method is extended to the scMEB method that considering two or more potential types of cells or unknown labels scRNA-seq dataset DEGs identification. biocViews: DifferentialExpression, GeneExpression, Normalization, Classification, Sequencing Author: Yan Zhou, Jiadi Zhu Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>, Yan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEB git_branch: RELEASE_3_22 git_last_commit: 75bb07c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MEB_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEB_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MEB_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MEB_1.24.0.tgz vignettes: vignettes/MEB/inst/doc/NIMEB.html vignetteTitles: MEB Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEB/inst/doc/NIMEB.R dependencyCount: 98 Package: MEDIPS Version: 1.62.0 Depends: R (>= 3.0), BSgenome, Rsamtools Imports: GenomicRanges, Biostrings, graphics, gtools, IRanges, methods, stats, utils, edgeR, DNAcopy, biomaRt, rtracklayer, preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, BiocStyle License: GPL (>=2) MD5sum: c0bc2e60fb680c3967f534e8c964cada NeedsCompilation: no Title: DNA IP-seq data analysis Description: MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis. biocViews: DNAMethylation, CpGIsland, DifferentialExpression, Sequencing, ChIPSeq, Preprocessing, QualityControl, Visualization, Microarray, Genetics, Coverage, GenomeAnnotation, CopyNumberVariation, SequenceMatching Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich, Isaac Lopez Moyado Maintainer: Lukas Chavez git_url: https://git.bioconductor.org/packages/MEDIPS git_branch: RELEASE_3_22 git_last_commit: 8748557 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MEDIPS_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEDIPS_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MEDIPS_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MEDIPS_1.62.0.tgz vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf vignetteTitles: MEDIPS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDIPS/inst/doc/MEDIPS.R suggestsMe: MEDIPSData dependencyCount: 104 Package: MEDME Version: 1.70.0 Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils Imports: Biostrings, MASS, drc Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9 License: GPL (>= 2) MD5sum: f368198d631d195a74f56e8318993015 NeedsCompilation: yes Title: Modelling Experimental Data from MeDIP Enrichment Description: MEDME allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments biocViews: Microarray, CpGIsland, DNAMethylation Author: Mattia Pelizzola and Annette Molinaro Maintainer: Mattia Pelizzola git_url: https://git.bioconductor.org/packages/MEDME git_branch: RELEASE_3_22 git_last_commit: 55c9b50 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MEDME_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEDME_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MEDME_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MEDME_1.70.0.tgz vignettes: vignettes/MEDME/inst/doc/MEDME.pdf vignetteTitles: MEDME.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDME/inst/doc/MEDME.R dependencyCount: 87 Package: megadepth Version: 1.20.0 Imports: xfun, utils, fs, GenomicRanges, readr, cmdfun, dplyr, magrittr Suggests: covr, knitr, BiocStyle, sessioninfo, rmarkdown, rtracklayer, derfinder, GenomeInfoDb, tools, RefManageR, testthat License: Artistic-2.0 MD5sum: 0dc129aaf84189b1ee8b35c198309272 NeedsCompilation: no Title: megadepth: BigWig and BAM related utilities Description: This package provides an R interface to Megadepth by Christopher Wilks available at https://github.com/ChristopherWilks/megadepth. It is particularly useful for computing the coverage of a set of genomic regions across bigWig or BAM files. With this package, you can build base-pair coverage matrices for regions or annotations of your choice from BigWig files. Megadepth was used to create the raw files provided by https://bioconductor.org/packages/recount3. biocViews: Software, Coverage, DataImport, Transcriptomics, RNASeq, Preprocessing Author: Leonardo Collado-Torres [aut] (ORCID: ), David Zhang [aut, cre] (ORCID: ) Maintainer: David Zhang URL: https://github.com/LieberInstitute/megadepth SystemRequirements: megadepth () VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/megadepth git_url: https://git.bioconductor.org/packages/megadepth git_branch: RELEASE_3_22 git_last_commit: 8dd30d5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/megadepth_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/megadepth_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/megadepth_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/megadepth_1.20.0.tgz vignettes: vignettes/megadepth/inst/doc/megadepth.html vignetteTitles: megadepth quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/megadepth/inst/doc/megadepth.R importsMe: chevreulProcess dependencyCount: 76 Package: MEIGOR Version: 1.44.0 Depends: R (>= 4.0), Rsolnp, snowfall, deSolve, CNORode Suggests: CellNOptR, knitr, BiocStyle License: GPL-3 MD5sum: a4dad33910bb56ef2e89511805b62d94 NeedsCompilation: no Title: MEIGOR - MEtaheuristics for bIoinformatics Global Optimization Description: MEIGOR provides a comprehensive environment for performing global optimization tasks in bioinformatics and systems biology. It leverages advanced metaheuristic algorithms to efficiently search the solution space and is specifically tailored to handle the complexity and high-dimensionality of biological datasets. This package supports various optimization routines and is integrated with Bioconductor's infrastructure for a seamless analysis workflow. biocViews: SystemsBiology, Optimization, Software Author: Jose A. Egea [aut, cre], David Henriques [aut], Alexandre Fdez. Villaverde [aut], Thomas Cokelaer [aut] Maintainer: Jose A. Egea VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEIGOR git_branch: RELEASE_3_22 git_last_commit: 47d6492 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MEIGOR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEIGOR_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MEIGOR_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MEIGOR_1.44.0.tgz vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.html vignetteTitles: MEIGOR: a software suite based on metaheuristics for global optimization in systems biology and bioinformatics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R dependencyCount: 80 Package: memes Version: 1.18.0 Depends: R (>= 4.1) Imports: Biostrings, dplyr, cmdfun (>= 1.0.2), GenomicRanges, ggplot2, ggseqlogo, magrittr, matrixStats, methods, patchwork, processx, purrr, rlang, readr, stats, tools, tibble, tidyr, utils, usethis, universalmotif (>= 1.9.3), xml2 Suggests: cowplot, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm6, forcats, testthat (>= 2.1.0), knitr, MotifDb, pheatmap, PMCMRplus, plyranges (>= 1.9.1), rmarkdown, covr License: MIT + file LICENSE MD5sum: 5a500dd6a401a815ca066fb190e3f25a NeedsCompilation: no Title: motif matching, comparison, and de novo discovery using the MEME Suite Description: A seamless interface to the MEME Suite family of tools for motif analysis. 'memes' provides data aware utilities for using GRanges objects as entrypoints to motif analysis, data structures for examining & editing motif lists, and novel data visualizations. 'memes' functions and data structures are amenable to both base R and tidyverse workflows. biocViews: DataImport, FunctionalGenomics, GeneRegulation, MotifAnnotation, MotifDiscovery, SequenceMatching, Software Author: Spencer Nystrom [aut, cre, cph] (ORCID: ) Maintainer: Spencer Nystrom URL: https://snystrom.github.io/memes/, https://github.com/snystrom/memes SystemRequirements: Meme Suite (v5.3.3 or above) VignetteBuilder: knitr BugReports: https://github.com/snystrom/memes/issues git_url: https://git.bioconductor.org/packages/memes git_branch: RELEASE_3_22 git_last_commit: ca59d4c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/memes_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/memes_1.17.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/memes_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/memes_1.18.0.tgz vignettes: vignettes/memes/inst/doc/core_ame.html, vignettes/memes/inst/doc/core_dreme.html, vignettes/memes/inst/doc/core_fimo.html, vignettes/memes/inst/doc/core_tomtom.html, vignettes/memes/inst/doc/install_guide.html, vignettes/memes/inst/doc/integrative_analysis.html, vignettes/memes/inst/doc/tidy_motifs.html vignetteTitles: Motif Enrichment Testing using AME, Denovo Motif Discovery Using DREME, Motif Scanning using FIMO, Motif Comparison using TomTom, Install MEME, ChIP-seq Analysis, Tidying Motif Metadata hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/memes/inst/doc/core_ame.R, vignettes/memes/inst/doc/core_dreme.R, vignettes/memes/inst/doc/core_fimo.R, vignettes/memes/inst/doc/core_tomtom.R, vignettes/memes/inst/doc/install_guide.R, vignettes/memes/inst/doc/integrative_analysis.R, vignettes/memes/inst/doc/tidy_motifs.R importsMe: MotifPeeker dependencyCount: 99 Package: Mergeomics Version: 1.38.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 03087aabc797e9f8c777718625320ffd NeedsCompilation: no Title: Integrative network analysis of omics data Description: The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA). biocViews: Software Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang Maintainer: Zeyneb Kurt git_url: https://git.bioconductor.org/packages/Mergeomics git_branch: RELEASE_3_22 git_last_commit: a71f54d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Mergeomics_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Mergeomics_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Mergeomics_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Mergeomics_1.38.0.tgz vignettes: vignettes/Mergeomics/inst/doc/Mergeomics.pdf vignetteTitles: Mergeomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mergeomics/inst/doc/Mergeomics.R dependencyCount: 0 Package: MeSHDbi Version: 1.46.0 Depends: R (>= 3.0.1) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: testthat License: Artistic-2.0 Archs: x64 MD5sum: 8dcaf670864f1418c4d517675cd825a4 NeedsCompilation: no Title: DBI to construct MeSH-related package from sqlite file Description: The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db. biocViews: Annotation, AnnotationData, Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/MeSHDbi git_branch: RELEASE_3_22 git_last_commit: 4b20d70 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MeSHDbi_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MeSHDbi_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MeSHDbi_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MeSHDbi_1.46.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: meshes, meshr, scTensor dependencyCount: 43 Package: meshes Version: 1.36.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, DOSE, enrichplot, GOSemSim (>= 2.31.2), methods, utils, AnnotationHub, MeSHDbi, yulab.utils (>= 0.1.5) Suggests: knitr, rmarkdown, prettydoc License: Artistic-2.0 MD5sum: 438883272c0e64bf66051872a993d077 NeedsCompilation: no Title: MeSH Enrichment and Semantic analyses Description: MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH terms were associated by Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH. This association is fundamental for enrichment and semantic analyses. meshes supports enrichment analysis (over-representation and gene set enrichment analysis) of gene list or whole expression profile. The semantic comparisons of MeSH terms provide quantitative ways to compute similarities between genes and gene groups. meshes implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively and supports more than 70 species. biocViews: Annotation, Clustering, MultipleComparison, Software Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/meshes/issues git_url: https://git.bioconductor.org/packages/meshes git_branch: RELEASE_3_22 git_last_commit: 6d7a37c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/meshes_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/meshes_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/meshes_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/meshes_1.36.0.tgz vignettes: vignettes/meshes/inst/doc/meshes.html vignetteTitles: meshes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshes/inst/doc/meshes.R dependencyCount: 144 Package: meshr Version: 2.16.0 Depends: R (>= 4.1.0) Imports: markdown, rmarkdown, BiocStyle, knitr, methods, stats, utils, fdrtool, MeSHDbi, Category, S4Vectors, BiocGenerics, RSQLite License: Artistic-2.0 MD5sum: 2b9e286e5383d3f1d0764aa79bf534b5 NeedsCompilation: no Title: Tools for conducting enrichment analysis of MeSH Description: A set of annotation maps describing the entire MeSH assembled using data from MeSH. biocViews: AnnotationData, FunctionalAnnotation, Bioinformatics, Statistics, Annotation, MultipleComparisons, MeSHDb Author: Koki Tsuyuzaki, Itoshi Nikaido, Gota Morota Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr BugReports: https://github.com/rikenbit/meshr/issues git_url: https://git.bioconductor.org/packages/meshr git_branch: RELEASE_3_22 git_last_commit: 43697db git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/meshr_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/meshr_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/meshr_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/meshr_2.16.0.tgz vignettes: vignettes/meshr/inst/doc/MeSH.html vignetteTitles: AnnotationHub-style MeSH ORA Framework from BioC 3.14 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshr/inst/doc/MeSH.R importsMe: scTensor dependencyCount: 83 Package: MesKit Version: 1.20.0 Depends: R (>= 4.0.0) Imports: methods, data.table, Biostrings, dplyr, tidyr (>= 1.0.0), ape (>= 5.4.1), ggrepel, pracma, ggridges, AnnotationDbi, IRanges, circlize, cowplot, mclust, phangorn, ComplexHeatmap (>= 1.9.3), ggplot2, RColorBrewer, grDevices, stats, utils, S4Vectors Suggests: shiny, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), org.Hs.eg.db, clusterProfiler, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: b3af20b3bcfebe670cdee87f3a1c19bb NeedsCompilation: no Title: A tool kit for dissecting cancer evolution from multi-region derived tumor biopsies via somatic alterations Description: MesKit provides commonly used analysis and visualization modules based on mutational data generated by multi-region sequencing (MRS). This package allows to depict mutational profiles, measure heterogeneity within or between tumors from the same patient, track evolutionary dynamics, as well as characterize mutational patterns on different levels. Shiny application was also developed for a need of GUI-based analysis. As a handy tool, MesKit can facilitate the interpretation of tumor heterogeneity and the understanding of evolutionary relationship between regions in MRS study. Author: Mengni Liu [aut, cre] (ORCID: ), Jianyu Chen [aut, ctb] (ORCID: ), Xin Wang [aut, ctb] (ORCID: ) Maintainer: Mengni Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MesKit git_branch: RELEASE_3_22 git_last_commit: 8a34315 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MesKit_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MesKit_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MesKit_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MesKit_1.20.0.tgz vignettes: vignettes/MesKit/inst/doc/MesKit.html vignetteTitles: Analyze and Visualize Multi-region Whole-exome Sequencing Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MesKit/inst/doc/MesKit.R importsMe: CaMutQC dependencyCount: 95 Package: messina Version: 1.46.0 Depends: R (>= 3.1.0), survival (>= 2.37-4), methods Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics LinkingTo: Rcpp Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>= 2.22.0), BiocStyle Enhances: doMC (>= 1.3.3) License: EPL (>= 1.0) MD5sum: 57e3910b826e488105bdc5c78c398cce NeedsCompilation: yes Title: Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems Description: Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Survival Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph] Maintainer: Mark Pinese VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/messina git_branch: RELEASE_3_22 git_last_commit: c37c4d7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/messina_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/messina_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/messina_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/messina_1.46.0.tgz vignettes: vignettes/messina/inst/doc/messina.pdf vignetteTitles: Using Messina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/messina/inst/doc/messina.R dependencyCount: 31 Package: metabCombiner Version: 1.20.0 Depends: R (>= 4.0) Imports: dplyr (>= 1.0), methods, mgcv, caret, S4Vectors, stats, utils, rlang, graphics, matrixStats, tidyr Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 2dd1777eb11f234af3a43ca455f231a4 NeedsCompilation: yes Title: Method for Combining LC-MS Metabolomics Feature Measurements Description: This package aligns LC-HRMS metabolomics datasets acquired from biologically similar specimens analyzed under similar, but not necessarily identical, conditions. Peak-picked and simply aligned metabolomics feature tables (consisting of m/z, rt, and per-sample abundance measurements, plus optional identifiers & adduct annotations) are accepted as input. The package outputs a combined table of feature pair alignments, organized into groups of similar m/z, and ranked by a similarity score. Input tables are assumed to be acquired using similar (but not necessarily identical) analytical methods. biocViews: Software, MassSpectrometry, Metabolomics Author: Hani Habra [aut, cre], Alla Karnovsky [ths] Maintainer: Hani Habra VignetteBuilder: knitr BugReports: https://www.github.com/hhabra/metabCombiner/issues git_url: https://git.bioconductor.org/packages/metabCombiner git_branch: RELEASE_3_22 git_last_commit: 32c5536 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metabCombiner_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metabCombiner_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metabCombiner_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metabCombiner_1.20.0.tgz vignettes: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.html vignetteTitles: Combine LC-MS Metabolomics Datasets with metabCombiner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.R dependencyCount: 87 Package: metabinR Version: 1.12.0 Depends: R (>= 4.3) Imports: methods, rJava Suggests: BiocStyle, cvms, data.table, dplyr, ggplot2, gridExtra, knitr, R.utils, rmarkdown, sabre, spelling, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: d4e1bf7ff3769d94c6fc7dd9ec5dfdd6 NeedsCompilation: no Title: Abundance and Compositional Based Binning of Metagenomes Description: Provide functions for performing abundance and compositional based binning on metagenomic samples, directly from FASTA or FASTQ files. Functions are implemented in Java and called via rJava. Parallel implementation that operates directly on input FASTA/FASTQ files for fast execution. biocViews: Classification, Clustering, Microbiome, Sequencing, Software Author: Anestis Gkanogiannis [aut, cre] (ORCID: ) Maintainer: Anestis Gkanogiannis URL: https://github.com/gkanogiannis/metabinR SystemRequirements: Java (>= 8) VignetteBuilder: knitr BugReports: https://github.com/gkanogiannis/metabinR/issues git_url: https://git.bioconductor.org/packages/metabinR git_branch: RELEASE_3_22 git_last_commit: 5af1469 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metabinR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metabinR_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metabinR_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metabinR_1.12.0.tgz vignettes: vignettes/metabinR/inst/doc/metabinR_vignette.html vignetteTitles: metabinR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabinR/inst/doc/metabinR_vignette.R dependencyCount: 2 Package: MetaboAnnotation Version: 1.14.0 Depends: R (>= 4.0.0) Imports: BiocGenerics, MsCoreUtils, MetaboCoreUtils, ProtGenerics, methods, S4Vectors, Spectra (>= 1.17.6), BiocParallel, SummarizedExperiment, QFeatures, AnnotationHub, graphics, CompoundDb Suggests: testthat, knitr, msdata, BiocStyle, rmarkdown, plotly, shiny, shinyjs, msentropy, DT, microbenchmark, mzR Enhances: RMariaDB, RSQLite License: Artistic-2.0 Archs: x64 MD5sum: 0c5e01390564278e447158fc56d2401d NeedsCompilation: no Title: Utilities for Annotation of Metabolomics Data Description: High level functions to assist in annotation of (metabolomics) data sets. These include functions to perform simple tentative annotations based on mass matching but also functions to consider m/z and retention times for annotation of LC-MS features given that respective reference values are available. In addition, the function provides high-level functions to simplify matching of LC-MS/MS spectra against spectral libraries and objects and functionality to represent and manage such matched data. biocViews: Infrastructure, Metabolomics, MassSpectrometry Author: Michael Witting [aut] (ORCID: ), Johannes Rainer [aut, cre] (ORCID: ), Andrea Vicini [aut] (ORCID: ), Carolin Huber [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Nir Shachaf [ctb] Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MetaboAnnotation VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MetaboAnnotation/issues git_url: https://git.bioconductor.org/packages/MetaboAnnotation git_branch: RELEASE_3_22 git_last_commit: 790d88f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MetaboAnnotation_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaboAnnotation_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaboAnnotation_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaboAnnotation_1.14.0.tgz vignettes: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.html vignetteTitles: Annotation of MS-based Metabolomics Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.R dependencyCount: 138 Package: MetaboCoreUtils Version: 1.18.0 Depends: R (>= 4.0) Imports: utils, MsCoreUtils, BiocParallel, methods, stats Suggests: BiocStyle, testthat, knitr, rmarkdown, robustbase License: Artistic-2.0 MD5sum: 085d08a6590bcaa3dfca3a6cebf0ceab NeedsCompilation: no Title: Core Utils for Metabolomics Data Description: MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound mass-to-charge ratios and masses or functions to work with chemical formulas. The package provides also a set of adduct definitions and information on some commercially available internal standard mixes commonly used in MS experiments. biocViews: Infrastructure, Metabolomics, MassSpectrometry Author: Johannes Rainer [aut, cre] (ORCID: ), Michael Witting [aut] (ORCID: ), Andrea Vicini [aut], Liesa Salzer [ctb] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Michael Stravs [ctb] (ORCID: ), Roger Gine [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MetaboCoreUtils VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MetaboCoreUtils/issues git_url: https://git.bioconductor.org/packages/MetaboCoreUtils git_branch: RELEASE_3_22 git_last_commit: c4fa33d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MetaboCoreUtils_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaboCoreUtils_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaboCoreUtils_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaboCoreUtils_1.18.0.tgz vignettes: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.html vignetteTitles: Core Utils for Metabolomics Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.R importsMe: CompoundDb, MetaboAnnotation, Spectra, xcms dependencyCount: 24 Package: MetaboDynamics Version: 1.99.12 Depends: R (>= 4.4.0) Imports: dplyr, ggplot2, KEGGREST, methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), rstantools (>= 2.4.0), S4Vectors, stringr, SummarizedExperiment, tidyr, dynamicTreeCut, rlang, ape, ggtree, patchwork LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: 381a6a6d285b051a0b6f2b3b4c04a0d3 NeedsCompilation: yes Title: Bayesian analysis of longitudinal metabolomics data Description: MetaboDynamics is an R-package that provides a framework of probabilistic models to analyze longitudinal metabolomics data. It enables robust estimation of mean concentrations despite varying spread between timepoints and reports differences between timepoints as well as metabolite specific dynamics profiles that can be used for identifying "dynamics clusters" of metabolites of similar dynamics. Provides probabilistic over-representation analysis of KEGG functional modules and pathways as well as comparison between clusters of different experimental conditions. biocViews: Software,Metabolomics,Bayesian,FunctionalPrediction,MultipleComparison,KEGG,Pathways,TimeCourse, Clustering Author: Katja Danielzik [aut, cre] (ORCID: ), Simo Kitanovski [ctb] (ORCID: ), Johann Matschke [ctb] (ORCID: ), Daniel Hoffmann [ctb] (ORCID: ) Maintainer: Katja Danielzik URL: https://github.com/KatjaDanielzik/MetaboDynamics SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/KatjaDanielzik/MetaboDynamics/issues git_url: https://git.bioconductor.org/packages/MetaboDynamics git_branch: devel git_last_commit: b91af21 git_last_commit_date: 2025-10-24 Date/Publication: 2025-10-24 source.ver: src/contrib/MetaboDynamics_1.99.12.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaboDynamics_1.1.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaboDynamics_1.99.12.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaboDynamics_1.99.12.tgz vignettes: vignettes/MetaboDynamics/inst/doc/MetaboDynamics_dataframes.html, vignettes/MetaboDynamics/inst/doc/MetaboDynamics.html vignetteTitles: 2. MetaboDynamics_dataframes, 1. MetaboDynamics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboDynamics/inst/doc/MetaboDynamics_dataframes.R, vignettes/MetaboDynamics/inst/doc/MetaboDynamics.R dependencyCount: 126 Package: metabomxtr Version: 1.44.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2 Suggests: xtable, reshape2 License: GPL-2 MD5sum: 2cf611dea65ff2a3a3b024c8431f71f4 NeedsCompilation: no Title: A package to run mixture models for truncated metabolomics data with normal or lognormal distributions Description: The functions in this package return optimized parameter estimates and log likelihoods for mixture models of truncated data with normal or lognormal distributions. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens Maintainer: Michael Nodzenski git_url: https://git.bioconductor.org/packages/metabomxtr git_branch: RELEASE_3_22 git_last_commit: db88c37 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metabomxtr_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metabomxtr_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metabomxtr_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metabomxtr_1.44.0.tgz vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.pdf vignetteTitles: metabomxtr, mixnorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.R dependencyCount: 49 Package: MetaboSignal Version: 1.40.0 Depends: R(>= 3.3) Imports: KEGGgraph, hpar, igraph, RCurl, KEGGREST, EnsDb.Hsapiens.v75, stats, graphics, utils, org.Hs.eg.db, biomaRt, AnnotationDbi, MWASTools, mygene Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: eb3a2812edf7654210aa197091b1f4a0 NeedsCompilation: no Title: MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways Description: MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes. biocViews: GraphAndNetwork, GeneSignaling, GeneTarget, Network, Pathways, KEGG, Reactome, Software Author: Andrea Rodriguez-Martinez, Rafael Ayala, Joram M. Posma, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaboSignal git_branch: RELEASE_3_22 git_last_commit: 4db76ce git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MetaboSignal_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaboSignal_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaboSignal_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaboSignal_1.40.0.tgz vignettes: vignettes/MetaboSignal/inst/doc/MetaboSignal.html, vignettes/MetaboSignal/inst/doc/MetaboSignal2.html vignetteTitles: MetaboSignal, MetaboSignal 2: merging KEGG with additional interaction resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboSignal/inst/doc/MetaboSignal.R, vignettes/MetaboSignal/inst/doc/MetaboSignal2.R dependencyCount: 200 Package: metaCCA Version: 1.38.0 Suggests: knitr License: MIT + file LICENSE MD5sum: 4f8195eb994c78937b7e8ded637444d9 NeedsCompilation: no Title: Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis Description: metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. biocViews: GenomeWideAssociation, SNP, Genetics, Regression, StatisticalMethod, Software Author: Anna Cichonska Maintainer: Anna Cichonska URL: https://doi.org/10.1093/bioinformatics/btw052 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metaCCA git_branch: RELEASE_3_22 git_last_commit: bd511af git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metaCCA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metaCCA_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metaCCA_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metaCCA_1.38.0.tgz vignettes: vignettes/metaCCA/inst/doc/metaCCA.pdf vignetteTitles: metaCCA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metaCCA/inst/doc/metaCCA.R dependencyCount: 0 Package: MetaCyto Version: 1.32.0 Depends: R (>= 3.4) Imports: flowCore (>= 1.4),tidyr (>= 0.7),fastcluster,ggplot2,metafor,cluster,FlowSOM, grDevices, graphics, stats, utils Suggests: knitr, dplyr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: b4e2e6e257fc0e8f7fe3f7233a74d384 NeedsCompilation: no Title: MetaCyto: A package for meta-analysis of cytometry data Description: This package provides functions for preprocessing, automated gating and meta-analysis of cytometry data. It also provides functions that facilitate the collection of cytometry data from the ImmPort database. biocViews: ImmunoOncology, CellBiology, FlowCytometry, Clustering, StatisticalMethod, Software, CellBasedAssays, Preprocessing Author: Zicheng Hu, Chethan Jujjavarapu, Sanchita Bhattacharya, Atul J. Butte Maintainer: Zicheng Hu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/MetaCyto git_branch: RELEASE_3_22 git_last_commit: f3c39a8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MetaCyto_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaCyto_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaCyto_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaCyto_1.32.0.tgz vignettes: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.R dependencyCount: 109 Package: MetaDICT Version: 1.0.0 Depends: R (>= 4.2.0) Imports: stats, RANN, igraph, vegan, edgeR, ecodist, ggplot2, viridis, ggpubr, ape, cluster, matrixStats Suggests: BiocStyle, knitr, rmarkdown, DT, ggraph, tidyverse, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 0ca058f956d9520fe4f92e85423225a6 NeedsCompilation: no Title: Microbiome data integration method via shared dictionary learning Description: MetaDICT is a method for the integration of microbiome data. This method is designed to remove batch effects and preserve biological variation while integrating heterogeneous datasets. MetaDICT can better avoid overcorrection when unobserved confounding variables are present. biocViews: Microbiome, BatchEffect, Sequencing, Clustering, Software Author: Bo Yuan [aut, cre] (ORCID: ), Shulei Wang [aut] Maintainer: Bo Yuan URL: https://github.com/BoYuan07/MetaDICT VignetteBuilder: knitr BugReports: https://github.com/BoYuan07/MetaDICT/issues git_url: https://git.bioconductor.org/packages/MetaDICT git_branch: RELEASE_3_22 git_last_commit: 2dfcaa8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MetaDICT_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaDICT_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaDICT_1.0.0.tgz vignettes: vignettes/MetaDICT/inst/doc/MetaDICT.html vignetteTitles: MetaDICT Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaDICT/inst/doc/MetaDICT.R dependencyCount: 92 Package: metagene2 Version: 1.26.0 Depends: R (>= 4.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, tools, GenomicAlignments, GenomeInfoDb, IRanges, ggplot2, Rsamtools, purrr, data.table, methods, dplyr, magrittr, reshape2 Suggests: BiocGenerics, RUnit, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: b9b43faf4f855fbe0fff543aebcc7b2c NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare coverages of sequencing experiments at selected groups of genomic regions. It can be used for such analyses as assessing the binding of DNA-interacting proteins at promoter regions or surveying antisense transcription over the length of a gene. The metagene2 package can manage all aspects of the analysis, from normalization of coverages to plot facetting according to experimental metadata. Bootstraping analysis is used to provide confidence intervals of per-sample mean coverages. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Eric Fournier [cre, aut], Charles Joly Beauparlant [aut], Cedric Lippens [aut], Arnaud Droit [aut] Maintainer: Eric Fournier URL: https://github.com/ArnaudDroitLab/metagene2 VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/metagene2/issues git_url: https://git.bioconductor.org/packages/metagene2 git_branch: RELEASE_3_22 git_last_commit: 6ab755a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metagene2_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metagene2_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metagene2_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metagene2_1.26.0.tgz vignettes: vignettes/metagene2/inst/doc/metagene2.html vignetteTitles: Introduction to metagene2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagene2/inst/doc/metagene2.R dependencyCount: 88 Package: metagenomeSeq Version: 1.52.0 Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer Imports: parallel, matrixStats, foreach, Matrix, gplots, graphics, grDevices, stats, utils, Wrench Suggests: annotate, BiocGenerics, biomformat, knitr, gss, testthat (>= 0.8), vegan, interactiveDisplay, IHW License: Artistic-2.0 Archs: x64 MD5sum: c0a18c6864307263a97f65dc9b27c927 NeedsCompilation: no Title: Statistical analysis for sparse high-throughput sequencing Description: metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. biocViews: ImmunoOncology, Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software Author: Joseph Nathaniel Paulson, Nathan D. Olson, Domenick J. Braccia, Justin Wagner, Hisham Talukder, Mihai Pop, Hector Corrada Bravo Maintainer: Joseph N. Paulson URL: https://github.com/nosson/metagenomeSeq/ VignetteBuilder: knitr BugReports: https://github.com/nosson/metagenomeSeq/issues git_url: https://git.bioconductor.org/packages/metagenomeSeq git_branch: RELEASE_3_22 git_last_commit: 4135a42 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metagenomeSeq_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metagenomeSeq_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metagenomeSeq_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metagenomeSeq_1.52.0.tgz vignettes: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.pdf, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf vignetteTitles: fitTimeSeries: differential abundance analysis through time or location, metagenomeSeq: statistical analysis for sparse high-throughput sequencing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.R, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.R dependsOnMe: microbiomeExplorer, etec16s importsMe: mbQTL, microbiomeDASim suggestsMe: dar, interactiveDisplay, phyloseq, scTreeViz, Wrench, ggpicrust2 dependencyCount: 32 Package: metahdep Version: 1.68.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 MD5sum: afc18082b05271dab29f90c92a6040ec NeedsCompilation: yes Title: Hierarchical Dependence in Meta-Analysis Description: Tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies biocViews: Microarray, DifferentialExpression Author: John R. Stevens, Gabriel Nicholas Maintainer: John R. Stevens git_url: https://git.bioconductor.org/packages/metahdep git_branch: RELEASE_3_22 git_last_commit: d46b885 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metahdep_1.68.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metahdep_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metahdep_1.68.0.tgz vignettes: vignettes/metahdep/inst/doc/metahdep.pdf vignetteTitles: metahdep Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metahdep/inst/doc/metahdep.R dependencyCount: 1 Package: metaMS Version: 1.46.0 Depends: R (>= 4.0), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics, graphics, stats, utils Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: 54ce3f88ff9e47504fb226e30a55eebd NeedsCompilation: no Title: MS-based metabolomics annotation pipeline Description: MS-based metabolomics data processing and compound annotation pipeline. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Ron Wehrens [aut] (author of GC-MS part, Initial Maintainer), Pietro Franceschi [aut] (author of LC-MS part), Nir Shahaf [ctb], Matthias Scholz [ctb], Georg Weingart [ctb] (development of GC-MS approach), Elisabete Carvalho [ctb] (testing and feedback of GC-MS pipeline), Yann Guitton [ctb, cre] (ORCID: ), Julien Saint-Vanne [ctb] Maintainer: Yann Guitton URL: https://github.com/yguitton/metaMS git_url: https://git.bioconductor.org/packages/metaMS git_branch: RELEASE_3_22 git_last_commit: d4799db git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metaMS_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metaMS_1.45.0.zip vignettes: vignettes/metaMS/inst/doc/runGC.pdf, vignettes/metaMS/inst/doc/runLC.pdf vignetteTitles: runGC, runLC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaMS/inst/doc/runGC.R, vignettes/metaMS/inst/doc/runLC.R suggestsMe: CluMSID dependencyCount: 156 Package: MetaNeighbor Version: 1.29.0 Depends: R(>= 3.5) Imports: grDevices, graphics, methods, stats (>= 3.4), utils (>= 3.4), Matrix (>= 1.2), matrixStats (>= 0.54), beanplot (>= 1.2), gplots (>= 3.0.1), RColorBrewer (>= 1.1.2), SummarizedExperiment (>= 1.12), SingleCellExperiment, igraph, dplyr, tidyr, tibble, ggplot2 Suggests: knitr (>= 1.17), rmarkdown (>= 1.6), testthat (>= 1.0.2), UpSetR License: MIT + file LICENSE MD5sum: 1635a436e4b8e4f6cb06d08ad2b9bbaa NeedsCompilation: no Title: Single cell replicability analysis Description: MetaNeighbor allows users to quantify cell type replicability across datasets using neighbor voting. biocViews: ImmunoOncology, GeneExpression, GO, MultipleComparison, SingleCell, Transcriptomics Author: Megan Crow [aut, cre], Sara Ballouz [ctb], Manthan Shah [ctb], Stephan Fischer [ctb], Jesse Gillis [aut] Maintainer: Stephan Fischer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaNeighbor git_branch: devel git_last_commit: 7e89527 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/MetaNeighbor_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaNeighbor_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaNeighbor_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaNeighbor_1.29.0.tgz vignettes: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.pdf vignetteTitles: MetaNeighbor user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.R dependencyCount: 61 Package: MetaPhOR Version: 1.12.0 Depends: R (>= 4.2.0) Imports: utils, ggplot2, ggrepel, stringr, pheatmap, grDevices, stats, clusterProfiler, RecordLinkage, RCy3 Suggests: BiocStyle, knitr, rmarkdown, kableExtra License: Artistic-2.0 MD5sum: fb815f0adf0165e578560a03ee0bb694 NeedsCompilation: no Title: Metabolic Pathway Analysis of RNA Description: MetaPhOR was developed to enable users to assess metabolic dysregulation using transcriptomic-level data (RNA-sequencing and Microarray data) and produce publication-quality figures. A list of differentially expressed genes (DEGs), which includes fold change and p value, from DESeq2 or limma, can be used as input, with sample size for MetaPhOR, and will produce a data frame of scores for each KEGG pathway. These scores represent the magnitude and direction of transcriptional change within the pathway, along with estimated p-values.MetaPhOR then uses these scores to visualize metabolic profiles within and between samples through a variety of mechanisms, including: bubble plots, heatmaps, and pathway models. biocViews: Metabolomics, RNASeq, Pathways, GeneExpression, DifferentialExpression, KEGG, Sequencing, Microarray Author: Emily Isenhart [aut, cre], Spencer Rosario [aut] Maintainer: Emily Isenhart SystemRequirements: Cytoscape (>= 3.9.0) for the cytoPath() examples VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaPhOR git_branch: RELEASE_3_22 git_last_commit: ef1fc18 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MetaPhOR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaPhOR_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaPhOR_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaPhOR_1.12.0.tgz vignettes: vignettes/MetaPhOR/inst/doc/MetaPhOR-vignette.html vignetteTitles: MetaPhOR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaPhOR/inst/doc/MetaPhOR-vignette.R dependencyCount: 179 Package: metapod Version: 1.18.0 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 3a1343aa16bcf614d2d2b443e59f7754 NeedsCompilation: yes Title: Meta-Analyses on P-Values of Differential Analyses Description: Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate. biocViews: MultipleComparison, DifferentialPeakCalling Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapod git_branch: RELEASE_3_22 git_last_commit: 6552a64 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metapod_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metapod_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metapod_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metapod_1.18.0.tgz vignettes: vignettes/metapod/inst/doc/overview.html vignetteTitles: Meta-analysis strategies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metapod/inst/doc/overview.R importsMe: csaw, mumosa, scp, scran suggestsMe: TSCAN dependencyCount: 3 Package: metapone Version: 1.16.0 Depends: R (>= 4.1.0), BiocParallel, fields, markdown, fdrtool, fgsea, ggplot2, ggrepel Imports: methods Suggests: rmarkdown, knitr License: Artistic-2.0 MD5sum: d1513e68646835e1df17fb6ce844161f NeedsCompilation: no Title: Conducts pathway test of metabolomics data using a weighted permutation test Description: The package conducts pathway testing from untargetted metabolomics data. It requires the user to supply feature-level test results, from case-control testing, regression, or other suitable feature-level tests for the study design. Weights are given to metabolic features based on how many metabolites they could potentially match to. The package can combine positive and negative mode results in pathway tests. biocViews: Technology, MassSpectrometry, Metabolomics, Pathways Author: Leqi Tian [aut], Tianwei Yu [aut], Tianwei Yu [cre] Maintainer: Tianwei Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapone git_branch: RELEASE_3_22 git_last_commit: 1bdf6f5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metapone_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metapone_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metapone_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metapone_1.16.0.tgz vignettes: vignettes/metapone/inst/doc/metapone.html vignetteTitles: metapone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metapone/inst/doc/metapone.R dependencyCount: 50 Package: metaSeq Version: 1.50.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 Archs: x64 MD5sum: 02b4a989a0093f0492e00651af48f6ba NeedsCompilation: no Title: Meta-analysis of RNA-Seq count data in multiple studies Description: The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method biocViews: RNASeq, DifferentialExpression, Sequencing, ImmunoOncology Author: Koki Tsuyuzaki, Itoshi Nikaido Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/metaSeq git_branch: RELEASE_3_22 git_last_commit: 6be5378 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metaSeq_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metaSeq_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metaSeq_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metaSeq_1.50.0.tgz vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf vignetteTitles: metaSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaSeq/inst/doc/metaSeq.R dependencyCount: 15 Package: metaseqR2 Version: 1.22.0 Depends: R (>= 4.0.0), DESeq2, limma, locfit, splines Imports: ABSSeq, Biobase, BiocGenerics, BiocParallel, biomaRt, Biostrings, corrplot, DSS, DT, EDASeq, edgeR, genefilter, Seqinfo, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, gplots, graphics, grDevices, harmonicmeanp, heatmaply, htmltools, httr, IRanges, jsonlite, lattice, log4r, magrittr, MASS, Matrix, methods, NBPSeq, pander, parallel, qvalue, rmarkdown, rmdformats, Rsamtools, RSQLite, rtracklayer, S4Vectors, stats, stringr, SummarizedExperiment, survcomp, txdbmaker, utils, VennDiagram, vsn, yaml, zoo Suggests: BiocStyle, BiocManager, BSgenome, knitr, RMySQL, RUnit Enhances: TCC License: GPL (>= 3) MD5sum: 8a5e8980f2fde413d9aec731b8414190 NeedsCompilation: yes Title: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms Description: Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way. biocViews: Software, GeneExpression, DifferentialExpression, WorkflowStep, Preprocessing, QualityControl, Normalization, ReportWriting, RNASeq, Transcription, Sequencing, Transcriptomics, Bayesian, Clustering, CellBiology, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, ImmunoOncology, AlternativeSplicing, DifferentialSplicing, MultipleComparison, TimeCourse, DataImport, ATACSeq, Epigenetics, Regression, ProprietaryPlatforms, GeneSetEnrichment, BatchEffect, ChIPSeq Author: Panagiotis Moulos [aut, cre] Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr BugReports: https://github.com/pmoulos/metaseqR2/issues git_url: https://git.bioconductor.org/packages/metaseqR2 git_branch: RELEASE_3_22 git_last_commit: e5e7809 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/metaseqR2_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metaseqR2_1.21.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metaseqR2_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metaseqR2_1.22.0.tgz vignettes: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.html, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.html vignetteTitles: Building an annotation database for metaseqR2, RNA-Seq data analysis with metaseqR2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.R, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.R dependencyCount: 239 Package: MetCirc Version: 1.40.0 Depends: R (>= 4.4), amap (>= 0.8), circlize (>= 0.4.16), scales (>= 1.3.0), shiny (>= 1.8.1.1), Spectra (>= 1.15.3) Imports: ggplot2 (>= 3.5.1), MsCoreUtils (>= 1.17.0), S4Vectors (>= 0.43.1) Suggests: BiocGenerics, graphics (>= 4.4), grDevices (>= 4.4), knitr (>= 1.48), testthat (>= 3.2.1.1) License: GPL (>= 3) MD5sum: 65839ba76586a6aee3b1a4445722626a NeedsCompilation: no Title: Navigating mass spectral similarity in high-resolution MS/MS metabolomics data metabolomics data Description: MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectra object infrastructure defined in the package Spectra that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features. biocViews: ShinyApps, Metabolomics, MassSpectrometry, Visualization Author: Thomas Naake , Johannes Rainer and Emmanuel Gaquerel Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetCirc git_branch: RELEASE_3_22 git_last_commit: 59436a4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MetCirc_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetCirc_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetCirc_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetCirc_1.40.0.tgz vignettes: vignettes/MetCirc/inst/doc/MetCirc.html vignetteTitles: Workflow for Metabolomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetCirc/inst/doc/MetCirc.R dependencyCount: 74 Package: methimpute Version: 1.32.0 Depends: R (>= 3.5.0), GenomicRanges, ggplot2 Imports: Rcpp (>= 0.12.4.5), methods, utils, grDevices, stats, GenomeInfoDb, IRanges, Biostrings, reshape2, minpack.lm, data.table LinkingTo: Rcpp Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: 80f7efcf01b05a1516acfa4b3efa8962 NeedsCompilation: yes Title: Imputation-guided re-construction of complete methylomes from WGBS data Description: This package implements functions for calling methylation for all cytosines in the genome. biocViews: ImmunoOncology, Software, DNAMethylation, Epigenetics, HiddenMarkovModel, Sequencing, Coverage Author: Aaron Taudt Maintainer: Aaron Taudt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methimpute git_branch: RELEASE_3_22 git_last_commit: 655a3e7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methimpute_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methimpute_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methimpute_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methimpute_1.32.0.tgz vignettes: vignettes/methimpute/inst/doc/methimpute.pdf vignetteTitles: Methylation status calling with METHimpute hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methimpute/inst/doc/methimpute.R dependencyCount: 50 Package: methInheritSim Version: 1.32.0 Depends: R (>= 3.5.0) Imports: methylKit, GenomicRanges, Seqinfo, parallel, BiocGenerics, S4Vectors, methods, stats, IRanges, msm Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance License: Artistic-2.0 MD5sum: 2d44184da76dbe07b0a28b2f4899f501 NeedsCompilation: no Title: Simulating Whole-Genome Inherited Bisulphite Sequencing Data Description: Simulate a multigeneration methylation case versus control experiment with inheritance relation using a real control dataset. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Pascal Belleau, Astrid Deschênes and Arnaud Droit Maintainer: Pascal Belleau URL: https://github.com/belleau/methInheritSim VignetteBuilder: knitr BugReports: https://github.com/belleau/methInheritSim/issues git_url: https://git.bioconductor.org/packages/methInheritSim git_branch: RELEASE_3_22 git_last_commit: b52e28a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methInheritSim_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methInheritSim_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methInheritSim_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methInheritSim_1.32.0.tgz vignettes: vignettes/methInheritSim/inst/doc/methInheritSim.html vignetteTitles: Simulating Whole-Genome Inherited Bisulphite Sequencing Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methInheritSim/inst/doc/methInheritSim.R suggestsMe: methylInheritance dependencyCount: 107 Package: methodical Version: 1.6.0 Depends: GenomicRanges, ggplot2, R (>= 4.0), SummarizedExperiment Imports: AnnotationHub, annotatr, BiocCheck, BiocManager, BiocParallel, BiocStyle, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, cowplot, data.table, DelayedArray, devtools, dplyr, ExperimentHub, foreach, GenomeInfoDb, HDF5Array, IRanges, knitr, MatrixGenerics, R.utils, rcmdcheck, RcppRoll, remotes, rhdf5, rtracklayer, S4Vectors, scales, tibble, tidyr, tools, TumourMethData, usethis Suggests: BSgenome.Athaliana.TAIR.TAIR9, DESeq2, methrix, rmarkdown License: GPL (>= 3) MD5sum: 9986eefe7f914866d813fa214cbc99bb NeedsCompilation: no Title: Discovering genomic regions where methylation is strongly associated with transcriptional activity Description: DNA methylation is generally considered to be associated with transcriptional silencing. However, comprehensive, genome-wide investigation of this relationship requires the evaluation of potentially millions of correlation values between the methylation of individual genomic loci and expression of associated transcripts in a relatively large numbers of samples. Methodical makes this process quick and easy while keeping a low memory footprint. It also provides a novel method for identifying regions where a number of methylation sites are consistently strongly associated with transcriptional expression. In addition, Methodical enables housing DNA methylation data from diverse sources (e.g. WGBS, RRBS and methylation arrays) with a common framework, lifting over DNA methylation data between different genome builds and creating base-resolution plots of the association between DNA methylation and transcriptional activity at transcriptional start sites. biocViews: DNAMethylation, MethylationArray, Transcription, GenomeWideAssociation, Software Author: Richard Heery [aut, cre] (ORCID: ) Maintainer: Richard Heery URL: https://github.com/richardheery/methodical SystemRequirements: kallisto VignetteBuilder: knitr BugReports: https://github.com/richardheery/methodical/issues git_url: https://git.bioconductor.org/packages/methodical git_branch: RELEASE_3_22 git_last_commit: ebf4ab4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methodical_1.6.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methodical_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methodical_1.6.0.tgz vignettes: vignettes/methodical/inst/doc/calculating_methylation_transcription_correlations.html, vignettes/methodical/inst/doc/working_with_meth_rses.html vignetteTitles: calculating_methylation_transcription_correlations, working_with_meth_rses hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/methodical/inst/doc/calculating_methylation_transcription_correlations.R, vignettes/methodical/inst/doc/working_with_meth_rses.R dependencyCount: 210 Package: MethPed Version: 1.38.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 Archs: x64 MD5sum: 796041888e5a789b2135ce93fef21fce NeedsCompilation: no Title: A DNA methylation classifier tool for the identification of pediatric brain tumor subtypes Description: Classification of pediatric tumors into biologically defined subtypes is challenging and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro). biocViews: ImmunoOncology, DNAMethylation, Classification, Epigenetics Author: Mohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut], Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph] Maintainer: Helena Carén VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethPed git_branch: RELEASE_3_22 git_last_commit: fcd2101 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MethPed_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethPed_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MethPed_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MethPed_1.38.0.tgz vignettes: vignettes/MethPed/inst/doc/MethPed-vignette.html vignetteTitles: MethPed User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R dependencyCount: 9 Package: MethReg Version: 1.20.0 Depends: R (>= 4.0) Imports: dplyr, plyr, GenomicRanges, SummarizedExperiment, DelayedArray, ggplot2, ggpubr, tibble, tidyr, S4Vectors, sesameData, sesame, AnnotationHub, ExperimentHub, stringr, readr, methods, stats, Matrix, MASS, rlang, pscl, IRanges, sfsmisc, progress, utils, openxlsx, JASPAR2024, RSQLite, TFBSTools Suggests: rmarkdown, BiocStyle, testthat (>= 2.1.0), parallel, R.utils, doParallel, reshape2, motifmatchr, matrixStats, biomaRt, dorothea, viper, stageR, BiocFileCache, png, htmltools, knitr, jpeg, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, data.table, downloader License: GPL-3 Archs: x64 MD5sum: d202c1b3478aac2494029784f7c4a75a NeedsCompilation: no Title: Assessing the regulatory potential of DNA methylation regions or sites on gene transcription Description: Epigenome-wide association studies (EWAS) detects a large number of DNA methylation differences, often hundreds of differentially methylated regions and thousands of CpGs, that are significantly associated with a disease, many are located in non-coding regions. Therefore, there is a critical need to better understand the functional impact of these CpG methylations and to further prioritize the significant changes. MethReg is an R package for integrative modeling of DNA methylation, target gene expression and transcription factor binding sites data, to systematically identify and rank functional CpG methylations. MethReg evaluates, prioritizes and annotates CpG sites with high regulatory potential using matched methylation and gene expression data, along with external TF-target interaction databases based on manually curation, ChIP-seq experiments or gene regulatory network analysis. biocViews: MethylationArray, Regression, GeneExpression, Epigenetics, GeneTarget, Transcription Author: Tiago Silva [aut, cre] (ORCID: ), Lily Wang [aut] Maintainer: Tiago Silva VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/MethReg/issues/ git_url: https://git.bioconductor.org/packages/MethReg git_branch: RELEASE_3_22 git_last_commit: a1982d5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MethReg_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethReg_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MethReg_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MethReg_1.20.0.tgz vignettes: vignettes/MethReg/inst/doc/MethReg.html vignetteTitles: MethReg: estimating regulatory potential of DNA methylation in gene transcription hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethReg/inst/doc/MethReg.R dependencyCount: 171 Package: methrix Version: 1.24.0 Depends: R (>= 3.6), data.table (>= 1.12.4), SummarizedExperiment Imports: rtracklayer, DelayedArray, HDF5Array, BSgenome, DelayedMatrixStats, parallel, methods, ggplot2, S4Vectors, matrixStats, graphics, stats, utils, GenomicRanges, IRanges Suggests: knitr, rmarkdown, DSS, bsseq, plotly, BSgenome.Mmusculus.UCSC.mm9, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, BSgenome.Hsapiens.UCSC.hg19, GenomicScores, Biostrings, RColorBrewer, GenomeInfoDb, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: a52cbb100daccb429b81e67469da156a NeedsCompilation: no Title: Fast and efficient summarization of generic bedGraph files from Bisufite sequencing Description: Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions. biocViews: DNAMethylation, Sequencing, Coverage Author: Anand Mayakonda [aut, cre] (ORCID: ), Reka Toth [aut] (ORCID: ), Rajbir Batra [ctb], Clarissa Feuerstein-Akgöz [ctb], Joschka Hey [ctb], Maximilian Schönung [ctb], Pavlo Lutsik [ctb] Maintainer: Anand Mayakonda URL: https://github.com/CompEpigen/methrix VignetteBuilder: knitr BugReports: https://github.com/CompEpigen/methrix/issues git_url: https://git.bioconductor.org/packages/methrix git_branch: RELEASE_3_22 git_last_commit: 3c2cf85 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methrix_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methrix_1.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methrix_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methrix_1.24.0.tgz vignettes: vignettes/methrix/inst/doc/methrix.html vignetteTitles: Methrix tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methrix/inst/doc/methrix.R suggestsMe: methodical dependencyCount: 82 Package: MethTargetedNGS Version: 1.42.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings, pwalign Imports: utils, graphics, stats License: Artistic-2.0 MD5sum: 3860ee1b9d205f60200dd62c3de3bae9 NeedsCompilation: no Title: Perform Methylation Analysis on Next Generation Sequencing Data Description: Perform step by step methylation analysis of Next Generation Sequencing data. biocViews: ResearchField, Genetics, Sequencing, Alignment, SequenceMatching, DataImport Author: Muhammad Ahmer Jamil with Contribution of Prof. Holger Frohlich and Priv.-Doz. Dr. Osman El-Maarri Maintainer: Muhammad Ahmer Jamil SystemRequirements: HMMER3 git_url: https://git.bioconductor.org/packages/MethTargetedNGS git_branch: RELEASE_3_22 git_last_commit: db19216 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MethTargetedNGS_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethTargetedNGS_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MethTargetedNGS_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MethTargetedNGS_1.42.0.tgz vignettes: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.pdf vignetteTitles: Introduction to MethTargetedNGS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.R dependencyCount: 40 Package: MethylAid Version: 1.44.0 Depends: R (>= 3.4) Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase, grDevices, graphics, hexbin, matrixStats, minfi (>= 1.22.0), methods, RColorBrewer, shiny, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, MethylAidData, minfiData, minfiDataEPIC, RUnit License: GPL (>= 2) MD5sum: 907fcd8cccda6710d0a4677d3fad12b4 NeedsCompilation: no Title: Visual and interactive quality control of large Illumina DNA Methylation array data sets Description: A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored. biocViews: DNAMethylation, MethylationArray, Microarray, TwoChannel, QualityControl, BatchEffect, Visualization, GUI Author: Maarten van Iterson [aut, cre], Elmar Tobi[ctb], Roderick Slieker[ctb], Wouter den Hollander[ctb], Rene Luijk[ctb] and Bas Heijmans[ctb] Maintainer: L.J.Sinke VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylAid git_branch: RELEASE_3_22 git_last_commit: b29b803 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MethylAid_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethylAid_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MethylAid_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MethylAid_1.44.0.tgz vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf vignetteTitles: MethylAid: Visual and Interactive quality control of Illumina Human DNA Methylation array data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R dependsOnMe: MethylAidData dependencyCount: 167 Package: methylCC Version: 1.24.0 Depends: R (>= 3.6), FlowSorted.Blood.450k Imports: Biobase, GenomicRanges, IRanges, S4Vectors, dplyr, magrittr, minfi, bsseq, quadprog, plyranges, stats, utils, bumphunter, genefilter, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19 Suggests: rmarkdown, knitr, testthat (>= 2.1.0), BiocGenerics, BiocStyle, tidyr, ggplot2 License: GPL-3 MD5sum: 6ab44cae93bae7208ebdf884c8b724f1 NeedsCompilation: no Title: Estimate the cell composition of whole blood in DNA methylation samples Description: A tool to estimate the cell composition of DNA methylation whole blood sample measured on any platform technology (microarray and sequencing). biocViews: Microarray, Sequencing, DNAMethylation, MethylationArray, MethylSeq, WholeGenome Author: Stephanie C. Hicks [aut, cre] (ORCID: ), Rafael Irizarry [aut] (ORCID: ) Maintainer: Stephanie C. Hicks URL: https://github.com/stephaniehicks/methylCC/ VignetteBuilder: knitr BugReports: https://github.com/stephaniehicks/methylCC/ git_url: https://git.bioconductor.org/packages/methylCC git_branch: RELEASE_3_22 git_last_commit: 98b2564 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methylCC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylCC_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylCC_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylCC_1.24.0.tgz vignettes: vignettes/methylCC/inst/doc/methylCC.html vignetteTitles: The methylCC user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylCC/inst/doc/methylCC.R dependencyCount: 157 Package: methylclock Version: 1.16.0 Depends: R (>= 4.1.0), methylclockData, devtools, quadprog Imports: Rcpp (>= 1.0.6), ExperimentHub, dplyr, impute, PerformanceAnalytics, Biobase, ggpmisc, tidyverse, ggplot2, ggpubr, minfi, tibble, RPMM, stats, graphics, tidyr, gridExtra, preprocessCore, dynamicTreeCut, planet LinkingTo: Rcpp Suggests: BiocStyle, knitr, GEOquery, rmarkdown License: MIT + file LICENSE MD5sum: e697bc677eaec3772f42109001128d68 NeedsCompilation: yes Title: Methylclock - DNA methylation-based clocks Description: This package allows to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. Chronological DNAm age (in years) : Horvath's clock, Hannum's clock, BNN, Horvath's skin+blood clock, PedBE clock and Wu's clock. Gestational DNAm age : Knight's clock, Bohlin's clock, Mayne's clock and Lee's clocks. Biological DNAm clocks : Levine's clock and Telomere Length's clock. biocViews: DNAMethylation, BiologicalQuestion, Preprocessing, StatisticalMethod, Normalization Author: Dolors Pelegri-Siso [aut, cre] (ORCID: ), Juan R. Gonzalez [aut] (ORCID: ) Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/methylclock VignetteBuilder: knitr BugReports: https://github.com/isglobal-brge/methylclock/issues git_url: https://git.bioconductor.org/packages/methylclock git_branch: RELEASE_3_22 git_last_commit: 79b4e8b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methylclock_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylclock_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylclock_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylclock_1.16.0.tgz vignettes: vignettes/methylclock/inst/doc/methylclock.html vignetteTitles: DNAm age using diffrent methylation clocks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methylclock/inst/doc/methylclock.R dependencyCount: 301 Package: methylGSA Version: 1.28.0 Depends: R (>= 3.5) Imports: RobustRankAggreg, ggplot2, stringr, stats, clusterProfiler, missMethyl, org.Hs.eg.db, reactome.db, BiocParallel, GO.db, AnnotationDbi, shiny, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Suggests: knitr, rmarkdown, testthat, enrichplot License: GPL-2 MD5sum: 71bee282202bf346ef650d37cd3a0a12 NeedsCompilation: no Title: Gene Set Analysis Using the Outcome of Differential Methylation Description: The main functions for methylGSA are methylglm and methylRRA. methylGSA implements logistic regression adjusting number of probes as a covariate. methylRRA adjusts multiple p-values of each gene by Robust Rank Aggregation. For more detailed help information, please see the vignette. biocViews: DNAMethylation,DifferentialMethylation,GeneSetEnrichment,Regression, GeneRegulation,Pathways Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/methylGSA VignetteBuilder: knitr BugReports: https://github.com/reese3928/methylGSA/issues git_url: https://git.bioconductor.org/packages/methylGSA git_branch: RELEASE_3_22 git_last_commit: 66c5dc9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methylGSA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylGSA_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylGSA_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylGSA_1.28.0.tgz vignettes: vignettes/methylGSA/inst/doc/methylGSA-vignette.html vignetteTitles: methylGSA: Gene Set Analysis for DNA Methylation Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylGSA/inst/doc/methylGSA-vignette.R dependencyCount: 226 Package: methyLImp2 Version: 1.6.0 Depends: R (>= 4.3.0), ChAMPdata Imports: BiocParallel, parallel, stats, methods, corpcor, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, spelling, testthat (>= 3.0.0) License: GPL-3 MD5sum: 56f33b532658b0590cef310e16990092 NeedsCompilation: no Title: Missing value estimation of DNA methylation data Description: This package allows to estimate missing values in DNA methylation data. methyLImp method is based on linear regression since methylation levels show a high degree of inter-sample correlation. Implementation is parallelised over chromosomes since probes on different chromosomes are usually independent. Mini-batch approach to reduce the runtime in case of large number of samples is available. biocViews: DNAMethylation, Microarray, Software, MethylationArray, Regression Author: Pietro Di Lena [aut] (ORCID: ), Anna Plaksienko [aut, cre] (ORCID: ), Claudia Angelini [aut] (ORCID: ), Christine Nardini [aut] (ORCID: ) Maintainer: Anna Plaksienko URL: https://github.com/annaplaksienko/methyLImp2 VignetteBuilder: knitr BugReports: https://github.com/annaplaksienko/methyLImp2/issues git_url: https://git.bioconductor.org/packages/methyLImp2 git_branch: RELEASE_3_22 git_last_commit: 58cc470 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methyLImp2_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methyLImp2_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methyLImp2_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methyLImp2_1.6.0.tgz vignettes: vignettes/methyLImp2/inst/doc/methyLImp2_vignette.html vignetteTitles: methyLImp2 vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methyLImp2/inst/doc/methyLImp2_vignette.R dependencyCount: 37 Package: methylInheritance Version: 1.34.0 Depends: R (>= 3.5) Imports: methylKit, BiocParallel, GenomicRanges, IRanges, S4Vectors, methods, parallel, ggplot2, gridExtra, rebus Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit, methInheritSim, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 6666032c1dd3c65b1ad569f7fe63868f NeedsCompilation: no Title: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect Description: Permutation analysis, based on Monte Carlo sampling, for testing the hypothesis that the number of conserved differentially methylated elements, between several generations, is associated to an effect inherited from a treatment and that stochastic effect can be dismissed. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Astrid Deschênes [cre, aut] (ORCID: ), Pascal Belleau [aut] (ORCID: ), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/methylInheritance VignetteBuilder: knitr BugReports: https://github.com/adeschen/methylInheritance/issues git_url: https://git.bioconductor.org/packages/methylInheritance git_branch: RELEASE_3_22 git_last_commit: 43a28dd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methylInheritance_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylInheritance_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylInheritance_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylInheritance_1.34.0.tgz vignettes: vignettes/methylInheritance/inst/doc/methylInheritance.html vignetteTitles: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylInheritance/inst/doc/methylInheritance.R suggestsMe: methInheritSim dependencyCount: 106 Package: methylKit Version: 1.36.0 Depends: R (>= 3.5.0), GenomicRanges (>= 1.18.1), methods Imports: IRanges, data.table (>= 1.9.6), parallel, S4Vectors (>= 0.13.13), Seqinfo, KernSmooth, qvalue, emdbook, Rsamtools, gtools, fastseg, rtracklayer, mclust, mgcv, Rcpp, R.utils, limma, grDevices, graphics, stats, utils LinkingTo: Rcpp, Rhtslib (>= 1.13.1) Suggests: testthat (>= 2.1.0), knitr, rmarkdown, genomation, BiocManager License: Artistic-2.0 MD5sum: a584ee85ef483ff891f20baf032874a4 NeedsCompilation: yes Title: DNA methylation analysis from high-throughput bisulfite sequencing results Description: methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files. biocViews: DNAMethylation, Sequencing, MethylSeq Author: Altuna Akalin [aut, cre], Matthias Kormaksson [aut], Sheng Li [aut], Arsene Wabo [ctb], Adrian Bierling [aut], Alexander Blume [aut], Katarzyna Wreczycka [ctb] Maintainer: Altuna Akalin , Alexander Blume URL: https://github.com/al2na/methylKit SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/al2na/methylKit/issues git_url: https://git.bioconductor.org/packages/methylKit git_branch: RELEASE_3_22 git_last_commit: c65437a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methylKit_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylKit_1.35.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylKit_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylKit_1.36.0.tgz vignettes: vignettes/methylKit/inst/doc/methylKit.html vignetteTitles: methylKit: User Guide v`r packageVersion('methylKit')` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylKit/inst/doc/methylKit.R importsMe: deconvR, methInheritSim, methylInheritance dependencyCount: 99 Package: MethylMix Version: 2.40.0 Depends: R (>= 3.2.0) Imports: foreach, RPMM, RColorBrewer, ggplot2, RCurl, impute, data.table, limma, R.matlab, digest Suggests: BiocStyle, doParallel, testthat, knitr, rmarkdown License: GPL-2 MD5sum: 9db5b525e2f0064495a18208a27aeb97 NeedsCompilation: no Title: MethylMix: Identifying methylation driven cancer genes Description: MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,DifferentialExpression,Pathways,Network Author: Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylMix git_branch: RELEASE_3_22 git_last_commit: b1e6c56 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MethylMix_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethylMix_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MethylMix_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MethylMix_2.40.0.tgz vignettes: vignettes/MethylMix/inst/doc/vignettes.html vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/vignettes.R dependencyCount: 39 Package: methylMnM Version: 1.48.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 MD5sum: e17d21e1e567af947a2b071a5b2eb534 NeedsCompilation: yes Title: detect different methylation level (DMR) Description: To give the exactly p-value and q-value of MeDIP-seq and MRE-seq data for different samples comparation. biocViews: Software, DNAMethylation, Sequencing Author: Yan Zhou, Bo Zhang, Nan Lin, BaoXue Zhang and Ting Wang Maintainer: Yan Zhou git_url: https://git.bioconductor.org/packages/methylMnM git_branch: RELEASE_3_22 git_last_commit: 084ea47 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methylMnM_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylMnM_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylMnM_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylMnM_1.48.0.tgz vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf vignetteTitles: methylMnM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylMnM/inst/doc/methylMnM.R importsMe: SIMD dependencyCount: 11 Package: methylPipe Version: 1.44.0 Depends: R (>= 3.5.0), methods, grDevices, graphics, stats, utils, GenomicRanges, SummarizedExperiment (>= 0.2.0), Rsamtools Imports: marray, gplots, IRanges, BiocGenerics, Gviz, GenomicAlignments, Biostrings, parallel, data.table, Seqinfo, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg18, TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR License: GPL(>=2) MD5sum: 760bce3fc976d70a8e69a235f5200534 NeedsCompilation: yes Title: Base resolution DNA methylation data analysis Description: Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data. biocViews: MethylSeq, DNAMethylation, Coverage, Sequencing Author: Mattia Pelizzola [aut], Kamal Kishore [aut], Mattia Furlan [ctb, cre] Maintainer: Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylPipe git_branch: RELEASE_3_22 git_last_commit: ba28f43 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methylPipe_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylPipe_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylPipe_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylPipe_1.44.0.tgz vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf vignetteTitles: methylPipe.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylPipe/inst/doc/methylPipe.R dependsOnMe: ListerEtAlBSseq importsMe: compEpiTools dependencyCount: 159 Package: methylscaper Version: 1.18.0 Depends: R (>= 4.4.0) Imports: shiny, shinyjs, seriation, BiocParallel, seqinr, Biostrings, pwalign, Rfast, grDevices, graphics, stats, utils, tools, methods, shinyFiles, data.table, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, devtools, R.utils License: GPL-2 MD5sum: 26e9b7142d4ce0ec9ebd165ec685ad8e NeedsCompilation: no Title: Visualization of Methylation Data Description: methylscaper is an R package for processing and visualizing data jointly profiling methylation and chromatin accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The package supports both single-cell and single-molecule data, and a common interface for jointly visualizing both data types through the generation of ordered representational methylation-state matrices. The Shiny app allows for an interactive seriation process of refinement and re-weighting that optimally orders the cells or DNA molecules to discover methylation patterns and nucleosome positioning. biocViews: DNAMethylation, Epigenetics, Sequencing, Visualization, SingleCell, NucleosomePositioning Author: Bacher Rhonda [aut, cre], Parker Knight [aut] Maintainer: Bacher Rhonda URL: https://github.com/rhondabacher/methylscaper/ VignetteBuilder: knitr BugReports: https://github.com/rhondabacher/methylscaper/issues git_url: https://git.bioconductor.org/packages/methylscaper git_branch: RELEASE_3_22 git_last_commit: 10fab04 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methylscaper_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylscaper_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylscaper_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylscaper_1.18.0.tgz vignettes: vignettes/methylscaper/inst/doc/methylScaper.html vignetteTitles: Using methylscaper to visualize joint methylation and nucleosome occupancy data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylscaper/inst/doc/methylScaper.R dependencyCount: 101 Package: MethylSeekR Version: 1.50.0 Depends: rtracklayer (>= 1.16.3), parallel (>= 2.15.1), mhsmm (>= 0.4.4) Imports: IRanges (>= 1.16.3), BSgenome (>= 1.26.1), GenomicRanges (>= 1.10.5), geneplotter (>= 1.34.0), graphics (>= 2.15.2), grDevices (>= 2.15.2), parallel (>= 2.15.2), stats (>= 2.15.2), utils (>= 2.15.2), GenomeInfoDb Suggests: BSgenome.Hsapiens.UCSC.hg38 License: GPL (>=2) MD5sum: 9ecaf49b89d5d5db9aa12b8f80e952b8 NeedsCompilation: no Title: Segmentation of Bis-seq data Description: This is a package for the discovery of regulatory regions from Bis-seq data biocViews: Sequencing, MethylSeq, DNAMethylation Author: Lukas Burger, Dimos Gaidatzis, Dirk Schubeler and Michael Stadler Maintainer: Lukas Burger git_url: https://git.bioconductor.org/packages/MethylSeekR git_branch: RELEASE_3_22 git_last_commit: f26dd67 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MethylSeekR_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethylSeekR_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MethylSeekR_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MethylSeekR_1.50.0.tgz vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf vignetteTitles: MethylSeekR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylSeekR/inst/doc/MethylSeekR.R suggestsMe: methylPipe, RnBeads dependencyCount: 84 Package: methylSig Version: 1.22.0 Depends: R (>= 3.6) Imports: bsseq, DelayedArray, DelayedMatrixStats, DSS, IRanges, Seqinfo, GenomicRanges, methods, parallel, stats, S4Vectors Suggests: BiocStyle, bsseqData, knitr, rmarkdown, testthat (>= 2.1.0), covr License: GPL-3 MD5sum: 4e046ee91b59b2a3ab861c0e8a75332e NeedsCompilation: no Title: MethylSig: Differential Methylation Testing for WGBS and RRBS Data Description: MethylSig is a package for testing for differentially methylated cytosines (DMCs) or regions (DMRs) in whole-genome bisulfite sequencing (WGBS) or reduced representation bisulfite sequencing (RRBS) experiments. MethylSig uses a beta binomial model to test for significant differences between groups of samples. Several options exist for either site-specific or sliding window tests, and variance estimation. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, Regression, MethylSeq Author: Yongseok Park [aut], Raymond G. Cavalcante [aut, cre] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://github.com/sartorlab/methylSig/issues git_url: https://git.bioconductor.org/packages/methylSig git_branch: RELEASE_3_22 git_last_commit: e0dc461 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methylSig_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylSig_1.21.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylSig_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylSig_1.22.0.tgz vignettes: vignettes/methylSig/inst/doc/updating-methylSig-code.html, vignettes/methylSig/inst/doc/using-methylSig.html vignetteTitles: Updating methylSig code, Using methylSig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylSig/inst/doc/updating-methylSig-code.R, vignettes/methylSig/inst/doc/using-methylSig.R dependencyCount: 89 Package: methylumi Version: 2.56.0 Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2, matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, Biobase, graphics, lattice, annotate, genefilter, AnnotationDbi, minfi, stats4, illuminaio, GenomicFeatures Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, TCGAMethylation450k, IlluminaHumanMethylation450kanno.ilmn12.hg19, FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr License: GPL-2 MD5sum: bb2f41eebcd0a5b2cc71a72e6de979f5 NeedsCompilation: no Title: Handle Illumina methylation data Description: This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl, CpGIsland Author: Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr., Moiz Bootwalla Maintainer: Sean Davis VignetteBuilder: knitr BugReports: https://github.com/seandavi/methylumi/issues/new git_url: https://git.bioconductor.org/packages/methylumi git_branch: RELEASE_3_22 git_last_commit: baf2fdd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/methylumi_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylumi_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylumi_2.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylumi_2.56.0.tgz vignettes: vignettes/methylumi/inst/doc/methylumi.pdf, vignettes/methylumi/inst/doc/methylumi450k.pdf vignetteTitles: An Introduction to the methylumi package, Working with Illumina 450k Arrays using methylumi hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylumi/inst/doc/methylumi.R, vignettes/methylumi/inst/doc/methylumi450k.R dependsOnMe: bigmelon, RnBeads, skewr, wateRmelon importsMe: ffpe, lumi, missMethyl dependencyCount: 157 Package: MetID Version: 1.28.0 Depends: R (>= 3.5) Imports: utils (>= 3.3.1), stats (>= 3.4.2), devtools (>= 1.13.0), stringr (>= 1.3.0), Matrix (>= 1.2-12), igraph (>= 1.2.1), ChemmineR (>= 2.30.2) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8) License: Artistic-2.0 MD5sum: e448bd2a13bd8721f7e5a4ca1a82717d NeedsCompilation: no Title: Network-based prioritization of putative metabolite IDs Description: This package uses an innovative network-based approach that will enhance our ability to determine the identities of significant ions detected by LC-MS. biocViews: AssayDomain, BiologicalQuestion, Infrastructure, ResearchField, StatisticalMethod, Technology, WorkflowStep, Network, KEGG Author: Zhenzhi Li Maintainer: Zhenzhi Li URL: https://github.com/ressomlab/MetID VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetID git_branch: RELEASE_3_22 git_last_commit: dad5fe3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MetID_1.28.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetID_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetID_1.28.0.tgz vignettes: vignettes/MetID/inst/doc/Introduction_to_MetID.html vignetteTitles: Introduction to MetID hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetID/inst/doc/Introduction_to_MetID.R dependencyCount: 128 Package: MetNet Version: 1.28.0 Depends: R (>= 4.1), S4Vectors (>= 0.28.1), SummarizedExperiment (>= 1.20.0) Imports: bnlearn (>= 4.3), BiocParallel (>= 1.12.0), corpcor (>= 1.6.10), dplyr (>= 1.0.3), ggplot2 (>= 3.3.3), GeneNet (>= 1.2.15), GENIE3 (>= 1.7.0), methods (>= 4.1), parmigene (>= 1.0.2), psych (>= 2.1.6), rlang (>= 0.4.10), stabs (>= 0.6), stats (>= 4.1), tibble (>= 3.0.5), tidyr (>= 1.1.2) Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), glmnet (>= 4.1-1), igraph (>= 1.1.2), knitr (>= 1.11), rmarkdown (>= 1.15), testthat (>= 2.2.1), Spectra (>= 1.4.1), MsCoreUtils (>= 1.6.0) License: GPL (>= 3) MD5sum: d4280377b75d9a98060f2917794159f7 NeedsCompilation: no Title: Inferring metabolic networks from untargeted high-resolution mass spectrometry data Description: MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Network, Regression Author: Thomas Naake [aut, cre], Liesa Salzer [ctb], Elva Maria Novoa-del-Toro [ctb] (ORCID: ) Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetNet git_branch: RELEASE_3_22 git_last_commit: ac9d855 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MetNet_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetNet_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetNet_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetNet_1.28.0.tgz vignettes: vignettes/MetNet/inst/doc/MetNet.html vignetteTitles: Workflow for high-resolution metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetNet/inst/doc/MetNet.R dependencyCount: 77 Package: Mfuzz Version: 2.70.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: ea0e93cd8afd5c890a2fda3203933be6 NeedsCompilation: no Title: Soft clustering of omics time series data Description: The Mfuzz package implements noise-robust soft clustering of omics time-series data, including transcriptomic, proteomic or metabolomic data. It is based on the use of c-means clustering. For convenience, it includes a graphical user interface. biocViews: Microarray, Clustering, TimeCourse, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://mfuzz.sysbiolab.eu/ git_url: https://git.bioconductor.org/packages/Mfuzz git_branch: RELEASE_3_22 git_last_commit: 8b2e1eb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Mfuzz_2.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Mfuzz_2.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Mfuzz_2.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Mfuzz_2.70.0.tgz vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf vignetteTitles: Introduction to Mfuzz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mfuzz/inst/doc/Mfuzz.R dependsOnMe: cycle, MultiRNAflow importsMe: Patterns suggestsMe: DAPAR, pctax dependencyCount: 17 Package: MGFM Version: 1.44.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 Archs: x64 MD5sum: 342fd82e784abb22e84a66eb14d004bd NeedsCompilation: no Title: Marker Gene Finder in Microarray gene expression data Description: The package is designed to detect marker genes from Microarray gene expression data sets biocViews: Genetics, GeneExpression, Microarray Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFM git_branch: RELEASE_3_22 git_last_commit: 3775829 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MGFM_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MGFM_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MGFM_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MGFM_1.44.0.tgz vignettes: vignettes/MGFM/inst/doc/MGFM.pdf vignetteTitles: Using MGFM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFM/inst/doc/MGFM.R dependencyCount: 46 Package: MGnifyR Version: 1.6.0 Depends: R(>= 4.4.0), MultiAssayExperiment, TreeSummarizedExperiment, SummarizedExperiment, BiocGenerics Imports: mia, ape, dplyr, httr, methods, plyr, reshape2, S4Vectors, urltools, utils, tidyjson Suggests: biomformat, broom, ggplot2, knitr, rmarkdown, testthat, xml2, BiocStyle, miaViz, vegan, scater, phyloseq, magick License: Artistic-2.0 | file LICENSE MD5sum: 7c9583a7e2d511ef203d24a8351ab2d4 NeedsCompilation: no Title: R interface to EBI MGnify metagenomics resource Description: Utility package to facilitate integration and analysis of EBI MGnify data in R. The package can be used to import microbial data for instance into TreeSummarizedExperiment (TreeSE). In TreeSE format, the data is directly compatible with miaverse framework. biocViews: Infrastructure, DataImport, Metagenomics, Microbiome, MicrobiomeData Author: Tuomas Borman [aut, cre] (ORCID: ), Ben Allen [aut], Leo Lahti [aut] (ORCID: ) Maintainer: Tuomas Borman URL: https://github.com/EBI-Metagenomics/MGnifyR VignetteBuilder: knitr BugReports: https://github.com/EBI-Metagenomics/MGnifyR/issues git_url: https://git.bioconductor.org/packages/MGnifyR git_branch: RELEASE_3_22 git_last_commit: 0aee502 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MGnifyR_1.6.0.tar.gz vignettes: vignettes/MGnifyR/inst/doc/MGnify_course.html, vignettes/MGnifyR/inst/doc/MGnifyR_long.html, vignettes/MGnifyR/inst/doc/MGnifyR.html vignetteTitles: MGnifyR,, extended vignette, MGnifyR,, extended vignette, MGnifyR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MGnifyR/inst/doc/MGnify_course.R, vignettes/MGnifyR/inst/doc/MGnifyR_long.R, vignettes/MGnifyR/inst/doc/MGnifyR.R suggestsMe: HoloFoodR dependencyCount: 169 Package: mgsa Version: 1.58.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 MD5sum: ede78b63a2414b7ca0f759cf49ee7b61 NeedsCompilation: yes Title: Model-based gene set analysis Description: Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology. biocViews: Pathways, GO, GeneSetEnrichment Author: Sebastian Bauer , Julien Gagneur Maintainer: Sebastian Bauer URL: https://github.com/sba1/mgsa-bioc git_url: https://git.bioconductor.org/packages/mgsa git_branch: RELEASE_3_22 git_last_commit: b056a17 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mgsa_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mgsa_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mgsa_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mgsa_1.58.0.tgz vignettes: vignettes/mgsa/inst/doc/mgsa.pdf vignetteTitles: Overview of the mgsa package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mgsa/inst/doc/mgsa.R dependencyCount: 9 Package: mia Version: 1.18.0 Depends: R (>= 4.1.0), MultiAssayExperiment, SingleCellExperiment, SummarizedExperiment, TreeSummarizedExperiment (>= 1.99.3) Imports: ape, BiocGenerics, BiocParallel, Biostrings, bluster, DECIPHER, decontam, DelayedArray, DelayedMatrixStats, DirichletMultinomial, dplyr, IRanges, MASS, MatrixGenerics, methods, rbiom, rlang, S4Vectors, scater, scuttle, stats, stringr, tibble, tidyr, utils, vegan, Rcpp LinkingTo: Rcpp Suggests: ade4, BiocStyle, biomformat, dada2, knitr, mediation, miaViz, microbiomeDataSets, NMF, patchwork, philr, phyloseq, reldist, rhdf5, rmarkdown, testthat, topicdoc, topicmodels, yaml License: Artistic-2.0 | file LICENSE MD5sum: fb1f3c58f03104206a2cb7b15e6ac2a5 NeedsCompilation: yes Title: Microbiome analysis Description: mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization. biocViews: Microbiome, Software, DataImport Author: Tuomas Borman [aut, cre] (ORCID: ), Felix G.M. Ernst [aut] (ORCID: ), Sudarshan A. Shetty [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Yang Cao [ctb], Nathan D. Olson [ctb], Levi Waldron [ctb], Marcel Ramos [ctb], Héctor Corrada Bravo [ctb], Jayaram Kancherla [ctb], Domenick Braccia [ctb], Basil Courbayre [ctb], Geraldson Muluh [ctb], Giulio Benedetti [ctb], Moritz Emanuel Beber [ctb] (ORCID: ), Chouaib Benchraka [ctb], Akewak Jeba [ctb] (ORCID: ), Himmi Lindgren [ctb], Noah De Gunst [ctb], Théotime Pralas [ctb], Shadman Ishraq [ctb], Eineje Ameh [ctb], Artur Sannikov [ctb], Hervé Pagès [ctb], Rajesh Shigdel [ctb], Katariina Pärnänen [ctb], Pande Erawijantari [ctb], Danielle Callan [ctb], Sam Hillman [ctb], Jesse Pasanen [ctb], Eetu Tammi [ctb] Maintainer: Tuomas Borman URL: https://microbiome.github.io/mia/, https://github.com/microbiome/mia VignetteBuilder: knitr BugReports: https://github.com/microbiome/mia/issues git_url: https://git.bioconductor.org/packages/mia git_branch: RELEASE_3_22 git_last_commit: ad28035 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mia_1.18.0.tar.gz vignettes: vignettes/mia/inst/doc/mia.html vignetteTitles: mia hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mia/inst/doc/mia.R dependsOnMe: miaTime, miaViz importsMe: dar, iSEEtree, lefser, MGnifyR, miaDash, curatedMetagenomicData suggestsMe: anansi, ANCOMBC, DspikeIn, HoloFoodR, miaSim, philr, bugphyzz, MicrobiomeBenchmarkData, MiscMetabar dependencyCount: 159 Package: miaDash Version: 1.2.0 Depends: R (>= 4.4.0), iSEE (>= 2.19.4), shiny Imports: ape, bluster, htmltools, iSEEtree (>= 1.1.4), mia, rintrojs, scater, scuttle, shinydashboard, shinyjs, shinyWidgets, S4Vectors, SingleCellExperiment, SummarizedExperiment, TreeSummarizedExperiment, utils, vegan Suggests: BiocStyle, knitr, philr, remotes, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 47fa4e656a53ae2c3daaeed51ff90726 NeedsCompilation: no Title: Dashboard for the interactive analysis and exploration of microbiome data Description: miaDash provides a Graphical User Interface for the exploration of microbiome data. This way, no knowledge of programming is required to perform analyses. Datasets can be imported, manipulated, analysed and visualised with a user-friendly interface. biocViews: Microbiome, Software, Visualization, GUI, ShinyApps, DataImport Author: Giulio Benedetti [aut, cre] (ORCID: ), Akewak Jeba [ctb] (ORCID: ), Leo Lahti [aut] (ORCID: ) Maintainer: Giulio Benedetti URL: https://github.com/microbiome/miaDash VignetteBuilder: knitr BugReports: https://github.com/microbiome/miaDash/issues git_url: https://git.bioconductor.org/packages/miaDash git_branch: RELEASE_3_22 git_last_commit: b230dcf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miaDash_1.2.0.tar.gz vignettes: vignettes/miaDash/inst/doc/miaDash.html vignetteTitles: miaDash hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miaDash/inst/doc/miaDash.R dependencyCount: 228 Package: miaSim Version: 1.16.0 Depends: TreeSummarizedExperiment Imports: SummarizedExperiment, deSolve, stats, poweRlaw, MatrixGenerics, S4Vectors Suggests: ape, cluster, foreach, doParallel, dplyr, GGally, ggplot2, igraph, network, reshape2, sna, vegan, rmarkdown, knitr, BiocStyle, testthat, mia, miaViz, colourvalues, philentropy License: Artistic-2.0 | file LICENSE MD5sum: 3025924c5e47dff145bc2c23cb575709 NeedsCompilation: no Title: Microbiome Data Simulation Description: Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects. biocViews: Microbiome, Software, Sequencing, DNASeq, ATACSeq, Coverage, Network Author: Yagmur Simsek [cre, aut], Karoline Faust [aut], Yu Gao [aut], Emma Gheysen [aut], Daniel Rios Garza [aut], Tuomas Borman [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Geraldson Muluh [ctb], Akewak Jeba [ctb] (ORCID: ) Maintainer: Yagmur Simsek URL: https://github.com/microbiome/miaSim VignetteBuilder: knitr BugReports: https://github.com/microbiome/miaSim/issues git_url: https://git.bioconductor.org/packages/miaSim git_branch: RELEASE_3_22 git_last_commit: 7f36b16 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miaSim_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miaSim_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miaSim_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miaSim_1.16.0.tgz vignettes: vignettes/miaSim/inst/doc/miaSim.html vignetteTitles: miaSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaSim/inst/doc/miaSim.R dependencyCount: 71 Package: miaTime Version: 1.0.0 Depends: R (>= 4.5.0), mia Imports: dplyr, methods, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tidyr, TreeSummarizedExperiment Suggests: BiocStyle, devtools, ggplot2, knitr, lubridate, miaViz, rmarkdown, scater, testthat, vegan License: Artistic-2.0 | file LICENSE MD5sum: fabf6a9fdcf462aee466ef400f8c63c6 NeedsCompilation: no Title: Microbiome Time Series Analysis Description: The `miaTime` package provides tools for microbiome time series analysis based on (Tree)SummarizedExperiment infrastructure. biocViews: Microbiome, Software, Sequencing Author: Leo Lahti [aut] (ORCID: ), Tuomas Borman [aut, cre] (ORCID: ), Yagmur Simsek [aut], Sudarshan Shetty [ctb], Chouaib Benchraka [ctb], Muluh Muluh [ctb], Ali Hajj [ctb] Maintainer: Tuomas Borman URL: https://github.com/microbiome/miaTime VignetteBuilder: knitr BugReports: https://github.com/microbiome/miaTime/issues git_url: https://git.bioconductor.org/packages/miaTime git_branch: RELEASE_3_22 git_last_commit: 26f2a82 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miaTime_1.0.0.tar.gz vignettes: vignettes/miaTime/inst/doc/miaTime.html vignetteTitles: miaTime hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaTime/inst/doc/miaTime.R suggestsMe: miaViz dependencyCount: 160 Package: miaViz Version: 1.17.10 Depends: R (>= 4.1.0), ggplot2, ggraph (>= 2.0), mia (>= 1.13.0), SummarizedExperiment, TreeSummarizedExperiment Imports: ape, BiocGenerics, BiocParallel, DelayedArray, DirichletMultinomial, dplyr, ggnewscale, ggrepel, ggtree, methods, rlang, S4Vectors, scales, scater, SingleCellExperiment, stats, tibble, tidygraph, tidyr, tidytext, tidytree, viridis Suggests: beeswarm, BiocStyle, bluster, circlize, ComplexHeatmap, ggh4x, knitr, mediation, miaTime, patchwork, rmarkdown, shadowtext, testthat, vegan, vipor License: Artistic-2.0 | file LICENSE MD5sum: 7be6ba994a028e3868ff9f77039e1356 NeedsCompilation: no Title: Microbiome Analysis Plotting and Visualization Description: The miaViz package implements functions to visualize TreeSummarizedExperiment objects especially in the context of microbiome analysis. Part of the mia family of R/Bioconductor packages. biocViews: Microbiome, Software, Visualization Author: Tuomas Borman [aut, cre] (ORCID: ), Felix G.M. Ernst [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Basil Courbayre [ctb], Giulio Benedetti [ctb] (ORCID: ), Théotime Pralas [ctb], Chouaib Benchraka [ctb], Sam Hillman [ctb], Geraldson Muluh [ctb], Noah De Gunst [ctb], Ely Seraidarian [ctb], Himmi Lindgren [ctb], Akewak Jeba [ctb] (ORCID: ), Vivian Ikeh [ctb] Maintainer: Tuomas Borman URL: https://github.com/microbiome/miaViz VignetteBuilder: knitr BugReports: https://github.com/microbiome/miaViz/issues git_url: https://git.bioconductor.org/packages/miaViz git_branch: devel git_last_commit: 530a086 git_last_commit_date: 2025-10-14 Date/Publication: 2025-10-15 source.ver: src/contrib/miaViz_1.17.10.tar.gz vignettes: vignettes/miaViz/inst/doc/miaViz.html vignetteTitles: miaViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaViz/inst/doc/miaViz.R importsMe: iSEEtree suggestsMe: HoloFoodR, MGnifyR, mia, miaSim, miaTime dependencyCount: 197 Package: MiChip Version: 1.64.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: 20a9fc3a9aee57914839c4ca467219d1 NeedsCompilation: no Title: MiChip Parsing and Summarizing Functions Description: This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages. biocViews: Microarray, Preprocessing Author: Jonathon Blake Maintainer: Jonathon Blake git_url: https://git.bioconductor.org/packages/MiChip git_branch: RELEASE_3_22 git_last_commit: a8ba87d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MiChip_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MiChip_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MiChip_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MiChip_1.64.0.tgz vignettes: vignettes/MiChip/inst/doc/MiChip.pdf vignetteTitles: MiChip miRNA Microarray Processing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiChip/inst/doc/MiChip.R dependencyCount: 7 Package: microbiome Version: 1.32.0 Depends: R (>= 3.6.0), phyloseq, ggplot2 Imports: Biostrings, compositions, dplyr, reshape2, Rtsne, scales, stats, tibble, tidyr, utils, vegan Suggests: BiocGenerics, BiocStyle, Cairo, knitr, rmarkdown, testthat License: BSD_2_clause + file LICENSE MD5sum: a977ebb2a7d3017612e99026e9446271 NeedsCompilation: no Title: Microbiome Analytics Description: Utilities for microbiome analysis. biocViews: Metagenomics,Microbiome,Sequencing,SystemsBiology Author: Leo Lahti [aut, cre] (ORCID: ), Sudarshan Shetty [aut] (ORCID: ) Maintainer: Leo Lahti URL: http://microbiome.github.io/microbiome VignetteBuilder: knitr BugReports: https://github.com/microbiome/microbiome/issues git_url: https://git.bioconductor.org/packages/microbiome git_branch: RELEASE_3_22 git_last_commit: 57d7b1a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/microbiome_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/microbiome_1.31.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/microbiome_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/microbiome_1.32.0.tgz vignettes: vignettes/microbiome/inst/doc/vignette.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiome/inst/doc/vignette.R importsMe: DspikeIn, MicrobiomeSurv suggestsMe: ANCOMBC, dar, zitools dependencyCount: 83 Package: microbiomeDASim Version: 1.24.0 Depends: R (>= 3.6.0) Imports: graphics, ggplot2, MASS, tmvtnorm, Matrix, mvtnorm, pbapply, stats, phyloseq, metagenomeSeq, Biobase Suggests: testthat (>= 2.1.0), knitr, devtools License: MIT + file LICENSE MD5sum: c1ef8cd30b9b3b92e817346e2060b78e NeedsCompilation: no Title: Microbiome Differential Abundance Simulation Description: A toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance. biocViews: Microbiome, Visualization, Software Author: Justin Williams, Hector Corrada Bravo, Jennifer Tom, Joseph Nathaniel Paulson Maintainer: Justin Williams URL: https://github.com/williazo/microbiomeDASim VignetteBuilder: knitr BugReports: https://github.com/williazo/microbiomeDASim/issues git_url: https://git.bioconductor.org/packages/microbiomeDASim git_branch: RELEASE_3_22 git_last_commit: 95a20d9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/microbiomeDASim_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/microbiomeDASim_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/microbiomeDASim_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/microbiomeDASim_1.24.0.tgz vignettes: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.pdf vignetteTitles: microbiomeDASim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.R dependencyCount: 90 Package: microbiomeExplorer Version: 1.20.0 Depends: shiny, magrittr, metagenomeSeq, Biobase Imports: shinyjs (>= 2.0.0), shinydashboard, shinycssloaders, shinyWidgets, rmarkdown (>= 1.9.0), DESeq2, RColorBrewer, dplyr, tidyr, purrr, rlang, knitr, readr, DT (>= 0.12.0), biomformat, tools, stringr, vegan, matrixStats, heatmaply, car, broom, limma, reshape2, tibble, forcats, lubridate, methods, plotly (>= 4.9.1) Suggests: V8, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 16e9768f7ea04efe8dc7ad93c6f2056c NeedsCompilation: no Title: Microbiome Exploration App Description: The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation. biocViews: Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software, ImmunoOncology Author: Joseph Paulson [aut], Janina Reeder [aut, cre], Mo Huang [aut], Genentech [cph, fnd] Maintainer: Janina Reeder VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/microbiomeExplorer git_branch: RELEASE_3_22 git_last_commit: 307275f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/microbiomeExplorer_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/microbiomeExplorer_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/microbiomeExplorer_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/microbiomeExplorer_1.20.0.tgz vignettes: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.html vignetteTitles: microbiomeExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.R dependencyCount: 192 Package: MicrobiomeProfiler Version: 1.16.0 Depends: R (>= 4.2.0) Imports: clusterProfiler (>= 4.5.2), config, DT, enrichplot, golem, gson, methods, magrittr, shiny (>= 1.6.0), shinyWidgets, shinycustomloader, htmltools, ggplot2, graphics, stats, utils, yulab.utils Suggests: rmarkdown, knitr, testthat (>= 3.0.0), prettydoc License: GPL-2 MD5sum: 1fe0c1ccadc505d9926b35cdf6f37425 NeedsCompilation: no Title: An R/shiny package for microbiome functional enrichment analysis Description: This is an R/shiny package to perform functional enrichment analysis for microbiome data. This package was based on clusterProfiler. Moreover, MicrobiomeProfiler support KEGG enrichment analysis, COG enrichment analysis, Microbe-Disease association enrichment analysis, Metabo-Pathway analysis. biocViews: Microbiome, Software, Visualization,KEGG Author: Guangchuang Yu [cre, aut] (ORCID: ), Meijun Chen [aut] (ORCID: ) Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/MicrobiomeProfiler/, https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiomeProfiler/issues git_url: https://git.bioconductor.org/packages/MicrobiomeProfiler git_branch: RELEASE_3_22 git_last_commit: 0abe0d0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MicrobiomeProfiler_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MicrobiomeProfiler_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MicrobiomeProfiler_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MicrobiomeProfiler_1.16.0.tgz vignettes: vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.html vignetteTitles: Introduction to MicrobiotaProcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.R dependencyCount: 154 Package: MicrobiotaProcess Version: 1.21.2 Depends: R (>= 4.0.0) Imports: ape, tidyr, ggplot2, magrittr, dplyr, Biostrings, ggrepel, vegan, zoo, ggtree, tidytree (>= 0.4.2), MASS, methods, rlang, tibble, grDevices, stats, utils, coin, ggsignif, patchwork, ggstar, tidyselect, SummarizedExperiment, foreach, treeio (>= 1.17.2), pillar, cli, plyr, dtplyr, ggtreeExtra, data.table, ggfun (>= 0.1.1) Suggests: rmarkdown, prettydoc, testthat, knitr, nlme, phangorn, DECIPHER, randomForest, jsonlite, biomformat, scales, yaml, withr, S4Vectors, purrr, seqmagick, glue, ggupset, ggVennDiagram, ggalluvial (>= 0.11.1), forcats, phyloseq, aplot, ggnewscale, ggside, ggh4x, hopach, parallel, shadowtext, DirichletMultinomial, ggpp, BiocManager License: GPL (>= 3.0) MD5sum: ac791ab3233ca7996c0be19558acfb8c NeedsCompilation: no Title: A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework Description: MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework). biocViews: Visualization, Microbiome, Software, MultipleComparison, FeatureExtraction Author: Shuangbin Xu [aut, cre] (ORCID: ), Guangchuang Yu [aut, ctb] (ORCID: ) Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/MicrobiotaProcess/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiotaProcess/issues git_url: https://git.bioconductor.org/packages/MicrobiotaProcess git_branch: devel git_last_commit: 0822a21 git_last_commit_date: 2025-09-23 Date/Publication: 2025-10-07 source.ver: src/contrib/MicrobiotaProcess_1.21.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/MicrobiotaProcess_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MicrobiotaProcess_1.21.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MicrobiotaProcess_1.21.2.tgz vignettes: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.html vignetteTitles: Introduction to MicrobiotaProcess hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.R dependencyCount: 124 Package: microRNA Version: 1.68.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) License: Artistic-2.0 MD5sum: 34bcf3f773592558db99172476a6266c NeedsCompilation: yes Title: Data and functions for dealing with microRNAs Description: Different data resources for microRNAs and some functions for manipulating them. biocViews: Infrastructure, GenomeAnnotation, SequenceMatching Author: R. Gentleman, S. Falcon Maintainer: "Michael Lawrence" git_url: https://git.bioconductor.org/packages/microRNA git_branch: RELEASE_3_22 git_last_commit: ff3bb2e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/microRNA_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/microRNA_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/microRNA_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/microRNA_1.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: rtracklayer dependencyCount: 15 Package: MICSQTL Version: 1.8.0 Depends: R (>= 4.3.0), SummarizedExperiment, stats Imports: TCA, nnls, purrr, TOAST, magrittr, BiocParallel, ggplot2, ggpubr, ggridges, glue, S4Vectors, dirmult Suggests: testthat (>= 3.0.0), rmarkdown, knitr, BiocStyle License: GPL-3 Archs: x64 MD5sum: 6cc9a7893aa2339bab16a72fb7954556 NeedsCompilation: no Title: MICSQTL (Multi-omic deconvolution, Integration and Cell-type-specific Quantitative Trait Loci) Description: Our pipeline, MICSQTL, utilizes scRNA-seq reference and bulk transcriptomes to estimate cellular composition in the matched bulk proteomes. The expression of genes and proteins at either bulk level or cell type level can be integrated by Angle-based Joint and Individual Variation Explained (AJIVE) framework. Meanwhile, MICSQTL can perform cell-type-specic quantitative trait loci (QTL) mapping to proteins or transcripts based on the input of bulk expression data and the estimated cellular composition per molecule type, without the need for single cell sequencing. We use matched transcriptome-proteome from human brain frontal cortex tissue samples to demonstrate the input and output of our tool. biocViews: GeneExpression, Genetics, Proteomics, RNASeq, Sequencing, SingleCell, Software, Visualization, CellBasedAssays, Coverage Author: Yue Pan [aut] (ORCID: ), Qian Li [aut, cre] (ORCID: ), Iain Carmichael [ctb] Maintainer: Qian Li URL: https://bioconductor.org/packages/MICSQTL, https://github.com/YuePan027/MICSQTL VignetteBuilder: knitr BugReports: https://github.com/YuePan027/MICSQTL/issues git_url: https://git.bioconductor.org/packages/MICSQTL git_branch: RELEASE_3_22 git_last_commit: d51e216 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MICSQTL_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MICSQTL_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MICSQTL_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MICSQTL_1.8.0.tgz vignettes: vignettes/MICSQTL/inst/doc/MICSQTL.html vignetteTitles: MICSQTL: Multi-omic deconvolution,, Integration and Cell-type-specific Quantitative Trait Loci hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MICSQTL/inst/doc/MICSQTL.R dependencyCount: 135 Package: midasHLA Version: 1.18.0 Depends: R (>= 4.1), MultiAssayExperiment (>= 1.8.3) Imports: assertthat (>= 0.2.0), broom (>= 0.5.1), dplyr (>= 0.8.0.1), formattable (>= 0.2.0.1), HardyWeinberg (>= 1.6.3), kableExtra (>= 1.1.0), knitr (>= 1.21), magrittr (>= 1.5), methods, stringi (>= 1.2.4), rlang (>= 0.3.1), S4Vectors (>= 0.20.1), stats, SummarizedExperiment (>= 1.12.0), tibble (>= 2.0.1), utils, qdapTools (>= 1.3.3) Suggests: broom.mixed (>= 0.2.4), cowplot (>= 1.0.0), devtools (>= 2.0.1), ggplot2 (>= 3.1.0), ggpubr (>= 0.2.5), rmarkdown, seqinr (>= 3.4-5), survival (>= 2.43-3), testthat (>= 2.0.1), tidyr (>= 1.1.2) License: MIT + file LICENCE Archs: x64 MD5sum: 49774d252c49ef66d7adbd487cfbf238 NeedsCompilation: no Title: R package for immunogenomics data handling and association analysis Description: MiDAS is a R package for immunogenetics data transformation and statistical analysis. MiDAS accepts input data in the form of HLA alleles and KIR types, and can transform it into biologically meaningful variables, enabling HLA amino acid fine mapping, analyses of HLA evolutionary divergence, KIR gene presence, as well as validated HLA-KIR interactions. Further, it allows comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS closes a gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to T cell, Natural Killer cell, and disease biology. biocViews: CellBiology, Genetics, StatisticalMethod Author: Christian Hammer [aut], Maciej Migdał [aut, cre] Maintainer: Maciej Migdał VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/midasHLA git_branch: RELEASE_3_22 git_last_commit: ba6d0cb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/midasHLA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/midasHLA_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/midasHLA_1.18.0.tgz vignettes: vignettes/midasHLA/inst/doc/MiDAS_tutorial.html, vignettes/midasHLA/inst/doc/MiDAS_vignette.html vignetteTitles: MiDAS tutorial, MiDAS quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/midasHLA/inst/doc/MiDAS_tutorial.R, vignettes/midasHLA/inst/doc/MiDAS_vignette.R dependencyCount: 138 Package: miloR Version: 2.6.0 Depends: R (>= 4.0.0), edgeR Imports: BiocNeighbors, BiocGenerics, SingleCellExperiment, Matrix (>= 1.3-0), MatrixGenerics, S4Vectors, stats, stringr, methods, igraph, irlba, utils, cowplot, BiocParallel, BiocSingular, limma, ggplot2, tibble, matrixStats, ggraph, gtools, SummarizedExperiment, patchwork, tidyr, dplyr, ggrepel, ggbeeswarm, RColorBrewer, grDevices, Rcpp, pracma, numDeriv LinkingTo: Rcpp, RcppArmadillo, RcppEigen, RcppML Suggests: testthat, mvtnorm, scater, scran, covr, knitr, rmarkdown, uwot, scuttle, BiocStyle, MouseGastrulationData, MouseThymusAgeing, magick, RCurl, MASS, curl, scRNAseq, graphics, sparseMatrixStats License: GPL-3 + file LICENSE MD5sum: 7641ddc2a124e7fb4f20ac335f125373 NeedsCompilation: yes Title: Differential neighbourhood abundance testing on a graph Description: Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using either a negative bionomial generalized linear model or negative binomial generalized linear mixed model. biocViews: SingleCell, MultipleComparison, FunctionalGenomics, Software Author: Mike Morgan [aut, cre] (ORCID: ), Emma Dann [aut, ctb] Maintainer: Mike Morgan URL: https://marionilab.github.io/miloR VignetteBuilder: knitr BugReports: https://github.com/MarioniLab/miloR/issues git_url: https://git.bioconductor.org/packages/miloR git_branch: RELEASE_3_22 git_last_commit: 374663b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miloR_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miloR_2.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miloR_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miloR_2.6.0.tgz vignettes: vignettes/miloR/inst/doc/milo_contrasts.html, vignettes/miloR/inst/doc/milo_demo.html, vignettes/miloR/inst/doc/milo_gastrulation.html, vignettes/miloR/inst/doc/milo_glmm.html vignetteTitles: Using contrasts for differential abundance testing, Differential abundance testing with Milo, Differential abundance testing with Milo - Mouse gastrulation example, Mixed effect models for Milo DA testing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miloR/inst/doc/milo_contrasts.R, vignettes/miloR/inst/doc/milo_demo.R, vignettes/miloR/inst/doc/milo_gastrulation.R, vignettes/miloR/inst/doc/milo_glmm.R dependencyCount: 103 Package: mimager Version: 1.34.0 Depends: Biobase Imports: BiocGenerics, S4Vectors, preprocessCore, grDevices, methods, grid, gtable, scales, DBI, affy, affyPLM, oligo, oligoClasses Suggests: knitr, rmarkdown, BiocStyle, testthat, lintr, Matrix, abind, affydata, hgu95av2cdf, oligoData, pd.hugene.1.0.st.v1 License: MIT + file LICENSE MD5sum: 2d5617e33a6be462aa2c0f007111ec45 NeedsCompilation: no Title: mimager: The Microarray Imager Description: Easily visualize and inspect microarrays for spatial artifacts. biocViews: Infrastructure, Visualization, Microarray Author: Aaron Wolen [aut, cre, cph] Maintainer: Aaron Wolen URL: https://github.com/aaronwolen/mimager VignetteBuilder: knitr BugReports: https://github.com/aaronwolen/mimager/issues git_url: https://git.bioconductor.org/packages/mimager git_branch: RELEASE_3_22 git_last_commit: f6a1e99 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mimager_1.34.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mimager_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mimager_1.34.0.tgz vignettes: vignettes/mimager/inst/doc/introduction.html vignetteTitles: mimager overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mimager/inst/doc/introduction.R dependencyCount: 64 Package: mina Version: 1.18.0 Depends: R (>= 4.0.0) Imports: methods, stats, Rcpp, MCL, RSpectra, apcluster, bigmemory, foreach, ggplot2, parallel, parallelDist, reshape2, plyr, biganalytics, stringr, Hmisc, utils LinkingTo: Rcpp, RcppParallel, RcppArmadillo Suggests: knitr, rmarkdown Enhances: doMC License: GPL MD5sum: 4e48253c8c8f9ff14a2446695ea9f18f NeedsCompilation: yes Title: Microbial community dIversity and Network Analysis Description: An increasing number of microbiome datasets have been generated and analyzed with the help of rapidly developing sequencing technologies. At present, analysis of taxonomic profiling data is mainly conducted using composition-based methods, which ignores interactions between community members. Besides this, a lack of efficient ways to compare microbial interaction networks limited the study of community dynamics. To better understand how community diversity is affected by complex interactions between its members, we developed a framework (Microbial community dIversity and Network Analysis, mina), a comprehensive framework for microbial community diversity analysis and network comparison. By defining and integrating network-derived community features, we greatly reduce noise-to-signal ratio for diversity analyses. A bootstrap and permutation-based method was implemented to assess community network dissimilarities and extract discriminative features in a statistically principled way. biocViews: Software, WorkflowStep Author: Rui Guan [aut, cre], Ruben Garrido-Oter [ctb] Maintainer: Rui Guan VignetteBuilder: knitr BugReports: https://github.com/Guan06/mina git_url: https://git.bioconductor.org/packages/mina git_branch: RELEASE_3_22 git_last_commit: 575681a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mina_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mina_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mina_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mina_1.18.0.tgz vignettes: vignettes/mina/inst/doc/mina.html vignetteTitles: Microbial dIversity and Network Analysis with MINA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mina/inst/doc/mina.R dependencyCount: 85 Package: MineICA Version: 1.49.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.8), Biobase, plyr, ggplot2, scales, foreach, xtable, biomaRt, gtools, GOstats, cluster, marray, mclust, RColorBrewer, colorspace, igraph, Rgraphviz, graph, annotate, Hmisc, fastICA, JADE Imports: AnnotationDbi, lumi, fpc, lumiHumanAll.db Suggests: biomaRt, GOstats, cluster, hgu133a.db, mclust, igraph, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerVDX, future, future.apply Enhances: doMC License: GPL-2 MD5sum: c32de2877f9a1c9a19389e9e7f8ab292 NeedsCompilation: no Title: Analysis of an ICA decomposition obtained on genomics data Description: The goal of MineICA is to perform Independent Component Analysis (ICA) on multiple transcriptome datasets, integrating additional data (e.g molecular, clinical and pathological). This Integrative ICA helps the biological interpretation of the components by studying their association with variables (e.g sample annotations) and gene sets, and enables the comparison of components from different datasets using correlation-based graph. biocViews: Visualization, MultipleComparison Author: Anne Biton Maintainer: Anne Biton git_url: https://git.bioconductor.org/packages/MineICA git_branch: devel git_last_commit: 839876d git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/MineICA_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MineICA_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MineICA_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MineICA_1.49.0.tgz vignettes: vignettes/MineICA/inst/doc/MineICA.pdf vignetteTitles: MineICA: Independent component analysis of genomic data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MineICA/inst/doc/MineICA.R dependencyCount: 219 Package: minet Version: 3.68.0 Imports: infotheo License: Artistic-2.0 Archs: x64 MD5sum: fc1e11cc8030fcf8564e23b335fa181a NeedsCompilation: yes Title: Mutual Information NETworks Description: This package implements various algorithms for inferring mutual information networks from data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference Author: Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi Maintainer: Patrick E. Meyer URL: http://minet.meyerp.com git_url: https://git.bioconductor.org/packages/minet git_branch: RELEASE_3_22 git_last_commit: ce17cab git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/minet_3.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/minet_3.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/minet_3.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/minet_3.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: BioNERO, epiNEM, RTN, PRANA, TGS suggestsMe: CNORfeeder, TCGAbiolinks, WGCNA dependencyCount: 1 Package: minfi Version: 1.56.0 Depends: methods, BiocGenerics (>= 0.15.3), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), Biostrings (>= 2.77.2), bumphunter (>= 1.1.9) Imports: S4Vectors, Seqinfo, Biobase (>= 2.33.2), IRanges, beanplot, RColorBrewer, lattice, nor1mix, siggenes, limma, preprocessCore, illuminaio (>= 0.23.2), DelayedMatrixStats (>= 1.3.4), mclust, genefilter, nlme, reshape, MASS, quadprog, data.table, GEOquery, stats, grDevices, graphics, utils, DelayedArray (>= 0.15.16), HDF5Array, BiocParallel Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1), minfiData (>= 0.18.0), minfiDataEPIC, FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest, BiocStyle, knitr, rmarkdown, tools License: Artistic-2.0 MD5sum: a29714a83632cc9d4333ec1dc14547f2 NeedsCompilation: no Title: Analyze Illumina Infinium DNA methylation arrays Description: Tools to analyze & visualize Illumina Infinium methylation arrays. biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, MultiChannel, TwoChannel, DataImport, Normalization, Preprocessing, QualityControl Author: Kasper Daniel Hansen [cre, aut], Martin Aryee [aut], Rafael A. Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb], E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim Triche [ctb], Shan V. Andrews [ctb], Peter F. Hickey [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/hansenlab/minfi VignetteBuilder: knitr BugReports: https://github.com/hansenlab/minfi/issues git_url: https://git.bioconductor.org/packages/minfi git_branch: RELEASE_3_22 git_last_commit: e6780b4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/minfi_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/minfi_1.55.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/minfi_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/minfi_1.56.0.tgz vignettes: vignettes/minfi/inst/doc/minfi.html vignetteTitles: minfi User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/minfi/inst/doc/minfi.R dependsOnMe: bigmelon, ChAMP, conumee, methylumi, REMP, IlluminaHumanMethylation27kanno.ilmn12.hg19, IlluminaHumanMethylation27kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICanno.ilm10b3.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylationEPICv2anno.20a1.hg38, IlluminaHumanMethylationEPICv2manifest, IlluminaHumanMethylationMSAanno.ilm10a1.hg38, IlluminaHumanMethylationMSAmanifest, BeadSorted.Saliva.EPIC, FlowSorted.Blood.450k, FlowSorted.Blood.EPIC, FlowSorted.CordBlood.450k, FlowSorted.CordBloodCombined.450k, FlowSorted.CordBloodNorway.450k, FlowSorted.DLPFC.450k, minfiData, minfiDataEPIC, methylationArrayAnalysis importsMe: deconvR, DMRcate, epimutacions, funtooNorm, MEAL, MEAT, MethylAid, methylCC, methylclock, methylumi, missMethyl, quantro, recountmethylation, shinyepico, shinyMethyl, skewr, HiBED, EMAS suggestsMe: dmGsea, epivizr, epivizrChart, GeoTcgaData, Harman, MultiDataSet, planet, RnBeads, brgedata, epimutacionsData, GSE159526, MLML2R dependencyCount: 142 Package: MinimumDistance Version: 1.54.0 Depends: R (>= 3.5.0), VanillaICE (>= 1.47.1) Imports: methods, BiocGenerics, MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges, Seqinfo, GenomicRanges (>= 1.17.16), SummarizedExperiment (>= 1.15.4), oligoClasses, DNAcopy, ff, foreach, matrixStats, lattice, data.table, grid, stats, utils Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, RUnit Enhances: snow, doSNOW License: Artistic-2.0 MD5sum: ee3d58624326a38bc015b200fbd4c8a5 NeedsCompilation: no Title: A Package for De Novo CNV Detection in Case-Parent Trios Description: Analysis of de novo copy number variants in trios from high-dimensional genotyping platforms. biocViews: Microarray, SNP, CopyNumberVariation Author: Robert B Scharpf and Ingo Ruczinski Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/MinimumDistance git_branch: RELEASE_3_22 git_last_commit: ac79af7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MinimumDistance_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MinimumDistance_1.53.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MinimumDistance_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MinimumDistance_1.54.0.tgz vignettes: vignettes/MinimumDistance/inst/doc/MinimumDistance.pdf vignetteTitles: Detection of de novo copy number alterations in case-parent trios hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MinimumDistance/inst/doc/MinimumDistance.R dependencyCount: 98 Package: MiPP Version: 1.82.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: 5845a0d9fa36795d5b2681a8a7ad0f11 NeedsCompilation: no Title: Misclassification Penalized Posterior Classification Description: This package finds optimal sets of genes that seperate samples into two or more classes. biocViews: Microarray, Classification Author: HyungJun Cho , Sukwoo Kim , Mat Soukup , and Jae K. Lee Maintainer: Sukwoo Kim URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/MiPP git_branch: RELEASE_3_22 git_last_commit: 4b64387 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MiPP_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MiPP_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MiPP_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MiPP_1.82.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: miQC Version: 1.17.0 Depends: R (>= 3.5.0) Imports: SingleCellExperiment, flexmix, ggplot2, splines Suggests: scRNAseq, scater, BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: fb726dc81746358f493d06106a48ffd4 NeedsCompilation: no Title: Flexible, probabilistic metrics for quality control of scRNA-seq data Description: Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. biocViews: SingleCell, QualityControl, GeneExpression, Preprocessing, Sequencing Author: Ariel Hippen [aut, cre], Stephanie Hicks [aut] Maintainer: Ariel Hippen URL: https://github.com/greenelab/miQC VignetteBuilder: knitr BugReports: https://github.com/greenelab/miQC/issues git_url: https://git.bioconductor.org/packages/miQC git_branch: devel git_last_commit: 44cd292 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/miQC_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miQC_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miQC_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miQC_1.18.0.tgz vignettes: vignettes/miQC/inst/doc/miQC.html vignetteTitles: miQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miQC/inst/doc/miQC.R dependencyCount: 46 Package: MIRA Version: 1.32.0 Depends: R (>= 3.5) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, ggplot2, Biobase, stats, bsseq, methods Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: 69fc60eb5ede4c7117fb87f30c03f25e NeedsCompilation: no Title: Methylation-Based Inference of Regulatory Activity Description: DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets. biocViews: ImmunoOncology, DNAMethylation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing, Epigenetics, Coverage Author: Nathan Sheffield [aut], Christoph Bock [ctb], John Lawson [aut, cre] Maintainer: John Lawson URL: http://databio.org/mira VignetteBuilder: knitr BugReports: https://github.com/databio/MIRA git_url: https://git.bioconductor.org/packages/MIRA git_branch: RELEASE_3_22 git_last_commit: bb55c9a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MIRA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MIRA_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MIRA_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MIRA_1.32.0.tgz vignettes: vignettes/MIRA/inst/doc/BiologicalApplication.html, vignettes/MIRA/inst/doc/GettingStarted.html vignetteTitles: Applying MIRA to a Biological Question, Getting Started with Methylation-based Inference of Regulatory Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIRA/inst/doc/BiologicalApplication.R, vignettes/MIRA/inst/doc/GettingStarted.R importsMe: COCOA dependencyCount: 93 Package: MiRaGE Version: 1.52.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: BiocGenerics, S4Vectors, AnnotationDbi, BiocManager Suggests: seqinr (>= 3.0.7), biomaRt (>= 2.19.1), GenomicFeatures (>= 1.15.4), Biostrings (>= 2.31.3), BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, miRNATarget, humanStemCell, IRanges, GenomicRanges (>= 1.8.3), BSgenome, beadarrayExampleData License: GPL MD5sum: 117d9faf24d17254c4bbc1dcca0aa054 NeedsCompilation: no Title: MiRNA Ranking by Gene Expression Description: The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile. biocViews: ImmunoOncology, Microarray, GeneExpression, RNASeq, Sequencing, SAGE Author: Y-h. Taguchi Maintainer: Y-h. Taguchi git_url: https://git.bioconductor.org/packages/MiRaGE git_branch: RELEASE_3_22 git_last_commit: e04e687 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MiRaGE_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MiRaGE_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MiRaGE_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MiRaGE_1.52.0.tgz vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf vignetteTitles: How to use MiRaGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R dependencyCount: 44 Package: miRBaseConverter Version: 1.34.0 Depends: R (>= 3.4) Imports: stats Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils, rmarkdown License: GPL (>= 2) MD5sum: 846cad5232dd14e29735d88ed9903c8a NeedsCompilation: no Title: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions Description: A comprehensive tool for converting and retrieving the miRNA Name, Accession, Sequence, Version, History and Family information in different miRBase versions. It can process a huge number of miRNAs in a short time without other depends. biocViews: Software, miRNA Author: Taosheng Xu Taosheng Xu [aut, cre] (ORCID: ) Maintainer: Taosheng Xu Taosheng Xu URL: https://github.com/taoshengxu/miRBaseConverter VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/miRBaseConverter/issues git_url: https://git.bioconductor.org/packages/miRBaseConverter git_branch: RELEASE_3_22 git_last_commit: 163891b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miRBaseConverter_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRBaseConverter_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRBaseConverter_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRBaseConverter_1.34.0.tgz vignettes: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.html vignetteTitles: "miRBaseConverter: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.R suggestsMe: EpiMix dependencyCount: 1 Package: miRcomp Version: 1.40.0 Depends: R (>= 3.5.0), Biobase (>= 2.22.0), miRcompData Imports: utils, methods, graphics, KernSmooth, stats Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, shiny License: GPL-3 | file LICENSE MD5sum: 24d0288947f10234d8c4a1b53c3bda4e NeedsCompilation: no Title: Tools to assess and compare miRNA expression estimatation methods Description: Based on a large miRNA dilution study, this package provides tools to read in the raw amplification data and use these data to assess the performance of methods that estimate expression from the amplification curves. biocViews: Software, qPCR, Preprocessing, QualityControl Author: Matthew N. McCall , Lauren Kemperman Maintainer: Matthew N. McCall VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRcomp git_branch: RELEASE_3_22 git_last_commit: ef417a8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miRcomp_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRcomp_1.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRcomp_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRcomp_1.40.0.tgz vignettes: vignettes/miRcomp/inst/doc/miRcomp.html vignetteTitles: Assessment and comparison of miRNA expression estimation methods (miRcomp) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R dependencyCount: 9 Package: mirIntegrator Version: 1.40.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: bf312618fea409c56542cc80f37839aa NeedsCompilation: no Title: Integrating microRNA expression into signaling pathways for pathway analysis Description: Tools for augmenting signaling pathways to perform pathway analysis of microRNA and mRNA expression levels. biocViews: Network, Microarray, GraphAndNetwork, Pathways, KEGG Author: Diana Diaz Maintainer: Diana Diaz URL: http://datad.github.io/mirIntegrator/ git_url: https://git.bioconductor.org/packages/mirIntegrator git_branch: RELEASE_3_22 git_last_commit: 1bd2140 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mirIntegrator_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mirIntegrator_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mirIntegrator_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mirIntegrator_1.40.0.tgz vignettes: vignettes/mirIntegrator/inst/doc/mirIntegrator.pdf vignetteTitles: mirIntegrator Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mirIntegrator/inst/doc/mirIntegrator.R dependencyCount: 63 Package: MIRit Version: 1.6.0 Depends: MultiAssayExperiment, R (>= 4.4.0) Imports: AnnotationDbi, BiocFileCache, BiocParallel, DESeq2, edgeR, fgsea, genekitr, geneset, ggplot2, ggpubr, graph, graphics, graphite, grDevices, httr, limma, methods, Rcpp, Rgraphviz (>= 2.44.0), rlang, stats, utils LinkingTo: Rcpp Suggests: BiocStyle, biomaRt, BSgenome.Hsapiens.UCSC.hg38, GenomicRanges, ggrepel, ggridges, Gviz, gwasrapidd, knitr, MonoPoly, org.Hs.eg.db, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: bfdc6b1cc5ed8811f2b9bf4973460f5e NeedsCompilation: yes Title: Integrate microRNA and gene expression to decipher pathway complexity Description: MIRit is an R package that provides several methods for investigating the relationships between miRNAs and genes in different biological conditions. In particular, MIRit allows to explore the functions of dysregulated miRNAs, and makes it possible to identify miRNA-gene regulatory axes that control biological pathways, thus enabling the users to unveil the complexity of miRNA biology. MIRit is an all-in-one framework that aims to help researchers in all the central aspects of an integrative miRNA-mRNA analyses, from differential expression analysis to network characterization. biocViews: Software, GeneRegulation, NetworkEnrichment, NetworkInference, Epigenetics, FunctionalGenomics, SystemsBiology, Network, Pathways, GeneExpression, DifferentialExpression Author: Jacopo Ronchi [aut, cre] (ORCID: ), Maria Foti [fnd] (ORCID: ) Maintainer: Jacopo Ronchi URL: https://github.com/jacopo-ronchi/MIRit VignetteBuilder: knitr BugReports: https://github.com/jacopo-ronchi/MIRit/issues git_url: https://git.bioconductor.org/packages/MIRit git_branch: RELEASE_3_22 git_last_commit: 2274523 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MIRit_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MIRit_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MIRit_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MIRit_1.6.0.tgz vignettes: vignettes/MIRit/inst/doc/MIRit.html vignetteTitles: Integrate miRNA and gene expression data with MIRit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIRit/inst/doc/MIRit.R dependencyCount: 213 Package: miRLAB Version: 1.40.0 Imports: methods, stats, utils, RCurl, httr, stringr, Hmisc, energy, entropy, gplots, glmnet, impute, limma, pcalg,TCGAbiolinks,dplyr,SummarizedExperiment, ctc, InvariantCausalPrediction, Category, GOstats, org.Hs.eg.db Suggests: knitr,BiocGenerics, AnnotationDbi,RUnit,rmarkdown License: GPL (>=2) Archs: x64 MD5sum: 8511ce33a1889e93abe572a2838a41d7 NeedsCompilation: no Title: Dry lab for exploring miRNA-mRNA relationships Description: Provide tools exploring miRNA-mRNA relationships, including popular miRNA target prediction methods, ensemble methods that integrate individual methods, functions to get data from online resources, functions to validate the results, and functions to conduct enrichment analyses. biocViews: miRNA, GeneExpression, NetworkInference, Network Author: Thuc Duy Le, Junpeng Zhang, Mo Chen, Vu Viet Hoang Pham Maintainer: Thuc Duy Le URL: https://github.com/pvvhoang/miRLAB VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRLAB git_branch: RELEASE_3_22 git_last_commit: d343103 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miRLAB_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRLAB_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRLAB_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRLAB_1.40.0.tgz vignettes: vignettes/miRLAB/inst/doc/miRLAB-vignette.html vignetteTitles: miRLAB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRLAB/inst/doc/miRLAB-vignette.R dependencyCount: 190 Package: miRNAmeConverter Version: 1.38.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi, reshape2 Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 61433a2e9dc206da1ee1ad617a6a276b NeedsCompilation: no Title: Convert miRNA Names to Different miRBase Versions Description: Translating mature miRNA names to different miRBase versions, sequence retrieval, checking names for validity and detecting miRBase version of a given set of names (data from http://www.mirbase.org/). biocViews: Preprocessing, miRNA Author: Stefan Haunsberger [aut, cre] Maintainer: Stefan J. Haunsberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRNAmeConverter git_branch: RELEASE_3_22 git_last_commit: 8422462 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miRNAmeConverter_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRNAmeConverter_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRNAmeConverter_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRNAmeConverter_1.38.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 51 Package: miRNApath Version: 1.70.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: db0b0a62a1c5b0eb461de7e96ed945b0 NeedsCompilation: no Title: miRNApath: Pathway Enrichment for miRNA Expression Data Description: This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes. biocViews: Annotation, Pathways, DifferentialExpression, NetworkEnrichment, miRNA Author: James M. Ward with contributions from Yunling Shi, Cindy Richards, John P. Cogswell Maintainer: James M. Ward git_url: https://git.bioconductor.org/packages/miRNApath git_branch: RELEASE_3_22 git_last_commit: f2a584f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miRNApath_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRNApath_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRNApath_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRNApath_1.70.0.tgz vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNApath/inst/doc/miRNApath.R dependencyCount: 1 Package: miRNAtap Version: 1.44.0 Depends: R (>= 3.3.0), AnnotationDbi Imports: DBI, RSQLite, stringr, sqldf, plyr, methods Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat License: GPL-2 Archs: x64 MD5sum: 6c0e1a1de9f9fda1e0e684d13fe5b027 NeedsCompilation: no Title: miRNAtap: microRNA Targets - Aggregated Predictions Description: The package facilitates implementation of workflows requiring miRNA predictions, it allows to integrate ranked miRNA target predictions from multiple sources available online and aggregate them with various methods which improves quality of predictions above any of the single sources. Currently predictions are available for Homo sapiens, Mus musculus and Rattus norvegicus (the last one through homology translation). biocViews: Software, Classification, Microarray, Sequencing, miRNA Author: Maciej Pajak, T. Ian Simpson Maintainer: T. Ian Simpson git_url: https://git.bioconductor.org/packages/miRNAtap git_branch: RELEASE_3_22 git_last_commit: 96a79bc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miRNAtap_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRNAtap_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRNAtap_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRNAtap_1.44.0.tgz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R dependsOnMe: miRNAtap.db importsMe: miRNAtap.db dependencyCount: 52 Package: miRSM Version: 2.6.0 Depends: R (>= 4.4.0) Imports: WGCNA, flashClust, dynamicTreeCut, GFA, igraph, RColorBrewer, grid, MCL, fabia, NMF, biclust, iBBiG, BicARE, isa2, methods, rJava, BiBitR, rqubic, Biobase, PMA, stats, dbscan, mclust, SOMbrero, ppclust, Rcpp, utils, SummarizedExperiment, GSEABase, org.Hs.eg.db, clusterProfiler, ReactomePA, DOSE, MatrixCorrelation, energy Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: e03e178f50f626e18eb623137184916c NeedsCompilation: yes Title: Inferring miRNA sponge modules in heterogeneous data Description: The package aims to identify miRNA sponge or ceRNA modules in heterogeneous data. It provides several functions to study miRNA sponge modules at single-sample and multi-sample levels, including popular methods for inferring gene modules (candidate miRNA sponge or ceRNA modules), and two functions to identify miRNA sponge modules at single-sample and multi-sample levels, as well as several functions to conduct modular analysis of miRNA sponge modules. biocViews: GeneExpression, BiomedicalInformatics, Clustering, GeneSetEnrichment, Microarray, Software, GeneRegulation, GeneTarget Author: Junpeng Zhang [aut, cre] Maintainer: Junpeng Zhang URL: https://github.com/zhangjunpeng411/miRSM VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRSM/issues git_url: https://git.bioconductor.org/packages/miRSM git_branch: RELEASE_3_22 git_last_commit: d79d504 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miRSM_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRSM_2.5.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRSM_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRSM_2.6.0.tgz vignettes: vignettes/miRSM/inst/doc/miRSM.html vignetteTitles: miRSM: inferring miRNA sponge modules in heterogeneous data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRSM/inst/doc/miRSM.R dependencyCount: 260 Package: miRspongeR Version: 2.14.0 Depends: R (>= 4.4.0) Imports: corpcor, SPONGE, parallel, igraph, MCL, clusterProfiler, ReactomePA, DOSE, survival, grDevices, graphics, stats, utils, Rcpp, RColorBrewer, grid, org.Hs.eg.db, foreach, doParallel Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 955622886fe7dd46f272a07d2df87c60 NeedsCompilation: yes Title: Identification and analysis of miRNA sponge regulation Description: This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network. biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment, Survival, Microarray, Software, SingleCell, Spatial, RNASeq Author: Junpeng Zhang [aut, cre] Maintainer: Junpeng Zhang URL: VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRspongeR/issues git_url: https://git.bioconductor.org/packages/miRspongeR git_branch: RELEASE_3_22 git_last_commit: b0b2997 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/miRspongeR_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRspongeR_2.13.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRspongeR_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRspongeR_2.14.0.tgz vignettes: vignettes/miRspongeR/inst/doc/miRspongeR.html vignetteTitles: Identification and analysis of miRNA sponge regulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRspongeR/inst/doc/miRspongeR.R dependencyCount: 281 Package: mirTarRnaSeq Version: 1.18.0 Depends: R (>= 4.1.0), ggplot2 Imports: purrr, MASS, pscl, assertthat, caTools, dplyr, pheatmap, reshape2, corrplot, grDevices, graphics, stats, utils, data.table, R.utils, viridis Suggests: BiocStyle, knitr, rmarkdown, R.cache, SPONGE License: MIT + file LICENSE MD5sum: 09a787541b56b535140211a3934b5ce5 NeedsCompilation: no Title: mirTarRnaSeq Description: mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments. biocViews: miRNA, Regression, Software, Sequencing, SmallRNA, TimeCourse, DifferentialExpression Author: Mercedeh Movassagh [aut, cre] (ORCID: ), Sarah Morton [aut], Rafael Irizarry [aut], Jeffrey Bailey [aut], Joseph N Paulson [aut] Maintainer: Mercedeh Movassagh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mirTarRnaSeq git_branch: RELEASE_3_22 git_last_commit: 439bc89 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mirTarRnaSeq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mirTarRnaSeq_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mirTarRnaSeq_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mirTarRnaSeq_1.18.0.tgz vignettes: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.pdf vignetteTitles: mirTarRnaSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.R dependencyCount: 51 Package: missMethyl Version: 1.44.0 Depends: R (>= 3.6.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICv2anno.20a1.hg38 Imports: AnnotationDbi, BiasedUrn, Biobase, BiocGenerics, GenomeInfoDb, GenomicRanges, GO.db, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylationEPICv2manifest, IRanges, limma, methods, methylumi, minfi, org.Hs.eg.db, ruv, S4Vectors, statmod, stringr, SummarizedExperiment Suggests: BiocStyle, edgeR, knitr, minfiData, rmarkdown, tweeDEseqCountData, DMRcate, ExperimentHub License: GPL-2 MD5sum: 8efededc2e77f639f7886abdfc613dea NeedsCompilation: no Title: Analysing Illumina HumanMethylation BeadChip Data Description: Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes. biocViews: Normalization, DNAMethylation, MethylationArray, GenomicVariation, GeneticVariability, DifferentialMethylation, GeneSetEnrichment Author: Belinda Phipson and Jovana Maksimovic Maintainer: Belinda Phipson , Jovana Maksimovic , Andrew Lonsdale , Calandra Grima VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missMethyl git_branch: RELEASE_3_22 git_last_commit: 3ca88f7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/missMethyl_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/missMethyl_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/missMethyl_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/missMethyl_1.44.0.tgz vignettes: vignettes/missMethyl/inst/doc/missMethyl.html vignetteTitles: missMethyl: Analysing Illumina HumanMethylation BeadChip Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R dependsOnMe: methylationArrayAnalysis importsMe: DMRcate, MEAL, methylGSA suggestsMe: RnBeads dependencyCount: 168 Package: missRows Version: 1.30.0 Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment Imports: plyr, stats, gtools, S4Vectors Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: c0641ec334450e350c8a647c7b276d04 NeedsCompilation: no Title: Handling Missing Individuals in Multi-Omics Data Integration Description: The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values. biocViews: Software, StatisticalMethod, DimensionReduction, PrincipalComponent, MathematicalBiology, Visualization Author: Ignacio Gonzalez and Valentin Voillet Maintainer: Gonzalez Ignacio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missRows git_branch: RELEASE_3_22 git_last_commit: 2560586 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/missRows_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/missRows_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/missRows_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/missRows_1.30.0.tgz vignettes: vignettes/missRows/inst/doc/missRows.pdf vignetteTitles: missRows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missRows/inst/doc/missRows.R dependencyCount: 58 Package: mist Version: 1.2.0 Depends: R (>= 4.5.0) Imports: BiocParallel, MCMCpack, Matrix, S4Vectors, methods, rtracklayer, car, mvtnorm, SummarizedExperiment, SingleCellExperiment, BiocGenerics, stats, rlang Suggests: knitr, rmarkdown, RUnit, ggplot2, BiocStyle License: MIT + file LICENSE MD5sum: 1dd7075c9ed4029aa9b8210a0a125308 NeedsCompilation: no Title: Differential Methylation Analysis for scDNAm Data Description: mist (Methylation Inference for Single-cell along Trajectory) is a hierarchical Bayesian framework for modeling DNA methylation trajectories and performing differential methylation (DM) analysis in single-cell DNA methylation (scDNAm) data. It estimates developmental-stage-specific variations, identifies genomic features with drastic changes along pseudotime, and, for two phenotypic groups, detects features with distinct temporal methylation patterns. mist uses Gibbs sampling to estimate parameters for temporal changes and stage-specific variations. biocViews: Epigenetics, DifferentialMethylation, DNAMethylation, SingleCell, Software Author: Daoyu Duan [aut, cre] (ORCID: ) Maintainer: Daoyu Duan URL: https://https://github.com/dxd429/mist VignetteBuilder: knitr BugReports: https://https://github.com/dxd429/mist/issues git_url: https://git.bioconductor.org/packages/mist git_branch: RELEASE_3_22 git_last_commit: 2a8026a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mist_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mist_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mist_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mist_1.2.0.tgz vignettes: vignettes/mist/inst/doc/mist_vignette.html vignetteTitles: mist_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mist/inst/doc/mist_vignette.R dependencyCount: 118 Package: mistyR Version: 1.18.0 Depends: R (>= 4.0) Imports: assertthat, caret, deldir, digest, distances, dplyr (>= 1.1.0), filelock, furrr (>= 0.2.0), ggplot2, methods, purrr, ranger, readr (>= 2.0.0), ridge, rlang, rlist, R.utils, stats, stringr, tibble, tidyr, tidyselect (>= 1.2.0), utils, withr Suggests: BiocStyle, covr, earth, future, igraph (>= 1.2.7), iml, kernlab, knitr, MASS, rmarkdown, RSNNS, testthat (>= 3.0.0), xgboost License: GPL-3 MD5sum: d58307a41c59495378df56b17362cfe7 NeedsCompilation: no Title: Multiview Intercellular SpaTial modeling framework Description: mistyR is an implementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution. biocViews: Software, BiomedicalInformatics, CellBiology, SystemsBiology, Regression, DecisionTree, SingleCell, Spatial Author: Jovan Tanevski [cre, aut] (ORCID: ), Ricardo Omar Ramirez Flores [ctb] (ORCID: ), Philipp Schäfer [ctb] Maintainer: Jovan Tanevski URL: https://saezlab.github.io/mistyR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/mistyR/issues git_url: https://git.bioconductor.org/packages/mistyR git_branch: RELEASE_3_22 git_last_commit: 1401996 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mistyR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mistyR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mistyR_1.18.0.tgz vignettes: vignettes/mistyR/inst/doc/mistyR.html vignetteTitles: Getting started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mistyR/inst/doc/mistyR.R dependencyCount: 107 Package: mitch Version: 1.22.0 Depends: R (>= 4.4) Imports: stats, grDevices, graphics, utils, MASS, plyr, reshape2, parallel, GGally, grid, gridExtra, knitr, rmarkdown, ggplot2, gplots, beeswarm, echarts4r, kableExtra, dplyr, network Suggests: stringi, testthat (>= 2.1.0), HGNChelper, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 License: CC BY-SA 4.0 + file LICENSE MD5sum: 611412ae15450b36f252431072b57754 NeedsCompilation: no Title: Multi-Contrast Gene Set Enrichment Analysis Description: mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. We have an established routine for pathway enrichment of Infinium Methylation Array data (see vignette). The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments. biocViews: GeneExpression, GeneSetEnrichment, SingleCell, Transcriptomics, Epigenetics, Proteomics, DifferentialExpression, Reactome, DNAMethylation, MethylationArray, DataImport Author: Mark Ziemann [aut, cre, cph] (ORCID: ), Antony Kaspi [aut, cph] Maintainer: Mark Ziemann URL: https://github.com/markziemann/mitch VignetteBuilder: knitr BugReports: https://github.com/markziemann/mitch git_url: https://git.bioconductor.org/packages/mitch git_branch: RELEASE_3_22 git_last_commit: cae4ecb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mitch_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mitch_1.21.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mitch_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mitch_1.22.0.tgz vignettes: vignettes/mitch/inst/doc/infiniumMethArrayWorkflow.html, vignettes/mitch/inst/doc/mitchWorkflow.html vignetteTitles: Applying mitch to pathway analysis of Infinium Methylation array data, mitch Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mitch/inst/doc/infiniumMethArrayWorkflow.R, vignettes/mitch/inst/doc/mitchWorkflow.R dependencyCount: 102 Package: mitoClone2 Version: 1.16.0 Depends: R (>= 4.4.0) Imports: reshape2, GenomicRanges, pheatmap, deepSNV, grDevices, Matrix, graphics, stats, utils, S4Vectors, Rhtslib, parallel, methods, ggplot2 LinkingTo: Rhtslib (>= 1.13.1) Suggests: knitr, rmarkdown, Biostrings, testthat License: GPL-3 MD5sum: c5b00767c11f6fe795d152296bcb1a3a NeedsCompilation: yes Title: Clonal Population Identification in Single-Cell RNA-Seq Data using Mitochondrial and Somatic Mutations Description: This package primarily identifies variants in mitochondrial genomes from BAM alignment files. It filters these variants to remove RNA editing events then estimates their evolutionary relationship (i.e. their phylogenetic tree) and groups single cells into clones. It also visualizes the mutations and providing additional genomic context. biocViews: Annotation, DataImport, Genetics, SNP, Software, SingleCell, Alignment Author: Benjamin Story [aut, cre], Lars Velten [aut], Gregor Mönke [aut] Maintainer: Benjamin Story URL: https://github.com/benstory/mitoClone2 SystemRequirements: GNU make, PhISCS (optional) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mitoClone2 git_branch: RELEASE_3_22 git_last_commit: 43b5799 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mitoClone2_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mitoClone2_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mitoClone2_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mitoClone2_1.16.0.tgz vignettes: vignettes/mitoClone2/inst/doc/clustering.html, vignettes/mitoClone2/inst/doc/overview.html vignetteTitles: Computation of phylogenetic trees and clustering of mutations, Variant Calling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitoClone2/inst/doc/clustering.R, vignettes/mitoClone2/inst/doc/overview.R dependencyCount: 98 Package: mitology Version: 1.1.3 Depends: R (>= 4.5.0) Imports: AnnotationDbi, ape, circlize, clusterProfiler, ComplexHeatmap, ggplot2, ggtree, magrittr, org.Hs.eg.db, ReactomePA, scales Suggests: Biobase, BiocStyle, GSVA, methods, rmarkdown, knitr, SummarizedExperiment, testthat License: AGPL-3 Archs: x64 MD5sum: a163b6fd4cbf0052c5775fcf956e47e0 NeedsCompilation: no Title: Study of mitochondrial activity from RNA-seq data Description: mitology allows to study the mitochondrial activity throught high-throughput RNA-seq data. It is based on a collection of genes whose proteins localize in to the mitochondria. From these, mitology provides a reorganization of the pathways related to mitochondria activity from Reactome and Gene Ontology. Further a ready-to-use implementation of MitoCarta3.0 pathways is included. biocViews: GeneExpression, RNASeq, Visualization, SingleCell, Spatial, Pathways, Reactome, GO Author: Stefania Pirrotta [cre, aut] (ORCID: ), Enrica Calura [aut, fnd] (ORCID: ) Maintainer: Stefania Pirrotta URL: https://github.com/CaluraLab/mitology VignetteBuilder: knitr BugReports: https://github.com/CaluraLab/mitology/issues git_url: https://git.bioconductor.org/packages/mitology git_branch: devel git_last_commit: 3513589 git_last_commit_date: 2025-10-22 Date/Publication: 2025-10-23 source.ver: src/contrib/mitology_1.1.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/mitology_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mitology_1.1.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mitology_1.1.3.tgz vignettes: vignettes/mitology/inst/doc/mitology.html vignetteTitles: mitology vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitology/inst/doc/mitology.R dependencyCount: 163 Package: mixOmics Version: 6.34.0 Depends: R (>= 4.4.0), MASS, lattice, ggplot2 Imports: igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra, grDevices, graphics, stats, ggrepel, BiocParallel, utils, gsignal, rgl Suggests: BiocStyle, knitr, rmarkdown, mime, testthat, microbenchmark, magick, vdiffr, kableExtra, devtools License: GPL (>= 2) MD5sum: be187fd2e61f3e88e6890f362d4e1af6 NeedsCompilation: no Title: Omics Data Integration Project Description: Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares. biocViews: ImmunoOncology, Microarray, Sequencing, Metabolomics, Metagenomics, Proteomics, GenePrediction, MultipleComparison, Classification, Regression Author: Kim-Anh Le Cao [aut], Florian Rohart [aut], Ignacio Gonzalez [aut], Sebastien Dejean [aut], Al J Abadi [ctb], Max Bladen [ctb], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb], Eva Hamrud [ctb, cre] Maintainer: Eva Hamrud URL: http://www.mixOmics.org VignetteBuilder: knitr BugReports: https://github.com/mixOmicsTeam/mixOmics/issues/ git_url: https://git.bioconductor.org/packages/mixOmics git_branch: RELEASE_3_22 git_last_commit: 553e6cc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mixOmics_6.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mixOmics_6.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mixOmics_6.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mixOmics_6.34.0.tgz vignettes: vignettes/mixOmics/inst/doc/vignette.html vignetteTitles: mixOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mixOmics/inst/doc/vignette.R dependsOnMe: timeOmics, mixKernel, sgPLS importsMe: AlpsNMR, DepecheR, PLSDAbatch, POMA, RFLOMICS, Coxmos, CytoProfile, Holomics, iTensor, MSclassifR, plsmod, plsRcox, SISIR suggestsMe: autonomics, notameStats, planet, eoPredData, fastml, MetabolomicsBasics, pctax, RVAideMemoire, SelectBoost, sharp dependencyCount: 85 Package: MLInterfaces Version: 1.90.0 Depends: R (>= 3.5), Rcpp, methods, BiocGenerics (>= 0.13.11), Biobase, annotate, cluster Imports: gdata, pls, sfsmisc, MASS, rpart, genefilter, fpc, ggvis, shiny, gbm, RColorBrewer, hwriter, threejs (>= 0.2.2), mlbench, stats4, tools, grDevices, graphics, stats, magrittr, SummarizedExperiment Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, hgu95av2.db, som, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party, klaR, BiocStyle, knitr, testthat, airway Enhances: parallel License: LGPL MD5sum: 87740bcb98ea86217140856ba05876aa NeedsCompilation: no Title: Uniform interfaces to R machine learning procedures for data in Bioconductor containers Description: This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers. biocViews: Classification, Clustering Author: Vincent Carey [cre, aut] (ORCID: ), Jess Mar [aut], Jason Vertrees [ctb], Laurent Gatto [ctb], Phylis Atieno [ctb] (Translated vignettes from Sweave to Rmarkdown / HTML.) Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLInterfaces git_branch: RELEASE_3_22 git_last_commit: 2d5da27 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MLInterfaces_1.90.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MLInterfaces_1.89.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MLInterfaces_1.90.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MLInterfaces_1.90.0.tgz vignettes: vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf, vignettes/MLInterfaces/inst/doc/MLint_devel.html, vignettes/MLInterfaces/inst/doc/MLprac2_2.html vignetteTitles: MLInterfaces Computer Cluster, MLInterfaces 2.0 -- a new design, A machine learning tutorial tutorial: applications of the Bioconductor MLInterfaces package to gene expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R, vignettes/MLInterfaces/inst/doc/MLprac2_2.R, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R dependsOnMe: pRoloc, SigCheck, dGAselID, nlcv dependencyCount: 121 Package: MLP Version: 1.58.0 Imports: AnnotationDbi, gplots, graphics, stats, utils Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Cf.eg.db, org.Mmu.eg.db, KEGGREST, annotate, Rgraphviz, GOstats, graph, limma, mouse4302.db, reactome.db License: GPL-3 MD5sum: 45c13ec8ad80e1f3453b8da6f8ba3539 NeedsCompilation: no Title: Mean Log P Analysis Description: Pathway analysis based on p-values associated to genes from a genes expression analysis of interest. Utility functions enable to extract pathways from the Gene Ontology Biological Process (GOBP), Molecular Function (GOMF) and Cellular Component (GOCC), Kyoto Encyclopedia of Genes of Genomes (KEGG) and Reactome databases. Methodology, and helper functions to display the results as a table, barplot of pathway significance, Gene Ontology graph and pathway significance are available. biocViews: Genetics, GeneExpression, Pathways, Reactome, KEGG, GO Author: Nandini Raghavan [aut], Tobias Verbeke [aut], An De Bondt [aut], Javier Cabrera [ctb], Dhammika Amaratunga [ctb], Tine Casneuf [ctb], Willem Ligtenberg [ctb], Laure Cougnaud [cre], Katarzyna Gorczak [ctb] Maintainer: Tobias Verbeke git_url: https://git.bioconductor.org/packages/MLP git_branch: RELEASE_3_22 git_last_commit: 10c7161 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MLP_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MLP_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MLP_1.58.0.tgz vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf vignetteTitles: UsingMLP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLP/inst/doc/UsingMLP.R importsMe: esetVis suggestsMe: a4 dependencyCount: 48 Package: MLSeq Version: 2.28.0 Depends: caret, ggplot2 Imports: testthat, VennDiagram, pamr, methods, DESeq2, edgeR, limma, Biobase, SummarizedExperiment, plyr, foreach, utils, sSeq, xtable Suggests: knitr, e1071, kernlab License: GPL(>=2) Archs: x64 MD5sum: e5482099756a6433b048c4dcf6c5e73d NeedsCompilation: no Title: Machine Learning Interface for RNA-Seq Data Description: This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, Classification, Clustering Author: Gokmen Zararsiz [aut, cre], Dincer Goksuluk [aut], Selcuk Korkmaz [aut], Vahap Eldem [aut], Izzet Parug Duru [ctb], Ahmet Ozturk [aut], Ahmet Ergun Karaagaoglu [aut, ths] Maintainer: Gokmen Zararsiz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLSeq git_branch: RELEASE_3_22 git_last_commit: e4f82c9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MLSeq_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MLSeq_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MLSeq_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MLSeq_2.28.0.tgz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: Beginner's guide to the "MLSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R importsMe: GARS dependencyCount: 133 Package: MMDiff2 Version: 1.38.0 Depends: R (>= 3.5.0), Rsamtools, Biobase Imports: GenomicRanges, locfit, BSgenome, Biostrings, shiny, ggplot2, RColorBrewer, graphics, grDevices, parallel, S4Vectors, methods Suggests: MMDiffBamSubset, MotifDb, knitr, BiocStyle, BSgenome.Mmusculus.UCSC.mm9 License: Artistic-2.0 MD5sum: 09da0898e6695da6dc601641db9dfd3e NeedsCompilation: no Title: Statistical Testing for ChIP-Seq data sets Description: This package detects statistically significant differences between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD). biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Software Author: Gabriele Schweikert [cre, aut], David Kuo [aut] Maintainer: Gabriele Schweikert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMDiff2 git_branch: RELEASE_3_22 git_last_commit: 3712313 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MMDiff2_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MMDiff2_1.37.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MMDiff2_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MMDiff2_1.38.0.tgz vignettes: vignettes/MMDiff2/inst/doc/MMDiff2.pdf vignetteTitles: An Introduction to the MMDiff2 method hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMDiff2/inst/doc/MMDiff2.R suggestsMe: MMDiffBamSubset dependencyCount: 96 Package: mnem Version: 1.26.0 Depends: R (>= 4.1) Imports: cluster, graph, Rgraphviz, flexclust, lattice, naturalsort, snowfall, stats4, tsne, methods, graphics, stats, utils, Linnorm, data.table, Rcpp, RcppEigen, matrixStats, grDevices, e1071, ggplot2, wesanderson LinkingTo: Rcpp, RcppEigen Suggests: knitr, devtools, rmarkdown, BiocGenerics, RUnit, epiNEM, BiocStyle License: GPL-3 Archs: x64 MD5sum: 8790a96d25f573905e409d17c2a62c18 NeedsCompilation: yes Title: Mixture Nested Effects Models Description: Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm. biocViews: Pathways, SystemsBiology, NetworkInference, Network, RNASeq, PooledScreens, SingleCell, CRISPR, ATACSeq, DNASeq, GeneExpression Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/mnem/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/mnem/issues git_url: https://git.bioconductor.org/packages/mnem git_branch: RELEASE_3_22 git_last_commit: ecde75f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mnem_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mnem_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mnem_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mnem_1.26.0.tgz vignettes: vignettes/mnem/inst/doc/mnem.html vignetteTitles: mnem hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mnem/inst/doc/mnem.R dependsOnMe: nempi importsMe: bnem, epiNEM dependencyCount: 78 Package: moanin Version: 1.18.0 Depends: R (>= 4.0), SummarizedExperiment, topGO, stats Imports: S4Vectors, MASS (>= 1.0.0), limma, viridis, edgeR, graphics, methods, grDevices, reshape2, NMI, zoo, ClusterR, splines, matrixStats Suggests: testthat (>= 1.0.0), timecoursedata, knitr, rmarkdown, markdown, covr, BiocStyle License: BSD 3-clause License + file LICENSE Archs: x64 MD5sum: 67f4d3fdaedc216642fba9cd3bbdbdc6 NeedsCompilation: no Title: An R Package for Time Course RNASeq Data Analysis Description: Simple and efficient workflow for time-course gene expression data, built on publictly available open-source projects hosted on CRAN and bioconductor. moanin provides helper functions for all the steps required for analysing time-course data using functional data analysis: (1) functional modeling of the timecourse data; (2) differential expression analysis; (3) clustering; (4) downstream analysis. biocViews: TimeCourse, GeneExpression, RNASeq, Microarray, DifferentialExpression, Clustering Author: Elizabeth Purdom [aut] (ORCID: ), Nelle Varoquaux [aut, cre] (ORCID: ) Maintainer: Nelle Varoquaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/moanin git_branch: RELEASE_3_22 git_last_commit: 6b72807 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/moanin_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/moanin_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/moanin_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/moanin_1.18.0.tgz vignettes: vignettes/moanin/inst/doc/documentation.html vignetteTitles: The Moanin Package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/moanin/inst/doc/documentation.R dependencyCount: 87 Package: mobileRNA Version: 1.5.1 Depends: R (>= 4.3.0) Imports: dplyr, tidyr, ggplot2, BiocGenerics, DESeq2, edgeR, ggrepel, grDevices, pheatmap, utils, tidyselect, progress, RColorBrewer, GenomicRanges, rtracklayer, data.table, SimDesign, scales, IRanges, stats, methods, Biostrings, reticulate, S4Vectors, Seqinfo, SummarizedExperiment, rlang, bioseq, grid Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 6c385a651d83b57f548195e40d09fc52 NeedsCompilation: no Title: mobileRNA: Investigate the RNA mobilome & population-scale changes Description: Genomic analysis can be utilised to identify differences between RNA populations in two conditions, both in production and abundance. This includes the identification of RNAs produced by multiple genomes within a biological system. For example, RNA produced by pathogens within a host or mobile RNAs in plant graft systems. The mobileRNA package provides methods to pre-process, analyse and visualise the sRNA and mRNA populations based on the premise of mapping reads to all genotypes at the same time. biocViews: Visualization, RNASeq, Sequencing, SmallRNA, GenomeAssembly, Clustering, ExperimentalDesign, QualityControl, WorkflowStep, Alignment, Preprocessing Author: Katie Jeynes-Cupper [aut, cre] (ORCID: ), Marco Catoni [aut] (ORCID: ) Maintainer: Katie Jeynes-Cupper SystemRequirements: GNU make, ShortStack (>= 4.0), HTSeq, HISAT2, SAMtools, Conda VignetteBuilder: knitr BugReports: https://github.com/KJeynesCupper/mobileRNA/issues git_url: https://git.bioconductor.org/packages/mobileRNA git_branch: devel git_last_commit: 072fb25 git_last_commit_date: 2025-07-23 Date/Publication: 2025-10-07 source.ver: src/contrib/mobileRNA_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/mobileRNA_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mobileRNA_1.5.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mobileRNA_1.5.1.tgz vignettes: vignettes/mobileRNA/inst/doc/mobileRNA.html vignetteTitles: mobileRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mobileRNA/inst/doc/mobileRNA.R dependencyCount: 140 Package: MODA Version: 1.36.0 Depends: R (>= 3.3) Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut, igraph, cluster, AMOUNTAIN, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: b9da62a6c430f9d392a74afe271dc3fb NeedsCompilation: no Title: MODA: MOdule Differential Analysis for weighted gene co-expression network Description: MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, James B. Brown, Luisa Orsini, Zhisong Pan, Guyu Hu and Shan He Maintainer: Dong Li git_url: https://git.bioconductor.org/packages/MODA git_branch: RELEASE_3_22 git_last_commit: 2387620 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MODA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MODA_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MODA_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MODA_1.36.0.tgz vignettes: vignettes/MODA/inst/doc/MODA.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 106 Package: ModCon Version: 1.18.0 Depends: data.table, parallel, utils, stats, R (>= 4.1) Suggests: testthat, knitr, rmarkdown, dplyr, shinycssloaders, shiny, shinyFiles, shinydashboard, shinyjs License: GPL-3 + file LICENSE MD5sum: 2aa134baef10ac0d546396c451c28e6d NeedsCompilation: no Title: Modifying splice site usage by changing the mRNP code, while maintaining the genetic code Description: Collection of functions to calculate a nucleotide sequence surrounding for splice donors sites to either activate or repress donor usage. The proposed alternative nucleotide sequence encodes the same amino acid and could be applied e.g. in reporter systems to silence or activate cryptic splice donor sites. biocViews: FunctionalGenomics, AlternativeSplicing Author: Johannes Ptok [aut, cre] (ORCID: ) Maintainer: Johannes Ptok URL: https://github.com/caggtaagtat/ModCon SystemRequirements: Perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ModCon git_branch: RELEASE_3_22 git_last_commit: 069420d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ModCon_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ModCon_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ModCon_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ModCon_1.18.0.tgz vignettes: vignettes/ModCon/inst/doc/ModCon.html vignetteTitles: Designing SD context with ModCon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ModCon/inst/doc/ModCon.R dependencyCount: 5 Package: Modstrings Version: 1.26.0 Depends: R (>= 3.6), Biostrings (>= 2.51.5) Imports: methods, BiocGenerics, GenomicRanges, S4Vectors, IRanges, XVector, stringi, stringr, crayon, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: 7a99260c347614f01c3e5d02eeb26893 NeedsCompilation: no Title: Working with modified nucleotide sequences Description: Representing nucleotide modifications in a nucleotide sequence is usually done via special characters from a number of sources. This represents a challenge to work with in R and the Biostrings package. The Modstrings package implements this functionallity for RNA and DNA sequences containing modified nucleotides by translating the character internally in order to work with the infrastructure of the Biostrings package. For this the ModRNAString and ModDNAString classes and derivates and functions to construct and modify these objects despite the encoding issues are implemenented. In addition the conversion from sequences to list like location information (and the reverse operation) is implemented as well. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Modstrings/issues git_url: https://git.bioconductor.org/packages/Modstrings git_branch: RELEASE_3_22 git_last_commit: a4e48ba git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Modstrings_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Modstrings_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Modstrings_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Modstrings_1.26.0.tgz vignettes: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.html, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.html, vignettes/Modstrings/inst/doc/Modstrings.html vignetteTitles: Modstrings-DNA-alphabet, Modstrings-RNA-alphabet, Modstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.R, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.R, vignettes/Modstrings/inst/doc/Modstrings.R dependsOnMe: EpiTxDb, RNAmodR, tRNAdbImport importsMe: tRNA suggestsMe: EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3 dependencyCount: 24 Package: MOFA2 Version: 1.20.0 Depends: R (>= 4.0) Imports: rhdf5, dplyr, tidyr, reshape2, pheatmap, ggplot2, methods, RColorBrewer, cowplot, ggrepel, reticulate, HDF5Array, grDevices, stats, magrittr, forcats, utils, corrplot, DelayedArray, Rtsne, uwot, basilisk, stringi Suggests: knitr, testthat, Seurat, SeuratObject, ggpubr, foreach, psych, MultiAssayExperiment, SummarizedExperiment, SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown, data.table, tidyverse, BiocStyle, Matrix, markdown License: file LICENSE MD5sum: 5294aca949b4e24bb82d55e57827bfcd NeedsCompilation: yes Title: Multi-Omics Factor Analysis v2 Description: The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, vizualisation, imputation etc are available. biocViews: DimensionReduction, Bayesian, Visualization Author: Ricard Argelaguet [aut, cre] (ORCID: ), Damien Arnol [aut] (ORCID: ), Danila Bredikhin [aut] (ORCID: ), Britta Velten [aut] (ORCID: ) Maintainer: Ricard Argelaguet URL: https://biofam.github.io/MOFA2/index.html SystemRequirements: Python (>=3), numpy, pandas, h5py, scipy, argparse, sklearn, mofapy2 VignetteBuilder: knitr BugReports: https://github.com/bioFAM/MOFA2 git_url: https://git.bioconductor.org/packages/MOFA2 git_branch: RELEASE_3_22 git_last_commit: 4dd4d60 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MOFA2_1.20.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MOFA2_1.20.0.tgz vignettes: vignettes/MOFA2/inst/doc/downstream_analysis.html, vignettes/MOFA2/inst/doc/getting_started_R.html, vignettes/MOFA2/inst/doc/MEFISTO_temporal.html vignetteTitles: Downstream analysis: Overview, MOFA2: How to train a model in R, MEFISTO on simulated data (temporal) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOFA2/inst/doc/downstream_analysis.R, vignettes/MOFA2/inst/doc/getting_started_R.R, vignettes/MOFA2/inst/doc/MEFISTO_temporal.R importsMe: RFLOMICS suggestsMe: HoloFoodR, SUMO dependencyCount: 84 Package: MOGAMUN Version: 1.20.0 Imports: stats, utils, RCy3, stringr, graphics, grDevices, RUnit, BiocParallel, igraph Suggests: knitr, markdown License: GPL-3 + file LICENSE MD5sum: b3fdebaa1f6c6f68b61a648f20d93556 NeedsCompilation: no Title: MOGAMUN: A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks Description: MOGAMUN is a multi-objective genetic algorithm that identifies active modules in a multiplex biological network. This allows analyzing different biological networks at the same time. MOGAMUN is based on NSGA-II (Non-Dominated Sorting Genetic Algorithm, version II), which we adapted to work on networks. biocViews: SystemsBiology, GraphAndNetwork, DifferentialExpression, BiomedicalInformatics, Transcriptomics, Clustering, Network Author: Elva-María Novoa-del-Toro [aut, cre] (ORCID: ) Maintainer: Elva-María Novoa-del-Toro URL: https://github.com/elvanov/MOGAMUN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MOGAMUN git_branch: RELEASE_3_22 git_last_commit: ca2045f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MOGAMUN_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MOGAMUN_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MOGAMUN_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MOGAMUN_1.19.0.tgz vignettes: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.html vignetteTitles: Finding active modules with MOGAMUN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.R dependencyCount: 68 Package: mogsa Version: 1.44.0 Depends: R (>= 3.4.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase, parallel, corpcor, svd, cluster, grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, org.Hs.eg.db License: GPL-2 Archs: x64 MD5sum: 3c46b74262cfa7fbe11137d1aaf0659f NeedsCompilation: no Title: Multiple omics data integrative clustering and gene set analysis Description: This package provide a method for doing gene set analysis based on multiple omics data. biocViews: GeneExpression, PrincipalComponent, StatisticalMethod, Clustering, Software Author: Chen Meng Maintainer: Chen Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mogsa git_branch: RELEASE_3_22 git_last_commit: f89a88e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mogsa_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mogsa_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mogsa_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mogsa_1.44.0.tgz vignettes: vignettes/mogsa/inst/doc/moCluster-knitr.pdf, vignettes/mogsa/inst/doc/mogsa-knitr.pdf vignetteTitles: moCluster: Integrative clustering using multiple omics data, mogsa: gene set analysis on multiple omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mogsa/inst/doc/moCluster-knitr.R, vignettes/mogsa/inst/doc/mogsa-knitr.R dependencyCount: 71 Package: MoleculeExperiment Version: 1.10.0 Depends: R (>= 4.1.0) Imports: SpatialExperiment, Matrix, purrr, data.table, dplyr (>= 1.1.1), magrittr, rjson, utils, methods, terra, ggplot2, rlang, cli, EBImage, rhdf5, BiocParallel, S4Vectors, stats Suggests: knitr, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: ab1884231ee6dd1e4d4cf9b20ade7c3d NeedsCompilation: no Title: Prioritising a molecule-level storage of Spatial Transcriptomics Data Description: MoleculeExperiment contains functions to create and work with objects from the new MoleculeExperiment class. We introduce this class for analysing molecule-based spatial transcriptomics data (e.g., Xenium by 10X, Cosmx SMI by Nanostring, and Merscope by Vizgen). This allows researchers to analyse spatial transcriptomics data at the molecule level, and to have standardised data formats accross vendors. biocViews: DataImport, DataRepresentation, Infrastructure, Software, Spatial, Transcriptomics Author: Bárbara Zita Peters Couto [aut], Nicholas Robertson [aut], Ellis Patrick [aut], Shila Ghazanfar [aut, cre] Maintainer: Shila Ghazanfar URL: https://github.com/SydneyBioX/MoleculeExperiment VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/MoleculeExperiment/issues git_url: https://git.bioconductor.org/packages/MoleculeExperiment git_branch: RELEASE_3_22 git_last_commit: b604184 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MoleculeExperiment_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MoleculeExperiment_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MoleculeExperiment_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MoleculeExperiment_1.10.0.tgz vignettes: vignettes/MoleculeExperiment/inst/doc/MoleculeExperiment.html vignetteTitles: "Introduction to MoleculeExperiment" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MoleculeExperiment/inst/doc/MoleculeExperiment.R dependencyCount: 116 Package: MOMA Version: 1.22.0 Depends: R (>= 4.0) Imports: circlize, cluster, ComplexHeatmap, dplyr, ggplot2, graphics, grid, grDevices, magrittr, methods, MKmisc, MultiAssayExperiment, parallel, qvalue, RColorBrewer, readr, reshape2, rlang, stats, stringr, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, viper License: GPL-3 MD5sum: 17180989706bdda4d4ee086f64b8de4c NeedsCompilation: no Title: Multi Omic Master Regulator Analysis Description: This package implements the inference of candidate master regulator proteins from multi-omics' data (MOMA) algorithm, as well as ancillary analysis and visualization functions. biocViews: Software, NetworkEnrichment, NetworkInference, Network, FeatureExtraction, Clustering, FunctionalGenomics, Transcriptomics, SystemsBiology Author: Evan Paull [aut], Sunny Jones [aut, cre], Mariano Alvarez [aut] Maintainer: Sunny Jones VignetteBuilder: knitr BugReports: https://github.com/califano-lab/MOMA/issues git_url: https://git.bioconductor.org/packages/MOMA git_branch: RELEASE_3_22 git_last_commit: 4a7d444 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MOMA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MOMA_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MOMA_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MOMA_1.22.0.tgz vignettes: vignettes/MOMA/inst/doc/moma.html vignetteTitles: MOMA - Multi Omic Master Regulator Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOMA/inst/doc/moma.R dependencyCount: 91 Package: monaLisa Version: 1.16.0 Depends: R (>= 4.1) Imports: BiocGenerics, BiocParallel, Biostrings, BSgenome, circlize, ComplexHeatmap (>= 2.11.1), Seqinfo, GenomicRanges, cli, ggplot2 (>= 4.0.0), glmnet, grDevices, grid, IRanges, methods, rlang, RSQLite, stabs, stats, SummarizedExperiment, S4Vectors, TFBSTools, tidyr, tools, utils, XVector Suggests: BiocManager, BiocStyle, BSgenome.Mmusculus.UCSC.mm10, ggrepel, gridExtra, JASPAR2020, JASPAR2024, knitr, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL (>= 3) MD5sum: 325abae762ec044e8b19e163d2767be8 NeedsCompilation: no Title: Binned Motif Enrichment Analysis and Visualization Description: Useful functions to work with sequence motifs in the analysis of genomics data. These include methods to annotate genomic regions or sequences with predicted motif hits and to identify motifs that drive observed changes in accessibility or expression. Functions to produce informative visualizations of the obtained results are also provided. biocViews: MotifAnnotation, Visualization, FeatureExtraction, Epigenetics Author: Dania Machlab [aut] (ORCID: ), Lukas Burger [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Dany Mukesha [ctb] (ORCID: ), Michael Stadler [aut, cre] (ORCID: ) Maintainer: Michael Stadler URL: https://github.com/fmicompbio/monaLisa, https://bioconductor.org/packages/monaLisa/, https://fmicompbio.github.io/monaLisa/ VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/monaLisa/issues git_url: https://git.bioconductor.org/packages/monaLisa git_branch: RELEASE_3_22 git_last_commit: fb7bf5b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/monaLisa_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/monaLisa_1.15.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/monaLisa_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/monaLisa_1.16.0.tgz vignettes: vignettes/monaLisa/inst/doc/monaLisa.html, vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.html vignetteTitles: monaLisa - MOtif aNAlysis with Lisa, selecting_motifs_with_randLassoStabSel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monaLisa/inst/doc/monaLisa.R, vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.R dependencyCount: 119 Package: monocle Version: 2.38.0 Depends: R (>= 2.10.0), methods, Matrix (>= 1.2-6), Biobase, ggplot2 (>= 1.0.0), VGAM (>= 1.0-6), DDRTree (>= 0.1.4), Imports: parallel, igraph (>= 1.0.1), BiocGenerics, HSMMSingleCell (>= 0.101.5), plyr, cluster, combinat, fastICA, grid, irlba (>= 2.0.0), matrixStats, Rtsne, MASS, reshape2, leidenbase (>= 0.1.9), limma, tibble, dplyr, pheatmap, stringr, proxy, slam, viridis, stats, biocViews, RANN(>= 2.5), Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: destiny, Hmisc, knitr, Seurat, scater, testthat License: Artistic-2.0 Archs: x64 MD5sum: 13ab3cb95ed9bb194e375e1284898031 NeedsCompilation: yes Title: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq Description: Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: Cole Trapnell Maintainer: Cole Trapnell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/monocle git_branch: RELEASE_3_22 git_last_commit: 9f2ab5b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/monocle_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/monocle_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/monocle_2.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/monocle_2.38.0.tgz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Cell counting,, differential expression,, and trajectory analysis for single-cell RNA-Seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R dependsOnMe: cicero importsMe: uSORT, scPOEM suggestsMe: sincell, ClusterGVis, grandR, Seurat dependencyCount: 74 Package: Moonlight2R Version: 1.8.0 Depends: R (>= 4.5), doParallel, foreach Imports: parmigene, randomForest, gplots, circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase, grDevices, graphics, GEOquery, stats, purrr, RISmed, grid, utils, ComplexHeatmap, GenomicRanges, dplyr, fuzzyjoin, rtracklayer, magrittr, qpdf, readr, seqminer, stringr, tibble, tidyHeatmap, tidyr, AnnotationHub, easyPubMed, org.Hs.eg.db, EpiMix, BiocGenerics, ggplot2, ExperimentHub, rlang, withr, data.table Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), devtools, roxygen2, png License: GPL-3 MD5sum: 496a1ec65de9f48341e64fc3ac932900 NeedsCompilation: no Title: Identify oncogenes and tumor suppressor genes from omics data Description: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. An additional mechanistic layer evaluates if there are mutations affecting the protein stability of the transcription factors (TFs) of the TSGs and OCGs, as that may have an effect on the expression of the genes. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Mona Nourbakhsh [aut], Astrid Saksager [aut], Nikola Tom [aut], Katrine Meldgård [aut], Anna Melidi [aut], Xi Steven Chen [aut], Antonio Colaprico [aut], Catharina Olsen [aut], Alessia Campo [aut], Matteo Tiberti [cre, aut], Elena Papaleo [aut] Maintainer: Matteo Tiberti URL: https://github.com/ELELAB/Moonlight2R SystemRequirements: CScapeSomatic VignetteBuilder: knitr BugReports: https://github.com/ELELAB/Moonlight2R/issues git_url: https://git.bioconductor.org/packages/Moonlight2R git_branch: RELEASE_3_22 git_last_commit: 7f42b79 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Moonlight2R_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Moonlight2R_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Moonlight2R_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Moonlight2R_1.8.0.tgz vignettes: vignettes/Moonlight2R/inst/doc/Moonlight2R.html vignetteTitles: A workflow to study mechanistic indicators for driver gene prediction with Moonlight hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/Moonlight2R/inst/doc/Moonlight2R.R dependencyCount: 225 Package: MoonlightR Version: 1.36.0 Depends: R (>= 3.5), doParallel, foreach Imports: parmigene, randomForest, SummarizedExperiment, gplots, circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase, limma, grDevices, graphics, TCGAbiolinks, GEOquery, stats, RISmed, grid, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, png, edgeR License: GPL (>= 3) MD5sum: 4c66e5cfd7b153d93dba7b0175217283 NeedsCompilation: no Title: Identify oncogenes and tumor suppressor genes from omics data Description: Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Antonio Colaprico [aut], Catharina Olsen [aut], Matthew H. Bailey [aut], Gabriel J. Odom [aut], Thilde Terkelsen [aut], Mona Nourbakhsh [aut], Astrid Saksager [aut], Tiago C. Silva [aut], André V. Olsen [aut], Laura Cantini [aut], Andrei Zinovyev [aut], Emmanuel Barillot [aut], Houtan Noushmehr [aut], Gloria Bertoli [aut], Isabella Castiglioni [aut], Claudia Cava [aut], Gianluca Bontempi [aut], Xi Steven Chen [aut], Elena Papaleo [aut], Matteo Tiberti [cre, aut] Maintainer: Matteo Tiberti URL: https://github.com/ELELAB/MoonlightR VignetteBuilder: knitr BugReports: https://github.com/ELELAB/MoonlightR/issues git_url: https://git.bioconductor.org/packages/MoonlightR git_branch: RELEASE_3_22 git_last_commit: af4023f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MoonlightR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MoonlightR_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MoonlightR_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MoonlightR_1.36.0.tgz vignettes: vignettes/MoonlightR/inst/doc/Moonlight.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoonlightR/inst/doc/Moonlight.R dependencyCount: 188 Package: mosaics Version: 2.48.0 Depends: R (>= 3.0.0), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, Seqinfo, S4Vectors LinkingTo: Rcpp Suggests: mosaicsExample Enhances: parallel License: GPL (>= 2) MD5sum: 424e7a221bd65e63aed64f434d291e83 NeedsCompilation: yes Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Description: This package provides functions for fitting MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data of transcription factor binding and histone modification. biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics Author: Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles Maintainer: Dongjun Chung URL: http://groups.google.com/group/mosaics_user_group SystemRequirements: Perl git_url: https://git.bioconductor.org/packages/mosaics git_branch: RELEASE_3_22 git_last_commit: 80f962c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mosaics_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mosaics_2.47.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mosaics_2.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mosaics_2.48.0.tgz vignettes: vignettes/mosaics/inst/doc/mosaics-example.pdf vignetteTitles: MOSAiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mosaics/inst/doc/mosaics-example.R dependencyCount: 45 Package: mosbi Version: 1.16.0 Depends: R (>= 4.1) Imports: Rcpp, BH, xml2, methods, igraph, fabia, RcppParallel, biclust, isa2, QUBIC, akmbiclust, RColorBrewer LinkingTo: Rcpp, BH, RcppParallel Suggests: knitr, rmarkdown, BiocGenerics, runibic, BiocStyle, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE MD5sum: 7c99ece109c8cda6512eda6249904ee7 NeedsCompilation: yes Title: Molecular Signature identification using Biclustering Description: This package is a implementation of biclustering ensemble method MoSBi (Molecular signature Identification from Biclustering). MoSBi provides standardized interfaces for biclustering results and can combine their results with a multi-algorithm ensemble approach to compute robust ensemble biclusters on molecular omics data. This is done by computing similarity networks of biclusters and filtering for overlaps using a custom error model. After that, the louvain modularity it used to extract bicluster communities from the similarity network, which can then be converted to ensemble biclusters. Additionally, MoSBi includes several network visualization methods to give an intuitive and scalable overview of the results. MoSBi comes with several biclustering algorithms, but can be easily extended to new biclustering algorithms. biocViews: Software, StatisticalMethod, Clustering, Network Author: Tim Daniel Rose [cre, aut], Josch Konstantin Pauling [aut], Nikolai Koehler [aut] Maintainer: Tim Daniel Rose SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mosbi git_branch: RELEASE_3_22 git_last_commit: 798cd83 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mosbi_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mosbi_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mosbi_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mosbi_1.16.0.tgz vignettes: vignettes/mosbi/inst/doc/example-workflow.html, vignettes/mosbi/inst/doc/similarity-metrics-evaluation.html vignetteTitles: example-workflow, similarity-metrics-evaluation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mosbi/inst/doc/example-workflow.R, vignettes/mosbi/inst/doc/similarity-metrics-evaluation.R dependencyCount: 59 Package: MOSClip Version: 1.4.0 Depends: R (>= 4.4.0) Imports: MultiAssayExperiment, methods, survminer, graph, graphite, AnnotationDbi, checkmate, ggplot2, gridExtra, igraph, pheatmap, survival, RColorBrewer, SuperExactTest, reshape, NbClust, S4Vectors, grDevices, graphics, stats, utils, ComplexHeatmap, FactoMineR, circlize, corpcor, coxrobust, elasticnet, gRbase, ggplotify, qpgraph, org.Hs.eg.db, Matrix Suggests: RUnit, BiocGenerics, MASS, BiocStyle, knitr, EDASeq, rmarkdown, kableExtra, testthat (>= 3.0.0) License: AGPL-3 MD5sum: e66fb554d3011f5404c256e01f2a3990 NeedsCompilation: no Title: Multi Omics Survival Clip Description: Topological pathway analysis tool able to integrate multi-omics data. It finds survival-associated modules or significant modules for two-class analysis. This tool have two main methods: pathway tests and module tests. The latter method allows the user to dig inside the pathways itself. biocViews: Software, StatisticalMethod, GraphAndNetwork, Survival, Regression, DimensionReduction, Pathways, Reactome Author: Paolo Martini [aut, cre] (ORCID: ), Anna Bortolato [aut] (ORCID: ), Anna Tanada [aut] (ORCID: ), Enrica Calura [aut] (ORCID: ), Stefania Pirrotta [aut] (ORCID: ), Federico Agostinis [aut] Maintainer: Paolo Martini URL: https://github.com/CaluraLab/MOSClip/ VignetteBuilder: knitr BugReports: https://github.com/CaluraLab/MOSClip/issues git_url: https://git.bioconductor.org/packages/MOSClip git_branch: RELEASE_3_22 git_last_commit: b231f21 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MOSClip_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MOSClip_1.3.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MOSClip_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MOSClip_1.4.0.tgz vignettes: vignettes/MOSClip/inst/doc/mosclip_vignette.html vignetteTitles: MOSClip hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSClip/inst/doc/mosclip_vignette.R dependencyCount: 219 Package: mosdef Version: 1.6.0 Depends: R (>= 4.4.0) Imports: DT, ggplot2, ggforce, ggrepel, graphics, grDevices, htmltools, methods, AnnotationDbi, topGO, GO.db, clusterProfiler, goseq, utils, RColorBrewer, rlang, DESeq2, scales, SummarizedExperiment, S4Vectors, stats Suggests: knitr, rmarkdown, macrophage, org.Hs.eg.db, GeneTonic, testthat (>= 3.0.0), TxDb.Hsapiens.UCSC.hg38.knownGene, BiocStyle License: MIT + file LICENSE MD5sum: 38e5d6aebee963ec9b5927a0ff51b107 NeedsCompilation: no Title: MOSt frequently used and useful Differential Expression Functions Description: This package provides functionality to run a number of tasks in the differential expression analysis workflow. This encompasses the most widely used steps, from running various enrichment analysis tools with a unified interface to creating plots and beautifying table components linking to external websites and databases. This streamlines the generation of comprehensive analysis reports. biocViews: GeneExpression, Software, Transcription, Transcriptomics, DifferentialExpression, Visualization, ReportWriting, GeneSetEnrichment, GO Author: Leon Dammer [aut] (ORCID: ), Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/imbeimainz/mosdef VignetteBuilder: knitr BugReports: https://github.com/imbeimainz/mosdef/issues git_url: https://git.bioconductor.org/packages/mosdef git_branch: RELEASE_3_22 git_last_commit: b5bb63e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mosdef_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mosdef_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mosdef_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mosdef_1.6.0.tgz vignettes: vignettes/mosdef/inst/doc/mosdef_userguide.html vignetteTitles: The mosdef User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mosdef/inst/doc/mosdef_userguide.R importsMe: ideal, pcaExplorer suggestsMe: DeeDeeExperiment dependencyCount: 184 Package: MOSim Version: 2.6.0 Depends: R (>= 4.2.0) Imports: HiddenMarkov, zoo, IRanges, S4Vectors, dplyr, ggplot2, lazyeval, matrixStats, methods, rlang, stringi, stringr, scran, Seurat, Signac, edgeR, Rcpp LinkingTo: cpp11, Rcpp Suggests: testthat, knitr, rmarkdown, codetools, BiocStyle, stats, utils, purrr, scales, tibble, tidyr, Biobase, scater, SingleCellExperiment, decor, markdown, Rsamtools, igraph, leiden, bluster License: GPL-3 MD5sum: b02bb5652294be3caa3a52268d597573 NeedsCompilation: yes Title: Multi-Omics Simulation (MOSim) Description: MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups. biocViews: Software, TimeCourse, ExperimentalDesign, RNASeq Author: Carolina Monzó [aut], Carlos Martínez [aut], Sonia Tarazona [cre, aut] Maintainer: Sonia Tarazona URL: https://github.com/ConesaLab/MOSim VignetteBuilder: knitr BugReports: https://github.com/ConesaLab/MOSim/issues git_url: https://git.bioconductor.org/packages/MOSim git_branch: RELEASE_3_22 git_last_commit: 3eff748 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MOSim_2.6.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MOSim_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MOSim_2.6.0.tgz vignettes: vignettes/MOSim/inst/doc/MOSim.html, vignettes/MOSim/inst/doc/scMOSim.html vignetteTitles: Wiki of how to use mosim, Wiki of how to use sc_mosim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSim/inst/doc/MOSim.R, vignettes/MOSim/inst/doc/scMOSim.R dependencyCount: 196 Package: Motif2Site Version: 1.14.0 Depends: R (>= 4.1) Imports: S4Vectors, stats, utils, methods, grDevices, graphics, BiocGenerics, BSgenome, GenomeInfoDb, MASS, IRanges, GenomicRanges, Biostrings, GenomicAlignments, edgeR, mixtools Suggests: BiocStyle, rmarkdown, knitr, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Ecoli.NCBI.20080805 License: GPL-2 MD5sum: df0a29cf73dcdc6cc12ec03a1b70d339 NeedsCompilation: no Title: Detect binding sites from motifs and ChIP-seq experiments, and compare binding sites across conditions Description: Detect binding sites using motifs IUPAC sequence or bed coordinates and ChIP-seq experiments in bed or bam format. Combine/compare binding sites across experiments, tissues, or conditions. All normalization and differential steps are done using TMM-GLM method. Signal decomposition is done by setting motifs as the centers of the mixture of normal distribution curves. biocViews: Software, Sequencing, ChIPSeq, DifferentialPeakCalling, Epigenetics, SequenceMatching Author: Peyman Zarrineh [cre, aut] (ORCID: ) Maintainer: Peyman Zarrineh VignetteBuilder: knitr BugReports: https://github.com/fls-bioinformatics-core/Motif2Site/issues git_url: https://git.bioconductor.org/packages/Motif2Site git_branch: RELEASE_3_22 git_last_commit: ce0607c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Motif2Site_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Motif2Site_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Motif2Site_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Motif2Site_1.14.0.tgz vignettes: vignettes/Motif2Site/inst/doc/Motif2Site.html vignetteTitles: Motif2Site hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Motif2Site/inst/doc/Motif2Site.R dependencyCount: 124 Package: motifbreakR Version: 2.24.0 Depends: R (>= 4.4.0), grid, MotifDb Imports: methods, grDevices, stringr, parallel, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Biostrings, BSgenome, rtracklayer, VariantAnnotation, BiocParallel, motifStack, Gviz, matrixStats, TFMPvalue, SummarizedExperiment, pwalign, DT, bsicons, BiocFileCache, biomaRt, bslib, shiny, vroom Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP155.GRCh37, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer7, BiocStyle, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Hsapiens.UCSC.hg38 License: GPL-2 Archs: x64 MD5sum: 2a0ac301b9290636a2b3ef07b0558303 NeedsCompilation: no Title: A Package For Predicting The Disruptiveness Of Single Nucleotide Polymorphisms On Transcription Factor Binding Sites Description: We introduce motifbreakR, which allows the biologist to judge in the first place whether the sequence surrounding the polymorphism is a good match, and in the second place how much information is gained or lost in one allele of the polymorphism relative to another. MotifbreakR is both flexible and extensible over previous offerings; giving a choice of algorithms for interrogation of genomes with motifs from public sources that users can choose from; these are 1) a weighted-sum probability matrix, 2) log-probabilities, and 3) weighted by relative entropy. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within Bioconductor (currently there are 32 species, a total of 109 versions). biocViews: ChIPSeq, Visualization, MotifAnnotation, Transcription Author: Simon Gert Coetzee [aut, cre] (ORCID: ), Dennis J. Hazelett [aut] Maintainer: Simon Gert Coetzee VignetteBuilder: knitr BugReports: https://github.com/Simon-Coetzee/motifbreakR/issues git_url: https://git.bioconductor.org/packages/motifbreakR git_branch: RELEASE_3_22 git_last_commit: 79ca231 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/motifbreakR_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/motifbreakR_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/motifbreakR_2.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/motifbreakR_2.24.0.tgz vignettes: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html vignetteTitles: motifbreakR: an Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.R dependencyCount: 179 Package: motifcounter Version: 1.34.0 Depends: R(>= 3.0) Imports: Biostrings, methods Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc License: GPL-2 MD5sum: bc87846c1f9648ef1bc2de1bbee31f34 NeedsCompilation: yes Title: R package for analysing TFBSs in DNA sequences Description: 'motifcounter' provides motif matching, motif counting and motif enrichment functionality based on position frequency matrices. The main features of the packages include the utilization of higher-order background models and accounting for self-overlapping motif matches when determining motif enrichment. The background model allows to capture dinucleotide (or higher-order nucleotide) composition adequately which may reduced model biases and misleading results compared to using simple GC background models. When conducting a motif enrichment analysis based on the motif match count, the package relies on a compound Poisson distribution or alternatively a combinatorial model. These distribution account for self-overlapping motif structures as exemplified by repeat-like or palindromic motifs, and allow to determine the p-value and fold-enrichment for a set of observed motif matches. biocViews: Transcription,MotifAnnotation,SequenceMatching,Software Author: Wolfgang Kopp [aut, cre] Maintainer: Wolfgang Kopp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifcounter git_branch: RELEASE_3_22 git_last_commit: 64fa306 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/motifcounter_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/motifcounter_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/motifcounter_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/motifcounter_1.34.0.tgz vignettes: vignettes/motifcounter/inst/doc/motifcounter.html vignetteTitles: Introduction to the `motifcounter` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifcounter/inst/doc/motifcounter.R dependencyCount: 15 Package: MotifDb Version: 1.52.0 Depends: R (>= 3.5.0), methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings Imports: rtracklayer, splitstackshape Suggests: RUnit, seqLogo, BiocStyle, knitr, rmarkdown, formatR, markdown License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: 53b7d86f9d2df0bc519ef8435b2272a4 NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 9900 annotated position frequency matrices from 14 public sources, for multiple organisms. biocViews: MotifAnnotation Author: Paul Shannon, Matt Richards Maintainer: Paul Shannon VignetteBuilder: knitr, rmarkdown, formatR, markdown git_url: https://git.bioconductor.org/packages/MotifDb git_branch: RELEASE_3_22 git_last_commit: 84d7458 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MotifDb_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MotifDb_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MotifDb_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MotifDb_1.52.0.tgz vignettes: vignettes/MotifDb/inst/doc/MotifDb.html vignetteTitles: "A collection of PWMs" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R dependsOnMe: motifbreakR, generegulation importsMe: rTRMui, TENET suggestsMe: ATACseqQC, DiffLogo, enhancerHomologSearch, igvR, memes, MMDiff2, motifcounter, motifStack, motifTestR, profileScoreDist, PWMEnrich, rTRM, TFutils, universalmotif, vtpnet dependencyCount: 59 Package: motifmatchr Version: 1.32.0 Depends: R (>= 3.3) Imports: Matrix, Rcpp, methods, TFBSTools, Biostrings, BSgenome, S4Vectors, SummarizedExperiment, GenomicRanges, IRanges, Rsamtools, Seqinfo LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 + file LICENSE Archs: x64 MD5sum: 3287cf01b93c9965ea1875a273fc6c48 NeedsCompilation: yes Title: Fast Motif Matching in R Description: Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. biocViews: MotifAnnotation Author: Alicia Schep [aut, cre], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifmatchr git_branch: RELEASE_3_22 git_last_commit: ef46b48 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/motifmatchr_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/motifmatchr_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/motifmatchr_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/motifmatchr_1.32.0.tgz vignettes: vignettes/motifmatchr/inst/doc/motifmatchr.html vignetteTitles: motifmatchr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/motifmatchr/inst/doc/motifmatchr.R importsMe: ATACseqTFEA, enhancerHomologSearch, epiregulon, esATAC, pageRank suggestsMe: chromVAR, GRaNIE, MethReg, CAGEWorkflow, Signac dependencyCount: 82 Package: MotifPeeker Version: 1.2.0 Depends: R (>= 4.4.0) Imports: BiocFileCache, BiocParallel, DT, ggplot2, plotly, universalmotif, GenomicRanges, IRanges, rtracklayer, tools, htmltools, rmarkdown, viridis, SummarizedExperiment, htmlwidgets, Rsamtools, GenomicAlignments, Seqinfo, Biostrings, BSgenome, memes, S4Vectors, dplyr, purrr, tidyr, heatmaply, stats, utils Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, downloadthis, knitr, markdown, methods, remotes, rworkflows, testthat (>= 3.0.0), withr, emoji, curl, jsonlite License: GPL (>= 3) MD5sum: b01824afdf3d530c8e7c1a828a368ea8 NeedsCompilation: no Title: Benchmarking Epigenomic Profiling Methods Using Motif Enrichment Description: MotifPeeker is used to compare and analyse datasets from epigenomic profiling methods with motif enrichment as the key benchmark. The package outputs an HTML report consisting of three sections: (1. General Metrics) Overview of peaks-related general metrics for the datasets (FRiP scores, peak widths and motif-summit distances). (2. Known Motif Enrichment Analysis) Statistics for the frequency of user-provided motifs enriched in the datasets. (3. De-Novo Motif Enrichment Analysis) Statistics for the frequency of de-novo discovered motifs enriched in the datasets and compared with known motifs. biocViews: Epigenetics, Genetics, QualityControl, ChIPSeq, MultipleComparison, FunctionalGenomics, MotifDiscovery, SequenceMatching, Software, Alignment Author: Hiranyamaya Dash [cre, aut] (ORCID: ), Thomas Roberts [aut] (ORCID: ), Maria Weinert [aut] (ORCID: ), Nathan Skene [aut] (ORCID: ) Maintainer: Hiranyamaya Dash URL: https://github.com/neurogenomics/MotifPeeker SystemRequirements: MEME Suite (v5.3.3 or above) VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/MotifPeeker/issues git_url: https://git.bioconductor.org/packages/MotifPeeker git_branch: RELEASE_3_22 git_last_commit: aee3e06 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MotifPeeker_1.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MotifPeeker_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MotifPeeker_1.2.0.tgz vignettes: vignettes/MotifPeeker/inst/doc/MotifPeeker.html, vignettes/MotifPeeker/inst/doc/troubleshooting.html vignetteTitles: MotifPeeker, troubleshooting hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/MotifPeeker/inst/doc/MotifPeeker.R, vignettes/MotifPeeker/inst/doc/troubleshooting.R dependencyCount: 186 Package: motifStack Version: 1.54.0 Depends: R (>= 2.15.1), methods, grid Imports: ade4, Biostrings, ggplot2, grDevices, graphics, htmlwidgets, stats, stats4, utils, XML, TFBSTools Suggests: Cairo, grImport, grImport2, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr, RUnit, rmarkdown, JASPAR2020 License: GPL (>= 2) Archs: x64 MD5sum: b334fda15ba2bbb4eaaed2c8055e2c9d NeedsCompilation: no Title: Plot stacked logos for single or multiple DNA, RNA and amino acid sequence Description: The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motif and amino acid sequence motif. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors. biocViews: SequenceMatching, Visualization, Sequencing, Microarray, Alignment, ChIPchip, ChIPSeq, MotifAnnotation, DataImport Author: Jianhong Ou, Michael Brodsky, Scot Wolfe and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifStack git_branch: RELEASE_3_22 git_last_commit: 6d8937d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/motifStack_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/motifStack_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/motifStack_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/motifStack_1.54.0.tgz vignettes: vignettes/motifStack/inst/doc/motifStack_HTML.html vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R dependsOnMe: generegulation importsMe: ATACseqQC, atSNP, dagLogo, motifbreakR, ribosomeProfilingQC suggestsMe: ChIPpeakAnno, TFutils, trackViewer, tripr, universalmotif dependencyCount: 112 Package: motifTestR Version: 1.6.0 Depends: Biostrings, GenomicRanges, ggplot2 (>= 4.0.0), R (>= 4.5.0), Imports: Seqinfo, graphics, harmonicmeanp, IRanges, matrixStats, methods, parallel, patchwork, rlang, S4Vectors, stats, universalmotif, Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, extraChIPs (>= 1.13.3), ggdendro, knitr, MASS, MotifDb, rmarkdown, rtracklayer, SimpleUpset, testthat (>= 3.0.0), VGAM License: GPL-3 MD5sum: b2e5d490d117b670e88778633c51be2e NeedsCompilation: no Title: Perform key tests for binding motifs in sequence data Description: Taking a set of sequence motifs as PWMs, test a set of sequences for over-representation of these motifs, as well as any positional features within the set of motifs. Enrichment analysis can be undertaken using multiple statistical approaches. The package also contains core functions to prepare data for analysis, and to visualise results. biocViews: MotifAnnotation, ChIPSeq, ChipOnChip, SequenceMatching, Software Author: Stevie Pederson [aut, cre] (ORCID: ) Maintainer: Stevie Pederson URL: https://github.com/smped/motifTestR VignetteBuilder: knitr BugReports: https://github.com/smped/motifTestR/issues git_url: https://git.bioconductor.org/packages/motifTestR git_branch: RELEASE_3_22 git_last_commit: 1ae0d82 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/motifTestR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/motifTestR_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/motifTestR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/motifTestR_1.6.0.tgz vignettes: vignettes/motifTestR/inst/doc/motifAnalysis.html vignetteTitles: Motif Analysis Using motifTestR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifTestR/inst/doc/motifAnalysis.R dependencyCount: 44 Package: MouseFM Version: 1.20.0 Depends: R (>= 4.0.0) Imports: httr, curl, GenomicRanges, dplyr, ggplot2, reshape2, scales, gtools, tidyr, data.table, jsonlite, rlist, Seqinfo, methods, biomaRt, stats, IRanges Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 349f1f02049420720e3e1022e5901981 NeedsCompilation: no Title: In-silico methods for genetic finemapping in inbred mice Description: This package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%). biocViews: Genetics, SNP, GeneTarget, VariantAnnotation, GenomicVariation, MultipleComparison, SystemsBiology, MathematicalBiology, PatternLogic, GenePrediction, BiomedicalInformatics, FunctionalGenomics Author: Matthias Munz [aut, cre] (ORCID: ), Inken Wohlers [aut] (ORCID: ), Hauke Busch [aut] (ORCID: ) Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/MouseFM/issues git_url: https://git.bioconductor.org/packages/MouseFM git_branch: RELEASE_3_22 git_last_commit: d76d60a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MouseFM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MouseFM_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MouseFM_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MouseFM_1.20.0.tgz vignettes: vignettes/MouseFM/inst/doc/fetch.html, vignettes/MouseFM/inst/doc/finemap.html, vignettes/MouseFM/inst/doc/prio.html vignetteTitles: Fetch, Finemapping, Prioritization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MouseFM/inst/doc/fetch.R, vignettes/MouseFM/inst/doc/finemap.R, vignettes/MouseFM/inst/doc/prio.R dependencyCount: 83 Package: MPAC Version: 1.4.0 Depends: R (>= 4.4.0) Imports: data.table (>= 1.14.2), SummarizedExperiment (>= 1.30.2), BiocParallel (>= 1.28.3), fitdistrplus (>= 1.1), igraph (>= 1.4.3), BiocSingular (>= 1.10.0), S4Vectors (>= 0.32.3), SingleCellExperiment (>= 1.16.0), bluster (>= 1.4.0), fgsea (>= 1.20.0), scran (>= 1.22.1), ComplexHeatmap (>= 2.16.0), circlize (>= 0.4.16), scales (>= 1.3.0), stringr (>= 1.5.1), viridis (>= 0.6.5), ggplot2 (>= 3.5.1), ggraph (>= 2.2.1), survival (>= 3.7), survminer (>= 0.4.9), grid, stats Suggests: rmarkdown, knitr, svglite, bookdown(>= 0.34), testthat (>= 3.0.0) License: GPL-3 MD5sum: 74e2614023d34f5c1a8e1e04a95589eb NeedsCompilation: no Title: Multi-omic Pathway Analysis of Cells Description: Multi-omic Pathway Analysis of Cells (MPAC), integrates multi-omic data for understanding cellular mechanisms. It predicts novel patient groups with distinct pathway profiles as well as identifying key pathway proteins with potential clinical associations. From CNA and RNA-seq data, it determines genes’ DNA and RNA states (i.e., repressed, normal, or activated), which serve as the input for PARADIGM to calculate Inferred Pathway Levels (IPLs). It also permutes DNA and RNA states to create a background distribution to filter IPLs as a way to remove events observed by chance. It provides multiple methods for downstream analysis and visualization. biocViews: Software, Technology, Sequencing, RNASeq, Survival, Clustering, ImmunoOncology Author: Peng Liu [aut, cre] (ORCID: ), Paul Ahlquist [aut], Irene Ong [aut], Anthony Gitter [aut] Maintainer: Peng Liu URL: https://github.com/pliu55/MPAC VignetteBuilder: knitr BugReports: https://github.com/pliu55/MPAC/issues git_url: https://git.bioconductor.org/packages/MPAC git_branch: RELEASE_3_22 git_last_commit: 778e22d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MPAC_1.4.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MPAC_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MPAC_1.4.0.tgz vignettes: vignettes/MPAC/inst/doc/MPAC.html vignetteTitles: MPAC: Multi-omic Pathway Analysis of Cells hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPAC/inst/doc/MPAC.R dependencyCount: 174 Package: MPFE Version: 1.46.0 License: GPL (>= 3) MD5sum: dc5d2b0d04a7531cfc47be5c62c59948 NeedsCompilation: no Title: Estimation of the amplicon methylation pattern distribution from bisulphite sequencing data Description: Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and read error rate. biocViews: HighThroughputSequencingData, DNAMethylation, MethylSeq Author: Peijie Lin, Sylvain Foret, Conrad Burden Maintainer: Conrad Burden git_url: https://git.bioconductor.org/packages/MPFE git_branch: RELEASE_3_22 git_last_commit: d80a98f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MPFE_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MPFE_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MPFE_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MPFE_1.46.0.tgz vignettes: vignettes/MPFE/inst/doc/MPFE.pdf vignetteTitles: MPFE hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPFE/inst/doc/MPFE.R dependencyCount: 0 Package: mpra Version: 1.32.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, limma Imports: S4Vectors, scales, stats, graphics, statmod Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 78fdb827c21c84eced61f6373b19eb89 NeedsCompilation: no Title: Analyze massively parallel reporter assays Description: Tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA). biocViews: Software, GeneRegulation, Sequencing, FunctionalGenomics Author: Leslie Myint [cre, aut], Kasper D. Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/mpra VignetteBuilder: knitr BugReports: https://github.com/hansenlab/mpra/issues git_url: https://git.bioconductor.org/packages/mpra git_branch: RELEASE_3_22 git_last_commit: 43074f5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mpra_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mpra_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mpra_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mpra_1.32.0.tgz vignettes: vignettes/mpra/inst/doc/mpra.html vignetteTitles: mpra User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mpra/inst/doc/mpra.R dependencyCount: 37 Package: MPRAnalyze Version: 1.28.0 Imports: BiocParallel, methods, progress, stats, SummarizedExperiment Suggests: knitr License: GPL-3 MD5sum: c7f277cd73480d7fa8d73676f50d6ebd NeedsCompilation: no Title: Statistical Analysis of MPRA data Description: MPRAnalyze provides statistical framework for the analysis of data generated by Massively Parallel Reporter Assays (MPRAs), used to directly measure enhancer activity. MPRAnalyze can be used for quantification of enhancer activity, classification of active enhancers and comparative analyses of enhancer activity between conditions. MPRAnalyze construct a nested pair of generalized linear models (GLMs) to relate the DNA and RNA observations, easily adjustable to various experimental designs and conditions, and provides a set of rigorous statistical testig schemes. biocViews: ImmunoOncology, Software, StatisticalMethod, Sequencing, GeneExpression, CellBiology, CellBasedAssays, DifferentialExpression, ExperimentalDesign, Classification Author: Tal Ashuach [aut, cre], David S Fischer [aut], Anat Kriemer [ctb], Fabian J Theis [ctb], Nir Yosef [ctb], Maintainer: Tal Ashuach URL: https://github.com/YosefLab/MPRAnalyze VignetteBuilder: knitr BugReports: https://github.com/YosefLab/MPRAnalyze git_url: https://git.bioconductor.org/packages/MPRAnalyze git_branch: RELEASE_3_22 git_last_commit: 6417179 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MPRAnalyze_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MPRAnalyze_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MPRAnalyze_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MPRAnalyze_1.28.0.tgz vignettes: vignettes/MPRAnalyze/inst/doc/vignette.html vignetteTitles: Analyzing MPRA data with MPRAnalyze hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPRAnalyze/inst/doc/vignette.R dependencyCount: 46 Package: msa Version: 1.42.0 Depends: R (>= 3.3.0), methods, Biostrings (>= 2.40.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr, seqinr, ape (>= 5.1), phangorn, pwalign License: GPL (>= 2) MD5sum: 25fd612fbd98d45aede78f9e596998aa NeedsCompilation: yes Title: Multiple Sequence Alignment Description: The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade. biocViews: MultipleSequenceAlignment, Alignment, MultipleComparison, Sequencing Author: Enrico Bonatesta [aut], Christoph Kainrath [aut], Ulrich Bodenhofer [aut, cre, ths] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/msa SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/msa git_branch: RELEASE_3_22 git_last_commit: 92481b4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/msa_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msa_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msa_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/msa_1.42.0.tgz vignettes: vignettes/msa/inst/doc/msa.pdf vignetteTitles: msa - An R Package for Multiple Sequence Alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msa/inst/doc/msa.R importsMe: DspikeIn, LymphoSeq, odseq, rhinotypeR, surfaltr suggestsMe: idpr, AntibodyForests, bio3d, BOLDconnectR, SVAlignR dependencyCount: 16 Package: MSA2dist Version: 1.14.0 Depends: R (>= 4.4.0) Imports: Rcpp, Biostrings, GenomicRanges, IRanges, ape, doParallel, dplyr, foreach, methods, parallel, pwalign, rlang, seqinr, stats, stringi, stringr, tibble, tidyr, utils LinkingTo: Rcpp, RcppThread Suggests: rmarkdown, knitr, devtools, testthat, ggplot2, BiocStyle License: GPL-3 + file LICENSE MD5sum: 43cbb6ef7cdd1a2827b085aea393670e NeedsCompilation: yes Title: MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis Description: MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis. It uses scoring matrices to be used in these pairwise distance calculations which can be adapted to any scoring for DNA or AA characters. E.g. by using literal distances MSA2dist calculates pairwise IUPAC distances. DNAStringSet alignments can be analysed as codon alignments to look for synonymous and nonsynonymous substitutions (dN/dS) in a parallelised fashion using a variety of substitution models. Non-aligned coding sequences can be directly used to construct pairwise codon alignments (global/local) and calculate dN/dS without any external dependencies. biocViews: Alignment, Sequencing, Genetics, GO Author: Kristian K Ullrich [aut, cre] (ORCID: ) Maintainer: Kristian K Ullrich URL: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist, https://mpievolbio-it.pages.gwdg.de/MSA2dist/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist/issues git_url: https://git.bioconductor.org/packages/MSA2dist git_branch: RELEASE_3_22 git_last_commit: 010a9f3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSA2dist_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSA2dist_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSA2dist_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSA2dist_1.14.0.tgz vignettes: vignettes/MSA2dist/inst/doc/MSA2dist.html vignetteTitles: MSA2dist Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MSA2dist/inst/doc/MSA2dist.R importsMe: doubletrouble, rhinotypeR dependencyCount: 55 Package: MsBackendMassbank Version: 1.18.0 Depends: R (>= 4.0), Spectra (>= 1.15.10) Imports: BiocParallel, S4Vectors, IRanges, methods, ProtGenerics (>= 1.35.3), MsCoreUtils, DBI, utils Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), RSQLite, rmarkdown License: Artistic-2.0 MD5sum: e29af42086ac50bade806b3737dd6578 NeedsCompilation: no Title: Mass Spectrometry Data Backend for MassBank record Files Description: Mass spectrometry (MS) data backend supporting import and export of MS/MS library spectra from MassBank record files. Different backends are available that allow handling of data in plain MassBank text file format or allow also to interact directly with MassBank SQL databases. Objects from this package are supposed to be used with the Spectra Bioconductor package. This package thus adds MassBank support to the Spectra package. biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport Author: RforMassSpectrometry Package Maintainer [cre], Michael Witting [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Michael Stravs [ctb] Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsBackendMassbank VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMassbank/issues git_url: https://git.bioconductor.org/packages/MsBackendMassbank git_branch: RELEASE_3_22 git_last_commit: 1be9f71 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MsBackendMassbank_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendMassbank_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMassbank_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMassbank_1.18.0.tgz vignettes: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.html vignetteTitles: Description and usage of MsBackendMassbank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.R dependencyCount: 31 Package: MsBackendMetaboLights Version: 1.4.0 Depends: R (>= 4.2.0), Spectra (>= 1.15.12) Imports: curl, ProtGenerics, BiocFileCache, S4Vectors, methods, progress Suggests: testthat, rmarkdown, mzR, knitr, BiocStyle License: Artistic-2.0 MD5sum: a5f711342f7c200c3005c9cec291d543 NeedsCompilation: no Title: Retrieve Mass Spectrometry Data from MetaboLights Description: MetaboLights is one of the main public repositories for storage of metabolomics experiments, which includes analysis results as well as raw data. The MsBackendMetaboLights package provides functionality to retrieve and represent mass spectrometry (MS) data from MetaboLights. Data files are downloaded and cached locally avoiding repetitive downloads. MS data from metabolomics experiments can thus be directly and seamlessly integrated into R-based analysis workflows with the Spectra and MsBackendMetaboLights package. biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport, Proteomics Author: Johannes Rainer [aut, cre] (ORCID: ), Philippine Louail [aut] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsBackendMetaboLights VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMetaboLights/issues git_url: https://git.bioconductor.org/packages/MsBackendMetaboLights git_branch: RELEASE_3_22 git_last_commit: b2bca58 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MsBackendMetaboLights_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendMetaboLights_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMetaboLights_1.3.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMetaboLights_1.4.0.tgz vignettes: vignettes/MsBackendMetaboLights/inst/doc/MsBackendMetaboLights.html vignetteTitles: Retrieve and Use Mass Spectrometry Data from MetaboLights hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMetaboLights/inst/doc/MsBackendMetaboLights.R suggestsMe: Chromatograms dependencyCount: 70 Package: MsBackendMgf Version: 1.18.0 Depends: R (>= 4.0), Spectra (>= 1.5.14) Imports: ProtGenerics (>= 1.35.3), BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 MD5sum: 2c5ee1e763557d924c902a3e1d5e77ff NeedsCompilation: no Title: Mass Spectrometry Data Backend for Mascot Generic Format (mgf) Files Description: Mass spectrometry (MS) data backend supporting import and export of MS/MS spectra data from Mascot Generic Format (mgf) files. Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, DataImport Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Michael Witting [ctb] (ORCID: ), Adriano Rutz [ctb] (ORCID: ), Corey Broeckling [ctb] (ORCID: ) Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsBackendMgf VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMgf/issues git_url: https://git.bioconductor.org/packages/MsBackendMgf git_branch: RELEASE_3_22 git_last_commit: 6656145 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MsBackendMgf_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendMgf_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMgf_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMgf_1.18.0.tgz vignettes: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.html vignetteTitles: Description and usage of MsBackendMgf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.R suggestsMe: CompoundDb, MsBackendRawFileReader, SpectriPy, xcms dependencyCount: 30 Package: MsBackendMsp Version: 1.14.0 Depends: R (>= 4.1.0), Spectra (>= 1.5.14) Imports: ProtGenerics (>= 1.35.3), BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 MD5sum: 5afad68330fe13f7bd928e466f92efaf NeedsCompilation: no Title: Mass Spectrometry Data Backend for NIST msp Files Description: Mass spectrometry (MS) data backend supporting import and handling of MS/MS spectra from NIST MSP Format (msp) files. Import of data from files with different MSP *flavours* is supported. Objects from this package add support for MSP files to Bioconductor's Spectra package. This package is thus not supposed to be used without the Spectra package that provides a complete infrastructure for MS data handling. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, DataImport Author: Neumann Steffen [aut] (ORCID: ), Johannes Rainer [aut, cre] (ORCID: ), Michael Witting [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsBackendMsp VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMsp/issues git_url: https://git.bioconductor.org/packages/MsBackendMsp git_branch: RELEASE_3_22 git_last_commit: 58e9f8b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MsBackendMsp_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendMsp_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMsp_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMsp_1.14.0.tgz vignettes: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.html vignetteTitles: MsBackendMsp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.R dependencyCount: 30 Package: MsBackendRawFileReader Version: 1.16.0 Depends: R (>= 4.1), methods, Spectra (>= 1.15.10) Imports: ProtGenerics (>= 1.35.3), MsCoreUtils, S4Vectors, IRanges, rawrr (>= 1.17.2), utils, BiocParallel Suggests: BiocStyle (>= 2.5), ExperimentHub, MsBackendMgf, knitr, lattice, mzR, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 Archs: x64 MD5sum: 9ab835174e86cb9310192a07329bc227 NeedsCompilation: yes Title: Mass Spectrometry Backend for Reading Thermo Fisher Scientific raw Files Description: implements a MsBackend for the Spectra package using Thermo Fisher Scientific's NewRawFileReader .Net libraries. The package is generalizing the functionality introduced by the rawrr package Methods defined in this package are supposed to extend the Spectra Bioconductor package. biocViews: MassSpectrometry, Proteomics, Metabolomics Author: Christian Panse [aut, cre] (ORCID: ), Tobias Kockmann [aut] (ORCID: ), Roger Gine Bertomeu [ctb] (ORCID: ) Maintainer: Christian Panse URL: https://github.com/fgcz/MsBackendRawFileReader SystemRequirements: mono-runtime 4.x or higher (including System.Data library) on Linux/macOS, .Net Framework (>= 4.5.1) on Microsoft Windows. VignetteBuilder: knitr BugReports: https://github.com/fgcz/MsBackendRawFileReader/issues git_url: https://git.bioconductor.org/packages/MsBackendRawFileReader git_branch: RELEASE_3_22 git_last_commit: 53064fe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MsBackendRawFileReader_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendRawFileReader_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendRawFileReader_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendRawFileReader_1.16.0.tgz vignettes: vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.html vignetteTitles: On Using and Extending the `MsBackendRawFileReader` Backend. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.R dependencyCount: 31 Package: MsBackendSql Version: 1.10.0 Depends: R (>= 4.2.0), Spectra (>= 1.19.8) Imports: BiocParallel, S4Vectors, methods, ProtGenerics (>= 1.35.3), DBI, MsCoreUtils, IRanges, data.table, progress, stringi, fastmatch, BiocGenerics Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), RSQLite, msdata, rmarkdown, microbenchmark, mzR License: Artistic-2.0 MD5sum: 2a99a4b267f4173484728f94d4b65371 NeedsCompilation: no Title: SQL-based Mass Spectrometry Data Backend Description: SQL-based mass spectrometry (MS) data backend supporting also storange and handling of very large data sets. Objects from this package are supposed to be used with the Spectra Bioconductor package. Through the MsBackendSql with its minimal memory footprint, this package thus provides an alternative MS data representation for very large or remote MS data sets. biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport, Proteomics Author: Johannes Rainer [aut, cre] (ORCID: ), Chong Tang [ctb], Laurent Gatto [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsBackendSql VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendSql/issues git_url: https://git.bioconductor.org/packages/MsBackendSql git_branch: RELEASE_3_22 git_last_commit: 1d3a770 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MsBackendSql_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendSql_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendSql_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendSql_1.10.0.tgz vignettes: vignettes/MsBackendSql/inst/doc/MsBackendSql.html vignetteTitles: Storing Mass Spectrometry Data in SQL Databases hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendSql/inst/doc/MsBackendSql.R suggestsMe: MsExperiment dependencyCount: 45 Package: MsCoreUtils Version: 1.21.0 Depends: R (>= 3.6.0) Imports: methods, S4Vectors, MASS, stats, clue LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, roxygen2, imputeLCMD, impute, norm, pcaMethods, vsn, Matrix, preprocessCore, missForest Enhances: HDF5Array License: Artistic-2.0 MD5sum: d1787ba81fed9d9ff1c6a79c6a758943 NeedsCompilation: yes Title: Core Utils for Mass Spectrometry Data Description: MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...), misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Adriaan Sticker [ctb], Sigurdur Smarason [ctb], Thomas Naake [ctb], Josep Maria Badia Aparicio [ctb] (ORCID: ), Michael Witting [ctb] (ORCID: ), Samuel Wieczorek [ctb], Roger Gine Bertomeu [ctb] (ORCID: ), Mar Garcia-Aloy [ctb] (ORCID: ) Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsCoreUtils VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsCoreUtils/issues git_url: https://git.bioconductor.org/packages/MsCoreUtils git_branch: devel git_last_commit: 3181c2a git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/MsCoreUtils_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsCoreUtils_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsCoreUtils_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsCoreUtils_1.21.0.tgz vignettes: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.html vignetteTitles: Core Utils for Mass Spectrometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.R importsMe: Chromatograms, CompoundDb, hdxmsqc, MetaboAnnotation, MetaboCoreUtils, MetCirc, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MsFeatures, MSnbase, PSMatch, QFeatures, qmtools, scp, SmartPhos, Spectra, SpectraQL, SpectriPy, xcms suggestsMe: MetNet, msqrob2 dependencyCount: 13 Package: MsDataHub Version: 1.10.0 Imports: ExperimentHub, utils Suggests: ExperimentHubData, DT, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), Spectra, mzR, PSMatch, QFeatures (>= 1.13.3) License: Artistic-2.0 MD5sum: 4ad62928fb49233f7aefc50a560ad9df NeedsCompilation: no Title: Mass Spectrometry Data on ExperimentHub Description: The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments. biocViews: ExperimentHubSoftware, MassSpectrometry, Proteomics, Metabolomics Author: Laurent Gatto [aut, cre] (ORCID: ), Kristina Gomoryova [ctb] (ORCID: ), Johannes Rainer [aut] (ORCID: ) Maintainer: Laurent Gatto URL: https://rformassspectrometry.github.io/MsDataHub VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsDataHub/issues git_url: https://git.bioconductor.org/packages/MsDataHub git_branch: RELEASE_3_22 git_last_commit: c362bfe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MsDataHub_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsDataHub_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsDataHub_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsDataHub_1.10.0.tgz vignettes: vignettes/MsDataHub/inst/doc/MsDataHub.html vignetteTitles: Mass Spectrometry Data on ExperimentHub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsDataHub/inst/doc/MsDataHub.R suggestsMe: msqrob2, QFeatures, scp, SpectriPy dependencyCount: 65 Package: MsExperiment Version: 1.12.0 Depends: R (>= 4.2), ProtGenerics (>= 1.35.2), Imports: methods, S4Vectors, IRanges, Spectra, SummarizedExperiment, QFeatures, DBI, BiocGenerics Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown, rpx, mzR, msdata, MsBackendSql (>= 1.3.2), RSQLite License: Artistic-2.0 MD5sum: 209e50be728a1eae1792a9f6067ff698 NeedsCompilation: no Title: Infrastructure for Mass Spectrometry Experiments Description: Infrastructure to store and manage all aspects related to a complete proteomics or metabolomics mass spectrometry (MS) experiment. The MsExperiment package provides light-weight and flexible containers for MS experiments building on the new MS infrastructure provided by the Spectra, QFeatures and related packages. Along with raw data representations, links to original data files and sample annotations, additional metadata or annotations can also be stored within the MsExperiment container. To guarantee maximum flexibility only minimal constraints are put on the type and content of the data within the containers. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, ExperimentalDesign, DataImport Author: Laurent Gatto [aut, cre] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Tuomas Borman [ctb] (ORCID: ) Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/MsExperiment VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsExperiment/issues git_url: https://git.bioconductor.org/packages/MsExperiment git_branch: RELEASE_3_22 git_last_commit: 85369cf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MsExperiment_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsExperiment_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsExperiment_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsExperiment_1.12.0.tgz vignettes: vignettes/MsExperiment/inst/doc/MsExperiment.html vignetteTitles: Managing Mass Spectrometry Experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsExperiment/inst/doc/MsExperiment.R importsMe: MsQuality, squallms, xcms dependencyCount: 112 Package: MsFeatures Version: 1.18.0 Depends: R (>= 4.0) Imports: methods, ProtGenerics (>= 1.23.5), MsCoreUtils, SummarizedExperiment, stats Suggests: testthat, roxygen2, BiocStyle, pheatmap, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 32f901b7d2cfba19b7d1f6840f368f8a NeedsCompilation: no Title: Functionality for Mass Spectrometry Features Description: The MsFeature package defines functionality for Mass Spectrometry features. This includes functions to group (LC-MS) features based on some of their properties, such as retention time (coeluting features), or correlation of signals across samples. This packge hence allows to group features, and its results can be used as an input for the `QFeatures` package which allows to aggregate abundance levels of features within each group. This package defines concepts and functions for base and common data types, implementations for more specific data types are expected to be implemented in the respective packages (such as e.g. `xcms`). All functionality of this package is implemented in a modular way which allows combination of different grouping approaches and enables its re-use in other R packages. biocViews: Infrastructure, MassSpectrometry, Metabolomics Author: Johannes Rainer [aut, cre] (ORCID: ), Johan Lassen [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsFeatures VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsFeatures/issues git_url: https://git.bioconductor.org/packages/MsFeatures git_branch: RELEASE_3_22 git_last_commit: ac1808c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MsFeatures_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsFeatures_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsFeatures_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsFeatures_1.18.0.tgz vignettes: vignettes/MsFeatures/inst/doc/MsFeatures.html vignetteTitles: Grouping Mass Spectrometry Features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsFeatures/inst/doc/MsFeatures.R importsMe: xcms suggestsMe: qmtools dependencyCount: 31 Package: msgbsR Version: 1.34.0 Depends: R (>= 3.5.0), GenomicRanges, methods Imports: BSgenome, easyRNASeq, edgeR, GenomicAlignments, GenomicFeatures, Seqinfo, ggbio, ggplot2, IRanges, parallel, plyr, Rsamtools, R.utils, stats, SummarizedExperiment, S4Vectors, utils Suggests: roxygen2, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-2 Archs: x64 MD5sum: fa70ae3507dc044490ccd16110a73769 NeedsCompilation: no Title: msgbsR: methylation sensitive genotyping by sequencing (MS-GBS) R functions Description: Pipeline for the anaysis of a MS-GBS experiment. biocViews: ImmunoOncology, DifferentialMethylation, DataImport, Epigenetics, MethylSeq Author: Benjamin Mayne Maintainer: Benjamin Mayne git_url: https://git.bioconductor.org/packages/msgbsR git_branch: RELEASE_3_22 git_last_commit: 1016b01 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/msgbsR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msgbsR_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msgbsR_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/msgbsR_1.34.0.tgz vignettes: vignettes/msgbsR/inst/doc/msgbsR_Vignette.pdf vignetteTitles: msgbsR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msgbsR/inst/doc/msgbsR_Vignette.R dependencyCount: 172 Package: msImpute Version: 1.20.0 Depends: R (>= 3.5.0) Imports: softImpute, methods, stats, graphics, pdist, reticulate, scran, data.table, FNN, matrixStats, limma, mvtnorm, tidyr, dplyr Suggests: BiocStyle, knitr, rmarkdown, ComplexHeatmap, imputeLCMD License: GPL (>=2) MD5sum: 6b6dbc584bd5dc744bf43c1c37358725 NeedsCompilation: no Title: Imputation of label-free mass spectrometry peptides Description: MsImpute is a package for imputation of peptide intensity in proteomics experiments. It additionally contains tools for MAR/MNAR diagnosis and assessment of distortions to the probability distribution of the data post imputation. The missing values are imputed by low-rank approximation of the underlying data matrix if they are MAR (method = "v2"), by Barycenter approach if missingness is MNAR ("v2-mnar"), or by Peptide Identity Propagation (PIP). biocViews: MassSpectrometry, Proteomics, Software Author: Soroor Hediyeh-zadeh [aut, cre] (ORCID: ) Maintainer: Soroor Hediyeh-zadeh SystemRequirements: python VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/msImpute/issues git_url: https://git.bioconductor.org/packages/msImpute git_branch: RELEASE_3_22 git_last_commit: c038506 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/msImpute_1.20.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msImpute_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/msImpute_1.20.0.tgz vignettes: vignettes/msImpute/inst/doc/msImpute-vignette.html vignetteTitles: msImpute: proteomics missing values imputation and diagnosis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msImpute/inst/doc/msImpute-vignette.R dependencyCount: 86 Package: mslp Version: 1.12.0 Depends: R (>= 4.2.0) Imports: data.table (>= 1.13.0), doRNG, fmsb, foreach, magrittr, org.Hs.eg.db, pROC, randomForest, RankProd, stats, utils Suggests: BiocStyle, doFuture, future, knitr, rmarkdown, roxygen2, tinytest License: GPL-3 MD5sum: eb96d0f146c9e6eb55f0dc909843c0d3 NeedsCompilation: no Title: Predict synthetic lethal partners of tumour mutations Description: An integrated pipeline to predict the potential synthetic lethality partners (SLPs) of tumour mutations, based on gene expression, mutation profiling and cell line genetic screens data. It has builtd-in support for data from cBioPortal. The primary SLPs correlating with muations in WT and compensating for the loss of function of mutations are predicted by random forest based methods (GENIE3) and Rank Products, respectively. Genetic screens are employed to identfy consensus SLPs leads to reduced cell viability when perturbed. biocViews: Pharmacogenetics, Pharmacogenomics Author: Chunxuan Shao [aut, cre] Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mslp git_branch: RELEASE_3_22 git_last_commit: 47d65b8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mslp_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mslp_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mslp_1.12.0.tgz vignettes: vignettes/mslp/inst/doc/mslp.html vignetteTitles: mslp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mslp/inst/doc/mslp.R dependencyCount: 60 Package: msmsEDA Version: 1.48.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: c34a1787478d0bf004d982530955a243 NeedsCompilation: no Title: Exploratory Data Analysis of LC-MS/MS data by spectral counts Description: Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori git_url: https://git.bioconductor.org/packages/msmsEDA git_branch: RELEASE_3_22 git_last_commit: 7709fc3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/msmsEDA_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msmsEDA_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msmsEDA_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/msmsEDA_1.48.0.tgz vignettes: vignettes/msmsEDA/inst/doc/msmsData-Vignette.pdf vignetteTitles: msmsEDA: Batch effects detection in LC-MSMS experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsEDA/inst/doc/msmsData-Vignette.R dependsOnMe: msmsTests suggestsMe: Harman, RforProteomics dependencyCount: 136 Package: msmsTests Version: 1.48.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue Suggests: xtable License: GPL-2 MD5sum: 68cc7fda8623a3e9a5514a09b2992f28 NeedsCompilation: no Title: LC-MS/MS Differential Expression Tests Description: Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package.The three models admit blocking factors to control for nuissance variables.To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori i Font git_url: https://git.bioconductor.org/packages/msmsTests git_branch: RELEASE_3_22 git_last_commit: 51afab8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/msmsTests_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msmsTests_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msmsTests_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/msmsTests_1.48.0.tgz vignettes: vignettes/msmsTests/inst/doc/msmsTests-Vignette.pdf, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.pdf vignetteTitles: msmsTests: post test filters to improve reproducibility, msmsTests: controlling batch effects by blocking hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsTests/inst/doc/msmsTests-Vignette.R, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.R importsMe: MSnID suggestsMe: RforProteomics dependencyCount: 141 Package: MSnbase Version: 2.36.0 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR (>= 2.29.3), S4Vectors, ProtGenerics (>= 1.29.1) Imports: MsCoreUtils, PSMatch (>= 1.13.2), BiocParallel, IRanges (>= 2.13.28), plyr, vsn, grid, stats4, affy, impute, pcaMethods, MALDIquant (>= 1.16), mzID (>= 1.5.2), digest, lattice, ggplot2, scales, MASS, Rcpp LinkingTo: Rcpp Suggests: testthat, pryr, gridExtra, microbenchmark, zoo, knitr (>= 1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.43.3), magick, msdata (>= 0.19.3), roxygen2, rgl, rpx, AnnotationHub, BiocStyle (>= 2.5.19), rmarkdown, imputeLCMD, norm, gplots, XML, shiny, magrittr, SummarizedExperiment, Spectra License: Artistic-2.0 MD5sum: fca6a622aba513014d20d91a294ab481 NeedsCompilation: yes Title: Base Functions and Classes for Mass Spectrometry and Proteomics Description: MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto, Johannes Rainer and Sebastian Gibb with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie Cotton, Arne Smits, Martina Fisher, Ludger Goeminne, Adriaan Sticker, Lieven Clement and Pascal Maas. Maintainer: Laurent Gatto URL: https://lgatto.github.io/MSnbase VignetteBuilder: knitr BugReports: https://github.com/lgatto/MSnbase/issues git_url: https://git.bioconductor.org/packages/MSnbase git_branch: RELEASE_3_22 git_last_commit: 5d00871 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSnbase_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSnbase_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSnbase_2.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSnbase_2.36.0.tgz vignettes: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.html, vignettes/MSnbase/inst/doc/v02-MSnbase-io.html, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.html, vignettes/MSnbase/inst/doc/v04-benchmarking.html, vignettes/MSnbase/inst/doc/v05-MSnbase-development.html vignetteTitles: Base Functions and Classes for MS-based Proteomics, MSnbase IO capabilities, MSnbase: centroiding of profile-mode MS data, MSnbase benchmarking, A short introduction to `MSnbase` development hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.R, vignettes/MSnbase/inst/doc/v02-MSnbase-io.R, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.R, vignettes/MSnbase/inst/doc/v04-benchmarking.R, vignettes/MSnbase/inst/doc/v05-MSnbase-development.R dependsOnMe: bandle, msmsEDA, msmsTests, pRoloc, pRolocGUI, qPLEXanalyzer, DAPARdata, pRolocdata, RforProteomics importsMe: cliqueMS, CluMSID, DAPAR, DEP, MSnID, MSstatsQC, omXplore, peakPantheR, PrInCE, PRONE, ptairMS, RMassBank, squallms, topdownr, xcms, qPLEXdata suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, msPurity, msqrob2, proDA, qcmetrics, wpm, faahKO, msdata, mtbls2, LCMSQA, PepMapViz dependencyCount: 130 Package: MSnID Version: 1.44.0 Depends: R (>= 2.10), Rcpp Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach, doParallel, parallel, methods, iterators, data.table, Biobase, ProtGenerics, reshape2, dplyr, mzR, BiocStyle, msmsTests, ggplot2, RUnit, BiocGenerics, Biostrings, purrr, rlang, stringr, tibble, AnnotationHub, AnnotationDbi, xtable License: Artistic-2.0 MD5sum: b79fe84c2b10c5477d258b832e2555ae NeedsCompilation: no Title: Utilities for Exploration and Assessment of Confidence of LC-MSn Proteomics Identifications Description: Extracts MS/MS ID data from mzIdentML (leveraging mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. Also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc. biocViews: Proteomics, MassSpectrometry, ImmunoOncology Author: Vlad Petyuk with contributions from Laurent Gatto Maintainer: Vlad Petyuk git_url: https://git.bioconductor.org/packages/MSnID git_branch: RELEASE_3_22 git_last_commit: 80a4b14 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSnID_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSnID_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSnID_1.44.0.tgz vignettes: vignettes/MSnID/inst/doc/handling_mods.pdf, vignettes/MSnID/inst/doc/msnid_vignette.pdf vignetteTitles: Handling Modifications with MSnID, MSnID Package for Handling MS/MS Identifications hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnID/inst/doc/handling_mods.R, vignettes/MSnID/inst/doc/msnid_vignette.R suggestsMe: RforProteomics dependencyCount: 167 Package: mspms Version: 1.2.0 Depends: R (>= 4.4.0) Imports: QFeatures, limma, SummarizedExperiment, magrittr, rlang, dplyr, purrr, stats, tidyr, stringr, ggplot2, ggseqlogo, heatmaply, readr, rstatix, tibble, ggpubr Suggests: knitr, testthat (>= 3.0.0), downloadthis, DT, rmarkdown, BiocStyle, imputeLCMD License: MIT + file LICENSE MD5sum: 452df04c1dc6c05ca1d61a115c24416e NeedsCompilation: no Title: Tools for the analysis of MSP-MS data Description: This package provides functions for the analysis of data generated by the multiplex substrate profiling by mass spectrometry for proteases (MSP-MS) method. Data exported from upstream proteomics software is accepted as input and subsequently processed for analysis. Tools for statistical analysis, visualization, and interpretation of the data are provided. biocViews: Proteomics, MassSpectrometry, Preprocessing Author: Charlie Bayne [aut, cre] (ORCID: ) Maintainer: Charlie Bayne URL: https://github.com/baynec2/mspms VignetteBuilder: knitr BugReports: https://github.com/baynec2/mspms/issues git_url: https://git.bioconductor.org/packages/mspms git_branch: RELEASE_3_22 git_last_commit: 04133d6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mspms_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mspms_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mspms_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mspms_1.2.0.tgz vignettes: vignettes/mspms/inst/doc/mspms_vignette.html vignetteTitles: mspms_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mspms/inst/doc/mspms_vignette.R dependencyCount: 172 Package: MSPrep Version: 1.19.0 Depends: R (>= 4.1.0) Imports: SummarizedExperiment, S4Vectors, pcaMethods (>= 1.24.0), crmn, preprocessCore, dplyr (>= 0.7), tidyr, tibble (>= 1.2), magrittr, rlang, stats, stringr, methods, missForest, sva, VIM, Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 1.0.2) License: GPL-3 Archs: x64 MD5sum: 37569700d0de5f80d7aa47dd1e68a703 NeedsCompilation: no Title: Package for Summarizing, Filtering, Imputing, and Normalizing Metabolomics Data Description: Package performs summarization of replicates, filtering by frequency, several different options for imputing missing data, and a variety of options for transforming, batch correcting, and normalizing data. biocViews: Metabolomics, MassSpectrometry, Preprocessing Author: Max McGrath [aut, cre], Matt Mulvahill [aut], Grant Hughes [aut], Sean Jacobson [aut], Harrison Pielke-lombardo [aut], Katerina Kechris [aut, cph, ths] Maintainer: Max McGrath URL: https://github.com/KechrisLab/MSPrep VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/MSPrep/issues git_url: https://git.bioconductor.org/packages/MSPrep git_branch: devel git_last_commit: 1d38ba2 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/MSPrep_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSPrep_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSPrep_1.19.0.tgz vignettes: vignettes/MSPrep/inst/doc/using_MSPrep.html vignetteTitles: Using MSPrep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSPrep/inst/doc/using_MSPrep.R dependencyCount: 148 Package: msPurity Version: 1.36.0 Depends: Rcpp Imports: plyr, dplyr, dbplyr, magrittr, foreach, parallel, doSNOW, stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite Suggests: MSnbase, testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData, CAMERA, RPostgres, RMySQL License: GPL-3 + file LICENSE MD5sum: 8f0af6e38eac8becd2c9099577a026db NeedsCompilation: no Title: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics Description: msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the "precursor ion purity". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call "Precursor ion purity" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation. biocViews: MassSpectrometry, Metabolomics, Software Author: Thomas N. Lawson [aut, cre] (ORCID: ), Ralf Weber [ctb], Martin Jones [ctb], Julien Saint-Vanne [ctb], Andris Jankevics [ctb], Mark Viant [ths], Warwick Dunn [ths] Maintainer: Thomas N. Lawson URL: https://github.com/computational-metabolomics/msPurity/ VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/msPurity/issues/new git_url: https://git.bioconductor.org/packages/msPurity git_branch: RELEASE_3_22 git_last_commit: 98f4ee5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/msPurity_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msPurity_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msPurity_1.35.0.tgz vignettes: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.html, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.html, vignettes/msPurity/inst/doc/msPurity-vignette.html vignetteTitles: msPurity spectral matching, msPurity spectral database schema, msPurity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.R, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.R, vignettes/msPurity/inst/doc/msPurity-vignette.R dependencyCount: 62 Package: msqrob2 Version: 1.18.0 Depends: R (>= 4.1), QFeatures (>= 1.1.2) Imports: stats, methods, lme4, purrr, BiocParallel, Matrix, MASS, limma, SummarizedExperiment, MultiAssayExperiment, codetools Suggests: multcomp, gridExtra, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown, testthat, tidyverse, plotly, MsDataHub, MSnbase, matrixStats, MsCoreUtils, covr License: Artistic-2.0 MD5sum: 62fcf4e9acec76c056326fdc1c156cf3 NeedsCompilation: no Title: Robust statistical inference for quantitative LC-MS proteomics Description: msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, MultipleComparison, Regression, ExperimentalDesign, Software, ImmunoOncology, Normalization, TimeCourse, Preprocessing Author: Lieven Clement [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Oliver M. Crook [aut] (ORCID: ), Adriaan Sticker [ctb], Ludger Goeminne [ctb], Milan Malfait [ctb] (ORCID: ), Stijn Vandenbulcke [aut] Maintainer: Lieven Clement URL: https://github.com/statOmics/msqrob2 VignetteBuilder: knitr BugReports: https://github.com/statOmics/msqrob2/issues git_url: https://git.bioconductor.org/packages/msqrob2 git_branch: RELEASE_3_22 git_last_commit: eb5e42c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/msqrob2_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msqrob2_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msqrob2_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/msqrob2_1.18.0.tgz vignettes: vignettes/msqrob2/inst/doc/cptac.html vignetteTitles: A. label-free workflow with two group design hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msqrob2/inst/doc/cptac.R dependencyCount: 121 Package: MsQuality Version: 1.10.0 Depends: R (>= 4.2.0) Imports: BiocParallel (>= 1.32.0), ggplot2 (>= 3.3.5), htmlwidgets (>= 1.5.3), methods (>= 4.2.0), msdata (>= 0.32.0), MsExperiment (>= 0.99.0), plotly (>= 4.9.4.1), ProtGenerics (>= 1.24.0), rlang (>= 1.1.1), rmzqc (>= 0.7.0), shiny (>= 1.6.0), shinydashboard (>= 0.7.1), Spectra (>= 1.13.2), stats (>= 4.2.0), stringr (>= 1.4.0), tibble (>= 3.1.4), tidyr (>= 1.1.3), utils (>= 4.2.0) Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), dplyr (>= 1.0.5), knitr (>= 1.11), mzR (>= 2.32.0), rmarkdown (>= 2.7), S4Vectors (>= 0.29.17), testthat (>= 2.2.1) License: GPL-3 MD5sum: 60950c7172206073861a03c515469c69 NeedsCompilation: no Title: MsQuality - Quality metric calculation from Spectra and MsExperiment objects Description: The MsQuality provides functionality to calculate quality metrics for mass spectrometry-derived, spectral data at the per-sample level. MsQuality relies on the mzQC framework of quality metrics defined by the Human Proteom Organization-Proteomics Standards Initiative (HUPO-PSI). These metrics quantify the quality of spectral raw files using a controlled vocabulary. The package is especially addressed towards users that acquire mass spectrometry data on a large scale (e.g. data sets from clinical settings consisting of several thousands of samples). The MsQuality package allows to calculate low-level quality metrics that require minimum information on mass spectrometry data: retention time, m/z values, and associated intensities. MsQuality relies on the Spectra package, or alternatively the MsExperiment package, and its infrastructure to store spectral data. biocViews: Metabolomics, Proteomics, MassSpectrometry, QualityControl Author: Thomas Naake [aut, cre] (ORCID: ), Johannes Rainer [aut] (ORCID: ) Maintainer: Thomas Naake URL: https://www.github.com/tnaake/MsQuality/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MsQuality git_branch: RELEASE_3_22 git_last_commit: 04fa814 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MsQuality_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsQuality_1.9.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsQuality_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsQuality_1.10.0.tgz vignettes: vignettes/MsQuality/inst/doc/MsQuality.html vignetteTitles: QC for metabolomics and proteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsQuality/inst/doc/MsQuality.R dependencyCount: 139 Package: MSstats Version: 4.18.0 Depends: R (>= 4.0) Imports: MSstatsConvert, data.table, checkmate, MASS, htmltools, limma, lme4, preprocessCore, survival, utils, Rcpp, ggplot2, ggrepel, gplots, plotly, marray, stats, grDevices, graphics, methods, statmod, parallel, rlang LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, tinytest, covr, markdown, mockery, kableExtra License: Artistic-2.0 MD5sum: b2b65575bd7bb9ceb81945dd7eb9a79b NeedsCompilation: yes Title: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments Description: A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, Normalization, QualityControl, TimeCourse Author: Meena Choi [aut, cre], Mateusz Staniak [aut], Devon Kohler [aut], Tony Wu [aut], Deril Raju [aut], Tsung-Heng Tsai [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Meena Choi URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstats git_branch: RELEASE_3_22 git_last_commit: 1173853 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstats_4.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstats_4.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstats_4.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstats_4.18.0.tgz vignettes: vignettes/MSstats/inst/doc/MSstats.html, vignettes/MSstats/inst/doc/MSstatsPlus.html, vignettes/MSstats/inst/doc/MSstatsWorkflow.html vignetteTitles: MSstats: Protein/Peptide significance analysis, MSstats+: Peak quality-weighted differential analysis, MSstats: End to End Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MSstats/inst/doc/MSstats.R, vignettes/MSstats/inst/doc/MSstatsPlus.R, vignettes/MSstats/inst/doc/MSstatsWorkflow.R dependsOnMe: MSstatsBioNet importsMe: artMS, MSstatsBig, MSstatsLiP, MSstatsPTM, MSstatsShiny, MSstatsTMT suggestsMe: MSstatsResponse dependencyCount: 101 Package: MSstatsBig Version: 1.8.0 Imports: arrow, DBI, dplyr, MSstats, MSstatsConvert, readr, sparklyr, utils Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 104a1399ecc928ae5a9a36a0fe854c3d NeedsCompilation: no Title: MSstats Preprocessing for Larger than Memory Data Description: MSstats package provide tools for preprocessing, summarization and differential analysis of mass spectrometry (MS) proteomics data. Recently, some MS protocols enable acquisition of data sets that result in larger than memory quantitative data. MSstats functions are not able to process such data. MSstatsBig package provides additional converter functions that enable processing larger than memory data sets. biocViews: MassSpectrometry, Proteomics, Software Author: Mateusz Staniak [aut, cre], Devon Kohler [aut] Maintainer: Mateusz Staniak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSstatsBig git_branch: RELEASE_3_22 git_last_commit: 1b12cd9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstatsBig_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsBig_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsBig_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsBig_1.8.0.tgz vignettes: vignettes/MSstatsBig/inst/doc/MSstatsBig_Workflow.html vignetteTitles: MSstatsBig Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsBig/inst/doc/MSstatsBig_Workflow.R dependencyCount: 124 Package: MSstatsBioNet Version: 1.2.0 Depends: R (>= 4.4.0), MSstats Imports: RCy3, httr, jsonlite, r2r, tidyr Suggests: data.table, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), mockery, MSstatsConvert License: file LICENSE MD5sum: 8364c8d4b79ba470c430fc461f26959d NeedsCompilation: no Title: Network Analysis for MS-based Proteomics Experiments Description: A set of tools for network analysis using mass spectrometry-based proteomics data and network databases. The package takes as input the output of MSstats differential abundance analysis and provides functions to perform enrichment analysis and visualization in the context of prior knowledge from past literature. Notably, this package integrates with INDRA, which is a database of biological networks extracted from the literature using text mining techniques. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, QualityControl, NetworkEnrichment, Network Author: Anthony Wu [aut, cre] (ORCID: ), Olga Vitek [aut] (ORCID: ) Maintainer: Anthony Wu URL: http://msstats.org, https://vitek-lab.github.io/MSstatsBioNet/ VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsBioNet git_branch: RELEASE_3_22 git_last_commit: 21e0165 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstatsBioNet_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsBioNet_1.1.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsBioNet_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsBioNet_1.2.0.tgz vignettes: vignettes/MSstatsBioNet/inst/doc/MSstatsBioNet.html, vignettes/MSstatsBioNet/inst/doc/PTM-Analysis.html vignetteTitles: MSstatsBioNet: Introduction, MSstatsBioNet: PTM Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MSstatsBioNet/inst/doc/MSstatsBioNet.R, vignettes/MSstatsBioNet/inst/doc/PTM-Analysis.R importsMe: MSstatsShiny dependencyCount: 117 Package: MSstatsConvert Version: 1.20.0 Depends: R (>= 4.0) Imports: data.table, log4r, methods, checkmate, utils, stringi, Rcpp, parallel LinkingTo: Rcpp Suggests: tinytest, covr, knitr, arrow, rmarkdown License: Artistic-2.0 MD5sum: 472e4f87202e9d7504c0483d20a4f1d6 NeedsCompilation: yes Title: Import Data from Various Mass Spectrometry Signal Processing Tools to MSstats Format Description: MSstatsConvert provides tools for importing reports of Mass Spectrometry data processing tools into R format suitable for statistical analysis using the MSstats and MSstatsTMT packages. biocViews: MassSpectrometry, Proteomics, Software, DataImport, QualityControl Author: Mateusz Staniak [aut, cre], Devon Kohler [aut], Anthony Wu [aut], Meena Choi [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Mateusz Staniak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSstatsConvert git_branch: RELEASE_3_22 git_last_commit: 23a63ff git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstatsConvert_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsConvert_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsConvert_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsConvert_1.20.0.tgz vignettes: vignettes/MSstatsConvert/inst/doc/msstats_data_format.html vignetteTitles: Working with MSstatsConvert hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsConvert/inst/doc/msstats_data_format.R importsMe: MSstats, MSstatsBig, MSstatsLiP, MSstatsPTM, MSstatsShiny, MSstatsTMT suggestsMe: MSstatsBioNet dependencyCount: 11 Package: MSstatsLiP Version: 1.16.0 Depends: R (>= 4.1) Imports: dplyr, gridExtra, stringr, ggplot2, grDevices, MSstats, MSstatsConvert, data.table, Biostrings, MSstatsPTM, Rcpp, checkmate, factoextra, ggpubr, purrr, tibble, tidyr, tidyverse, scales, stats, plotly, htmltools LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, gghighlight License: Artistic-2.0 Archs: x64 MD5sum: 7e40c5142dee6067154ae77b3aafddfe NeedsCompilation: yes Title: LiP Significance Analysis in shotgun mass spectrometry-based proteomic experiments Description: Tools for LiP peptide and protein significance analysis. Provides functions for summarization, estimation of LiP peptide abundance, and detection of changes across conditions. Utilizes functionality across the MSstats family of packages. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut], Anthony Wu [aut, cre], Tsung-Heng Tsai [aut], Deril Raju [aut], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Valentina Cappelletti [aut], Liliana Malinovska [aut], Olga Vitek [aut] Maintainer: Anthony Wu VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLiP/issues git_url: https://git.bioconductor.org/packages/MSstatsLiP git_branch: RELEASE_3_22 git_last_commit: 2030167 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstatsLiP_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsLiP_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsLiP_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsLiP_1.16.0.tgz vignettes: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.html, vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.html vignetteTitles: MSstatsLiP Workflow: An example workflow and analysis of the MSstatsLiP package, MSstatsLiP Proteolytic Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.R, vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.R dependencyCount: 193 Package: MSstatsLOBD Version: 1.18.0 Depends: R (>= 4.0) Imports: minpack.lm, ggplot2, utils, stats, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, dplyr License: Artistic-2.0 MD5sum: 093b02cf3f56d2d1b888c6fe7447e7bb NeedsCompilation: no Title: Assay characterization: estimation of limit of blanc(LoB) and limit of detection(LOD) Description: The MSstatsLOBD package allows calculation and visualization of limit of blac (LOB) and limit of detection (LOD). We define the LOB as the highest apparent concentration of a peptide expected when replicates of a blank sample containing no peptides are measured. The LOD is defined as the measured concentration value for which the probability of falsely claiming the absence of a peptide in the sample is 0.05, given a probability 0.05 of falsely claiming its presence. These functionalities were previously a part of the MSstats package. The methodology is described in Galitzine (2018) . biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Mateusz Staniak [aut], Cyril Galitzine [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLODQ/issues git_url: https://git.bioconductor.org/packages/MSstatsLOBD git_branch: RELEASE_3_22 git_last_commit: 3fe21ff git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstatsLOBD_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsLOBD_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsLOBD_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsLOBD_1.18.0.tgz vignettes: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.html vignetteTitles: LOB/LOD Estimation Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.R dependencyCount: 24 Package: MSstatsPTM Version: 2.12.0 Depends: R (>= 4.3) Imports: dplyr, gridExtra, stringr, stats, ggplot2, stringi, grDevices, MSstatsTMT, MSstatsConvert, MSstats, data.table, Rcpp, Biostrings, checkmate, ggrepel, plotly, htmltools LinkingTo: Rcpp Suggests: knitr, rmarkdown, tinytest, covr, mockery, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 913e3bc8eff20d864cea7f7f50bc717b NeedsCompilation: yes Title: Statistical Characterization of Post-translational Modifications Description: MSstatsPTM provides general statistical methods for quantitative characterization of post-translational modifications (PTMs). Supports DDA, DIA, SRM, and tandem mass tag (TMT) labeling. Typically, the analysis involves the quantification of PTM sites (i.e., modified residues) and their corresponding proteins, as well as the integration of the quantification results. MSstatsPTM provides functions for summarization, estimation of PTM site abundance, and detection of changes in PTMs across experimental conditions. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut], Tsung-Heng Tsai [aut], Anthony Wu [aut, cre], Deril Raju [aut], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Anthony Wu VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsPTM/issues git_url: https://git.bioconductor.org/packages/MSstatsPTM git_branch: RELEASE_3_22 git_last_commit: 6245a63 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstatsPTM_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsPTM_2.11.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsPTM_2.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsPTM_2.12.0.tgz vignettes: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.html, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.html vignetteTitles: MSstatsPTM LabelFree Workflow, MSstatsPTM TMT Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.R, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.R importsMe: MSstatsLiP, MSstatsShiny dependencyCount: 114 Package: MSstatsQC Version: 2.28.0 Depends: R (>= 3.5.0) Imports: dplyr,plotly,ggplot2,ggExtra, stats,grid, MSnbase, qcmetrics Suggests: knitr,rmarkdown, testthat, RforProteomics License: Artistic License 2.0 MD5sum: 8a7e10ec53135dc502d8ca2655cb72d4 NeedsCompilation: no Title: Longitudinal system suitability monitoring and quality control for proteomic experiments Description: MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQC git_branch: RELEASE_3_22 git_last_commit: 718daeb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstatsQC_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsQC_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsQC_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsQC_2.28.0.tgz vignettes: vignettes/MSstatsQC/inst/doc/MSstatsQC.html vignetteTitles: MSstatsQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQC/inst/doc/MSstatsQC.R importsMe: MSstatsQCgui dependencyCount: 142 Package: MSstatsQCgui Version: 1.30.0 Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, grid Suggests: knitr License: Artistic License 2.0 MD5sum: b53a52f3d80e8f68b3e018a6472d39b7 NeedsCompilation: no Title: A graphical user interface for MSstatsQC package Description: MSstatsQCgui is a Shiny app which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry, GUI Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQCgui git_branch: RELEASE_3_22 git_last_commit: 7b4256b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstatsQCgui_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsQCgui_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsQCgui_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsQCgui_1.30.0.tgz vignettes: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.html vignetteTitles: MSstatsQCgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.R dependencyCount: 144 Package: MSstatsResponse Version: 1.0.0 Depends: R (>= 4.5.0) Imports: BiocParallel, ggplot2, dplyr, stats, parallel, data.table Suggests: MSstats, MSstatsTMT, tidyverse, boot, purrr, gridExtra, knitr, rmarkdown, BiocStyle, testthat License: Artistic-2.0 MD5sum: bf262b1f8b3858ddc40c250bbd3e4bff NeedsCompilation: no Title: Statistical Methods for Chemoproteomics Dose-Response Analysis Description: Tools for detecting drug-protein interactions and estimating IC50 values from chemoproteomics data. Implements semi-parametric isotonic regression, bootstrapping, and curve fitting to evaluate compound effects on protein abundance. biocViews: Proteomics, MassSpectrometry, StatisticalMethod, Software, Regression Author: Sarah Szvetecz [aut, cre], Devon Kohler [aut], Olga Vitek [aut] Maintainer: Sarah Szvetecz URL: https://github.com/Vitek-Lab/MSstatsResponse VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsResponse/issues git_url: https://git.bioconductor.org/packages/MSstatsResponse git_branch: RELEASE_3_22 git_last_commit: 7b935df git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstatsResponse_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsResponse_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsResponse_1.0.0.tgz vignettes: vignettes/MSstatsResponse/inst/doc/MSstatsResponse.html vignetteTitles: MSstatsResponse User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsResponse/inst/doc/MSstatsResponse.R dependencyCount: 41 Package: MSstatsShiny Version: 1.12.0 Depends: R (>= 4.2) Imports: shiny, shinyBS, shinyjs, shinybusy, dplyr, ggplot2, plotly, data.table, Hmisc, MSstats, MSstatsTMT, MSstatsPTM, MSstatsConvert, gplots, marray, DT, readxl, ggrepel, uuid, utils, stats, htmltools, methods, tidyr, grDevices, graphics, mockery, MSstatsBioNet, shinydashboard, arrow, tools Suggests: rmarkdown, tinytest, sessioninfo, knitr, testthat (>= 3.0.0), shinytest2, License: Artistic-2.0 MD5sum: 171cf764515bcdb0cf381d0745ba98b1 NeedsCompilation: no Title: MSstats GUI for Statistical Anaylsis of Proteomics Experiments Description: MSstatsShiny is an R-Shiny graphical user interface (GUI) integrated with the R packages MSstats, MSstatsTMT, and MSstatsPTM. It provides a point and click end-to-end analysis pipeline applicable to a wide variety of experimental designs. These include data-dependedent acquisitions (DDA) which are label-free or tandem mass tag (TMT)-based, as well as DIA, SRM, and PRM acquisitions and those targeting post-translational modifications (PTMs). The application automatically saves users selections and builds an R script that recreates their analysis, supporting reproducible data analysis. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, ShinyApps, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl, GUI Author: Devon Kohler [aut], Anthony Wu [aut, cre], Deril Raju [aut], Maanasa Kaza [aut], Cristina Pasi [aut], Ting Huang [aut], Mateusz Staniak [aut], Dhaval Mohandas [aut], Eduard Sabido [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Anthony Wu VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsShiny/issues git_url: https://git.bioconductor.org/packages/MSstatsShiny git_branch: RELEASE_3_22 git_last_commit: bc76da8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstatsShiny_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsShiny_1.11.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsShiny_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsShiny_1.12.0.tgz vignettes: vignettes/MSstatsShiny/inst/doc/MSstatsShiny_Launch_Instructions.html vignetteTitles: MSstatsPTM LabelFree Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsShiny/inst/doc/MSstatsShiny_Launch_Instructions.R dependencyCount: 170 Package: MSstatsTMT Version: 2.18.0 Depends: R (>= 4.2) Imports: limma, lme4, lmerTest, methods, data.table, stats, utils, ggplot2, grDevices, graphics, MSstats, MSstatsConvert, checkmate, plotly, htmltools Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 1fdda6b1266986f68d56cfb4e4b59497 NeedsCompilation: no Title: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling Description: The package provides statistical tools for detecting differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. It provides multiple functionalities, including aata visualization, protein quantification and normalization, and statistical modeling and inference. Furthermore, it is inter-operable with other data processing tools, such as Proteome Discoverer, MaxQuant, OpenMS and SpectroMine. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software Author: Devon Kohler [aut, cre], Ting Huang [aut], Meena Choi [aut], Mateusz Staniak [aut], Tony Wu [aut], Deril Raju [aut], Sicheng Hao [aut], Olga Vitek [aut] Maintainer: Devon Kohler URL: http://msstats.org/msstatstmt/ VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsTMT git_branch: RELEASE_3_22 git_last_commit: ad356e0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MSstatsTMT_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsTMT_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsTMT_2.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsTMT_2.18.0.tgz vignettes: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.html vignetteTitles: MSstatsTMT User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.R importsMe: MSstatsPTM, MSstatsShiny suggestsMe: MSstatsResponse dependencyCount: 104 Package: MuData Version: 1.14.0 Depends: Matrix, S4Vectors, rhdf5 (>= 2.45) Imports: methods, stats, MultiAssayExperiment, SingleCellExperiment, SummarizedExperiment, DelayedArray, S4Vectors Suggests: HDF5Array, rmarkdown, knitr, fs, testthat, BiocStyle, covr, SingleCellMultiModal, CiteFuse, scater License: GPL-3 MD5sum: cbb0c2b36ac493fd3a203b484ab071cf NeedsCompilation: no Title: Serialization for MultiAssayExperiment Objects Description: Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage. biocViews: DataImport Author: Danila Bredikhin [aut] (ORCID: ), Ilia Kats [aut, cre] (ORCID: ) Maintainer: Ilia Kats URL: https://github.com/ilia-kats/MuData VignetteBuilder: knitr BugReports: https://github.com/ilia-kats/MuData/issues git_url: https://git.bioconductor.org/packages/MuData git_branch: RELEASE_3_22 git_last_commit: b15275d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MuData_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MuData_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MuData_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MuData_1.14.0.tgz vignettes: vignettes/MuData/inst/doc/Blood-CITE-seq.html, vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.html, vignettes/MuData/inst/doc/Getting-Started.html vignetteTitles: Blood CITE-seq with MuData, Cord Blood CITE-seq with MuData, Getting started with MuDataMae hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MuData/inst/doc/Blood-CITE-seq.R, vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.R, vignettes/MuData/inst/doc/Getting-Started.R dependencyCount: 50 Package: Mulcom Version: 1.60.0 Depends: R (>= 2.10), Biobase Imports: graphics, grDevices, stats, methods, fields License: GPL-2 MD5sum: 081eeffddc3877105664823d4e6abac0 NeedsCompilation: yes Title: Calculates Mulcom test Description: Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test. biocViews: StatisticalMethod, MultipleComparison, Microarray, DifferentialExpression, GeneExpression Author: Claudio Isella Maintainer: Claudio Isella git_url: https://git.bioconductor.org/packages/Mulcom git_branch: RELEASE_3_22 git_last_commit: cd22ca4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Mulcom_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Mulcom_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Mulcom_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Mulcom_1.60.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 16 Package: MultiAssayExperiment Version: 1.36.0 Depends: SummarizedExperiment, R (>= 4.5.0) Imports: Biobase, BiocBaseUtils, BiocGenerics, DelayedArray, GenomicRanges, IRanges, MatrixGenerics, methods, S4Vectors, tidyr, utils Suggests: BiocStyle, HDF5Array, h5mread, knitr, maftools, RaggedExperiment, reshape2, rmarkdown, survival, survminer, testthat, UpSetR License: Artistic-2.0 MD5sum: dca7cf3d949f3c8cbb039939434d2b32 NeedsCompilation: no Title: Software for the integration of multi-omics experiments in Bioconductor Description: Harmonize data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. Facilities are provided for reshaping data into wide and long formats for adaptability to graphing and downstream analysis. biocViews: Infrastructure, DataRepresentation Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut, ctb], Lori Shepherd [ctb], Hervé Pagès [ctb], Vincent J Carey [aut, ctb], Levi Waldron [aut], MultiAssay SIG [ctb] Maintainer: Marcel Ramos URL: http://waldronlab.io/MultiAssayExperiment/ VignetteBuilder: knitr Video: https://youtu.be/w6HWAHaDpyk, https://youtu.be/Vh0hVVUKKFM BugReports: https://github.com/waldronlab/MultiAssayExperiment/issues git_url: https://git.bioconductor.org/packages/MultiAssayExperiment git_branch: RELEASE_3_22 git_last_commit: 1b8ac6c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MultiAssayExperiment_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MultiAssayExperiment_1.35.7.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MultiAssayExperiment_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MultiAssayExperiment_1.36.0.tgz vignettes: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment_cheatsheet.html, vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.html vignetteTitles: MultiAssayExperiment Cheatsheet, Coordinating Analysis of Multi-Assay Experiments, Quick-start Guide, HDF5Array and MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.R, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.R, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.R dependsOnMe: alabaster.mae, CAGEr, cBioPortalData, ClassifyR, evaluomeR, HoloFoodR, InTAD, MGnifyR, mia, midasHLA, MIRit, missRows, QFeatures, RFLOMICS, terraTCGAdata, curatedPCaData, curatedTCGAData, microbiomeDataSets, OMICsPCAdata, scMultiome, SingleCellMultiModal importsMe: AffiXcan, AMARETTO, anansi, animalcules, autonomics, biosigner, CoreGx, corral, ELMER, FindIT2, gDRcore, gDRimport, gDRutils, glmSparseNet, GOpro, hermes, Lheuristic, LinkHD, MOMA, MOSClip, msqrob2, MuData, MultiBaC, MultimodalExperiment, nipalsMCIA, OMICsPCA, omicsPrint, omXplore, padma, PDATK, PharmacoGx, phenomis, ropls, scGraphVerse, scp, scPipe, SmartPhos, survClust, TCGAutils, TENET, vsclust, xcore, curatedTBData, HMP2Data, LegATo, MetaScope, TENET.ExperimentHub suggestsMe: BatchQC, BiocGenerics, CNVRanger, funOmics, maftools, MOFA2, MultiDataSet, RaggedExperiment, updateObject, brgedata, MOFAdata, teal, teal.slice dependencyCount: 45 Package: MultiBaC Version: 1.20.0 Imports: Matrix, ggplot2, MultiAssayExperiment, ropls, graphics, methods, plotrix, grDevices, pcaMethods Suggests: knitr, rmarkdown, BiocStyle, devtools License: GPL-3 MD5sum: 3c06d9564262b08476620fc07bb8fb52 NeedsCompilation: no Title: Multiomic Batch effect Correction Description: MultiBaC is a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. MultiBaC is the first Batch effect correction algorithm that dealing with batch effect correction in multiomics datasets. MultiBaC is able to remove batch effects across different omics generated within separate batches provided that at least one common omic data type is included in all the batches considered. biocViews: Software, StatisticalMethod, PrincipalComponent, DataRepresentation, GeneExpression, Transcription, BatchEffect Author: person("Manuel", "Ugidos", email = "manuelugidos@gmail.com"), person("Sonia", "Tarazona", email = "sotacam@gmail.com"), person("María José", "Nueda", email = "mjnueda@ua.es") Maintainer: The package maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiBaC git_branch: RELEASE_3_22 git_last_commit: e44e678 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MultiBaC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MultiBaC_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MultiBaC_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MultiBaC_1.20.0.tgz vignettes: vignettes/MultiBaC/inst/doc/MultiBaC.html vignetteTitles: MultiBaC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiBaC/inst/doc/MultiBaC.R dependencyCount: 100 Package: multiClust Version: 1.40.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, rmarkdown, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) Archs: x64 MD5sum: 1d4c6d3e0cddd0a4ddddcbf4fd459eaf NeedsCompilation: no Title: multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles Description: Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies. biocViews: FeatureExtraction, Clustering, GeneExpression, Survival Author: Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri [aut], Krish Karuturi [aut], Joshy George [aut] Maintainer: Nathan Lawlor VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiClust git_branch: RELEASE_3_22 git_last_commit: 9efff1c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/multiClust_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiClust_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiClust_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiClust_1.40.0.tgz vignettes: vignettes/multiClust/inst/doc/multiClust.html vignetteTitles: "A Guide to multiClust" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiClust/inst/doc/multiClust.R dependencyCount: 35 Package: multicrispr Version: 1.20.0 Depends: R (>= 4.0) Imports: BiocGenerics, Biostrings, BSgenome, CRISPRseek, data.table, Seqinfo, GenomicFeatures, GenomicRanges, ggplot2, grid, karyoploteR, magrittr, methods, parallel, plyranges, Rbowtie, reticulate, rtracklayer, stats, stringi, tidyr, tidyselect, utils Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Scerevisiae.UCSC.sacCer1, ensembldb, IRanges, GenomeInfoDb, knitr, magick, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-2 MD5sum: 29e85f34cbefc8f1161baa5d6ea801eb NeedsCompilation: no Title: Multi-locus multi-purpose Crispr/Cas design Description: This package is for designing Crispr/Cas9 and Prime Editing experiments. It contains functions to (1) define and transform genomic targets, (2) find spacers (4) count offtarget (mis)matches, and (5) compute Doench2016/2014 targeting efficiency. Care has been taken for multicrispr to scale well towards large target sets, enabling the design of large Crispr/Cas9 libraries. biocViews: CRISPR, Software Author: Aditya Bhagwat [aut, cre], Richie ´Cotton [aut], Rene Wiegandt [ctb], Mette Bentsen [ctb], Jens Preussner [ctb], Michael Lawrence [ctb], Hervé Pagès [ctb], Johannes Graumann [sad], Mario Looso [sad, rth] Maintainer: Aditya Bhagwat URL: https://github.com/bhagwataditya/multicrispr VignetteBuilder: knitr BugReports: https://github.com/bhagwataditya/multicrispr/issues git_url: https://git.bioconductor.org/packages/multicrispr git_branch: RELEASE_3_22 git_last_commit: 1e84407 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/multicrispr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multicrispr_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multicrispr_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multicrispr_1.20.0.tgz vignettes: vignettes/multicrispr/inst/doc/crispr_grna_design.html, vignettes/multicrispr/inst/doc/genome_arithmetics.html, vignettes/multicrispr/inst/doc/prime_editing.html vignetteTitles: grna_design, genome_arithmetics, prime_editing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multicrispr/inst/doc/crispr_grna_design.R, vignettes/multicrispr/inst/doc/genome_arithmetics.R, vignettes/multicrispr/inst/doc/prime_editing.R dependencyCount: 189 Package: MultiDataSet Version: 1.38.0 Depends: R (>= 4.1), Biobase Imports: BiocGenerics, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, methods, utils, ggplot2, ggrepel, qqman, limma Suggests: brgedata, minfi, minfiData, knitr, rmarkdown, testthat, omicade4, iClusterPlus, GEOquery, MultiAssayExperiment, BiocStyle, RaggedExperiment License: file LICENSE MD5sum: a459a2808c3e70af5cac69b8600cba31 NeedsCompilation: no Title: Implementation of MultiDataSet and ResultSet Description: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages. biocViews: Software, DataRepresentation Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut] Maintainer: Xavier Escrib Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiDataSet git_branch: RELEASE_3_22 git_last_commit: 65bb120 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MultiDataSet_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MultiDataSet_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MultiDataSet_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MultiDataSet_1.38.0.tgz vignettes: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.html, vignettes/MultiDataSet/inst/doc/MultiDataSet.html vignetteTitles: Adding a new type of data to MultiDataSet objects, Introduction to MultiDataSet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.R, vignettes/MultiDataSet/inst/doc/MultiDataSet.R dependsOnMe: MEAL importsMe: biosigner, omicRexposome, phenomis, ropls dependencyCount: 48 Package: multiGSEA Version: 1.20.0 Depends: R (>= 4.0.0) Imports: magrittr, graphite, AnnotationDbi, metaboliteIDmapping, dplyr, fgsea, metap, rappdirs, rlang, methods Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Ss.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Xl.eg.db, org.Cf.eg.db, knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: GPL-3 Archs: x64 MD5sum: 6b3cc8b0dce1091740d2e16ec97e8bb4 NeedsCompilation: no Title: Combining GSEA-based pathway enrichment with multi omics data integration Description: Extracted features from pathways derived from 8 different databases (KEGG, Reactome, Biocarta, etc.) can be used on transcriptomic, proteomic, and/or metabolomic level to calculate a combined GSEA-based enrichment score. biocViews: GeneSetEnrichment, Pathways, Reactome, BioCarta Author: Sebastian Canzler [aut, cre] (ORCID: ), Jörg Hackermüller [aut] (ORCID: ) Maintainer: Sebastian Canzler URL: https://github.com/yigbt/multiGSEA VignetteBuilder: knitr BugReports: https://github.com/yigbt/multiGSEA/issues git_url: https://git.bioconductor.org/packages/multiGSEA git_branch: RELEASE_3_22 git_last_commit: 89609e9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/multiGSEA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiGSEA_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiGSEA_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiGSEA_1.20.0.tgz vignettes: vignettes/multiGSEA/inst/doc/multiGSEA.html vignetteTitles: multiGSEA.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiGSEA/inst/doc/multiGSEA.R dependencyCount: 119 Package: multiHiCcompare Version: 1.28.0 Depends: R (>= 4.0.0) Imports: data.table, dplyr, HiCcompare, edgeR, BiocParallel, qqman, pheatmap, methods, GenomicRanges, graphics, stats, utils, pbapply, GenomeInfoDbData, GenomeInfoDb, aggregation Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 9b2e92768b0bf12f7fea0062dd9f9148 NeedsCompilation: no Title: Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available Description: multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner. biocViews: Software, HiC, Sequencing, Normalization Author: Mikhail Dozmorov [aut, cre] (ORCID: ), John Stansfield [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/multiHiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/multiHiCcompare/issues git_url: https://git.bioconductor.org/packages/multiHiCcompare git_branch: RELEASE_3_22 git_last_commit: 5d0766c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/multiHiCcompare_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiHiCcompare_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiHiCcompare_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiHiCcompare_1.28.0.tgz vignettes: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.html, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.html vignetteTitles: juiceboxVisualization, multiHiCcompare hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.R, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.R importsMe: HiCDOC, OHCA suggestsMe: HiCcompare dependencyCount: 91 Package: MultiMed Version: 2.32.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: a85b8e341cc10f75f5d6cef2ca58d92e NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements methods for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Ruth Heller, Joshua N. Sampson Maintainer: Simina M. Boca git_url: https://git.bioconductor.org/packages/MultiMed git_branch: RELEASE_3_22 git_last_commit: 5b10c46 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MultiMed_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MultiMed_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MultiMed_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MultiMed_2.32.0.tgz vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf vignetteTitles: MultiMedTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiMed/inst/doc/MultiMed.R dependencyCount: 0 Package: multiMiR Version: 1.32.0 Depends: R (>= 3.4) Imports: stats, XML, RCurl, purrr (>= 0.2.2), tibble (>= 2.0), methods, BiocGenerics, AnnotationDbi, dplyr, Suggests: BiocStyle, edgeR, knitr, rmarkdown, testthat (>= 1.0.2) License: MIT + file LICENSE MD5sum: b6ae37ef2a74420c5ab7e7555cc0366d NeedsCompilation: no Title: Integration of multiple microRNA-target databases with their disease and drug associations Description: A collection of microRNAs/targets from external resources, including validated microRNA-target databases (miRecords, miRTarBase and TarBase), predicted microRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan) and microRNA-disease/drug databases (miR2Disease, Pharmaco-miR VerSe and PhenomiR). biocViews: miRNAData, Homo_sapiens_Data, Mus_musculus_Data, Rattus_norvegicus_Data, OrganismData Author: Yuanbin Ru [aut], Matt Mulvahill [aut], Spencer Mahaffey [cre, aut], Katerina Kechris [aut, cph, ths] Maintainer: Spencer Mahaffey URL: https://github.com/KechrisLab/multiMiR VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/multiMiR/issues git_url: https://git.bioconductor.org/packages/multiMiR git_branch: RELEASE_3_22 git_last_commit: 0cd565e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/multiMiR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiMiR_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiMiR_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiMiR_1.32.0.tgz vignettes: vignettes/multiMiR/inst/doc/multiMiR.html vignetteTitles: The multiMiR user's guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiMiR/inst/doc/multiMiR.R suggestsMe: EpiMix dependencyCount: 54 Package: MultimodalExperiment Version: 1.10.0 Depends: R (>= 4.3.0), IRanges, S4Vectors Imports: BiocGenerics, MultiAssayExperiment, methods, utils Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 4618f39c6f9bd63e27fe89819de06f59 NeedsCompilation: no Title: Integrative Bulk and Single-Cell Experiment Container Description: MultimodalExperiment is an S4 class that integrates bulk and single-cell experiment data; it is optimally storage-efficient, and its methods are exceptionally fast. It effortlessly represents multimodal data of any nature and features normalized experiment, subject, sample, and cell annotations, which are related to underlying biological experiments through maps. Its coordination methods are opt-in and employ database-like join operations internally to deliver fast and flexible management of multimodal data. biocViews: DataRepresentation, Infrastructure, SingleCell Author: Lucas Schiffer [aut, cre] (ORCID: ) Maintainer: Lucas Schiffer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultimodalExperiment git_branch: RELEASE_3_22 git_last_commit: ed79028 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MultimodalExperiment_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MultimodalExperiment_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MultimodalExperiment_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MultimodalExperiment_1.10.0.tgz vignettes: vignettes/MultimodalExperiment/inst/doc/MultimodalExperiment.html vignetteTitles: MultimodalExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultimodalExperiment/inst/doc/MultimodalExperiment.R dependencyCount: 46 Package: MultiRNAflow Version: 1.8.0 Depends: Mfuzz (>= 2.64.0), R (>= 4.4) Imports: Biobase (>= 2.54.0), ComplexHeatmap (>= 2.20.0), DESeq2 (>= 1.44.0), factoextra (>= 1.0.7), FactoMineR (>= 2.11), ggalluvial (>= 0.12.5), ggplot2 (>= 3.5.1), ggplotify (>= 0.1.2), ggrepel (>= 0.9.5), gprofiler2 (>= 0.2.3), graphics (>= 4.2.2), grDevices (>= 4.2.2), grid (>= 4.2.2), plot3D (>= 1.4.1), plot3Drgl (>= 1.0.4), reshape2 (>= 1.4.4), rlang (>= 1.1.6), S4Vectors (>= 0.42.0), stats (>= 4.2.2), SummarizedExperiment (>= 1.34.0), UpSetR (>= 1.4.0), utils (>= 4.2.2) Suggests: BiocGenerics (>= 0.40.0), BiocStyle (>= 2.32.1), e1071 (>= 1.7.12), knitr (>= 1.47), rmarkdown (>= 2.27), testthat (>= 3.0.0) License: GPL-3 | file LICENSE MD5sum: 29641e591ba3b47af846d203466dbeb1 NeedsCompilation: no Title: An R package for integrated analysis of temporal RNA-seq data with multiple biological conditions Description: Our R package MultiRNAflow provides an easy to use unified framework allowing to automatically make both unsupervised and supervised (DE) analysis for datasets with an arbitrary number of biological conditions and time points. In particular, our code makes a deep downstream analysis of DE information, e.g. identifying temporal patterns across biological conditions and DE genes which are specific to a biological condition for each time. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, TimeCourse, Preprocessing, Visualization, Normalization, PrincipalComponent, Clustering, DifferentialExpression, GeneSetEnrichment, Pathways Author: Rodolphe Loubaton [aut, cre] (ORCID: ), Nicolas Champagnat [aut, ths] (ORCID: ), Laurent Vallat [aut, ths] (ORCID: ), Pierre Vallois [aut] (ORCID: ), Région Grand Est [fnd], Cancéropôle Est [fnd] Maintainer: Rodolphe Loubaton URL: https://github.com/loubator/MultiRNAflow VignetteBuilder: knitr BugReports: https://github.com/loubator/MultiRNAflow/issues git_url: https://git.bioconductor.org/packages/MultiRNAflow git_branch: RELEASE_3_22 git_last_commit: 6e77950 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MultiRNAflow_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MultiRNAflow_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MultiRNAflow_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MultiRNAflow_1.8.0.tgz vignettes: vignettes/MultiRNAflow/inst/doc/MultiRNAflow_vignette-knitr.pdf, vignettes/MultiRNAflow/inst/doc/Running_analysis_with_MultiRNAflow.html vignetteTitles: MultiRNAflow: A R package for analysing RNA-seq raw counts with different time points and several biological conditions., Running_analysis_with_MultiRNAflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiRNAflow/inst/doc/MultiRNAflow_vignette-knitr.R, vignettes/MultiRNAflow/inst/doc/Running_analysis_with_MultiRNAflow.R dependencyCount: 187 Package: multiscan Version: 1.70.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) Archs: x64 MD5sum: f95ece4ba1ae1a0bd8be9c30063965d1 NeedsCompilation: yes Title: R package for combining multiple scans Description: Estimates gene expressions from several laser scans of the same microarray biocViews: Microarray, Preprocessing Author: Mizanur Khondoker , Chris Glasbey, Bruce Worton. Maintainer: Mizanur Khondoker git_url: https://git.bioconductor.org/packages/multiscan git_branch: RELEASE_3_22 git_last_commit: 386b33c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/multiscan_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiscan_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiscan_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiscan_1.70.0.tgz vignettes: vignettes/multiscan/inst/doc/multiscan.pdf vignetteTitles: An R Package for Estimating Gene Expressions using Multiple Scans hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiscan/inst/doc/multiscan.R dependencyCount: 7 Package: multistateQTL Version: 2.2.0 Depends: QTLExperiment, SummarizedExperiment, ComplexHeatmap, collapse Imports: methods, S4Vectors, data.table, grid, dplyr, tidyr, matrixStats, stats, fitdistrplus, viridis, ggplot2, circlize, mashr, grDevices Suggests: testthat, BiocStyle, knitr, covr, rmarkdown License: GPL-3 MD5sum: fb15442fe00fbc152bc6859a7408ad5e NeedsCompilation: no Title: Toolkit for the analysis of multi-state QTL data Description: A collection of tools for doing various analyses of multi-state QTL data, with a focus on visualization and interpretation. The package 'multistateQTL' contains functions which can remove or impute missing data, identify significant associations, as well as categorise features into global, multi-state or unique. The analysis results are stored in a 'QTLExperiment' object, which is based on the 'SummarisedExperiment' framework. biocViews: FunctionalGenomics, GeneExpression, Sequencing, Visualization, SNP, Software Author: Christina Del Azodi [aut], Davis McCarthy [ctb], Amelia Dunstone [cre, aut] (ORCID: ) Maintainer: Amelia Dunstone URL: https://github.com/dunstone-a/multistateQTL VignetteBuilder: knitr BugReports: https://github.com/dunstone-a/multistateQTL/issues git_url: https://git.bioconductor.org/packages/multistateQTL git_branch: RELEASE_3_22 git_last_commit: ccf4a9e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/multistateQTL_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multistateQTL_2.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multistateQTL_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multistateQTL_2.2.0.tgz vignettes: vignettes/multistateQTL/inst/doc/multistateQTL.html vignetteTitles: multistateQTL: Orchestrating multi-state QTL analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multistateQTL/inst/doc/multistateQTL.R dependencyCount: 104 Package: multiWGCNA Version: 1.7.0 Depends: R (>= 4.3.0), ggalluvial Imports: stringr, readr, WGCNA, dplyr, reshape2, data.table, patchwork, scales, igraph, flashClust, ggplot2, dcanr, cowplot, ggrepel, methods, SummarizedExperiment Suggests: BiocStyle, doParallel, ExperimentHub, knitr, markdown, rmarkdown, testthat (>= 3.0.0), vegan License: GPL-3 Archs: x64 MD5sum: ec79d505567174e77651242ce18e3672 NeedsCompilation: no Title: multiWGCNA Description: An R package for deeping mining gene co-expression networks in multi-trait expression data. Provides functions for analyzing, comparing, and visualizing WGCNA networks across conditions. multiWGCNA was designed to handle the common case where there are multiple biologically meaningful sample traits, such as disease vs wildtype across development or anatomical region. biocViews: Sequencing, RNASeq, GeneExpression, DifferentialExpression, Regression, Clustering Author: Dario Tommasini [aut, cre] (ORCID: ), Brent Fogel [aut, ctb] Maintainer: Dario Tommasini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiWGCNA git_branch: devel git_last_commit: b6102c0 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/multiWGCNA_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiWGCNA_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiWGCNA_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiWGCNA_1.7.0.tgz vignettes: vignettes/multiWGCNA/inst/doc/astrocyte_map_v2.html, vignettes/multiWGCNA/inst/doc/autism_full_workflow.html vignetteTitles: Astrocyte multiWGCNA network, General Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiWGCNA/inst/doc/astrocyte_map_v2.R, vignettes/multiWGCNA/inst/doc/autism_full_workflow.R suggestsMe: multiWGCNAdata dependencyCount: 140 Package: multtest Version: 2.66.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL MD5sum: 267407926d63a1a6d1f35019bfd3e0b5 NeedsCompilation: yes Title: Resampling-based multiple hypothesis testing Description: Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra Taylor, Sandrine Dudoit Maintainer: Katherine S. Pollard git_url: https://git.bioconductor.org/packages/multtest git_branch: RELEASE_3_22 git_last_commit: 2722ca2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/multtest_2.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multtest_2.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multtest_2.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multtest_2.66.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: aCGH, BicARE, KCsmart, PREDA, rain, REDseq, siggenes, webbioc, cp4p, DiffCorr, PCS importsMe: a4Base, ABarray, adSplit, ALDEx2, anota, BulkSignalR, ChIPpeakAnno, GUIDEseq, metabomxtr, nethet, OCplus, phyloseq, RTopper, singleCellTK, webbioc, mutoss, nlcv, pRF, Qploidy, structSSI, TcGSA suggestsMe: annaffy, CAMERA, ecolitk, factDesign, GOstats, GSEAlm, ropls, topGO, xcms, cherry, POSTm dependencyCount: 15 Package: mumosa Version: 1.18.0 Depends: SingleCellExperiment Imports: stats, utils, methods, igraph, Matrix, BiocGenerics, BiocParallel, IRanges, S4Vectors, DelayedArray, DelayedMatrixStats, SummarizedExperiment, BiocNeighbors, BiocSingular, ScaledMatrix, beachmat, scuttle, metapod, scran, batchelor, uwot Suggests: testthat, knitr, BiocStyle, rmarkdown, scater, bluster, DropletUtils, scRNAseq License: GPL-3 MD5sum: 50febcbd5612dde446792c22fae1f635 NeedsCompilation: no Title: Multi-Modal Single-Cell Analysis Methods Description: Assorted utilities for multi-modal analyses of single-cell datasets. Includes functions to combine multiple modalities for downstream analysis, perform MNN-based batch correction across multiple modalities, and to compute correlations between assay values for different modalities. biocViews: ImmunoOncology, SingleCell, RNASeq Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: http://bioconductor.org/packages/mumosa VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/mumosa git_branch: RELEASE_3_22 git_last_commit: 18e57eb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mumosa_1.18.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mumosa_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mumosa_1.18.0.tgz vignettes: vignettes/mumosa/inst/doc/overview.html vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mumosa/inst/doc/overview.R dependsOnMe: OSCA.advanced suggestsMe: Ibex dependencyCount: 73 Package: MungeSumstats Version: 1.18.0 Depends: R(>= 4.1) Imports: data.table, utils, R.utils, dplyr, stats, GenomicRanges, GenomeInfoDb, IRanges, ieugwasr(>= 1.0.1), BSgenome, Biostrings, stringr, VariantAnnotation, methods, parallel, rtracklayer(>= 1.59.1), RCurl Suggests: SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BiocGenerics, S4Vectors, rmarkdown, markdown, knitr, testthat (>= 3.0.0), UpSetR, BiocStyle, covr, Rsamtools, MatrixGenerics, badger, BiocParallel, GenomicFiles License: Artistic-2.0 MD5sum: 607d742c93aabd515a1234ae4296f320 NeedsCompilation: no Title: Standardise summary statistics from GWAS Description: The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences. biocViews: SNP, WholeGenome, Genetics, ComparativeGenomics, GenomeWideAssociation, GenomicVariation, Preprocessing Author: Alan Murphy [aut, cre] (ORCID: ), Brian Schilder [aut, ctb] (ORCID: ), Nathan Skene [aut] (ORCID: ) Maintainer: Alan Murphy URL: https://github.com/neurogenomics/MungeSumstats, https://al-murphy.github.io/MungeSumstats/ VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/MungeSumstats/issues git_url: https://git.bioconductor.org/packages/MungeSumstats git_branch: RELEASE_3_22 git_last_commit: e2c1431 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MungeSumstats_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MungeSumstats_1.17.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MungeSumstats_1.17.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MungeSumstats_1.18.0.tgz vignettes: vignettes/MungeSumstats/inst/doc/docker.html, vignettes/MungeSumstats/inst/doc/MungeSumstats.html, vignettes/MungeSumstats/inst/doc/OpenGWAS.html vignetteTitles: docker, MungeSumstats, OpenGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MungeSumstats/inst/doc/docker.R, vignettes/MungeSumstats/inst/doc/MungeSumstats.R, vignettes/MungeSumstats/inst/doc/OpenGWAS.R dependencyCount: 94 Package: muscat Version: 1.24.0 Depends: R (>= 4.5) Imports: BiocParallel, blme, ComplexHeatmap, data.table, DESeq2, dplyr, edgeR, ggplot2, glmmTMB, grDevices, grid, IHW, limma, lmerTest, lme4, Matrix, matrixStats, methods, progress, purrr, rlang, S4Vectors, scales, scater, scuttle, sctransform, stats, SingleCellExperiment, SummarizedExperiment, variancePartition Suggests: BiocStyle, cowplot, countsimQC, AnnotationHub, ExperimentHub, iCOBRA, knitr, patchwork, phylogram, RColorBrewer, reshape2, rmarkdown, statmod, stageR, testthat, tidyr, UpSetR License: GPL-3 MD5sum: 5e48daf929cfc2484096445dc35ae56d NeedsCompilation: no Title: Multi-sample multi-group scRNA-seq data analysis tools Description: `muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data. biocViews: ImmunoOncology, DifferentialExpression, Sequencing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre] (ORCID: ), Pierre-Luc Germain [aut], Charlotte Soneson [aut], Anthony Sonrel [aut], Jeroen Gilis [aut], Davide Risso [aut], Lieven Clement [aut], Mark D. Robinson [aut, fnd] Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/muscat VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/muscat/issues git_url: https://git.bioconductor.org/packages/muscat git_branch: RELEASE_3_22 git_last_commit: a6749ec git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/muscat_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/muscat_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/muscat_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/muscat_1.24.0.tgz vignettes: vignettes/muscat/inst/doc/analysis.html, vignettes/muscat/inst/doc/bbhw.html, vignettes/muscat/inst/doc/detection.html, vignettes/muscat/inst/doc/simulation.html vignetteTitles: "1. DS analysis", "4. Increasing power with bulk-based hypothesis weighing", "3. Differential detection", "2. Data simulation" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscat/inst/doc/analysis.R, vignettes/muscat/inst/doc/bbhw.R, vignettes/muscat/inst/doc/detection.R, vignettes/muscat/inst/doc/simulation.R suggestsMe: crumblr, dreamlet, muscData dependencyCount: 175 Package: muscle Version: 3.52.0 Depends: Biostrings License: Unlimited MD5sum: e7f74672139343077839d02c200cfbfa NeedsCompilation: yes Title: Multiple Sequence Alignment with MUSCLE Description: MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences. biocViews: MultipleSequenceAlignment, Alignment, Sequencing, Genetics, SequenceMatching, DataImport Author: Algorithm by Robert C. Edgar. R port by Alex T. Kalinka. Maintainer: Alex T. Kalinka URL: http://www.drive5.com/muscle/ git_url: https://git.bioconductor.org/packages/muscle git_branch: RELEASE_3_22 git_last_commit: 1c5d26f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/muscle_3.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/muscle_3.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/muscle_3.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/muscle_3.52.0.tgz vignettes: vignettes/muscle/inst/doc/muscle-vignette.pdf vignetteTitles: A guide to using muscle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscle/inst/doc/muscle-vignette.R suggestsMe: BOLDconnectR, orthGS, seqmagick dependencyCount: 15 Package: musicatk Version: 2.4.0 Depends: R (>= 4.4.0), NMF Imports: SummarizedExperiment, VariantAnnotation, Biostrings, base, methods, magrittr, tibble, tidyr, gtools, gridExtra, MCMCprecision, MASS, matrixTests, data.table, dplyr, rlang, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, S4Vectors, uwot, ggplot2, stringr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, decompTumor2Sig, topicmodels, ggrepel, plotly, utils, factoextra, cluster, ComplexHeatmap, philentropy, maftools, shiny, stringi, tidyverse, ggpubr, Matrix (>= 1.6.1), scales Suggests: TCGAbiolinks, shinyBS, shinyalert, shinybusy, shinydashboard, shinyjs, shinyjqui, sortable, testthat, BiocStyle, knitr, rmarkdown, survival, XVector, qpdf, covr, shinyWidgets, cowplot, withr License: LGPL-3 MD5sum: 366fa3792affb0747912826ac42cfc45 NeedsCompilation: no Title: Mutational Signature Comprehensive Analysis Toolkit Description: Mutational signatures are carcinogenic exposures or aberrant cellular processes that can cause alterations to the genome. We created musicatk (MUtational SIgnature Comprehensive Analysis ToolKit) to address shortcomings in versatility and ease of use in other pre-existing computational tools. Although many different types of mutational data have been generated, current software packages do not have a flexible framework to allow users to mix and match different types of mutations in the mutational signature inference process. Musicatk enables users to count and combine multiple mutation types, including SBS, DBS, and indels. Musicatk calculates replication strand, transcription strand and combinations of these features along with discovery from unique and proprietary genomic feature associated with any mutation type. Musicatk also implements several methods for discovery of new signatures as well as methods to infer exposure given an existing set of signatures. Musicatk provides functions for visualization and downstream exploratory analysis including the ability to compare signatures between cohorts and find matching signatures in COSMIC V2 or COSMIC V3. biocViews: Software, BiologicalQuestion, SomaticMutation, VariantAnnotation Author: Aaron Chevalier [aut] (ORCHID: 0000-0002-3968-9250), Natasha Gurevich [aut] (ORCHID: 0000-0002-0747-6840), Tao Guo [aut] (ORCHID: 0009-0005-8960-9203), Joshua D. Campbell [aut, cre] (ORCID: ) Maintainer: Joshua D. Campbell URL: https://www.camplab.net/musicatk/ VignetteBuilder: knitr BugReports: https://github.com/campbio/musicatk/issues git_url: https://git.bioconductor.org/packages/musicatk git_branch: RELEASE_3_22 git_last_commit: a2d2204 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/musicatk_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/musicatk_2.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/musicatk_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/musicatk_2.4.0.tgz vignettes: vignettes/musicatk/inst/doc/musicatk.html vignetteTitles: Mutational Signature Comprehensive Analysis Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/musicatk/inst/doc/musicatk.R dependencyCount: 267 Package: MutationalPatterns Version: 3.19.1 Depends: R (>= 4.2.0), GenomicRanges (>= 1.24.0), NMF (>= 0.20.6) Imports: stats, S4Vectors, BiocGenerics (>= 0.18.0), BSgenome (>= 1.40.0), VariantAnnotation (>= 1.18.1), dplyr (>= 0.8.3), tibble(>= 2.1.3), purrr (>= 0.3.2), tidyr (>= 1.0.0), stringr (>= 1.4.0), magrittr (>= 1.5), ggplot2 (>= 2.1.0), pracma (>= 1.8.8), IRanges (>= 2.6.0), Seqinfo, GenomeInfoDb (>= 1.45.9), Biostrings (>= 2.40.0), ggdendro (>= 0.1-20), cowplot (>= 0.9.2), ggalluvial (>= 0.12.2), RColorBrewer, methods Suggests: BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), BiocStyle (>= 2.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), biomaRt (>= 2.28.0), gridExtra (>= 2.2.1), rtracklayer (>= 1.32.2), ccfindR (>= 1.6.0), GenomicFeatures, AnnotationDbi, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 4de2512f0263e8b205f95f86d3e31c1c NeedsCompilation: no Title: Comprehensive genome-wide analysis of mutational processes Description: Mutational processes leave characteristic footprints in genomic DNA. This package provides a comprehensive set of flexible functions that allows researchers to easily evaluate and visualize a multitude of mutational patterns in base substitution catalogues of e.g. healthy samples, tumour samples, or DNA-repair deficient cells. The package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes. The package works with single nucleotide variants (SNVs), insertions and deletions (Indels), double base substitutions (DBSs) and larger multi base substitutions (MBSs). The package provides functionalities for both extracting mutational signatures de novo and determining the contribution of previously identified mutational signatures on a single sample level. MutationalPatterns integrates with common R genomic analysis workflows and allows easy association with (publicly available) annotation data. biocViews: Genetics, SomaticMutation Author: Freek Manders [aut] (ORCID: ), Francis Blokzijl [aut] (ORCID: ), Roel Janssen [aut] (ORCID: ), Jurrian de Kanter [ctb] (ORCID: ), Rurika Oka [ctb] (ORCID: ), Mark van Roosmalen [cre], Ruben van Boxtel [aut, cph] (ORCID: ), Edwin Cuppen [aut] (ORCID: ) Maintainer: Mark van Roosmalen URL: https://doi.org/doi:10.1186/s12864-022-08357-3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MutationalPatterns git_branch: devel git_last_commit: a1969dd git_last_commit_date: 2025-07-31 Date/Publication: 2025-10-07 source.ver: src/contrib/MutationalPatterns_3.19.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MutationalPatterns_3.19.1.zip vignettes: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.html vignetteTitles: Introduction to MutationalPatterns hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.R importsMe: RESOLVE suggestsMe: SUITOR dependencyCount: 120 Package: mutscan Version: 1.0.0 Depends: R (>= 4.5.0) Imports: BiocGenerics, S4Vectors, methods, SummarizedExperiment, Rcpp, edgeR (>= 3.42.0), dplyr, Matrix, limma, tidyr, stats, GGally, ggplot2, tidyselect (>= 1.2.0), tibble, rlang, grDevices, csaw, rmarkdown, xfun, DT, ggrepel, IRanges, utils, DelayedArray, tools LinkingTo: Rcpp Suggests: testthat (>= 3.0.0), BiocStyle, knitr, Biostrings, pwalign, plotly, scattermore, BiocManager License: MIT + file LICENSE MD5sum: 2d0666fcfae5997a59f3cf92327e03d2 NeedsCompilation: yes Title: Preprocessing and Analysis of Deep Mutational Scanning Data Description: Provides functionality for processing and statistical analysis of multiplexed assays of variant effect (MAVE) and similar data. The package contains functions covering the full workflow from raw FASTQ files to publication-ready visualizations. A broad range of library designs can be processed with a single, unified interface. biocViews: GeneticVariability, GenomicVariation, Preprocessing Author: Charlotte Soneson [aut, cre] (ORCID: ), Michael Stadler [aut] (ORCID: ), Friedrich Miescher Institute for Biomedical Research [cph] Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/mutscan SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/mutscan/issues git_url: https://git.bioconductor.org/packages/mutscan git_branch: RELEASE_3_22 git_last_commit: 79672e3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mutscan_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mutscan_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mutscan_1.0.0.tgz vignettes: vignettes/mutscan/inst/doc/mutscan.html vignetteTitles: mutscan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mutscan/inst/doc/mutscan.R dependencyCount: 110 Package: MVCClass Version: 1.84.0 Depends: R (>= 2.1.0), methods License: LGPL Archs: x64 MD5sum: 0dbc874160ce5ffa30fd05e86e7ebf00 NeedsCompilation: no Title: Model-View-Controller (MVC) Classes Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/MVCClass git_branch: RELEASE_3_22 git_last_commit: 978002b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MVCClass_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MVCClass_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MVCClass_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MVCClass_1.84.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BioMVCClass dependencyCount: 1 Package: MWASTools Version: 1.34.0 Depends: R (>= 3.5.0) Imports: glm2, ppcor, qvalue, car, boot, grid, ggplot2, gridExtra, igraph, SummarizedExperiment, KEGGgraph, RCurl, KEGGREST, ComplexHeatmap, stats, utils Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: CC BY-NC-ND 4.0 Archs: x64 MD5sum: 21d25fdaaec8cea2deeee398b91e3ad9 NeedsCompilation: no Title: MWASTools: an integrated pipeline to perform metabolome-wide association studies Description: MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results. biocViews: Metabolomics, Lipidomics, Cheminformatics, SystemsBiology, QualityControl Author: Andrea Rodriguez-Martinez, Joram M. Posma, Rafael Ayala, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MWASTools git_branch: RELEASE_3_22 git_last_commit: 58721f5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/MWASTools_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MWASTools_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MWASTools_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MWASTools_1.34.0.tgz vignettes: vignettes/MWASTools/inst/doc/MWASTools.html vignetteTitles: MWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MWASTools/inst/doc/MWASTools.R importsMe: MetaboSignal dependencyCount: 122 Package: mygene Version: 1.46.0 Depends: R (>= 3.2.1), GenomicFeatures, txdbmaker Imports: methods, utils, stats, httr (>= 0.3), jsonlite (>= 0.9.7), Hmisc, sqldf, plyr, S4Vectors Suggests: BiocStyle License: Artistic-2.0 MD5sum: ba20a759a98892ea18beff1a2150c61b NeedsCompilation: no Title: Access MyGene.Info_ services Description: MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. biocViews: Annotation Author: Adam Mark, Ryan Thompson, Cyrus Afrasiabi, Chunlei Wu Maintainer: Adam Mark, Cyrus Afrasiabi, Chunlei Wu git_url: https://git.bioconductor.org/packages/mygene git_branch: RELEASE_3_22 git_last_commit: 7e59021 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mygene_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mygene_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mygene_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mygene_1.46.0.tgz vignettes: vignettes/mygene/inst/doc/mygene.pdf vignetteTitles: Using mygene.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mygene/inst/doc/mygene.R importsMe: MetaboSignal dependencyCount: 143 Package: myvariant Version: 1.39.1 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, Seqinfo, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: 3022e1750f40c6170e5f55f9e6a437a7 NeedsCompilation: no Title: Accesses MyVariant.info variant query and annotation services Description: MyVariant.info is a comprehensive aggregation of variant annotation resources. myvariant is a wrapper for querying MyVariant.info services biocViews: VariantAnnotation, Annotation, GenomicVariation Author: Adam Mark Maintainer: Adam Mark, Chunlei Wu git_url: https://git.bioconductor.org/packages/myvariant git_branch: devel git_last_commit: 236c7a9 git_last_commit_date: 2025-07-22 Date/Publication: 2025-10-07 source.ver: src/contrib/myvariant_1.39.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/myvariant_1.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/myvariant_1.39.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/myvariant_1.40.0.tgz vignettes: vignettes/myvariant/inst/doc/myvariant.pdf vignetteTitles: Using MyVariant.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/myvariant/inst/doc/myvariant.R dependencyCount: 124 Package: mzID Version: 1.48.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 5b1160da18271053174e06a7314b5881 NeedsCompilation: no Title: An mzIdentML parser for R Description: A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly. biocViews: ImmunoOncology, DataImport, MassSpectrometry, Proteomics Author: Laurent Gatto [ctb, cre] (ORCID: ), Thomas Pedersen [aut] (ORCID: ), Vladislav Petyuk [ctb] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mzID git_branch: RELEASE_3_22 git_last_commit: 96e9485 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mzID_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mzID_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mzID_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mzID_1.48.0.tgz vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf vignetteTitles: Using mzID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzID/inst/doc/HOWTO_mzID.R importsMe: MSnbase, MSnID, TargetDecoy suggestsMe: mzR, PSMatch, RforProteomics, PepMapViz dependencyCount: 11 Package: mzR Version: 2.44.0 Depends: R (>= 4.0.0), Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.17.3), ncdf4 LinkingTo: Rcpp, Rhdf5lib (>= 1.1.4) Suggests: msdata (>= 0.15.1), RUnit, mzID, BiocStyle (>= 2.5.19), knitr, XML, rmarkdown License: Artistic-2.0 MD5sum: 7718ab7f48d6edcf53fb0a8c437cae77 NeedsCompilation: yes Title: parser for netCDF, mzXML and mzML and mzIdentML files (mass spectrometry data) Description: mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. biocViews: ImmunoOncology, Infrastructure, DataImport, Proteomics, Metabolomics, MassSpectrometry Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou, Johannes Rainer Maintainer: Steffen Neumann URL: https://github.com/sneumann/mzR/ SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/sneumann/mzR/issues/ git_url: https://git.bioconductor.org/packages/mzR git_branch: RELEASE_3_22 git_last_commit: 2637134 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/mzR_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mzR_2.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mzR_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mzR_2.44.0.tgz vignettes: vignettes/mzR/inst/doc/mzR.html vignetteTitles: Accessin raw mass spectrometry and identification data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzR/inst/doc/mzR.R dependsOnMe: MSnbase importsMe: adductomicsR, CluMSID, MSnID, msPurity, peakPantheR, RMassBank, SIMAT, TargetDecoy, topdownr, xcms, yamss suggestsMe: AnnotationHub, Chromatograms, koinar, MetaboAnnotation, MsBackendMetaboLights, MsBackendRawFileReader, MsBackendSql, MsDataHub, MsExperiment, MsQuality, PSMatch, qcmetrics, Spectra, SpectraQL, SpectriPy, msdata, RforProteomics, chromConverter, erah dependencyCount: 11 Package: NADfinder Version: 1.34.0 Depends: R (>= 3.5.0), BiocGenerics, IRanges, GenomicRanges, S4Vectors, SummarizedExperiment Imports: graphics, methods, baseline, signal, GenomicAlignments, GenomeInfoDb, rtracklayer, limma, trackViewer, stats, utils, Rsamtools, metap, EmpiricalBrownsMethod,ATACseqQC, corrplot, csaw Suggests: RUnit, BiocStyle, knitr, BSgenome.Mmusculus.UCSC.mm10, testthat, BiocManager, rmarkdown License: GPL (>= 2) MD5sum: a49cc419630ace2f2660e3abb7759ac3 NeedsCompilation: no Title: Call wide peaks for sequencing data Description: Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation. biocViews: Sequencing, DNASeq, GeneRegulation, PeakDetection Author: Jianhong Ou, Haibo Liu, Jun Yu, Hervé Pagès, Paul Kaufman, Lihua Julie Zhu Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NADfinder git_branch: RELEASE_3_22 git_last_commit: 24bbc3f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NADfinder_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NADfinder_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NADfinder_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NADfinder_1.34.0.tgz vignettes: vignettes/NADfinder/inst/doc/NADfinder.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R dependencyCount: 225 Package: NanoMethViz Version: 3.6.0 Depends: R (>= 4.0.0), methods, ggplot2 (>= 3.4.0) Imports: cpp11 (>= 0.2.5), readr, cli, S4Vectors, SummarizedExperiment, BiocSingular, bsseq, forcats, assertthat, AnnotationDbi, Rcpp, dplyr, dbscan, e1071, fs, GenomicRanges, Biostrings, ggrastr, glue, graphics, IRanges, limma (>= 3.44.0), patchwork, purrr, rlang, R.utils, Rsamtools, scales (>= 1.2.0), stats, stringr, tibble, tidyr, utils, withr LinkingTo: Rcpp Suggests: BiocStyle, DSS, Mus.musculus (>= 1.3.1), Homo.sapiens (>= 1.3.1), org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, knitr, rmarkdown, rtracklayer, testthat (>= 3.0.0), covr License: Apache License (>= 2.0) MD5sum: 0d83e786a6361cac8ae039238b80c29c NeedsCompilation: yes Title: Visualise methylation data from Oxford Nanopore sequencing Description: NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features. biocViews: Software, LongRead, Visualization, DifferentialMethylation, DNAMethylation, Epigenetics, DataImport Author: Shian Su [cre, aut] Maintainer: Shian Su URL: https://github.com/shians/NanoMethViz, https://shians.github.io/NanoMethViz/ SystemRequirements: C++20 VignetteBuilder: knitr BugReports: https://github.com/Shians/NanoMethViz/issues git_url: https://git.bioconductor.org/packages/NanoMethViz git_branch: RELEASE_3_22 git_last_commit: 836d7c6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NanoMethViz_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NanoMethViz_3.5.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NanoMethViz_3.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NanoMethViz_3.6.0.tgz vignettes: vignettes/NanoMethViz/inst/doc/UsersGuide.html vignetteTitles: User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoMethViz/inst/doc/UsersGuide.R dependencyCount: 145 Package: NanoStringDiff Version: 1.40.0 Depends: Biobase Imports: matrixStats, methods, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle License: GPL Archs: x64 MD5sum: 51a87ceb939e70ddfe1e167e46e80786 NeedsCompilation: yes Title: Differential Expression Analysis of NanoString nCounter Data Description: This Package utilizes a generalized linear model(GLM) of the negative binomial family to characterize count data and allows for multi-factor design. NanoStrongDiff incorporate size factors, calculated from positive controls and housekeeping controls, and background level, obtained from negative controls, in the model framework so that all the normalization information provided by NanoString nCounter Analyzer is fully utilized. biocViews: DifferentialExpression, Normalization Author: hong wang , tingting zhai , chi wang Maintainer: tingting zhai ,hong wang git_url: https://git.bioconductor.org/packages/NanoStringDiff git_branch: RELEASE_3_22 git_last_commit: ab375a4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NanoStringDiff_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NanoStringDiff_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NanoStringDiff_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NanoStringDiff_1.40.0.tgz vignettes: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf vignetteTitles: NanoStringDiff Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.R suggestsMe: NanoTube dependencyCount: 9 Package: NanoStringNCTools Version: 1.18.0 Depends: R (>= 3.6), Biobase, S4Vectors, ggplot2 Imports: BiocGenerics, Biostrings, ggbeeswarm, ggiraph, ggthemes, grDevices, IRanges, methods, pheatmap, RColorBrewer, stats, utils Suggests: biovizBase, ggbio, RUnit, rmarkdown, knitr, qpdf License: MIT MD5sum: 6723ca0ff82e91388d0e9eb2ec3b8522 NeedsCompilation: no Title: NanoString nCounter Tools Description: Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, mRNAMicroarray, ProprietaryPlatforms, RNASeq Author: Patrick Aboyoun [aut], Nicole Ortogero [aut], Maddy Griswold [cre], Zhi Yang [ctb] Maintainer: Maddy Griswold VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringNCTools git_branch: RELEASE_3_22 git_last_commit: 00af008 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NanoStringNCTools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NanoStringNCTools_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NanoStringNCTools_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NanoStringNCTools_1.18.0.tgz vignettes: vignettes/NanoStringNCTools/inst/doc/Introduction.html vignetteTitles: Introduction to the NanoStringRCCSet Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NanoStringNCTools/inst/doc/Introduction.R dependsOnMe: GeomxTools, GeoMxWorkflows importsMe: GeoDiff dependencyCount: 78 Package: NanoTube Version: 1.16.0 Depends: R (>= 4.1), Biobase, ggplot2, limma Imports: fgsea, methods, reshape, stats, utils Suggests: grid, kableExtra, knitr, NanoStringDiff, pheatmap, plotly, rlang, rmarkdown, ruv, RUVSeq, shiny, testthat, xlsx License: GPL-3 + file LICENSE MD5sum: 231107a2dbfd332d878d34cab2057fd0 NeedsCompilation: no Title: An Easy Pipeline for NanoString nCounter Data Analysis Description: NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages. biocViews: Software, GeneExpression, DifferentialExpression, QualityControl Author: Caleb Class [cre, aut] (ORCID: ), Caiden Lukan [ctb] Maintainer: Caleb Class VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoTube git_branch: RELEASE_3_22 git_last_commit: b78a412 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NanoTube_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NanoTube_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NanoTube_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NanoTube_1.16.0.tgz vignettes: vignettes/NanoTube/inst/doc/NanoTube.html vignetteTitles: NanoTube Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NanoTube/inst/doc/NanoTube.R dependencyCount: 46 Package: NBAMSeq Version: 1.26.0 Depends: R (>= 3.6), SummarizedExperiment, S4Vectors Imports: DESeq2, mgcv(>= 1.8-24), BiocParallel, genefilter, methods, stats, Suggests: knitr, rmarkdown, testthat, ggplot2 License: GPL-2 MD5sum: 358ef7c94bfcd36ee7a1d7472f539bfb NeedsCompilation: no Title: Negative Binomial Additive Model for RNA-Seq Data Description: High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. biocViews: RNASeq, DifferentialExpression, GeneExpression, Sequencing, Coverage Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/NBAMSeq VignetteBuilder: knitr BugReports: https://github.com/reese3928/NBAMSeq/issues git_url: https://git.bioconductor.org/packages/NBAMSeq git_branch: RELEASE_3_22 git_last_commit: 4d8f91b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NBAMSeq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NBAMSeq_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NBAMSeq_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NBAMSeq_1.26.0.tgz vignettes: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.html vignetteTitles: Negative Binomial Additive Model for RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.R dependencyCount: 85 Package: ncdfFlow Version: 2.56.0 Depends: R (>= 2.14.0), flowCore(>= 1.51.7), methods, BH Imports: Biobase,BiocGenerics,flowCore LinkingTo: cpp11,BH, Rhdf5lib Suggests: testthat,parallel,flowStats,knitr License: AGPL-3.0-only MD5sum: b7ea9a69a6d82273d02d25a3bea8b3b2 NeedsCompilation: yes Title: ncdfFlow: A package that provides HDF5 based storage for flow cytometry data. Description: Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. biocViews: ImmunoOncology, FlowCytometry Author: Mike Jiang,Greg Finak,N. Gopalakrishnan Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ncdfFlow git_branch: RELEASE_3_22 git_last_commit: 8e7c415 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ncdfFlow_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ncdfFlow_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ncdfFlow_2.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ncdfFlow_2.56.0.tgz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R dependsOnMe: ggcyto importsMe: flowStats, flowWorkspace, openCyto suggestsMe: COMPASS, cydar dependencyCount: 18 Package: ncGTW Version: 1.24.0 Depends: methods, BiocParallel, xcms Imports: Rcpp, grDevices, graphics, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-2 MD5sum: 638ae9c22137b498d5199003c9aa10ac NeedsCompilation: yes Title: Alignment of LC-MS Profiles by Neighbor-wise Compound-specific Graphical Time Warping with Misalignment Detection Description: The purpose of ncGTW is to help XCMS for LC-MS data alignment. Currently, ncGTW can detect the misaligned feature groups by XCMS, and the user can choose to realign these feature groups by ncGTW or not. biocViews: Software, MassSpectrometry, Metabolomics, Alignment Author: Chiung-Ting Wu Maintainer: Chiung-Ting Wu VignetteBuilder: knitr BugReports: https://github.com/ChiungTingWu/ncGTW/issues git_url: https://git.bioconductor.org/packages/ncGTW git_branch: RELEASE_3_22 git_last_commit: 2eb1b4f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ncGTW_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ncGTW_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ncGTW_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ncGTW_1.24.0.tgz vignettes: vignettes/ncGTW/inst/doc/ncGTW.html vignetteTitles: ncGTW User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncGTW/inst/doc/ncGTW.R dependencyCount: 140 Package: NCIgraph Version: 1.58.0 Depends: R (>= 4.0.0) Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.oo Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: 99788722cd9ed544cffcfa0313edd95d NeedsCompilation: no Title: Pathways from the NCI Pathways Database Description: Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them. biocViews: Pathways, GraphAndNetwork Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/NCIgraph git_branch: RELEASE_3_22 git_last_commit: eb4dd86 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NCIgraph_1.58.0.tar.gz vignettes: vignettes/NCIgraph/inst/doc/NCIgraph.pdf vignetteTitles: NCIgraph: networks from the NCI pathway integrated database as graphNEL objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NCIgraph/inst/doc/NCIgraph.R importsMe: DEGraph suggestsMe: DEGraph dependencyCount: 57 Package: ncRNAtools Version: 1.20.0 Imports: httr, xml2, utils, methods, grDevices, ggplot2, IRanges, GenomicRanges, S4Vectors Suggests: knitr, BiocStyle, rmarkdown, RUnit, BiocGenerics License: GPL-3 MD5sum: 625e9e116102df8e4d408c7af1195cdc NeedsCompilation: no Title: An R toolkit for non-coding RNA Description: ncRNAtools provides a set of basic tools for handling and analyzing non-coding RNAs. These include tools to access the RNAcentral database and to predict and visualize the secondary structure of non-coding RNAs. The package also provides tools to read, write and interconvert the file formats most commonly used for representing such secondary structures. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, StructuralPrediction Author: Lara Selles Vidal [cre, aut] (ORCID: ), Rafael Ayala [aut] (ORCID: ), Guy-Bart Stan [aut] (ORCID: ), Rodrigo Ledesma-Amaro [aut] (ORCID: ) Maintainer: Lara Selles Vidal VignetteBuilder: knitr BugReports: https://github.com/LaraSellesVidal/ncRNAtools/issues git_url: https://git.bioconductor.org/packages/ncRNAtools git_branch: RELEASE_3_22 git_last_commit: 38a582e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ncRNAtools_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ncRNAtools_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ncRNAtools_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ncRNAtools_1.20.0.tgz vignettes: vignettes/ncRNAtools/inst/doc/ncRNAtools.html vignetteTitles: rfaRm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncRNAtools/inst/doc/ncRNAtools.R dependencyCount: 38 Package: ndexr Version: 1.32.0 Depends: RCX Imports: httr, jsonlite, plyr, tidyr Suggests: BiocStyle, testthat, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: b9edfe818e3370419327564f75c53300 NeedsCompilation: no Title: NDEx R client library Description: This package offers an interface to NDEx servers, e.g. the public server at http://ndexbio.org/. It can retrieve and save networks via the API. Networks are offered as RCX object and as igraph representation. biocViews: Pathways, DataImport, Network Author: Florian Auer [cre, aut] (ORCID: ), Frank Kramer [ctb], Alex Ishkin [ctb], Dexter Pratt [ctb] Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/ndexr VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/ndexr/issues git_url: https://git.bioconductor.org/packages/ndexr git_branch: RELEASE_3_22 git_last_commit: 5dbac8e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ndexr_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ndexr_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ndexr_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ndexr_1.32.0.tgz vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html vignetteTitles: NDExR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R dependencyCount: 40 Package: nearBynding Version: 1.20.0 Depends: R (>= 4.0) Imports: R.utils, matrixStats, plyranges, transport, Rsamtools, S4Vectors, grDevices, graphics, rtracklayer, dplyr, Seqinfo, methods, GenomicRanges, utils, stats, magrittr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, ggplot2, gplots, BiocGenerics, rlang Suggests: knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 2b064fc9c336038d7cf9c19d384c30b6 NeedsCompilation: no Title: Discern RNA structure proximal to protein binding Description: Provides a pipeline to discern RNA structure at and proximal to the site of protein binding within regions of the transcriptome defined by the user. CLIP protein-binding data can be input as either aligned BAM or peak-called bedGraph files. RNA structure can either be predicted internally from sequence or users have the option to input their own RNA structure data. RNA structure binding profiles can be visually and quantitatively compared across multiple formats. biocViews: Visualization, MotifDiscovery, DataRepresentation, StructuralPrediction, Clustering, MultipleComparison Author: Veronica Busa [cre] Maintainer: Veronica Busa SystemRequirements: bedtools (>= 2.28.0), Stereogene (>= v2.22), CapR (>= 1.1.1) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nearBynding git_branch: RELEASE_3_22 git_last_commit: a5f36da git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/nearBynding_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nearBynding_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nearBynding_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nearBynding_1.20.0.tgz vignettes: vignettes/nearBynding/inst/doc/nearBynding.pdf vignetteTitles: nearBynding Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nearBynding/inst/doc/nearBynding.R dependencyCount: 106 Package: Nebulosa Version: 1.20.0 Depends: R (>= 4.0), ggplot2, patchwork Imports: SingleCellExperiment, SummarizedExperiment, SeuratObject, ks, Matrix, stats, methods, ggrastr Suggests: testthat, BiocStyle, knitr, rmarkdown, covr, scater, scran, DropletUtils, igraph, BiocFileCache, Seurat License: GPL-3 MD5sum: 21dfb39952381d96504ee13d3517ce1e NeedsCompilation: no Title: Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation Description: This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa. biocViews: Software, GeneExpression, SingleCell, Visualization, DimensionReduction Author: Jose Alquicira-Hernandez [aut, cre] (ORCID: ) Maintainer: Jose Alquicira-Hernandez URL: https://github.com/powellgenomicslab/Nebulosa VignetteBuilder: knitr BugReports: https://github.com/powellgenomicslab/Nebulosa/issues git_url: https://git.bioconductor.org/packages/Nebulosa git_branch: RELEASE_3_22 git_last_commit: 561343b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Nebulosa_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Nebulosa_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Nebulosa_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Nebulosa_1.20.0.tgz vignettes: vignettes/Nebulosa/inst/doc/introduction.html, vignettes/Nebulosa/inst/doc/nebulosa_seurat.html vignetteTitles: Visualization of gene expression with Nebulosa, Visualization of gene expression with Nebulosa (in Seurat) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Nebulosa/inst/doc/introduction.R, vignettes/Nebulosa/inst/doc/nebulosa_seurat.R suggestsMe: scCustomize, SCpubr dependencyCount: 82 Package: nempi Version: 1.18.0 Depends: R (>= 4.1), mnem Imports: e1071, nnet, randomForest, naturalsort, graphics, stats, utils, matrixStats, epiNEM Suggests: knitr, BiocGenerics, rmarkdown, RUnit, BiocStyle License: GPL-3 MD5sum: 9c844027fcd7e3fc000f7425b2c0f003 NeedsCompilation: no Title: Inferring unobserved perturbations from gene expression data Description: Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models. biocViews: Software, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneSignaling, Pathways, Network, Classification, NeuralNetwork, NetworkInference, ATACSeq, DNASeq, RNASeq, PooledScreens, CRISPR, SingleCell, SystemsBiology Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/nempi/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/nempi/issues git_url: https://git.bioconductor.org/packages/nempi git_branch: RELEASE_3_22 git_last_commit: 2f9cff2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/nempi_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nempi_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nempi_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nempi_1.18.0.tgz vignettes: vignettes/nempi/inst/doc/nempi.html vignetteTitles: nempi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nempi/inst/doc/nempi.R dependencyCount: 111 Package: NetActivity Version: 1.12.0 Depends: R (>= 4.1.0) Imports: airway, DelayedArray, DelayedMatrixStats, DESeq2, methods, methods, NetActivityData, SummarizedExperiment, utils Suggests: AnnotationDbi, BiocStyle, Fletcher2013a, knitr, org.Hs.eg.db, rmarkdown, testthat (>= 3.0.0), tidyverse License: MIT + file LICENSE MD5sum: e19d1cd4fa29ce2dae062acf9cf61d4a NeedsCompilation: no Title: Compute gene set scores from a deep learning framework Description: #' NetActivity enables to compute gene set scores from previously trained sparsely-connected autoencoders. The package contains a function to prepare the data (`prepareSummarizedExperiment`) and a function to compute the gene set scores (`computeGeneSetScores`). The package `NetActivityData` contains different pre-trained models to be directly applied to the data. Alternatively, the users might use the package to compute gene set scores using custom models. biocViews: RNASeq, Microarray, Transcription, FunctionalGenomics, GO, GeneExpression, Pathways, Software Author: Carlos Ruiz-Arenas [aut, cre] Maintainer: Carlos Ruiz-Arenas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetActivity git_branch: RELEASE_3_22 git_last_commit: 2ba9ebf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NetActivity_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NetActivity_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NetActivity_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NetActivity_1.12.0.tgz vignettes: vignettes/NetActivity/inst/doc/NetActivity.html vignetteTitles: "Gene set scores computation with NetActivity" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NetActivity/inst/doc/NetActivity.R dependencyCount: 59 Package: netboost Version: 2.18.0 Depends: R (>= 4.0.0) Imports: Rcpp, RcppParallel, parallel, grDevices, graphics, stats, utils, dynamicTreeCut, WGCNA, impute, colorspace, methods, BiocStyle, R.utils LinkingTo: Rcpp, RcppParallel Suggests: knitr, rmarkdown License: GPL-3 OS_type: unix MD5sum: b35622dd0b7f916bc1655706501d67a3 NeedsCompilation: yes Title: Network Analysis Supported by Boosting Description: Boosting supported network analysis for high-dimensional omics applications. This package comes bundled with the MC-UPGMA clustering package by Yaniv Loewenstein. biocViews: Software, StatisticalMethod, GraphAndNetwork, Network, Clustering, DimensionReduction, BiomedicalInformatics, Epigenetics, Metabolomics, Transcriptomics Author: Pascal Schlosser [aut, cre], Jochen Knaus [aut, ctb], Yaniv Loewenstein [aut] Maintainer: Pascal Schlosser URL: https://bioconductor.org/packages/release/bioc/html/netboost.html SystemRequirements: GNU make, Bash, Perl, Gzip VignetteBuilder: knitr BugReports: pascal.schlosser@uniklinik-freiburg.de git_url: https://git.bioconductor.org/packages/netboost git_branch: RELEASE_3_22 git_last_commit: 76f8650 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/netboost_2.18.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/netboost_2.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/netboost_2.18.0.tgz vignettes: vignettes/netboost/inst/doc/netboost.html vignetteTitles: The Netboost users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netboost/inst/doc/netboost.R dependencyCount: 111 Package: nethet Version: 1.42.0 Imports: glasso, mvtnorm, GeneNet, huge, CompQuadForm, ggm, mclust, parallel, GSA, limma, multtest, ICSNP, glmnet, network, ggplot2, grDevices, graphics, stats, utils Suggests: knitr, xtable, BiocStyle, testthat License: GPL-2 MD5sum: 1efdaecbc764bfc420f7a7f63fecc08f NeedsCompilation: yes Title: A bioconductor package for high-dimensional exploration of biological network heterogeneity Description: Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013). biocViews: Clustering, GraphAndNetwork Author: Nicolas Staedler, Frank Dondelinger Maintainer: Nicolas Staedler , Frank Dondelinger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nethet git_branch: RELEASE_3_22 git_last_commit: bc1b9c9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/nethet_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nethet_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nethet_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nethet_1.42.0.tgz vignettes: vignettes/nethet/inst/doc/nethet.pdf vignetteTitles: nethet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nethet/inst/doc/nethet.R dependencyCount: 74 Package: NetPathMiner Version: 1.46.0 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, graph, knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: df0df8638e4605e757d5143a58b788c3 NeedsCompilation: yes Title: NetPathMiner for Biological Network Construction, Path Mining and Visualization Description: NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation. biocViews: GraphAndNetwork, Pathways, Network, Clustering, Classification Author: Ahmed Mohamed [aut, cre] (ORCID: ), Tim Hancock [aut], Tim Hancock [aut] Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/NetPathMiner SystemRequirements: libxml2, libSBML (>= 5.5) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetPathMiner git_branch: RELEASE_3_22 git_last_commit: ee312b1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NetPathMiner_1.46.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NetPathMiner_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NetPathMiner_1.46.0.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.html vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R dependencyCount: 17 Package: netprioR Version: 1.36.0 Depends: methods, graphics, R(>= 3.3) Imports: stats, Matrix, dplyr, doParallel, foreach, parallel, sparseMVN, ggplot2, gridExtra, pROC Suggests: knitr, BiocStyle, pander License: GPL-3 MD5sum: 8bf07657008db4c4b82483b54abd6e5d NeedsCompilation: no Title: A model for network-based prioritisation of genes Description: A model for semi-supervised prioritisation of genes integrating network data, phenotypes and additional prior knowledge about TP and TN gene labels from the literature or experts. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Network Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://bioconductor.org/packages/netprioR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netprioR git_branch: RELEASE_3_22 git_last_commit: d579329 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/netprioR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/netprioR_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/netprioR_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/netprioR_1.36.0.tgz vignettes: vignettes/netprioR/inst/doc/netprioR.html vignetteTitles: netprioR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netprioR/inst/doc/netprioR.R dependencyCount: 41 Package: netresponse Version: 1.70.0 Depends: R (>= 2.15.1), BiocStyle, Rgraphviz, rmarkdown, methods, minet, mclust, reshape2 Imports: ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer Suggests: knitr License: GPL (>=2) MD5sum: 7c6505ef862b15fe7a4a94eb1239305f NeedsCompilation: yes Title: Functional Network Analysis Description: Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling. biocViews: CellBiology, Clustering, GeneExpression, Genetics, Network, GraphAndNetwork, DifferentialExpression, Microarray, NetworkInference, Transcription Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso Parkkinen Maintainer: Leo Lahti URL: https://github.com/antagomir/netresponse VignetteBuilder: knitr BugReports: https://github.com/antagomir/netresponse/issues git_url: https://git.bioconductor.org/packages/netresponse git_branch: RELEASE_3_22 git_last_commit: 2ffd016 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/netresponse_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/netresponse_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/netresponse_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/netresponse_1.70.0.tgz vignettes: vignettes/netresponse/inst/doc/NetResponse.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netresponse/inst/doc/NetResponse.R dependencyCount: 69 Package: NetSAM Version: 1.50.0 Depends: R (>= 3.0.0), seriation (>= 1.0-6), igraph (>= 2.0.0), tools (>= 3.0.0), WGCNA (>= 1.34.0), biomaRt (>= 2.18.0) Imports: methods, AnnotationDbi (>= 1.28.0), doParallel (>= 1.0.10), foreach (>= 1.4.0), survival (>= 2.37-7), GO.db (>= 2.10.0), R2HTML (>= 2.2.0), DBI (>= 0.5-1) Suggests: RUnit, BiocGenerics, org.Sc.sgd.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Dr.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.At.tair.db, rmarkdown, knitr, markdown License: LGPL Archs: x64 MD5sum: d1a5e62c8ea64f61f3002b1fa31276a6 NeedsCompilation: no Title: Network Seriation And Modularization Description: The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a weighted or unweighted network as an input, performs network seriation and modularization analysis, and generates as files that can be used as an input for the one-dimensional network visualization tool NetGestalt (http://www.netgestalt.org) or other network analysis. The NetSAM package can also generate correlation network (e.g. co-expression network) based on the input matrix data, perform seriation and modularization analysis for the correlation network and calculate the associations between the sample features and modules or identify the associated GO terms for the modules. Author: Jing Wang Maintainer: Zhiao Shi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetSAM git_branch: RELEASE_3_22 git_last_commit: 61a9843 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NetSAM_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NetSAM_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NetSAM_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NetSAM_1.50.0.tgz vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf vignetteTitles: NetSAM User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R dependencyCount: 133 Package: netSmooth Version: 1.30.0 Depends: R (>= 3.5), scater (>= 1.15.11), clusterExperiment (>= 2.1.6) Imports: entropy, SummarizedExperiment, SingleCellExperiment, Matrix, cluster, data.table, stats, methods, DelayedArray, HDF5Array (>= 1.15.13) Suggests: knitr, testthat, Rtsne, biomaRt, igraph, STRINGdb, NMI, pheatmap, ggplot2, BiocStyle, rmarkdown, BiocParallel, uwot License: GPL-3 MD5sum: 1d491ce22c00b587b7810eb0859db2dc NeedsCompilation: no Title: Network smoothing for scRNAseq Description: netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data. biocViews: Network, GraphAndNetwork, SingleCell, RNASeq, GeneExpression, Sequencing, Transcriptomics, Normalization, Preprocessing, Clustering, DimensionReduction Author: Jonathan Ronen [aut, cre], Altuna Akalin [aut] Maintainer: Jonathan Ronen URL: https://github.com/BIMSBbioinfo/netSmooth VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/netSmooth/issues git_url: https://git.bioconductor.org/packages/netSmooth git_branch: RELEASE_3_22 git_last_commit: e53eba9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/netSmooth_1.30.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/netSmooth_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/netSmooth_1.30.0.tgz vignettes: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.html, vignettes/netSmooth/inst/doc/netSmoothIntro.html vignetteTitles: Generation of PPI graph, netSmooth example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.R, vignettes/netSmooth/inst/doc/netSmoothIntro.R dependencyCount: 174 Package: NewWave Version: 1.20.0 Depends: R (>= 4.0), SummarizedExperiment Imports: methods, SingleCellExperiment, parallel, irlba, Matrix, DelayedArray, BiocSingular, SharedObject, stats Suggests: testthat, rmarkdown, splatter, mclust, Rtsne, ggplot2, Rcpp, BiocStyle, knitr License: GPL-3 MD5sum: a3ad212525ce7c3f32d9e5086607669a NeedsCompilation: no Title: Negative binomial model for scRNA-seq Description: A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise. biocViews: Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Sequencing, Coverage, Regression Author: Federico Agostinis [aut, cre], Chiara Romualdi [aut], Gabriele Sales [aut], Davide Risso [aut] Maintainer: Federico Agostinis VignetteBuilder: knitr BugReports: https://github.com/fedeago/NewWave/issues git_url: https://git.bioconductor.org/packages/NewWave git_branch: RELEASE_3_22 git_last_commit: abfdb4d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NewWave_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NewWave_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NewWave_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NewWave_1.20.0.tgz vignettes: vignettes/NewWave/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NewWave/inst/doc/vignette.R dependencyCount: 44 Package: ngsReports Version: 2.12.0 Depends: R (>= 4.2.0), BiocGenerics, ggplot2 (>= 4.0.0), patchwork (>= 1.1.1), tibble (>= 1.3.1) Imports: Biostrings, checkmate, dplyr (>= 1.1.0), forcats, ggdendro, grDevices (>= 3.6.0), grid, jsonlite, lifecycle, lubridate, methods, plotly (>= 4.9.4), rlang, rmarkdown, scales, stats, stringr, tidyr, tidyselect (>= 0.2.3), utils, zoo Suggests: BiocStyle, Cairo, DT, knitr, pander, readr, testthat, truncnorm License: LGPL-3 MD5sum: 2480a2508d9139c53095115220f83334 NeedsCompilation: no Title: Load FastqQC reports and other NGS related files Description: This package provides methods and object classes for parsing FastQC reports and output summaries from other NGS tools into R. As well as parsing files, multiple plotting methods have been implemented for visualising the parsed data. Plots can be generated as static ggplot objects or interactive plotly objects. biocViews: QualityControl, ReportWriting Author: Stevie Pederson [aut, cre] (ORCID: ), Christopher Ward [aut], Thu-Hien To [aut] Maintainer: Stevie Pederson URL: https://github.com/smped/ngsReports VignetteBuilder: knitr BugReports: https://github.com/smped/ngsReports/issues git_url: https://git.bioconductor.org/packages/ngsReports git_branch: RELEASE_3_22 git_last_commit: 35d18a8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ngsReports_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ngsReports_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ngsReports_2.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ngsReports_2.12.0.tgz vignettes: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.html vignetteTitles: An Introduction To ngsReports hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.R dependencyCount: 89 Package: nipalsMCIA Version: 1.8.0 Depends: R (>= 4.3.0) Imports: ComplexHeatmap, dplyr, fgsea, ggplot2 (>= 3.0.0), graphics, grid, methods, MultiAssayExperiment, SummarizedExperiment, pracma, rlang, RSpectra, scales, stats Suggests: BiocFileCache, BiocStyle, circlize, ggpubr, KernSmooth, knitr, piggyback, reshape2, rmarkdown, rpart, Seurat (>= 4.0.0), spatstat.explore, stringr, survival, tidyverse, testthat (>= 3.0.0) License: GPL-3 MD5sum: 78e294777461a9d0327d078f73d43a27 NeedsCompilation: no Title: Multiple Co-Inertia Analysis via the NIPALS Method Description: Computes Multiple Co-Inertia Analysis (MCIA), a dimensionality reduction (jDR) algorithm, for a multi-block dataset using a modification to the Nonlinear Iterative Partial Least Squares method (NIPALS) proposed in (Hanafi et. al, 2010). Allows multiple options for row- and table-level preprocessing, and speeds up computation of variance explained. Vignettes detail application to bulk- and single cell- multi-omics studies. biocViews: Software, Clustering, Classification, MultipleComparison, Normalization, Preprocessing, SingleCell Author: Maximilian Mattessich [cre] (ORCID: ), Joaquin Reyna [aut] (ORCID: ), Edel Aron [aut] (ORCID: ), Ferhat Ay [aut] (ORCID: ), Steven Kleinstein [aut] (ORCID: ), Anna Konstorum [aut] (ORCID: ) Maintainer: Maximilian Mattessich URL: https://github.com/Muunraker/nipalsMCIA VignetteBuilder: knitr BugReports: https://github.com/Muunraker/nipalsMCIA/issues git_url: https://git.bioconductor.org/packages/nipalsMCIA git_branch: RELEASE_3_22 git_last_commit: 5c9ad2e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/nipalsMCIA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nipalsMCIA_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nipalsMCIA_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nipalsMCIA_1.8.0.tgz vignettes: vignettes/nipalsMCIA/inst/doc/Analysis-of-MCIA-Decomposition.html, vignettes/nipalsMCIA/inst/doc/Predicting-New-Scores.html, vignettes/nipalsMCIA/inst/doc/Single-Cell-Analysis.html vignetteTitles: Analysis of MCIA Decomposition, Predicting New MCIA scores, Single Cell Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nipalsMCIA/inst/doc/Analysis-of-MCIA-Decomposition.R, vignettes/nipalsMCIA/inst/doc/Predicting-New-Scores.R, vignettes/nipalsMCIA/inst/doc/Single-Cell-Analysis.R dependencyCount: 87 Package: nnNorm Version: 2.74.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL Archs: x64 MD5sum: 936977a7ebae200e6b802128f5bb9e59 NeedsCompilation: no Title: Spatial and intensity based normalization of cDNA microarray data based on robust neural nets Description: This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting. biocViews: Microarray, TwoChannel, Preprocessing Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/tarca/ git_url: https://git.bioconductor.org/packages/nnNorm git_branch: RELEASE_3_22 git_last_commit: d63e597 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/nnNorm_2.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nnNorm_2.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nnNorm_2.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nnNorm_2.74.0.tgz vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf vignetteTitles: nnNorm Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nnNorm/inst/doc/nnNorm.R dependencyCount: 9 Package: nnSVG Version: 1.14.0 Depends: R (>= 4.2) Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, BRISC, BiocParallel, Matrix, matrixStats, stats, methods Suggests: BiocStyle, knitr, rmarkdown, STexampleData, WeberDivechaLCdata, scran, ggplot2, testthat License: MIT + file LICENSE MD5sum: e8072a02e1a505b20354a4966014157d NeedsCompilation: no Title: Scalable identification of spatially variable genes in spatially-resolved transcriptomics data Description: Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Preprocessing Author: Lukas M. Weber [aut, cre] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/nnSVG VignetteBuilder: knitr BugReports: https://github.com/lmweber/nnSVG/issues git_url: https://git.bioconductor.org/packages/nnSVG git_branch: RELEASE_3_22 git_last_commit: f7af058 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/nnSVG_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nnSVG_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nnSVG_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nnSVG_1.14.0.tgz vignettes: vignettes/nnSVG/inst/doc/nnSVG.html vignetteTitles: nnSVG Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nnSVG/inst/doc/nnSVG.R importsMe: spoon, OSTA suggestsMe: SEraster, tpSVG dependencyCount: 81 Package: NOISeq Version: 2.54.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 Archs: x64 MD5sum: e65bb7d1826a31623729b4e9576fdba9 NeedsCompilation: no Title: Exploratory analysis and differential expression for RNA-seq data Description: Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. biocViews: ImmunoOncology, RNASeq, DifferentialExpression, Visualization, Sequencing Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto Ferrer and Ana Conesa Maintainer: Sonia Tarazona git_url: https://git.bioconductor.org/packages/NOISeq git_branch: RELEASE_3_22 git_last_commit: 3cf0ca5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NOISeq_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NOISeq_2.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NOISeq_2.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NOISeq_2.54.0.tgz vignettes: vignettes/NOISeq/inst/doc/NOISeq.pdf, vignettes/NOISeq/inst/doc/QCreport.pdf vignetteTitles: NOISeq User's Guide, QCreport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NOISeq/inst/doc/NOISeq.R dependsOnMe: metaSeq importsMe: CNVPanelizer, ExpHunterSuite suggestsMe: compcodeR, GeoTcgaData dependencyCount: 12 Package: nondetects Version: 2.40.0 Depends: R (>= 3.2), Biobase (>= 2.22.0) Imports: limma, mvtnorm, utils, methods, arm, HTqPCR (>= 1.16.0) Suggests: knitr, rmarkdown, BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL-3 MD5sum: 916ae05403bbe9b76fbce46084724a65 NeedsCompilation: no Title: Non-detects in qPCR data Description: Methods to model and impute non-detects in the results of qPCR experiments. biocViews: Software, AssayDomain, GeneExpression, Technology, qPCR, WorkflowStep, Preprocessing Author: Matthew N. McCall , Valeriia Sherina Maintainer: Valeriia Sherina VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nondetects git_branch: RELEASE_3_22 git_last_commit: 2d09856 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/nondetects_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nondetects_2.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nondetects_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nondetects_2.40.0.tgz vignettes: vignettes/nondetects/inst/doc/nondetects.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nondetects/inst/doc/nondetects.R dependencyCount: 43 Package: NoRCE Version: 1.22.0 Depends: R (>= 4.4.0) Imports: KEGGREST,png,dplyr,graphics,RSQLite,DBI,tidyr,grDevices,stringr,Seqinfo, S4Vectors,SummarizedExperiment,reactome.db,rWikiPathways,RCurl, dbplyr,utils,ggplot2,igraph,stats,reshape2,readr, GO.db, biomaRt,rtracklayer,IRanges,GenomicRanges,GenomicFeatures,AnnotationDbi,methods Suggests: knitr, TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Mmusculus.UCSC.mm10.knownGene,TxDb.Dmelanogaster.UCSC.dm6.ensGene, testthat,TxDb.Celegans.UCSC.ce11.refGene,rmarkdown, TxDb.Rnorvegicus.UCSC.rn6.refGene,TxDb.Hsapiens.UCSC.hg19.knownGene, org.Mm.eg.db, org.Rn.eg.db,org.Hs.eg.db,org.Dr.eg.db,BiocGenerics, org.Sc.sgd.db, org.Ce.eg.db,org.Dm.eg.db, markdown License: MIT + file LICENSE MD5sum: e21868b92e58b060f0bfa294923a9c1a NeedsCompilation: no Title: NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment Description: While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint to a functional association. We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. biocViews: BiologicalQuestion, DifferentialExpression, GenomeAnnotation, GeneSetEnrichment, GeneTarget, GenomeAssembly, GO Author: Gulden Olgun [aut, cre] Maintainer: Gulden Olgun VignetteBuilder: knitr BugReports: https://github.com/guldenolgun/NoRCE/issues git_url: https://git.bioconductor.org/packages/NoRCE git_branch: RELEASE_3_22 git_last_commit: 355a24c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NoRCE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NoRCE_1.21.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NoRCE_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NoRCE_1.22.0.tgz vignettes: vignettes/NoRCE/inst/doc/NoRCE.html vignetteTitles: Noncoding RNA Set Cis Annotation and Enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NoRCE/inst/doc/NoRCE.R dependencyCount: 120 Package: normalize450K Version: 1.38.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: fcb85e0b22904767fd41bf8facda28dc NeedsCompilation: no Title: Preprocessing of Illumina Infinium 450K data Description: Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K '.idat' files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values. biocViews: Normalization, DNAMethylation, Microarray, TwoChannel, Preprocessing, MethylationArray Author: Jonathan Alexander Heiss Maintainer: Jonathan Alexander Heiss git_url: https://git.bioconductor.org/packages/normalize450K git_branch: RELEASE_3_22 git_last_commit: d38a6c7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/normalize450K_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/normalize450K_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/normalize450K_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/normalize450K_1.38.0.tgz vignettes: vignettes/normalize450K/inst/doc/read_and_normalize450K.pdf vignetteTitles: Normalization of 450K data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/normalize450K/inst/doc/read_and_normalize450K.R dependencyCount: 13 Package: NormalyzerDE Version: 1.28.0 Depends: R (>= 4.1.0) Imports: vsn, preprocessCore, limma, MASS, ape, car, ggplot2, methods, utils, stats, SummarizedExperiment, matrixStats, ggforce Suggests: knitr, testthat, rmarkdown, roxygen2, hexbin, BiocStyle License: Artistic-2.0 MD5sum: 64968f50e7e01539b1c9fc3167e6375e NeedsCompilation: no Title: Evaluation of normalization methods and calculation of differential expression analysis statistics Description: NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis. biocViews: Normalization, MultipleComparison, Visualization, Bayesian, Proteomics, Metabolomics, DifferentialExpression Author: Jakob Willforss Maintainer: Jakob Willforss URL: https://github.com/ComputationalProteomics/NormalyzerDE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NormalyzerDE git_branch: RELEASE_3_22 git_last_commit: 93b5996 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NormalyzerDE_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NormalyzerDE_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NormalyzerDE_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NormalyzerDE_1.28.0.tgz vignettes: vignettes/NormalyzerDE/inst/doc/vignette.html vignetteTitles: Differential expression and countering technical biases using NormalyzerDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormalyzerDE/inst/doc/vignette.R importsMe: PRONE dependencyCount: 99 Package: NormqPCR Version: 1.56.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: f6bc620af9169be9a52ab86b70389616 NeedsCompilation: no Title: Functions for normalisation of RT-qPCR data Description: Functions for the selection of optimal reference genes and the normalisation of real-time quantitative PCR data. biocViews: MicrotitrePlateAssay, GeneExpression, qPCR Author: Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html git_url: https://git.bioconductor.org/packages/NormqPCR git_branch: RELEASE_3_22 git_last_commit: 3162613 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NormqPCR_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NormqPCR_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NormqPCR_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NormqPCR_1.56.0.tgz vignettes: vignettes/NormqPCR/inst/doc/NormqPCR.pdf vignetteTitles: NormqPCR: Functions for normalisation of RT-qPCR data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormqPCR/inst/doc/NormqPCR.R dependencyCount: 48 Package: normr Version: 1.36.0 Depends: R (>= 3.3.0) Imports: methods, stats, utils, grDevices, parallel, GenomeInfoDb, GenomicRanges, IRanges, Rcpp (>= 0.11), qvalue (>= 2.2), bamsignals (>= 1.4), rtracklayer (>= 1.32) LinkingTo: Rcpp Suggests: BiocStyle, testthat (>= 1.0), knitr, rmarkdown Enhances: BiocParallel License: GPL-2 Archs: x64 MD5sum: 061f3b68b4cd273a8e7034e0c92ed12d NeedsCompilation: yes Title: Normalization and difference calling in ChIP-seq data Description: Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions. biocViews: Bayesian, DifferentialPeakCalling, Classification, DataImport, ChIPSeq, RIPSeq, FunctionalGenomics, Genetics, MultipleComparison, Normalization, PeakDetection, Preprocessing, Alignment Author: Johannes Helmuth [aut, cre], Ho-Ryun Chung [aut] Maintainer: Johannes Helmuth URL: https://github.com/your-highness/normR SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/your-highness/normR/issues git_url: https://git.bioconductor.org/packages/normr git_branch: RELEASE_3_22 git_last_commit: 9a5bb3a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/normr_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/normr_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/normr_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/normr_1.36.0.tgz vignettes: vignettes/normr/inst/doc/normr.html vignetteTitles: Introduction to the normR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/normr/inst/doc/normr.R dependencyCount: 83 Package: notame Version: 1.0.0 Depends: R (>= 4.5.0), ggplot2, SummarizedExperiment Imports: BiocGenerics, BiocParallel, dplyr, futile.logger, methods, openxlsx, S4Vectors, scales, stringr, tidyr, utils Suggests: BiocStyle, fpc, igraph, knitr, missForest, notameViz, notameStats, pcaMethods, RUVSeq, testthat License: MIT + file LICENSE MD5sum: 006a2ae778ae37b46007754842e16a80 NeedsCompilation: no Title: Workflow for non-targeted LC-MS metabolic profiling Description: Provides functionality for untargeted LC-MS metabolomics research as specified in the associated protocol article in the 'Metabolomics Data Processing and Data Analysis—Current Best Practices' special issue of the Metabolites journal (2020). This includes tabular data preprocessing and quality control, uni- and multivariate analysis as well as quality control visualizations, feature-wise visualizations and results visualizations. Raw data preprocessing and functionality related to biological context, such as pathway analysis, is not included. biocViews: BiomedicalInformatics, Metabolomics, DataImport, MassSpectrometry, BatchEffect, MultipleComparison, Normalization, QualityControl, Visualization, Preprocessing Author: Anton Klåvus [aut, cph] (ORCID: ), Jussi Paananen [aut, cph] (ORCID: ), Oskari Timonen [aut, cph] (ORCID: ), Atte Lihtamo [aut], Vilhelm Suksi [aut, cre] (ORCID: ), Retu Haikonen [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Kati Hanhineva [aut] (ORCID: ), Ville Koistinen [ctb] (ORCID: ), Olli Kärkkäinen [ctb] (ORCID: ), Artur Sannikov [ctb] Maintainer: Vilhelm Suksi URL: https://github.com/hanhineva-lab/notame, https://hanhineva-lab.github.io/notame/ VignetteBuilder: knitr BugReports: https://github.com/hanhineva-lab/notame/issues git_url: https://git.bioconductor.org/packages/notame git_branch: RELEASE_3_22 git_last_commit: e9edc04 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/notame_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/notame_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/notame_1.0.0.tgz vignettes: vignettes/notame/inst/doc/introduction.html, vignettes/notame/inst/doc/project_example.html vignetteTitles: Non-targeted metabolomics preprocessing and wrangling, Project example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/notame/inst/doc/introduction.R, vignettes/notame/inst/doc/project_example.R importsMe: notameStats, notameViz dependencyCount: 65 Package: notameStats Version: 1.0.0 Depends: R (>= 4.5.0), SummarizedExperiment, Imports: BiocGenerics, BiocParallel, broom, dplyr, methods, notame, stats, tibble, tidyr, utils Suggests: BiocStyle, car, knitr, lmerTest, mixOmics, MuMIn, MUVR2, notameViz, PERMANOVA, PK, randomForest, rmcorr, testthat License: MIT + file LICENSE MD5sum: 493b29ae5054ca4f47b3a9bddd9bc468 NeedsCompilation: no Title: Workflow for non-targeted LC-MS metabolic profiling Description: Provides univariate and multivariate statistics for feature prioritization in untargeted LC-MS metabolomics research. biocViews: BiomedicalInformatics, Metabolomics, DataImport, MassSpectrometry, BatchEffect, MultipleComparison, Normalization, QualityControl, Visualization, Preprocessing Author: Anton Klåvus [aut, cph] (ORCID: ), Jussi Paananen [aut, cph] (ORCID: ), Oskari Timonen [aut, cph] (ORCID: ), Atte Lihtamo [aut], Vilhelm Suksi [aut, cre] (ORCID: ), Retu Haikonen [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Kati Hanhineva [aut] (ORCID: ), Ville Koistinen [ctb] (ORCID: ), Olli Kärkkäinen [ctb] (ORCID: ), Artur Sannikov [ctb] Maintainer: Vilhelm Suksi URL: https://github.com/hanhineva-lab/notameStats VignetteBuilder: knitr BugReports: https://github.com/hanhineva-lab/notameStats/issues git_url: https://git.bioconductor.org/packages/notameStats git_branch: RELEASE_3_22 git_last_commit: 58a6cb5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/notameStats_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/notameStats_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/notameStats_1.0.0.tgz vignettes: vignettes/notameStats/inst/doc/Non-targeted_metabolomics_feature_prioritization.html vignetteTitles: Non-targeted metabolomics feature prioritization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/notameStats/inst/doc/Non-targeted_metabolomics_feature_prioritization.R suggestsMe: notame, notameViz dependencyCount: 68 Package: notameViz Version: 1.0.0 Depends: R (>= 4.5.0), ggplot2, SummarizedExperiment Imports: BiocGenerics, cowplot, devEMF, dplyr, ggbeeswarm, ggdendro, ggrepel, grDevices, methods, notame, scales, stringr, stats, tibble, tidyr, utils Suggests: batchCorr, BiocStyle, igraph, knitr, notameStats, pcaMethods, Rtsne, testthat License: MIT + file LICENSE MD5sum: 23b7d43de9f4a769bfc01a7f94cb0e23 NeedsCompilation: no Title: Workflow for non-targeted LC-MS metabolic profiling Description: Provides visualization functionality for untargeted LC-MS metabolomics research. Includes quality control visualizations, feature-wise visualizations and results visualizations. biocViews: BiomedicalInformatics, Metabolomics, DataImport, MassSpectrometry, BatchEffect, MultipleComparison, Normalization, QualityControl, Visualization, Preprocessing Author: Anton Klåvus [aut, cph] (ORCID: ), Jussi Paananen [aut, cph] (ORCID: ), Oskari Timonen [aut, cph] (ORCID: ), Atte Lihtamo [aut], Vilhelm Suksi [aut, cre] (ORCID: ), Retu Haikonen [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Kati Hanhineva [aut] (ORCID: ), Ville Koistinen [ctb] (ORCID: ), Olli Kärkkäinen [ctb] (ORCID: ), Artur Sannikov [ctb] Maintainer: Vilhelm Suksi URL: https://github.com/hanhineva-lab/notameViz VignetteBuilder: knitr BugReports: https://github.com/hanhineva-lab/notameViz/issues git_url: https://git.bioconductor.org/packages/notameViz git_branch: RELEASE_3_22 git_last_commit: bb6e677 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/notameViz_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/notameViz_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/notameViz_1.0.0.tgz vignettes: vignettes/notameViz/inst/doc/Non_targeted_metabolomics_visualization.html vignetteTitles: Non-targeted metabolomics visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/notameViz/inst/doc/Non_targeted_metabolomics_visualization.R suggestsMe: notame, notameStats dependencyCount: 74 Package: NPARC Version: 1.22.0 Depends: R (>= 4.0.0) Imports: dplyr, tidyr, BiocParallel, broom, MASS, rlang, magrittr, stats, methods Suggests: testthat, devtools, knitr, rprojroot, rmarkdown, ggplot2, BiocStyle License: GPL-3 MD5sum: e1a7354978a4a542815ac457e3652896 NeedsCompilation: no Title: Non-parametric analysis of response curves for thermal proteome profiling experiments Description: Perform non-parametric analysis of response curves as described by Childs, Bach, Franken et al. (2019): Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins. biocViews: Software, Proteomics Author: Dorothee Childs, Nils Kurzawa Maintainer: Nils Kurzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NPARC git_branch: RELEASE_3_22 git_last_commit: 92e6972 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NPARC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NPARC_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NPARC_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NPARC_1.22.0.tgz vignettes: vignettes/NPARC/inst/doc/NPARC.html vignetteTitles: Analysing thermal proteome profiling data with the NPARC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NPARC/inst/doc/NPARC.R dependencyCount: 38 Package: npGSEA Version: 1.46.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 23d30d70f5e14e8aa8fb4def9bfde1cd NeedsCompilation: no Title: Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA) Description: Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results. biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways Author: Jessica Larson and Art Owen Maintainer: Jessica Larson git_url: https://git.bioconductor.org/packages/npGSEA git_branch: RELEASE_3_22 git_last_commit: e75f387 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/npGSEA_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/npGSEA_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/npGSEA_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/npGSEA_1.46.0.tgz vignettes: vignettes/npGSEA/inst/doc/npGSEA.pdf vignetteTitles: Running gene set enrichment analysis with the "npGSEA" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/npGSEA/inst/doc/npGSEA.R dependencyCount: 48 Package: NTW Version: 1.60.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: 74b8e8ee02dcddaa434e50606d566413 NeedsCompilation: no Title: Predict gene network using an Ordinary Differential Equation (ODE) based method Description: This package predicts the gene-gene interaction network and identifies the direct transcriptional targets of the perturbation using an ODE (Ordinary Differential Equation) based method. biocViews: Preprocessing Author: Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang, Yuanhua Liu, Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/NTW git_branch: RELEASE_3_22 git_last_commit: 67cd56f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NTW_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NTW_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NTW_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NTW_1.60.0.tgz vignettes: vignettes/NTW/inst/doc/NTW.pdf vignetteTitles: NTW vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NTW/inst/doc/NTW.R dependencyCount: 3 Package: nucleoSim Version: 1.38.0 Imports: stats, IRanges, S4Vectors, graphics, methods Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 53669fc62fdfcf63e814257cb9075f7b NeedsCompilation: no Title: Generate synthetic nucleosome maps Description: This package can generate a synthetic map with reads covering the nucleosome regions as well as a synthetic map with forward and reverse reads emulating next-generation sequencing. The synthetic hybridization data of “Tiling Arrays” can also be generated. The user has choice between three different distributions for the read positioning: Normal, Student and Uniform. In addition, a visualization tool is provided to explore the synthetic nucleosome maps. biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment Author: Rawane Samb [aut], Astrid Deschênes [cre, aut] (ORCID: ), Pascal Belleau [aut] (ORCID: ), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/arnauddroitlab/nucleoSim VignetteBuilder: knitr BugReports: https://github.com/arnauddroitlab/nucleoSim/issues git_url: https://git.bioconductor.org/packages/nucleoSim git_branch: RELEASE_3_22 git_last_commit: ed553bf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/nucleoSim_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nucleoSim_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nucleoSim_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nucleoSim_1.38.0.tgz vignettes: vignettes/nucleoSim/inst/doc/nucleoSim.html vignetteTitles: Generate synthetic nucleosome maps hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R suggestsMe: RJMCMCNucleosomes dependencyCount: 9 Package: nucleR Version: 2.42.0 Depends: R (>= 3.5.0), methods Imports: Biobase, BiocGenerics, Biostrings, Seqinfo, GenomicRanges, IRanges, Rsamtools, S4Vectors, ShortRead, dplyr, ggplot2, magrittr, parallel, stats, utils, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat License: LGPL (>= 3) MD5sum: de887ed9a7b294a2381bf3bd5bc19034 NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and NGS experiments. biocViews: NucleosomePositioning, Coverage, ChIPSeq, Microarray, Sequencing, Genetics, QualityControl, DataImport Author: Oscar Flores, Ricard Illa Maintainer: Alba Sala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nucleR git_branch: RELEASE_3_22 git_last_commit: 90978c6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/nucleR_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nucleR_2.41.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nucleR_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nucleR_2.42.0.tgz vignettes: vignettes/nucleR/inst/doc/nucleR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleR/inst/doc/nucleR.R dependencyCount: 76 Package: nuCpos Version: 1.28.0 Depends: R (>= 4.2.0) Imports: graphics, methods Suggests: NuPoP, Biostrings, testthat License: GPL-2 MD5sum: 3f8f4904c171abd9177486cd6d500bf4 NeedsCompilation: yes Title: An R package for prediction of nucleosome positions Description: nuCpos, a derivative of NuPoP, is an R package for prediction of nucleosome positions. nuCpos calculates local and whole nucleosomal histone binding affinity (HBA) scores for a given 147-bp sequence. Note: This package was designed to demonstrate the use of chemical maps in prediction. As the parental package NuPoP now provides chemical-map-based prediction, the function for dHMM-based prediction was removed from this package. nuCpos continues to provide functions for HBA calculation. biocViews: Genetics, Epigenetics, NucleosomePositioning Author: Hiroaki Kato, Takeshi Urano Maintainer: Hiroaki Kato git_url: https://git.bioconductor.org/packages/nuCpos git_branch: RELEASE_3_22 git_last_commit: 1e6f2f2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/nuCpos_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nuCpos_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nuCpos_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nuCpos_1.28.0.tgz vignettes: vignettes/nuCpos/inst/doc/nuCpos-intro.pdf vignetteTitles: An R package for prediction of nucleosome positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nuCpos/inst/doc/nuCpos-intro.R dependencyCount: 2 Package: nullranges Version: 1.16.0 Depends: R (>= 4.2.0) Imports: stats, IRanges, GenomicRanges, Seqinfo, methods, rlang, S4Vectors, scales, InteractionSet, ggplot2, grDevices, plyranges, data.table, progress, ggridges Suggests: testthat, knitr, rmarkdown, ks, DNAcopy, RcppHMM, AnnotationHub, ExperimentHub, GenomeInfoDb, nullrangesData, excluderanges, ensembldb, EnsDb.Hsapiens.v86, BSgenome.Hsapiens.UCSC.hg38, patchwork, plotgardener, dplyr, magrittr, tidyr, cobalt, DiagrammeR, MatchIt, mariner License: GPL-3 MD5sum: 2c2d4db1d1f0af3cec2295bf6e7a3377 NeedsCompilation: no Title: Generation of null ranges via bootstrapping or covariate matching Description: Modular package for generation of sets of ranges representing the null hypothesis. These can take the form of bootstrap samples of ranges (using the block bootstrap framework of Bickel et al 2010), or sets of control ranges that are matched across one or more covariates. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package. biocViews: Visualization, GeneSetEnrichment, FunctionalGenomics, Epigenetics, GeneRegulation, GeneTarget, GenomeAnnotation, Annotation, GenomeWideAssociation, HistoneModification, ChIPSeq, ATACSeq, DNaseSeq, RNASeq, HiddenMarkovModel Author: Michael Love [aut, cre] (ORCID: ), Wancen Mu [aut] (ORCID: ), Eric Davis [aut] (ORCID: ), Douglas Phanstiel [aut] (ORCID: ), Stuart Lee [aut] (ORCID: ), Mikhail Dozmorov [ctb], Tim Triche [ctb], CZI [fnd] Maintainer: Michael Love URL: https://nullranges.github.io/nullranges, https://github.com/nullranges/nullranges VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/nullranges/ git_url: https://git.bioconductor.org/packages/nullranges git_branch: RELEASE_3_22 git_last_commit: 9352bd0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/nullranges_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nullranges_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nullranges_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nullranges_1.16.0.tgz vignettes: vignettes/nullranges/inst/doc/bootRanges.html, vignettes/nullranges/inst/doc/matching_ginteractions.html, vignettes/nullranges/inst/doc/matching_granges.html, vignettes/nullranges/inst/doc/matching_pool_set.html, vignettes/nullranges/inst/doc/matchRanges.html, vignettes/nullranges/inst/doc/nullranges.html vignetteTitles: 1. Introduction to bootRanges, 4. Matching case study II: CTCF orientation, 3. Matching case study I: CTCF occupancy, 5. Creating a pool set for matchRanges, 2. Introduction to matchRanges, 0. Introduction to nullranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nullranges/inst/doc/bootRanges.R, vignettes/nullranges/inst/doc/matching_ginteractions.R, vignettes/nullranges/inst/doc/matching_granges.R, vignettes/nullranges/inst/doc/matching_pool_set.R, vignettes/nullranges/inst/doc/matchRanges.R, vignettes/nullranges/inst/doc/nullranges.R suggestsMe: tidyomics dependencyCount: 87 Package: NuPoP Version: 2.18.0 Depends: R (>= 4.0) Imports: graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: dff2ad4b2a7ac40a584bcc89c2fa6f64 NeedsCompilation: yes Title: An R package for nucleosome positioning prediction Description: NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at https://github.com/jipingw. The Fortran codes have complete functonality as the R package. Note: NuPoP has two separate functions for prediction of nucleosome positioning, one for MNase-map trained models and the other for chemical map-trained models. The latter was implemented for four species including yeast, S.pombe, mouse and human, trained based on our recent publications. We noticed there is another package nuCpos by another group for prediction of nucleosome positioning trained with chemicals. A report to compare recent versions of NuPoP with nuCpos can be found at https://github.com/jiping/NuPoP_doc. Some more information can be found and will be posted at https://github.com/jipingw/NuPoP. biocViews: Genetics,Visualization,Classification,NucleosomePositioning, HiddenMarkovModel Author: Ji-Ping Wang ; Liqun Xi ; Oscar Zarate Maintainer: Ji-Ping Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NuPoP git_branch: RELEASE_3_22 git_last_commit: 496dcb6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/NuPoP_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NuPoP_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NuPoP_2.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NuPoP_2.18.0.tgz vignettes: vignettes/NuPoP/inst/doc/NuPoP.html vignetteTitles: NuPoP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NuPoP/inst/doc/NuPoP.R suggestsMe: nuCpos dependencyCount: 2 Package: occugene Version: 1.70.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: d36cb86647d70fc1985964d5ed872281 NeedsCompilation: no Title: Functions for Multinomial Occupancy Distribution Description: Statistical tools for building random mutagenesis libraries for prokaryotes. The package has functions for handling the occupancy distribution for a multinomial and for estimating the number of essential genes in random transposon mutagenesis libraries. biocViews: Annotation, Pathways Author: Oliver Will Maintainer: Oliver Will git_url: https://git.bioconductor.org/packages/occugene git_branch: RELEASE_3_22 git_last_commit: ce1dc67 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/occugene_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/occugene_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/occugene_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/occugene_1.70.0.tgz vignettes: vignettes/occugene/inst/doc/occugene.pdf vignetteTitles: occugene hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/occugene/inst/doc/occugene.R dependencyCount: 0 Package: OCplus Version: 1.84.0 Depends: R (>= 2.1.0) Imports: multtest (>= 1.7.3), graphics, grDevices, stats, interp License: LGPL MD5sum: fdafe37d78c6c4e1e4ac45499bd220f5 NeedsCompilation: no Title: Operating characteristics plus sample size and local fdr for microarray experiments Description: This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes). biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yudi Pawitan and Alexander Ploner Maintainer: Alexander Ploner git_url: https://git.bioconductor.org/packages/OCplus git_branch: RELEASE_3_22 git_last_commit: 322d769 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OCplus_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OCplus_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OCplus_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OCplus_1.84.0.tgz vignettes: vignettes/OCplus/inst/doc/OCplus.pdf vignetteTitles: OCplus Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OCplus/inst/doc/OCplus.R dependencyCount: 20 Package: octad Version: 1.12.0 Depends: R (>= 4.2.0), magrittr, dplyr, ggplot2, edgeR, RUVSeq, DESeq2, limma, rhdf5, foreach, Rfast, octad.db, stats, httr, qpdf, ExperimentHub, AnnotationHub, Biobase, S4Vectors Imports: EDASeq, GSVA, data.table, htmlwidgets, plotly, reshape2, grDevices, utils Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: ed509e69719d2414ea260f040dc46474 NeedsCompilation: no Title: Open Cancer TherApeutic Discovery (OCTAD) Description: OCTAD provides a platform for virtually screening compounds targeting precise cancer patient groups. The essential idea is to identify drugs that reverse the gene expression signature of disease by tamping down over-expressed genes and stimulating weakly expressed ones. The package offers deep-learning based reference tissue selection, disease gene expression signature creation, pathway enrichment analysis, drug reversal potency scoring, cancer cell line selection, drug enrichment analysis and in silico hit validation. It currently covers ~20,000 patient tissue samples covering 50 cancer types, and expression profiles for ~12,000 distinct compounds. biocViews: Classification, GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, GeneSetEnrichment Author: E. Chekalin [aut, cre], S. Paithankar [aut], B. Zeng [aut], B. Glicksberg [ctb], P. Newbury [ctb], J. Xing [ctb], K. Liu [ctb], A. Wen [ctb], D. Joseph [ctb], B. Chen [aut] Maintainer: E. Chekalin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/octad git_branch: RELEASE_3_22 git_last_commit: e8beea0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/octad_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/octad_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/octad_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/octad_1.12.0.tgz vignettes: vignettes/octad/inst/doc/octad.html vignetteTitles: octad hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/octad/inst/doc/octad.R dependencyCount: 185 Package: odseq Version: 1.38.0 Depends: R (>= 3.2.3) Imports: msa (>= 1.2.1), kebabs (>= 1.4.1), mclust (>= 5.1) Suggests: knitr(>= 1.11) License: MIT + file LICENSE Archs: x64 MD5sum: 2f3b27cd9cbc1bffeb85ac8a76ec308c NeedsCompilation: no Title: Outlier detection in multiple sequence alignments Description: Performs outlier detection of sequences in a multiple sequence alignment using bootstrap of predefined distance metrics. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This package implements the OD-seq algorithm proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for aligned sequences and a variant using string kernels for unaligned sequences. biocViews: Alignment, MultipleSequenceAlignment Author: José Jiménez Maintainer: José Jiménez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/odseq git_branch: RELEASE_3_22 git_last_commit: d937efc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/odseq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/odseq_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/odseq_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/odseq_1.38.0.tgz vignettes: vignettes/odseq/inst/doc/vignette.pdf vignetteTitles: A quick tutorial to outlier detection in MSAs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/odseq/inst/doc/vignette.R dependencyCount: 29 Package: OGRE Version: 1.14.0 Depends: R (>= 4.2.0), S4Vectors Imports: GenomicRanges, methods, data.table, assertthat, ggplot2, Gviz, IRanges, AnnotationHub, grDevices, stats, Seqinfo, GenomeInfoDb, shiny, shinyFiles, DT, rtracklayer, shinydashboard, shinyBS,tidyr Suggests: testthat (>= 3.0.0), knitr (>= 1.36), rmarkdown (>= 2.11) License: Artistic-2.0 MD5sum: d1d9307053195db4f3418de1cea12e62 NeedsCompilation: no Title: Calculate, visualize and analyse overlap between genomic regions Description: OGRE calculates overlap between user defined genomic region datasets. Any regions can be supplied i.e. genes, SNPs, or reads from sequencing experiments. Key numbers help analyse the extend of overlaps which can also be visualized at a genomic level. biocViews: Software, WorkflowStep, BiologicalQuestion, Annotation, Metagenomics, Visualization, Sequencing Author: Sven Berres [aut, cre], Jörg Gromoll [ctb], Marius Wöste [ctb], Sarah Sandmann [ctb], Sandra Laurentino [ctb] Maintainer: Sven Berres URL: https://github.com/svenbioinf/OGRE/ VignetteBuilder: knitr BugReports: https://github.com/svenbioinf/OGRE/issues git_url: https://git.bioconductor.org/packages/OGRE git_branch: RELEASE_3_22 git_last_commit: 0f074f4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OGRE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OGRE_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OGRE_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OGRE_1.14.0.tgz vignettes: vignettes/OGRE/inst/doc/OGRE.html vignetteTitles: OGRE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OGRE/inst/doc/OGRE.R dependencyCount: 170 Package: oligo Version: 1.74.0 Depends: R (>= 3.2.0), BiocGenerics (>= 0.13.11), oligoClasses (>= 1.29.6), Biobase (>= 2.27.3), Biostrings (>= 2.35.12) Imports: affyio (>= 1.35.0), affxparser (>= 1.39.4), DBI (>= 0.3.1), ff, graphics, methods, preprocessCore (>= 1.29.0), RSQLite (>= 1.0.0), splines, stats, stats4, utils, bit LinkingTo: preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg18, hapmap100kxba, pd.hg.u95av2, pd.mapping50k.xba240, pd.huex.1.0.st.v2, pd.hg18.60mer.expr, pd.hugene.1.0.st.v1, maqcExpression4plex, genefilter, limma, RColorBrewer, oligoData, BiocStyle, knitr, RUnit, biomaRt, AnnotationDbi, ACME, RCurl Enhances: doMC, doMPI License: LGPL (>= 2) MD5sum: 58b1b34f18141fb209591bc14e633a9e NeedsCompilation: yes Title: Preprocessing tools for oligonucleotide arrays Description: A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, SNP, DifferentialExpression, ExonArray, GeneExpression, DataImport Author: Benilton Carvalho and Rafael Irizarry Maintainer: Benilton Carvalho URL: https://github.com/benilton/oligo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oligo git_branch: RELEASE_3_22 git_last_commit: 03318a4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/oligo_1.74.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/oligo_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/oligo_1.74.0.tgz vignettes: vignettes/oligo/inst/doc/oug.pdf vignetteTitles: oligo User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, puma, SCAN.UPC, oligoData, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling, pumadata, maEndToEnd importsMe: ArrayExpress, cn.farms, frma, ITALICS, mimager suggestsMe: frmaTools, hapmap100khind, hapmap100kxba, hapmap500knsp, hapmap500ksty, hapmapsnp5, hapmapsnp6, maqcExpression4plex, aroma.affymetrix, maGUI, RCPA dependencyCount: 53 Package: oligoClasses Version: 1.72.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.27.1), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>= 1.23.2), foreach, BiocManager, utils, S4Vectors (>= 0.9.25), RSQLite, DBI, ff Suggests: hapmapsnp5, hapmapsnp6, pd.genomewidesnp.6, pd.genomewidesnp.5, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.mapping250k.sty, pd.mapping250k.nsp, genomewidesnp6Crlmm (>= 1.0.7), genomewidesnp5Crlmm (>= 1.0.6), RUnit, human370v1cCrlmm, VanillaICE, crlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: GPL (>= 2) MD5sum: c172493532761f09c2465db87fc08305 NeedsCompilation: no Title: Classes for high-throughput arrays supported by oligo and crlmm Description: This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. biocViews: Infrastructure Author: Benilton Carvalho and Robert Scharpf Maintainer: Benilton Carvalho and Robert Scharpf git_url: https://git.bioconductor.org/packages/oligoClasses git_branch: RELEASE_3_22 git_last_commit: d8ec574 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/oligoClasses_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oligoClasses_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/oligoClasses_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/oligoClasses_1.72.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling, maEndToEnd importsMe: affycoretools, frma, ITALICS, mimager, MinimumDistance, pdInfoBuilder, puma, VanillaICE suggestsMe: hapmapsnp6, aroma.affymetrix, scrime dependencyCount: 49 Package: OLIN Version: 1.88.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 Archs: x64 MD5sum: c18c7625ee8482eacb8ec0c47eeb4e0b NeedsCompilation: no Title: Optimized local intensity-dependent normalisation of two-color microarrays Description: Functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLIN git_branch: RELEASE_3_22 git_last_commit: d16f4f9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OLIN_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OLIN_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OLIN_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OLIN_1.88.0.tgz vignettes: vignettes/OLIN/inst/doc/OLIN.pdf vignetteTitles: Introduction to OLIN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLIN/inst/doc/OLIN.R dependsOnMe: OLINgui importsMe: OLINgui dependencyCount: 11 Package: OLINgui Version: 1.84.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: e1918d796fa0fc1e1d8595c8a9e70d4b NeedsCompilation: no Title: Graphical user interface for OLIN Description: Graphical user interface for the OLIN package biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLINgui git_branch: RELEASE_3_22 git_last_commit: 65f1149 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OLINgui_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OLINgui_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OLINgui_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OLINgui_1.84.0.tgz vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf vignetteTitles: Introduction to OLINgui hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLINgui/inst/doc/OLINgui.R dependencyCount: 17 Package: omada Version: 1.12.0 Depends: pdfCluster (>= 1.0-3), kernlab (>= 0.9-29), R (>= 4.2), fpc (>= 2.2-9), Rcpp (>= 1.0.7), diceR (>= 0.6.0), ggplot2 (>= 3.3.5), reshape (>= 0.8.8), genieclust (>= 1.1.3), clValid (>= 0.7), glmnet (>= 4.1.3), dplyr(>= 1.0.7), stats (>= 4.1.2), clValid(>= 0.7) Suggests: rmarkdown, knitr, testthat License: GPL-3 MD5sum: 145a45eae8b03d4a7a2a08bbd1e689b7 NeedsCompilation: no Title: Machine learning tools for automated transcriptome clustering analysis Description: Symptomatic heterogeneity in complex diseases reveals differences in molecular states that need to be investigated. However, selecting the numerous parameters of an exploratory clustering analysis in RNA profiling studies requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent and further gene association analyses need to be performed independently. We have developed a suite of tools to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with four datasets characterised by different expression signal strengths. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Even in datasets with less clear biological distinctions, stable subgroups with different expression profiles and clinical associations were found. biocViews: Software, Clustering, RNASeq, GeneExpression Author: Sokratis Kariotis [aut, cre] (ORCID: ) Maintainer: Sokratis Kariotis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omada git_branch: RELEASE_3_22 git_last_commit: 53ba5ea git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/omada_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omada_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omada_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omada_1.12.0.tgz vignettes: vignettes/omada/inst/doc/omada-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omada/inst/doc/omada-vignette.R dependencyCount: 137 Package: OmaDB Version: 2.25.0 Depends: R (>= 3.5), httr (>= 1.2.1), plyr(>= 1.8.4) Imports: utils, ape, Biostrings, GenomicRanges, IRanges, methods, topGO, jsonlite Suggests: knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: e376cf1c4d860bb15b27a1dde5b39ed1 NeedsCompilation: no Title: R wrapper for the OMA REST API Description: A package for the orthology prediction data download from OMA database. biocViews: Software, ComparativeGenomics, FunctionalGenomics, Genetics, Annotation, GO, FunctionalPrediction Author: Klara Kaleb Maintainer: Klara Kaleb , Adrian Altenhoff URL: https://github.com/DessimozLab/OmaDB VignetteBuilder: knitr BugReports: https://github.com/DessimozLab/OmaDB/issues git_url: https://git.bioconductor.org/packages/OmaDB git_branch: devel git_last_commit: 3f1ab42 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-09 source.ver: src/contrib/OmaDB_2.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OmaDB_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OmaDB_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OmaDB_2.26.0.tgz vignettes: vignettes/OmaDB/inst/doc/exploring_hogs.html, vignettes/OmaDB/inst/doc/OmaDB.html, vignettes/OmaDB/inst/doc/sequence_mapping.html, vignettes/OmaDB/inst/doc/tree_visualisation.html vignetteTitles: Exploring Hierarchical orthologous groups with OmaDB, Get started with OmaDB, Sequence Mapping with OmaDB, Exploring Taxonomic trees with OmaDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmaDB/inst/doc/exploring_hogs.R, vignettes/OmaDB/inst/doc/OmaDB.R, vignettes/OmaDB/inst/doc/sequence_mapping.R, vignettes/OmaDB/inst/doc/tree_visualisation.R suggestsMe: orthogene, PhyloProfile dependencyCount: 57 Package: omicade4 Version: 1.50.0 Depends: R (>= 3.0.0), ade4 Imports: made4, Biobase Suggests: BiocStyle License: GPL-2 MD5sum: 196daec15996ac4052efc9343b42f336 NeedsCompilation: no Title: Multiple co-inertia analysis of omics datasets Description: This package performes multiple co-inertia analysis of omics datasets. biocViews: Software, Clustering, Classification, MultipleComparison Author: Chen Meng, Aedin Culhane, Amin M. Gholami. Maintainer: Chen Meng git_url: https://git.bioconductor.org/packages/omicade4 git_branch: RELEASE_3_22 git_last_commit: da74ccc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/omicade4_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omicade4_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omicade4_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omicade4_1.50.0.tgz vignettes: vignettes/omicade4/inst/doc/omicade4.pdf vignetteTitles: Using omicade4 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicade4/inst/doc/omicade4.R importsMe: omicRexposome suggestsMe: biosigner, MultiDataSet, phenomis, ropls dependencyCount: 39 Package: OmicCircos Version: 1.48.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: 7c2686cabf87d7cf275c6f4181b0aa33 NeedsCompilation: no Title: High-quality circular visualization of omics data Description: OmicCircos is an R application and package for generating high-quality circular plots for omics data. biocViews: Visualization,Statistics,Annotation Author: Ying Hu Chunhua Yan Maintainer: Ying Hu git_url: https://git.bioconductor.org/packages/OmicCircos git_branch: RELEASE_3_22 git_last_commit: de926fd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OmicCircos_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OmicCircos_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OmicCircos_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OmicCircos_1.48.0.tgz vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf vignetteTitles: OmicCircos vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.R dependencyCount: 11 Package: omicplotR Version: 1.30.0 Depends: R (>= 3.6), ALDEx2 (>= 1.18.0) Imports: compositions, DT, grDevices, knitr, jsonlite, matrixStats, rmarkdown, shiny, stats, vegan, zCompositions License: MIT + file LICENSE MD5sum: 819717041c1ff0dafeb70225e1014d16 NeedsCompilation: no Title: Visual Exploration of Omic Datasets Using a Shiny App Description: A Shiny app for visual exploration of omic datasets as compositions, and differential abundance analysis using ALDEx2. Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata. biocViews: Software, DifferentialExpression, GeneExpression, GUI, RNASeq, DNASeq, Metagenomics, Transcriptomics, Bayesian, Microbiome, Visualization, Sequencing, ImmunoOncology Author: Daniel Giguere [aut, cre], Jean Macklaim [aut], Greg Gloor [aut] Maintainer: Daniel Giguere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicplotR git_branch: RELEASE_3_22 git_last_commit: e4df327 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/omicplotR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omicplotR_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omicplotR_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omicplotR_1.30.0.tgz vignettes: vignettes/omicplotR/inst/doc/omicplotR.html vignetteTitles: omicplotR: A tool for visualization of omic datasets as compositions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicplotR/inst/doc/omicplotR.R dependencyCount: 106 Package: omicRexposome Version: 1.32.0 Depends: R (>= 3.5.0), Biobase Imports: stats, utils, grDevices, graphics, methods, rexposome, limma, sva, ggplot2, ggrepel, PMA, omicade4, gridExtra, MultiDataSet, SmartSVA, isva, parallel, SummarizedExperiment, stringr Suggests: BiocStyle, knitr, rmarkdown, snpStats, brgedata License: MIT + file LICENSE MD5sum: 45da8e48417b726f10840a7f82d10b3c NeedsCompilation: no Title: Exposome and omic data associatin and integration analysis Description: omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA). biocViews: ImmunoOncology, WorkflowStep, MultipleComparison, Visualization, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneRegulation, Epigenetics, Proteomics, Transcriptomics, StatisticalMethod, Regression Author: Carles Hernandez-Ferrer [aut, cre], Juan R. González [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicRexposome git_branch: RELEASE_3_22 git_last_commit: 0868a1b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/omicRexposome_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omicRexposome_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omicRexposome_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omicRexposome_1.32.0.tgz vignettes: vignettes/omicRexposome/inst/doc/exposome_omic_integration.html vignetteTitles: Exposome Data Integration with Omic Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicRexposome/inst/doc/exposome_omic_integration.R dependencyCount: 224 Package: omicsGMF Version: 1.0.0 Depends: R (>= 4.5.0), sgdGMF, SingleCellExperiment, scuttle, scater Imports: stats, utils, Matrix, S4Vectors, SummarizedExperiment, DelayedArray, MatrixGenerics, BiocSingular, BiocParallel, beachmat, ggplot2, methods, QFeatures Suggests: knitr, dplyr, testthat, BiocGenerics, BiocStyle, graphics, grDevices License: Artistic-2.0 MD5sum: 0b35cfa0ea9e5844d21a7f1cd86d54be NeedsCompilation: no Title: Dimensionality reduction of (single-cell) omics data in R using omicsGMF Description: omicsGMF is a Bioconductor package that uses the sgdGMF-framework of the \code{sgdGMF} package for highly performant and fast matrix factorization that can be used for dimensionality reduction, visualization and imputation of omics data. It considers data from the general exponential family as input, and therefore suits the use of both RNA-seq (Poisson or Negative Binomial data) and proteomics data (Gaussian data). It does not require prior transformation of counts to the log-scale, because it rather optimizes the deviances from the data family specified. Also, it allows to correct for known sample-level and feature-level covariates, therefore enabling visualization and dimensionality reduction upon batch correction. Last but not least, it deals with missing values, and allows to impute these after matrix factorization, useful for proteomics data. This Bioconductor package allows input of SummarizedExperiment, SingleCellExperiment, and QFeature classes. biocViews: SingleCell, RNASeq, Proteomics, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, DataRepresentation, MassSpectrometry Author: Alexandre Segers [aut, cre, fnd], Cristian Castiglione [ctb], Christophe Vanderaa [ctb], Davide Risso [ctb, fnd], Lieven Clement [ctb, fnd] Maintainer: Alexandre Segers URL: https://github.com/statOmics/omicsGMF VignetteBuilder: knitr BugReports: https://github.com/statOmics/omicsGMF/issues git_url: https://git.bioconductor.org/packages/omicsGMF git_branch: RELEASE_3_22 git_last_commit: 665af6c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/omicsGMF_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omicsGMF_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omicsGMF_1.0.0.tgz vignettes: vignettes/omicsGMF/inst/doc/Proteomics-vignette.html, vignettes/omicsGMF/inst/doc/RNASeq-vignette.html vignetteTitles: Proteomics-vignette: omicsGMF, RNASeq-vignette: omicsGMF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsGMF/inst/doc/Proteomics-vignette.R, vignettes/omicsGMF/inst/doc/RNASeq-vignette.R dependencyCount: 181 Package: OMICsPCA Version: 1.28.0 Depends: R (>= 3.5.0), OMICsPCAdata Imports: HelloRanges, fpc, stats, MultiAssayExperiment, pdftools, methods, grDevices, utils,clValid, NbClust, cowplot, rmarkdown, kableExtra, rtracklayer, IRanges, Seqinfo, reshape2, ggplot2, factoextra, rgl, corrplot, MASS, graphics, FactoMineR, PerformanceAnalytics, tidyr, data.table, cluster, magick Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 61cfa8891fd03be1e786a067e18fae75 NeedsCompilation: no Title: An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples Description: OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals. biocViews: ImmunoOncology, MultipleComparison, PrincipalComponent, DataRepresentation, Workflow, Visualization, DimensionReduction, Clustering, BiologicalQuestion, EpigeneticsWorkflow, Transcription, GeneticVariability, GUI, BiomedicalInformatics, Epigenetics, FunctionalGenomics, SingleCell Author: Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb] Maintainer: Subhadeep Das VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OMICsPCA git_branch: RELEASE_3_22 git_last_commit: 57b6111 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OMICsPCA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OMICsPCA_1.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OMICsPCA_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OMICsPCA_1.28.0.tgz vignettes: vignettes/OMICsPCA/inst/doc/vignettes.html vignetteTitles: OMICsPCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OMICsPCA/inst/doc/vignettes.R dependencyCount: 206 Package: omicsPrint Version: 1.30.0 Depends: R (>= 3.5), MASS Imports: methods, matrixStats, graphics, stats, SummarizedExperiment, MultiAssayExperiment, RaggedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat, GEOquery, VariantAnnotation, Rsamtools, BiocParallel, GenomicRanges, FDb.InfiniumMethylation.hg19, snpStats License: GPL (>= 2) MD5sum: 5f53d3f6365ca0482fe87a253d86d360 NeedsCompilation: no Title: Cross omic genetic fingerprinting Description: omicsPrint provides functionality for cross omic genetic fingerprinting, for example, to verify sample relationships between multiple omics data types, i.e. genomic, transcriptomic and epigenetic (DNA methylation). biocViews: QualityControl, Genetics, Epigenetics, Transcriptomics, DNAMethylation, Transcription, GeneticVariability, ImmunoOncology Author: Maarten van Iterson [aut], Davy Cats [cre] Maintainer: Davy Cats VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicsPrint git_branch: RELEASE_3_22 git_last_commit: f147664 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/omicsPrint_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omicsPrint_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omicsPrint_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omicsPrint_1.30.0.tgz vignettes: vignettes/omicsPrint/inst/doc/omicsPrint.html vignetteTitles: omicsPrint: detection of data linkage errors in multiple omics studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsPrint/inst/doc/omicsPrint.R dependencyCount: 48 Package: omicsViewer Version: 1.14.0 Depends: R (>= 4.2) Imports: survminer, survival, fastmatch, reshape2, stringr, beeswarm, grDevices, DT, shiny, shinythemes, shinyWidgets, plotly, networkD3, httr, matrixStats, RColorBrewer, Biobase, fgsea, openxlsx, psych, shinybusy, ggseqlogo, htmlwidgets, graphics, grid, stats, utils, methods, shinyjs, curl, flatxml, ggplot2, S4Vectors, SummarizedExperiment, RSQLite, Matrix, shinycssloaders, ROCR, drc Suggests: BiocStyle, knitr, rmarkdown, unittest License: GPL-2 MD5sum: ac75c3bd4b66c0fa701c4e1b6881b997 NeedsCompilation: no Title: Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer Description: omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisher’s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. Otherwise, a standalone version of omicsViewer together with designated omics data could be easily created by integrating it with portable R, which can be shared with collaborators or submitted as supplementary data together with a manuscript. biocViews: Software, Visualization, GeneSetEnrichment, DifferentialExpression, MotifDiscovery, Network, NetworkEnrichment Author: Chen Meng [aut, cre] Maintainer: Chen Meng URL: https://github.com/mengchen18/omicsViewer VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=0nirB-exquY&list=PLo2m88lJf-RRoLKMY8UEGqCpraKYrX5lk BugReports: https://github.com/mengchen18/omicsViewer git_url: https://git.bioconductor.org/packages/omicsViewer git_branch: RELEASE_3_22 git_last_commit: 5a2ceb1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/omicsViewer_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omicsViewer_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omicsViewer_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omicsViewer_1.14.0.tgz vignettes: vignettes/omicsViewer/inst/doc/quickStart.html vignetteTitles: quickStart.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsViewer/inst/doc/quickStart.R dependencyCount: 194 Package: Omixer Version: 1.20.0 Depends: R (>= 4.0.0) Imports: dplyr, ggplot2, forcats, tibble, gridExtra, magrittr, readr, tidyselect, grid, stats, stringr Suggests: knitr, rmarkdown, BiocStyle, magick, testthat License: MIT + file LICENSE Archs: x64 MD5sum: de9972e47ba40891f8f5b814480f501f NeedsCompilation: no Title: Omixer: multivariate and reproducible sample randomization to proactively counter batch effects in omics studies Description: Omixer - an Bioconductor package for multivariate and reproducible sample randomization, which ensures optimal sample distribution across batches with well-documented methods. It outputs lab-friendly sample layouts, reducing the risk of sample mixups when manually pipetting randomized samples. biocViews: DataRepresentation, ExperimentalDesign, QualityControl, Software, Visualization Author: Lucy Sinke [cre, aut] Maintainer: Lucy Sinke VignetteBuilder: knitr BugReports: https://github.com/molepi/Omixer/issues git_url: https://git.bioconductor.org/packages/Omixer git_branch: RELEASE_3_22 git_last_commit: af80d3c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Omixer_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Omixer_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Omixer_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Omixer_1.20.0.tgz vignettes: vignettes/Omixer/inst/doc/omixer-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Omixer/inst/doc/omixer-vignette.R dependencyCount: 46 Package: ompBAM Version: 1.14.0 Imports: utils, Rcpp Suggests: RcppProgress, knitr, rmarkdown, roxygen2, devtools, usethis, desc, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 07b9ed7aec0d08c6b82a0616611862b7 NeedsCompilation: no Title: C++ Library for OpenMP-based multi-threaded sequential profiling of Binary Alignment Map (BAM) files Description: This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged included within ompBAM. biocViews: Alignment, DataImport, RNASeq, Software, Sequencing, Transcriptomics, SingleCell Author: Alex Chit Hei Wong [aut, cre, cph] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/ompBAM VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/ompBAM git_branch: RELEASE_3_22 git_last_commit: bbcbda1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ompBAM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ompBAM_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ompBAM_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ompBAM_1.14.0.tgz vignettes: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.html vignetteTitles: ompBAM API Documentation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.R importsMe: SpliceWiz linksToMe: SpliceWiz dependencyCount: 3 Package: omXplore Version: 1.3.0 Depends: R (>= 4.4.0), methods Imports: DT, shiny, bs4Dash, waiter, thematic, MSnbase, PSMatch, SummarizedExperiment, MultiAssayExperiment, shinyBS, shinyjs, shinyjqui, RColorBrewer, gplots, highcharter, visNetwork, tibble, grDevices, stats, utils, htmlwidgets, vioplot, graphics, FactoMineR, dendextend, dplyr, factoextra, tidyr, nipals Suggests: knitr, rmarkdown, BiocStyle, testthat, Matrix, graph License: Artistic-2.0 Archs: x64 MD5sum: 7003662bcaee3589492f17767149f968 NeedsCompilation: no Title: Vizualization tools for 'omics' datasets with R Description: This package contains a collection of functions (written as shiny modules) for the visualisation and the statistical analysis of omics data. These plots can be displayed individually or embedded in a global Shiny module. Additionaly, it is possible to integrate third party modules to the main interface of the package omXplore. biocViews: Software, ShinyApps, MassSpectrometry, DataRepresentation, GUI, QualityControl Author: Samuel Wieczorek [aut, cre] (ORCID: ), Thomas Burger [aut], Enora Fremy [ctb], Cyril Ariztegui [ctb] Maintainer: Samuel Wieczorek URL: https://github.com/prostarproteomics/omXplore, https://prostarproteomics.github.io/omXplore/ VignetteBuilder: knitr BugReports: https://github.com/prostarproteomics/omXplore/issues git_url: https://git.bioconductor.org/packages/omXplore git_branch: devel git_last_commit: 8318898 git_last_commit_date: 2025-06-10 Date/Publication: 2025-10-07 source.ver: src/contrib/omXplore_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omXplore_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omXplore_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omXplore_1.3.0.tgz vignettes: vignettes/omXplore/inst/doc/addingThirdPartyPlots.html, vignettes/omXplore/inst/doc/omXplore.html vignetteTitles: Adding third party plots, omXplore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omXplore/inst/doc/addingThirdPartyPlots.R, vignettes/omXplore/inst/doc/omXplore.R dependencyCount: 213 Package: oncomix Version: 1.32.0 Depends: R (>= 3.4.0) Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats, SummarizedExperiment Suggests: knitr, rmarkdown, testthat, RMySQL License: GPL-3 Archs: x64 MD5sum: 8a9ffce0a5f243551c9e85ce383b83ce NeedsCompilation: no Title: Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data Description: This package helps identify mRNAs that are overexpressed in subsets of tumors relative to normal tissue. Ideal inputs would be paired tumor-normal data from the same tissue from many patients (>15 pairs). This unsupervised approach relies on the observation that oncogenes are characteristically overexpressed in only a subset of tumors in the population, and may help identify oncogene candidates purely based on differences in mRNA expression between previously unknown subtypes. biocViews: GeneExpression, Sequencing Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oncomix git_branch: RELEASE_3_22 git_last_commit: 735c801 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/oncomix_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oncomix_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/oncomix_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/oncomix_1.32.0.tgz vignettes: vignettes/oncomix/inst/doc/oncomix.html vignetteTitles: OncoMix Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oncomix/inst/doc/oncomix.R dependencyCount: 44 Package: oncoscanR Version: 1.12.0 Depends: R (>= 4.2), IRanges (>= 2.30.0), GenomicRanges (>= 1.48.0), magrittr Imports: readr, S4Vectors, methods, utils Suggests: testthat (>= 3.1.4), jsonlite, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: cd45ce6f4c543605c137782b779eca67 NeedsCompilation: no Title: Secondary analyses of CNV data (HRD and more) Description: The software uses the copy number segments from a text file and identifies all chromosome arms that are globally altered and computes various genome-wide scores. The following HRD scores (characteristic of BRCA-mutated cancers) are included: LST, HR-LOH, nLST and gLOH. the package is tailored for the ThermoFisher Oncoscan assay analyzed with their Chromosome Alteration Suite (ChAS) but can be adapted to any input. biocViews: CopyNumberVariation, Microarray, Software Author: Yann Christinat [aut, cre], Geneva University Hospitals [aut, cph] Maintainer: Yann Christinat URL: https://github.com/yannchristinat/oncoscanR VignetteBuilder: knitr BugReports: https://github.com/yannchristinat/oncoscanR/issues git_url: https://git.bioconductor.org/packages/oncoscanR git_branch: RELEASE_3_22 git_last_commit: 11be7a3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/oncoscanR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oncoscanR_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/oncoscanR_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/oncoscanR_1.12.0.tgz vignettes: vignettes/oncoscanR/inst/doc/oncoscanR.html vignetteTitles: oncoscanR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/oncoscanR/inst/doc/oncoscanR.R dependencyCount: 36 Package: OncoScore Version: 1.38.0 Depends: R (>= 4.1.0), Imports: biomaRt, grDevices, graphics, utils, methods, Suggests: BiocGenerics, BiocStyle, knitr, testthat, License: file LICENSE MD5sum: 2b815f98de861df4f8cb5ac3848d78c6 NeedsCompilation: no Title: A tool to identify potentially oncogenic genes Description: OncoScore is a tool to measure the association of genes to cancer based on citation frequencies in biomedical literature. The score is evaluated from PubMed literature by dynamically updatable web queries. biocViews: BiomedicalInformatics Author: Luca De Sano [cre, aut] (ORCID: ), Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb], Daniele Ramazzotti [aut] (ORCID: ), Roberta Spinelli [ctb] Maintainer: Luca De Sano URL: https://github.com/danro9685/OncoScore VignetteBuilder: knitr BugReports: https://github.com/danro9685/OncoScore git_url: https://git.bioconductor.org/packages/OncoScore git_branch: RELEASE_3_22 git_last_commit: c34bf8c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OncoScore_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OncoScore_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OncoScore_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OncoScore_1.38.0.tgz vignettes: vignettes/OncoScore/inst/doc/v1_introduction.html, vignettes/OncoScore/inst/doc/v2_running_OncoScore.html vignetteTitles: Introduction, Running OncoScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OncoScore/inst/doc/v1_introduction.R, vignettes/OncoScore/inst/doc/v2_running_OncoScore.R dependencyCount: 64 Package: OncoSimulR Version: 4.12.0 Depends: R (>= 3.5.0) Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr, smatr, ggplot2, ggrepel, stringr LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown, pander License: GPL (>= 3) MD5sum: 009baa6fee18bc27329a4309e80febb2 NeedsCompilation: yes Title: Forward Genetic Simulation of Cancer Progression with Epistasis Description: Functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Fitness (including just birth, just death, or both birth and death) can also be a function of the relative and absolute frequencies of other genotypes (i.e., frequency-dependent fitness). Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulating multi-species scenarios and therapeutic interventions, including adaptive therapy, is also possible. Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for: simulating random DAGs of the type found in Oncogenetic Trees, Conjunctive Bayesian Networks, and other cancer progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (Rough Mount Fuji, House of Cards, additive, NK, Ising, and Eggbox models) and plotting them. biocViews: BiologicalQuestion, SomaticMutation Author: Ramon Diaz-Uriarte [aut, cre], Sergio Sanchez-Carrillo [aut], Juan Antonio Miguel Gonzalez [aut], Alberto Gonzalez Klein [aut], Javier Mu\~noz Haro [aut], Javier Lopez Cano [aut], Niklas Endres [ctb], Mark Taylor [ctb], Arash Partow [ctb], Sophie Brouillet [ctb], Sebastian Matuszewski [ctb], Harry Annoni [ctb], Luca Ferretti [ctb], Guillaume Achaz [ctb], Tymoteusz Wolodzko [ctb], Guillermo Gorines Cordero [ctb], Ivan Lorca Alonso [ctb], Francisco Mu\~noz Lopez [ctb], David Roncero Moro\~no [ctb], Alvaro Quevedo [ctb], Pablo Perez [ctb], Cristina Devesa [ctb], Alejandro Herrador [ctb], Holger Froehlich [ctb], Florian Markowetz [ctb], Achim Tresch [ctb], Theresa Niederberger [ctb], Christian Bender [ctb], Matthias Maneck [ctb], Claudio Lottaz [ctb], Tim Beissbarth [ctb], Sara Dorado Alfaro [ctb], Miguel Hernandez del Valle [ctb], Alvaro Huertas Garcia [ctb], Diego Ma\~nanes Cayero [ctb], Alejandro Martin Mu\~noz [ctb], Marta Couce Iglesias [ctb], Silvia Garcia Cobos [ctb], Carlos Madariaga Aramendi [ctb], Ana Rodriguez Ronchel [ctb], Lucia Sanchez Garcia [ctb], Yolanda Benitez Quesada [ctb], Asier Fernandez Pato [ctb], Esperanza Lopez Lopez [ctb], Alberto Manuel Parra Perez [ctb], Jorge Garcia Calleja [ctb], Ana del Ramo Galian [ctb], Alejandro de los Reyes Benitez [ctb], Guillermo Garcia Hoyos [ctb], Rosalia Palomino Cabrera [ctb], Rafael Barrero Rodriguez [ctb], Silvia Talavera Marcos [ctb] Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/OncoSimul, https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/ VignetteBuilder: knitr BugReports: https://github.com/rdiaz02/OncoSimul/issues git_url: https://git.bioconductor.org/packages/OncoSimulR git_branch: RELEASE_3_22 git_last_commit: 0528208 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OncoSimulR_4.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OncoSimulR_4.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OncoSimulR_4.12.0.tgz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.html vignetteTitles: OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R dependencyCount: 79 Package: onlineFDR Version: 2.18.0 Imports: stats, Rcpp, progress LinkingTo: Rcpp, RcppProgress Suggests: knitr, rmarkdown, testthat, covr License: GPL-3 Archs: x64 MD5sum: f9278dbf78028ceb389ef270200176da NeedsCompilation: yes Title: Online error rate control Description: This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online multiple hypothesis testing, where hypotheses arrive in a stream. In this framework, a null hypothesis is rejected based on the evidence against it and on the previous rejection decisions. biocViews: MultipleComparison, Software, StatisticalMethod Author: David S. Robertson [aut, cre], Lathan Liou [aut], Aaditya Ramdas [aut], Adel Javanmard [ctb], Andrea Montanari [ctb], Jinjin Tian [ctb], Tijana Zrnic [ctb], Natasha A. Karp [aut] Maintainer: David S. Robertson URL: https://dsrobertson.github.io/onlineFDR/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/onlineFDR git_branch: RELEASE_3_22 git_last_commit: 8df9eef git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/onlineFDR_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/onlineFDR_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/onlineFDR_2.18.0.tgz vignettes: vignettes/onlineFDR/inst/doc/advanced-usage.html, vignettes/onlineFDR/inst/doc/onlineFDR.html, vignettes/onlineFDR/inst/doc/theory.html vignetteTitles: Advanced usage of onlineFDR, Using the onlineFDR package, The theory behind onlineFDR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/onlineFDR/inst/doc/advanced-usage.R, vignettes/onlineFDR/inst/doc/onlineFDR.R, vignettes/onlineFDR/inst/doc/theory.R dependencyCount: 17 Package: ontoProc Version: 2.4.0 Depends: R (>= 4.1), ontologyIndex Imports: Biobase, S4Vectors, methods, stats, utils, BiocFileCache, shiny, graph, Rgraphviz, ontologyPlot, dplyr, magrittr, DT, igraph, AnnotationHub, SummarizedExperiment, reticulate, R.utils, httr, basilisk, jsonlite, RBGL, ellmer Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle, SingleCellExperiment, celldex, rmarkdown, AnnotationDbi, magick, License: Artistic-2.0 Archs: x64 MD5sum: 8c5d6d502e72184587c3e4eaa5191063 NeedsCompilation: no Title: processing of ontologies of anatomy, cell lines, and so on Description: Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies. biocViews: Infrastructure, GO Author: Vincent Carey [ctb, cre] (ORCID: ), Sara Stankiewicz [ctb], Victor Tarca [ctb] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/ontoProc VignetteBuilder: knitr BugReports: https://github.com/vjcitn/ontoProc/issues git_url: https://git.bioconductor.org/packages/ontoProc git_branch: RELEASE_3_22 git_last_commit: f2c1b3f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ontoProc_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ontoProc_2.3.9.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ontoProc_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ontoProc_2.4.0.tgz vignettes: vignettes/ontoProc/inst/doc/ontoProc.html, vignettes/ontoProc/inst/doc/owlents.html vignetteTitles: ontoProc: some ontology-oriented utilites with single-cell focus for Bioconductor, owlents: using OWL directly in ontoProc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ontoProc/inst/doc/ontoProc.R, vignettes/ontoProc/inst/doc/owlents.R dependsOnMe: SingleRBook importsMe: pogos, tenXplore suggestsMe: scDiffCom dependencyCount: 123 Package: openCyto Version: 2.22.0 Depends: R (>= 3.5.0) Imports: methods, Biobase, BiocGenerics, flowCore(>= 1.99.17), flowViz, ncdfFlow(>= 2.11.34), flowWorkspace(>= 3.99.1), flowClust(>= 3.11.4), RBGL, graph, data.table, RColorBrewer, grDevices LinkingTo: cpp11, BH(>= 1.62.0-1) Suggests: flowWorkspaceData, knitr, rmarkdown, markdown, testthat, utils, tools, parallel, ggcyto, CytoML, flowStats(>= 4.5.2), MASS License: AGPL-3.0-only MD5sum: 590a797c16436e9a367d797f31967f15 NeedsCompilation: yes Title: Hierarchical Gating Pipeline for flow cytometry data Description: This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openCyto git_branch: RELEASE_3_22 git_last_commit: bbeacaf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/openCyto_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/openCyto_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/openCyto_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/openCyto_2.22.0.tgz vignettes: vignettes/openCyto/inst/doc/HowToAutoGating.html, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html vignetteTitles: How to use different auto gating functions, How to write a csv gating template, An Introduction to the openCyto package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/openCyto/inst/doc/HowToAutoGating.R, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R, vignettes/openCyto/inst/doc/openCytoVignette.R importsMe: CytoML suggestsMe: CATALYST, flowClust, flowCore, flowStats, flowTime, flowWorkspace, ggcyto dependencyCount: 72 Package: openPrimeR Version: 1.32.0 Depends: R (>= 4.0.0) Imports: Biostrings (>= 2.38.4), pwalign, XML (>= 3.98-1.4), scales (>= 0.4.0), reshape2 (>= 1.4.1), seqinr (>= 3.3-3), IRanges (>= 2.4.8), GenomicRanges (>= 1.22.4), ggplot2 (>= 2.1.0), plyr (>= 1.8.4), dplyr (>= 0.5.0), stringdist (>= 0.9.4.1), stringr (>= 1.0.0), RColorBrewer (>= 1.1-2), DECIPHER (>= 1.16.1), lpSolveAPI (>= 5.5.2.0-17), digest (>= 0.6.9), Hmisc (>= 3.17-4), ape (>= 3.5), BiocGenerics (>= 0.16.1), S4Vectors (>= 0.8.11), foreach (>= 1.4.3), magrittr (>= 1.5), uniqtag (>= 1.0), openxlsx (>= 4.0.17), grid (>= 3.1.0), grDevices (>= 3.1.0), stats (>= 3.1.0), utils (>= 3.1.0), methods (>= 3.1.0) Suggests: testthat (>= 1.0.2), knitr (>= 1.13), rmarkdown (>= 1.0), devtools (>= 1.12.0), doParallel (>= 1.0.10), pander (>= 0.6.0), learnr (>= 0.9) License: GPL-2 MD5sum: ff37f6162daf94107af766811ecd5ce9 NeedsCompilation: no Title: Multiplex PCR Primer Design and Analysis Description: An implementation of methods for designing, evaluating, and comparing primer sets for multiplex PCR. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. A Shiny app providing a user interface for the functionalities of this package is provided by the 'openPrimeRui' package. biocViews: Software, Technology, Coverage, MultipleComparison Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring SystemRequirements: MAFFT (>= 7.305), OligoArrayAux (>= 3.8), ViennaRNA (>= 2.4.1), MELTING (>= 5.1.1), Pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeR git_branch: RELEASE_3_22 git_last_commit: 7d12b40 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/openPrimeR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/openPrimeR_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/openPrimeR_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/openPrimeR_1.32.0.tgz vignettes: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.html vignetteTitles: openPrimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.R dependencyCount: 103 Package: OpenStats Version: 1.22.0 Depends: nlme Imports: MASS, jsonlite, Hmisc, methods, knitr, AICcmodavg, car, rlist, summarytools, graphics, stats, utils Suggests: rmarkdown License: GPL (>= 2) MD5sum: 698e7d1228181dc92da0b884f8d59860 NeedsCompilation: no Title: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput genotype-phenotype association Description: Package contains several methods for statistical analysis of genotype to phenotype association in high-throughput screening pipelines. biocViews: StatisticalMethod, BatchEffect, Bayesian Author: Hamed Haseli Mashhadi Maintainer: Marina Kan URL: https://git.io/Jv5w0 VignetteBuilder: knitr BugReports: https://git.io/Jv5wg git_url: https://git.bioconductor.org/packages/OpenStats git_branch: RELEASE_3_22 git_last_commit: c2e418f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OpenStats_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OpenStats_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OpenStats_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OpenStats_1.22.0.tgz vignettes: vignettes/OpenStats/inst/doc/OpenStats.html vignetteTitles: OpenStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OpenStats/inst/doc/OpenStats.R dependencyCount: 127 Package: oposSOM Version: 2.28.0 Depends: R (>= 4.0.0), igraph (>= 1.0.0) Imports: fastICA, tsne, scatterplot3d, pixmap, fdrtool, ape, biomaRt, Biobase, RcppParallel, Rcpp, methods, graph, XML, png, RCurl LinkingTo: RcppParallel, Rcpp License: GPL (>=2) MD5sum: 17676e7dce4780bf489dfe4bea8a87a0 NeedsCompilation: yes Title: Comprehensive analysis of transcriptome data Description: This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Visualization Author: Henry Loeffler-Wirth , Hoang Thanh Le and Martin Kalcher Maintainer: Henry Loeffler-Wirth URL: http://som.izbi.uni-leipzig.de git_url: https://git.bioconductor.org/packages/oposSOM git_branch: RELEASE_3_22 git_last_commit: c1a99a8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/oposSOM_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oposSOM_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/oposSOM_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/oposSOM_2.28.0.tgz vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf vignetteTitles: The oposSOM users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oposSOM/inst/doc/Vignette.R dependencyCount: 83 Package: oppar Version: 1.38.0 Depends: R (>= 3.3) Imports: Biobase, methods, GSEABase, GSVA Suggests: knitr, rmarkdown, limma, org.Hs.eg.db, GO.db, snow, parallel License: GPL-2 MD5sum: e16ea4a252e9a307ae481f65a1a46c32 NeedsCompilation: yes Title: Outlier profile and pathway analysis in R Description: The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis. biocViews: Pathways, GeneSetEnrichment, SystemsBiology, GeneExpression, Software Author: Chenwei Wang [aut], Alperen Taciroglu [aut], Stefan R Maetschke [aut], Colleen C Nelson [aut], Mark Ragan [aut], Melissa Davis [aut], Soroor Hediyeh zadeh [cre], Momeneh Foroutan [ctr] Maintainer: Soroor Hediyeh zadeh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oppar git_branch: RELEASE_3_22 git_last_commit: ef174de git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/oppar_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oppar_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/oppar_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/oppar_1.38.0.tgz vignettes: vignettes/oppar/inst/doc/oppar.html vignetteTitles: OPPAR: Outlier Profile and Pathway Analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppar/inst/doc/oppar.R dependencyCount: 103 Package: optimalFlow Version: 1.22.0 Depends: dplyr, optimalFlowData, rlang (>= 0.4.0) Imports: transport, parallel, Rfast, robustbase, dbscan, randomForest, foreach, graphics, doParallel, stats, flowMeans, rgl, ellipse Suggests: knitr, BiocStyle, rmarkdown, magick License: Artistic-2.0 MD5sum: 1eaa06faa1e497b00679f277c32dee38 NeedsCompilation: no Title: optimalFlow Description: Optimal-transport techniques applied to supervised flow cytometry gating. biocViews: Software, FlowCytometry, Technology Author: Hristo Inouzhe Maintainer: Hristo Inouzhe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/optimalFlow git_branch: RELEASE_3_22 git_last_commit: ea616b7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/optimalFlow_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/optimalFlow_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/optimalFlow_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/optimalFlow_1.22.0.tgz vignettes: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.html vignetteTitles: optimalFlow: optimal-transport approach to Flow Cytometry analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.R dependencyCount: 95 Package: OPWeight Version: 1.32.0 Depends: R (>= 3.4.0), Imports: graphics, qvalue, MASS, tibble, stats, Suggests: airway, BiocStyle, cowplot, DESeq2, devtools, ggplot2, gridExtra, knitr, Matrix, rmarkdown, scales, testthat License: Artistic-2.0 Archs: x64 MD5sum: 75fb8164091404dbe78a4d546a81113e NeedsCompilation: no Title: Optimal p-value weighting with independent information Description: This package perform weighted-pvalue based multiple hypothesis test and provides corresponding information such as ranking probability, weight, significant tests, etc . To conduct this testing procedure, the testing method apply a probabilistic relationship between the test rank and the corresponding test effect size. biocViews: ImmunoOncology, BiomedicalInformatics, MultipleComparison, Regression, RNASeq, SNP Author: Mohamad Hasan [aut, cre], Paul Schliekelman [aut] Maintainer: Mohamad Hasan URL: https://github.com/mshasan/OPWeight VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OPWeight git_branch: RELEASE_3_22 git_last_commit: acab1d1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OPWeight_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OPWeight_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OPWeight_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OPWeight_1.32.0.tgz vignettes: vignettes/OPWeight/inst/doc/OPWeight.html vignetteTitles: "Introduction to OPWeight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OPWeight/inst/doc/OPWeight.R dependencyCount: 36 Package: OrderedList Version: 1.82.0 Depends: R (>= 3.6.1), Biobase, twilight Imports: methods License: GPL (>= 2) MD5sum: d94731b9397392029a4c9f4e62162f6f NeedsCompilation: no Title: Similarities of Ordered Gene Lists Description: Detection of similarities between ordered lists of genes. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Xinan Yang, Stefanie Scheid, Claudio Lottaz Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/OrderedList.shtml git_url: https://git.bioconductor.org/packages/OrderedList git_branch: RELEASE_3_22 git_last_commit: 38b4454 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OrderedList_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OrderedList_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OrderedList_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OrderedList_1.82.0.tgz vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf vignetteTitles: Similarities of Ordered Gene Lists hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrderedList/inst/doc/tr_2006_01.R dependencyCount: 10 Package: ORFhunteR Version: 1.18.0 Depends: Biostrings, rtracklayer, Peptides Imports: Rcpp (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg38, data.table, stringr, randomForest, xfun, stats, utils, parallel, graphics LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown License: MIT License Archs: x64 MD5sum: 6c293e3d2b98e62c5563d654710e98f3 NeedsCompilation: yes Title: Predict open reading frames in nucleotide sequences Description: The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules from the NCBI RefSeq and Ensembl databases. The package can be used in basic and applied biomedical research related to the study of the transcriptome of normal as well as altered (for example, cancer) human cells. biocViews: Technology, StatisticalMethod, Sequencing, RNASeq, Classification, FeatureExtraction Author: Vasily V. Grinev [aut, cre] (ORCID: ), Mikalai M. Yatskou [aut], Victor V. Skakun [aut], Maryna Chepeleva [aut] (ORCID: ), Petr V. Nazarov [aut] (ORCID: ) Maintainer: Vasily V. Grinev VignetteBuilder: knitr BugReports: https://github.com/rfctbio-bsu/ORFhunteR/issues git_url: https://git.bioconductor.org/packages/ORFhunteR git_branch: RELEASE_3_22 git_last_commit: 92b6d57 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ORFhunteR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ORFhunteR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ORFhunteR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ORFhunteR_1.18.0.tgz vignettes: vignettes/ORFhunteR/inst/doc/ORFhunteR.html vignetteTitles: The ORFhunteR package: User’s manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ORFhunteR/inst/doc/ORFhunteR.R dependencyCount: 74 Package: ORFik Version: 1.30.0 Depends: R (>= 4.1.0), IRanges (>= 2.17.1), GenomicRanges (>= 1.35.1), GenomicAlignments (>= 1.19.0) Imports: AnnotationDbi (>= 1.45.0), Biostrings (>= 2.51.1), biomaRt, biomartr (>= 1.0.7), BiocFileCache, BiocGenerics (>= 0.29.1), BiocParallel (>= 1.19.0), BSgenome, cowplot (>= 1.0.0), data.table (>= 1.11.8), DESeq2 (>= 1.24.0), fst (>= 0.9.2), GenomeInfoDb (>= 1.15.5), GenomicFeatures (>= 1.31.10), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), httr (>= 1.3.0), jsonlite, methods (>= 3.6.0), qs, R.utils, Rcpp (>= 1.0.0), Rsamtools (>= 1.35.0), rtracklayer (>= 1.43.0), stats, SummarizedExperiment (>= 1.14.0), S4Vectors (>= 0.21.3), tools, txdbmaker, utils, XML, xml2 (>= 1.2.0), withr LinkingTo: Rcpp Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDbData License: MIT + file LICENSE MD5sum: 3b916c9a6c4e49332a3cfffa0b69ab33 NeedsCompilation: yes Title: Open Reading Frames in Genomics Description: R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more. biocViews: ImmunoOncology, Software, Sequencing, RiboSeq, RNASeq, FunctionalGenomics, Coverage, Alignment, DataImport Author: Haakon Tjeldnes [aut, cre, dtc], Kornel Labun [aut, cph], Michal Swirski [ctb], Katarzyna Chyzynska [ctb, dtc], Yamila Torres Cleuren [ctb, ths], Eivind Valen [ths, fnd] Maintainer: Haakon Tjeldnes URL: https://github.com/Roleren/ORFik VignetteBuilder: knitr BugReports: https://github.com/Roleren/ORFik/issues git_url: https://git.bioconductor.org/packages/ORFik git_branch: RELEASE_3_22 git_last_commit: b860267 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ORFik_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ORFik_1.29.7.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ORFik_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ORFik_1.30.0.tgz vignettes: vignettes/ORFik/inst/doc/Annotation_Alignment.html, vignettes/ORFik/inst/doc/Importing_Data.html, vignettes/ORFik/inst/doc/ORFikExperiment.html, vignettes/ORFik/inst/doc/ORFikOverview.html, vignettes/ORFik/inst/doc/Ribo-seq_pipeline-human.html, vignettes/ORFik/inst/doc/Ribo-seq_pipeline-yeast.html, vignettes/ORFik/inst/doc/Working_with_transcripts.html vignetteTitles: Annotation & Alignment, Importing data, Data management, ORFik Overview, Ribo-seq pipeline (Human), Ribo-seq pipeline (Yeast), Working with transcripts hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ORFik/inst/doc/Annotation_Alignment.R, vignettes/ORFik/inst/doc/Importing_Data.R, vignettes/ORFik/inst/doc/ORFikExperiment.R, vignettes/ORFik/inst/doc/ORFikOverview.R, vignettes/ORFik/inst/doc/Ribo-seq_pipeline-human.R, vignettes/ORFik/inst/doc/Ribo-seq_pipeline-yeast.R, vignettes/ORFik/inst/doc/Working_with_transcripts.R dependsOnMe: RiboCrypt importsMe: TFHAZ dependencyCount: 137 Package: Organism.dplyr Version: 1.37.1 Depends: R (>= 4.1.0), dplyr (>= 0.7.0), AnnotationFilter (>= 1.1.3) Imports: RSQLite, S4Vectors, Seqinfo, IRanges, GenomicRanges (>= 1.61.1), GenomicFeatures (>= 1.61.4), AnnotationDbi, rlang, methods, tools, utils, BiocFileCache, DBI, dbplyr, tibble Suggests: GenomeInfoDb, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.ensGene, testthat, knitr, rmarkdown, magick, BiocStyle, ggplot2 License: Artistic-2.0 MD5sum: c218443d2cd09ccbf5250f7357c13d21 NeedsCompilation: no Title: dplyr-based Access to Bioconductor Annotation Resources Description: This package provides an alternative interface to Bioconductor 'annotation' resources, in particular the gene identifier mapping functionality of the 'org' packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the 'TxDb' packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene). biocViews: Annotation, Sequencing, GenomeAnnotation Author: Martin Morgan [aut, cre], Daniel van Twisk [ctb], Yubo Cheng [aut] Maintainer: Martin Morgan VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Organism.dplyr/issues git_url: https://git.bioconductor.org/packages/Organism.dplyr git_branch: devel git_last_commit: 93d938b git_last_commit_date: 2025-06-23 Date/Publication: 2025-10-07 source.ver: src/contrib/Organism.dplyr_1.37.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/Organism.dplyr_1.37.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Organism.dplyr_1.37.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Organism.dplyr_1.37.1.tgz vignettes: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.html vignetteTitles: Organism.dplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.R dependsOnMe: annotation importsMe: linkSet, Ularcirc dependencyCount: 94 Package: OrganismDbi Version: 1.52.0 Depends: R (>= 2.14.0), BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.33.15), Seqinfo, GenomicFeatures (>= 1.61.4) Imports: methods, utils, stats, DBI, BiocManager, Biobase, graph, RBGL, S4Vectors, IRanges, GenomicRanges (>= 1.61.1) Suggests: txdbmaker, GenomeInfoDbData, Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, AnnotationHub, FDb.UCSC.tRNAs, rtracklayer, biomaRt, RUnit, RMariaDB, BiocStyle, knitr License: Artistic-2.0 MD5sum: 388d995de51d9d8cfb13372218c842f3 NeedsCompilation: no Title: Software to enable the smooth interfacing of different database packages Description: The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods. biocViews: Annotation, Infrastructure Author: Marc Carlson [aut], Martin Morgan [aut], Valerie Obenchain [aut], Aliyu Atiku Mustapha [ctb] (Converted 'OrganismDbi' vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OrganismDbi git_branch: RELEASE_3_22 git_last_commit: bd13a8e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OrganismDbi_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OrganismDbi_1.51.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OrganismDbi_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OrganismDbi_1.52.0.tgz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.html vignetteTitles: OrganismDbi: A meta framework for Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R dependsOnMe: Homo.sapiens, Mus.musculus, Rattus.norvegicus importsMe: AnnotationHubData, epivizrData, ggbio, uncoverappLib suggestsMe: ChIPpeakAnno, epivizrStandalone dependencyCount: 79 Package: orthogene Version: 1.15.02 Depends: R (>= 4.1) Imports: dplyr, methods, stats, utils, Matrix, jsonlite, homologene, gprofiler2, babelgene, data.table, parallel, ggplot2, ggpubr, patchwork, DelayedArray, grr, repmis, ggtree, tools Suggests: rworkflows, remotes, knitr, BiocStyle, markdown, rmarkdown, testthat (>= 3.0.0), piggyback, magick, GenomeInfoDbData, ape, phytools, rphylopic (>= 1.0.0), TreeTools, ggimage, OmaDB License: GPL-3 MD5sum: 184f034158670bba1dc96e7b8f7da6dc NeedsCompilation: no Title: Interspecies gene mapping Description: `orthogene` is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across **700+ organisms**. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using **1:1**, **many:1**, **1:many** or **many:many** gene mappings, both within- and between-species. biocViews: Genetics, ComparativeGenomics, Preprocessing, Phylogenetics, Transcriptomics, GeneExpression Author: Brian Schilder [cre] (ORCID: ) Maintainer: Brian Schilder URL: https://github.com/neurogenomics/orthogene VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/orthogene/issues git_url: https://git.bioconductor.org/packages/orthogene git_branch: devel git_last_commit: 349fee0 git_last_commit_date: 2025-09-27 Date/Publication: 2025-10-07 source.ver: src/contrib/orthogene_1.15.02.tar.gz win.binary.ver: bin/windows/contrib/4.5/orthogene_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/orthogene_1.15.02.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/orthogene_1.15.02.tgz vignettes: vignettes/orthogene/inst/doc/docker.html, vignettes/orthogene/inst/doc/infer_species.html, vignettes/orthogene/inst/doc/orthogene.html vignetteTitles: docker, Infer species, orthogene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/orthogene/inst/doc/docker.R, vignettes/orthogene/inst/doc/infer_species.R, vignettes/orthogene/inst/doc/orthogene.R importsMe: BulkSignalR, EWCE suggestsMe: sparrow dependencyCount: 160 Package: orthos Version: 1.8.0 Depends: R (>= 4.3), SummarizedExperiment Imports: AnnotationHub, basilisk, BiocParallel, colorspace, cowplot, DelayedArray, dplyr, ExperimentHub, ggplot2 (>= 3.4.0), ggpubr, ggrepel, ggsci, grDevices, grid, HDF5Array, keras (>= 2.16.0), methods, orthosData, parallel, plyr, reticulate, rlang, S4Vectors, stats, tensorflow, tidyr Suggests: BiocManager, BiocStyle, htmltools, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 94fa2812241aa95773e486d21599f987 NeedsCompilation: no Title: `orthos` is an R package for variance decomposition using conditional variational auto-encoders Description: `orthos` decomposes RNA-seq contrasts, for example obtained from a gene knock-out or compound treatment experiment, into unspecific and experiment-specific components. Original and decomposed contrasts can be efficiently queried against a large database of contrasts (derived from ARCHS4, https://maayanlab.cloud/archs4/) to identify similar experiments. `orthos` furthermore provides plotting functions to visualize the results of such a search for similar contrasts. biocViews: RNASeq, DifferentialExpression, GeneExpression Author: Panagiotis Papasaikas [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Michael Stadler [aut] (ORCID: ), Friedrich Miescher Institute for Biomedical Research [cph] Maintainer: Panagiotis Papasaikas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/orthos git_branch: RELEASE_3_22 git_last_commit: 53e77e2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-30 source.ver: src/contrib/orthos_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/orthos_1.7.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/orthos_1.7.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/orthos_1.7.4.tgz vignettes: vignettes/orthos/inst/doc/orthosIntro.html vignetteTitles: 1. Introduction to orthos hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/orthos/inst/doc/orthosIntro.R dependencyCount: 157 Package: OSAT Version: 1.58.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: 10c851ab466d6d994dd5c7b927b4144f NeedsCompilation: no Title: OSAT: Optimal Sample Assignment Tool Description: A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. To minimize batch effects, a careful experiment design should ensure the even distribution of biological groups and confounding factors across batches. OSAT (Optimal Sample Assignment Tool) is developed to facilitate the allocation of collected samples to different batches. With minimum steps, it produces setup that optimizes the even distribution of samples in groups of biological interest into different batches, reducing the confounding or correlation between batches and the biological variables of interest. It can also optimize the even distribution of confounding factors across batches. Our tool can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideal balanced RCBD. OSAT provides a number of predefined layout for some of the most commonly used genomics platform. Related paper can be find at http://www.biomedcentral.com/1471-2164/13/689 . biocViews: DataRepresentation, Visualization, ExperimentalDesign, QualityControl Author: Li Yan Maintainer: Li Yan URL: http://www.biomedcentral.com/1471-2164/13/689 git_url: https://git.bioconductor.org/packages/OSAT git_branch: RELEASE_3_22 git_last_commit: 25c4dfa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OSAT_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OSAT_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OSAT_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OSAT_1.58.0.tgz vignettes: vignettes/OSAT/inst/doc/OSAT.pdf vignetteTitles: An introduction to OSAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OSAT/inst/doc/OSAT.R suggestsMe: designit dependencyCount: 2 Package: Oscope Version: 1.40.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: 299759f1efd2ad9b1d363285a1e3a837 NeedsCompilation: no Title: Oscope - A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq Description: Oscope is a statistical pipeline developed to identifying and recovering the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group. biocViews: ImmunoOncology, StatisticalMethod,RNASeq, Sequencing, GeneExpression Author: Ning Leng Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/Oscope git_branch: RELEASE_3_22 git_last_commit: 2767f6a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Oscope_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Oscope_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Oscope_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Oscope_1.40.0.tgz vignettes: vignettes/Oscope/inst/doc/Oscope_vignette.pdf vignetteTitles: Oscope_vigette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Oscope/inst/doc/Oscope_vignette.R importsMe: scDDboost dependencyCount: 53 Package: OSTA.data Version: 1.1.2 Depends: R (>= 4.5) Imports: osfr, utils, BiocFileCache Suggests: BiocStyle, DropletUtils, knitr, VisiumIO, SpatialExperimentIO License: Artistic-2.0 MD5sum: d29c77641c03920fa13618277fc28248 NeedsCompilation: no Title: OSTA book data Description: 'OSTA.data' is a companion package for the "Orchestrating Spatial Transcriptomics Analysis" (OSTA) with Bioconductor online book. Throughout OSTA, we rely on a set of publicly available datasets that cover different sequencing- and imaging-based platforms, such as Visium, Visium HD, Xenium (10x Genomics) and CosMx (NanoString). In addition, we rely on scRNA-seq (Chromium) data for tasks, e.g., spot deconvolution and label transfer (i.e., supervised clustering). These data been deposited in an Open Storage Framework (OSF) repository, and can be queried and downloaded using functions from the 'osfr' package. For convenience, we have implemented 'OSTA.data' to query and retrieve data from our OSF node, and cache retrieved Zip archives using 'BiocFileCache'. biocViews: DataImport, DataRepresentation, ExperimentHubSoftware, Infrastructure, ImmunoOncology, GeneExpression, Transcriptomics, SingleCell, Spatial Author: Yixing E. Dong [aut, cre] (ORCID: ), Helena L. Crowell [aut] (ORCID: ), Vince Carey [aut] (ORCID: ) Maintainer: Yixing E. Dong URL: https://github.com/estellad/OSTA.data VignetteBuilder: knitr BugReports: https://github.com/estellad/OSTA.data git_url: https://git.bioconductor.org/packages/OSTA.data git_branch: devel git_last_commit: 4c52a2a git_last_commit_date: 2025-08-29 Date/Publication: 2025-10-07 source.ver: src/contrib/OSTA.data_1.1.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/OSTA.data_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OSTA.data_1.1.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OSTA.data_1.1.2.tgz vignettes: vignettes/OSTA.data/inst/doc/OSTA.data.html vignetteTitles: OSTA.data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OSTA.data/inst/doc/OSTA.data.R importsMe: OSTA suggestsMe: ggspavis dependencyCount: 54 Package: OTUbase Version: 1.60.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: 06d1b549a47b5be2e81df2945f6a55d4 NeedsCompilation: no Title: Provides structure and functions for the analysis of OTU data Description: Provides a platform for Operational Taxonomic Unit based analysis biocViews: Sequencing, DataImport Author: Daniel Beck, Matt Settles, and James A. Foster Maintainer: Daniel Beck git_url: https://git.bioconductor.org/packages/OTUbase git_branch: RELEASE_3_22 git_last_commit: 5c945e4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OTUbase_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OTUbase_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OTUbase_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OTUbase_1.60.0.tgz vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf vignetteTitles: An introduction to OTUbase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.R dependencyCount: 60 Package: OUTRIDER Version: 1.28.0 Depends: R (>= 3.6), BiocParallel, GenomicFeatures, SummarizedExperiment, methods Imports: BBmisc, BiocGenerics, data.table, DESeq2 (>= 1.16.1), generics, GenomicRanges, ggplot2, ggrepel, graphics, grDevices, heatmaply, IRanges, matrixStats, pcaMethods, pheatmap, plotly, plyr, pracma, PRROC, RColorBrewer, reshape2, RMTstat, S4Vectors, scales, splines, stats, txdbmaker, utils LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, RMariaDB, AnnotationDbi, beeswarm, covr, GenomeInfoDb, ggbio, biovizBase License: file LICENSE MD5sum: d896533d86f2f95e63d1911f13914c66 NeedsCompilation: yes Title: OUTRIDER - OUTlier in RNA-Seq fInDER Description: Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results. biocViews: ImmunoOncology, RNASeq, Transcriptomics, Alignment, Sequencing, GeneExpression, Genetics Author: Felix Brechtmann [aut] (ORCID: ), Christian Mertes [aut, cre] (ORCID: ), Agne Matuseviciute [aut], Michaela Fee Müller [ctb], Andrea Raithel [ctb], Vicente Yepez [aut] (ORCID: ), Julien Gagneur [aut] (ORCID: ) Maintainer: Christian Mertes URL: https://github.com/gagneurlab/OUTRIDER VignetteBuilder: knitr BugReports: https://github.com/gagneurlab/OUTRIDER/issues git_url: https://git.bioconductor.org/packages/OUTRIDER git_branch: RELEASE_3_22 git_last_commit: 5a0b0f3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OUTRIDER_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OUTRIDER_1.27.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OUTRIDER_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OUTRIDER_1.28.0.tgz vignettes: vignettes/OUTRIDER/inst/doc/OUTRIDER.pdf vignetteTitles: OUTRIDER: OUTlier in RNA-seq fInDER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OUTRIDER/inst/doc/OUTRIDER.R importsMe: FRASER dependencyCount: 173 Package: OutSplice Version: 1.10.0 Depends: R(>= 4.3) Imports: AnnotationDbi (>= 1.60.0), GenomicRanges (>= 1.49.0), GenomicFeatures (>= 1.50.2), IRanges (>= 2.32.0), org.Hs.eg.db (>= 3.16.0), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.16.0), S4Vectors (>= 0.36.0) Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 45232d58e3692fe176cadfa15e6303b2 NeedsCompilation: no Title: Comparison of Splicing Events between Tumor and Normal Samples Description: An easy to use tool that can compare splicing events in tumor and normal tissue samples using either a user generated matrix, or data from The Cancer Genome Atlas (TCGA). This package generates a matrix of splicing outliers that are significantly over or underexpressed in tumors samples compared to normal denoted by chromosome location. The package also will calculate the splicing burden in each tumor and characterize the types of splicing events that occur. biocViews: AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, RNASeq, Software, VariantAnnotation Author: Joseph Bendik [aut] (ORCID: ), Sandhya Kalavacherla [aut] (ORCID: ), Michael Considine [aut] (ORCID: ), Bahman Afsari [aut] (ORCID: ), Michael F. Ochs [aut], Joseph Califano [aut] (ORCID: ), Daria A. Gaykalova [aut] (ORCID: ), Elana Fertig [aut] (ORCID: ), Theresa Guo [cre, aut] (ORCID: ) Maintainer: Theresa Guo URL: https://github.com/GuoLabUCSD/OutSplice VignetteBuilder: knitr BugReports: https://github.com/GuoLabUCSD/OutSplice/issues git_url: https://git.bioconductor.org/packages/OutSplice git_branch: RELEASE_3_22 git_last_commit: 394473e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OutSplice_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OutSplice_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OutSplice_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OutSplice_1.10.0.tgz vignettes: vignettes/OutSplice/inst/doc/OutSplice.html vignetteTitles: Find Splicing Outliers in Tumor Samples with OutSplice hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OutSplice/inst/doc/OutSplice.R dependencyCount: 79 Package: OVESEG Version: 1.26.0 Depends: R (>= 3.6) Imports: stats, utils, methods, BiocParallel, SummarizedExperiment, limma, fdrtool, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, ggplot2, gridExtra, grid, reshape2, scales License: GPL-2 MD5sum: 291c9f5760a04577aced67e94b899821 NeedsCompilation: yes Title: OVESEG-test to detect tissue/cell-specific markers Description: An R package for multiple-group comparison to detect tissue/cell-specific marker genes among subtypes. It provides functions to compute OVESEG-test statistics, derive component weights in the mixture null distribution model and estimate p-values from weightedly aggregated permutations. Obtained posterior probabilities of component null hypotheses can also portrait all kinds of upregulation patterns among subtypes. biocViews: Software, MultipleComparison, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Lululuella/OVESEG git_url: https://git.bioconductor.org/packages/OVESEG git_branch: RELEASE_3_22 git_last_commit: 67b37c3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/OVESEG_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OVESEG_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OVESEG_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OVESEG_1.26.0.tgz vignettes: vignettes/OVESEG/inst/doc/OVESEG.html vignetteTitles: OVESEG User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OVESEG/inst/doc/OVESEG.R dependencyCount: 39 Package: PAA Version: 1.44.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.6) Imports: e1071, gplots, gtools, limma, MASS, mRMRe, randomForest, ROCR, sva LinkingTo: Rcpp Suggests: BiocStyle, RUnit, BiocGenerics, vsn License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 7dd97b2793c45e54a9fd57f29019249f NeedsCompilation: yes Title: PAA (Protein Array Analyzer) Description: PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After preprocessing (background correction, batch filtering, normalization) univariate feature preselection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation. biocViews: Classification, Microarray, OneChannel, Proteomics Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre] Maintainer: Michael Turewicz , Martin Eisenacher URL: http://www.ruhr-uni-bochum.de/mpc/software/PAA/ SystemRequirements: C++ software package Random Jungle git_url: https://git.bioconductor.org/packages/PAA git_branch: RELEASE_3_22 git_last_commit: 5b94516 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PAA_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PAA_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PAA_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PAA_1.44.0.tgz vignettes: vignettes/PAA/inst/doc/PAA_1.7.1.pdf, vignettes/PAA/inst/doc/PAA_vignette.pdf vignetteTitles: PAA_1.7.1.pdf, PAA tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R dependencyCount: 85 Package: packFinder Version: 1.22.0 Depends: R (>= 4.1.0) Imports: Biostrings, GenomicRanges, kmer, ape, methods, IRanges, S4Vectors Suggests: biomartr, knitr, rmarkdown, testthat, dendextend, biocViews, BiocCheck, BiocStyle License: GPL-2 MD5sum: 44b19b03d90cb7e060963dd349d09b34 NeedsCompilation: no Title: de novo Annotation of Pack-TYPE Transposable Elements Description: Algorithm and tools for in silico pack-TYPE transposon discovery. Filters a given genome for properties unique to DNA transposons and provides tools for the investigation of returned matches. Sequences are input in DNAString format, and ranges are returned as a dataframe (in the format returned by as.dataframe(GRanges)). biocViews: Genetics, SequenceMatching, Annotation Author: Jack Gisby [aut, cre] (ORCID: ), Marco Catoni [aut] (ORCID: ) Maintainer: Jack Gisby URL: https://github.com/jackgisby/packFinder VignetteBuilder: knitr BugReports: https://github.com/jackgisby/packFinder/issues git_url: https://git.bioconductor.org/packages/packFinder git_branch: RELEASE_3_22 git_last_commit: 360ddfb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/packFinder_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/packFinder_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/packFinder_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/packFinder_1.22.0.tgz vignettes: vignettes/packFinder/inst/doc/packFinder.html vignetteTitles: packFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/packFinder/inst/doc/packFinder.R dependencyCount: 28 Package: padma Version: 1.20.0 Depends: R (>= 4.1.0), SummarizedExperiment, S4Vectors Imports: FactoMineR, MultiAssayExperiment, methods, graphics, stats, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, KEGGREST, missMDA, ggplot2, ggrepel, car, cowplot, reshape2 License: GPL (>=3) MD5sum: f0fd3b40924bc56bc66c3feb70f2560f NeedsCompilation: no Title: Individualized Multi-Omic Pathway Deviation Scores Using Multiple Factor Analysis Description: Use multiple factor analysis to calculate individualized pathway-centric scores of deviation with respect to the sampled population based on multi-omic assays (e.g., RNA-seq, copy number alterations, methylation, etc). Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles. biocViews: Software, StatisticalMethod, PrincipalComponent, GeneExpression, Pathways, RNASeq, BioCarta, MethylSeq Author: Andrea Rau [cre, aut] (ORCID: ), Regina Manansala [aut], Florence Jaffrézic [ctb], Denis Laloë [aut], Paul Auer [aut] Maintainer: Andrea Rau URL: https://github.com/andreamrau/padma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/padma git_branch: RELEASE_3_22 git_last_commit: 620ccc1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/padma_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/padma_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/padma_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/padma_1.20.0.tgz vignettes: vignettes/padma/inst/doc/padma.html vignetteTitles: padma package:Quick-start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/padma/inst/doc/padma.R dependencyCount: 125 Package: PADOG Version: 1.52.0 Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db, hgu133a.db, KEGGREST, nlme Suggests: doParallel, parallel License: GPL (>= 2) MD5sum: 6b048ff0dd53c8af9c2f24a9d5a5d89d NeedsCompilation: no Title: Pathway Analysis with Down-weighting of Overlapping Genes (PADOG) Description: This package implements a general purpose gene set analysis method called PADOG that downplays the importance of genes that apear often accross the sets of genes to be analyzed. The package provides also a benchmark for gene set analysis methods in terms of sensitivity and ranking using 24 public datasets from KEGGdzPathwaysGEO package. biocViews: Microarray, OneChannel, TwoChannel Author: Adi Laurentiu Tarca ; Zhonghui Xu Maintainer: Adi L. Tarca git_url: https://git.bioconductor.org/packages/PADOG git_branch: RELEASE_3_22 git_last_commit: 08a7421 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PADOG_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PADOG_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PADOG_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PADOG_1.52.0.tgz vignettes: vignettes/PADOG/inst/doc/PADOG.pdf vignetteTitles: PADOG hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PADOG/inst/doc/PADOG.R dependsOnMe: BLMA importsMe: EGSEA suggestsMe: ReporterScore dependencyCount: 60 Package: pageRank Version: 1.20.0 Depends: R (>= 4.0) Imports: GenomicRanges, igraph, motifmatchr, stats, utils, grDevices, graphics Suggests: bcellViper, BSgenome.Hsapiens.UCSC.hg19, JASPAR2018, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, TFBSTools, GenomicFeatures, annotate License: GPL-2 MD5sum: 7525bd69d630eccc9712e0db3bbefa2c NeedsCompilation: no Title: Temporal and Multiplex PageRank for Gene Regulatory Network Analysis Description: Implemented temporal PageRank analysis as defined by Rozenshtein and Gionis. Implemented multiplex PageRank as defined by Halu et al. Applied temporal and multiplex PageRank in gene regulatory network analysis. biocViews: StatisticalMethod, GeneTarget, Network Author: Hongxu Ding [aut, cre, ctb, cph] Maintainer: Hongxu Ding URL: https://github.com/hd2326/pageRank BugReports: https://github.com/hd2326/pageRank/issues git_url: https://git.bioconductor.org/packages/pageRank git_branch: RELEASE_3_22 git_last_commit: 77c4d03 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pageRank_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pageRank_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pageRank_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pageRank_1.20.0.tgz vignettes: vignettes/pageRank/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pageRank/inst/doc/introduction.R dependencyCount: 85 Package: PAIRADISE Version: 1.26.0 Depends: R (>= 3.6), nloptr Imports: SummarizedExperiment, S4Vectors, stats, methods, abind, BiocParallel Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 6e7b3c8cddd01ae09298e064170695b2 NeedsCompilation: no Title: PAIRADISE: Paired analysis of differential isoform expression Description: This package implements the PAIRADISE procedure for detecting differential isoform expression between matched replicates in paired RNA-Seq data. biocViews: RNASeq, DifferentialExpression, AlternativeSplicing, StatisticalMethod, ImmunoOncology Author: Levon Demirdjian, Ying Nian Wu, Yi Xing Maintainer: Qiang Hu , Levon Demirdjian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PAIRADISE git_branch: RELEASE_3_22 git_last_commit: d2d3a60 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PAIRADISE_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PAIRADISE_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PAIRADISE_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PAIRADISE_1.26.0.tgz vignettes: vignettes/PAIRADISE/inst/doc/pairadise.html vignetteTitles: PAIRADISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAIRADISE/inst/doc/pairadise.R dependencyCount: 36 Package: paircompviz Version: 1.48.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: b537a23eaddcc1229b7ea68be018fa25 NeedsCompilation: no Title: Multiple comparison test visualization Description: This package provides visualization of the results from the multiple (i.e. pairwise) comparison tests such as pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test. The groups being compared are visualized as nodes in Hasse diagram. Such approach enables very clear and vivid depiction of which group is significantly greater than which others, especially if comparing a large number of groups. biocViews: GraphAndNetwork Author: Michal Burda Maintainer: Michal Burda git_url: https://git.bioconductor.org/packages/paircompviz git_branch: RELEASE_3_22 git_last_commit: 0185a4f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/paircompviz_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/paircompviz_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/paircompviz_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/paircompviz_1.48.0.tgz vignettes: vignettes/paircompviz/inst/doc/vignette.pdf vignetteTitles: Using paircompviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paircompviz/inst/doc/vignette.R dependencyCount: 11 Package: pairedGSEA Version: 1.10.0 Depends: R (>= 4.4.0) Imports: DESeq2, DEXSeq, limma, fgsea, msigdbr, sva, SummarizedExperiment, S4Vectors, BiocParallel, ggplot2, aggregation, stats, utils, methods, showtext Suggests: writexl, readxl, readr, rhdf5, plotly, testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, covr License: MIT + file LICENSE MD5sum: 41a92194d6447210ac448b3ba41a29a1 NeedsCompilation: no Title: Paired DGE and DGS analysis for gene set enrichment analysis Description: pairedGSEA makes it simple to run a paired Differential Gene Expression (DGE) and Differencital Gene Splicing (DGS) analysis. The package allows you to store intermediate results for further investiation, if desired. pairedGSEA comes with a wrapper function for running an Over-Representation Analysis (ORA) and functionalities for plotting the results. biocViews: DifferentialExpression, AlternativeSplicing, DifferentialSplicing, GeneExpression, ImmunoOncology, GeneSetEnrichment, Pathways, RNASeq, Software, Transcription, Author: Søren Helweg Dam [cre, aut] (ORCID: ), Lars Rønn Olsen [aut] (ORCID: ), Kristoffer Vitting-Seerup [aut] (ORCID: ) Maintainer: Søren Helweg Dam URL: https://github.com/shdam/pairedGSEA VignetteBuilder: knitr BugReports: https://github.com/shdam/pairedGSEA/issues git_url: https://git.bioconductor.org/packages/pairedGSEA git_branch: RELEASE_3_22 git_last_commit: 44e676a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pairedGSEA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pairedGSEA_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pairedGSEA_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pairedGSEA_1.10.0.tgz vignettes: vignettes/pairedGSEA/inst/doc/User-Guide.html vignetteTitles: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pairedGSEA/inst/doc/User-Guide.R dependencyCount: 126 Package: pairkat Version: 1.16.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, KEGGREST, igraph, data.table, methods, stats, magrittr, CompQuadForm, tibble Suggests: rmarkdown, knitr, BiocStyle, dplyr License: GPL-3 Archs: x64 MD5sum: a0daf471808ae5fa24b2fa3a281654ce NeedsCompilation: no Title: PaIRKAT Description: PaIRKAT is model framework for assessing statistical relationships between networks of metabolites (pathways) and an outcome of interest (phenotype). PaIRKAT queries the KEGG database to determine interactions between metabolites from which network connectivity is constructed. This model framework improves testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. PaIRKAT uses known biological connections between high dimensional variables by representing them as edges of ‘graphs’ or ‘networks.’ It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or ‘smoothing’ approach for managing this issue. biocViews: Software, Metabolomics, KEGG, Pathways, Network, GraphAndNetwork, Regression Author: Charlie Carpenter [aut], Cameron Severn [aut], Max McGrath [cre, aut] Maintainer: Max McGrath VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/pairkat/issues git_url: https://git.bioconductor.org/packages/pairkat git_branch: RELEASE_3_22 git_last_commit: d3657b1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pairkat_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pairkat_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pairkat_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pairkat_1.16.0.tgz vignettes: vignettes/pairkat/inst/doc/using-pairkat.html vignetteTitles: using-pairkat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pairkat/inst/doc/using-pairkat.R dependencyCount: 51 Package: pandaR Version: 1.42.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 46a3a1ecff115eb861f6cd3cfd6c9b6a NeedsCompilation: no Title: PANDA Algorithm Description: Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources. biocViews: StatisticalMethod, GraphAndNetwork, Microarray, GeneRegulation, NetworkInference, GeneExpression, Transcription, Network Author: Dan Schlauch, Joseph N. Paulson, Albert Young, John Quackenbush, Kimberly Glass Maintainer: Joseph N. Paulson , Dan Schlauch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pandaR git_branch: RELEASE_3_22 git_last_commit: 6b47106 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pandaR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pandaR_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pandaR_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pandaR_1.42.0.tgz vignettes: vignettes/pandaR/inst/doc/pandaR.html vignetteTitles: pandaR Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pandaR/inst/doc/pandaR.R dependencyCount: 37 Package: panelcn.mops Version: 1.32.0 Depends: R (>= 3.5), cn.mops, methods, utils, stats, graphics Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, Seqinfo, grDevices Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: LGPL (>= 2.0) MD5sum: 4c3c145edb21b2cde8089978d665e792 NeedsCompilation: no Title: CNV detection tool for targeted NGS panel data Description: CNV detection tool for targeted NGS panel data. Extension of the cn.mops package. biocViews: Sequencing, CopyNumberVariation, CellBiology, GenomicVariation, VariantDetection, Genetics Author: Verena Haunschmid [aut], Gundula Povysil [aut, cre] Maintainer: Gundula Povysil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/panelcn.mops git_branch: RELEASE_3_22 git_last_commit: 55f3146 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/panelcn.mops_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/panelcn.mops_1.31.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/panelcn.mops_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/panelcn.mops_1.32.0.tgz vignettes: vignettes/panelcn.mops/inst/doc/panelcn.mops.pdf vignetteTitles: panelcn.mops: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panelcn.mops/inst/doc/panelcn.mops.R suggestsMe: CopyNumberPlots dependencyCount: 31 Package: PanomiR Version: 1.14.0 Depends: R (>= 4.2.0) Imports: clusterProfiler, dplyr, forcats, GSEABase, igraph, limma, metap, org.Hs.eg.db, parallel, preprocessCore, RColorBrewer, rlang, tibble, withr, utils Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 5a186c0eab68e2588d63b816370ddab5 NeedsCompilation: no Title: Detection of miRNAs that regulate interacting groups of pathways Description: PanomiR is a package to detect miRNAs that target groups of pathways from gene expression data. This package provides functionality for generating pathway activity profiles, determining differentially activated pathways between user-specified conditions, determining clusters of pathways via the PCxN package, and generating miRNAs targeting clusters of pathways. These function can be used separately or sequentially to analyze RNA-Seq data. biocViews: GeneExpression, GeneSetEnrichment, GeneTarget, miRNA, Pathways Author: Pourya Naderi [aut, cre], Yue Yang (Alan) Teo [aut], Ilya Sytchev [aut], Winston Hide [aut] Maintainer: Pourya Naderi URL: https://github.com/pouryany/PanomiR VignetteBuilder: knitr BugReports: https://github.com/pouryany/PanomiR/issues git_url: https://git.bioconductor.org/packages/PanomiR git_branch: RELEASE_3_22 git_last_commit: ec73906 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PanomiR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PanomiR_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PanomiR_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PanomiR_1.14.0.tgz vignettes: vignettes/PanomiR/inst/doc/PanomiR.html vignetteTitles: PanomiR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PanomiR/inst/doc/PanomiR.R dependencyCount: 170 Package: panp Version: 1.80.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: b62d6d7a582d708ceea451ba5d8f619e NeedsCompilation: no Title: Presence-Absence Calls from Negative Strand Matching Probesets Description: A function to make gene presence/absence calls based on distance from negative strand matching probesets (NSMP) which are derived from Affymetrix annotation. PANP is applied after gene expression values are created, and therefore can be used after any preprocessing method such as MAS5 or GCRMA, or PM-only methods like RMA. NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date. biocViews: Infrastructure Author: Peter Warren Maintainer: Peter Warren git_url: https://git.bioconductor.org/packages/panp git_branch: RELEASE_3_22 git_last_commit: a031b51 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/panp_1.80.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/panp_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/panp_1.80.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/panp_1.80.0.tgz vignettes: vignettes/panp/inst/doc/panp.pdf vignetteTitles: gene presence/absence calls hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panp/inst/doc/panp.R dependencyCount: 12 Package: PANR Version: 1.56.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: a23d182b171871484f19f60f0a7af739 NeedsCompilation: no Title: Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations Description: This package provides S4 classes and methods for inferring functional gene networks with edges encoding posterior beliefs of gene association types and nodes encoding perturbation effects. biocViews: ImmunoOncology, NetworkInference, Visualization, GraphAndNetwork, Clustering, CellBasedAssays Author: Xin Wang Maintainer: Xin Wang git_url: https://git.bioconductor.org/packages/PANR git_branch: RELEASE_3_22 git_last_commit: 3682d47 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PANR_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PANR_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PANR_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PANR_1.56.0.tgz vignettes: vignettes/PANR/inst/doc/PANR-Vignette.pdf vignetteTitles: Main vignette:Posterior association network and enriched functional gene modules inferred from rich phenotypes of gene perturbations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PANR/inst/doc/PANR-Vignette.R dependencyCount: 26 Package: parglms Version: 1.42.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges, gwascat, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 2695f40b923b89caa9090e3c09528965 NeedsCompilation: no Title: support for parallelized estimation of GLMs/GEEs Description: This package provides support for parallelized estimation of GLMs/GEEs, catering for dispersed data. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parglms git_branch: RELEASE_3_22 git_last_commit: 65caa9b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/parglms_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/parglms_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/parglms_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/parglms_1.42.0.tgz vignettes: vignettes/parglms/inst/doc/parglms.pdf vignetteTitles: parglms: parallelized GLM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parglms/inst/doc/parglms.R dependencyCount: 38 Package: parody Version: 1.68.0 Depends: R (>= 3.5.0), tools, utils Suggests: knitr, BiocStyle, testthat, rmarkdown License: Artistic-2.0 MD5sum: 575685b4a3919c86c310258f504951be NeedsCompilation: no Title: Parametric And Resistant Outlier DYtection Description: Provide routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics. biocViews: MultipleComparison Author: Vince Carey [aut, cre] (ORCID: ) Maintainer: Vince Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parody git_branch: RELEASE_3_22 git_last_commit: 7525536 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/parody_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/parody_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/parody_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/parody_1.68.0.tgz vignettes: vignettes/parody/inst/doc/parody.html vignetteTitles: parody: parametric and resistant outlier dytection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parody/inst/doc/parody.R dependsOnMe: arrayMvout dependencyCount: 2 Package: partCNV Version: 1.8.0 Depends: R (>= 3.5.0) Imports: stats, data.table, depmixS4, Seurat, SingleCellExperiment, AnnotationHub, magrittr, GenomicRanges, BiocStyle Suggests: rmarkdown, knitr, IRanges, testthat (>= 3.0.0) License: GPL-2 MD5sum: c07aea12d4c66640e5dcb164032724b3 NeedsCompilation: no Title: Infer locally aneuploid cells using single cell RNA-seq data Description: This package uses a statistical framework for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. Our method uses an EM algorithm with mixtures of Poisson distributions while incorporating cytogenetics information (e.g., regional deletion or amplification) to guide the classification (partCNV). When applicable, we further improve the accuracy by integrating a Hidden Markov Model for feature selection (partCNVH). biocViews: Software, CopyNumberVariation, HiddenMarkovModel, SingleCell, Classification Author: Ziyi Li [aut, cre, ctb], Ruoxing Li [ctb] Maintainer: Ziyi Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/partCNV git_branch: RELEASE_3_22 git_last_commit: d29e8a0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/partCNV_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/partCNV_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/partCNV_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/partCNV_1.8.0.tgz vignettes: vignettes/partCNV/inst/doc/partCNV_vignette.html vignetteTitles: partCNV_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/partCNV/inst/doc/partCNV_vignette.R dependencyCount: 191 Package: PAST Version: 1.26.0 Depends: R (>= 4.0) Imports: stats, utils, dplyr, rlang, iterators, parallel, foreach, doParallel, qvalue, rtracklayer, ggplot2, GenomicRanges, S4Vectors Suggests: knitr, rmarkdown License: GPL (>=3) + file LICENSE MD5sum: 872db8bc58c652289b17d7844331817c NeedsCompilation: no Title: Pathway Association Study Tool (PAST) Description: PAST takes GWAS output and assigns SNPs to genes, uses those genes to find pathways associated with the genes, and plots pathways based on significance. Implements methods for reading GWAS input data, finding genes associated with SNPs, calculating enrichment score and significance of pathways, and plotting pathways. biocViews: Pathways, GeneSetEnrichment Author: Thrash Adam [cre, aut], DeOrnellis Mason [aut] Maintainer: Thrash Adam URL: https://github.com/IGBB/past VignetteBuilder: knitr BugReports: https://github.com/IGBB/past/issues git_url: https://git.bioconductor.org/packages/PAST git_branch: RELEASE_3_22 git_last_commit: aa1cbad git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PAST_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PAST_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PAST_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PAST_1.26.0.tgz vignettes: vignettes/PAST/inst/doc/past.html vignetteTitles: PAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAST/inst/doc/past.R dependencyCount: 89 Package: Path2PPI Version: 1.40.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 11a58370037bf837932ae5904aadaf51 NeedsCompilation: no Title: Prediction of pathway-related protein-protein interaction networks Description: Package to predict protein-protein interaction (PPI) networks in target organisms for which only a view information about PPIs is available. Path2PPI predicts PPI networks based on sets of proteins which can belong to a certain pathway from well-established model organisms. It helps to combine and transfer information of a certain pathway or biological process from several reference organisms to one target organism. Path2PPI only depends on the sequence similarity of the involved proteins. biocViews: NetworkInference, SystemsBiology, Network, Proteomics, Pathways Author: Oliver Philipp [aut, cre], Ina Koch [ctb] Maintainer: Oliver Philipp URL: http://www.bioinformatik.uni-frankfurt.de/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Path2PPI git_branch: RELEASE_3_22 git_last_commit: b8e1181 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Path2PPI_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Path2PPI_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Path2PPI_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Path2PPI_1.40.0.tgz vignettes: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html vignetteTitles: Path2PPI - A brief tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R dependencyCount: 17 Package: pathifier Version: 1.48.0 Imports: R.oo, princurve (>= 2.0.4) License: Artistic-1.0 MD5sum: 30435c25a9b447a3d48da958860d140d NeedsCompilation: no Title: Quantify deregulation of pathways in cancer Description: Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. biocViews: Network Author: Yotam Drier Maintainer: Assif Yitzhaky git_url: https://git.bioconductor.org/packages/pathifier git_branch: RELEASE_3_22 git_last_commit: edfa704 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pathifier_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pathifier_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pathifier_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pathifier_1.48.0.tgz vignettes: vignettes/pathifier/inst/doc/Overview.pdf vignetteTitles: Quantify deregulation of pathways in cancer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathifier/inst/doc/Overview.R importsMe: funOmics dependencyCount: 9 Package: pathlinkR Version: 1.6.0 Depends: R (>= 4.4.0) Imports: circlize, clusterProfiler, ComplexHeatmap, dplyr, fgsea, ggforce, ggplot2, ggpubr, ggraph, ggrepel, grid, igraph, purrr, sigora, stringr, tibble, tidygraph, tidyr, vegan, visNetwork Suggests: AnnotationDbi, BiocStyle, biomaRt, covr, DESeq2, jsonlite, knitr, org.Hs.eg.db, rmarkdown, scales, testthat (>= 3.0.0), vdiffr License: GPL-3 + file LICENSE MD5sum: 1b20920d5c44246e3801507a6951503d NeedsCompilation: no Title: Analyze and interpret RNA-Seq results Description: pathlinkR is an R package designed to facilitate analysis of RNA-Seq results. Specifically, our aim with pathlinkR was to provide a number of tools which take a list of DE genes and perform different analyses on them, aiding with the interpretation of results. Functions are included to perform pathway enrichment, with muliplte databases supported, and tools for visualizing these results. Genes can also be used to create and plot protein-protein interaction networks, all from inside of R. biocViews: GeneSetEnrichment, Network, Pathways, Reactome, RNASeq, NetworkEnrichment Author: Travis Blimkie [cre] (ORCID: ), Andy An [aut] Maintainer: Travis Blimkie URL: https://github.com/hancockinformatics/pathlinkR VignetteBuilder: knitr BugReports: https://github.com/hancockinformatics/pathlinkR/issues git_url: https://git.bioconductor.org/packages/pathlinkR git_branch: RELEASE_3_22 git_last_commit: 4a3af75 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pathlinkR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pathlinkR_1.5.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pathlinkR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pathlinkR_1.6.0.tgz vignettes: vignettes/pathlinkR/inst/doc/pathlinkR.html vignetteTitles: Analyze and visualize RNA-Seq data with pathlinkR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pathlinkR/inst/doc/pathlinkR.R dependencyCount: 193 Package: pathMED Version: 1.2.0 Depends: R (>= 4.5.0) Imports: BiocParallel, caret, caretEnsemble, decoupleR, ggplot2, GSVA, factoextra, FactoMineR, magrittr, matrixStats, methods, metrica, pbapply, reshape2, singscore, stats, stringi, dplyr, Suggests: ada, AUCell, Biobase, BiocGenerics, BiocStyle, fgsea (>= 1.15.4), gam, GSEABase, import, kernlab, klaR, knitr, mboost, MLeval, randomForest, ranger, rmarkdown, RUnit, SummarizedExperiment, utils, xgboost License: GPL-2 MD5sum: 63ad8ef9978c4a84c9c447f9c219a926 NeedsCompilation: no Title: Scoring Personalized Molecular Portraits Description: PathMED is a collection of tools to facilitate precision medicine studies with omics data (e.g. transcriptomics). Among its funcionalities, genesets scores for individual samples may be calculated with several methods. These scores may be used to train machine learning models and to predict clinical features on new data. For this, several machine learning methods are evaluated in order to select the best method based on internal validation and to tune the hyperparameters. Performance metrics and a ready-to-use model to predict the outcomes for new patients are returned. biocViews: Pathways, Classification, FeatureExtraction, Transcriptomics Author: Jordi Martorell-Marugán [cre, aut] (ORCID: ), Daniel Toro-Domínguez [aut] (ORCID: ), Raúl López-Domínguez [aut] (ORCID: ), Iván Ellson [aut] (ORCID: ) Maintainer: Jordi Martorell-Marugán URL: https://github.com/jordimartorell/pathMED VignetteBuilder: knitr BugReports: https://github.com/jordimartorell/pathMED/issues git_url: https://git.bioconductor.org/packages/pathMED git_branch: RELEASE_3_22 git_last_commit: b17f51c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pathMED_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pathMED_1.1.4.zip vignettes: vignettes/pathMED/inst/doc/pathMED.html vignetteTitles: pathMED hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathMED/inst/doc/pathMED.R dependencyCount: 238 Package: PathNet Version: 1.50.0 Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: dcc69467d6eb510148612c7330b2db6d NeedsCompilation: no Title: An R package for pathway analysis using topological information Description: PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10. biocViews: Pathways, DifferentialExpression, MultipleComparison, KEGG, NetworkEnrichment, Network Author: Bhaskar Dutta , Anders Wallqvist , and Jaques Reifman Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/PathNet git_branch: RELEASE_3_22 git_last_commit: 4d1784c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PathNet_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PathNet_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PathNet_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PathNet_1.50.0.tgz vignettes: vignettes/PathNet/inst/doc/PathNet.pdf vignetteTitles: PathNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathNet/inst/doc/PathNet.R dependencyCount: 0 Package: PathoStat Version: 1.36.0 Depends: R (>= 3.5) Imports: limma, corpcor,matrixStats, reshape2, scales, ggplot2, rentrez, DT, tidyr, plyr, dplyr, phyloseq, shiny, stats, methods, XML, graphics, utils, BiocStyle, edgeR, DESeq2, ComplexHeatmap, plotly, webshot, vegan, shinyjs, glmnet, gmodels, ROCR, RColorBrewer, knitr, devtools, ape Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: fdf7cf6d71a18b3f84a1cb6ba2bd4c02 NeedsCompilation: no Title: PathoStat Statistical Microbiome Analysis Package Description: The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis. biocViews: Microbiome, Metagenomics, GraphAndNetwork, Microarray, PatternLogic, PrincipalComponent, Sequencing, Software, Visualization, RNASeq, ImmunoOncology Author: Solaiappan Manimaran , Matthew Bendall , Sandro Valenzuela Diaz , Eduardo Castro , Tyler Faits , Yue Zhao , Anthony Nicholas Federico , W. Evan Johnson Maintainer: Solaiappan Manimaran , Yue Zhao URL: https://github.com/mani2012/PathoStat VignetteBuilder: knitr BugReports: https://github.com/mani2012/PathoStat/issues git_url: https://git.bioconductor.org/packages/PathoStat git_branch: RELEASE_3_22 git_last_commit: a14c74c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PathoStat_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PathoStat_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PathoStat_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PathoStat_1.36.0.tgz vignettes: vignettes/PathoStat/inst/doc/PathoStat-vignette.html vignetteTitles: PathoStat intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathoStat/inst/doc/PathoStat-vignette.R dependencyCount: 205 Package: pathRender Version: 1.78.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: b2da8dfc4341693d9d55de1b42aaac08 NeedsCompilation: no Title: Render molecular pathways Description: build graphs from pathway databases, render them by Rgraphviz. biocViews: GraphAndNetwork, Pathways, Visualization Author: Li Long Maintainer: Vince Carey URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/pathRender git_branch: RELEASE_3_22 git_last_commit: 4ea6df8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pathRender_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pathRender_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pathRender_1.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pathRender_1.78.0.tgz vignettes: vignettes/pathRender/inst/doc/pathRender.pdf, vignettes/pathRender/inst/doc/plotExG.pdf vignetteTitles: pathRender overview, pathway graphs colored by expression map hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathRender/inst/doc/pathRender.R, vignettes/pathRender/inst/doc/plotExG.R dependencyCount: 48 Package: pathview Version: 1.50.0 Depends: R (>= 3.5.0) Imports: KEGGgraph, XML, Rgraphviz, graph, png, AnnotationDbi, org.Hs.eg.db, KEGGREST, methods, utils Suggests: gage, org.Mm.eg.db, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: 590c1c4bec660ca0d1e6d9ee7c7ebef4 NeedsCompilation: no Title: a tool set for pathway based data integration and visualization Description: Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis. biocViews: Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/pathview, https://pathview.uncc.edu/ git_url: https://git.bioconductor.org/packages/pathview git_branch: RELEASE_3_22 git_last_commit: 5efd147 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pathview_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pathview_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pathview_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pathview_1.50.0.tgz vignettes: vignettes/pathview/inst/doc/pathview.pdf vignetteTitles: Pathview: pathway based data integration and visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathview/inst/doc/pathview.R dependsOnMe: EGSEA, SBGNview importsMe: debrowser, EnrichmentBrowser, GDCRNATools, lilikoi, SQMtools suggestsMe: gage, TCGAbiolinks, gageData, CAGEWorkflow, ReporterScore dependencyCount: 51 Package: pathwayPCA Version: 1.26.0 Depends: R (>= 3.1) Imports: lars, methods, parallel, stats, survival, utils Suggests: airway, circlize, grDevices, knitr, RCurl, reshape2, rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse License: GPL-3 MD5sum: 4460dcb0094e9f8a9f20b4c31cef7c90 NeedsCompilation: no Title: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection Description: pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) ; Chen et al. (2010) ; and Chen (2011) . biocViews: CopyNumberVariation, DNAMethylation, GeneExpression, SNP, Transcription, GenePrediction, GeneSetEnrichment, GeneSignaling, GeneTarget, GenomeWideAssociation, GenomicVariation, CellBiology, Epigenetics, FunctionalGenomics, Genetics, Lipidomics, Metabolomics, Proteomics, SystemsBiology, Transcriptomics, Classification, DimensionReduction, FeatureExtraction, PrincipalComponent, Regression, Survival, MultipleComparison, Pathways Author: Gabriel Odom [aut, cre], James Ban [aut], Lizhong Liu [aut], Lily Wang [aut], Steven Chen [aut] Maintainer: Gabriel Odom URL: VignetteBuilder: knitr BugReports: https://github.com/gabrielodom/pathwayPCA/issues git_url: https://git.bioconductor.org/packages/pathwayPCA git_branch: RELEASE_3_22 git_last_commit: e20c694 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pathwayPCA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pathwayPCA_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pathwayPCA_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pathwayPCA_1.26.0.tgz vignettes: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.html, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.html, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.html, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.html, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.html, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.html vignetteTitles: Integrative Pathway Analysis with pathwayPCA, Suppl. 1. Quickstart Guide, Suppl. 2. Importing Data, Suppl. 3. Create Data Objects, Suppl. 4. Test Pathway Significance, Suppl. 5. Visualizing the Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.R, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.R, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.R, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.R, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.R, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.R dependencyCount: 12 Package: pcaExplorer Version: 3.4.0 Imports: DESeq2, SummarizedExperiment, mosdef (>= 1.1.0), GenomicRanges, IRanges, S4Vectors, genefilter, ggplot2 (>= 2.0.0), heatmaply, plotly, scales, NMF, plyr, topGO, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, ggrepel, DT, shinyAce, threejs, biomaRt, pheatmap, knitr, rmarkdown, base64enc, tidyr, grDevices, methods Suggests: testthat, BiocStyle, markdown, airway, org.Hs.eg.db, htmltools License: MIT + file LICENSE MD5sum: b3411ac2daa9f89b64e02d6e03944462 NeedsCompilation: no Title: Interactive Visualization of RNA-seq Data Using a Principal Components Approach Description: This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis. biocViews: ImmunoOncology, Visualization, RNASeq, DimensionReduction, PrincipalComponent, QualityControl, GUI, ReportWriting, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/pcaExplorer, https://federicomarini.github.io/pcaExplorer/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/pcaExplorer/issues git_url: https://git.bioconductor.org/packages/pcaExplorer git_branch: RELEASE_3_22 git_last_commit: 5b76435 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pcaExplorer_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pcaExplorer_3.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pcaExplorer_3.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pcaExplorer_3.4.0.tgz vignettes: vignettes/pcaExplorer/inst/doc/pcaExplorer.html, vignettes/pcaExplorer/inst/doc/upandrunning.html vignetteTitles: pcaExplorer User Guide, Up and running with pcaExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcaExplorer/inst/doc/pcaExplorer.R, vignettes/pcaExplorer/inst/doc/upandrunning.R dependencyCount: 235 Package: pcaMethods Version: 2.2.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) MD5sum: 498ad5899a75225d16867c7806d2a123 NeedsCompilation: yes Title: A collection of PCA methods Description: Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. biocViews: Bayesian Author: Wolfram Stacklies, Henning Redestig, Kevin Wright Maintainer: Henning Redestig URL: https://github.com/hredestig/pcamethods SystemRequirements: Rcpp BugReports: https://github.com/hredestig/pcamethods/issues git_url: https://git.bioconductor.org/packages/pcaMethods git_branch: RELEASE_3_22 git_last_commit: 0956371 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pcaMethods_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pcaMethods_2.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pcaMethods_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pcaMethods_2.2.0.tgz vignettes: vignettes/pcaMethods/inst/doc/missingValues.pdf, vignettes/pcaMethods/inst/doc/outliers.pdf, vignettes/pcaMethods/inst/doc/pcaMethods.pdf vignetteTitles: Missing value imputation, Data with outliers, Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R, vignettes/pcaMethods/inst/doc/outliers.R, vignettes/pcaMethods/inst/doc/pcaMethods.R dependsOnMe: crmn, DiffCorr, imputeLCMD importsMe: consensusDE, FRASER, MAI, MatrixQCvis, MSnbase, MSPrep, MultiBaC, OUTRIDER, PhosR, pmp, scde, SomaticSignatures, ADAPTS, CopSens, geneticae, lfproQC, LOST, MetabolomicsBasics, metamorphr, missCompare, multiDimBio, pmartR, polyRAD, promor, santaR, scMappR suggestsMe: autonomics, cardelino, MsCoreUtils, notame, notameViz, QFeatures, qmtools, mtbls2, pagoda2, rsvddpd dependencyCount: 10 Package: PCAN Version: 1.38.0 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: ff6834f436fe0a22be172c47d239b1cd NeedsCompilation: no Title: Phenotype Consensus ANalysis (PCAN) Description: Phenotypes comparison based on a pathway consensus approach. Assess the relationship between candidate genes and a set of phenotypes based on additional genes related to the candidate (e.g. Pathways or network neighbors). biocViews: Annotation, Sequencing, Genetics, FunctionalPrediction, VariantAnnotation, Pathways, Network Author: Matthew Page and Patrice Godard Maintainer: Matthew Page and Patrice Godard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAN git_branch: RELEASE_3_22 git_last_commit: 97b34b0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PCAN_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PCAN_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PCAN_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PCAN_1.38.0.tgz vignettes: vignettes/PCAN/inst/doc/PCAN.html vignetteTitles: Assessing gene relevance for a set of phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAN/inst/doc/PCAN.R dependencyCount: 14 Package: PDATK Version: 1.18.0 Depends: R (>= 4.1), SummarizedExperiment Imports: data.table, MultiAssayExperiment, ConsensusClusterPlus, igraph, ggplotify, matrixStats, RColorBrewer, clusterRepro, CoreGx, caret, survminer, methods, S4Vectors, BiocGenerics, survival, stats, plyr, dplyr, MatrixGenerics, BiocParallel, rlang, piano, scales, survcomp, genefu, ggplot2, switchBox, reportROC, pROC, verification, utils Suggests: testthat (>= 3.0.0), msigdbr, BiocStyle, rmarkdown, knitr, HDF5Array License: MIT + file LICENSE MD5sum: 3a62e5a3fe0fdf2e0639bd6d9ddb2fcb NeedsCompilation: no Title: Pancreatic Ductal Adenocarcinoma Tool-Kit Description: Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification, Survival, Clustering, GenePrediction Author: Vandana Sandhu [aut], Heewon Seo [aut], Christopher Eeles [aut], Neha Rohatgi [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/PDATK/issues git_url: https://git.bioconductor.org/packages/PDATK git_branch: RELEASE_3_22 git_last_commit: d15fa15 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PDATK_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PDATK_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PDATK_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PDATK_1.18.0.tgz vignettes: vignettes/PDATK/inst/doc/PCOSP_model_analysis.html, vignettes/PDATK/inst/doc/PDATK_introduction.html vignetteTitles: PCOSP: Pancreatic Cancer Overall Survival Predictor, PDATK_introduction.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PDATK/inst/doc/PCOSP_model_analysis.R, vignettes/PDATK/inst/doc/PDATK_introduction.R dependencyCount: 263 Package: pdInfoBuilder Version: 1.74.0 Depends: R (>= 3.2.0), methods, Biobase (>= 2.27.3), RSQLite (>= 1.0.0), affxparser (>= 1.39.4), oligo (>= 1.31.5) Imports: Biostrings (>= 2.35.12), BiocGenerics (>= 0.13.11), DBI (>= 0.3.1), IRanges (>= 2.1.43), oligoClasses (>= 1.29.6), S4Vectors (>= 0.5.22) License: Artistic-2.0 MD5sum: 3840929eee21b77165bd754057e7b647 NeedsCompilation: yes Title: Platform Design Information Package Builder Description: Builds platform design information packages. These consist of a SQLite database containing feature-level data such as x, y position on chip and featureSet ID. The database also incorporates featureSet-level annotation data. The products of this packages are used by the oligo pkg. biocViews: Annotation, Infrastructure Author: Seth Falcon, Vince Carey, Matt Settles, Kristof de Beuf, Benilton Carvalho Maintainer: Benilton Carvalho git_url: https://git.bioconductor.org/packages/pdInfoBuilder git_branch: RELEASE_3_22 git_last_commit: 4ce36e7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pdInfoBuilder_1.74.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pdInfoBuilder_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pdInfoBuilder_1.74.0.tgz vignettes: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.pdf, vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.pdf vignetteTitles: Building Annotation Packages with pdInfoBuilder for Use with the oligo Package, PDInfo Package Building Affymetrix Mapping Chips hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R suggestsMe: maqcExpression4plex, aroma.affymetrix, maGUI dependencyCount: 54 Package: PeacoQC Version: 1.20.0 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, flowCore, flowWorkspace, ggplot2, grDevices, grid, gridExtra, methods, plyr, stats, utils Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=3) MD5sum: 5e49d8de49cec4c01d2ea082e6d04006 NeedsCompilation: no Title: Peak-based selection of high quality cytometry data Description: This is a package that includes pre-processing and quality control functions that can remove margin events, compensate and transform the data and that will use PeacoQCSignalStability for quality control. This last function will first detect peaks in each channel of the flowframe. It will remove anomalies based on the IsolationTree function and the MAD outlier detection method. This package can be used for both flow- and mass cytometry data. biocViews: FlowCytometry, QualityControl, Preprocessing, PeakDetection Author: Annelies Emmaneel [aut, cre] Maintainer: Annelies Emmaneel URL: http://github.com/saeyslab/PeacoQC VignetteBuilder: knitr BugReports: http://github.com/saeyslab/PeacoQC/issues git_url: https://git.bioconductor.org/packages/PeacoQC git_branch: RELEASE_3_22 git_last_commit: 0408e9f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PeacoQC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PeacoQC_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PeacoQC_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PeacoQC_1.20.0.tgz vignettes: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.pdf vignetteTitles: PeacoQC_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.R importsMe: CytoPipeline dependencyCount: 78 Package: peakCombiner Version: 1.0.0 Depends: R (>= 4.5.0) Imports: tidyr, dplyr (>= 1.1.2), IRanges, GenomicRanges, tidyselect, purrr, readr (>= 2.1.2), tibble (>= 3.2.1), rlang, stringr, here, stats, Seqinfo Suggests: testthat (>= 3.0.0), tidyverse, rmarkdown, styler, cli, lintr, rtracklayer, knitr, devtools, ggplot2, BiocStyle, BiocManager, usethis, utils, AnnotationHub, GenomeInfoDb License: MIT + file LICENSE MD5sum: 96cf4b75d32415a0ec88988504abb61a NeedsCompilation: no Title: The R package to curate and merge enriched genomic regions into consensus peak sets Description: peakCombiner, a fully R based, user-friendly, transparent, and customizable tool that allows even novice R users to create a high-quality consensus peak list. The modularity of its functions allows an easy way to optimize input and output data. A broad range of accepted input data formats can be used to create a consensus peak set that can be exported to a file or used as the starting point for most downstream peak analyses. biocViews: WorkflowStep, Preprocessing, ChipOnChip Author: Markus Muckenhuber [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Michael Stadler [aut] (ORCID: ), Kathleen Sprouffske [aut] (ORCID: ), Novartis Biomedical Research [cph] Maintainer: Markus Muckenhuber URL: https://github.com/novartis/peakCombiner/, https://bioconductor.org/packages/peakCombiner VignetteBuilder: knitr BugReports: https://github.com/novartis/peakCombiner/issues git_url: https://git.bioconductor.org/packages/peakCombiner git_branch: RELEASE_3_22 git_last_commit: 152407d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/peakCombiner_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/peakCombiner_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/peakCombiner_1.0.0.tgz vignettes: vignettes/peakCombiner/inst/doc/peakCombiner.html vignetteTitles: peakCombiner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/peakCombiner/inst/doc/peakCombiner.R dependencyCount: 44 Package: peakPantheR Version: 1.24.0 Depends: R (>= 4.5) Imports: foreach (>= 1.4.4), doParallel (>= 1.0.11), ggplot2 (>= 3.5.0), gridExtra (>= 2.3), MSnbase (>= 2.4.0), mzR (>= 2.12.0), stringr (>= 1.2.0), methods (>= 3.4.0), XML (>= 3.98.1.10), minpack.lm (>= 1.2.1), scales(>= 0.5.0), shiny (>= 1.0.5), bslib, shinycssloaders (>= 1.0.0), DT (>= 0.15), pracma (>= 2.2.3), utils, lubridate, svglite (>= 2.1.1) Suggests: testthat, devtools, faahKO, msdata, knitr, rmarkdown, pander, BiocStyle License: GPL-3 MD5sum: 1089ad8f49cc54a62299e043ce2a6a04 NeedsCompilation: no Title: Peak Picking and Annotation of High Resolution Experiments Description: An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained. biocViews: MassSpectrometry, Metabolomics, PeakDetection Author: Arnaud Wolfer [aut, cre] (ORCID: ), Goncalo Correia [aut] (ORCID: ), Jake Pearce [ctb], Caroline Sands [ctb] Maintainer: Arnaud Wolfer URL: https://github.com/phenomecentre/peakPantheR VignetteBuilder: knitr BugReports: https://github.com/phenomecentre/peakPantheR/issues/new git_url: https://git.bioconductor.org/packages/peakPantheR git_branch: RELEASE_3_22 git_last_commit: 9bede65 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/peakPantheR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/peakPantheR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/peakPantheR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/peakPantheR_1.24.0.tgz vignettes: vignettes/peakPantheR/inst/doc/getting-started.html, vignettes/peakPantheR/inst/doc/parallel-annotation.html, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.html, vignettes/peakPantheR/inst/doc/real-time-annotation.html vignetteTitles: Getting Started with the peakPantheR package, Parallel Annotation, peakPantheR Graphical User Interface, Real Time Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peakPantheR/inst/doc/getting-started.R, vignettes/peakPantheR/inst/doc/parallel-annotation.R, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.R, vignettes/peakPantheR/inst/doc/real-time-annotation.R dependencyCount: 146 Package: PECA Version: 1.46.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: fce2eefc51a1e3d8fcd7705b5c087e1c NeedsCompilation: no Title: Probe-level Expression Change Averaging Description: Calculates Probe-level Expression Change Averages (PECA) to identify differential expression in Affymetrix gene expression microarray studies or in proteomic studies using peptide-level mesurements respectively. biocViews: Software, Proteomics, Microarray, DifferentialExpression, GeneExpression, ExonArray, DifferentialSplicing Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/PECA git_branch: RELEASE_3_22 git_last_commit: ea1bf85 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PECA_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PECA_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PECA_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PECA_1.46.0.tgz vignettes: vignettes/PECA/inst/doc/PECA.pdf vignetteTitles: PECA: Probe-level Expression Change Averaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PECA/inst/doc/PECA.R dependencyCount: 102 Package: peco Version: 1.22.0 Depends: R (>= 3.5.0) Imports: assertthat, circular, conicfit, doParallel, foreach, genlasso (>= 1.4), graphics, methods, parallel, scater, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown License: GPL (>= 3) Archs: x64 MD5sum: c36d0bc2919e98cb6794a422e49c5de7 NeedsCompilation: no Title: A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data Description: Our approach provides a way to assign continuous cell cycle phase using scRNA-seq data, and consequently, allows to identify cyclic trend of gene expression levels along the cell cycle. This package provides method and training data, which includes scRNA-seq data collected from 6 individual cell lines of induced pluripotent stem cells (iPSCs), and also continuous cell cycle phase derived from FUCCI fluorescence imaging data. biocViews: Sequencing, RNASeq, GeneExpression, Transcriptomics, SingleCell, Software, StatisticalMethod, Classification, Visualization Author: Chiaowen Joyce Hsiao [aut, cre], Matthew Stephens [aut], John Blischak [ctb], Peter Carbonetto [ctb] Maintainer: Chiaowen Joyce Hsiao URL: https://github.com/jhsiao999/peco VignetteBuilder: knitr BugReports: https://github.com/jhsiao999/peco/issues git_url: https://git.bioconductor.org/packages/peco git_branch: RELEASE_3_22 git_last_commit: a69498c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/peco_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/peco_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/peco_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/peco_1.22.0.tgz vignettes: vignettes/peco/inst/doc/vignette.html vignetteTitles: An example of predicting cell cycle phase using peco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peco/inst/doc/vignette.R dependencyCount: 102 Package: Pedixplorer Version: 1.6.0 Depends: R (>= 4.4.0) Imports: graphics, stats, methods, ggplot2, utils, grDevices, stringr, plyr, dplyr, tidyr, quadprog, Matrix, S4Vectors, shiny, readxl, DT, igraph, shinycssloaders, shinyhelper, shinyjs, shinyjqui, shinyWidgets, htmlwidgets, plotly, colourpicker, shinytoastr Suggests: diffviewer, gridExtra, testthat (>= 3.0.0), vdiffr, rmarkdown, BiocStyle, knitr, withr, qpdf, shinytest2, devtools, R.devices, usethis, rlang, magick, cowplot License: Artistic-2.0 Archs: x64 MD5sum: a14905d7d289a34c1a5e4966745d6f2e NeedsCompilation: no Title: Pedigree Functions Description: Routines to handle family data with a Pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for Pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the Pedigree object with various criteria, and kinship for the X chromosome. biocViews: Software, DataRepresentation, Genetics, GraphAndNetwork, Visualization Author: Louis Le Nezet [aut, cre, ctb] (ORCID: ), Jason Sinnwell [aut], Terry Therneau [aut], Daniel Schaid [ctb], Elizabeth Atkinson [ctb] Maintainer: Louis Le Nezet URL: https://louislenezet.github.io/Pedixplorer/ VignetteBuilder: knitr BugReports: https://github.com/LouisLeNezet/Pedixplorer/issues git_url: https://git.bioconductor.org/packages/Pedixplorer git_branch: RELEASE_3_22 git_last_commit: 4f31557 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Pedixplorer_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Pedixplorer_1.5.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Pedixplorer_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Pedixplorer_1.6.0.tgz vignettes: vignettes/Pedixplorer/inst/doc/pedigree_alignment.html, vignettes/Pedixplorer/inst/doc/pedigree_kinship.html, vignettes/Pedixplorer/inst/doc/pedigree_object.html, vignettes/Pedixplorer/inst/doc/pedigree_plot.html, vignettes/Pedixplorer/inst/doc/Pedixplorer.html vignetteTitles: Pedigree alignment details, Pedigree kinship() details, Pedigree object, Pedigree plotting details, Pedixplorer tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pedixplorer/inst/doc/pedigree_alignment.R, vignettes/Pedixplorer/inst/doc/pedigree_kinship.R, vignettes/Pedixplorer/inst/doc/pedigree_object.R, vignettes/Pedixplorer/inst/doc/pedigree_plot.R, vignettes/Pedixplorer/inst/doc/Pedixplorer.R dependsOnMe: pedgene dependencyCount: 102 Package: pengls Version: 1.16.0 Depends: R (>= 4.5.0) Imports: glmnet, nlme, stats, BiocParallel Suggests: knitr,rmarkdown,testthat License: GPL-2 MD5sum: e5841d6879cc5be6b3131a1eb87faccf NeedsCompilation: no Title: Fit Penalised Generalised Least Squares models Description: Combine generalised least squares methodology from the nlme package for dealing with autocorrelation with penalised least squares methods from the glmnet package to deal with high dimensionality. This pengls packages glues them together through an iterative loop. The resulting method is applicable to high dimensional datasets that exhibit autocorrelation, such as spatial or temporal data. biocViews: Transcriptomics, Regression, TimeCourse, Spatial Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel URL: https://github.com/sthawinke/pengls VignetteBuilder: knitr BugReports: https://github.com/sthawinke/pengls/issues git_url: https://git.bioconductor.org/packages/pengls git_branch: RELEASE_3_22 git_last_commit: 77f4c89 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pengls_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pengls_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pengls_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pengls_1.16.0.tgz vignettes: vignettes/pengls/inst/doc/penglsVignette.html vignetteTitles: Vignette of the pengls package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pengls/inst/doc/penglsVignette.R dependencyCount: 27 Package: PepSetTest Version: 1.4.0 Depends: R (>= 4.1.0) Imports: dplyr, limma, lme4, MASS, matrixStats, reshape2, stats, tibble, SummarizedExperiment, methods Suggests: statmod, BiocStyle, knitr, rmarkdown, tidyr License: GPL (>= 3) Archs: x64 MD5sum: 68aa386c2100e68dfe4a1af6bafac7b5 NeedsCompilation: no Title: Peptide Set Test Description: Peptide Set Test (PepSetTest) is a peptide-centric strategy to infer differentially expressed proteins in LC-MS/MS proteomics data. This test detects coordinated changes in the expression of peptides originating from the same protein and compares these changes against the rest of the peptidome. Compared to traditional aggregation-based approaches, the peptide set test demonstrates improved statistical power, yet controlling the Type I error rate correctly in most cases. This test can be valuable for discovering novel biomarkers and prioritizing drug targets, especially when the direct application of statistical analysis to protein data fails to provide substantial insights. biocViews: DifferentialExpression, Regression, Proteomics, MassSpectrometry Author: Junmin Wang [aut, cre] Maintainer: Junmin Wang URL: https://github.com/JmWangBio/PepSetTest VignetteBuilder: knitr BugReports: https://github.com/JmWangBio/PepSetTest/issues git_url: https://git.bioconductor.org/packages/PepSetTest git_branch: RELEASE_3_22 git_last_commit: f57d8e7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PepSetTest_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PepSetTest_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PepSetTest_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PepSetTest_1.4.0.tgz vignettes: vignettes/PepSetTest/inst/doc/PepSetTest.html vignetteTitles: A Tutorial for PepSetTest hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PepSetTest/inst/doc/PepSetTest.R dependencyCount: 58 Package: PepsNMR Version: 1.28.0 Depends: R (>= 3.6) Imports: Matrix, ptw, ggplot2, gridExtra, matrixStats, reshape2, methods, graphics, stats Suggests: knitr, markdown, rmarkdown, BiocStyle, PepsNMRData License: GPL-2 | file LICENSE MD5sum: d91444ef982e2d60e1da5d6ed42f7881 NeedsCompilation: no Title: Pre-process 1H-NMR FID signals Description: This package provides R functions for common pre-procssing steps that are applied on 1H-NMR data. It also provides a function to read the FID signals directly in the Bruker format. biocViews: Software, Preprocessing, Visualization, Metabolomics, DataImport Author: Manon Martin [aut, cre], Bernadette Govaerts [aut, ths], Benoît Legat [aut], Paul H.C. Eilers [aut], Pascal de Tullio [dtc], Bruno Boulanger [ctb], Julien Vanwinsberghe [ctb] Maintainer: Manon Martin URL: https://github.com/ManonMartin/PepsNMR VignetteBuilder: knitr BugReports: https://github.com/ManonMartin/PepsNMR/issues git_url: https://git.bioconductor.org/packages/PepsNMR git_branch: RELEASE_3_22 git_last_commit: 8e960db git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PepsNMR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PepsNMR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PepsNMR_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PepsNMR_1.28.0.tgz vignettes: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.html vignetteTitles: Application of PepsNMR on the Human Serum dataset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.R importsMe: ASICS dependencyCount: 36 Package: pepStat Version: 1.44.0 Depends: R (>= 3.0.0), Biobase, IRanges Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods, data.table Suggests: pepDat, Pviz, knitr, shiny License: Artistic-2.0 MD5sum: 17a7e1e7c4f405dafefa6152db06b0b1 NeedsCompilation: no Title: Statistical analysis of peptide microarrays Description: Statistical analysis of peptide microarrays biocViews: Microarray, Preprocessing Author: Raphael Gottardo, Gregory C Imholte, Renan Sauteraud, Mike Jiang Maintainer: Gregory C Imholte URL: https://github.com/RGLab/pepStat VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pepStat git_branch: RELEASE_3_22 git_last_commit: 9230a3a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pepStat_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pepStat_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pepStat_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pepStat_1.44.0.tgz vignettes: vignettes/pepStat/inst/doc/pepStat.pdf vignetteTitles: Full peptide microarray analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepStat/inst/doc/pepStat.R dependencyCount: 40 Package: pepXMLTab Version: 1.44.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: 58e890e1c7baeea5c3a343feac06efa0 NeedsCompilation: no Title: Parsing pepXML files and filter based on peptide FDR. Description: Parsing pepXML files based one XML package. The package tries to handle pepXML files generated from different softwares. The output will be a peptide-spectrum-matching tabular file. The package also provide function to filter the PSMs based on FDR. biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/pepXMLTab git_branch: RELEASE_3_22 git_last_commit: c7325dd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pepXMLTab_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pepXMLTab_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pepXMLTab_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pepXMLTab_1.44.0.tgz vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf vignetteTitles: Introduction to pepXMLTab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepXMLTab/inst/doc/pepXMLTab.R dependencyCount: 3 Package: periodicDNA Version: 1.20.0 Depends: R (>= 4.0), Biostrings, GenomicRanges, IRanges, BSgenome, BiocParallel Imports: S4Vectors, rtracklayer, stats, Seqinfo, magrittr, zoo, ggplot2, methods, parallel, cowplot Suggests: BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, reticulate, testthat, covr, knitr, rmarkdown, pkgdown License: GPL-3 + file LICENSE Archs: x64 MD5sum: ad093038f1d0e6ad98817af521ecd618 NeedsCompilation: no Title: Set of tools to identify periodic occurrences of k-mers in DNA sequences Description: This R package helps the user identify k-mers (e.g. di- or tri-nucleotides) present periodically in a set of genomic loci (typically regulatory elements). The functions of this package provide a straightforward approach to find periodic occurrences of k-mers in DNA sequences, such as regulatory elements. It is not aimed at identifying motifs separated by a conserved distance; for this type of analysis, please visit MEME website. biocViews: SequenceMatching, MotifDiscovery, MotifAnnotation, Sequencing, Coverage, Alignment, DataImport Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/periodicDNA VignetteBuilder: knitr BugReports: https://github.com/js2264/periodicDNA/issues git_url: https://git.bioconductor.org/packages/periodicDNA git_branch: RELEASE_3_22 git_last_commit: 4feee90 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/periodicDNA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/periodicDNA_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/periodicDNA_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/periodicDNA_1.20.0.tgz vignettes: vignettes/periodicDNA/inst/doc/periodicDNA.html vignetteTitles: Introduction to periodicDNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/periodicDNA/inst/doc/periodicDNA.R dependencyCount: 76 Package: pfamAnalyzeR Version: 1.10.0 Depends: R (>= 4.3.0), readr, stringr, dplyr Imports: utils, tibble, magrittr Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: a6dd9cce90164364eff0d0f50c69a671 NeedsCompilation: no Title: Identification of domain isotypes in pfam data Description: Protein domains is one of the most import annoation of proteins we have with the Pfam database/tool being (by far) the most used tool. This R package enables the user to read the pfam prediction from both webserver and stand-alone runs into R. We have recently shown most human protein domains exist as multiple distinct variants termed domain isotypes. Different domain isotypes are used in a cell, tissue, and disease-specific manner. Accordingly, we find that domain isotypes, compared to each other, modulate, or abolish the functionality of a protein domain. This R package enables the identification and classification of such domain isotypes from Pfam data. biocViews: AlternativeSplicing, TranscriptomeVariant, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, Annotation, FunctionalPrediction, GenePrediction, DataImport Author: Kristoffer Vitting-Seerup [cre, aut] (ORCID: ) Maintainer: Kristoffer Vitting-Seerup VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/pfamAnalyzeR/issues git_url: https://git.bioconductor.org/packages/pfamAnalyzeR git_branch: RELEASE_3_22 git_last_commit: f357ad4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pfamAnalyzeR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pfamAnalyzeR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pfamAnalyzeR_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pfamAnalyzeR_1.10.0.tgz vignettes: vignettes/pfamAnalyzeR/inst/doc/pfamAnalyzeR.html vignetteTitles: pfamAnalyzeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pfamAnalyzeR/inst/doc/pfamAnalyzeR.R dependsOnMe: IsoformSwitchAnalyzeR dependencyCount: 34 Package: pgca Version: 1.34.0 Imports: utils, stats Suggests: knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: 4464dffa04b3dc70617c800d03267d08 NeedsCompilation: no Title: PGCA: An Algorithm to Link Protein Groups Created from MS/MS Data Description: Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data. The algorithm connects two or more groups with overlapping accession numbers. In some cases, pairwise groups are mutually exclusive but they may still be connected by another group (or set of groups) with overlapping accession numbers. Thus, groups created by PGCA from multiple experimental runs (i.e., global groups) are called "connected" groups. These identified global protein groups enable the analysis of quantitative data available for protein groups instead of unique protein identifiers. biocViews: WorkflowStep,AssayDomain,Proteomics,MassSpectrometry,ImmunoOncology Author: Gabriela Cohen-Freue Maintainer: Gabriela Cohen-Freue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pgca git_branch: RELEASE_3_22 git_last_commit: c121bd3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pgca_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pgca_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pgca_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pgca_1.34.0.tgz vignettes: vignettes/pgca/inst/doc/intro.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pgca/inst/doc/intro.R dependencyCount: 2 Package: pgxRpi Version: 1.6.0 Depends: R (>= 4.2) Imports: utils, methods, grDevices, graphics, circlize, httr, dplyr, attempt, lubridate, survival, survminer, ggplot2, GenomicRanges, SummarizedExperiment, S4Vectors, yaml, parallel, future, future.apply Suggests: BiocStyle, rmarkdown, knitr, testthat License: Artistic-2.0 MD5sum: d155374a843c7e733c93c2a725b5ed3a NeedsCompilation: no Title: R wrapper for Progenetix Description: The package is an R wrapper for Progenetix REST API built upon the Beacon v2 protocol. Its purpose is to provide a seamless way for retrieving genomic data from Progenetix database—an open resource dedicated to curated oncogenomic profiles. Empowered by this package, users can effortlessly access and visualize data from Progenetix. biocViews: CopyNumberVariation, GenomicVariation, DataImport, Software Author: Hangjia Zhao [aut, cre] (ORCID: ), Michael Baudis [aut] (ORCID: ) Maintainer: Hangjia Zhao URL: https://github.com/progenetix/pgxRpi VignetteBuilder: knitr BugReports: https://github.com/progenetix/pgxRpi/issues git_url: https://git.bioconductor.org/packages/pgxRpi git_branch: RELEASE_3_22 git_last_commit: 2c9aee6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pgxRpi_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pgxRpi_1.5.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pgxRpi_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pgxRpi_1.6.0.tgz vignettes: vignettes/pgxRpi/inst/doc/Introduction_1_load_metadata.html, vignettes/pgxRpi/inst/doc/Introduction_2_query_variants.html, vignettes/pgxRpi/inst/doc/Introduction_3_access_cnv_frequency.html, vignettes/pgxRpi/inst/doc/Introduction_4_process_pgxseg.html vignetteTitles: Introduction_1_load_metadata, Introduction_2_query_variants, Introduction_3_access_cnv_frequency, Introduction_4_process_pgxseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pgxRpi/inst/doc/Introduction_1_load_metadata.R, vignettes/pgxRpi/inst/doc/Introduction_2_query_variants.R, vignettes/pgxRpi/inst/doc/Introduction_3_access_cnv_frequency.R, vignettes/pgxRpi/inst/doc/Introduction_4_process_pgxseg.R dependencyCount: 136 Package: phantasus Version: 1.29.0 Depends: R (>= 4.3) Imports: ggplot2, protolite, Biobase, GEOquery, Rook, htmltools, httpuv, jsonlite, limma, edgeR, opencpu, assertthat, methods, httr, rhdf5, utils, parallel, stringr, fgsea (>= 1.9.4), svglite, gtable, stats, Matrix, pheatmap, scales, ccaPP, grid, grDevices, AnnotationDbi, DESeq2, data.table, curl, apeglm, tidyr, config (>= 0.3.2), rhdf5client (>= 1.25.1), yaml, fs, phantasusLite, XML Suggests: testthat, BiocStyle, knitr, rmarkdown, org.Hs.eg.db, org.Mm.eg.db License: MIT + file LICENSE MD5sum: 6e700d43ba6ecaef613a9636a3d81988 NeedsCompilation: no Title: Visual and interactive gene expression analysis Description: Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported. biocViews: GeneExpression, GUI, Visualization, DataRepresentation, Transcriptomics, RNASeq, Microarray, Normalization, Clustering, DifferentialExpression, PrincipalComponent, ImmunoOncology Author: Maxim Kleverov [aut], Daria Zenkova [aut], Vladislav Kamenev [aut], Margarita Sablina [ctb], Maxim Artyomov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://alserglab.wustl.edu/phantasus VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasus/issues git_url: https://git.bioconductor.org/packages/phantasus git_branch: devel git_last_commit: 5b63f9c git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-13 source.ver: src/contrib/phantasus_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phantasus_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/phantasus_1.30.0.tgz vignettes: vignettes/phantasus/inst/doc/phantasus-tutorial.html vignetteTitles: Using phantasus application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/phantasus/inst/doc/phantasus-tutorial.R dependencyCount: 159 Package: phantasusLite Version: 1.8.0 Depends: R (>= 4.2) Imports: data.table, rhdf5client(>= 1.25.1), httr, stringr, stats, utils, Biobase, methods Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, rhdf5, GEOquery License: MIT + file LICENSE Archs: x64 MD5sum: 0dd3702467265cf4f7a4c8d2798616ba NeedsCompilation: no Title: Loading and annotation RNA-seq counts matrices Description: PhantasusLite – a lightweight package with helper functions of general interest extracted from phantasus package. In parituclar it simplifies working with public RNA-seq datasets from GEO by providing access to the remote HSDS repository with the precomputed gene counts from ARCHS4 and DEE2 projects. biocViews: GeneExpression, Transcriptomics, RNASeq Author: Rita Sablina [aut], Maxim Kleverov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/phantasusLite/ VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasusLite/issues git_url: https://git.bioconductor.org/packages/phantasusLite git_branch: RELEASE_3_22 git_last_commit: cad195c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/phantasusLite_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/phantasusLite_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phantasusLite_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/phantasusLite_1.8.0.tgz vignettes: vignettes/phantasusLite/inst/doc/phantasusLite-tutorial.html vignetteTitles: phantasusLite tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/phantasusLite/inst/doc/phantasusLite-tutorial.R importsMe: phantasus dependencyCount: 41 Package: PharmacoGx Version: 3.14.0 Depends: R (>= 4.1.0), CoreGx Imports: BiocGenerics, Biobase, S4Vectors, SummarizedExperiment, MultiAssayExperiment, BiocParallel, ggplot2, RColorBrewer, magicaxis, parallel, caTools, methods, downloader, stats, utils, graphics, grDevices, reshape2, jsonlite, data.table, checkmate, boot, coop LinkingTo: Rcpp Suggests: pander, rmarkdown, knitr, knitcitations, crayon, testthat, markdown, BiocStyle, R.utils License: GPL (>= 3) Archs: x64 MD5sum: 205307e3d32998d5928fda74a3394bfa NeedsCompilation: yes Title: Analysis of Large-Scale Pharmacogenomic Data Description: Contains a set of functions to perform large-scale analysis of pharmaco-genomic data. These include the PharmacoSet object for storing the results of pharmacogenomic experiments, as well as a number of functions for computing common summaries of drug-dose response and correlating them with the molecular features in a cancer cell-line. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Petr Smirnov [aut], Christopher Eeles [aut], Jermiah Joseph [aut], Zhaleh Safikhani [aut], Mark Freeman [aut], Feifei Li [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/PharmacoGx/issues git_url: https://git.bioconductor.org/packages/PharmacoGx git_branch: RELEASE_3_22 git_last_commit: 96dc940 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PharmacoGx_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PharmacoGx_3.13.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PharmacoGx_3.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PharmacoGx_3.14.0.tgz vignettes: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.html, vignettes/PharmacoGx/inst/doc/DetectingDrugSynergyAndAntagonism.html, vignettes/PharmacoGx/inst/doc/PharmacoGx.html vignetteTitles: Creating a PharmacoSet Object, Detecting Drug Synergy and Antagonism with PharmacoGx 3.0+, PharmacoGx: An R Package for Analysis of Large Pharmacogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.R, vignettes/PharmacoGx/inst/doc/DetectingDrugSynergyAndAntagonism.R, vignettes/PharmacoGx/inst/doc/PharmacoGx.R importsMe: gDRimport, Xeva suggestsMe: ToxicoGx dependencyCount: 143 Package: PhenoGeneRanker Version: 1.18.0 Imports: igraph, Matrix, foreach, doParallel, dplyr, stats, utils, parallel Suggests: knitr, rmarkdown License: Creative Commons Attribution 4.0 International License MD5sum: 9bdddbddf0675a5e2127ea9e6afc0d7a NeedsCompilation: no Title: PhenoGeneRanker: A gene and phenotype prioritization tool Description: This package is a gene/phenotype prioritization tool that utilizes multiplex heterogeneous gene phenotype network. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p-values of gene/phenotype ranking using random stratified sampling of genes/phenotypes based on their connectivity degree in the network. https://dl.acm.org/doi/10.1145/3307339.3342155. biocViews: BiomedicalInformatics, GenePrediction, GraphAndNetwork, Network, NetworkInference, Pathways, Software, SystemsBiology Author: Cagatay Dursun [aut, cre] Maintainer: Cagatay Dursun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhenoGeneRanker git_branch: RELEASE_3_22 git_last_commit: 440d35d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PhenoGeneRanker_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PhenoGeneRanker_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PhenoGeneRanker_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PhenoGeneRanker_1.18.0.tgz vignettes: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.html vignetteTitles: PhenoGeneRanker hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.R dependencyCount: 30 Package: phenomis Version: 1.12.0 Depends: SummarizedExperiment Imports: Biobase, biodb, biodbChebi, data.table, futile.logger, ggplot2, ggrepel, graphics, grDevices, grid, htmlwidgets, igraph, limma, methods, MultiAssayExperiment, MultiDataSet, PMCMRplus, plotly, ranger, RColorBrewer, ropls, stats, tibble, tidyr, utils, VennDiagram Suggests: BiocGenerics, BiocStyle, biosigner, CLL, knitr, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 4c4ba9442e7dfd01a49573df612b54f2 NeedsCompilation: no Title: Postprocessing and univariate analysis of omics data Description: The 'phenomis' package provides methods to perform post-processing (i.e. quality control and normalization) as well as univariate statistical analysis of single and multi-omics data sets. These methods include quality control metrics, signal drift and batch effect correction, intensity transformation, univariate hypothesis testing, but also clustering (as well as annotation of metabolomics data). The data are handled in the standard Bioconductor formats (i.e. SummarizedExperiment and MultiAssayExperiment for single and multi-omics datasets, respectively; the alternative ExpressionSet and MultiDataSet formats are also supported for convenience). As a result, all methods can be readily chained as workflows. The pipeline can be further enriched by multivariate analysis and feature selection, by using the 'ropls' and 'biosigner' packages, which support the same formats. Data can be conveniently imported from and exported to text files. Although the methods were initially targeted to metabolomics data, most of the methods can be applied to other types of omics data (e.g., transcriptomics, proteomics). biocViews: BatchEffect, Clustering, Coverage, KEGG, MassSpectrometry, Metabolomics, Normalization, Proteomics, QualityControl, Sequencing, StatisticalMethod, Transcriptomics Author: Etienne A. Thevenot [aut, cre] (ORCID: ), Natacha Lenuzza [ctb], Marie Tremblay-Franco [ctb], Alyssa Imbert [ctb], Pierrick Roger [ctb], Eric Venot [ctb], Sylvain Dechaumet [ctb] Maintainer: Etienne A. Thevenot URL: https://doi.org/10.1038/s41597-021-01095-3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phenomis git_branch: RELEASE_3_22 git_last_commit: 7918c7b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/phenomis_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/phenomis_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phenomis_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/phenomis_1.12.0.tgz vignettes: vignettes/phenomis/inst/doc/phenomis-vignette.html vignetteTitles: phenomis-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenomis/inst/doc/phenomis-vignette.R suggestsMe: ropls dependencyCount: 150 Package: phenopath Version: 1.34.0 Imports: Rcpp (>= 0.12.8), SummarizedExperiment, methods, stats, dplyr, tibble, ggplot2, tidyr LinkingTo: Rcpp Suggests: knitr, rmarkdown, forcats, testthat, BiocStyle, SingleCellExperiment License: Apache License (== 2.0) MD5sum: ca5f1c4f27ccf1c8a0623db5d1b79550 NeedsCompilation: yes Title: Genomic trajectories with heterogeneous genetic and environmental backgrounds Description: PhenoPath infers genomic trajectories (pseudotimes) in the presence of heterogeneous genetic and environmental backgrounds and tests for interactions between them. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell, PrincipalComponent Author: Kieran Campbell Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phenopath git_branch: RELEASE_3_22 git_last_commit: f09cdb5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/phenopath_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/phenopath_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phenopath_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/phenopath_1.34.0.tgz vignettes: vignettes/phenopath/inst/doc/introduction_to_phenopath.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenopath/inst/doc/introduction_to_phenopath.R suggestsMe: splatter dependencyCount: 54 Package: phenoTest Version: 1.58.0 Depends: R (>= 3.6.0), Biobase, methods, annotate, Heatplus, BMA, ggplot2, Hmisc Imports: survival, limma, gplots, Category, AnnotationDbi, hopach, biomaRt, GSEABase, genefilter, xtable, annotate, mgcv, hgu133a.db, ellipse Suggests: GSEABase, GO.db Enhances: parallel, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, org.Dm.eg.db License: GPL (>=2) MD5sum: a5a501682864448fc6b6cc93c9c6e1b7 NeedsCompilation: no Title: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation. Description: Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Evarist Planet Maintainer: Evarist Planet git_url: https://git.bioconductor.org/packages/phenoTest git_branch: RELEASE_3_22 git_last_commit: 25bf541 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/phenoTest_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/phenoTest_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phenoTest_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/phenoTest_1.58.0.tgz vignettes: vignettes/phenoTest/inst/doc/phenoTest.pdf vignetteTitles: Manual for the phenoTest library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoTest/inst/doc/phenoTest.R importsMe: canceR dependencyCount: 140 Package: philr Version: 1.35.0 Imports: ape, phangorn, tidyr, ggplot2, ggtree, methods Suggests: testthat, knitr, ecodist, rmarkdown, BiocStyle, phyloseq, SummarizedExperiment, TreeSummarizedExperiment, glmnet, dplyr, mia License: GPL-3 MD5sum: 1564f54326e9c9c06afe258eb84a14f9 NeedsCompilation: no Title: Phylogenetic partitioning based ILR transform for metagenomics data Description: PhILR is short for Phylogenetic Isometric Log-Ratio Transform. This package provides functions for the analysis of compositional data (e.g., data representing proportions of different variables/parts). Specifically this package allows analysis of compositional data where the parts can be related through a phylogenetic tree (as is common in microbiota survey data) and makes available the Isometric Log Ratio transform built from the phylogenetic tree and utilizing a weighted reference measure. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Software Author: Justin Silverman [aut, cre], Leo Lahti [ctb] (ORCID: ) Maintainer: Justin Silverman URL: https://github.com/jsilve24/philr VignetteBuilder: knitr BugReports: https://github.com/jsilve24/philr/issues git_url: https://git.bioconductor.org/packages/philr git_branch: devel git_last_commit: 418de15 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/philr_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/philr_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/philr_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/philr_1.35.0.tgz vignettes: vignettes/philr/inst/doc/philr-intro.html vignetteTitles: Introduction to PhILR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/philr/inst/doc/philr-intro.R suggestsMe: mia, miaDash dependencyCount: 84 Package: PhIPData Version: 1.18.0 Depends: R (>= 4.1.0), SummarizedExperiment (>= 1.3.81) Imports: BiocFileCache, BiocGenerics, methods, GenomicRanges, IRanges, S4Vectors, edgeR, cli, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, covr, dplyr, readr, withr License: MIT + file LICENSE MD5sum: a1f64e56bd974105a064f1a609389b82 NeedsCompilation: no Title: Container for PhIP-Seq Experiments Description: PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters. biocViews: Infrastructure, DataRepresentation, Sequencing, Coverage Author: Athena Chen [aut, cre] (ORCID: ), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen VignetteBuilder: knitr BugReports: https://github.com/athchen/PhIPData/issues git_url: https://git.bioconductor.org/packages/PhIPData git_branch: RELEASE_3_22 git_last_commit: 2d43e86 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PhIPData_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PhIPData_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PhIPData_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PhIPData_1.18.0.tgz vignettes: vignettes/PhIPData/inst/doc/PhIPData.html vignetteTitles: PhIPData: A Container for PhIP-Seq Experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhIPData/inst/doc/PhIPData.R dependsOnMe: beer dependencyCount: 66 Package: phosphonormalizer Version: 1.34.0 Depends: R (>= 4.0) Imports: plyr, stats, graphics, matrixStats, methods Suggests: knitr, rmarkdown, testthat Enhances: MSnbase License: GPL (>= 2) MD5sum: 8904e7cbed96b73257d078eaafce732b NeedsCompilation: no Title: Compensates for the bias introduced by median normalization in Description: It uses the overlap between enriched and non-enriched datasets to compensate for the bias introduced in global phosphorylation after applying median normalization. biocViews: Software, StatisticalMethod, WorkflowStep, Normalization, Proteomics Author: Sohrab Saraei [aut, cre], Tomi Suomi [ctb], Otto Kauko [ctb], Laura Elo [ths] Maintainer: Sohrab Saraei VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phosphonormalizer git_branch: RELEASE_3_22 git_last_commit: fc6df69 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/phosphonormalizer_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/phosphonormalizer_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phosphonormalizer_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/phosphonormalizer_1.34.0.tgz vignettes: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.pdf, vignettes/phosphonormalizer/inst/doc/vignette.html vignetteTitles: phosphonormalizer: Phosphoproteomics Normalization, Pairwise normalization of phosphoproteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.R, vignettes/phosphonormalizer/inst/doc/vignette.R dependencyCount: 7 Package: PhosR Version: 1.20.0 Depends: R (>= 4.2.0) Imports: ruv, e1071, dendextend, limma, pcaMethods, stats, RColorBrewer, circlize, dplyr, igraph, pheatmap, preprocessCore, tidyr, rlang, graphics, grDevices, utils, SummarizedExperiment, methods, S4Vectors, BiocGenerics, ggplot2, GGally, ggdendro, ggpubr, network, reshape2, ggtext, stringi Suggests: testthat, knitr, rgl, sna, ClueR, directPA, rmarkdown, org.Rn.eg.db, org.Mm.eg.db, reactome.db, annotate, BiocStyle, stringr, calibrate License: GPL-3 + file LICENSE MD5sum: cdb602968b8efa2536146e3fbf40be08 NeedsCompilation: no Title: A set of methods and tools for comprehensive analysis of phosphoproteomics data Description: PhosR is a package for the comprenhensive analysis of phosphoproteomic data. There are two major components to PhosR: processing and downstream analysis. PhosR consists of various processing tools for phosphoproteomics data including filtering, imputation, normalisation, and functional analysis for inferring active kinases and signalling pathways. biocViews: Software, ResearchField, Proteomics Author: Pengyi Yang [aut], Di Xiao [aut, cre], Hani Jieun Kim [aut] Maintainer: Di Xiao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhosR git_branch: RELEASE_3_22 git_last_commit: d795fca git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PhosR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PhosR_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PhosR_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PhosR_1.20.0.tgz vignettes: vignettes/PhosR/inst/doc/PhosR.html vignetteTitles: An introduction to PhosR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhosR/inst/doc/PhosR.R suggestsMe: SmartPhos dependencyCount: 133 Package: PhyloProfile Version: 2.2.0 Depends: R (>= 4.5.0) Imports: ape, bioDist, BiocStyle, Biostrings, bsplus, colourpicker, data.table, dplyr, DT, energy, fastcluster, ggplot2, gridExtra, htmlwidgets, pbapply, plotly, RColorBrewer, RCurl, Rfast, scattermore, shiny, shinycssloaders, shinyFiles, shinyjs, stringr, tsne, svglite, umap, xml2, zoo, yaml Suggests: knitr, rmarkdown, testthat, OmaDB License: MIT + file LICENSE Archs: x64 MD5sum: 996b6cbff12084eba1c329f39878b867 NeedsCompilation: no Title: PhyloProfile Description: PhyloProfile is a tool for exploring complex phylogenetic profiles. Phylogenetic profiles, presence/absence patterns of genes over a set of species, are commonly used to trace the functional and evolutionary history of genes across species and time. With PhyloProfile we can enrich regular phylogenetic profiles with further data like sequence/structure similarity, to make phylogenetic profiling more meaningful. Besides the interactive visualisation powered by R-Shiny, the package offers a set of further analysis features to gain insights like the gene age estimation or core gene identification. biocViews: Software, Visualization, DataRepresentation, MultipleComparison, FunctionalPrediction, DimensionReduction Author: Vinh Tran [aut, cre] (ORCID: ), Bastian Greshake Tzovaras [aut], Ingo Ebersberger [aut], Carla Mölbert [ctb] Maintainer: Vinh Tran URL: https://github.com/BIONF/PhyloProfile/ VignetteBuilder: knitr BugReports: https://github.com/BIONF/PhyloProfile/issues git_url: https://git.bioconductor.org/packages/PhyloProfile git_branch: RELEASE_3_22 git_last_commit: aa891b5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PhyloProfile_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PhyloProfile_2.1.8.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PhyloProfile_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PhyloProfile_2.2.0.tgz vignettes: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.html vignetteTitles: PhyloProfile hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.R dependencyCount: 131 Package: phyloseq Version: 1.54.0 Depends: R (>= 3.3.0) Imports: ade4 (>= 1.7-4), ape (>= 5.0), Biobase (>= 2.36.2), BiocGenerics (>= 0.22.0), biomformat (>= 1.0.0), Biostrings (>= 2.40.0), cluster (>= 2.0.4), data.table (>= 1.10.4), foreach (>= 1.4.3), ggplot2 (>= 2.1.0), igraph (>= 1.0.1), methods (>= 3.3.0), multtest (>= 2.28.0), plyr (>= 1.8.3), reshape2 (>= 1.4.1), scales (>= 0.4.0), vegan (>= 2.5) Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58), knitr (>= 1.16), magrittr (>= 1.5), metagenomeSeq (>= 1.14), rmarkdown (>= 1.6), testthat (>= 1.0.2) Enhances: doParallel (>= 1.0.10) License: AGPL-3 MD5sum: fdf4b791b6253fef2fe28a3e67e5a9ba NeedsCompilation: no Title: Handling and analysis of high-throughput microbiome census data Description: phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Clustering, Classification, MultipleComparison, GeneticVariability Author: Paul J. McMurdie , Susan Holmes , with contributions from Gregory Jordan and Scott Chamberlain Maintainer: Paul J. McMurdie URL: http://dx.plos.org/10.1371/journal.pone.0061217 VignetteBuilder: knitr BugReports: https://github.com/joey711/phyloseq/issues git_url: https://git.bioconductor.org/packages/phyloseq git_branch: RELEASE_3_22 git_last_commit: 457587e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/phyloseq_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/phyloseq_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phyloseq_1.54.0.tgz vignettes: vignettes/phyloseq/inst/doc/phyloseq-analysis.html, vignettes/phyloseq/inst/doc/phyloseq-basics.html, vignettes/phyloseq/inst/doc/phyloseq-FAQ.html, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html vignetteTitles: analysis vignette, phyloseq basics vignette, phyloseq Frequently Asked Questions (FAQ), phyloseq and DESeq2 on Colorectal Cancer Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phyloseq/inst/doc/phyloseq-analysis.R, vignettes/phyloseq/inst/doc/phyloseq-basics.R, vignettes/phyloseq/inst/doc/phyloseq-FAQ.R, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.R dependsOnMe: microbiome, SIAMCAT, MiscMetabar, phyloseqGraphTest importsMe: ADAPT, combi, dar, DspikeIn, FinfoMDS, MBECS, microbiomeDASim, PathoStat, RCM, reconsi, RPA, SimBu, SPsimSeq, zitools, HMP2Data, adaptiveGPCA, BaHZING, breakaway, chem16S, eDNAfuns, FLORAL, holobiont, HybridMicrobiomes, microbial, mixKernel, multimedia, Rsearch, speedytax, structSSI, TaxaNorm, treeDA suggestsMe: ANCOMBC, decontam, lefser, MGnifyR, mia, MicrobiotaProcess, philr, HMP16SData, corncob, demulticoder, FAVA, fido, file2meco, MIDASim, parafac4microbiome, pctax, phyloregion, readyomics, SQMtools dependencyCount: 69 Package: piano Version: 2.26.0 Depends: R (>= 3.5) Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray, fgsea, shiny, DT, htmlwidgets, shinyjs, shinydashboard, visNetwork, scales, grDevices, graphics, stats, utils, methods Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM, gtools, biomaRt, snowfall, AnnotationDbi, knitr, rmarkdown, BiocStyle License: GPL (>=2) Archs: x64 MD5sum: 78bbb821a9afbd140c0a97f4a78e78bf NeedsCompilation: no Title: Platform for integrative analysis of omics data Description: Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses. biocViews: Microarray, Preprocessing, QualityControl, DifferentialExpression, Visualization, GeneExpression, GeneSetEnrichment, Pathways Author: Leif Varemo Wigge and Intawat Nookaew Maintainer: Leif Varemo Wigge URL: http://www.sysbio.se/piano VignetteBuilder: knitr BugReports: https://github.com/varemo/piano/issues git_url: https://git.bioconductor.org/packages/piano git_branch: RELEASE_3_22 git_last_commit: ce8c4de git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/piano_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/piano_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/piano_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/piano_2.26.0.tgz vignettes: vignettes/piano/inst/doc/piano-vignette.pdf, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.html vignetteTitles: Piano - Platform for Integrative Analysis of Omics data, Running gene-set anaysis with piano hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/piano/inst/doc/piano-vignette.R, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.R importsMe: CoreGx, PDATK, SmartPhos suggestsMe: cosmosR, BloodCancerMultiOmics2017 dependencyCount: 94 Package: PICB Version: 1.2.0 Imports: utils, Seqinfo, GenomicRanges, GenomicAlignments, Biostrings, Rsamtools, data.table, IRanges, seqinr, stats, openxlsx, dplyr, S4Vectors, methods Suggests: GenomeInfoDb, knitr, rtracklayer, testthat, BiocStyle, prettydoc, BSgenome, BSgenome.Dmelanogaster.UCSC.dm6, BiocManager, rmarkdown, ggplot2 License: CC0 Archs: x64 MD5sum: 953db5a8a3497e13275a59057f0f75e9 NeedsCompilation: no Title: piRNA Cluster Builder Description: piRNAs (short for PIWI-interacting RNAs) and their PIWI protein partners play a key role in fertility and maintaining genome integrity by restricting mobile genetic elements (transposons) in germ cells. piRNAs originate from genomic regions known as piRNA clusters. The piRNA Cluster Builder (PICB) is a versatile toolkit designed to identify genomic regions with a high density of piRNAs. It constructs piRNA clusters through a stepwise integration of unique and multimapping piRNAs and offers wide-ranging parameter settings, supported by an optimization function that allows users to test different parameter combinations to tailor the analysis to their specific piRNA system. The output includes extensive metadata columns, enabling researchers to rank clusters and extract cluster characteristics. biocViews: Genetics, GenomeAnnotation, Sequencing, FunctionalPrediction, Coverage, Transcriptomics Author: Pavol Genzor [aut], Aleksandr Friman [aut], Daniel Stoyko [aut], Parthena Konstantinidou [aut], Franziska Ahrend [aut, cre] (ORCID: ), Zuzana Loubalova [aut], Yuejun Wang [aut], Hernan Lorenzi [aut], Astrid D Haase [aut] Maintainer: Franziska Ahrend URL: https://github.com/HaaseLab/PICB VignetteBuilder: knitr BugReports: https://github.com/HaaseLab/PICB/issues git_url: https://git.bioconductor.org/packages/PICB git_branch: RELEASE_3_22 git_last_commit: b93bc74 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PICB_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PICB_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PICB_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PICB_1.2.0.tgz vignettes: vignettes/PICB/inst/doc/PICB.html vignetteTitles: Introduction to the piRNA Cluster Builder (PICB) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PICB/inst/doc/PICB.R dependencyCount: 69 Package: pickgene Version: 1.82.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 0e703091270e8d69a7bc0211fbb9565e NeedsCompilation: no Title: Adaptive Gene Picking for Microarray Expression Data Analysis Description: Functions to Analyze Microarray (Gene Expression) Data. biocViews: Microarray, DifferentialExpression Author: Brian S. Yandell Maintainer: Brian S. Yandell URL: http://www.stat.wisc.edu/~yandell/statgen git_url: https://git.bioconductor.org/packages/pickgene git_branch: RELEASE_3_22 git_last_commit: da16f28 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pickgene_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pickgene_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pickgene_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pickgene_1.82.0.tgz vignettes: vignettes/pickgene/inst/doc/pickgene.pdf vignetteTitles: Adaptive Gene Picking for Microarray Expression Data Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: pipeComp Version: 1.20.0 Depends: R (>= 4.1) Imports: BiocParallel, S4Vectors, ComplexHeatmap, SingleCellExperiment, SummarizedExperiment, Seurat, matrixStats, Matrix, cluster, aricode, methods, utils, dplyr, grid, scales, scran, viridisLite, clue, randomcoloR, ggplot2, cowplot, intrinsicDimension, scater, knitr, reshape2, stats, Rtsne, uwot, circlize, RColorBrewer Suggests: BiocStyle, rmarkdown License: GPL MD5sum: a1b1fede82d54b780615884f13ffa44b NeedsCompilation: no Title: pipeComp pipeline benchmarking framework Description: A simple framework to facilitate the comparison of pipelines involving various steps and parameters. The `pipelineDefinition` class represents pipelines as, minimally, a set of functions consecutively executed on the output of the previous one, and optionally accompanied by step-wise evaluation and aggregation functions. Given such an object, a set of alternative parameters/methods, and benchmark datasets, the `runPipeline` function then proceeds through all combinations arguments, avoiding recomputing the same step twice and compiling evaluations on the fly to avoid storing potentially large intermediate data. biocViews: GeneExpression, Transcriptomics, Clustering, DataRepresentation Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Anthony Sonrel [aut] (ORCID: ), Mark D. Robinson [aut, fnd] (ORCID: ) Maintainer: Pierre-Luc Germain URL: https://doi.org/10.1186/s13059-020-02136-7 VignetteBuilder: knitr BugReports: https://github.com/plger/pipeComp git_url: https://git.bioconductor.org/packages/pipeComp git_branch: RELEASE_3_22 git_last_commit: e37c3d3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pipeComp_1.20.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pipeComp_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pipeComp_1.20.0.tgz vignettes: vignettes/pipeComp/inst/doc/pipeComp_dea.html, vignettes/pipeComp/inst/doc/pipeComp_scRNA.html, vignettes/pipeComp/inst/doc/pipeComp.html vignetteTitles: pipeComp_dea, pipeComp_scRNA, pipeComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pipeComp/inst/doc/pipeComp_dea.R, vignettes/pipeComp/inst/doc/pipeComp_scRNA.R, vignettes/pipeComp/inst/doc/pipeComp.R dependencyCount: 215 Package: pipeFrame Version: 1.26.0 Depends: R (>= 4.0.0), Imports: BSgenome, digest, visNetwork, magrittr, methods, Biostrings, Seqinfo, parallel, stats, utils, rmarkdown Suggests: BiocManager, knitr, rtracklayer, testthat, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: df961544c66d21390fcedfcea7c55300 NeedsCompilation: no Title: Pipeline framework for bioinformatics in R Description: pipeFrame is an R package for building a componentized bioinformatics pipeline. Each step in this pipeline is wrapped in the framework, so the connection among steps is created seamlessly and automatically. Users could focus more on fine-tuning arguments rather than spending a lot of time on transforming file format, passing task outputs to task inputs or installing the dependencies. Componentized step elements can be customized into other new pipelines flexibly as well. This pipeline can be split into several important functional steps, so it is much easier for users to understand the complex arguments from each step rather than parameter combination from the whole pipeline. At the same time, componentized pipeline can restart at the breakpoint and avoid rerunning the whole pipeline, which may save a lot of time for users on pipeline tuning or such issues as power off or process other interrupts. biocViews: Software, Infrastructure, WorkflowStep Author: Zheng Wei, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/pipeFrame VignetteBuilder: knitr BugReports: https://github.com/wzthu/pipeFrame/issues git_url: https://git.bioconductor.org/packages/pipeFrame git_branch: RELEASE_3_22 git_last_commit: baee5fd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pipeFrame_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pipeFrame_1.25.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pipeFrame_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pipeFrame_1.26.0.tgz vignettes: vignettes/pipeFrame/inst/doc/pipeFrame.html vignetteTitles: An Introduction to pipeFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pipeFrame/inst/doc/pipeFrame.R dependsOnMe: esATAC dependencyCount: 83 Package: PIPETS Version: 1.6.0 Depends: R (>= 4.4.0) Imports: dplyr, utils, stats, GenomicRanges, BiocGenerics, methods Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: f8b1736db65f64ee278859c152f872fb NeedsCompilation: no Title: Poisson Identification of PEaks from Term-Seq data Description: PIPETS provides statistically robust analysis for 3'-seq/term-seq data. It utilizes a sliding window approach to apply a Poisson Distribution test to identify genomic positions with termination read coverage that is significantly higher than the surrounding signal. PIPETS then condenses proximal signal and produces strand specific results that contain all significant termination peaks. biocViews: Sequencing, Transcription, GeneRegulation, PeakDetection, Genetics, Transcriptomics, Coverage Author: Quinlan Furumo [aut, cre] (ORCID: ) Maintainer: Quinlan Furumo URL: https://github.com/qfurumo/PIPETS VignetteBuilder: knitr BugReports: https://github.com/qfurumo/PIPETS/issues git_url: https://git.bioconductor.org/packages/PIPETS git_branch: RELEASE_3_22 git_last_commit: 6b112c5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PIPETS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PIPETS_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PIPETS_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PIPETS_1.6.0.tgz vignettes: vignettes/PIPETS/inst/doc/PIPETS.html vignetteTitles: PIPETS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PIPETS/inst/doc/PIPETS.R dependencyCount: 26 Package: PIUMA Version: 1.5.0 Depends: R (>= 4.3), ggplot2 Imports: cluster, umap, tsne, kernlab, vegan, dbscan, igraph, scales, Hmisc, patchwork, grDevices, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-3 + file LICENSE MD5sum: 7248bbbbd4ca946776dd009ed3c8330a NeedsCompilation: no Title: Phenotypes Identification Using Mapper from topological data Analysis Description: The PIUMA package offers a tidy pipeline of Topological Data Analysis frameworks to identify and characterize communities in high and heterogeneous dimensional data. biocViews: Clustering, GraphAndNetwork, DimensionReduction, Network, Classification Author: Mattia Chiesa [aut, cre] (ORCID: ), Arianna Dagliati [aut] (ORCID: ), Alessia Gerbasi [aut] (ORCID: ), Giuseppe Albi [aut], Laura Ballarini [aut], Luca Piacentini [aut] (ORCID: ) Maintainer: Mattia Chiesa URL: https://github.com/BioinfoMonzino/PIUMA VignetteBuilder: knitr BugReports: https://github.com/BioinfoMonzino/PIUMA/issues git_url: https://git.bioconductor.org/packages/PIUMA git_branch: devel git_last_commit: a82e3e6 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/PIUMA_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PIUMA_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PIUMA_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PIUMA_1.5.0.tgz vignettes: vignettes/PIUMA/inst/doc/PIUMA_vignette.html vignetteTitles: PIUMA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PIUMA/inst/doc/PIUMA_vignette.R dependencyCount: 104 Package: planet Version: 1.18.0 Depends: R (>= 4.3) Imports: methods, tibble, magrittr, dplyr Suggests: ExperimentHub, mixOmics, ggplot2, testthat, tidyr, scales, minfi, EpiDISH, knitr, rmarkdown License: GPL-2 MD5sum: 5707e0e96cf7f827f5c78b173438a036 NeedsCompilation: no Title: Placental DNA methylation analysis tools Description: This package contains R functions to predict biological variables to from placnetal DNA methylation data generated from infinium arrays. This includes inferring ethnicity/ancestry, gestational age, and cell composition from placental DNA methylation array (450k/850k) data. biocViews: Software, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, DNAMethylation, CpGIsland Author: Victor Yuan [aut, cre], Wendy P. Robinson [aut, ctb], Icíar Fernández-Boyano [aut, ctb] Maintainer: Victor Yuan URL: http://github.com/wvictor14/planet, http://victoryuan.com/planet/ VignetteBuilder: knitr BugReports: http://github.com/wvictor14/planet/issues git_url: https://git.bioconductor.org/packages/planet git_branch: RELEASE_3_22 git_last_commit: 40a737f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/planet_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/planet_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/planet_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/planet_1.18.0.tgz vignettes: vignettes/planet/inst/doc/planet.html vignetteTitles: planet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/planet/inst/doc/planet.R importsMe: methylclock suggestsMe: eoPredData dependencyCount: 19 Package: planttfhunter Version: 1.10.0 Depends: R (>= 4.2.0) Imports: Biostrings, SummarizedExperiment, utils, methods Suggests: BiocStyle, covr, sessioninfo, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 183821066a7ce8bc2ab5b9d51064f097 NeedsCompilation: no Title: Identification and classification of plant transcription factors Description: planttfhunter is used to identify plant transcription factors (TFs) from protein sequence data and classify them into families and subfamilies using the classification scheme implemented in PlantTFDB. TFs are identified using pre-built hidden Markov model profiles for DNA-binding domains. Then, auxiliary and forbidden domains are used with DNA-binding domains to classify TFs into families and subfamilies (when applicable). Currently, TFs can be classified in 58 different TF families/subfamilies. biocViews: Software, Transcription, FunctionalPrediction, GenomeAnnotation, FunctionalGenomics, HiddenMarkovModel, Sequencing, Classification Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/planttfhunter SystemRequirements: HMMER VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/planttfhunter git_url: https://git.bioconductor.org/packages/planttfhunter git_branch: RELEASE_3_22 git_last_commit: 77ef3f0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/planttfhunter_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/planttfhunter_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/planttfhunter_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/planttfhunter_1.10.0.tgz vignettes: vignettes/planttfhunter/inst/doc/vignette_planttfhunter.html vignetteTitles: Genome-wide identification and classification of transcription factors in plant genomes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/planttfhunter/inst/doc/vignette_planttfhunter.R dependencyCount: 27 Package: plasmut Version: 1.8.0 Depends: R (>= 4.3.0) Imports: tibble, stats, dplyr Suggests: knitr, rmarkdown, tidyverse, ggrepel, magrittr, qpdf, BiocStyle, biocViews, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 9545d82f2703bf9ec1b6cd2746013773 NeedsCompilation: no Title: Stratifying mutations observed in cell-free DNA and white blood cells as germline, hematopoietic, or somatic Description: A Bayesian method for quantifying the liklihood that a given plasma mutation arises from clonal hematopoesis or the underlying tumor. It requires sequencing data of the mutation in plasma and white blood cells with the number of distinct and mutant reads in both tissues. We implement a Monte Carlo importance sampling method to assess the likelihood that a mutation arises from the tumor relative to non-tumor origin. biocViews: Bayesian, SomaticMutation, GermlineMutation, Sequencing Author: Adith Arun [aut, cre], Robert Scharpf [aut] Maintainer: Adith Arun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/plasmut git_branch: RELEASE_3_22 git_last_commit: 6901916 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/plasmut_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plasmut_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plasmut_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plasmut_1.8.0.tgz vignettes: vignettes/plasmut/inst/doc/plasmut.html vignetteTitles: Modeling the origin of mutations in a liquid biopsy: cancer or clonal hematopoiesis? hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plasmut/inst/doc/plasmut.R dependencyCount: 20 Package: plgem Version: 1.82.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS, methods License: GPL-2 MD5sum: 3370378aec05cfc2f3f6a44f088aff12 NeedsCompilation: no Title: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM) Description: The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets. biocViews: ImmunoOncology, Microarray, DifferentialExpression, Proteomics, GeneExpression, MassSpectrometry Author: Mattia Pelizzola and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it git_url: https://git.bioconductor.org/packages/plgem git_branch: RELEASE_3_22 git_last_commit: 57535f8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/plgem_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plgem_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plgem_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plgem_1.82.0.tgz vignettes: vignettes/plgem/inst/doc/plgem.pdf vignetteTitles: An introduction to PLGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plgem/inst/doc/plgem.R importsMe: INSPEcT dependencyCount: 9 Package: plier Version: 1.80.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) MD5sum: e0de3e20bab58cbff357e3d4a5fbb284 NeedsCompilation: yes Title: Implements the Affymetrix PLIER algorithm Description: The PLIER (Probe Logarithmic Error Intensity Estimate) method produces an improved signal by accounting for experimentally observed patterns in probe behavior and handling error at the appropriately at low and high signal values. biocViews: Software Author: Affymetrix Inc., Crispin J Miller, PICR Maintainer: Crispin Miller git_url: https://git.bioconductor.org/packages/plier git_branch: RELEASE_3_22 git_last_commit: 14a702b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/plier_1.80.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plier_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plier_1.80.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plier_1.80.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano dependencyCount: 12 Package: plotgardener Version: 1.16.0 Depends: R (>= 4.2.0) Imports: curl, data.table, dplyr, GenomeInfoDb, GenomicRanges, glue, grDevices, grid, ggplotify, IRanges, methods, plyranges, purrr, Rcpp, RColorBrewer, rhdf5, rlang, stats, strawr, tools, utils, withr LinkingTo: Rcpp Suggests: AnnotationDbi, AnnotationHub, BSgenome, BSgenome.Hsapiens.UCSC.hg19, ComplexHeatmap, GenomicFeatures, ggplot2, InteractionSet, knitr, org.Hs.eg.db, rtracklayer, plotgardenerData, pdftools, png, rmarkdown, scales, showtext, testthat (>= 3.0.0), TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene License: MIT + file LICENSE MD5sum: 158c15b4f98786dc04d82111ce3441c5 NeedsCompilation: yes Title: Coordinate-Based Genomic Visualization Package for R Description: Coordinate-based genomic visualization package for R. It grants users the ability to programmatically produce complex, multi-paneled figures. Tailored for genomics, plotgardener allows users to visualize large complex genomic datasets and provides exquisite control over how plots are placed and arranged on a page. biocViews: Visualization, GenomeAnnotation, FunctionalGenomics, GenomeAssembly, HiC Author: Nicole Kramer [aut, cre], Eric S. Davis [aut], Craig Wenger [aut], Sarah Parker [ctb], Erika Deoudes [art], Michael Love [ctb], Douglas H. Phanstiel [aut, cre, cph] Maintainer: Nicole Kramer , Douglas Phanstiel URL: https://phanstiellab.github.io/plotgardener, https://github.com/PhanstielLab/plotgardener VignetteBuilder: knitr BugReports: https://github.com/PhanstielLab/plotgardener/issues git_url: https://git.bioconductor.org/packages/plotgardener git_branch: RELEASE_3_22 git_last_commit: eb3d8c8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/plotgardener_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plotgardener_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plotgardener_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plotgardener_1.16.0.tgz vignettes: vignettes/plotgardener/inst/doc/introduction_to_plotgardener.html vignetteTitles: Introduction to plotgardener hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/plotgardener/inst/doc/introduction_to_plotgardener.R importsMe: DegCre, mariner suggestsMe: nullranges, rigvf dependencyCount: 95 Package: plotGrouper Version: 1.28.0 Depends: R (>= 3.5) Imports: ggplot2 (>= 3.0.0), dplyr (>= 0.7.6), tidyr (>= 0.2.0), tibble (>= 1.4.2), stringr (>= 1.3.1), readr (>= 1.1.1), readxl (>= 1.1.0), scales (>= 1.0.0), stats, grid, gridExtra (>= 2.3), egg (>= 0.4.0), gtable (>= 0.2.0), ggpubr (>= 0.1.8), shiny (>= 1.1.0), shinythemes (>= 1.1.1), colourpicker (>= 1.0), magrittr (>= 1.5), Hmisc (>= 4.1.1), rlang (>= 0.2.2) Suggests: knitr, htmltools, BiocStyle, rmarkdown, testthat License: GPL-3 MD5sum: e4401bb2c96c670a81a0923ccce88489 NeedsCompilation: no Title: Shiny app GUI wrapper for ggplot with built-in statistical analysis Description: A shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed). biocViews: ImmunoOncology, FlowCytometry, GraphAndNetwork, StatisticalMethod, DataImport, GUI, MultipleComparison Author: John D. Gagnon [aut, cre] Maintainer: John D. Gagnon URL: https://jdgagnon.github.io/plotGrouper/ VignetteBuilder: knitr BugReports: https://github.com/jdgagnon/plotGrouper/issues git_url: https://git.bioconductor.org/packages/plotGrouper git_branch: RELEASE_3_22 git_last_commit: 35a0af6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/plotGrouper_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plotGrouper_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plotGrouper_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plotGrouper_1.28.0.tgz vignettes: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.html vignetteTitles: plotGrouper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.R dependencyCount: 135 Package: PLPE Version: 1.70.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: 15fbec14fb6549878649e91110dc6c31 NeedsCompilation: no Title: Local Pooled Error Test for Differential Expression with Paired High-throughput Data Description: This package performs tests for paired high-throughput data. biocViews: Proteomics, Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/PLPE git_branch: RELEASE_3_22 git_last_commit: efb5c2a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PLPE_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PLPE_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PLPE_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PLPE_1.70.0.tgz vignettes: vignettes/PLPE/inst/doc/PLPE.pdf vignetteTitles: PLPE Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLPE/inst/doc/PLPE.R dependencyCount: 10 Package: PLSDAbatch Version: 1.6.0 Depends: R (>= 4.3.0) Imports: mixOmics, scales, Rdpack, ggplot2, gridExtra, ggpubr, lmerTest, performance, grid, stats, pheatmap, vegan, Biobase, BiocStyle, TreeSummarizedExperiment Suggests: knitr, rmarkdown, testthat, badger License: GPL-3 MD5sum: f9ada93dfb0b4bf835599a74c6abc632 NeedsCompilation: no Title: PLSDA-batch Description: A novel framework to correct for batch effects prior to any downstream analysis in microbiome data based on Projection to Latent Structures Discriminant Analysis. The main method is named “PLSDA-batch”. It first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data whilst preserving biological variation of interest. PLSDA-batch is highly suitable for microbiome data as it is non-parametric, multivariate and allows for ordination and data visualisation. Combined with centered log-ratio transformation for addressing uneven library sizes and compositional structure, PLSDA-batch addresses all characteristics of microbiome data that existing correction methods have ignored so far. Two other variants are proposed for 1/ unbalanced batch x treatment designs that are commonly encountered in studies with small sample sizes, and for 2/ selection of discriminative variables amongst treatment groups to avoid overfitting in classification problems. These two variants have widened the scope of applicability of PLSDA-batch to different data settings. biocViews: StatisticalMethod, DimensionReduction, PrincipalComponent, Classification, Microbiome, BatchEffect, Normalization, Visualization Author: Yiwen (Eva) Wang [aut, cre] (ORCID: ), Kim-Anh Le Cao [aut] Maintainer: Yiwen (Eva) Wang URL: https://github.com/EvaYiwenWang/PLSDAbatch VignetteBuilder: knitr BugReports: https://github.com/EvaYiwenWang/PLSDAbatch/issues/ git_url: https://git.bioconductor.org/packages/PLSDAbatch git_branch: RELEASE_3_22 git_last_commit: 77783a7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PLSDAbatch_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PLSDAbatch_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PLSDAbatch_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PLSDAbatch_1.6.0.tgz vignettes: vignettes/PLSDAbatch/inst/doc/brief_vignette.html vignetteTitles: PLSDA-batch Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLSDAbatch/inst/doc/brief_vignette.R dependencyCount: 154 Package: plyinteractions Version: 1.8.0 Depends: R (>= 4.3.0) Imports: InteractionSet, Seqinfo, BiocGenerics, GenomicRanges, plyranges, IRanges, S4Vectors, rlang, dplyr, tibble, tidyselect, methods, utils Suggests: tidyverse, BSgenome.Mmusculus.UCSC.mm10, Biostrings, BiocParallel, GenomeInfoDb, scales, HiContactsData, rtracklayer, BiocStyle, covr, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0), RefManageR License: Artistic-2.0 MD5sum: 4f6b5839866eccae821bdc84f63f4236 NeedsCompilation: no Title: Extending tidy verbs to genomic interactions Description: Operate on `GInteractions` objects as tabular data using `dplyr`-like verbs. The functions and methods in `plyinteractions` provide a grammatical approach to manipulate `GInteractions`, to facilitate their integration in genomic analysis workflows. biocViews: Software, Infrastructure Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://github.com/js2264/plyinteractions VignetteBuilder: knitr BugReports: https://github.com/js2264/plyinteractions/issues git_url: https://git.bioconductor.org/packages/plyinteractions git_branch: RELEASE_3_22 git_last_commit: 18a50a3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/plyinteractions_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plyinteractions_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plyinteractions_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plyinteractions_1.8.0.tgz vignettes: vignettes/plyinteractions/inst/doc/plyinteractions.html, vignettes/plyinteractions/inst/doc/process_pairs.html vignetteTitles: plyinteractions, HiCarithmetic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plyinteractions/inst/doc/plyinteractions.R, vignettes/plyinteractions/inst/doc/process_pairs.R importsMe: OHCA suggestsMe: tidyomics dependencyCount: 73 Package: plyranges Version: 1.30.0 Depends: R (>= 3.5), BiocGenerics, IRanges (>= 2.12.0), GenomicRanges (>= 1.28.4) Imports: methods, dplyr, rlang (>= 0.2.0), magrittr, tidyselect (>= 1.0.0), rtracklayer, GenomicAlignments, Seqinfo, Rsamtools, S4Vectors (>= 0.23.10), utils Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 2.1.0), HelloRanges, HelloRangesData, BSgenome.Hsapiens.UCSC.hg19, pasillaBamSubset, covr, ggplot2 License: Artistic-2.0 MD5sum: 2feb7f423a46d8f42ad0330bedf3287a NeedsCompilation: no Title: A fluent interface for manipulating GenomicRanges Description: A dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessiblity for new Bioconductor users is hopefully increased. biocViews: Infrastructure, DataRepresentation, WorkflowStep, Coverage Author: Stuart Lee [aut] (ORCID: ), Michael Lawrence [aut, ctb], Dianne Cook [aut, ctb], Spencer Nystrom [ctb] (ORCID: ), Pierre-Paul Axisa [ctb], Michael Love [ctb, cre] Maintainer: Michael Love VignetteBuilder: knitr BugReports: https://github.com/tidyomics/plyranges git_url: https://git.bioconductor.org/packages/plyranges git_branch: RELEASE_3_22 git_last_commit: 6243859 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/plyranges_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plyranges_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plyranges_1.30.0.tgz vignettes: vignettes/plyranges/inst/doc/an-introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plyranges/inst/doc/an-introduction.R importsMe: BOBaFIT, BUSpaRse, cfDNAPro, Damsel, GenomicPlot, InPAS, katdetectr, mariner, methylCC, multicrispr, nearBynding, nullranges, plotgardener, plyinteractions, SingleMoleculeFootprinting, STADyUM, tidyomics, fluentGenomics suggestsMe: EpiCompare, extraChIPs, memes, rigvf, svaNUMT, svaRetro, tidyCoverage, CTCF dependencyCount: 70 Package: plyxp Version: 1.4.0 Depends: R (>= 4.5.0) Imports: dplyr, purrr, rlang, SummarizedExperiment, tidyr, tidyselect, vctrs, tibble, pillar, cli, glue, S7, S4Vectors, utils, methods Suggests: devtools, knitr, rmarkdown, testthat, airway, IRanges, here License: MIT + file LICENSE MD5sum: 22664eaa1f2efd4d65e5f465abf0935f NeedsCompilation: no Title: Data masks for SummarizedExperiment enabling dplyr-like manipulation Description: The package provides `rlang` data masks for the SummarizedExperiment class. The enables the evaluation of unquoted expression in different contexts of the SummarizedExperiment object with optional access to other contexts. The goal for `plyxp` is for evaluation to feel like a data.frame object without ever needing to unwind to a rectangular data.frame. biocViews: Annotation, GenomeAnnotation, Transcriptomics Author: Justin Landis [aut, cre] (ORCID: ), Michael Love [aut] (ORCID: ) Maintainer: Justin Landis URL: https://github.com/jtlandis/plyxp, https://jtlandis.github.io/plyxp VignetteBuilder: knitr BugReports: https://www.github.com/jtlandis/plyxp/issues git_url: https://git.bioconductor.org/packages/plyxp git_branch: RELEASE_3_22 git_last_commit: 6fc93b3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/plyxp_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plyxp_1.3.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plyxp_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plyxp_1.4.0.tgz vignettes: vignettes/plyxp/inst/doc/plyxp.html vignetteTitles: plyxp Usage Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/plyxp/inst/doc/plyxp.R importsMe: tidySummarizedExperiment dependencyCount: 45 Package: pmm Version: 1.42.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: 4fef56ad028e95ec007e711ffd47153d NeedsCompilation: no Title: Parallel Mixed Model Description: The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines. biocViews: SystemsBiology, Regression Author: Anna Drewek Maintainer: Anna Drewek git_url: https://git.bioconductor.org/packages/pmm git_branch: RELEASE_3_22 git_last_commit: de74872 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pmm_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pmm_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pmm_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pmm_1.42.0.tgz vignettes: vignettes/pmm/inst/doc/pmm-package.pdf vignetteTitles: User manual for R-Package PMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmm/inst/doc/pmm-package.R dependencyCount: 22 Package: pmp Version: 1.21.0 Depends: R (>= 4.0) Imports: stats, impute, pcaMethods, missForest, ggplot2, methods, SummarizedExperiment, S4Vectors, matrixStats, grDevices, reshape2, utils Suggests: testthat, covr, knitr, rmarkdown, BiocStyle, gridExtra, magick License: GPL-3 MD5sum: 1004c01b28d94398ee19539a995bf935 NeedsCompilation: no Title: Peak Matrix Processing and signal batch correction for metabolomics datasets Description: Methods and tools for (pre-)processing of metabolomics datasets (i.e. peak matrices), including filtering, normalisation, missing value imputation, scaling, and signal drift and batch effect correction methods. Filtering methods are based on: the fraction of missing values (across samples or features); Relative Standard Deviation (RSD) calculated from the Quality Control (QC) samples; the blank samples. Normalisation methods include Probabilistic Quotient Normalisation (PQN) and normalisation to total signal intensity. A unified user interface for several commonly used missing value imputation algorithms is also provided. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and a constant small value. The generalised logarithm (glog) transformation algorithm is available to stabilise the variance across low and high intensity mass spectral features. Finally, this package provides an implementation of the Quality Control-Robust Spline Correction (QCRSC) algorithm for signal drift and batch effect correction of mass spectrometry-based datasets. biocViews: MassSpectrometry, Metabolomics, Software, QualityControl, BatchEffect Author: Andris Jankevics [aut], Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pmp git_branch: devel git_last_commit: 0899db6 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/pmp_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pmp_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pmp_1.21.0.tgz vignettes: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.html, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.html, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.html vignetteTitles: Peak Matrix Processing for metabolomics datasets, Signal drift and batch effect correction and mass spectral quality assessment, Signal drift and batch effect correction for mass spectrometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.R, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.R, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.R dependencyCount: 64 Package: PMScanR Version: 1.0.0 Imports: dplyr (>= 1.1.0), shiny, bslib, shinyFiles, plotly, rtracklayer, reshape2, ggseqlogo, ggplot2, seqinr, magrittr, rlang, utils, stringr, BiocFileCache Suggests: BiocStyle, knitr, seqLogo, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 31f1cdf3bc207f749f59d672ac5e96f9 NeedsCompilation: no Title: Protein motifs analysis and visualisation Description: Provides tools for large-scale protein motif analysis and visualization in R. PMScanR facilitates the identification of motifs using external tools like PROSITE's ps_scan (handling necessary file downloads and execution) and enables downstream analysis of results. Key features include parsing scan outputs, converting formats (e.g., to GFF-like structures), generating motif occurrence matrices, and creating informative visualizations such as heatmaps, sequence logos (via seqLogo/ggseqlogo). The package also offers an optional Shiny-based graphical user interface for interactive analysis, aiming to streamline the process of exploring motif patterns across multiple protein sequences. biocViews: MotifDiscovery, Visualization Author: Jan Pawel Jastrzebski [aut, cre] (ORCID: ), Monika Gawronska [ctb] (ORCID: ), Wiktor Babis [ctb] (ORCID: ), Miriana Quaranta [ctb] (ORCID: ), Damian Czopek [ctb, aut] (ORCID: ) Maintainer: Jan Pawel Jastrzebski URL: https://github.com/prodakt/PMScanR SystemRequirements: Perl VignetteBuilder: knitr BugReports: https://github.com/prodakt/PMScanR/issues git_url: https://git.bioconductor.org/packages/PMScanR git_branch: RELEASE_3_22 git_last_commit: b9f2642 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PMScanR_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PMScanR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PMScanR_1.0.0.tgz vignettes: vignettes/PMScanR/inst/doc/PMScanR.html vignetteTitles: PMScanR: Protein Motif Scanning and Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PMScanR/inst/doc/PMScanR.R dependencyCount: 137 Package: PoDCall Version: 1.18.0 Depends: R (>= 4.5) Imports: ggplot2, gridExtra, mclust, diptest, rlist, shiny, DT, LaplacesDemon, purrr, shinyjs, readr, grDevices, stats, utils Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: fcfa7d839b7b2862f419aa36473a7ab5 NeedsCompilation: no Title: Positive Droplet Calling for DNA Methylation Droplet Digital PCR Description: Reads files exported from 'QX Manager or QuantaSoft' containing amplitude values from a run of ddPCR (96 well plate) and robustly sets thresholds to determine positive droplets for each channel of each individual well. Concentration and normalized concentration in addition to other metrics is then calculated for each well. Results are returned as a table, optionally written to file, as well as optional plots (scatterplot and histogram) for both channels per well written to file. The package includes a shiny application which provides an interactive and user-friendly interface to the full functionality of PoDCall. biocViews: Classification, Epigenetics, ddPCR, DifferentialMethylation, CpGIsland, DNAMethylation, Author: Hans Petter Brodal [aut, cre], Marine Jeanmougin [aut], Guro Elisabeth Lind [aut] Maintainer: Hans Petter Brodal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoDCall git_branch: RELEASE_3_22 git_last_commit: 2b57b48 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PoDCall_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PoDCall_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PoDCall_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PoDCall_1.18.0.tgz vignettes: vignettes/PoDCall/inst/doc/PoDCall.html vignetteTitles: PoDCall: Positive Droplet Caller for DNA Methylation ddPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PoDCall/inst/doc/PoDCall.R dependencyCount: 84 Package: podkat Version: 1.42.0 Depends: R (>= 3.5.0), methods, Rsamtools (>= 1.99.1), GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats (>= 4.3.0), graphics, grDevices, utils, Biobase, BiocGenerics, Matrix, Seqinfo, IRanges, Biostrings, BSgenome (>= 1.32.0) LinkingTo: Rcpp, Rhtslib (>= 1.15.3) Suggests: BSgenome.Hsapiens.UCSC.hg38.masked, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24), VariantAnnotation, SummarizedExperiment, knitr License: GPL (>= 2) MD5sum: 58f53dcae61303bd8186a92ea456dbe1 NeedsCompilation: yes Title: Position-Dependent Kernel Association Test Description: This package provides an association test that is capable of dealing with very rare and even private variants. This is accomplished by a kernel-based approach that takes the positions of the variants into account. The test can be used for pre-processed matrix data, but also directly for variant data stored in VCF files. Association testing can be performed whole-genome, whole-exome, or restricted to pre-defined regions of interest. The test is complemented by tools for analyzing and visualizing the results. biocViews: Genetics, WholeGenome, Annotation, VariantAnnotation, Sequencing, DataImport Author: Ulrich Bodenhofer [aut, cre] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/podkat SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/podkat git_branch: RELEASE_3_22 git_last_commit: a5e8201 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/podkat_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/podkat_1.41.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/podkat_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/podkat_1.42.0.tgz vignettes: vignettes/podkat/inst/doc/podkat.pdf vignetteTitles: PODKAT - An R Package for Association Testing Involving Rare and Private Variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/podkat/inst/doc/podkat.R dependencyCount: 59 Package: poem Version: 1.2.0 Depends: R (>= 4.1.0) Imports: aricode, BiocNeighbors, BiocParallel, bluster, clevr, clue, cluster, elsa, fclust, igraph, Matrix, matrixStats, mclustcomp, methods, pdist, sp, spdep, stats, utils, SpatialExperiment, SummarizedExperiment Suggests: testthat (>= 3.0.0), BiocStyle, knitr, DT, dplyr, kableExtra, scico, cowplot, ggnetwork, ggplot2, tidyr, STexampleData License: GPL (>= 3) MD5sum: 236ddc5b076e9f2aab99497b7dded6b9 NeedsCompilation: no Title: POpulation-based Evaluation Metrics Description: This package provides a comprehensive set of external and internal evaluation metrics. It includes metrics for assessing partitions or fuzzy partitions derived from clustering results, as well as for evaluating subpopulation identification results within embeddings or graph representations. Additionally, it provides metrics for comparing spatial domain detection results against ground truth labels, and tools for visualizing spatial errors. biocViews: DimensionReduction, Clustering, GraphAndNetwork, Spatial, ATACSeq, SingleCell, RNASeq, Software, Visualization Author: Siyuan Luo [cre, aut] (ORCID: ), Pierre-Luc Germain [aut, ctb] (ORCID: ) Maintainer: Siyuan Luo URL: https://roseyuan.github.io/poem/ VignetteBuilder: knitr BugReports: https://github.com/RoseYuan/poem/issues git_url: https://git.bioconductor.org/packages/poem git_branch: RELEASE_3_22 git_last_commit: e248fd3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/poem_1.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/poem_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/poem_1.2.0.tgz vignettes: vignettes/poem/inst/doc/MetricsInPoem.html, vignettes/poem/inst/doc/poem.html, vignettes/poem/inst/doc/PoemOnSpatialExperiment.html vignetteTitles: MetricsInPoem.html, 1_introduction, 3_SpatialExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/poem/inst/doc/MetricsInPoem.R, vignettes/poem/inst/doc/poem.R, vignettes/poem/inst/doc/PoemOnSpatialExperiment.R dependencyCount: 109 Package: pogos Version: 1.30.0 Depends: R (>= 3.5.0), rjson (>= 0.2.15), httr (>= 1.3.1) Imports: methods, S4Vectors, utils, shiny, ontoProc, ggplot2, graphics Suggests: knitr, DT, ontologyPlot, testthat, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: bc046c1e8ee67c94d6f656eac51b311e NeedsCompilation: no Title: PharmacOGenomics Ontology Support Description: Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies. biocViews: Pharmacogenomics, PooledScreens, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pogos git_branch: RELEASE_3_22 git_last_commit: 924b319 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pogos_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pogos_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pogos_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pogos_1.30.0.tgz vignettes: vignettes/pogos/inst/doc/pogos.html vignetteTitles: pogos -- simple interface to bhklab PharmacoDB with emphasis on ontology hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pogos/inst/doc/pogos.R dependencyCount: 133 Package: PolySTest Version: 1.4.0 Depends: R (>= 4.4.0) Imports: fdrtool (>= 1.2.15), limma (>= 3.61.3), matrixStats (>= 0.57.0), qvalue (>= 2.22.0), shiny (>= 1.5.0), SummarizedExperiment (>= 1.20.0), knitr (>= 1.33), plotly (>= 4.9.4), heatmaply (>= 1.1.1), circlize (>= 0.4.12), UpSetR (>= 1.4.0), gplots (>= 3.1.1), S4Vectors (>= 0.30.0), parallel (>= 4.1.0), grDevices (>= 4.1.0), graphics (>= 4.1.0), stats (>= 4.1.0), utils (>= 4.1.0) Suggests: testthat (>= 3.0.0), BiocStyle License: GPL-2 MD5sum: c080a08f9f9b89e48b170430a8b36479 NeedsCompilation: no Title: PolySTest: Detection of differentially regulated features. Combined statistical testing for data with few replicates and missing values Description: The complexity of high-throughput quantitative omics experiments often leads to low replicates numbers and many missing values. We implemented a new test to simultaneously consider missing values and quantitative changes, which we combined with well-performing statistical tests for high confidence detection of differentially regulated features. The package contains functions to run the test and to visualize the results. biocViews: MassSpectrometry, Proteomics, Software, DifferentialExpression Author: Veit Schwämmle [aut, cre] (ORCID: ) Maintainer: Veit Schwämmle URL: https://github.com/computproteomics/PolySTest VignetteBuilder: knitr BugReports: https://github.com/computproteomics/PolySTest/issues git_url: https://git.bioconductor.org/packages/PolySTest git_branch: RELEASE_3_22 git_last_commit: 2c8f878 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PolySTest_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PolySTest_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PolySTest_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PolySTest_1.4.0.tgz vignettes: vignettes/PolySTest/inst/doc/StatisticalTest.html vignetteTitles: PolySTest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PolySTest/inst/doc/StatisticalTest.R dependencyCount: 137 Package: Polytect Version: 1.2.0 Depends: R (>= 4.4.0) Imports: stats, utils, grDevices, mvtnorm, sn, dplyr, flowPeaks, ggplot2, tidyverse, cowplot, mlrMBO, DiceKriging, smoof, ParamHelpers, lhs, rgenoud, BiocManager Suggests: testthat (>= 3.0.0), knitr, rmarkdown, ddPCRclust License: Artistic-2.0 MD5sum: 1ec30217f0d601aabe811a5914776e7d NeedsCompilation: no Title: An R package for digital data clustering Description: Polytect is an advanced computational tool designed for the analysis of multi-color digital PCR data. It provides automatic clustering and labeling of partitions into distinct groups based on clusters first identified by the flowPeaks algorithm. Polytect is particularly useful for researchers in molecular biology and bioinformatics, enabling them to gain deeper insights into their experimental results through precise partition classification and data visualization. biocViews: ddPCR, Clustering, MultiChannel, Classification Author: Yao Chen [aut, cre] (ORCID: ) Maintainer: Yao Chen URL: https://github.com/emmachenlingo/Polytect VignetteBuilder: knitr BugReports: https://github.com/emmachenlingo/Polytect/issues git_url: https://git.bioconductor.org/packages/Polytect git_branch: RELEASE_3_22 git_last_commit: 8145906 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Polytect_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Polytect_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Polytect_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Polytect_1.2.0.tgz vignettes: vignettes/Polytect/inst/doc/introduction.pdf vignetteTitles: Polytect Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Polytect/inst/doc/introduction.R dependencyCount: 137 Package: POMA Version: 1.20.0 Depends: R (>= 4.0) Imports: broom, caret, ComplexHeatmap, dbscan, dplyr, DESeq2, fgsea, FSA, ggcorrplot, ggplot2, ggrepel, glmnet, grid, impute, janitor, limma, lme4, magrittr, MASS, mixOmics, multcomp, msigdbr, purrr, randomForest, RankProd (>= 3.14), rlang, SummarizedExperiment, sva, tibble, tidyr, utils, uwot, vegan Suggests: BiocStyle, covr, ggraph, ggtext, knitr, patchwork, plotly, tidyverse, testthat (>= 2.3.2) License: GPL-3 MD5sum: 2e2c8a7a8c49d1aaefcebddc1c32477f NeedsCompilation: no Title: Tools for Omics Data Analysis Description: The POMA package offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness, POMA leverages the standardized SummarizedExperiment class from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, making POMA an essential asset for researchers handling omics datasets. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) for more details. biocViews: BatchEffect, Classification, Clustering, DecisionTree, DimensionReduction, MultidimensionalScaling, Normalization, Preprocessing, PrincipalComponent, Regression, RNASeq, Software, StatisticalMethod, Visualization Author: Pol Castellano-Escuder [aut, cre] (ORCID: ) Maintainer: Pol Castellano-Escuder URL: https://github.com/pcastellanoescuder/POMA VignetteBuilder: knitr BugReports: https://github.com/pcastellanoescuder/POMA/issues git_url: https://git.bioconductor.org/packages/POMA git_branch: RELEASE_3_22 git_last_commit: 49dcbbe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/POMA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/POMA_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/POMA_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/POMA_1.20.0.tgz vignettes: vignettes/POMA/inst/doc/POMA-normalization.html, vignettes/POMA/inst/doc/POMA-workflow.html vignetteTitles: Normalization Methods, Get Started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POMA/inst/doc/POMA-normalization.R, vignettes/POMA/inst/doc/POMA-workflow.R importsMe: PRONE suggestsMe: fobitools dependencyCount: 230 Package: powerTCR Version: 1.30.0 Imports: cubature, doParallel, evmix, foreach, magrittr, methods, parallel, purrr, stats, truncdist, vegan, VGAM Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 22944c23d68464a22eb3fbe15e386d60 NeedsCompilation: no Title: Model-Based Comparative Analysis of the TCR Repertoire Description: This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits. biocViews: Software, Clustering, BiomedicalInformatics Author: Hillary Koch Maintainer: Hillary Koch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/powerTCR git_branch: RELEASE_3_22 git_last_commit: d26ba80 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/powerTCR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/powerTCR_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/powerTCR_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/powerTCR_1.30.0.tgz vignettes: vignettes/powerTCR/inst/doc/powerTCR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/powerTCR/inst/doc/powerTCR.R dependencyCount: 36 Package: POWSC Version: 1.18.0 Depends: R (>= 4.1), Biobase, SingleCellExperiment, MAST Imports: pheatmap, ggplot2, RColorBrewer, grDevices, SummarizedExperiment, limma Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-2 MD5sum: df8358493102220e6e0381c46337ecc9 NeedsCompilation: no Title: Simulation, power evaluation, and sample size recommendation for single cell RNA-seq Description: Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship. biocViews: DifferentialExpression, ImmunoOncology, SingleCell, Software Author: Kenong Su [aut, cre], Hao Wu [aut] Maintainer: Kenong Su VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/POWSC git_branch: RELEASE_3_22 git_last_commit: 8098daa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/POWSC_1.18.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/POWSC_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/POWSC_1.18.0.tgz vignettes: vignettes/POWSC/inst/doc/POWSC.html vignetteTitles: The POWSC User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POWSC/inst/doc/POWSC.R dependencyCount: 59 Package: ppcseq Version: 1.18.0 Depends: R (>= 4.1.0), rstan (>= 2.18.1) Imports: benchmarkme, dplyr, edgeR, foreach, ggplot2, graphics, lifecycle, magrittr, methods, parallel, purrr, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rlang, rstantools (>= 2.1.1), stats, tibble, tidybayes, tidyr (>= 0.8.3.9000), utils LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: knitr, testthat, BiocStyle, rmarkdown License: GPL-3 MD5sum: 913757f254b5289395b79ce89766456e NeedsCompilation: yes Title: Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models Description: Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers. biocViews: RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] (ORCID: ) Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/ppcseq SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/stemangiola/ppcseq/issues git_url: https://git.bioconductor.org/packages/ppcseq git_branch: RELEASE_3_22 git_last_commit: cf30bec git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ppcseq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ppcseq_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ppcseq_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ppcseq_1.18.0.tgz vignettes: vignettes/ppcseq/inst/doc/introduction.html vignetteTitles: Overview of the ppcseq package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppcseq/inst/doc/introduction.R dependencyCount: 88 Package: PPInfer Version: 1.36.0 Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb, yeastExpData Imports: httr, grDevices, graphics, stats, utils License: Artistic-2.0 MD5sum: 8d770efd539ca4cf5f03e613b2a4d205 NeedsCompilation: no Title: Inferring functionally related proteins using protein interaction networks Description: Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, NetworkEnrichment, Pathways Author: Dongmin Jung, Xijin Ge Maintainer: Dongmin Jung git_url: https://git.bioconductor.org/packages/PPInfer git_branch: RELEASE_3_22 git_last_commit: d01a2dd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PPInfer_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PPInfer_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PPInfer_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PPInfer_1.36.0.tgz vignettes: vignettes/PPInfer/inst/doc/PPInfer.pdf vignetteTitles: User manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PPInfer/inst/doc/PPInfer.R dependsOnMe: gsean dependencyCount: 107 Package: pqsfinder Version: 2.26.0 Depends: R (>= 3.5.0), Biostrings Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods LinkingTo: Rcpp, BH (>= 1.78.0) Suggests: BiocStyle, knitr, rmarkdown, Gviz, rtracklayer, ggplot2, BSgenome.Hsapiens.UCSC.hg38, testthat, stringr, stringi License: BSD_2_clause + file LICENSE Archs: x64 MD5sum: 9944bb962f4bc546cb114143e92af54a NeedsCompilation: yes Title: Identification of potential quadruplex forming sequences Description: Pqsfinder detects DNA and RNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, pqsfinder is able to detect G4s folded from imperfect G-runs containing bulges or mismatches or G4s having long loops. Pqsfinder also assigns an integer score to each hit that was fitted on G4 sequencing data and corresponds to expected stability of the folded G4. biocViews: MotifDiscovery, SequenceMatching, GeneRegulation Author: Jiri Hon, Dominika Labudova, Matej Lexa and Tomas Martinek Maintainer: Jiri Hon URL: https://pqsfinder.fi.muni.cz SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pqsfinder git_branch: RELEASE_3_22 git_last_commit: 82b47c5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pqsfinder_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pqsfinder_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pqsfinder_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pqsfinder_2.26.0.tgz vignettes: vignettes/pqsfinder/inst/doc/pqsfinder.html vignetteTitles: pqsfinder: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R dependencyCount: 18 Package: pram Version: 1.26.0 Depends: R (>= 3.6) Imports: methods, BiocParallel, tools, utils, data.table (>= 1.11.8), GenomicAlignments (>= 1.16.0), rtracklayer (>= 1.40.6), BiocGenerics (>= 0.26.0), Seqinfo, GenomicRanges (>= 1.32.0), IRanges (>= 2.14.12), Rsamtools (>= 1.32.3), S4Vectors (>= 0.18.3) Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>= 3) MD5sum: 335c4e4b213c820d3f9c2385a7d527fb NeedsCompilation: no Title: Pooling RNA-seq datasets for assembling transcript models Description: Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by large collections of RNA-seq datasets has emerged as one of such analysis. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a new R package named Pooling RNA-seq and Assembling Models (PRAM), which builds transcript models in intergenic regions from pooled RNA-seq datasets. This package includes functions for defining intergenic regions, extracting and pooling related RNA-seq alignments, predicting, selected, and evaluating transcript models. biocViews: Software, Technology, Sequencing, RNASeq, BiologicalQuestion, GenePrediction, GenomeAnnotation, ResearchField, Transcriptomics Author: Peng Liu [aut, cre], Colin N. Dewey [aut], Sündüz Keleş [aut] Maintainer: Peng Liu URL: https://github.com/pliu55/pram VignetteBuilder: knitr BugReports: https://github.com/pliu55/pram/issues git_url: https://git.bioconductor.org/packages/pram git_branch: RELEASE_3_22 git_last_commit: 18e4eb0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pram_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pram_1.25.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pram_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pram_1.26.0.tgz vignettes: vignettes/pram/inst/doc/pram.html vignetteTitles: Pooling RNA-seq and Assembling Models hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pram/inst/doc/pram.R dependencyCount: 58 Package: prebs Version: 1.50.0 Depends: R (>= 2.14.0), GenomicAlignments, affy, RPA Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges, Biobase, Seqinfo, S4Vectors Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe License: Artistic-2.0 Archs: x64 MD5sum: 418ad3000c5bd98c4b9b761c4cd9d5a5 NeedsCompilation: no Title: Probe region expression estimation for RNA-seq data for improved microarray comparability Description: The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using a modified version of RMA algorithm. The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output. biocViews: ImmunoOncology, Microarray, RNASeq, Sequencing, GeneExpression, Preprocessing Author: Karolis Uziela and Antti Honkela Maintainer: Karolis Uziela git_url: https://git.bioconductor.org/packages/prebs git_branch: RELEASE_3_22 git_last_commit: a3b9bf7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/prebs_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/prebs_1.49.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/prebs_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/prebs_1.50.0.tgz vignettes: vignettes/prebs/inst/doc/prebs.pdf vignetteTitles: prebs User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prebs/inst/doc/prebs.R dependencyCount: 116 Package: preciseTAD Version: 1.20.0 Depends: R (>= 4.1) Imports: S4Vectors, IRanges, GenomicRanges, randomForest, ModelMetrics, e1071, PRROC, pROC, caret, utils, cluster, dbscan, doSNOW, foreach, pbapply, stats, parallel, gtools, rCGH Suggests: knitr, rmarkdown, testthat, BiocCheck, BiocManager, BiocStyle License: MIT + file LICENSE MD5sum: 4d7be17dc33fbd8072a5bfa248ead805 NeedsCompilation: no Title: preciseTAD: A machine learning framework for precise TAD boundary prediction Description: preciseTAD provides functions to predict the location of boundaries of topologically associated domains (TADs) and chromatin loops at base-level resolution. As an input, it takes BED-formatted genomic coordinates of domain boundaries detected from low-resolution Hi-C data, and coordinates of high-resolution genomic annotations from ENCODE or other consortia. preciseTAD employs several feature engineering strategies and resampling techniques to address class imbalance, and trains an optimized random forest model for predicting low-resolution domain boundaries. Translated on a base-level, preciseTAD predicts the probability for each base to be a boundary. Density-based clustering and scalable partitioning techniques are used to detect precise boundary regions and summit points. Compared with low-resolution boundaries, preciseTAD boundaries are highly enriched for CTCF, RAD21, SMC3, and ZNF143 signal and more conserved across cell lines. The pre-trained model can accurately predict boundaries in another cell line using CTCF, RAD21, SMC3, and ZNF143 annotation data for this cell line. biocViews: Software, HiC, Sequencing, Clustering, Classification, FunctionalGenomics, FeatureExtraction Author: Spiro Stilianoudakis [aut], Mikhail Dozmorov [aut, cre] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/preciseTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/preciseTAD/issues git_url: https://git.bioconductor.org/packages/preciseTAD git_branch: RELEASE_3_22 git_last_commit: 4158f87 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/preciseTAD_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/preciseTAD_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/preciseTAD_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/preciseTAD_1.20.0.tgz vignettes: vignettes/preciseTAD/inst/doc/preciseTAD.html vignetteTitles: preciseTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/preciseTAD/inst/doc/preciseTAD.R suggestsMe: preciseTADhub dependencyCount: 176 Package: PREDA Version: 1.56.0 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: 9b4c22065e2adf765207d1fc3cb418cc NeedsCompilation: no Title: Position Related Data Analysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari git_url: https://git.bioconductor.org/packages/PREDA git_branch: RELEASE_3_22 git_last_commit: dee4ae5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PREDA_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PREDA_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PREDA_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PREDA_1.56.0.tgz vignettes: vignettes/PREDA/inst/doc/PREDAclasses.pdf, vignettes/PREDA/inst/doc/PREDAtutorial.pdf vignetteTitles: PREDA S4-classes, PREDA tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PREDA/inst/doc/PREDAtutorial.R dependsOnMe: PREDAsampledata dependencyCount: 55 Package: preprocessCore Version: 1.72.0 Imports: stats License: LGPL (>= 2) MD5sum: c288c12d10a9a8b02904140e08aa97bd NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/preprocessCore git_url: https://git.bioconductor.org/packages/preprocessCore git_branch: RELEASE_3_22 git_last_commit: f8fc99a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/preprocessCore_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/preprocessCore_1.71.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/preprocessCore_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/preprocessCore_1.72.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, crlmm importsMe: affy, BloodGen3Module, bnbc, cn.farms, CPSM, crupR, cypress, EMDomics, ExiMiR, fastLiquidAssociation, frma, frmaTools, iCheck, InPAS, lumi, MBCB, MBQN, MEDIPS, methylclock, mimager, minfi, MSPrep, MSstats, NormalyzerDE, oligo, PanomiR, PECA, PhosR, PRONE, qPLEXanalyzer, quantiseqr, sesame, yarn, GSE13015, ADAPTS, bulkAnalyseR, cinaR, FARDEEP, HEMDAG, lilikoi, noise, noisyr, retriever, SMDIC, WGCNA suggestsMe: DAPAR, DspikeIn, MsCoreUtils, multiClust, QFeatures, roastgsa, scp, splatter, tidybulk, wateRmelon, aroma.affymetrix, aroma.core, corrselect, SCdeconR, wrMisc, wrTopDownFrag linksToMe: affy, affyPLM, crlmm, oligo dependencyCount: 1 Package: primirTSS Version: 1.28.0 Depends: R (>= 3.5.0) Imports: GenomicRanges (>= 1.32.2), S4Vectors (>= 0.18.2), rtracklayer (>= 1.40.3), dplyr (>= 0.7.6), stringr (>= 1.3.1), tidyr (>= 0.8.1), Biostrings (>= 2.48.0), purrr (>= 0.2.5), BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.1), phastCons100way.UCSC.hg38 (>= 3.7.1), GenomicScores (>= 1.4.1), shiny (>= 1.0.5), Gviz (>= 1.24.0), BiocGenerics (>= 0.26.0), IRanges (>= 2.14.10), TFBSTools (>= 1.18.0), JASPAR2018 (>= 1.1.1), tibble (>= 1.4.2), R.utils (>= 2.6.0), stats, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 58ee5103ba50829889e8fb193b78693c NeedsCompilation: no Title: Prediction of pri-miRNA Transcription Start Site Description: A fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs. biocViews: ImmunoOncology, Sequencing, RNASeq, Genetics, Preprocessing, Transcription, GeneRegulation Author: Pumin Li [aut, cre], Qi Xu [aut], Jie Li [aut], Jin Wang [aut] Maintainer: Pumin Li URL: https://github.com/ipumin/primirTSS VignetteBuilder: knitr BugReports: http://github.com/ipumin/primirTSS/issues git_url: https://git.bioconductor.org/packages/primirTSS git_branch: RELEASE_3_22 git_last_commit: 9d01dbb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/primirTSS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/primirTSS_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/primirTSS_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/primirTSS_1.28.0.tgz vignettes: vignettes/primirTSS/inst/doc/primirTSS.html vignetteTitles: primirTSS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/primirTSS/inst/doc/primirTSS.R dependencyCount: 183 Package: PrInCE Version: 1.26.0 Depends: R (>= 3.6.0) Imports: purrr (>= 0.2.4), dplyr (>= 0.7.4), tidyr (>= 0.8.99), forecast (>= 8.2), progress (>= 1.1.2), Hmisc (>= 4.0), naivebayes (>= 0.9.1), robustbase (>= 0.92-7), ranger (>= 0.8.0), LiblineaR (>= 2.10-8), speedglm (>= 0.3-2), tester (>= 0.1.7), magrittr (>= 1.5), Biobase (>= 2.40.0), MSnbase (>= 2.8.3), stats, utils, methods, Rdpack (>= 0.7) Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 92cbc77db45f3776e9ec0d6b1a0ad04d NeedsCompilation: no Title: Predicting Interactomes from Co-Elution Description: PrInCE (Predicting Interactomes from Co-Elution) uses a naive Bayes classifier trained on dataset-derived features to recover protein-protein interactions from co-elution chromatogram profiles. This package contains the R implementation of PrInCE. biocViews: Proteomics, SystemsBiology, NetworkInference Author: Michael Skinnider [aut, trl, cre], R. Greg Stacey [ctb], Nichollas Scott [ctb], Anders Kristensen [ctb], Leonard Foster [aut, led] Maintainer: Michael Skinnider VignetteBuilder: knitr BugReports: https://github.com/fosterlab/PrInCE/issues git_url: https://git.bioconductor.org/packages/PrInCE git_branch: RELEASE_3_22 git_last_commit: 9307f0b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PrInCE_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PrInCE_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PrInCE_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PrInCE_1.26.0.tgz vignettes: vignettes/PrInCE/inst/doc/intro-to-prince.html vignetteTitles: Interactome reconstruction from co-elution data with PrInCE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PrInCE/inst/doc/intro-to-prince.R dependencyCount: 171 Package: proActiv Version: 1.20.0 Depends: R (>= 4.0.0) Imports: AnnotationDbi, BiocParallel, data.table, dplyr, DESeq2, IRanges, GenomicRanges, GenomicFeatures, GenomicAlignments, GenomeInfoDb, ggplot2, gplots, graphics, methods, rlang, scales, S4Vectors, SummarizedExperiment, stats, tibble, txdbmaker Suggests: GenomeInfoDbData, testthat, rmarkdown, knitr, Rtsne, gridExtra License: MIT + file LICENSE MD5sum: 735382f5b7b36f2b99d7ac5169cce12d NeedsCompilation: no Title: Estimate Promoter Activity from RNA-Seq data Description: Most human genes have multiple promoters that control the expression of different isoforms. The use of these alternative promoters enables the regulation of isoform expression pre-transcriptionally. Alternative promoters have been found to be important in a wide number of cell types and diseases. proActiv is an R package that enables the analysis of promoters from RNA-seq data. proActiv uses aligned reads as input, and generates counts and normalized promoter activity estimates for each annotated promoter. In particular, proActiv accepts junction files from TopHat2 or STAR or BAM files as inputs. These estimates can then be used to identify which promoter is active, which promoter is inactive, and which promoters change their activity across conditions. proActiv also allows visualization of promoter activity across conditions. biocViews: RNASeq, GeneExpression, Transcription, AlternativeSplicing, GeneRegulation, DifferentialSplicing, FunctionalGenomics, Epigenetics, Transcriptomics, Preprocessing Author: Deniz Demircioglu [aut] (ORCID: ), Jonathan Göke [aut], Joseph Lee [cre] Maintainer: Joseph Lee URL: https://github.com/GoekeLab/proActiv VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proActiv git_branch: RELEASE_3_22 git_last_commit: 69c3302 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/proActiv_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/proActiv_1.19.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/proActiv_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/proActiv_1.20.0.tgz vignettes: vignettes/proActiv/inst/doc/proActiv.html vignetteTitles: Identifying Active and Alternative Promoters from RNA-Seq data with proActiv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/proActiv/inst/doc/proActiv.R dependencyCount: 118 Package: proBAMr Version: 1.44.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, txdbmaker, rtracklayer Suggests: GenomeInfoDbData, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 9c2e60caf3c43871e91705bfd1690850 NeedsCompilation: no Title: Generating SAM file for PSMs in shotgun proteomics data Description: Mapping PSMs back to genome. The package builds SAM file from shotgun proteomics data The package also provides function to prepare annotation from GTF file. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Software, Visualization Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/proBAMr git_branch: RELEASE_3_22 git_last_commit: 90383eb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/proBAMr_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/proBAMr_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/proBAMr_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/proBAMr_1.44.0.tgz vignettes: vignettes/proBAMr/inst/doc/proBAMr.pdf vignetteTitles: Introduction to proBAMr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBAMr/inst/doc/proBAMr.R dependencyCount: 100 Package: PROcess Version: 1.86.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: 1d95f3974d3aa090379298fe94d5901d NeedsCompilation: no Title: Ciphergen SELDI-TOF Processing Description: A package for processing protein mass spectrometry data. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Xiaochun Li Maintainer: Xiaochun Li git_url: https://git.bioconductor.org/packages/PROcess git_branch: RELEASE_3_22 git_last_commit: f8dee55 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PROcess_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PROcess_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PROcess_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PROcess_1.86.0.tgz vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf vignetteTitles: HOWTO PROcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROcess/inst/doc/howtoprocess.R dependencyCount: 11 Package: procoil Version: 2.38.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: 0fef837ceed077dc5316ea736b1a1c38 NeedsCompilation: no Title: Prediction of Oligomerization of Coiled Coil Proteins Description: The package allows for predicting whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. Additionally to the prediction itself, a prediction profile is computed which allows for determining the strengths to which the individual residues are indicative for either class. Prediction profiles can also be visualized as curves or heatmaps. biocViews: Proteomics, Classification, SupportVectorMachine Author: Ulrich Bodenhofer [aut, cre] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/procoil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/procoil git_branch: RELEASE_3_22 git_last_commit: e9b34f9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/procoil_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/procoil_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/procoil_2.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/procoil_2.38.0.tgz vignettes: vignettes/procoil/inst/doc/procoil.pdf vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting the Oligomerization of Coiled-Coil Proteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/procoil/inst/doc/procoil.R dependencyCount: 27 Package: proDA Version: 1.24.0 Imports: stats, utils, methods, BiocGenerics, SummarizedExperiment, S4Vectors, extraDistr Suggests: testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr, tibble, limma, DEP, numDeriv, pheatmap, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 1e63b09dc63c1360597cff27ce1c65d7 NeedsCompilation: no Title: Differential Abundance Analysis of Label-Free Mass Spectrometry Data Description: Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, Bayesian, Regression, Software, Normalization, QualityControl Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ), Simon Anders [ths] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/proDA VignetteBuilder: knitr BugReports: https://github.com/const-ae/proDA/issues git_url: https://git.bioconductor.org/packages/proDA git_branch: RELEASE_3_22 git_last_commit: cf3d455 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/proDA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/proDA_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/proDA_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/proDA_1.24.0.tgz vignettes: vignettes/proDA/inst/doc/data-import.html, vignettes/proDA/inst/doc/Introduction.html vignetteTitles: Data Import, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proDA/inst/doc/data-import.R, vignettes/proDA/inst/doc/Introduction.R importsMe: MatrixQCvis, SmartPhos suggestsMe: protti dependencyCount: 27 Package: profileScoreDist Version: 1.38.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE MD5sum: 5b0b8cf1a1e302147d07a765a48d6959 NeedsCompilation: yes Title: Profile score distributions Description: Regularization and score distributions for position count matrices. biocViews: Software, GeneRegulation, StatisticalMethod Author: Paal O. Westermark Maintainer: Paal O. Westermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileScoreDist git_branch: RELEASE_3_22 git_last_commit: 133a059 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/profileScoreDist_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/profileScoreDist_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/profileScoreDist_1.38.0.tgz vignettes: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.pdf vignetteTitles: Using profileScoreDist hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.R dependencyCount: 7 Package: progeny Version: 1.32.0 Depends: R (>= 3.6.0) Imports: Biobase, stats, dplyr, tidyr, ggplot2, ggrepel, gridExtra, decoupleR, reshape2 Suggests: airway, biomaRt, BiocFileCache, broom, Seurat, SingleCellExperiment, DESeq2, BiocStyle, knitr, readr, readxl, pheatmap, tibble, rmarkdown, testthat (>= 2.1.0) License: Apache License (== 2.0) | file LICENSE MD5sum: 64e4d3a0aaf2a5e3a95660ee0875b6c2 NeedsCompilation: no Title: Pathway RespOnsive GENes for activity inference from gene expression Description: PROGENy is resource that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway responsive genes for human and mouse. These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics. biocViews: SystemsBiology, GeneExpression, FunctionalPrediction, GeneRegulation Author: Michael Schubert [aut], Alberto Valdeolivas [ctb] (ORCID: ), Christian H. Holland [ctb] (ORCID: ), Igor Bulanov [ctb], Aurélien Dugourd [cre, ctb] Maintainer: Aurélien Dugourd URL: https://github.com/saezlab/progeny VignetteBuilder: knitr BugReports: https://github.com/saezlab/progeny/issues git_url: https://git.bioconductor.org/packages/progeny git_branch: RELEASE_3_22 git_last_commit: d29d813 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/progeny_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/progeny_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/progeny_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/progeny_1.32.0.tgz vignettes: vignettes/progeny/inst/doc/progeny.html vignetteTitles: PROGENy pathway signatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/progeny/inst/doc/progeny.R importsMe: easier suggestsMe: autonomics dependencyCount: 58 Package: projectR Version: 1.26.0 Depends: R (>= 4.0.0) Imports: SingleCellExperiment, methods, cluster, stats, limma, NMF, ROCR, ggalluvial, RColorBrewer, dplyr, fgsea, reshape2, viridis, scales, Matrix, MatrixModels, msigdbr, ggplot2, cowplot, ggrepel, umap, tsne Suggests: BiocStyle, CoGAPS, gridExtra, grid, testthat, devtools, knitr, rmarkdown, ComplexHeatmap, gplots, SeuratObject License: GPL (==2) MD5sum: f24c922b9c4b8298f8cb67affc1f12fd NeedsCompilation: no Title: Functions for the projection of weights from PCA, CoGAPS, NMF, correlation, and clustering Description: Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering. biocViews: FunctionalPrediction, GeneRegulation, BiologicalQuestion, Software Author: Gaurav Sharma, Charles Shin, Jared Slosberg, Loyal Goff, Genevieve Stein-O'Brien Maintainer: Genevieve Stein-O'Brien URL: https://github.com/genesofeve/projectR/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/projectR/ git_url: https://git.bioconductor.org/packages/projectR git_branch: RELEASE_3_22 git_last_commit: 8ac1c21 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/projectR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/projectR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/projectR_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/projectR_1.26.0.tgz vignettes: vignettes/projectR/inst/doc/projectR.html vignetteTitles: projectR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/projectR/inst/doc/projectR.R dependencyCount: 113 Package: pRoloc Version: 1.50.0 Depends: R (>= 3.5), MSnbase (>= 1.19.20), MLInterfaces (>= 1.67.10), methods, Rcpp (>= 0.10.3), BiocParallel Imports: stats4, Biobase, mclust (>= 4.3), caret, e1071, sampling, class, kernlab, lattice, nnet, randomForest, proxy, FNN, hexbin, BiocGenerics, stats, dendextend, RColorBrewer, scales, MASS, knitr, mvtnorm, LaplacesDemon, coda, mixtools, gtools, plyr, ggplot2, biomaRt, utils, grDevices, graphics, colorspace LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, rmarkdown, pRolocdata (>= 1.43.2), roxygen2, xtable, rgl, BiocStyle (>= 2.5.19), hpar (>= 1.41.0), dplyr, akima, fields, vegan, GO.db, AnnotationDbi, Rtsne (>= 0.13), nipals, reshape, magick, umap License: GPL-2 MD5sum: cc6a3e6056c8fd4d25813db29f1814da NeedsCompilation: yes Title: A unifying bioinformatics framework for spatial proteomics Description: The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Classification, Clustering, QualityControl Author: Laurent Gatto [aut], Lisa Breckels [aut, cre], Thomas Burger [ctb], Samuel Wieczorek [ctb], Charlotte Hutchings [ctb], Oliver Crook [aut] Maintainer: Lisa Breckels URL: https://github.com/lgatto/pRoloc VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRoloc/issues git_url: https://git.bioconductor.org/packages/pRoloc git_branch: RELEASE_3_22 git_last_commit: 264a1bf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pRoloc_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pRoloc_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pRoloc_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pRoloc_1.50.0.tgz vignettes: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.html, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.html, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.html, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.html vignetteTitles: Using pRoloc for spatial proteomics data analysis, Machine learning techniques available in pRoloc, Bayesian spatial proteomics with pRoloc, A transfer learning algorithm for spatial proteomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.R, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.R, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.R, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.R dependsOnMe: bandle, pRolocGUI suggestsMe: MSnbase, pRolocdata, RforProteomics dependencyCount: 228 Package: pRolocGUI Version: 2.20.0 Depends: methods, R (>= 3.1.0), pRoloc (>= 1.27.6), Biobase, MSnbase (>= 2.1.11) Imports: shiny (>= 0.9.1), scales, dplyr, DT (>= 0.1.40), graphics, utils, ggplot2, shinydashboardPlus (>= 2.0.0), colourpicker, shinyhelper, shinyWidgets, shinyjs, colorspace, stats, grDevices, grid, BiocGenerics, shinydashboard Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown, testthat (>= 3.0.0) License: GPL-2 MD5sum: 06955b292178545fe7301e6551ccc9ba NeedsCompilation: no Title: Interactive visualisation of spatial proteomics data Description: The package pRolocGUI comprises functions to interactively visualise spatial proteomics data on the basis of pRoloc, pRolocdata and shiny. biocViews: Proteomics, Visualization, GUI Author: Lisa Breckels [aut, cre] (ORCID: ), Thomas Naake [aut], Laurent Gatto [aut] (ORCID: ) Maintainer: Lisa Breckels URL: https://github.com/lgatto/pRolocGUI VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRolocGUI/issues git_url: https://git.bioconductor.org/packages/pRolocGUI git_branch: RELEASE_3_22 git_last_commit: 7531dcd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pRolocGUI_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pRolocGUI_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pRolocGUI_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pRolocGUI_2.20.0.tgz vignettes: vignettes/pRolocGUI/inst/doc/pRolocGUI.html vignetteTitles: pRolocGUI - Interactive visualisation of spatial proteomics data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R dependencyCount: 242 Package: PROMISE Version: 1.62.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: c592e1470c56c02255b0774eccee6f91 NeedsCompilation: no Title: PRojection Onto the Most Interesting Statistical Evidence Description: A general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019 biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression Author: Stan Pounds , Xueyuan Cao Maintainer: Stan Pounds , Xueyuan Cao git_url: https://git.bioconductor.org/packages/PROMISE git_branch: RELEASE_3_22 git_last_commit: 7bf04c1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PROMISE_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PROMISE_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PROMISE_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PROMISE_1.62.0.tgz vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf vignetteTitles: An introduction to PROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROMISE/inst/doc/PROMISE.R dependsOnMe: CCPROMISE dependencyCount: 48 Package: PRONE Version: 1.4.0 Depends: R (>= 4.4.0), SummarizedExperiment Imports: dplyr, magrittr, data.table, RColorBrewer, ggplot2, S4Vectors, ComplexHeatmap, stringr, NormalyzerDE, tibble, limma, MASS, edgeR, matrixStats, preprocessCore, stats, gtools, methods, ROTS, ComplexUpset, tidyr, purrr, circlize, gprofiler2, plotROC, MSnbase, UpSetR, dendsort, vsn, Biobase, reshape2, POMA, ggtext, scales, DEqMS, vegan Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, DT License: GPL (>= 3) Archs: x64 MD5sum: 07f6f29fe0eab2650361e771ddf79da7 NeedsCompilation: no Title: The PROteomics Normalization Evaluator Description: High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed. biocViews: Proteomics, Preprocessing, Normalization, DifferentialExpression, Visualization Author: Lis Arend [aut, cre] (ORCID: ) Maintainer: Lis Arend URL: https://github.com/daisybio/PRONE VignetteBuilder: knitr BugReports: https://github.com/daisybio/PRONE/issues git_url: https://git.bioconductor.org/packages/PRONE git_branch: RELEASE_3_22 git_last_commit: 2b31a49 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PRONE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PRONE_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PRONE_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PRONE_1.4.0.tgz vignettes: vignettes/PRONE/inst/doc/Differential_Expression.html, vignettes/PRONE/inst/doc/Imputation.html, vignettes/PRONE/inst/doc/Normalization.html, vignettes/PRONE/inst/doc/Preprocessing.html, vignettes/PRONE/inst/doc/PRONE.html, vignettes/PRONE/inst/doc/Spike_In_Data.html vignetteTitles: 5. Differential Expression Analysis, 4. Imputation, 3. Normalization, 2. Preprocessing, 1. Getting started with PRONE, 6. PRONE with Spike-In Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PRONE/inst/doc/Differential_Expression.R, vignettes/PRONE/inst/doc/Imputation.R, vignettes/PRONE/inst/doc/Normalization.R, vignettes/PRONE/inst/doc/Preprocessing.R, vignettes/PRONE/inst/doc/PRONE.R, vignettes/PRONE/inst/doc/Spike_In_Data.R dependencyCount: 286 Package: PROPER Version: 1.42.0 Depends: R (>= 3.3) Imports: edgeR Suggests: BiocStyle,DESeq2,DSS,knitr License: GPL MD5sum: 3245cb7f0e6d8c767d33172c52ea46ab NeedsCompilation: no Title: PROspective Power Evaluation for RNAseq Description: This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPER git_branch: RELEASE_3_22 git_last_commit: f2bf399 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PROPER_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PROPER_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PROPER_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PROPER_1.42.0.tgz vignettes: vignettes/PROPER/inst/doc/PROPER.pdf vignetteTitles: Power and Sample size analysis for gene expression from RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPER/inst/doc/PROPER.R importsMe: cypress dependencyCount: 11 Package: PROPS Version: 1.32.0 Imports: bnlearn, reshape2, sva, stats, utils, Biobase Suggests: knitr, rmarkdown License: GPL-2 MD5sum: bb615dd58fe93782d1a1974ded0e7f79 NeedsCompilation: no Title: PRObabilistic Pathway Score (PROPS) Description: This package calculates probabilistic pathway scores using gene expression data. Gene expression values are aggregated into pathway-based scores using Bayesian network representations of biological pathways. biocViews: Classification, Bayesian, GeneExpression Author: Lichy Han Maintainer: Lichy Han VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPS git_branch: RELEASE_3_22 git_last_commit: bf725c0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PROPS_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PROPS_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PROPS_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PROPS_1.32.0.tgz vignettes: vignettes/PROPS/inst/doc/props.html vignetteTitles: PRObabilistic Pathway Scores (PROPS) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPS/inst/doc/props.R dependencyCount: 77 Package: Prostar Version: 1.42.0 Depends: R (>= 4.4.0) Imports: DAPAR (>= 1.35.1), DAPARdata (>= 1.30.0), rhandsontable, data.table, shiny, shinyBS, shinyAce, highcharter, htmlwidgets, webshot, shinythemes, later, shinycssloaders, future, promises, shinyjqui, tibble, ggplot2, gplots, shinyjs, vioplot, Biobase, DT, R.utils, RColorBrewer, XML, colourpicker, gtools, markdown, rclipboard, sass, shinyTree, shinyWidgets Suggests: BiocStyle, BiocManager, testthat, knitr License: Artistic-2.0 Archs: x64 MD5sum: ef70ab7bfbd302a3b4f9a6e4c231e2d0 NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for the DAPAR package. The package Prostar (Proteomics statistical analysis with R) is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required. biocViews: Proteomics, MassSpectrometry, Normalization, Preprocessing, Software, GUI Author: Thomas Burger [aut], Florence Combes [aut], Samuel Wieczorek [cre, aut] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/edyp-lab/Prostar/issues git_url: https://git.bioconductor.org/packages/Prostar git_branch: RELEASE_3_22 git_last_commit: d48b236 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Prostar_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Prostar_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Prostar_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Prostar_1.42.0.tgz vignettes: vignettes/Prostar/inst/doc/Prostar_UserManual.html vignetteTitles: Prostar User Manual hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prostar/inst/doc/Prostar_UserManual.R dependencyCount: 183 Package: proteinProfiles Version: 1.50.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: a5a059eef8570cccaf6e3a815ddd0f72 NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/proteinProfiles git_branch: RELEASE_3_22 git_last_commit: 6dbf504 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/proteinProfiles_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/proteinProfiles_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/proteinProfiles_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/proteinProfiles_1.50.0.tgz vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf vignetteTitles: The proteinProfiles package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteinProfiles/inst/doc/proteinProfiles.R dependencyCount: 2 Package: ProteoDisco Version: 1.16.0 Depends: R (>= 4.1.0), Imports: BiocGenerics (>= 0.38.0), BiocParallel (>= 1.26.0), Biostrings (>= 2.60.1), checkmate (>= 2.0.0), cleaver (>= 1.30.0), dplyr (>= 1.0.6), GenomeInfoDb (>= 1.28.0), GenomicFeatures (>= 1.44.0), GenomicRanges (>= 1.44.0), IRanges (>= 2.26.0), methods (>= 4.1.0), ParallelLogger (>= 2.0.1), plyr (>= 1.8.6), rlang (>= 0.4.11), S4Vectors (>= 0.30.0), Seqinfo, tibble (>= 3.1.2), tidyr (>= 1.1.3), VariantAnnotation (>= 1.36.0), XVector (>= 0.32.0), Suggests: AnnotationDbi (>= 1.54.1), BSgenome (>= 1.60.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.3), BiocStyle (>= 2.20.1), DelayedArray (>= 0.18.0), devtools (>= 2.4.2), knitr (>= 1.33), matrixStats (>= 0.59.0), markdown (>= 1.1), org.Hs.eg.db (>= 3.13.0), purrr (>= 0.3.4), RCurl (>= 1.98.1.3), readr (>= 1.4.0), ggplot2 (>= 3.3.5), rmarkdown (>= 2.9), rtracklayer (>= 1.52.0), seqinr (>= 4.2.8), stringr (>= 1.4.0), reshape2 (>= 1.4.4), scales (>= 1.1.1), testthat (>= 3.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2) License: GPL-3 MD5sum: 9a4f576463dac2ef273c46e764da5821 NeedsCompilation: no Title: Generation of customized protein variant databases from genomic variants, splice-junctions and manual sequences Description: ProteoDisco is an R package to facilitate proteogenomics studies. It houses functions to create customized (variant) protein databases based on user-submitted genomic variants, splice-junctions, fusion genes and manual transcript sequences. The flexible workflow can be adopted to suit a myriad of research and experimental settings. biocViews: Software, Proteomics, RNASeq, SNP, Sequencing, VariantAnnotation, DataImport Author: Job van Riet [cre], Wesley van de Geer [aut], Harmen van de Werken [ths] Maintainer: Job van Riet URL: https://github.com/ErasmusMC-CCBC/ProteoDisco VignetteBuilder: knitr BugReports: https://github.com/ErasmusMC-CCBC/ProteoDisco/issues git_url: https://git.bioconductor.org/packages/ProteoDisco git_branch: RELEASE_3_22 git_last_commit: d4be16c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ProteoDisco_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ProteoDisco_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ProteoDisco_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ProteoDisco_1.16.0.tgz vignettes: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.html vignetteTitles: Overview_Proteodisco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.R dependencyCount: 100 Package: ProteoMM Version: 1.28.0 Depends: R (>= 3.5) Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics Suggests: BiocStyle, knitr, rmarkdown License: MIT MD5sum: b89671208ab751545fa46b29744faad3 NeedsCompilation: no Title: Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform Description: ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009). biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Normalization, DifferentialExpression Author: Yuliya V Karpievitch, Tim Stuart and Sufyaan Mohamed Maintainer: Yuliya V Karpievitch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ProteoMM git_branch: RELEASE_3_22 git_last_commit: e3b80ca git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ProteoMM_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ProteoMM_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ProteoMM_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ProteoMM_1.28.0.tgz vignettes: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.html vignetteTitles: Multi-Dataset Model-based Differential Expression Proteomics Platform hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.R suggestsMe: mi4p dependencyCount: 79 Package: protGear Version: 1.14.0 Depends: R (>= 4.2), dplyr (>= 0.8.0) , limma (>= 3.40.2) ,vsn (>= 3.54.0) Imports: magrittr (>= 1.5) , stats (>= 3.6) , ggplot2 (>= 3.3.0) , tidyr (>= 1.1.3) , data.table (>= 1.14.0), ggpubr (>= 0.4.0), gtools (>= 3.8.2) , tibble (>= 3.1.0) , rmarkdown (>= 2.9) , knitr (>= 1.33), utils (>= 3.6), genefilter (>= 1.74.0), readr (>= 2.0.1) , Biobase (>= 2.52.0), plyr (>= 1.8.6) , Kendall (>= 2.2) , shiny (>= 1.0.0) , purrr (>= 0.3.4), plotly (>= 4.9.0) , MASS (>= 7.3) , htmltools (>= 0.4.0) , flexdashboard (>= 0.5.2) , shinydashboard (>= 0.7.1) , GGally (>= 2.1.2) , pheatmap (>= 1.0.12) , grid(>= 4.1.1), styler (>= 1.6.1) , factoextra (>= 1.0.7) ,FactoMineR (>= 2.4) , rlang (>= 0.4.11), remotes (>= 2.4.0) Suggests: gridExtra (>= 2.3), png (>= 0.1-7) , magick (>= 2.7.3) , ggplotify (>= 0.1.0) , scales (>= 1.1.1) , shinythemes (>= 1.2.0) , shinyjs (>= 2.0.0) , shinyWidgets (>= 0.6.2) , shinycssloaders (>= 1.0.0) , shinyalert (>= 3.0.0) , shinyFiles (>= 0.9.1) , shinyFeedback (>= 0.3.0) License: GPL-3 MD5sum: c8f0a89d044620a6ce32640128bd4e27 NeedsCompilation: no Title: Protein Micro Array Data Management and Interactive Visualization Description: A generic three-step pre-processing package for protein microarray data. This package contains different data pre-processing procedures to allow comparison of their performance.These steps are background correction, the coefficient of variation (CV) based filtering, batch correction and normalization. biocViews: Microarray, OneChannel, Preprocessing , BiomedicalInformatics , Proteomics , BatchEffect, Normalization , Bayesian, Clustering, Regression,SystemsBiology, ImmunoOncology Author: Kennedy Mwai [cre, aut], James Mburu [aut], Jacqueline Waeni [ctb] Maintainer: Kennedy Mwai URL: https://github.com/Keniajin/protGear VignetteBuilder: knitr BugReports: https://github.com/Keniajin/protGear/issues git_url: https://git.bioconductor.org/packages/protGear git_branch: RELEASE_3_22 git_last_commit: 494bde5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/protGear_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/protGear_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/protGear_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/protGear_1.14.0.tgz vignettes: vignettes/protGear/inst/doc/vignette.html vignetteTitles: protGear hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/protGear/inst/doc/vignette.R dependencyCount: 187 Package: ProtGenerics Version: 1.42.0 Depends: methods Suggests: testthat License: Artistic-2.0 MD5sum: 096214a880a202170f14a2a72155a234 NeedsCompilation: no Title: Generic infrastructure for Bioconductor mass spectrometry packages Description: S4 generic functions and classes needed by Bioconductor proteomics packages. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto , Johannes Rainer Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/ProtGenerics git_url: https://git.bioconductor.org/packages/ProtGenerics git_branch: RELEASE_3_22 git_last_commit: 672cf15 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ProtGenerics_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ProtGenerics_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ProtGenerics_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ProtGenerics_1.42.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, Chromatograms, MsExperiment, MSnbase, SpectraQL, topdownr importsMe: CompoundDb, ensembldb, matter, MetaboAnnotation, MsBackendMassbank, MsBackendMetaboLights, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MsFeatures, MSnID, MsQuality, mzID, mzR, PSMatch, QFeatures, Spectra, SpectriPy, xcms dependencyCount: 1 Package: psichomics Version: 1.36.0 Depends: R (>= 4.0), shiny (>= 1.7.0), shinyBS Imports: AnnotationDbi, AnnotationHub, BiocFileCache, cluster, colourpicker, data.table, digest, dplyr, DT (>= 0.2), edgeR, fastICA, fastmatch, ggplot2, ggrepel, graphics, grDevices, highcharter (>= 0.5.0), htmltools, httr, jsonlite, limma, pairsD3, plyr, purrr, Rcpp (>= 0.12.14), recount, Rfast, R.utils, reshape2, shinyjs, stringr, stats, SummarizedExperiment, survival, tools, utils, XML, xtable, methods LinkingTo: Rcpp Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr, car, rstudioapi, spelling License: MIT + file LICENSE MD5sum: 1d3172e4cdc28f4e4d419c28f7e4f8e0 NeedsCompilation: yes Title: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation Description: Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, Transcription, GUI, PrincipalComponent, Survival, BiomedicalInformatics, Transcriptomics, ImmunoOncology, Visualization, MultipleComparison, GeneExpression, DifferentialExpression Author: Nuno Saraiva-Agostinho [aut, cre] (ORCID: ), Nuno Luís Barbosa-Morais [aut, led, ths] (ORCID: ), André Falcão [ths], Lina Gallego Paez [ctb], Marie Bordone [ctb], Teresa Maia [ctb], Mariana Ferreira [ctb], Ana Carolina Leote [ctb], Bernardo de Almeida [ctb] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/psichomics/, https://github.com/nuno-agostinho/psichomics/ VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/psichomics/issues git_url: https://git.bioconductor.org/packages/psichomics git_branch: RELEASE_3_22 git_last_commit: 7123c18 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/psichomics_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/psichomics_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/psichomics_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/psichomics_1.36.0.tgz vignettes: vignettes/psichomics/inst/doc/AS_events_preparation.html, vignettes/psichomics/inst/doc/CLI_tutorial.html, vignettes/psichomics/inst/doc/custom_data.html, vignettes/psichomics/inst/doc/GUI_tutorial.html vignetteTitles: Preparing an Alternative Splicing Annotation for psichomics, Case study: command-line interface (CLI) tutorial, Loading user-provided data, Case study: visual interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psichomics/inst/doc/AS_events_preparation.R, vignettes/psichomics/inst/doc/CLI_tutorial.R, vignettes/psichomics/inst/doc/custom_data.R, vignettes/psichomics/inst/doc/GUI_tutorial.R dependencyCount: 206 Package: PSMatch Version: 1.14.0 Depends: S4Vectors, R (>= 4.1.0) Imports: utils, stats, igraph, methods, Spectra (>= 1.17.10), Matrix, BiocParallel, BiocGenerics, ProtGenerics (>= 1.27.1), QFeatures, MsCoreUtils, IRanges Suggests: msdata, rpx, mzID, mzR, SummarizedExperiment, BiocStyle, rmarkdown, knitr, factoextra, vdiffr (>= 1.0.0), testthat License: Artistic-2.0 MD5sum: d9435ba43b9d7e4c9966658cc77feb83 NeedsCompilation: no Title: Handling and Managing Peptide Spectrum Matches Description: The PSMatch package helps proteomics practitioners to load, handle and manage Peptide Spectrum Matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and make informed decision about shared peptide filtering. The package also provides functions to calculate and visualise MS2 fragment ions. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto [aut, cre] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Samuel Wieczorek [ctb], Thomas Burger [ctb], Guillaume Deflandre [ctb] (ORCID: ) Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/PSM VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/PSM/issues git_url: https://git.bioconductor.org/packages/PSMatch git_branch: RELEASE_3_22 git_last_commit: eedaf3a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PSMatch_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PSMatch_1.13.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PSMatch_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PSMatch_1.14.0.tgz vignettes: vignettes/PSMatch/inst/doc/AdjacencyMatrix.html, vignettes/PSMatch/inst/doc/Fragments.html, vignettes/PSMatch/inst/doc/PSM.html vignetteTitles: Understanding protein groups with adjacency matrices, MS2 fragment ions, Working with PSM data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSMatch/inst/doc/AdjacencyMatrix.R, vignettes/PSMatch/inst/doc/Fragments.R, vignettes/PSMatch/inst/doc/PSM.R importsMe: MSnbase, omXplore, topdownr suggestsMe: MsDataHub dependencyCount: 111 Package: ptairMS Version: 1.18.0 Imports: Biobase, bit64, chron, data.table, doParallel, DT, enviPat, foreach, ggplot2, graphics, grDevices, ggpubr, gridExtra, Hmisc, methods, minpack.lm, MSnbase, parallel, plotly, rhdf5, rlang, Rcpp, shiny, shinyscreenshot, signal, scales, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), ptairData, ropls License: GPL-3 MD5sum: 0bf4823122363eac409e5a7a9f8c7671 NeedsCompilation: yes Title: Pre-processing PTR-TOF-MS Data Description: This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a singl VignetteBuilder: knitr BugReports: https://github.com/camilleroquencourt/ptairMS/issues git_url: https://git.bioconductor.org/packages/ptairMS git_branch: RELEASE_3_22 git_last_commit: 87f2663 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ptairMS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ptairMS_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ptairMS_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ptairMS_1.18.0.tgz vignettes: vignettes/ptairMS/inst/doc/ptairMS.html vignetteTitles: ptaiMS: Processing and analysis of PTR-TOF-MS data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ptairMS/inst/doc/ptairMS.R dependencyCount: 190 Package: puma Version: 3.52.0 Depends: R (>= 3.2.0), oligo (>= 1.32.0),graphics,grDevices, methods, stats, utils, mclust, oligoClasses Imports: Biobase (>= 2.5.5), affy (>= 1.46.0), affyio, oligoClasses Suggests: pumadata, affydata, snow, limma, ROCR,annotate License: LGPL MD5sum: aff9fdb31b2e1142b28333eed20ce0fe NeedsCompilation: yes Title: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) Description: Most analyses of Affymetrix GeneChip data (including tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. In additon to calculte gene expression from Affymetrix 3' arrays, puma also provides methods to process exon arrays and produces gene and isoform expression for alternative splicing study. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting functions. biocViews: Microarray, OneChannel, Preprocessing, DifferentialExpression, Clustering, ExonArray, GeneExpression, mRNAMicroarray, ChipOnChip, AlternativeSplicing, DifferentialSplicing, Bayesian, TwoChannel, DataImport, HTA2.0 Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo, Neil D. Lawrence, Guido Sanguinetti, Li Zhang Maintainer: Xuejun Liu URL: http://umber.sbs.man.ac.uk/resources/puma git_url: https://git.bioconductor.org/packages/puma git_branch: RELEASE_3_22 git_last_commit: fa4369b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/puma_3.52.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/puma_3.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/puma_3.52.0.tgz vignettes: vignettes/puma/inst/doc/puma.pdf vignetteTitles: puma User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/puma/inst/doc/puma.R suggestsMe: tigre dependencyCount: 56 Package: PureCN Version: 2.16.0 Depends: R (>= 3.5.0), DNAcopy, VariantAnnotation (>= 1.14.1) Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer, S4Vectors, data.table, grDevices, graphics, stats, utils, SummarizedExperiment, Seqinfo, GenomeInfoDb, GenomicFeatures, Rsamtools, Biobase, Biostrings, BiocGenerics, rtracklayer, ggplot2, gridExtra, futile.logger, VGAM, tools, methods, mclust, rhdf5, Matrix Suggests: BiocParallel, BiocStyle, PSCBS, R.utils, TxDb.Hsapiens.UCSC.hg19.knownGene, covr, knitr, optparse, org.Hs.eg.db, jsonlite, markdown, rmarkdown, testthat Enhances: genomicsdb (>= 0.0.3) License: Artistic-2.0 MD5sum: d1b67c31d5a3d275932e0b546707eac7 NeedsCompilation: no Title: Copy number calling and SNV classification using targeted short read sequencing Description: This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples. biocViews: CopyNumberVariation, Software, Sequencing, VariantAnnotation, VariantDetection, Coverage, ImmunoOncology Author: Markus Riester [aut, cre] (ORCID: ), Angad P. Singh [aut] Maintainer: Markus Riester URL: https://github.com/lima1/PureCN VignetteBuilder: knitr BugReports: https://github.com/lima1/PureCN/issues git_url: https://git.bioconductor.org/packages/PureCN git_branch: RELEASE_3_22 git_last_commit: c7ace8b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PureCN_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PureCN_2.15.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PureCN_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PureCN_2.16.0.tgz vignettes: vignettes/PureCN/inst/doc/PureCN.pdf, vignettes/PureCN/inst/doc/Quick.html vignetteTitles: Overview of the PureCN R package, Best practices,, quick start and command line usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PureCN/inst/doc/PureCN.R, vignettes/PureCN/inst/doc/Quick.R dependencyCount: 99 Package: pvac Version: 1.58.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: 4a4bf7bde824b3ca0a5a97e86d48ad50 NeedsCompilation: no Title: PCA-based gene filtering for Affymetrix arrays Description: The package contains the function for filtering genes by the proportion of variation accounted for by the first principal component (PVAC). biocViews: Microarray, OneChannel, QualityControl Author: Jun Lu and Pierre R. Bushel Maintainer: Jun Lu , Pierre R. Bushel git_url: https://git.bioconductor.org/packages/pvac git_branch: RELEASE_3_22 git_last_commit: 5928536 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pvac_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pvac_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pvac_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pvac_1.58.0.tgz vignettes: vignettes/pvac/inst/doc/pvac.pdf vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvac/inst/doc/pvac.R dependencyCount: 12 Package: pvca Version: 1.50.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: af6deb6fdd113e6910260866fd5ae192 NeedsCompilation: no Title: Principal Variance Component Analysis (PVCA) Description: This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12. biocViews: Microarray, BatchEffect Author: Pierre Bushel Maintainer: Jianying LI git_url: https://git.bioconductor.org/packages/pvca git_branch: RELEASE_3_22 git_last_commit: b6045e1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pvca_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pvca_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pvca_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pvca_1.50.0.tgz vignettes: vignettes/pvca/inst/doc/pvca.pdf vignetteTitles: Batch effect estimation in Microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvca/inst/doc/pvca.R importsMe: ExpressionNormalizationWorkflow dependencyCount: 48 Package: Pviz Version: 1.44.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 MD5sum: fbba1722b3f05a67cf298e6def1436f2 NeedsCompilation: no Title: Peptide Annotation and Data Visualization using Gviz Description: Pviz adapts the Gviz package for protein sequences and data. biocViews: Visualization, Proteomics, Microarray Author: Renan Sauteraud, Mike Jiang, Raphael Gottardo Maintainer: Renan Sauteraud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pviz git_branch: RELEASE_3_22 git_last_commit: b25a5c9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Pviz_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Pviz_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Pviz_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Pviz_1.44.0.tgz vignettes: vignettes/Pviz/inst/doc/Pviz.pdf vignetteTitles: The Pviz users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pviz/inst/doc/Pviz.R suggestsMe: pepStat dependencyCount: 152 Package: pwalign Version: 1.6.0 Depends: BiocGenerics, S4Vectors, IRanges, Biostrings (>= 2.71.5) Imports: methods, utils LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit Enhances: Rmpi License: Artistic-2.0 MD5sum: b6a8497c27e51bda092fcb2f5efe02ba NeedsCompilation: yes Title: Perform pairwise sequence alignments Description: The two main functions in the package are pairwiseAlignment() and stringDist(). The former solves (Needleman-Wunsch) global alignment, (Smith-Waterman) local alignment, and (ends-free) overlap alignment problems. The latter computes the Levenshtein edit distance or pairwise alignment score matrix for a set of strings. biocViews: Alignment, SequenceMatching, Sequencing, Genetics Author: Patrick Aboyoun [aut], Robert Gentleman [aut], Hervé Pagès [cre] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/pwalign BugReports: https://github.com/Bioconductor/pwalign/issues git_url: https://git.bioconductor.org/packages/pwalign git_branch: RELEASE_3_22 git_last_commit: fe44e6d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/pwalign_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pwalign_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pwalign_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pwalign_1.6.0.tgz vignettes: vignettes/pwalign/inst/doc/PairwiseAlignments.pdf vignetteTitles: Pairwise Sequence Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pwalign/inst/doc/PairwiseAlignments.R dependsOnMe: MethTargetedNGS, QSutils, sangeranalyseR, sangerseqR, CleanBSequences importsMe: ChIPpeakAnno, CNEr, crisprShiny, DominoEffect, enhancerHomologSearch, ggseqalign, GUIDEseq, IMMAN, IsoformSwitchAnalyzeR, methylscaper, motifbreakR, MSA2dist, openPrimeR, scanMiR, scifer, ShortRead, SPLINTER, StructuralVariantAnnotation, svaNUMT, TFBSTools, AntibodyForests, BIGr, longreadvqs suggestsMe: BiocGenerics, Biostrings, idpr, msa, mutscan, RSVSim, dowser, geneviewer, seqtrie dependencyCount: 15 Package: PWMEnrich Version: 4.46.0 Depends: R (>= 3.5.0), methods, BiocGenerics, Biostrings Imports: grid, seqLogo, gdata, evd, S4Vectors Suggests: MotifDb, BSgenome, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 4354b7d775c71f73aca444c5e6ab85b7 NeedsCompilation: no Title: PWM enrichment analysis Description: A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools. biocViews: MotifAnnotation, SequenceMatching, Software Author: Robert Stojnic, Diego Diez Maintainer: Diego Diez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PWMEnrich git_branch: RELEASE_3_22 git_last_commit: f38bb65 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/PWMEnrich_4.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PWMEnrich_4.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PWMEnrich_4.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PWMEnrich_4.46.0.tgz vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf vignetteTitles: Overview of the 'PWMEnrich' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PWMEnrich/inst/doc/PWMEnrich.R dependsOnMe: PWMEnrich.Dmelanogaster.background, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background suggestsMe: rTRM dependencyCount: 20 Package: qcmetrics Version: 1.48.0 Depends: R (>= 3.3) Imports: Biobase, methods, knitr, tools, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, mzR, BiocStyle, rmarkdown, markdown License: GPL-2 MD5sum: 6ed9dc10fbfba671f28cb4cabf87d20b NeedsCompilation: no Title: A Framework for Quality Control Description: The package provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats. biocViews: ImmunoOncology, Software, QualityControl, Proteomics, Microarray, MassSpectrometry, Visualization, ReportWriting Author: Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: http://lgatto.github.io/qcmetrics/articles/qcmetrics.html VignetteBuilder: knitr BugReports: https://github.com/lgatto/qcmetrics/issues git_url: https://git.bioconductor.org/packages/qcmetrics git_branch: RELEASE_3_22 git_last_commit: 7058a53 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/qcmetrics_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qcmetrics_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qcmetrics_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qcmetrics_1.48.0.tgz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.html vignetteTitles: Index file for the qcmetrics package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R importsMe: MSstatsQC dependencyCount: 20 Package: QDNAseq Version: 1.46.0 Depends: R (>= 3.1.0) Imports: graphics, methods, stats, utils, BiocGenerics, Biobase (>= 2.18.0), CGHbase (>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0), Seqinfo, GenomicRanges (>= 1.20), IRanges (>= 2.2), matrixStats (>= 0.60.0), R.utils (>= 2.9.0), Rsamtools (>= 1.20), future.apply (>= 1.8.1) Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>= 0.6.20), GenomeInfoDb (>= 1.6.0), future (>= 1.22.1), parallelly (>= 1.28.1), R.cache (>= 0.13.0), QDNAseq.hg19, QDNAseq.mm10 License: GPL MD5sum: 0364b9877a6768288a563b678b8d5cd3 NeedsCompilation: no Title: Quantitative DNA Sequencing for Chromosomal Aberrations Description: Quantitative DNA sequencing for chromosomal aberrations. The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively. biocViews: CopyNumberVariation, DNASeq, Genetics, GenomeAnnotation, Preprocessing, QualityControl, Sequencing Author: Ilari Scheinin [aut], Daoud Sie [aut, cre], Henrik Bengtsson [aut], Erik van Dijk [ctb] Maintainer: Daoud Sie URL: https://github.com/ccagc/QDNAseq BugReports: https://github.com/ccagc/QDNAseq/issues git_url: https://git.bioconductor.org/packages/QDNAseq git_branch: RELEASE_3_22 git_last_commit: 00b06a1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/QDNAseq_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QDNAseq_1.45.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QDNAseq_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QDNAseq_1.46.0.tgz vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf vignetteTitles: Introduction to QDNAseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QDNAseq/inst/doc/QDNAseq.R dependsOnMe: GeneBreak, QDNAseq.hg19, QDNAseq.mm10 importsMe: ACE, biscuiteer, cfdnakit dependencyCount: 48 Package: QFeatures Version: 1.20.0 Depends: R (>= 4.1), MultiAssayExperiment (>= 1.33.6) Imports: methods, stats, utils, S4Vectors, IRanges, SummarizedExperiment, BiocGenerics (>= 0.53.4), ProtGenerics (>= 1.35.1), AnnotationFilter, lazyeval, Biobase, MsCoreUtils (>= 1.7.2), igraph, grDevices, plotly, tidyr, tidyselect, reshape2 Suggests: SingleCellExperiment, MsDataHub (>= 1.3.3), Matrix, HDF5Array, msdata, ggplot2, gplots, dplyr, limma, DT, shiny, shinydashboard, testthat, knitr, BiocStyle, rmarkdown, vsn, preprocessCore, matrixStats, imputeLCMD, pcaMethods, impute, norm, ComplexHeatmap License: Artistic-2.0 MD5sum: 2cc5dcd802b0490a28f88a6e5cb56676 NeedsCompilation: no Title: Quantitative features for mass spectrometry data Description: The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format. biocViews: Infrastructure, MassSpectrometry, Proteomics, Metabolomics Author: Laurent Gatto [aut, cre] (ORCID: ), Christophe Vanderaa [aut] (ORCID: ), Léopold Guyot [ctb] Maintainer: Laurent Gatto URL: https://rformassspectrometry.github.io/QFeatures VignetteBuilder: knitr BugReports: https://github.com/rformassspectrometry/QFeatures/issues git_url: https://git.bioconductor.org/packages/QFeatures git_branch: RELEASE_3_22 git_last_commit: bde5b54 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/QFeatures_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QFeatures_1.19.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QFeatures_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QFeatures_1.20.0.tgz vignettes: vignettes/QFeatures/inst/doc/Processing.html, vignettes/QFeatures/inst/doc/QFeatures.html, vignettes/QFeatures/inst/doc/read_QFeatures.html, vignettes/QFeatures/inst/doc/Visualization.html vignetteTitles: Processing quantitative proteomics data with QFeatures, Quantitative features for mass spectrometry data, Load data using readQFeatures(), Data visualization from a QFeatures object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QFeatures/inst/doc/Processing.R, vignettes/QFeatures/inst/doc/QFeatures.R, vignettes/QFeatures/inst/doc/read_QFeatures.R, vignettes/QFeatures/inst/doc/Visualization.R dependsOnMe: hdxmsqc, msqrob2, scp, scpdata importsMe: MetaboAnnotation, MsExperiment, mspms, omicsGMF, PSMatch suggestsMe: MsDataHub dependencyCount: 99 Package: qmtools Version: 1.14.0 Depends: R (>= 4.2.0), SummarizedExperiment Imports: rlang, ggplot2, patchwork, heatmaply, methods, MsCoreUtils, stats, igraph, VIM, scales, grDevices, graphics, limma Suggests: Rtsne, missForest, vsn, pcaMethods, pls, MsFeatures, impute, imputeLCMD, nlme, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 1c769e238898126374696b51267f7bb9 NeedsCompilation: no Title: Quantitative Metabolomics Data Processing Tools Description: The qmtools (quantitative metabolomics tools) package provides basic tools for processing quantitative metabolomics data with the standard SummarizedExperiment class. This includes functions for imputation, normalization, feature filtering, feature clustering, dimension-reduction, and visualization to help users prepare data for statistical analysis. This package also offers a convenient way to compute empirical Bayes statistics for which metabolic features are different between two sets of study samples. Several functions in this package could also be used in other types of omics data. biocViews: Metabolomics, Preprocessing, Normalization, DimensionReduction, MassSpectrometry Author: Jaehyun Joo [aut, cre], Blanca Himes [aut] Maintainer: Jaehyun Joo URL: https://github.com/HimesGroup/qmtools VignetteBuilder: knitr BugReports: https://github.com/HimesGroup/qmtools/issues git_url: https://git.bioconductor.org/packages/qmtools git_branch: RELEASE_3_22 git_last_commit: b38de25 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/qmtools_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qmtools_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qmtools_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qmtools_1.14.0.tgz vignettes: vignettes/qmtools/inst/doc/qmtools.html vignetteTitles: Quantitative metabolomics data processing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qmtools/inst/doc/qmtools.R dependencyCount: 163 Package: qpcrNorm Version: 1.68.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) Archs: x64 MD5sum: 1a563a3b8aef538ef6d28ce9c354721e NeedsCompilation: no Title: Data-driven normalization strategies for high-throughput qPCR data. Description: The package contains functions to perform normalization of high-throughput qPCR data. Basic functions for processing raw Ct data plus functions to generate diagnostic plots are also available. biocViews: Preprocessing, GeneExpression Author: Jessica Mar Maintainer: Jessica Mar git_url: https://git.bioconductor.org/packages/qpcrNorm git_branch: RELEASE_3_22 git_last_commit: 393692f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/qpcrNorm_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qpcrNorm_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qpcrNorm_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qpcrNorm_1.68.0.tgz vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf vignetteTitles: qPCR Normalization Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpcrNorm/inst/doc/qpcrNorm.R dependencyCount: 14 Package: qpgraph Version: 2.44.0 Depends: R (>= 3.5) Imports: methods, parallel, Matrix (>= 1.5-0), grid, annotate, graph (>= 1.45.1), Biobase, S4Vectors, BiocParallel, AnnotationDbi, IRanges, Seqinfo, GenomicRanges, GenomicFeatures, mvtnorm, qtl, Rgraphviz Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db, rlecuyer, snow, Category, GOstats License: GPL (>= 2) Archs: x64 MD5sum: bbe1bd3c6c2c0019cc8ea8d98f53523a NeedsCompilation: yes Title: Estimation of Genetic and Molecular Regulatory Networks from High-Throughput Genomics Data Description: Estimate gene and eQTL networks from high-throughput expression and genotyping assays. biocViews: Microarray, GeneExpression, Transcription, Pathways, NetworkInference, GraphAndNetwork, GeneRegulation, Genetics, GeneticVariability, SNP, Software Author: Robert Castelo [aut, cre], Alberto Roverato [aut] Maintainer: Robert Castelo URL: https://github.com/rcastelo/qpgraph BugReports: https://github.com/rcastelo/qpgraph/issues git_url: https://git.bioconductor.org/packages/qpgraph git_branch: RELEASE_3_22 git_last_commit: 7de7e0c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/qpgraph_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qpgraph_2.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qpgraph_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qpgraph_2.44.0.tgz vignettes: vignettes/qpgraph/inst/doc/BasicUsersGuide.pdf, vignettes/qpgraph/inst/doc/eQTLnetworks.pdf, vignettes/qpgraph/inst/doc/qpgraphSimulate.pdf, vignettes/qpgraph/inst/doc/qpTxRegNet.pdf vignetteTitles: BasicUsersGuide.pdf, Estimate eQTL networks using qpgraph, Simulating molecular regulatory networks using qpgraph, Reverse-engineer transcriptional regulatory networks using qpgraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpgraph/inst/doc/eQTLnetworks.R, vignettes/qpgraph/inst/doc/qpgraphSimulate.R, vignettes/qpgraph/inst/doc/qpTxRegNet.R importsMe: clipper, MOSClip, topologyGSA dependencyCount: 82 Package: qPLEXanalyzer Version: 1.27.0 Depends: R (>= 4.0), Biobase, MSnbase Imports: assertthat, BiocGenerics, Biostrings, dplyr (>= 1.0.0), ggdendro, ggplot2, graphics, grDevices, IRanges, limma, magrittr, preprocessCore, purrr, RColorBrewer, readr, rlang, scales, stats, stringr, tibble, tidyr, tidyselect, utils Suggests: patchwork, knitr, qPLEXdata, rmarkdown, statmod, testthat, UniProt.ws, vdiffr License: GPL-2 MD5sum: 260172755bb3302508b46bb38b8ae1a4 NeedsCompilation: no Title: Tools for quantitative proteomics data analysis Description: Tools for TMT based quantitative proteomics data analysis. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Normalization, Preprocessing, QualityControl, DataImport Author: Matthew Eldridge [aut], Kamal Kishore [aut], Ashley Sawle [aut, cre] Maintainer: Ashley Sawle VignetteBuilder: knitr BugReports: https://github.com/crukci-bioinformatics/qPLEXanalyzer/issues git_url: https://git.bioconductor.org/packages/qPLEXanalyzer git_branch: devel git_last_commit: 2f9ab92 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/qPLEXanalyzer_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qPLEXanalyzer_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qPLEXanalyzer_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qPLEXanalyzer_1.27.0.tgz vignettes: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.html vignetteTitles: qPLEXanalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.R dependsOnMe: qPLEXdata dependencyCount: 144 Package: QRscore Version: 1.2.0 Depends: R (>= 4.4.0) Imports: MASS, pscl, arrangements, hitandrun, assertthat, dplyr, BiocParallel Suggests: devtools, DESeq2, knitr, rmarkdown, testthat, BiocStyle License: GPL (>= 3) MD5sum: b4cd32673821a1785a92d999ee893e3a NeedsCompilation: no Title: Quantile Rank Score Description: In genomics, differential analysis enables the discovery of groups of genes implicating important biological processes such as cell differentiation and aging. Non-parametric tests of differential gene expression usually detect shifts in centrality (such as mean or median), and therefore suffer from diminished power against alternative hypotheses characterized by shifts in spread (such as variance). This package provides a flexible family of non-parametric two-sample tests and K-sample tests, which is based on theoretical work around non-parametric tests, spacing statistics and local asymptotic normality (Erdmann-Pham et al., 2022+ [arXiv:2008.06664v2]; Erdmann-Pham, 2023+ [arXiv:2209.14235v2]). biocViews: StatisticalMethod, DifferentialExpression, GeneExpression, StructuralGenomics, GeneTarget Author: Fanding Zhou [cre, aut] (ORCID: ), Alan Aw [aut] (ORCID: ), Dan Erdmann-Pham [aut], Jonathan Fischer [aut] (ORCID: ), Xurui Chen [ctb] Maintainer: Fanding Zhou URL: https://github.com/songlab-cal/QRscore VignetteBuilder: BiocStyle BugReports: https://github.com/songlab-cal/QRscore/issues git_url: https://git.bioconductor.org/packages/QRscore git_branch: RELEASE_3_22 git_last_commit: 89dd601 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/QRscore_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QRscore_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QRscore_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QRscore_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 39 Package: qsea Version: 1.36.0 Depends: R (>= 4.3) Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy, rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges, limma, Seqinfo, BiocGenerics, grDevices, zoo, BiocParallel, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle, knitr, rmarkdown, BiocManager, MASS License: GPL-2 MD5sum: b0857eb2b650de4a24697a5d5d595761 NeedsCompilation: yes Title: IP-seq data analysis and vizualization Description: qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples. biocViews: Sequencing, DNAMethylation, CpGIsland, ChIPSeq, Preprocessing, Normalization, QualityControl, Visualization, CopyNumberVariation, ChipOnChip, DifferentialMethylation Author: Matthias Lienhard [aut, cre] (ORCID: ), Lukas Chavez [aut] (ORCID: ), Ralf Herwig [aut] (ORCID: ) Maintainer: Matthias Lienhard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsea git_branch: RELEASE_3_22 git_last_commit: a9ac816 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/qsea_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qsea_1.35.1.zip vignettes: vignettes/qsea/inst/doc/qsea_tutorial.html vignetteTitles: qsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsea/inst/doc/qsea_tutorial.R suggestsMe: MEDIPSData dependencyCount: 64 Package: qsmooth Version: 1.26.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, utils, sva, stats, methods, graphics, Hmisc Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 6689b4da6cc224509c97b22548514abf NeedsCompilation: no Title: Smooth quantile normalization Description: Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing, RNASeq, BatchEffect Author: Stephanie C. Hicks [aut, cre] (ORCID: ), Kwame Okrah [aut], Koen Van den Berge [ctb], Hector Corrada Bravo [aut] (ORCID: ), Rafael Irizarry [aut] (ORCID: ) Maintainer: Stephanie C. Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsmooth git_branch: RELEASE_3_22 git_last_commit: e068166 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/qsmooth_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qsmooth_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qsmooth_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qsmooth_1.26.0.tgz vignettes: vignettes/qsmooth/inst/doc/qsmooth.html vignetteTitles: The qsmooth user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsmooth/inst/doc/qsmooth.R importsMe: CleanUpRNAseq suggestsMe: metamorphr dependencyCount: 119 Package: QSutils Version: 1.28.0 Depends: R (>= 3.5), Biostrings, pwalign, BiocGenerics, methods Imports: ape, stats, psych Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: GPL-2 MD5sum: af9f9d82e839575b8c88984bb79b0baf NeedsCompilation: no Title: Quasispecies Diversity Description: Set of utility functions for viral quasispecies analysis with NGS data. Most functions are equally useful for metagenomic studies. There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data. biocViews: Software, Genetics, DNASeq, GeneticVariability, Sequencing, Alignment, SequenceMatching, DataImport Author: Mercedes Guerrero-Murillo [cre, aut] (ORCID: ), Josep Gregori i Font [aut] (ORCID: ) Maintainer: Mercedes Guerrero-Murillo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QSutils git_branch: RELEASE_3_22 git_last_commit: 8911641 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/QSutils_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QSutils_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QSutils_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QSutils_1.28.0.tgz vignettes: vignettes/QSutils/inst/doc/QSUtils-Alignment.html, vignettes/QSutils/inst/doc/QSutils-Diversity.html, vignettes/QSutils/inst/doc/QSutils-Simulation.html vignetteTitles: QSUtils-Alignment, QSutils-Diversity, QSutils-Simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QSutils/inst/doc/QSUtils-Alignment.R, vignettes/QSutils/inst/doc/QSutils-Diversity.R, vignettes/QSutils/inst/doc/QSutils-Simulation.R importsMe: longreadvqs dependencyCount: 26 Package: qsvaR Version: 1.14.0 Depends: R (>= 4.2), SummarizedExperiment Imports: dplyr, sva, stats, ggplot2, rlang, methods Suggests: BiocFileCache, BiocStyle, covr, knitr, limma, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 69269550070e832a2a68adc81fb81fb3 NeedsCompilation: no Title: Generate Quality Surrogate Variable Analysis for Degradation Correction Description: The qsvaR package contains functions for removing the effect of degration in rna-seq data from postmortem brain tissue. The package is equipped to help users generate principal components associated with degradation. The components can be used in differential expression analysis to remove the effects of degradation. biocViews: Software, WorkflowStep, Normalization, BiologicalQuestion, DifferentialExpression, Sequencing, Coverage Author: Joshua Stolz [aut] (ORCID: ), Hedia Tnani [ctb] (ORCID: ), Leonardo Collado-Torres [ctb] (ORCID: ), Nicholas J. Eagles [aut, cre] (ORCID: ) Maintainer: Nicholas J. Eagles URL: https://github.com/LieberInstitute/qsvaR VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/qsvaR git_url: https://git.bioconductor.org/packages/qsvaR git_branch: RELEASE_3_22 git_last_commit: f960098 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/qsvaR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qsvaR_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qsvaR_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qsvaR_1.14.0.tgz vignettes: vignettes/qsvaR/inst/doc/Intro_qsvaR.html vignetteTitles: Introduction to qsvaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsvaR/inst/doc/Intro_qsvaR.R dependencyCount: 92 Package: QTLExperiment Version: 2.2.0 Depends: SummarizedExperiment Imports: methods, rlang, checkmate, dplyr, collapse, vroom, tidyr, tibble, utils, stats, ashr, S4Vectors, BiocGenerics Suggests: testthat, BiocStyle, knitr, rmarkdown, covr License: GPL-3 MD5sum: 5c2e0f9e26594d9551f72b4ac24fa0ae NeedsCompilation: no Title: S4 classes for QTL summary statistics and metadata Description: QLTExperiment defines an S4 class for storing and manipulating summary statistics from QTL mapping experiments in one or more states. It is based on the 'SummarizedExperiment' class and contains functions for creating, merging, and subsetting objects. 'QTLExperiment' also stores experiment metadata and has checks in place to ensure that transformations apply correctly. biocViews: FunctionalGenomics, DataImport, DataRepresentation, Infrastructure, Sequencing, SNP, Software Author: Christina Del Azodi [aut], Davis McCarthy [ctb], Amelia Dunstone [cre, aut] (ORCID: ) Maintainer: Amelia Dunstone URL: https://github.com/dunstone-a/QTLExperiment VignetteBuilder: knitr BugReports: https://github.com/dunstone-a/QTLExperiment/issues git_url: https://git.bioconductor.org/packages/QTLExperiment git_branch: RELEASE_3_22 git_last_commit: c6ecd79 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/QTLExperiment_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QTLExperiment_2.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QTLExperiment_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QTLExperiment_2.2.0.tgz vignettes: vignettes/QTLExperiment/inst/doc/QTLExperiment.html vignetteTitles: An introduction to the QTLExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QTLExperiment/inst/doc/QTLExperiment.R dependsOnMe: multistateQTL dependencyCount: 64 Package: Qtlizer Version: 1.24.0 Depends: R (>= 3.6.0) Imports: httr, curl, GenomicRanges, stringi Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 713c9ea5af9335fcb4ce6f0c86ac8ded NeedsCompilation: no Title: Comprehensive QTL annotation of GWAS results Description: This R package provides access to the Qtlizer web server. Qtlizer annotates lists of common small variants (mainly SNPs) and genes in humans with associated changes in gene expression using the most comprehensive database of published quantitative trait loci (QTLs). biocViews: GenomeWideAssociation, SNP, Genetics, LinkageDisequilibrium Author: Matthias Munz [aut, cre] (ORCID: ), Julia Remes [aut] Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/Qtlizer/issues git_url: https://git.bioconductor.org/packages/Qtlizer git_branch: RELEASE_3_22 git_last_commit: 8ba83e7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Qtlizer_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Qtlizer_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Qtlizer_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Qtlizer_1.24.0.tgz vignettes: vignettes/Qtlizer/inst/doc/Qtlizer.html vignetteTitles: Qtlizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Qtlizer/inst/doc/Qtlizer.R dependencyCount: 21 Package: quantiseqr Version: 1.18.0 Depends: R (>= 4.1.0) Imports: Biobase, limSolve, MASS, methods, preprocessCore, stats, SummarizedExperiment, ggplot2, tidyr, rlang, utils Suggests: AnnotationDbi, BiocStyle, dplyr, ExperimentHub, GEOquery, knitr, macrophage, org.Hs.eg.db, reshape2, rmarkdown, testthat, tibble License: GPL-3 MD5sum: cf9e9069980b449bdac284ffdb173b01 NeedsCompilation: no Title: Quantification of the Tumor Immune contexture from RNA-seq data Description: This package provides a streamlined workflow for the quanTIseq method, developed to perform the quantification of the Tumor Immune contexture from RNA-seq data. The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data. biocViews: GeneExpression, Software, Transcription, Transcriptomics, Sequencing, Microarray, Visualization, Annotation, ImmunoOncology, FeatureExtraction, Classification, StatisticalMethod, ExperimentHubSoftware, FlowCytometry Author: Federico Marini [aut, cre] (ORCID: ), Francesca Finotello [aut] (ORCID: ) Maintainer: Federico Marini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantiseqr git_branch: RELEASE_3_22 git_last_commit: 4091b22 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/quantiseqr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/quantiseqr_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/quantiseqr_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/quantiseqr_1.18.0.tgz vignettes: vignettes/quantiseqr/inst/doc/using_quantiseqr.html vignetteTitles: Using quantiseqr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantiseqr/inst/doc/using_quantiseqr.R importsMe: easier dependencyCount: 58 Package: quantro Version: 1.44.0 Depends: R (>= 4.0) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: rmarkdown, knitr, RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: x64 MD5sum: 15d3395fc4efa90dde3a374649f67a1e NeedsCompilation: no Title: A test for when to use quantile normalization Description: A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing Author: Stephanie Hicks [aut, cre] (ORCID: ), Rafael Irizarry [aut] (ORCID: ) Maintainer: Stephanie Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantro git_branch: RELEASE_3_22 git_last_commit: d148fc5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/quantro_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/quantro_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/quantro_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/quantro_1.44.0.tgz vignettes: vignettes/quantro/inst/doc/quantro.html vignetteTitles: The quantro user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantro/inst/doc/quantro.R importsMe: yarn suggestsMe: extraChIPs, qsmooth dependencyCount: 152 Package: quantsmooth Version: 1.76.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: cb3865ee8f793ec8c5b9ed4c9f195e1c NeedsCompilation: no Title: Quantile smoothing and genomic visualization of array data Description: Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. biocViews: Visualization, CopyNumberVariation Author: Jan Oosting, Paul Eilers, Renee Menezes Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/quantsmooth git_branch: RELEASE_3_22 git_last_commit: d417779 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/quantsmooth_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/quantsmooth_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/quantsmooth_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/quantsmooth_1.76.0.tgz vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf vignetteTitles: quantsmooth hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantsmooth/inst/doc/quantsmooth.R importsMe: GWASTools, SIM suggestsMe: PREDA dependencyCount: 14 Package: QuasR Version: 1.50.0 Depends: R (>= 4.4), parallel, GenomicRanges, Rbowtie Imports: methods, grDevices, graphics, utils, stats, tools, BiocGenerics, S4Vectors, IRanges, Biobase, Biostrings, BSgenome, Rsamtools (>= 2.13.1), GenomicFeatures, txdbmaker, ShortRead, BiocParallel, Seqinfo, rtracklayer, GenomicFiles, AnnotationDbi LinkingTo: Rhtslib (>= 1.99.1) Suggests: Gviz, BiocStyle, GenomeInfoDbData, GenomicAlignments, Rhisat2, knitr, rmarkdown, covr, testthat License: GPL-2 MD5sum: db339f04bee5deac3fbedc50ffec9fb8 NeedsCompilation: yes Title: Quantify and Annotate Short Reads in R Description: This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. Read alignments are either generated through Rbowtie (data from DNA/ChIP/ATAC/Bis-seq experiments) or Rhisat2 (data from RNA-seq experiments that require spliced alignments), or can be provided in the form of bam files. biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq, MethylSeq, Coverage, Alignment, QualityControl, ImmunoOncology Author: Anita Lerch [aut], Adam Alexander Thil SMITH [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Dimos Gaidatzis [aut], Michael Stadler [aut, cre] (ORCID: ) Maintainer: Michael Stadler URL: https://bioconductor.org/packages/QuasR SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/QuasR/issues git_url: https://git.bioconductor.org/packages/QuasR git_branch: RELEASE_3_22 git_last_commit: 3274f2d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/QuasR_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QuasR_1.49.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QuasR_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QuasR_1.50.0.tgz vignettes: vignettes/QuasR/inst/doc/QuasR.html vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuasR/inst/doc/QuasR.R importsMe: SingleMoleculeFootprinting suggestsMe: eisaR dependencyCount: 116 Package: QuaternaryProd Version: 1.44.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18) LinkingTo: Rcpp Suggests: knitr License: GPL (>=3) MD5sum: d115c63a22b8e370c31051c48f3618ac NeedsCompilation: yes Title: Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs Description: QuaternaryProd is an R package that performs causal reasoning on biological networks, including publicly available networks such as STRINGdb. QuaternaryProd is an open-source alternative to commercial products such as Inginuity Pathway Analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test (Enrichment test). The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. The Vignette provides more details on the Quaternary Statistic and illustrates an example of how to perform causal reasoning using STRINGdb. biocViews: GraphAndNetwork, GeneExpression, Transcription Author: Carl Tony Fakhry [cre, aut], Ping Chen [ths], Kourosh Zarringhalam [aut, ths] Maintainer: Carl Tony Fakhry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuaternaryProd git_branch: RELEASE_3_22 git_last_commit: e2ad6e4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/QuaternaryProd_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QuaternaryProd_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QuaternaryProd_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QuaternaryProd_1.44.0.tgz vignettes: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.pdf vignetteTitles: QuaternaryProdVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.R dependencyCount: 21 Package: QUBIC Version: 1.38.0 Depends: R (>= 3.1), biclust Imports: Rcpp (>= 0.11.0), methods, Matrix LinkingTo: Rcpp, RcppArmadillo Suggests: QUBICdata, qgraph, fields, knitr, rmarkdown Enhances: RColorBrewer License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: ee979a47d82f9bcff2e0a32cb939f0da NeedsCompilation: yes Title: An R package for qualitative biclustering in support of gene co-expression analyses Description: The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape). biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization, GeneExpression, Network Author: Yu Zhang [aut, cre], Qin Ma [aut] Maintainer: Yu Zhang URL: http://github.com/zy26/QUBIC SystemRequirements: C++11, Rtools (>= 3.1) VignetteBuilder: knitr BugReports: http://github.com/zy26/QUBIC/issues git_url: https://git.bioconductor.org/packages/QUBIC git_branch: RELEASE_3_22 git_last_commit: d4c6579 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/QUBIC_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QUBIC_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QUBIC_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QUBIC_1.38.0.tgz vignettes: vignettes/QUBIC/inst/doc/qubic_vignette.pdf vignetteTitles: QUBIC Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QUBIC/inst/doc/qubic_vignette.R importsMe: mosbi suggestsMe: runibic dependencyCount: 49 Package: qusage Version: 2.44.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, emmeans, fftw License: GPL (>= 2) MD5sum: e4473859502845016246e700b4f65f02 NeedsCompilation: no Title: qusage: Quantitative Set Analysis for Gene Expression Description: This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. The QuSAGE package also includes a mixed effects model implementation, as described in (Turner JA et al, BMC Bioinformatics, 2015), and a meta-analysis framework as described in (Meng H, et al. PLoS Comput Biol. 2019). For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) biocViews: GeneSetEnrichment, Microarray, RNASeq, Software, ImmunoOncology Author: Christopher Bolen and Gur Yaari, with contributions from Juilee Thakar, Hailong Meng, Jacob Turner, Derek Blankenship, and Steven Kleinstein Maintainer: Christopher Bolen URL: http://clip.med.yale.edu/qusage git_url: https://git.bioconductor.org/packages/qusage git_branch: RELEASE_3_22 git_last_commit: 123902b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/qusage_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qusage_2.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qusage_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qusage_2.44.0.tgz vignettes: vignettes/qusage/inst/doc/qusage.pdf vignetteTitles: Running qusage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qusage/inst/doc/qusage.R importsMe: mExplorer suggestsMe: SigCheck dependencyCount: 19 Package: qvalue Version: 2.42.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL Archs: x64 MD5sum: 3085a05ef140e71c6b492800f6aa14cb NeedsCompilation: no Title: Q-value estimation for false discovery rate control Description: This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. biocViews: MultipleComparisons Author: John D. Storey [aut, cre], Andrew J. Bass [aut], Alan Dabney [aut], David Robinson [aut], Gregory Warnes [ctb] Maintainer: John D. Storey , Andrew J. Bass URL: http://github.com/jdstorey/qvalue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qvalue git_branch: RELEASE_3_22 git_last_commit: 6527d7b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/qvalue_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qvalue_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qvalue_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qvalue_2.42.0.tgz vignettes: vignettes/qvalue/inst/doc/qvalue.pdf vignetteTitles: qvalue Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qvalue/inst/doc/qvalue.R dependsOnMe: anota, DEGseq, DrugVsDisease, r3Cseq, webbioc, BonEV, cp4p, isva, ReAD, STAREG importsMe: Anaquin, anota, clusterProfiler, CTSV, DegCre, derfinder, DOSE, edge, erccdashboard, EventPointer, FindIT2, fishpond, LimROTS, metaseqR2, methylKit, MOMA, msmsTests, MWASTools, netresponse, normr, OPWeight, PAST, PolySTest, RiboDiPA, RNAsense, Rnits, RolDE, SDAMS, sights, signatureSearch, SpaceMarkers, subSeq, vsclust, webbioc, IHWpaper, AEenrich, cancerGI, fdrDiscreteNull, glmmSeq, groupedSurv, HDMT, jaccard, medScan, NBPSeq, qch, SeqFeatR, sffdr, shinyExprPortal, ssizeRNA, TFactSR suggestsMe: biobroom, LBE, PREDA, RnBeads, swfdr, BootstrapQTL, dartR, dartR.base, dartR.popgen, DGEobj.utils, easylabel, familiar, jackstraw, multiDEGGs, mutoss, readyomics, Rediscover, seqgendiff, volcano3D, wrMisc dependencyCount: 30 Package: R3CPET Version: 1.42.0 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: methods, parallel, ggplot2, pheatmap, clValid, igraph, data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors, IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), ggbio LinkingTo: Rcpp Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene, biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny, ChIPpeakAnno License: GPL (>=2) MD5sum: fb63bfa3ca329dcdb48261d62e79055c NeedsCompilation: yes Title: 3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process Description: The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results. biocViews: NetworkInference, GenePrediction, Bayesian, GraphAndNetwork, Network, GeneExpression, HiC Author: Djekidel MN, Yang Chen et al. Maintainer: Mohamed Nadhir Djekidel VignetteBuilder: knitr BugReports: https://github.com/sirusb/R3CPET/issues git_url: https://git.bioconductor.org/packages/R3CPET git_branch: RELEASE_3_22 git_last_commit: fd5a1f8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/R3CPET_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/R3CPET_1.41.0.zip vignettes: vignettes/R3CPET/inst/doc/R3CPET.pdf vignetteTitles: 3CPET: Finding Co-factor Complexes maintaining Chia-PET interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R3CPET/inst/doc/R3CPET.R dependencyCount: 141 Package: r3Cseq Version: 1.56.0 Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue Imports: methods, Seqinfo, IRanges, Biostrings, data.table, sqldf, RColorBrewer Suggests: BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Rnorvegicus.UCSC.rn5.masked License: GPL-3 Archs: x64 MD5sum: 9a05c19020dbfcdcded992f2a33f4de5 NeedsCompilation: no Title: Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq) Description: This package is used for the analysis of long-range chromatin interactions from 3C-seq assay. biocViews: Preprocessing, Sequencing Author: Supat Thongjuea, MRC WIMM Centre for Computational Biology, Weatherall Institute of Molecular Medicine, University of Oxford, UK Maintainer: Supat Thongjuea or URL: http://r3cseq.genereg.net,https://github.com/supatt-lab/r3Cseq/ git_url: https://git.bioconductor.org/packages/r3Cseq git_branch: RELEASE_3_22 git_last_commit: 4184f01 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/r3Cseq_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/r3Cseq_1.55.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/r3Cseq_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/r3Cseq_1.56.0.tgz vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf vignetteTitles: r3Cseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/r3Cseq/inst/doc/r3Cseq.R dependencyCount: 96 Package: R4RNA Version: 1.38.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 MD5sum: d8e155cda5ebe9cd63a0db9e56499ce8 NeedsCompilation: no Title: An R package for RNA visualization and analysis Description: A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each. biocViews: Alignment, MultipleSequenceAlignment, Preprocessing, Visualization, DataImport, DataRepresentation, MultipleComparison Author: Daniel Lai, Irmtraud Meyer Maintainer: Daniel Lai URL: http://www.e-rna.org/r-chie/ git_url: https://git.bioconductor.org/packages/R4RNA git_branch: RELEASE_3_22 git_last_commit: 90fdbbd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/R4RNA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/R4RNA_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/R4RNA_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/R4RNA_1.38.0.tgz vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R importsMe: ggmsa, rnaCrosslinkOO suggestsMe: rfaRm dependencyCount: 15 Package: RadioGx Version: 2.14.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, BiocGenerics, data.table, S4Vectors, Biobase, parallel, BiocParallel, RColorBrewer, caTools, magicaxis, methods, reshape2, scales, grDevices, graphics, stats, utils, assertthat, matrixStats, downloader Suggests: rmarkdown, BiocStyle, knitr, pander, markdown License: GPL-3 MD5sum: 5c6611270efefbc4d9d4a1b5876b57cf NeedsCompilation: no Title: Analysis of Large-Scale Radio-Genomic Data Description: Computational tool box for radio-genomic analysis which integrates radio-response data, radio-biological modelling and comprehensive cell line annotations for hundreds of cancer cell lines. The 'RadioSet' class enables creation and manipulation of standardized datasets including information about cancer cells lines, radio-response assays and dose-response indicators. Included methods allow fitting and plotting dose-response data using established radio-biological models along with quality control to validate results. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, references, as well as: Manem, V. et al (2018) . biocViews: Software, Pharmacogenetics, QualityControl, Survival, Pharmacogenomics, Classification Author: Venkata Manem [aut], Petr Smirnov [aut], Ian Smith [aut], Meghan Lambie [aut], Christopher Eeles [aut], Scott Bratman [aut], Jermiah Joseph [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RadioGx git_branch: RELEASE_3_22 git_last_commit: 0605239 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RadioGx_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RadioGx_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RadioGx_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RadioGx_2.14.0.tgz vignettes: vignettes/RadioGx/inst/doc/RadioGx.html vignetteTitles: RadioGx: An R Package for Analysis of Large Radiogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RadioGx/inst/doc/RadioGx.R dependencyCount: 142 Package: raer Version: 1.8.0 Imports: stats, methods, GenomicRanges, IRanges, Rsamtools, BSgenome, Biostrings, SummarizedExperiment, SingleCellExperiment, S4Vectors, Seqinfo, GenomeInfoDb, GenomicAlignments, GenomicFeatures, BiocGenerics, BiocParallel, rtracklayer, Matrix, cli LinkingTo: Rhtslib Suggests: testthat (>= 3.0.0), knitr, DESeq2, edgeR, limma, rmarkdown, BiocStyle, ComplexHeatmap, TxDb.Hsapiens.UCSC.hg38.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, BSgenome.Hsapiens.NCBI.GRCh38, scater, scran, scuttle, AnnotationHub, covr, raerdata, txdbmaker License: MIT + file LICENSE MD5sum: 8e198d4ebebaedd6cfb5c6e27d9d67cc NeedsCompilation: yes Title: RNA editing tools in R Description: Toolkit for identification and statistical testing of RNA editing signals from within R. Provides support for identifying sites from bulk-RNA and single cell RNA-seq datasets, and general methods for extraction of allelic read counts from alignment files. Facilitates annotation and exploratory analysis of editing signals using Bioconductor packages and resources. biocViews: MultipleComparison, RNASeq, SingleCell, Sequencing, Coverage, Epitranscriptomics, FeatureExtraction, Annotation, Alignment Author: Kent Riemondy [aut, cre] (ORCID: ), Kristen Wells-Wrasman [aut] (ORCID: ), Ryan Sheridan [ctb] (ORCID: ), Jay Hesselberth [ctb] (ORCID: ), RNA Bioscience Initiative [cph, fnd] Maintainer: Kent Riemondy URL: https://rnabioco.github.io/raer, https://github.com/rnabioco/raer SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/rnabioco/raer/issues git_url: https://git.bioconductor.org/packages/raer git_branch: RELEASE_3_22 git_last_commit: 1e83c0c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/raer_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/raer_1.7.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/raer_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/raer_1.8.0.tgz vignettes: vignettes/raer/inst/doc/raer.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/raer/inst/doc/raer.R dependencyCount: 80 Package: RaggedExperiment Version: 1.34.0 Depends: R (>= 4.5.0), GenomicRanges (>= 1.61.1) Imports: BiocBaseUtils, BiocGenerics, Seqinfo, IRanges, Matrix, MatrixGenerics, methods, S4Vectors, stats, SummarizedExperiment (>= 1.39.1), utils Suggests: BiocStyle, knitr, rmarkdown, testthat, GenomeInfoDb, MultiAssayExperiment License: Artistic-2.0 MD5sum: e191e056f9ee21bdb914e188d2eb2785 NeedsCompilation: no Title: Representation of Sparse Experiments and Assays Across Samples Description: This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset. biocViews: Infrastructure, DataRepresentation Author: Martin Morgan [aut], Marcel Ramos [aut, cre] (ORCID: ), Lydia King [ctb] Maintainer: Marcel Ramos URL: https://bioconductor.github.io/RaggedExperiment, https://bioconductor.org/packages/RaggedExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/RaggedExperiment/issues git_url: https://git.bioconductor.org/packages/RaggedExperiment git_branch: RELEASE_3_22 git_last_commit: 2cf1b41 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RaggedExperiment_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RaggedExperiment_1.33.7.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RaggedExperiment_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RaggedExperiment_1.34.0.tgz vignettes: vignettes/RaggedExperiment/inst/doc/ASCAT_to_RaggedExperiment.html, vignettes/RaggedExperiment/inst/doc/RaggedExperiment.html vignetteTitles: ASCAT to RaggedExperiment, RaggedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RaggedExperiment/inst/doc/ASCAT_to_RaggedExperiment.R, vignettes/RaggedExperiment/inst/doc/RaggedExperiment.R dependsOnMe: CNVRanger, curatedPCaData importsMe: cBioPortalData, omicsPrint, RTCGAToolbox, TCGAutils, terraTCGAdata suggestsMe: maftools, MultiAssayExperiment, MultiDataSet, TENxIO, curatedTCGAData, SingleCellMultiModal dependencyCount: 26 Package: RAIDS Version: 1.8.0 Depends: R (>= 4.2.0), gdsfmt, SNPRelate, stats, utils, GENESIS, dplyr, Rsamtools Imports: S4Vectors, GenomicRanges, ensembldb, BSgenome, AnnotationDbi, methods, class, pROC, IRanges, AnnotationFilter, rlang, VariantAnnotation, MatrixGenerics, ggplot2, stringr Suggests: testthat, knitr, rmarkdown, BiocStyle, withr, Seqinfo, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: Apache License (>= 2) Archs: x64 MD5sum: c82b7b83e2549858af45ff10fee87706 NeedsCompilation: no Title: Robust Ancestry Inference using Data Synthesis Description: This package implements specialized algorithms that enable genetic ancestry inference from various cancer sequences sources (RNA, Exome and Whole-Genome sequences). This package also implements a simulation algorithm that generates synthetic cancer-derived data. This code and analysis pipeline was designed and developed for the following publication: Belleau, P et al. Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms. Cancer Res 1 January 2023; 83 (1): 49–58. biocViews: Genetics, Software, Sequencing, WholeGenome, PrincipalComponent, GeneticVariability, DimensionReduction, BiocViews Author: Pascal Belleau [cre, aut] (ORCID: ), Astrid Deschênes [aut] (ORCID: ), David A. Tuveson [aut] (ORCID: ), Alexander Krasnitz [aut] Maintainer: Pascal Belleau URL: https://krasnitzlab.github.io/RAIDS/ VignetteBuilder: knitr BugReports: https://github.com/KrasnitzLab/RAIDS/issues git_url: https://git.bioconductor.org/packages/RAIDS git_branch: RELEASE_3_22 git_last_commit: 4c9a678 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RAIDS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RAIDS_1.7.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RAIDS_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RAIDS_1.8.0.tgz vignettes: vignettes/RAIDS/inst/doc/Create_Reference_GDS_File.html, vignettes/RAIDS/inst/doc/RAIDS.html, vignettes/RAIDS/inst/doc/Wrappers.html vignetteTitles: Population reference dataset GDS files, Robust Ancestry Inference using Data Synthesis, Using wrappper functionss hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RAIDS/inst/doc/Create_Reference_GDS_File.R, vignettes/RAIDS/inst/doc/RAIDS.R, vignettes/RAIDS/inst/doc/Wrappers.R dependencyCount: 168 Package: rain Version: 1.44.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: 272de1d5fc5aa579610b123237f5782a NeedsCompilation: no Title: Rhythmicity Analysis Incorporating Non-parametric Methods Description: This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements. biocViews: TimeCourse, Genetics, SystemsBiology, Proteomics, Microarray, MultipleComparison Author: Paul F. Thaben, Pål O. Westermark Maintainer: Paul F. Thaben git_url: https://git.bioconductor.org/packages/rain git_branch: RELEASE_3_22 git_last_commit: 7ff0c32 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rain_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rain_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rain_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rain_1.44.0.tgz vignettes: vignettes/rain/inst/doc/rain.pdf vignetteTitles: Rain Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rain/inst/doc/rain.R dependencyCount: 17 Package: ramr Version: 1.18.0 Depends: R (>= 4.1) Imports: methods, data.table, Seqinfo, GenomicRanges, IRanges, BiocGenerics, S4Vectors, Rcpp LinkingTo: Rcpp Suggests: RUnit, knitr, rmarkdown, ggplot2, gridExtra, annotatr, LOLA, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, parallel, doParallel, foreach, doRNG, matrixStats, EnvStats, ExtDist, gamlss, gamlss.dist License: Artistic-2.0 MD5sum: 3da04f4c08004ed3f0f6344bbf731dad NeedsCompilation: yes Title: Detection of Rare Aberrantly Methylated Regions in Array and NGS Data Description: ramr is an R package for detection of epimutations (i.e., infrequent aberrant DNA methylation events) in large data sets obtained by methylation profiling using array or high-throughput methylation sequencing. In addition, package provides functions to visualize found aberrantly methylated regions (AMRs), to generate sets of all possible regions to be used as reference sets for enrichment analysis, and to generate biologically relevant test data sets for performance evaluation of AMR/DMR search algorithms. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, MethylationArray, MethylSeq Author: Oleksii Nikolaienko [aut, cre] (ORCID: ) Maintainer: Oleksii Nikolaienko URL: https://github.com/BBCG/ramr SystemRequirements: C++20, GNU make VignetteBuilder: knitr BugReports: https://github.com/BBCG/ramr/issues git_url: https://git.bioconductor.org/packages/ramr git_branch: RELEASE_3_22 git_last_commit: f809d5f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ramr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ramr_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ramr_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ramr_1.18.0.tgz vignettes: vignettes/ramr/inst/doc/ramr.html vignetteTitles: ramr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramr/inst/doc/ramr.R dependencyCount: 13 Package: ramwas Version: 1.34.0 Depends: R (>= 3.3.0), methods, filematrix Imports: graphics, stats, utils, digest, glmnet, KernSmooth, grDevices, GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings, BiocGenerics Suggests: knitr, rmarkdown, pander, BiocStyle, BSgenome.Ecoli.NCBI.20080805 License: LGPL-3 MD5sum: 97706595fd4411e605b7fa50a19cff51 NeedsCompilation: yes Title: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms Description: A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) . biocViews: DNAMethylation, Sequencing, QualityControl, Coverage, Preprocessing, Normalization, BatchEffect, PrincipalComponent, DifferentialMethylation, Visualization Author: Andrey A Shabalin [aut, cre] (ORCID: ), Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut] Maintainer: Andrey A Shabalin URL: https://bioconductor.org/packages/ramwas/ VignetteBuilder: knitr BugReports: https://github.com/andreyshabalin/ramwas/issues git_url: https://git.bioconductor.org/packages/ramwas git_branch: RELEASE_3_22 git_last_commit: aedf8f9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ramwas_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ramwas_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ramwas_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ramwas_1.34.0.tgz vignettes: vignettes/ramwas/inst/doc/RW1_intro.html, vignettes/ramwas/inst/doc/RW2_CpG_sets.html, vignettes/ramwas/inst/doc/RW3_BAM_QCs.html, vignettes/ramwas/inst/doc/RW4_SNPs.html, vignettes/ramwas/inst/doc/RW5a_matrix.html, vignettes/ramwas/inst/doc/RW5c_matrix.html, vignettes/ramwas/inst/doc/RW6_param.html vignetteTitles: 1. Overview, 2. CpG sets, 3. BAM Quality Control Measures, 4. Joint Analysis of Methylation and Genotype Data, 5.a. Analyzing Illumina Methylation Array Data, 5.c. Analyzing data from other sources, 6. RaMWAS parameters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramwas/inst/doc/RW1_intro.R, vignettes/ramwas/inst/doc/RW2_CpG_sets.R, vignettes/ramwas/inst/doc/RW3_BAM_QCs.R, vignettes/ramwas/inst/doc/RW4_SNPs.R, vignettes/ramwas/inst/doc/RW5a_matrix.R, vignettes/ramwas/inst/doc/RW5c_matrix.R, vignettes/ramwas/inst/doc/RW6_param.R dependencyCount: 100 Package: randPack Version: 1.56.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: cd593cb5f620decf7ccc99ed22b17ba6 NeedsCompilation: no Title: Randomization routines for Clinical Trials Description: A suite of classes and functions for randomizing patients in clinical trials. biocViews: StatisticalMethod Author: Vincent Carey and Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/randPack git_branch: RELEASE_3_22 git_last_commit: cd1d7b6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/randPack_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/randPack_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/randPack_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/randPack_1.56.0.tgz vignettes: vignettes/randPack/inst/doc/randPack.pdf vignetteTitles: Clinical trial randomization infrastructure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randPack/inst/doc/randPack.R dependencyCount: 7 Package: randRotation Version: 1.22.0 Imports: methods, graphics, utils, stats, Rdpack (>= 0.7) Suggests: knitr, BiocParallel, lme4, nlme, rmarkdown, BiocStyle, testthat (>= 2.1.0), limma, sva License: GPL-3 MD5sum: ede9681a4989577aab75b14981e6d425 NeedsCompilation: no Title: Random Rotation Methods for High Dimensional Data with Batch Structure Description: A collection of methods for performing random rotations on high-dimensional, normally distributed data (e.g. microarray or RNA-seq data) with batch structure. The random rotation approach allows exact testing of dependent test statistics with linear models following arbitrary batch effect correction methods. biocViews: Software, Sequencing, BatchEffect, BiomedicalInformatics, RNASeq, Preprocessing, Microarray, DifferentialExpression, GeneExpression, Genetics, MicroRNAArray, Normalization, StatisticalMethod Author: Peter Hettegger [aut, cre] (ORCID: ) Maintainer: Peter Hettegger URL: https://github.com/phettegger/randRotation VignetteBuilder: knitr BugReports: https://github.com/phettegger/randRotation/issues git_url: https://git.bioconductor.org/packages/randRotation git_branch: RELEASE_3_22 git_last_commit: 4c9448f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/randRotation_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/randRotation_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/randRotation_1.22.0.tgz vignettes: vignettes/randRotation/inst/doc/randRotationIntro.pdf vignetteTitles: Random Rotation Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randRotation/inst/doc/randRotationIntro.R dependencyCount: 7 Package: RankProd Version: 3.36.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: 395ccf89b269d8eec46bc819f481160d NeedsCompilation: no Title: Rank Product method for identifying differentially expressed genes with application in meta-analysis Description: Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. biocViews: DifferentialExpression, StatisticalMethod, Software, ResearchField, Metabolomics, Lipidomics, Proteomics, SystemsBiology, GeneExpression, Microarray, GeneSignaling Author: Francesco Del Carratore , Andris Jankevics Fangxin Hong , Ben Wittner , Rainer Breitling , and Florian Battke Maintainer: Francesco Del Carratore git_url: https://git.bioconductor.org/packages/RankProd git_branch: RELEASE_3_22 git_last_commit: ed5e638 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RankProd_3.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RankProd_3.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RankProd_3.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RankProd_3.36.0.tgz vignettes: vignettes/RankProd/inst/doc/RankProd.pdf vignetteTitles: RankProd Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RankProd/inst/doc/RankProd.R dependsOnMe: tRanslatome importsMe: mslp, POMA, synlet suggestsMe: sigQC dependencyCount: 6 Package: RAREsim Version: 1.14.0 Depends: R (>= 4.1.0) Imports: nloptr Suggests: markdown, ggplot2, BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: de585232da9f618d76e93f83016937ee NeedsCompilation: no Title: Simulation of Rare Variant Genetic Data Description: Haplotype simulations of rare variant genetic data that emulates real data can be performed with RAREsim. RAREsim uses the expected number of variants in MAC bins - either as provided by default parameters or estimated from target data - and an abundance of rare variants as simulated HAPGEN2 to probabilistically prune variants. RAREsim produces haplotypes that emulate real sequencing data with respect to the total number of variants, allele frequency spectrum, haplotype structure, and variant annotation. biocViews: Genetics, Software, VariantAnnotation, Sequencing Author: Megan Null [aut], Ryan Barnard [cre] Maintainer: Ryan Barnard URL: https://github.com/meganmichelle/RAREsim VignetteBuilder: knitr BugReports: https://github.com/meganmichelle/RAREsim/issues git_url: https://git.bioconductor.org/packages/RAREsim git_branch: RELEASE_3_22 git_last_commit: 9740e84 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RAREsim_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RAREsim_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RAREsim_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RAREsim_1.14.0.tgz vignettes: vignettes/RAREsim/inst/doc/RAREsim_Vignette.html vignetteTitles: RAREsim Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RAREsim/inst/doc/RAREsim_Vignette.R dependencyCount: 1 Package: RareVariantVis Version: 2.38.0 Depends: BiocGenerics, VariantAnnotation, googleVis, GenomicFeatures Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, gtools, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, SummarizedExperiment, GenomicScores Suggests: knitr License: Artistic-2.0 MD5sum: 1973b0bbcccb2c53ec43edbb559600e2 NeedsCompilation: no Title: A suite for analysis of rare genomic variants in whole genome sequencing data Description: Second version of RareVariantVis package aims to provide comprehensive information about rare variants for your genome data. It annotates, filters and presents genomic variants (especially rare ones) in a global, per chromosome way. For discovered rare variants CRISPR guide RNAs are designed, so the user can plan further functional studies. Large structural variants, including copy number variants are also supported. Package accepts variants directly from variant caller - for example GATK or Speedseq. Output of package are lists of variants together with adequate visualization. Visualization of variants is performed in two ways - standard that outputs png figures and interactive that uses JavaScript d3 package. Interactive visualization allows to analyze trio/family data, for example in search for causative variants in rare Mendelian diseases, in point-and-click interface. The package includes homozygous region caller and allows to analyse whole human genomes in less than 30 minutes on a desktop computer. RareVariantVis disclosed novel causes of several rare monogenic disorders, including one with non-coding causative variant - keratolythic winter erythema. biocViews: GenomicVariation, Sequencing, WholeGenome Author: Adam Gudys and Tomasz Stokowy Maintainer: Tomasz Stokowy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RareVariantVis git_branch: RELEASE_3_22 git_last_commit: 883a165 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RareVariantVis_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RareVariantVis_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RareVariantVis_2.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RareVariantVis_2.38.0.tgz vignettes: vignettes/RareVariantVis/inst/doc/RareVariantsVis.pdf vignetteTitles: RareVariantVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RareVariantVis/inst/doc/RareVariantsVis.R dependencyCount: 111 Package: Rarr Version: 1.10.0 Depends: BiocGenerics, DelayedArray, R (>= 4.1.0) Imports: curl, jsonlite, methods, paws.storage, R.utils, utils Suggests: BiocStyle, covr, knitr, testthat (>= 3.0.0), withr License: MIT + file LICENSE Archs: x64 MD5sum: 6c38bbf52939b5ad86085d86c70d9fbd NeedsCompilation: yes Title: Read Zarr Files in R Description: The Zarr specification defines a format for chunked, compressed, N-dimensional arrays. It's design allows efficient access to subsets of the stored array, and supports both local and cloud storage systems. Rarr aims to implement this specification in R with minimal reliance on an external tools or libraries. biocViews: DataImport Author: Mike Smith [aut, ccp] (ORCID: , Maintainer from 2022 to 2025.), Hugo Gruson [aut, cre] (ORCID: ), Artür Manukyan [ctb], Sharla Gelfand [ctb], German Network for Bioinformatics Infrastructure - de.NBI [fnd] Maintainer: Hugo Gruson URL: https://huber-group-embl.github.io/Rarr/, https://github.com/Huber-group-EMBL/Rarr SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/Rarr/issues git_url: https://git.bioconductor.org/packages/Rarr git_branch: RELEASE_3_22 git_last_commit: 780b137 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rarr_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rarr_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rarr_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rarr_1.10.0.tgz vignettes: vignettes/Rarr/inst/doc/Rarr.html vignetteTitles: "Working with Zarr arrays in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rarr/inst/doc/Rarr.R dependencyCount: 45 Package: rawDiag Version: 1.6.0 Depends: R (>= 4.4) Imports: dplyr, ggplot2 (>= 3.4), grDevices, hexbin, htmltools, BiocManager, BiocParallel, rawrr (>= 1.15.5), rlang, reshape2, scales, shiny (>= 1.5), stats, utils Suggests: BiocStyle (>= 2.28), ExperimentHub, tartare, knitr, testthat License: GPL-3 MD5sum: 3d94d0cd0686b86a5020ff05bdeceb7f NeedsCompilation: no Title: Brings Orbitrap Mass Spectrometry Data to Life; Fast and Colorful Description: Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) poses a nontrivial challenge. The rawDiag package facilitates rational method optimization by generating MS operator-tailored diagnostic plots of scan-level metadata. The package is designed for use on the R shell or as a Shiny application on the Orbitrap instrument PC. biocViews: MassSpectrometry, Proteomics, Metabolomics, Infrastructure, Software, ShinyApps Author: Christian Panse [aut, cre] (ORCID: ), Christian Trachsel [aut], Tobias Kockmann [aut] Maintainer: Christian Panse URL: https://github.com/fgcz/rawDiag/ VignetteBuilder: knitr BugReports: https://github.com/fgcz/rawDiag/issues git_url: https://git.bioconductor.org/packages/rawDiag git_branch: RELEASE_3_22 git_last_commit: e72968b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rawDiag_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rawDiag_1.5.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rawDiag_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rawDiag_1.6.0.tgz vignettes: vignettes/rawDiag/inst/doc/rawDiag.html vignetteTitles: Brings Orbitrap Mass Spectrometry Data to Life; Fast and Colorful hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rawDiag/inst/doc/rawDiag.R dependencyCount: 72 Package: rawrr Version: 1.18.0 Depends: R (>= 4.5) Imports: grDevices, graphics, stats, utils Suggests: BiocStyle (>= 2.5), ExperimentHub, knitr, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 MD5sum: 4938b3cfcdf844a128ed046b9aae4f41 NeedsCompilation: no Title: Direct Access to Orbitrap Data and Beyond Description: This package wraps the functionality of the Thermo Fisher Scientic RawFileReader .NET 8.0 assembly. Within the R environment, spectra and chromatograms are represented by S3 objects. The package provides basic functions to download and install the required third-party libraries. The package is developed, tested, and used at the Functional Genomics Center Zurich, Switzerland. biocViews: MassSpectrometry, Proteomics, Metabolomics, Infrastructure, Software Author: Christian Panse [aut, cre] (ORCID: ), Leonardo Schwarz [ctb] (ORCID: ), Tobias Kockmann [aut] (ORCID: ) Maintainer: Christian Panse URL: https://github.com/fgcz/rawrr/ SystemRequirements: .NET 8.0 (optional; required only if you want to compile and link the C# code) VignetteBuilder: knitr BugReports: https://github.com/fgcz/rawrr/issues git_url: https://git.bioconductor.org/packages/rawrr git_branch: RELEASE_3_22 git_last_commit: 9dc5784 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rawrr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rawrr_1.17.13.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rawrr_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rawrr_1.18.0.tgz vignettes: vignettes/rawrr/inst/doc/rawrr.html vignetteTitles: Direct Access to Orbitrap Data and Beyond hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rawrr/inst/doc/rawrr.R importsMe: MsBackendRawFileReader, rawDiag dependencyCount: 4 Package: RbcBook1 Version: 1.78.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 Archs: x64 MD5sum: ca9027c0b5482bd782536383b6a5e284 NeedsCompilation: no Title: Support for Springer monograph on Bioconductor Description: tools for building book biocViews: Software Author: Vince Carey and Wolfgang Huber Maintainer: Vince Carey URL: http://www.biostat.harvard.edu/~carey git_url: https://git.bioconductor.org/packages/RbcBook1 git_branch: RELEASE_3_22 git_last_commit: 1e86750 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RbcBook1_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RbcBook1_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RbcBook1_1.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RbcBook1_1.78.0.tgz vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf vignetteTitles: RbcBook1 Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RbcBook1/inst/doc/RbcBook1.R dependencyCount: 11 Package: Rbec Version: 1.18.0 Imports: Rcpp (>= 1.0.6), dada2, ggplot2, readr, doParallel, foreach, grDevices, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: 56feddea179b5bf9437b21b7a66095fa NeedsCompilation: yes Title: Rbec: a tool for analysis of amplicon sequencing data from synthetic microbial communities Description: Rbec is a adapted version of DADA2 for analyzing amplicon sequencing data from synthetic communities (SynComs), where the reference sequences for each strain exists. Rbec can not only accurately profile the microbial compositions in SynComs, but also predict the contaminants in SynCom samples. biocViews: Sequencing, MicrobialStrain, Microbiome Author: Pengfan Zhang [aut, cre] Maintainer: Pengfan Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbec git_branch: RELEASE_3_22 git_last_commit: 1ce88ce git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rbec_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rbec_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rbec_1.18.0.tgz vignettes: vignettes/Rbec/inst/doc/Rbec.html vignetteTitles: Rbec hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbec/inst/doc/Rbec.R dependencyCount: 93 Package: RBGL Version: 1.86.0 Depends: graph, methods Imports: methods LinkingTo: BH Suggests: Rgraphviz, XML, RUnit, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 MD5sum: b9fdd333b124e0e5cd6c344269e1c6a1 NeedsCompilation: yes Title: An interface to the BOOST graph library Description: A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. biocViews: GraphAndNetwork, Network Author: Vince Carey [aut], Li Long [aut], R. Gentleman [aut], Emmanuel Taiwo [ctb] (Converted RBGL vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RBGL git_branch: RELEASE_3_22 git_last_commit: a15bdde git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RBGL_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RBGL_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RBGL_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RBGL_1.86.0.tgz vignettes: vignettes/RBGL/inst/doc/RBGL.html vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBGL/inst/doc/RBGL.R dependsOnMe: apComplex, BioNet, CellNOptR, fgga, PerfMeas importsMe: BiocPkgTools, biocViews, CAMERA, Category, ChIPpeakAnno, CHRONOS, CytoML, DEGraph, DEsubs, EventPointer, flowWorkspace, GenomicInteractionNodes, GOstats, NCIgraph, ontoProc, openCyto, OrganismDbi, Streamer, VariantFiltering, BiDAG, clustNet, eff2, HEMDAG, micd, pcalg, rags2ridges, RANKS, SEMgraph, SID suggestsMe: DEGraph, G4SNVHunter, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools, yeastExpData, archeofrag, maGUI dependencyCount: 9 Package: RBioFormats Version: 1.10.0 Imports: EBImage, methods, rJava (>= 0.9-6), S4Vectors, stats Suggests: BiocStyle, knitr, testthat, xml2 License: GPL-3 MD5sum: f0fdf86456f2d7da79b8b0d489defe7d NeedsCompilation: no Title: R interface to Bio-Formats Description: An R package which interfaces the OME Bio-Formats Java library to allow reading of proprietary microscopy image data and metadata. biocViews: DataImport Author: Andrzej Oleś [aut, cre] (ORCID: ), John Lee [ctb] (ORCID: ) Maintainer: Andrzej Oleś URL: https://github.com/aoles/RBioFormats SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr BugReports: https://github.com/aoles/RBioFormats/issues git_url: https://git.bioconductor.org/packages/RBioFormats git_branch: RELEASE_3_22 git_last_commit: 52b73ef git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RBioFormats_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RBioFormats_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RBioFormats_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RBioFormats_1.10.0.tgz vignettes: vignettes/RBioFormats/inst/doc/RBioFormats.html vignetteTitles: RBioFormats: an R interface to the Bio-Formats library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBioFormats/inst/doc/RBioFormats.R importsMe: alabaster.sfe, islify, SpatialOmicsOverlay suggestsMe: SpatialFeatureExperiment, Voyager dependencyCount: 49 Package: rBiopaxParser Version: 2.50.0 Depends: R (>= 4.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, RBGL, igraph License: GPL (>= 2) MD5sum: f0e40da309f8599cade2496fe15c489d NeedsCompilation: no Title: Parses BioPax files and represents them in R Description: Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported. biocViews: DataRepresentation Author: Frank Kramer Maintainer: Frank Kramer URL: https://github.com/frankkramer-lab/rBiopaxParser git_url: https://git.bioconductor.org/packages/rBiopaxParser git_branch: RELEASE_3_22 git_last_commit: 82754bf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rBiopaxParser_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rBiopaxParser_2.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rBiopaxParser_2.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rBiopaxParser_2.50.0.tgz vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf vignetteTitles: rBiopaxParser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.R suggestsMe: AnnotationHub, NetPathMiner dependencyCount: 4 Package: rBLAST Version: 1.6.0 Depends: Biostrings (>= 2.26.2) Imports: methods, utils, BiocFileCache Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: a1c2a49202262491184e00fa939994c4 NeedsCompilation: no Title: R Interface for the Basic Local Alignment Search Tool Description: Seamlessly interfaces the Basic Local Alignment Search Tool (BLAST) running locally to search genetic sequence data bases. This work was partially supported by grant no. R21HG005912 from the National Human Genome Research Institute. biocViews: Genetics, Sequencing, SequenceMatching, Alignment, DataImport Author: Michael Hahsler [aut, cre] (ORCID: ), Nagar Anurag [aut] Maintainer: Michael Hahsler URL: https://github.com/mhahsler/rBLAST SystemRequirements: ncbi-blast+ VignetteBuilder: knitr BugReports: https://github.com/mhahsler/rBLAST/issues git_url: https://git.bioconductor.org/packages/rBLAST git_branch: RELEASE_3_22 git_last_commit: 392b3db git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rBLAST_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rBLAST_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rBLAST_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rBLAST_1.6.0.tgz vignettes: vignettes/rBLAST/inst/doc/blast.html vignetteTitles: rBLAST: R Interface for the Basic Local Alignment Search Tool hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rBLAST/inst/doc/blast.R dependencyCount: 52 Package: RBM Version: 1.42.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) MD5sum: 5d6fe4b3e3fb2a81d2d38d07f510f7ac NeedsCompilation: no Title: RBM: a R package for microarray and RNA-Seq data analysis Description: Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets. biocViews: Microarray, DifferentialExpression Author: Dongmei Li and Chin-Yuan Liang Maintainer: Dongmei Li git_url: https://git.bioconductor.org/packages/RBM git_branch: RELEASE_3_22 git_last_commit: 0079f8c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RBM_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RBM_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RBM_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RBM_1.42.0.tgz vignettes: vignettes/RBM/inst/doc/RBM.pdf vignetteTitles: RBM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBM/inst/doc/RBM.R dependencyCount: 8 Package: Rbowtie Version: 1.50.0 Imports: utils Suggests: testthat, parallel, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 236998d9430982b8b3743b136ae588bb NeedsCompilation: yes Title: R bowtie wrapper Description: This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly. biocViews: Sequencing, Alignment Author: Florian Hahne [aut], Anita Lerch [aut], Michael Stadler [aut, cre] (ORCID: ) Maintainer: Michael Stadler URL: https://github.com/fmicompbio/Rbowtie SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rbowtie/issues git_url: https://git.bioconductor.org/packages/Rbowtie git_branch: RELEASE_3_22 git_last_commit: 8c9be9c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rbowtie_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rbowtie_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rbowtie_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rbowtie_1.50.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.html vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR importsMe: crisprBowtie, multicrispr, seqpac suggestsMe: crisprDesign, eisaR dependencyCount: 1 Package: Rbowtie2 Version: 2.16.0 Depends: R (>= 4.1.0) Imports: magrittr, Rsamtools Suggests: knitr, testthat (>= 3.0.0), rmarkdown License: GPL (>= 3) MD5sum: 5a4fbdc3a5e7f654b15996f57fc2b4af NeedsCompilation: yes Title: An R Wrapper for Bowtie2 and AdapterRemoval Description: This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. The package contains wrapper functions that allow for genome indexing and alignment to those indexes. The package also allows for the creation of .bam files via Rsamtools. biocViews: Sequencing, Alignment, Preprocessing Author: Zheng Wei [aut, cre], Wei Zhang [aut] Maintainer: Zheng Wei SystemRequirements: C++11, GNU make, samtools VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbowtie2 git_branch: RELEASE_3_22 git_last_commit: ab8fb3e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rbowtie2_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rbowtie2_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rbowtie2_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rbowtie2_2.16.0.tgz vignettes: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.html vignetteTitles: An Introduction to Rbowtie2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.R importsMe: CircSeqAlignTk, esATAC, UMI4Cats, MetaScope dependencyCount: 30 Package: RbowtieCuda Version: 1.2.0 Depends: R (>= 4.5.0) Imports: methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: d5810006727977aa74b93c7035f261df NeedsCompilation: yes Title: An R Wrapper for nvBowtie and nvBWT, a rewritten version of Bowtie2 for cuda Description: This package provides an R wrapper for the popular Bowtie2 sequencing read aligner, optimized to run on NVIDIA graphics cards. It includes wrapper functions that enable both genome indexing and alignment to the generated indexes, ensuring high performance and ease of use within the R environment. biocViews: Sequencing, Alignment, Preprocessing, Coverage Author: c(Jacopo Pantaleoni [aut], Nuno Subtil [aut], Samuel Simon Sanchez [aut], Franck RICHARD [aut, cre], role = c("aut", "cre")) Maintainer: Franck RICHARD URL: https://github.com/FranckRICHARD01/RbowtieCuda, https://belacqua-labo.ovh/bioinformatic/RbowtieCuda SystemRequirements: C++20, GNU make, cmake, CUDA Toolkit(>=10), MSVC, libthrust-dev, libcub-dev, gcc, g++ VignetteBuilder: knitr BugReports: https://github.com/FranckRICHARD01/RbowtieCuda/issues git_url: https://git.bioconductor.org/packages/RbowtieCuda git_branch: RELEASE_3_22 git_last_commit: 5051fea git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-30 source.ver: src/contrib/RbowtieCuda_1.2.0.tar.gz vignettes: vignettes/RbowtieCuda/inst/doc/RbowtieCuda-Introduction.html vignetteTitles: An Introduction to RbowtieCuda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/RbowtieCuda/inst/doc/RbowtieCuda-Introduction.R dependencyCount: 1 Package: rbsurv Version: 2.68.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: c3debb33bc59b1a4e1c71904091c7f91 NeedsCompilation: no Title: Robust likelihood-based survival modeling with microarray data Description: This package selects genes associated with survival. biocViews: Microarray Author: HyungJun Cho , Sukwoo Kim , Soo-heang Eo , Jaewoo Kang Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/rbsurv git_branch: RELEASE_3_22 git_last_commit: 7852a7f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rbsurv_2.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rbsurv_2.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rbsurv_2.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rbsurv_2.68.0.tgz vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rbsurv/inst/doc/rbsurv.R dependencyCount: 13 Package: Rbwa Version: 1.14.0 Depends: R (>= 4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE OS_type: unix MD5sum: 6f5d1adb9d202661c456088491a6334b NeedsCompilation: yes Title: R wrapper for BWA-backtrack and BWA-MEM aligners Description: Provides an R wrapper for BWA alignment algorithms. Both BWA-backtrack and BWA-MEM are available. Convenience function to build a BWA index from a reference genome is also provided. Currently not supported for Windows machines. biocViews: Sequencing, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/Rbwa SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/Rbwa/issues git_url: https://git.bioconductor.org/packages/Rbwa git_branch: RELEASE_3_22 git_last_commit: 7c00790 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rbwa_1.14.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rbwa_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rbwa_1.14.0.tgz vignettes: vignettes/Rbwa/inst/doc/Rbwa.html vignetteTitles: An introduction to Rbwa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbwa/inst/doc/Rbwa.R importsMe: crisprBwa suggestsMe: crisprDesign dependencyCount: 0 Package: RCAS Version: 1.36.0 Depends: R (>= 3.5.0), plotly (>= 4.5.2), DT (>= 0.2), data.table Imports: GenomicRanges, IRanges, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDb (>= 1.12.0), Biostrings, rtracklayer, GenomicFeatures, txdbmaker, rmarkdown (>= 0.9.5), genomation (>= 1.5.5), knitr (>= 1.12.3), BiocGenerics, S4Vectors, plotrix, pbapply, RSQLite, proxy, pheatmap, ggplot2, cowplot, seqLogo, utils, ranger, gprofiler2 Suggests: testthat, covr, BiocManager License: Artistic-2.0 MD5sum: 7e831ff3c3282c2cba8a734e22f7aa58 NeedsCompilation: no Title: RNA Centric Annotation System Description: RCAS is an R/Bioconductor package designed as a generic reporting tool for the functional analysis of transcriptome-wide regions of interest detected by high-throughput experiments. Such transcriptomic regions could be, for instance, signal peaks detected by CLIP-Seq analysis for protein-RNA interaction sites, RNA modification sites (alias the epitranscriptome), CAGE-tag locations, or any other collection of query regions at the level of the transcriptome. RCAS produces in-depth annotation summaries and coverage profiles based on the distribution of the query regions with respect to transcript features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions). Moreover, RCAS can carry out functional enrichment analyses and discriminative motif discovery. biocViews: Software, GeneTarget, MotifAnnotation, MotifDiscovery, GO, Transcriptomics, GenomeAnnotation, GeneSetEnrichment, Coverage Author: Bora Uyar [aut, cre], Dilmurat Yusuf [aut], Ricardo Wurmus [aut], Altuna Akalin [aut] Maintainer: Bora Uyar SystemRequirements: pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCAS git_branch: RELEASE_3_22 git_last_commit: 780ed18 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RCAS_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCAS_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCAS_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCAS_1.36.0.tgz vignettes: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.html, vignettes/RCAS/inst/doc/RCAS.vignette.html vignetteTitles: How to do meta-analysis of multiple samples, Introduction - single sample analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.R, vignettes/RCAS/inst/doc/RCAS.vignette.R importsMe: GenomicPlot dependencyCount: 156 Package: RCASPAR Version: 1.56.0 License: GPL (>=3) MD5sum: 967a86132f7c2af760b1812d71645aeb NeedsCompilation: no Title: A package for survival time prediction based on a piecewise baseline hazard Cox regression model. Description: The package is the R-version of the C-based software \bold{CASPAR} (Kaderali,2006: \url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine. biocViews: aCGH, GeneExpression, Genetics, Proteomics, Visualization Author: Douaa Mugahid, Lars Kaderali Maintainer: Douaa Mugahid , Lars Kaderali git_url: https://git.bioconductor.org/packages/RCASPAR git_branch: RELEASE_3_22 git_last_commit: 8f5e3b1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RCASPAR_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCASPAR_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCASPAR_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCASPAR_1.56.0.tgz vignettes: vignettes/RCASPAR/inst/doc/RCASPAR.pdf vignetteTitles: RCASPAR: Software for high-dimentional-data driven survival time prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCASPAR/inst/doc/RCASPAR.R dependencyCount: 0 Package: rcellminer Version: 2.32.0 Depends: R (>= 3.2), Biobase, rcellminerData (>= 2.0.0) Imports: stringr, gplots, ggplot2, methods, stats, utils, shiny Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat, BiocStyle, jsonlite, heatmaply, glmnet, foreach, doSNOW, parallel, rmarkdown License: LGPL-3 + file LICENSE Archs: x64 MD5sum: 981f8901bae57bbdabca787b82e6a012 NeedsCompilation: no Title: rcellminer: Molecular Profiles, Drug Response, and Chemical Structures for the NCI-60 Cell Lines Description: The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/ cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data. biocViews: aCGH, CellBasedAssays, CopyNumberVariation, GeneExpression, Pharmacogenomics, Pharmacogenetics, miRNA, Cheminformatics, Visualization, Software, SystemsBiology Author: Augustin Luna, Vinodh Rajapakse, Fabricio Sousa Maintainer: Augustin Luna , Vinodh Rajapakse , Fathi Elloumi URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rcellminer git_branch: RELEASE_3_22 git_last_commit: e315ee0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rcellminer_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rcellminer_2.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rcellminer_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rcellminer_2.32.0.tgz vignettes: vignettes/rcellminer/inst/doc/rcellminerUsage.html vignetteTitles: Using rcellminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R suggestsMe: rcellminerData dependencyCount: 58 Package: rCGH Version: 1.40.0 Depends: R (>= 3.4),methods,stats,utils,graphics Imports: plyr,DNAcopy,lattice,ggplot2,grid,shiny (>= 0.11.1), limma,affy,mclust,TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db,GenomicFeatures,Seqinfo,GenomicRanges,AnnotationDbi, parallel,IRanges,grDevices,aCGH Suggests: BiocStyle, knitr, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 9a758b35a1f0a6d6024f671f91182fed NeedsCompilation: no Title: Comprehensive Pipeline for Analyzing and Visualizing Array-Based CGH Data Description: A comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through commercial or custom aCGH arrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, Affymetrix SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt files exported from ChAS or Affymetrix Power Tools. rCGH also supports custom arrays, provided data complies with the expected format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to profiles segmentation and gene annotations. This package also provides several visualization functions (static or interactive) which facilitate individual profiles interpretation. Input files can be in compressed format, e.g. .bz2 or .gz. biocViews: aCGH,CopyNumberVariation,Preprocessing,FeatureExtraction Author: Frederic Commo [aut, cre] Maintainer: Frederic Commo URL: https://github.com/fredcommo/rCGH VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rCGH git_branch: RELEASE_3_22 git_last_commit: 2a97521 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rCGH_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rCGH_1.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rCGH_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rCGH_1.40.0.tgz vignettes: vignettes/rCGH/inst/doc/rCGH.pdf vignetteTitles: using rCGH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rCGH/inst/doc/rCGH.R importsMe: preciseTAD dependencyCount: 124 Package: RcisTarget Version: 1.29.0 Depends: R (>= 3.5.0) Imports: AUCell (>= 1.1.6), BiocGenerics, data.table, graphics, GenomeInfoDb, GenomicRanges, arrow (>= 2.0.0), dplyr, tibble, GSEABase, methods, R.utils, stats, SummarizedExperiment, S4Vectors, utils, zoo Suggests: Biobase, BiocStyle, BiocParallel, doParallel, DT, foreach, gplots, rtracklayer, igraph, knitr, RcisTarget.hg19.motifDBs.cisbpOnly.500bp, rmarkdown, testthat, visNetwork Enhances: doMC, doRNG License: GPL-3 Archs: x64 MD5sum: 938b50a05dca366cfb8e6d253fcc0865 NeedsCompilation: no Title: RcisTarget Identify transcription factor binding motifs enriched on a list of genes or genomic regions Description: RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge). biocViews: GeneRegulation, MotifAnnotation, Transcriptomics, Transcription, GeneSetEnrichment, GeneTarget Author: Sara Aibar, Gert Hulselmans, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium Maintainer: Gert Hulselmans URL: http://scenic.aertslab.org VignetteBuilder: knitr BugReports: https://github.com/aertslab/RcisTarget/issues git_url: https://git.bioconductor.org/packages/RcisTarget git_branch: devel git_last_commit: 8eb9126 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/RcisTarget_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RcisTarget_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RcisTarget_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RcisTarget_1.29.0.tgz vignettes: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.html, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.html, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.html vignetteTitles: RcisTarget: Transcription factor binding motif enrichment, RcisTarget - on regions, RcisTarget - with background hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.R, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.R, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.R dependencyCount: 122 Package: RCM Version: 1.26.0 Depends: R (>= 4.5.0) Imports: RColorBrewer, alabama, edgeR, reshape2, tseries, stats, VGAM, ggplot2 (>= 2.2.1.9000), nleqslv, phyloseq, tensor, MASS, grDevices, graphics, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 059f528b6f5d657f5b08b657fb145b83 NeedsCompilation: no Title: Fit row-column association models with the negative binomial distribution for the microbiome Description: Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. In addition, diagnostic plot to detect lack of fit are available. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel URL: https://bioconductor.org/packages/release/bioc/vignettes/RCM/inst/doc/RCMvignette.html/ VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/RCM/issues git_url: https://git.bioconductor.org/packages/RCM git_branch: RELEASE_3_22 git_last_commit: a722ed9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RCM_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCM_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCM_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCM_1.26.0.tgz vignettes: vignettes/RCM/inst/doc/RCMvignette.html vignetteTitles: Manual for the RCM pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCM/inst/doc/RCMvignette.R dependencyCount: 86 Package: Rcollectl Version: 1.10.0 Imports: utils, ggplot2, lubridate, processx Suggests: knitr, BiocStyle, knitcitations, sessioninfo, rmarkdown, testthat, covr License: Artistic-2.0 MD5sum: 31e4f1efa6d429ae135525d3f84d0284 NeedsCompilation: no Title: Help use collectl with R in Linux, to measure resource consumption in R processes Description: Provide functions to obtain instrumentation data on processes in a unix environment. Parse output of a collectl run. Vizualize aspects of system usage over time, with annotation. biocViews: Software, Infrastructure Author: Vincent Carey [aut, cre] (ORCID: ), Yubo Cheng [aut] Maintainer: Vincent Carey URL: https://github.com/vjcitn/Rcollectl SystemRequirements: collectl VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/Rcollectl git_url: https://git.bioconductor.org/packages/Rcollectl git_branch: RELEASE_3_22 git_last_commit: 1a76401 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rcollectl_1.10.0.tar.gz vignettes: vignettes/Rcollectl/inst/doc/Rcollectl.html vignetteTitles: Rcollectl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcollectl/inst/doc/Rcollectl.R dependencyCount: 28 Package: Rcpi Version: 1.46.0 Depends: R (>= 3.0.2) Imports: Biostrings, GOSemSim, curl, doParallel, foreach, httr2, jsonlite, methods, rlang, stats, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 | file LICENSE MD5sum: e39b108a783ad75527ed17c88bd1efab NeedsCompilation: no Title: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery Description: A molecular informatics toolkit with an integration of bioinformatics and chemoinformatics tools for drug discovery. biocViews: Software, DataImport, DataRepresentation, FeatureExtraction, Cheminformatics, BiomedicalInformatics, Proteomics, GO, SystemsBiology Author: Nan Xiao [aut, cre] (ORCID: ), Dong-Sheng Cao [aut], Qing-Song Xu [aut] Maintainer: Nan Xiao URL: https://nanx.me/Rcpi/, https://github.com/nanxstats/Rcpi VignetteBuilder: knitr BugReports: https://github.com/nanxstats/Rcpi/issues git_url: https://git.bioconductor.org/packages/Rcpi git_branch: RELEASE_3_22 git_last_commit: c114c2a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rcpi_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rcpi_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rcpi_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rcpi_1.46.0.tgz vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.html, vignettes/Rcpi/inst/doc/Rcpi.html vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package as an Integrated Informatics Platform for Drug Discovery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcpi/inst/doc/Rcpi.R dependencyCount: 61 Package: RCSL Version: 1.18.0 Depends: R (>= 4.1) Imports: RcppAnnoy, igraph, NbClust, Rtsne, ggplot2(>= 3.4.0), methods, pracma, umap, grDevices, graphics, stats, Rcpp (>= 0.11.0), MatrixGenerics, SingleCellExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown, mclust, tidyverse, tinytex License: Artistic-2.0 MD5sum: 3bb86b358f7c1e07b0492a9b8d5e5696 NeedsCompilation: no Title: Rank Constrained Similarity Learning for single cell RNA sequencing data Description: A novel clustering algorithm and toolkit RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both lo-cal similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similar-ity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similar-ity of a cell to other cells is a linear combination of its global similarity and local similarity. biocViews: SingleCell, Software, Clustering, DimensionReduction, RNASeq, Visualization, Sequencing Author: Qinglin Mei [cre, aut], Guojun Li [fnd], Zhengchang Su [fnd] Maintainer: Qinglin Mei URL: https://github.com/QinglinMei/RCSL VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCSL git_branch: RELEASE_3_22 git_last_commit: 98e4992 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RCSL_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCSL_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCSL_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCSL_1.18.0.tgz vignettes: vignettes/RCSL/inst/doc/RCSL.html vignetteTitles: RCSL package manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCSL/inst/doc/RCSL.R dependencyCount: 64 Package: Rcwl Version: 1.26.0 Depends: R (>= 3.6), yaml, methods, S4Vectors Imports: utils, stats, BiocParallel, batchtools, DiagrammeR, shiny, R.utils, codetools, basilisk Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 | file LICENSE MD5sum: 72887a533d80f378ca4081eded54756a NeedsCompilation: no Title: An R interface to the Common Workflow Language Description: The Common Workflow Language (CWL) is an open standard for development of data analysis workflows that is portable and scalable across different tools and working environments. Rcwl provides a simple way to wrap command line tools and build CWL data analysis pipelines programmatically within R. It increases the ease of usage, development, and maintenance of CWL pipelines. biocViews: Software, WorkflowStep, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut] Maintainer: Qiang Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rcwl git_branch: RELEASE_3_22 git_last_commit: 0b6281a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rcwl_1.26.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rcwl_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rcwl_1.25.0.tgz vignettes: vignettes/Rcwl/inst/doc/Rcwl.html vignetteTitles: Rcwl: An R interface to the Common Workflow Language hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcwl/inst/doc/Rcwl.R dependsOnMe: RcwlPipelines importsMe: ReUseData dependencyCount: 108 Package: RcwlPipelines Version: 1.26.0 Depends: R (>= 3.6), Rcwl, BiocFileCache Imports: rappdirs, methods, utils, git2r, httr, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 8c3b92b7c5a7ceb89c53c4ac69bc59a1 NeedsCompilation: no Title: Bioinformatics pipelines based on Rcwl Description: A collection of Bioinformatics tools and pipelines based on R and the Common Workflow Language. biocViews: Software, WorkflowStep, Alignment, Preprocessing, QualityControl, DNASeq, RNASeq, DataImport, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut], Shuang Gao [aut] Maintainer: Qiang Hu SystemRequirements: nodejs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RcwlPipelines git_branch: RELEASE_3_22 git_last_commit: f76b183 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RcwlPipelines_1.26.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RcwlPipelines_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RcwlPipelines_1.26.0.tgz vignettes: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.html vignetteTitles: RcwlPipelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.R importsMe: ReUseData dependencyCount: 122 Package: RCX Version: 1.14.0 Depends: R (>= 4.2.0) Imports: jsonlite, plyr, igraph, methods Suggests: BiocStyle, testthat, knitr, rmarkdown, base64enc, graph License: MIT + file LICENSE MD5sum: b1486056004aba5837dc7d6dbfbd007d NeedsCompilation: no Title: R package implementing the Cytoscape Exchange (CX) format Description: Create, handle, validate, visualize and convert networks in the Cytoscape exchange (CX) format to standard data types and objects. The package also provides conversion to and from objects of iGraph and graphNEL. The CX format is also used by the NDEx platform, a online commons for biological networks, and the network visualization software Cytocape. biocViews: Pathways, DataImport, Network Author: Florian Auer [aut, cre] (ORCID: ) Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/RCX VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/RCX/issues git_url: https://git.bioconductor.org/packages/RCX git_branch: RELEASE_3_22 git_last_commit: 6ce2c95 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RCX_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCX_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCX_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCX_1.14.0.tgz vignettes: vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.html, vignettes/RCX/inst/doc/Creating_RCX_from_scratch.html, vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.html, vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.html vignetteTitles: Appendix: The RCX and CX Data Model, 02. Creating RCX from scratch, 03. Extending the RCX Data Model, 01. RCX - an R package implementing the Cytoscape Exchange (CX) format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.R, vignettes/RCX/inst/doc/Creating_RCX_from_scratch.R, vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.R, vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.R dependsOnMe: ndexr dependencyCount: 20 Package: RCy3 Version: 2.30.0 Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, stats, graph, fs, uuid, stringi, glue, RCurl, base64url, base64enc, IRkernel, IRdisplay, RColorBrewer, gplots Suggests: BiocStyle, knitr, rmarkdown, igraph, grDevices License: MIT + file LICENSE Archs: x64 MD5sum: 7f32745cfc50ad7928c491491fccad14 NeedsCompilation: no Title: Functions to Access and Control Cytoscape Description: Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network Author: Alex Pico [aut, cre] (ORCID: ), Tanja Muetze [aut], Paul Shannon [aut], Ruth Isserlin [ctb], Shraddha Pai [ctb], Julia Gustavsen [ctb], Georgi Kolishovski [ctb], Yihang Xin [ctb] Maintainer: Alex Pico URL: https://github.com/cytoscape/RCy3 SystemRequirements: Cytoscape (>= 3.7.1), CyREST (>= 3.8.0) VignetteBuilder: knitr BugReports: https://github.com/cytoscape/RCy3/issues git_url: https://git.bioconductor.org/packages/RCy3 git_branch: RELEASE_3_22 git_last_commit: 0677632 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RCy3_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCy3_2.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCy3_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCy3_2.30.0.tgz vignettes: vignettes/RCy3/inst/doc/Cancer-networks-and-data.html, vignettes/RCy3/inst/doc/Custom-Graphics.html, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.html, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.html, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.html, vignettes/RCy3/inst/doc/Filtering-Networks.html, vignettes/RCy3/inst/doc/Group-nodes.html, vignettes/RCy3/inst/doc/Identifier-mapping.html, vignettes/RCy3/inst/doc/Importing-data.html, vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.html, vignettes/RCy3/inst/doc/Network-functions-and-visualization.html, vignettes/RCy3/inst/doc/Overview-of-RCy3.html, vignettes/RCy3/inst/doc/Phylogenetic-trees.html, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.html vignetteTitles: 06. Cancer networks and data ~40 min, 11. Custom Graphics and Labels ~10 min, 03. Cytoscape and graphNEL ~5 min, 02. Cytoscape and igraph ~5 min, 09. Cytoscape and NDEx ~20 min, 12. Filtering Networks ~10 min, 10. Group nodes ~15 min, 07. Identifier mapping ~20 min, 04. Importing data ~5 min, 14. Jupyter Bridge and RCy3 ~10 min, 05. Network functions and visualization ~15 min, 01. Overview of RCy3 ~25 min, 13. Phylogenetic Trees ~3 min, 08. Upgrading existing scripts ~15 min hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCy3/inst/doc/Cancer-networks-and-data.R, vignettes/RCy3/inst/doc/Custom-Graphics.R, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.R, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.R, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.R, vignettes/RCy3/inst/doc/Filtering-Networks.R, vignettes/RCy3/inst/doc/Group-nodes.R, vignettes/RCy3/inst/doc/Identifier-mapping.R, vignettes/RCy3/inst/doc/Importing-data.R, vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.R, vignettes/RCy3/inst/doc/Network-functions-and-visualization.R, vignettes/RCy3/inst/doc/Overview-of-RCy3.R, vignettes/RCy3/inst/doc/Phylogenetic-trees.R, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.R importsMe: categoryCompare, CeTF, enrichViewNet, fedup, GeneNetworkBuilder, MetaPhOR, MOGAMUN, MSstatsBioNet, NCIgraph, regutools, transomics2cytoscape, dendroNetwork, lilikoi, netgsa, ScriptMapR suggestsMe: graphite, rScudo, tidysbml, sharp dependencyCount: 49 Package: RCyjs Version: 2.32.0 Depends: R (>= 3.5.0), BrowserViz (>= 2.7.18), graph (>= 1.56.0) Imports: methods, httpuv (>= 1.5.0), BiocGenerics, base64enc, utils Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 7e26346738431caeccb8e822f7d1d608 NeedsCompilation: no Title: Display and manipulate graphs in cytoscape.js Description: Interactive viewing and exploration of graphs, connecting R to Cytoscape.js, using websockets. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCyjs git_branch: RELEASE_3_22 git_last_commit: abd521e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RCyjs_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCyjs_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCyjs_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCyjs_2.32.0.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.html vignetteTitles: "RCyjs: programmatic access to the web browser-based network viewer,, cytoscape.js" hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R dependencyCount: 23 Package: Rdisop Version: 1.70.0 Depends: R (>= 2.10), Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-2 MD5sum: a2e6a150d2c9e6882b059d6dc7639774 NeedsCompilation: yes Title: Decomposition of Isotopic Patterns Description: In high resolution mass spectrometry (HR-MS), the measured masses can be decomposed into potential element combinations (chemical sum formulas). Where additional mass/intensity information of respective isotopic peaks is available, decomposition can take this information into account to better rank the potential candidate sum formulas. To compare measured mass/intensity information with the theoretical distribution of candidate sum formulas, the latter needs to be calculated. This package implements fast algorithms to address both tasks, the calculation of isotopic distributions for arbitrary sum formulas (assuming a HR-MS resolution of roughly 30,000), and the ranked list of sum formulas fitting an observed peak or isotopic peak set. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Anton Pervukhin [aut], Steffen Neumann [aut, cre] (ORCID: ), Jan Lisec [ctb] (ORCID: ), Miao Yu [ctb], Roberto Canteri [ctb] Maintainer: Steffen Neumann URL: https://github.com/sneumann/Rdisop SystemRequirements: None VignetteBuilder: knitr BugReports: https://github.com/sneumann/Rdisop/issues/new git_url: https://git.bioconductor.org/packages/Rdisop git_branch: RELEASE_3_22 git_last_commit: 57124fd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rdisop_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rdisop_1.69.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rdisop_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rdisop_1.70.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.html vignetteTitles: Mass decomposition with the Rdisop package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE importsMe: enviGCMS suggestsMe: adductomicsR, MSnbase, RforProteomics, CorMID, InterpretMSSpectrum dependencyCount: 3 Package: RDRToolbox Version: 1.60.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: 28679872091e1dceae778c65f963beb9 NeedsCompilation: no Title: A package for nonlinear dimension reduction with Isomap and LLE. Description: A package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset. biocViews: DimensionReduction, FeatureExtraction, Visualization, Clustering, Microarray Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RDRToolbox git_branch: RELEASE_3_22 git_last_commit: e596d1f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RDRToolbox_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RDRToolbox_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RDRToolbox_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RDRToolbox_1.60.0.tgz vignettes: vignettes/RDRToolbox/inst/doc/vignette.pdf vignetteTitles: A package for nonlinear dimension reduction with Isomap and LLE. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDRToolbox/inst/doc/vignette.R suggestsMe: loon dependencyCount: 36 Package: ReactomeGSA Version: 1.23.0 Imports: Biobase, BiocSingular, dplyr, ggplot2, gplots, httr, igraph, jsonlite, methods, progress, RColorBrewer, SummarizedExperiment, tidyr Suggests: devtools, knitr, ReactomeGSA.data, rmarkdown, scater, scran, scRNAseq, scuttle, Seurat (>= 3.0), SingleCellExperiment, testthat License: MIT + file LICENSE MD5sum: a7aed7c2c2ebb91366c4f67aee852679 NeedsCompilation: no Title: Client for the Reactome Analysis Service for comparative multi-omics gene set analysis Description: The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited. biocViews: GeneSetEnrichment, Proteomics, Transcriptomics, SystemsBiology, GeneExpression, Reactome Author: Johannes Griss [aut, cre] () Maintainer: Johannes Griss URL: https://github.com/reactome/ReactomeGSA VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGSA/issues git_url: https://git.bioconductor.org/packages/ReactomeGSA git_branch: devel git_last_commit: d9a274e git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/ReactomeGSA_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ReactomeGSA_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ReactomeGSA_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ReactomeGSA_1.24.0.tgz vignettes: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.html, vignettes/ReactomeGSA/inst/doc/reanalysing-public-data.html, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.html vignetteTitles: Analysing single-cell RNAseq data, Loading and re-analysing public data through ReactomeGSA, Using the ReactomeGSA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.R, vignettes/ReactomeGSA/inst/doc/reanalysing-public-data.R, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.R dependsOnMe: ReactomeGSA.data importsMe: scPipeline dependencyCount: 86 Package: ReactomePA Version: 1.54.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2 (>= 3.3.5), ggraph, reactome.db, igraph, graphite, gson, yulab.utils (>= 0.1.5) Suggests: BiocStyle, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db, prettydoc, testthat License: GPL-2 MD5sum: 7df11f1d7c86a0c9d4386926322308eb NeedsCompilation: no Title: Reactome Pathway Analysis Description: This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. This package is not affiliated with the Reactome team. biocViews: Pathways, Visualization, Annotation, MultipleComparison, GeneSetEnrichment, Reactome Author: Guangchuang Yu [aut, cre], Vladislav Petyuk [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ReactomePA/issues git_url: https://git.bioconductor.org/packages/ReactomePA git_branch: RELEASE_3_22 git_last_commit: 36f3c2f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ReactomePA_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ReactomePA_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ReactomePA_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ReactomePA_1.54.0.tgz vignettes: vignettes/ReactomePA/inst/doc/ReactomePA.html vignetteTitles: An R package for Reactome Pathway Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R dependsOnMe: maEndToEnd importsMe: bioCancer, miRSM, miRspongeR, mitology, scTensor, ExpHunterSuite suggestsMe: CBNplot, ChIPseeker, cola, GRaNIE, scGPS, scGraphVerse dependencyCount: 147 Package: ReadqPCR Version: 1.56.0 Depends: R(>= 2.14.0), Biobase, methods Suggests: qpcR License: LGPL-3 MD5sum: 1a1b8ef409cb97983cc7bd91fd0040f6 NeedsCompilation: no Title: Read qPCR data Description: The package provides functions to read raw RT-qPCR data of different platforms. biocViews: DataImport, MicrotitrePlateAssay, GeneExpression, qPCR Author: James Perkins, Matthias Kohl, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html git_url: https://git.bioconductor.org/packages/ReadqPCR git_branch: RELEASE_3_22 git_last_commit: e09eee4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ReadqPCR_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ReadqPCR_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ReadqPCR_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ReadqPCR_1.56.0.tgz vignettes: vignettes/ReadqPCR/inst/doc/ReadqPCR.pdf vignetteTitles: Functions to load RT-qPCR data into R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReadqPCR/inst/doc/ReadqPCR.R dependsOnMe: NormqPCR dependencyCount: 7 Package: REBET Version: 1.28.0 Depends: ASSET Imports: stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 Archs: x64 MD5sum: 014c1d8243e81111efdea2fc9b03f9b5 NeedsCompilation: yes Title: The subREgion-based BurdEn Test (REBET) Description: There is an increasing focus to investigate the association between rare variants and diseases. The REBET package implements the subREgion-based BurdEn Test which is a powerful burden test that simultaneously identifies susceptibility loci and sub-regions. biocViews: Software, VariantAnnotation, SNP Author: Bill Wheeler [cre], Bin Zhu [aut], Lisa Mirabello [ctb], Nilanjan Chatterjee [ctb] Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/REBET git_branch: RELEASE_3_22 git_last_commit: 5b98138 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/REBET_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/REBET_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/REBET_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/REBET_1.28.0.tgz vignettes: vignettes/REBET/inst/doc/vignette.pdf vignetteTitles: REBET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REBET/inst/doc/vignette.R dependencyCount: 27 Package: rebook Version: 1.20.0 Imports: utils, methods, knitr (>= 1.32), rmarkdown, CodeDepends, dir.expiry, filelock, BiocStyle Suggests: testthat, igraph, XML, BiocManager, RCurl, bookdown, rappdirs, yaml, BiocParallel, OSCA.intro, OSCA.workflows License: GPL-3 MD5sum: 13364d4800a6a5b09c89093ea5321eec NeedsCompilation: no Title: Re-using Content in Bioconductor Books Description: Provides utilities to re-use content across chapters of a Bioconductor book. This is mostly based on functionality developed while writing the OSCA book, but generalized for potential use in other large books with heavy compute. Also contains some functions to assist book deployment. biocViews: Software, Infrastructure, ReportWriting Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rebook git_branch: RELEASE_3_22 git_last_commit: 0301643 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rebook_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rebook_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rebook_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rebook_1.20.0.tgz vignettes: vignettes/rebook/inst/doc/userguide.html vignetteTitles: Reusing book content hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rebook/inst/doc/userguide.R dependsOnMe: csawBook, OSCA, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook dependencyCount: 44 Package: reconsi Version: 1.22.0 Imports: phyloseq, ks, reshape2, ggplot2, stats, methods, graphics, grDevices, matrixStats, Matrix Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: beeed30fd2f904c2295f170791bdbe86 NeedsCompilation: no Title: Resampling Collapsed Null Distributions for Simultaneous Inference Description: Improves simultaneous inference under dependence of tests by estimating a collapsed null distribution through resampling. Accounting for the dependence between tests increases the power while reducing the variability of the false discovery proportion. This dependence is common in genomics applications, e.g. when combining flow cytometry measurements with microbiome sequence counts. biocViews: Metagenomics, Microbiome, MultipleComparison, FlowCytometry Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/reconsi/issues git_url: https://git.bioconductor.org/packages/reconsi git_branch: RELEASE_3_22 git_last_commit: 68b3cde git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/reconsi_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/reconsi_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/reconsi_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/reconsi_1.22.0.tgz vignettes: vignettes/reconsi/inst/doc/reconsiVignette.html vignetteTitles: Manual for the RCM pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/reconsi/inst/doc/reconsiVignette.R dependencyCount: 79 Package: recount Version: 1.36.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: BiocParallel, derfinder, downloader, GEOquery, GenomeInfoDb, GenomicRanges, IRanges, methods, RCurl, rentrez, rtracklayer (>= 1.35.3), S4Vectors, stats, utils Suggests: AnnotationDbi, BiocManager, BiocStyle (>= 2.5.19), DESeq2, sessioninfo, EnsDb.Hsapiens.v79, GenomicFeatures, txdbmaker, knitr (>= 1.6), org.Hs.eg.db, RefManageR, regionReport (>= 1.9.4), rmarkdown (>= 0.9.5), testthat (>= 2.1.0), covr, pheatmap, DT, edgeR, ggplot2, RColorBrewer License: Artistic-2.0 Archs: x64 MD5sum: 86c2343ae3ef868e1d4ce17cd0968dd7 NeedsCompilation: no Title: Explore and download data from the recount project Description: Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Abhinav Nellore [ctb], Andrew E. Jaffe [ctb] (ORCID: ), Margaret A. Taub [ctb], Kai Kammers [ctb], Shannon E. Ellis [ctb] (ORCID: ), Kasper Daniel Hansen [ctb] (ORCID: ), Ben Langmead [ctb] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/recount VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recount/ git_url: https://git.bioconductor.org/packages/recount git_branch: RELEASE_3_22 git_last_commit: 42cbd6e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/recount_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/recount_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/recount_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/recount_1.36.0.tgz vignettes: vignettes/recount/inst/doc/recount-quickstart.html, vignettes/recount/inst/doc/SRP009615-results.html vignetteTitles: recount quick start guide, Basic DESeq2 results exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount/inst/doc/recount-quickstart.R, vignettes/recount/inst/doc/SRP009615-results.R importsMe: psichomics, RNAAgeCalc, recountWorkflow suggestsMe: recount3 dependencyCount: 163 Package: recount3 Version: 1.20.0 Depends: SummarizedExperiment Imports: BiocFileCache, methods, rtracklayer, S4Vectors, utils, httr, data.table, R.utils, Matrix, GenomicRanges, sessioninfo, tools Suggests: BiocStyle, covr, knitcitations, knitr, RefManageR, rmarkdown, testthat, pryr, recount License: Artistic-2.0 MD5sum: 180e8458ef219094a6a8f15f86e60207 NeedsCompilation: no Title: Explore and download data from the recount3 project Description: The recount3 package enables access to a large amount of uniformly processed RNA-seq data from human and mouse. You can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level with sample metadata and QC statistics. In addition we provide access to sample coverage BigWig files. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Leonardo Collado-Torres [aut, cre] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/LieberInstitute/recount3 VignetteBuilder: knitr BugReports: https://github.com/LieberInstitute/recount3/issues git_url: https://git.bioconductor.org/packages/recount3 git_branch: RELEASE_3_22 git_last_commit: 33bfdcd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/recount3_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/recount3_1.19.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/recount3_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/recount3_1.20.0.tgz vignettes: vignettes/recount3/inst/doc/recount3-quickstart.html vignetteTitles: recount3 quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount3/inst/doc/recount3-quickstart.R suggestsMe: geyser, RNAseqQC dependencyCount: 93 Package: recountmethylation Version: 1.20.0 Depends: R (>= 4.1) Imports: minfi, HDF5Array, rhdf5, S4Vectors, utils, methods, RCurl, R.utils, BiocFileCache, basilisk, reticulate, DelayedMatrixStats Suggests: minfiData, minfiDataEPIC, knitr, testthat, ggplot2, gridExtra, rmarkdown, BiocStyle, GenomicRanges, limma, ExperimentHub, AnnotationHub License: Artistic-2.0 MD5sum: eac2ddcbb5fb35c93ac6e3befb430bd7 NeedsCompilation: no Title: Access and analyze public DNA methylation array data compilations Description: Resources for cross-study analyses of public DNAm array data from NCBI GEO repo, produced using Illumina's Infinium HumanMethylation450K (HM450K) and MethylationEPIC (EPIC) platforms. Provided functions enable download, summary, and filtering of large compilation files. Vignettes detail background about file formats, example analyses, and more. Note the disclaimer on package load and consult the main manuscripts for further info. biocViews: DNAMethylation, Epigenetics, Microarray, MethylationArray, ExperimentHub Author: Sean K Maden [cre, aut] (ORCID: ), Brian Walsh [aut] (ORCID: ), Kyle Ellrott [aut] (ORCID: ), Kasper D Hansen [aut] (ORCID: ), Reid F Thompson [aut] (ORCID: ), Abhinav Nellore [aut] (ORCID: ) Maintainer: Sean K Maden URL: https://github.com/metamaden/recountmethylation VignetteBuilder: knitr BugReports: https://github.com/metamaden/recountmethylation/issues git_url: https://git.bioconductor.org/packages/recountmethylation git_branch: RELEASE_3_22 git_last_commit: 3d96dab git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/recountmethylation_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/recountmethylation_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/recountmethylation_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/recountmethylation_1.20.0.tgz vignettes: vignettes/recountmethylation/inst/doc/cpg_probe_annotations.html, vignettes/recountmethylation/inst/doc/exporting_saving_data.html, vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.html, vignettes/recountmethylation/inst/doc/recountmethylation_glint.html, vignettes/recountmethylation/inst/doc/recountmethylation_pwrewas.html, vignettes/recountmethylation/inst/doc/recountmethylation_search_index.html, vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.html vignetteTitles: Practical uses for CpG annotations, Working with DNAm data types, Data Analyses, Determine population ancestry from DNAm arrays, Power analysis for DNAm arrays, Nearest neighbors analysis for DNAm arrays, recountmethylation User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recountmethylation/inst/doc/cpg_probe_annotations.R, vignettes/recountmethylation/inst/doc/exporting_saving_data.R, vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.R, vignettes/recountmethylation/inst/doc/recountmethylation_glint.R, vignettes/recountmethylation/inst/doc/recountmethylation_pwrewas.R, vignettes/recountmethylation/inst/doc/recountmethylation_search_index.R, vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.R dependencyCount: 152 Package: recoup Version: 1.38.0 Depends: R (>= 4.0.0), GenomicRanges, GenomicAlignments, ggplot2, ComplexHeatmap Imports: BiocGenerics, biomaRt, Biostrings, circlize, Seqinfo, GenomicFeatures, graphics, grDevices, httr, IRanges, methods, parallel, RSQLite, Rsamtools, rtracklayer, S4Vectors, stats, stringr, txdbmaker, utils Suggests: GenomeInfoDb, grid, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocManager, BSgenome, RMySQL License: GPL (>= 3) MD5sum: a599e780953b26eda9856d64ca61a7a1 NeedsCompilation: no Title: An R package for the creation of complex genomic profile plots Description: recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively. biocViews: ImmunoOncology, Software, GeneExpression, Preprocessing, QualityControl, RNASeq, ChIPSeq, Sequencing, Coverage, ATACSeq, ChipOnChip, Alignment, DataImport Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/recoup VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/recoup git_branch: RELEASE_3_22 git_last_commit: 7728d2d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/recoup_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/recoup_1.37.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/recoup_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/recoup_1.38.0.tgz vignettes: vignettes/recoup/inst/doc/recoup_intro.html vignetteTitles: Introduction to the recoup package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recoup/inst/doc/recoup_intro.R dependencyCount: 121 Package: RedeR Version: 3.6.0 Depends: R (>= 4.0), methods Imports: scales, igraph Suggests: knitr, rmarkdown, markdown, BiocStyle, TreeAndLeaf License: GPL-3 MD5sum: 6ded86bfe1025ed0b1a134fa650bd580 NeedsCompilation: no Title: Interactive visualization and manipulation of nested networks Description: RedeR is an R-based package combined with a stand-alone Java application for interactive visualization and manipulation of nested networks. Graph, node, and edge attributes can be configured using either graphical or command-line methods, following igraph syntax rules. biocViews: GUI, GraphAndNetwork, Network, NetworkEnrichment, NetworkInference, Software, SystemsBiology Author: Xin Wang [ctb], Florian Markowetz [ctb], Mauro Castro [aut, cre] (ORCID: ) Maintainer: Mauro Castro URL: https://doi.org/10.1186/gb-2012-13-4-r29 SystemRequirements: Java Runtime Environment (Java>= 11) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedeR git_branch: RELEASE_3_22 git_last_commit: bff7283 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RedeR_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RedeR_3.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RedeR_3.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RedeR_3.6.0.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.html vignetteTitles: "RedeR: nested networks" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R dependsOnMe: Fletcher2013b, dc3net importsMe: PANR, RTN, transcriptogramer, TreeAndLeaf suggestsMe: PathwaySpace dependencyCount: 23 Package: RedisParam Version: 1.12.0 Depends: R (>= 4.2.0), BiocParallel (>= 1.29.12) Imports: methods, redux, withr, futile.logger Suggests: rmarkdown, knitr, testthat, BiocStyle License: Artistic-2.0 MD5sum: dc36e237c1b81b5aebc31cb75c258f95 NeedsCompilation: no Title: Provide a 'redis' back-end for BiocParallel Description: This package provides a Redis-based back-end for BiocParallel, enabling an alternative mechanism for distributed computation. The The 'manager' distributes tasks to a 'worker' pool through a central Redis server, rather than directly to workers as with other BiocParallel implementations. This means that the worker pool can change dynamically during job evaluation. All features of BiocParallel are supported, including reproducible random number streams, logging to the manager, and alternative 'load balancing' task distributions. biocViews: Infrastructure Author: Martin Morgan [aut, cre] (ORCID: ), Jiefei Wang [aut] Maintainer: Martin Morgan SystemRequirements: hiredis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedisParam git_branch: RELEASE_3_22 git_last_commit: 5d13359 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RedisParam_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RedisParam_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RedisParam_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RedisParam_1.12.0.tgz vignettes: vignettes/RedisParam/inst/doc/RedisParamDeveloperGuide.html, vignettes/RedisParam/inst/doc/RedisParamUserGuide.html vignetteTitles: RedisParam for Developers, Using RedisParam hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/RedisParam/inst/doc/RedisParamDeveloperGuide.R, vignettes/RedisParam/inst/doc/RedisParamUserGuide.R dependencyCount: 20 Package: REDseq Version: 1.56.0 Depends: R (>= 3.5.0), BiocGenerics, BSgenome.Celegans.UCSC.ce2, multtest, Biostrings, BSgenome, ChIPpeakAnno Imports: AnnotationDbi, graphics, IRanges (>= 1.13.5), stats, utils License: GPL (>=2) MD5sum: 7ff324e55f34426522916d559c08a899 NeedsCompilation: no Title: Analysis of high-throughput sequencing data processed by restriction enzyme digestion Description: The package includes functions to build restriction enzyme cut site (RECS) map, distribute mapped sequences on the map with five different approaches, find enriched/depleted RECSs for a sample, and identify differentially enriched/depleted RECSs between samples. biocViews: Sequencing, SequenceMatching, Preprocessing Author: Lihua Julie Zhu, Junhui Li and Thomas Fazzio Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/REDseq git_branch: RELEASE_3_22 git_last_commit: 872f291 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/REDseq_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/REDseq_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/REDseq_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/REDseq_1.56.0.tgz vignettes: vignettes/REDseq/inst/doc/REDseq.pdf vignetteTitles: REDseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REDseq/inst/doc/REDseq.R dependencyCount: 129 Package: ReducedExperiment Version: 1.2.0 Depends: R (>= 4.4.0), SummarizedExperiment, methods Imports: WGCNA, ica, moments, clusterProfiler, msigdbr, RColorBrewer, car, lme4, lmerTest, pheatmap, biomaRt, stats, grDevices, BiocParallel, ggplot2, patchwork, BiocGenerics, S4Vectors Suggests: knitr, rmarkdown, testthat, biocViews, BiocCheck, BiocStyle, airway License: GPL (>= 3) MD5sum: 08094d431926e3503cfb79d6c9459900 NeedsCompilation: no Title: Containers and tools for dimensionally-reduced -omics representations Description: Provides SummarizedExperiment-like containers for storing and manipulating dimensionally-reduced assay data. The ReducedExperiment classes allow users to simultaneously manipulate their original dataset and their decomposed data, in addition to other method-specific outputs like feature loadings. Implements utilities and specialised classes for the application of stabilised independent component analysis (sICA) and weighted gene correlation network analysis (WGCNA). biocViews: GeneExpression, Infrastructure, DataRepresentation, Software, DimensionReduction, Network Author: Jack Gisby [aut, cre] (ORCID: ), Michael Barnes [aut] (ORCID: ) Maintainer: Jack Gisby URL: https://github.com/jackgisby/ReducedExperiment VignetteBuilder: knitr BugReports: https://github.com/jackgisby/ReducedExperiment/issues git_url: https://git.bioconductor.org/packages/ReducedExperiment git_branch: RELEASE_3_22 git_last_commit: 5622222 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ReducedExperiment_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ReducedExperiment_1.1.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ReducedExperiment_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ReducedExperiment_1.2.0.tgz vignettes: vignettes/ReducedExperiment/inst/doc/ReducedExperiment.html vignetteTitles: ReducedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReducedExperiment/inst/doc/ReducedExperiment.R dependencyCount: 203 Package: RegEnrich Version: 1.20.0 Depends: R (>= 4.0.0), S4Vectors, dplyr, tibble, BiocSet, SummarizedExperiment Imports: randomForest, fgsea, DOSE, BiocParallel, DESeq2, limma, WGCNA, ggplot2 (>= 2.2.0), methods, reshape2, magrittr, BiocStyle Suggests: GEOquery, rmarkdown, knitr, BiocManager, testthat License: GPL (>= 2) MD5sum: 4dc2db7d3bd7e095ec792e363fa0574e NeedsCompilation: no Title: Gene regulator enrichment analysis Description: This package is a pipeline to identify the key gene regulators in a biological process, for example in cell differentiation and in cell development after stimulation. There are four major steps in this pipeline: (1) differential expression analysis; (2) regulator-target network inference; (3) enrichment analysis; and (4) regulators scoring and ranking. biocViews: GeneExpression, Transcriptomics, RNASeq, TwoChannel, Transcription, GeneTarget, NetworkEnrichment, DifferentialExpression, Network, NetworkInference, GeneSetEnrichment, FunctionalPrediction Author: Weiyang Tao [cre, aut], Aridaman Pandit [aut] Maintainer: Weiyang Tao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RegEnrich git_branch: RELEASE_3_22 git_last_commit: 4833032 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RegEnrich_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RegEnrich_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RegEnrich_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RegEnrich_1.20.0.tgz vignettes: vignettes/RegEnrich/inst/doc/RegEnrich.html vignetteTitles: Gene regulator enrichment with RegEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RegEnrich/inst/doc/RegEnrich.R dependencyCount: 149 Package: RegionalST Version: 1.8.0 Depends: R (>= 4.3.0) Imports: stats, grDevices, utils, ggplot2, dplyr, scater, gridExtra, BiocStyle, BayesSpace, fgsea, magrittr, SingleCellExperiment, RColorBrewer, Seurat, S4Vectors, tibble, TOAST, assertthat, colorspace, shiny, SummarizedExperiment Suggests: knitr, rmarkdown, gplots, testthat (>= 3.0.0) License: GPL-3 MD5sum: 8a3b4bd4b76769795e516864f57068cc NeedsCompilation: no Title: Investigating regions of interest and performing regional cell type-specific analysis with spatial transcriptomics data Description: This package analyze spatial transcriptomics data through cross-regional cell type-specific analysis. It selects regions of interest (ROIs) and identifys cross-regional cell type-specific differential signals. The ROIs can be selected using automatic algorithm or through manual selection. It facilitates manual selection of ROIs using a shiny application. biocViews: Spatial, Transcriptomics, Reactome, KEGG Author: Ziyi Li [aut, cre] Maintainer: Ziyi Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RegionalST git_branch: RELEASE_3_22 git_last_commit: d77f4d3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RegionalST_1.8.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RegionalST_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RegionalST_1.8.0.tgz vignettes: vignettes/RegionalST/inst/doc/RegionalST.html vignetteTitles: RegionalST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RegionalST/inst/doc/RegionalST.R dependencyCount: 251 Package: regioneR Version: 1.42.0 Depends: GenomicRanges Imports: memoise, GenomicRanges, IRanges, BSgenome, Biostrings, rtracklayer, parallel, graphics, stats, utils, methods, Seqinfo, GenomeInfoDb, S4Vectors, tools Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 Archs: x64 MD5sum: ed43e0428e56018911846916258de8bb NeedsCompilation: no Title: Association analysis of genomic regions based on permutation tests Description: regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation Author: Anna Diez-Villanueva , Roberto Malinverni and Bernat Gel Maintainer: Bernat Gel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneR git_branch: RELEASE_3_22 git_last_commit: eb018a1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/regioneR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/regioneR_1.41.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/regioneR_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/regioneR_1.42.0.tgz vignettes: vignettes/regioneR/inst/doc/regioneR.html vignetteTitles: regioneR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneR/inst/doc/regioneR.R dependsOnMe: karyoploteR, regioneReloaded importsMe: annotatr, ChIPpeakAnno, CNVfilteR, CopyNumberPlots, karyoploteR, RgnTX, UMI4Cats suggestsMe: CNVRanger, EpiMix, UPDhmm, MitoHEAR dependencyCount: 64 Package: regioneReloaded Version: 1.12.0 Depends: R (>= 4.2), regioneR Imports: stats, RColorBrewer, Rtsne, umap, ggplot2, ggrepel, reshape2, methods, scales, cluster, grid, grDevices Suggests: rmarkdown, BiocStyle, GenomeInfoDb, knitr, testthat (>= 3.0.0) License: Artistic-2.0 Archs: x64 MD5sum: f511a8be892a97eedc913202865aba40 NeedsCompilation: no Title: RegioneReloaded: Multiple Association for Genomic Region Sets Description: RegioneReloaded is a package that allows simultaneous analysis of associations between genomic region sets, enabling clustering of data and the creation of ready-to-publish graphs. It takes over and expands on all the features of its predecessor regioneR. It also incorporates a strategy to improve p-value calculations and normalize z-scores coming from multiple analysis to allow for their direct comparison. RegioneReloaded builds upon regioneR by adding new plotting functions for obtaining publication-ready graphs. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation, Clustering, MultipleComparison Author: Roberto Malinverni [aut, cre] (ORCID: ), David Corujo [aut], Bernat Gel [aut] Maintainer: Roberto Malinverni URL: https://github.com/RMalinverni/regioneReload VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneReloaded git_branch: RELEASE_3_22 git_last_commit: f9a9462 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/regioneReloaded_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/regioneReloaded_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/regioneReloaded_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/regioneReloaded_1.12.0.tgz vignettes: vignettes/regioneReloaded/inst/doc/regioneReloaded.html vignetteTitles: regioneReloaded hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneReloaded/inst/doc/regioneReloaded.R dependencyCount: 97 Package: regionReport Version: 1.44.0 Depends: R(>= 3.2) Imports: BiocStyle (>= 2.5.19), derfinder (>= 1.25.3), DEFormats, DESeq2, Seqinfo, GenomeInfoDb, GenomicRanges, knitr (>= 1.6), knitrBootstrap (>= 0.9.0), methods, RefManageR, rmarkdown (>= 0.9.5), S4Vectors, SummarizedExperiment, utils Suggests: BiocManager, biovizBase, bumphunter (>= 1.7.6), derfinderPlot (>= 1.29.1), sessioninfo, DT, edgeR, ggbio (>= 1.35.2), ggplot2, grid, gridExtra, IRanges, mgcv, pasilla, pheatmap, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, whisker License: Artistic-2.0 Archs: x64 MD5sum: c192a814c3ace1cf66845372dfd12ff9 NeedsCompilation: no Title: Generate HTML or PDF reports for a set of genomic regions or DESeq2/edgeR results Description: Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, Transcription, Coverage, ReportWriting, DifferentialMethylation, DifferentialPeakCalling, ImmunoOncology, QualityControl Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/regionReport VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regionReport/ git_url: https://git.bioconductor.org/packages/regionReport git_branch: RELEASE_3_22 git_last_commit: 0ae7ca5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/regionReport_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/regionReport_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/regionReport_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/regionReport_1.44.0.tgz vignettes: vignettes/regionReport/inst/doc/bumphunterExample.html, vignettes/regionReport/inst/doc/regionReport.html vignetteTitles: Example report using bumphunter results, Introduction to regionReport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regionReport/inst/doc/bumphunterExample.R, vignettes/regionReport/inst/doc/regionReport.R importsMe: recountWorkflow suggestsMe: recount dependencyCount: 155 Package: regsplice Version: 1.36.0 Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats, pbapply, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 8962823619c4474e641f2c1ee7dcd864 NeedsCompilation: no Title: L1-regularization based methods for detection of differential splicing Description: Statistical methods for detection of differential splicing (differential exon usage) in RNA-seq and exon microarray data, using L1-regularization (lasso) to improve power. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, RNASeq, Microarray, ExonArray, ExperimentalDesign, Software Author: Lukas M. Weber [aut, cre] Maintainer: Lukas M. Weber URL: https://github.com/lmweber/regsplice VignetteBuilder: knitr BugReports: https://github.com/lmweber/regsplice/issues git_url: https://git.bioconductor.org/packages/regsplice git_branch: RELEASE_3_22 git_last_commit: d9c8d76 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/regsplice_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/regsplice_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/regsplice_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/regsplice_1.36.0.tgz vignettes: vignettes/regsplice/inst/doc/regsplice-workflow.html vignetteTitles: regsplice workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/regsplice/inst/doc/regsplice-workflow.R dependencyCount: 40 Package: regutools Version: 1.22.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationHub, Biostrings, DBI, GenomicRanges, Gviz, IRanges, RCy3, RSQLite, S4Vectors, methods, stats, utils, BiocFileCache Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 2.1.0), covr License: Artistic-2.0 MD5sum: cdd951da65d1c678619e264b1171f2b3 NeedsCompilation: no Title: regutools: an R package for data extraction from RegulonDB Description: RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools provides researchers with the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. biocViews: GeneRegulation, GeneExpression, SystemsBiology, Network,NetworkInference,Visualization, Transcription Author: Joselyn Chavez [aut, cre] (ORCID: ), Carmina Barberena-Jonas [aut] (ORCID: ), Jesus E. Sotelo-Fonseca [aut] (ORCID: ), Jose Alquicira-Hernandez [ctb] (ORCID: ), Heladia Salgado [ctb] (ORCID: ), Leonardo Collado-Torres [aut] (ORCID: ), Alejandro Reyes [aut] (ORCID: ) Maintainer: Joselyn Chavez URL: https://github.com/ComunidadBioInfo/regutools VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regutools git_url: https://git.bioconductor.org/packages/regutools git_branch: RELEASE_3_22 git_last_commit: f94aecc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/regutools_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/regutools_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/regutools_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/regutools_1.22.0.tgz vignettes: vignettes/regutools/inst/doc/regutools.html vignetteTitles: regutools: an R package for data extraction from RegulonDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regutools/inst/doc/regutools.R dependencyCount: 169 Package: REMP Version: 1.34.0 Depends: R (>= 3.6), SummarizedExperiment(>= 1.1.6), minfi (>= 1.22.0) Imports: readr, rtracklayer, graphics, stats, utils, methods, settings, BiocGenerics, S4Vectors, Biostrings, GenomicRanges, IRanges, Seqinfo, BiocParallel, doParallel, parallel, foreach, caret, kernlab, ranger, BSgenome, AnnotationHub, org.Hs.eg.db, impute, iterators Suggests: IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, minfiDataEPIC License: GPL-3 Archs: x64 MD5sum: 616c167d66b2be1daeaf0854de44fb5f NeedsCompilation: no Title: Repetitive Element Methylation Prediction Description: Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE. biocViews: DNAMethylation, Microarray, MethylationArray, Sequencing, GenomeWideAssociation, Epigenetics, Preprocessing, MultiChannel, TwoChannel, DifferentialMethylation, QualityControl, DataImport Author: Yinan Zheng [aut, cre], Lei Liu [aut], Wei Zhang [aut], Warren Kibbe [aut], Lifang Hou [aut, cph] Maintainer: Yinan Zheng URL: https://github.com/YinanZheng/REMP BugReports: https://github.com/YinanZheng/REMP/issues git_url: https://git.bioconductor.org/packages/REMP git_branch: RELEASE_3_22 git_last_commit: 7f2a04f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/REMP_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/REMP_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/REMP_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/REMP_1.34.0.tgz vignettes: vignettes/REMP/inst/doc/REMP.pdf vignetteTitles: An Introduction to the REMP Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REMP/inst/doc/REMP.R dependencyCount: 197 Package: ReportingTools Version: 2.50.0 Depends: methods, knitr, utils Imports: Biobase,hwriter,Category,GOstats,limma(>= 3.17.5),lattice,AnnotationDbi,edgeR, annotate,PFAM.db, GSEABase, BiocGenerics(>= 0.1.6), grid, XML, R.utils, DESeq2(>= 1.3.41), ggplot2, ggbio, IRanges Suggests: RUnit, ALL, hgu95av2.db, org.Mm.eg.db, shiny, pasilla, org.Sc.sgd.db, rmarkdown, markdown License: Artistic-2.0 MD5sum: 84f1f74336bd891303989bf8340aa8e3 NeedsCompilation: no Title: Tools for making reports in various formats Description: The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools. biocViews: ImmunoOncology, Software, Visualization, Microarray, RNASeq, GO, DataRepresentation, GeneSetEnrichment Author: Jason A. Hackney, Melanie Huntley, Jessica L. Larson, Christina Chaivorapol, Gabriel Becker, and Josh Kaminker Maintainer: Jason A. Hackney , Gabriel Becker , Jessica L. Larson VignetteBuilder: utils, rmarkdown git_url: https://git.bioconductor.org/packages/ReportingTools git_branch: RELEASE_3_22 git_last_commit: 9a53d36 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ReportingTools_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ReportingTools_2.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ReportingTools_2.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ReportingTools_2.50.0.tgz vignettes: vignettes/ReportingTools/inst/doc/basicReportingTools.pdf, vignettes/ReportingTools/inst/doc/microarrayAnalysis.pdf, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.pdf, vignettes/ReportingTools/inst/doc/shiny.pdf vignetteTitles: ReportingTools basics, Reporting on microarray differential expression, Reporting on RNA-seq differential expression, ReportingTools shiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReportingTools/inst/doc/basicReportingTools.R, vignettes/ReportingTools/inst/doc/microarrayAnalysis.R, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R, vignettes/ReportingTools/inst/doc/shiny.R importsMe: affycoretools suggestsMe: cpvSNP, EnrichmentBrowser, GSEABase, npGSEA dependencyCount: 158 Package: ResidualMatrix Version: 1.20.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 Archs: x64 MD5sum: 9c4fb15413aff2681eda81ec978f3d9b NeedsCompilation: no Title: Creating a DelayedMatrix of Regression Residuals Description: Provides delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. Also supports partial computation of residuals where selected factors are to be preserved in the output matrix. Implements a number of efficient methods for operating on the delayed matrix of residuals, most notably matrix multiplication and calculation of row/column sums or means. biocViews: Software, DataRepresentation, Regression, BatchEffect, ExperimentalDesign Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/ResidualMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/ResidualMatrix/issues git_url: https://git.bioconductor.org/packages/ResidualMatrix git_branch: RELEASE_3_22 git_last_commit: 87f8d9c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ResidualMatrix_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ResidualMatrix_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ResidualMatrix_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ResidualMatrix_1.20.0.tgz vignettes: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.html vignetteTitles: Using the ResidualMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.R importsMe: batchelor suggestsMe: alabaster.matrix, BiocSingular, chihaya, scran dependencyCount: 21 Package: RESOLVE Version: 1.12.0 Depends: R (>= 4.1.0) Imports: Biostrings, BSgenome, BSgenome.Hsapiens.1000genomes.hs37d5, cluster, data.table, GenomeInfoDb, GenomicRanges, glmnet, ggplot2, gridExtra, IRanges, lsa, MutationalPatterns, nnls, parallel, reshape2, S4Vectors, RhpcBLASctl, survival Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: 932940879bed5e60ef8c52e70b65dcf2 NeedsCompilation: no Title: RESOLVE: An R package for the efficient analysis of mutational signatures from cancer genomes Description: Cancer is a genetic disease caused by somatic mutations in genes controlling key biological functions such as cellular growth and division. Such mutations may arise both through cell-intrinsic and exogenous processes, generating characteristic mutational patterns over the genome named mutational signatures. The study of mutational signatures have become a standard component of modern genomics studies, since it can reveal which (environmental and endogenous) mutagenic processes are active in a tumor, and may highlight markers for therapeutic response. Mutational signatures computational analysis presents many pitfalls. First, the task of determining the number of signatures is very complex and depends on heuristics. Second, several signatures have no clear etiology, casting doubt on them being computational artifacts rather than due to mutagenic processes. Last, approaches for signatures assignment are greatly influenced by the set of signatures used for the analysis. To overcome these limitations, we developed RESOLVE (Robust EStimation Of mutationaL signatures Via rEgularization), a framework that allows the efficient extraction and assignment of mutational signatures. RESOLVE implements a novel algorithm that enables (i) the efficient extraction, (ii) exposure estimation, and (iii) confidence assessment during the computational inference of mutational signatures. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Luca De Sano [cre, aut] (ORCID: ) Maintainer: Luca De Sano URL: https://github.com/danro9685/RESOLVE VignetteBuilder: knitr BugReports: https://github.com/danro9685/RESOLVE/issues git_url: https://git.bioconductor.org/packages/RESOLVE git_branch: RELEASE_3_22 git_last_commit: 0daa88d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RESOLVE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RESOLVE_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RESOLVE_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RESOLVE_1.12.0.tgz vignettes: vignettes/RESOLVE/inst/doc/RESOLVE.html vignetteTitles: RESOLVE.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RESOLVE/inst/doc/RESOLVE.R dependencyCount: 133 Package: retrofit Version: 1.10.0 Depends: R (>= 4.2), Rcpp LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, testthat, DescTools, ggplot2, corrplot, cowplot, grid, colorspace, png, reshape2, pals, RCurl License: GPL-3 MD5sum: 49b9b5f51633dd804452493d81fcbb7b NeedsCompilation: yes Title: RETROFIT: Reference-free deconvolution of cell mixtures in spatial transcriptomics Description: RETROFIT is a Bayesian non-negative matrix factorization framework to decompose cell type mixtures in ST data without using external single-cell expression references. RETROFIT outperforms existing reference-based methods in estimating cell type proportions and reconstructing gene expressions in simulations with varying spot size and sample heterogeneity, irrespective of the quality or availability of the single-cell reference. RETROFIT recapitulates known cell-type localization patterns in a Slide-seq dataset of mouse cerebellum without using any single-cell data. biocViews: Transcriptomics, Visualization, RNASeq, Bayesian, Spatial, Software, GeneExpression, DimensionReduction, FeatureExtraction, SingleCell Author: Adam Park [aut, cre], Roopali Singh [aut] (ORCID: ), Xiang Zhu [aut] (ORCID: ), Qunhua Li [aut] (ORCID: ) Maintainer: Adam Park URL: https://github.com/qunhualilab/retrofit VignetteBuilder: knitr BugReports: https://github.com/qunhualilab/retrofit/issues git_url: https://git.bioconductor.org/packages/retrofit git_branch: RELEASE_3_22 git_last_commit: b0337e6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/retrofit_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/retrofit_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/retrofit_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/retrofit_1.10.0.tgz vignettes: vignettes/retrofit/inst/doc/ColonVignette.html, vignettes/retrofit/inst/doc/SimulationVignette.html vignetteTitles: Retrofit Colon Vignette, Retrofit Simulation Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/retrofit/inst/doc/ColonVignette.R, vignettes/retrofit/inst/doc/SimulationVignette.R dependencyCount: 3 Package: ReUseData Version: 1.10.0 Imports: Rcwl, RcwlPipelines, BiocFileCache, S4Vectors, stats, tools, utils, methods, jsonlite, yaml, basilisk Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: GPL-3 MD5sum: 4e608dc6bad186002b04ce212cda6252 NeedsCompilation: no Title: Reusable and reproducible Data Management Description: ReUseData is an _R/Bioconductor_ software tool to provide a systematic and versatile approach for standardized and reproducible data management. ReUseData facilitates transformation of shell or other ad hoc scripts for data preprocessing into workflow-based data recipes. Evaluation of data recipes generate curated data files in their generic formats (e.g., VCF, bed). Both recipes and data are cached using database infrastructure for easy data management and reuse. Prebuilt data recipes are available through ReUseData portal ("https://rcwl.org/dataRecipes/") with full annotation and user instructions. Pregenerated data are available through ReUseData cloud bucket that is directly downloadable through "getCloudData()". biocViews: Software, Infrastructure, DataImport, Preprocessing, ImmunoOncology Author: Qian Liu [aut, cre] (ORCID: ) Maintainer: Qian Liu URL: https://github.com/rworkflow/ReUseData VignetteBuilder: knitr BugReports: https://github.com/rworkflow/ReUseData/issues git_url: https://git.bioconductor.org/packages/ReUseData git_branch: RELEASE_3_22 git_last_commit: 2d4066b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ReUseData_1.10.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ReUseData_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ReUseData_1.9.0.tgz vignettes: vignettes/ReUseData/inst/doc/ReUseData_data.html, vignettes/ReUseData/inst/doc/ReUseData_quickStart.html, vignettes/ReUseData/inst/doc/ReUseData_recipe.html vignetteTitles: ReUseDataData, ReUseDataQS, ReUseDataRecipes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReUseData/inst/doc/ReUseData_data.R, vignettes/ReUseData/inst/doc/ReUseData_quickStart.R, vignettes/ReUseData/inst/doc/ReUseData_recipe.R dependencyCount: 123 Package: rexposome Version: 1.32.0 Depends: R (>= 3.5), Biobase Imports: methods, utils, stats, lsr, FactoMineR, stringr, circlize, corrplot, ggplot2, ggridges, reshape2, pryr, S4Vectors, imputeLCMD, scatterplot3d, glmnet, gridExtra, grid, Hmisc, gplots, gtools, scales, lme4, grDevices, graphics, ggrepel, mice Suggests: mclust, flexmix, testthat, BiocStyle, knitr, formatR, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 6266519a688acd841d88a7a07102062f NeedsCompilation: no Title: Exposome exploration and outcome data analysis Description: Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes. biocViews: Software, BiologicalQuestion, Infrastructure, DataImport, DataRepresentation, BiomedicalInformatics, ExperimentalDesign, MultipleComparison, Classification, Clustering Author: Carles Hernandez-Ferrer [aut, cre], Juan R. Gonzalez [aut], Xavier Escribà-Montagut [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rexposome git_branch: RELEASE_3_22 git_last_commit: 4e63989 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rexposome_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rexposome_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rexposome_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rexposome_1.32.0.tgz vignettes: vignettes/rexposome/inst/doc/exposome_data_analysis.html, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.html vignetteTitles: Exposome Data Analysis, Dealing with Multiple Imputations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rexposome/inst/doc/exposome_data_analysis.R, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.R importsMe: omicRexposome suggestsMe: brgedata dependencyCount: 167 Package: rfaRm Version: 1.22.0 Imports: httr, stringi, rsvg, magick, data.table, Biostrings, utils, rvest, xml2, IRanges, S4Vectors, jsonlite Suggests: R4RNA, treeio, knitr, BiocStyle, rmarkdown, BiocGenerics, RUnit License: GPL-3 MD5sum: 7f218e4dafc75b735e974d56c11b1744 NeedsCompilation: no Title: An R interface to the Rfam database Description: rfaRm provides a client interface to the Rfam database of RNA families. Data that can be retrieved include RNA families, secondary structure images, covariance models, sequences within each family, alignments leading to the identification of a family and secondary structures in the dot-bracket format. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, MultipleSequenceAlignment Author: Lara Selles Vidal, Rafael Ayala, Guy-Bart Stan, Rodrigo Ledesma-Amaro Maintainer: Lara Selles Vidal , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rfaRm git_branch: RELEASE_3_22 git_last_commit: affb9a4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rfaRm_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rfaRm_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rfaRm_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rfaRm_1.22.0.tgz vignettes: vignettes/rfaRm/inst/doc/rfaRm.html vignetteTitles: rfaRm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfaRm/inst/doc/rfaRm.R dependencyCount: 42 Package: RFLOMICS Version: 1.2.0 Depends: R (>= 4.4.0), SummarizedExperiment, MultiAssayExperiment, shinyBS, dplyr, ggplot2, htmltools, knitr, coseq Imports: vroom, org.At.tair.db, AnnotationDbi, clusterProfiler, ComplexHeatmap, data.table, DT, edgeR, enrichplot, FactoMineR, ggpubr, ggrepel, grDevices, grid, httr, limma, magrittr, methods, mixOmics, MOFA2, plotly, purrr, RColorBrewer, reshape2, reticulate, rmarkdown, S4Vectors, shiny, shinydashboard, shinyWidgets, stats, stringr, tidyr, tibble, tidyselect, UpSetR, Suggests: testthat, shinytest2, BiocStyle, org.Hs.eg.db License: Artistic-2.0 MD5sum: 5fe72e78cccc093fc78d5dbcc7f5dde6 NeedsCompilation: no Title: Interactive web application for Omics-data analysis Description: R-package with shiny interface, provides a framework for the analysis of transcriptomics, proteomics and/or metabolomics data. The interface offers a guided experience for the user, from the definition of the experimental design to the integration of several omics table together. A report can be generated with all settings and analysis results. biocViews: ShinyApps, DifferentialExpression, Metabolomics, Proteomics, Transcriptomics Author: Nadia Bessoltane [aut, cre] (ORCID: ), Delphine Charif [aut] (ORCID: ), Audrey Hulot [aut] (ORCID: ), Christine Paysant-Leroux [aut] (ORCID: ), Gwendal Cueff [aut] Maintainer: Nadia Bessoltane URL: https://github.com/RFLOMICS/RFLOMICS SystemRequirements: Python (>=3), numpy, pandas, h5py, scipy, argparse, sklearn, mofapy2 (>=0.7.1) VignetteBuilder: knitr BugReports: https://github.com/RFLOMICS/RFLOMICS/issues git_url: https://git.bioconductor.org/packages/RFLOMICS git_branch: RELEASE_3_22 git_last_commit: cb2ef8a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RFLOMICS_1.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RFLOMICS_1.2.0.tgz vignettes: vignettes/RFLOMICS/inst/doc/RFLOMICS-command-line.html, vignettes/RFLOMICS/inst/doc/RFLOMICS-input-data.html, vignettes/RFLOMICS/inst/doc/RFLOMICS.html vignetteTitles: RFLOMICS Command line, RFLOMICS input format, RFLOMICS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RFLOMICS/inst/doc/RFLOMICS-command-line.R, vignettes/RFLOMICS/inst/doc/RFLOMICS-input-data.R, vignettes/RFLOMICS/inst/doc/RFLOMICS.R dependencyCount: 271 Package: rfPred Version: 1.48.0 Depends: R (>= 3.5.0), methods Imports: utils, Seqinfo, data.table, IRanges, GenomicRanges, parallel, Rsamtools Suggests: BiocStyle License: GPL (>=2 ) MD5sum: de0bac15b94f226a8b9a43c98c3f695c NeedsCompilation: yes Title: Assign rfPred functional prediction scores to a missense variants list Description: Based on external numerous data files where rfPred scores are pre-calculated on all genomic positions of the human exome, the package gives rfPred scores to missense variants identified by the chromosome, the position (hg19 version), the referent and alternative nucleotids and the uniprot identifier of the protein. Note that for using the package, the user has to download the TabixFile and index (approximately 3.3 Go). biocViews: Software, Annotation, Classification Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais Maintainer: Hugo Varet git_url: https://git.bioconductor.org/packages/rfPred git_branch: RELEASE_3_22 git_last_commit: a86c9b0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rfPred_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rfPred_1.47.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rfPred_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rfPred_1.48.0.tgz vignettes: vignettes/rfPred/inst/doc/vignette.pdf vignetteTitles: CalculatingrfPredscoreswithpackagerfPred hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfPred/inst/doc/vignette.R dependencyCount: 30 Package: rGenomeTracks Version: 1.16.0 Depends: R (>= 4.1.0), Imports: imager, reticulate, methods, rGenomeTracksData Suggests: rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: cfb337667fa10622ec1efddd7782147d NeedsCompilation: no Title: Integerated visualization of epigenomic data Description: rGenomeTracks package leverages the power of pyGenomeTracks software with the interactivity of R. pyGenomeTracks is a python software that offers robust method for visualizing epigenetic data files like narrowPeak, Hic matrix, TADs and arcs, however though, here is no way currently to use it within R interactive session. rGenomeTracks wrapped the whole functionality of pyGenomeTracks with additional utilites to make to more pleasant for R users. biocViews: Software, HiC, Visualization Author: Omar Elashkar [aut, cre] (ORCID: ) Maintainer: Omar Elashkar SystemRequirements: pyGenomeTracks (prefered to use install_pyGenomeTracks()) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGenomeTracks git_branch: RELEASE_3_22 git_last_commit: c118752 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rGenomeTracks_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rGenomeTracks_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rGenomeTracks_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rGenomeTracks_1.16.0.tgz vignettes: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.html vignetteTitles: rGenomeTracks hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.R dependencyCount: 81 Package: RgnTX Version: 1.11.1 Depends: R (>= 4.2.0) Imports: Seqinfo, GenomicFeatures, GenomicRanges, ggplot2, graphics, IRanges, methods, regioneR, S4Vectors, stats, TxDb.Hsapiens.UCSC.hg19.knownGene Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: Artistic-2.0 Archs: x64 MD5sum: 599443992a6df487bd862566e6d73dc9 NeedsCompilation: no Title: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity Description: RgnTX allows the integration of transcriptome annotations so as to model the complex alternative splicing patterns. It supports the testing of transcriptome elements without clear isoform association, which is often the real scenario due to technical limitations. It involves functions that do permutaion test for evaluating association between features and transcriptome regions. biocViews: AlternativeSplicing, Sequencing, RNASeq, MethylSeq, Transcription, SplicedAlignment Author: Yue Wang [aut, cre], Jia Meng [aut] Maintainer: Yue Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RgnTX git_branch: devel git_last_commit: 8a121cf git_last_commit_date: 2025-07-22 Date/Publication: 2025-10-07 source.ver: src/contrib/RgnTX_1.11.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/RgnTX_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RgnTX_1.11.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RgnTX_1.11.1.tgz vignettes: vignettes/RgnTX/inst/doc/RgnTX.html vignetteTitles: RgnTX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RgnTX/inst/doc/RgnTX.R dependencyCount: 91 Package: rgoslin Version: 1.14.0 Imports: Rcpp (>= 1.0.3), dplyr LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), BiocStyle, knitr, rmarkdown, kableExtra, BiocManager, stringr, stringi, ggplot2, tibble, lipidr License: MIT + file LICENSE Archs: x64 MD5sum: 6191d196a90fa0ebe5faf3d5ccd53704 NeedsCompilation: yes Title: Lipid Shorthand Name Parsing and Normalization Description: The R implementation for the Grammar of Succint Lipid Nomenclature parses different short hand notation dialects for lipid names. It normalizes them to a standard name. It further provides calculated monoisotopic masses and sum formulas for each successfully parsed lipid name and supplements it with LIPID MAPS Category and Class information. Also, the structural level and further structural details about the head group, fatty acyls and functional groups are returned, where applicable. biocViews: Software, Lipidomics, Metabolomics, Preprocessing, Normalization, MassSpectrometry Author: Nils Hoffmann [aut, cre] (ORCID: ), Dominik Kopczynski [aut] (ORCID: ) Maintainer: Nils Hoffmann URL: https://github.com/lifs-tools/rgoslin VignetteBuilder: knitr BugReports: https://github.com/lifs-tools/rgoslin/issues git_url: https://git.bioconductor.org/packages/rgoslin git_branch: RELEASE_3_22 git_last_commit: 484ceb7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rgoslin_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rgoslin_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rgoslin_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rgoslin_1.14.0.tgz vignettes: vignettes/rgoslin/inst/doc/introduction.html vignetteTitles: Using R Goslin to parse and normalize lipid nomenclature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rgoslin/inst/doc/introduction.R dependencyCount: 20 Package: RGraph2js Version: 1.38.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 MD5sum: 3696252fb7b01ad7ca5f532eede9c440 NeedsCompilation: no Title: Convert a Graph into a D3js Script Description: Generator of web pages which display interactive network/graph visualizations with D3js, jQuery and Raphael. biocViews: Visualization, Network, GraphAndNetwork, ThirdPartyClient Author: Stephane Cano [aut, cre], Sylvain Gubian [aut], Florian Martin [aut] Maintainer: Stephane Cano SystemRequirements: jQuery, jQueryUI, qTip2, D3js and Raphael are required Javascript libraries made available via the online CDNJS service (http://cdnjs.cloudflare.com). git_url: https://git.bioconductor.org/packages/RGraph2js git_branch: RELEASE_3_22 git_last_commit: 2921c75 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RGraph2js_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RGraph2js_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RGraph2js_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RGraph2js_1.38.0.tgz vignettes: vignettes/RGraph2js/inst/doc/RGraph2js.pdf vignetteTitles: RGraph2js hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGraph2js/inst/doc/RGraph2js.R dependencyCount: 11 Package: Rgraphviz Version: 2.54.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL MD5sum: 7c56d75e456cbb5772ce70e28f252b49 NeedsCompilation: yes Title: Provides plotting capabilities for R graph objects Description: Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. biocViews: GraphAndNetwork, Visualization Author: Kasper Daniel Hansen [cre, aut], Jeff Gentry [aut], Li Long [aut], Robert Gentleman [aut], Seth Falcon [aut], Florian Hahne [aut], Deepayan Sarkar [aut] Maintainer: Kasper Daniel Hansen SystemRequirements: optionally Graphviz (>= 2.16), USE_C17 git_url: https://git.bioconductor.org/packages/Rgraphviz git_branch: RELEASE_3_22 git_last_commit: deef90a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rgraphviz_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rgraphviz_2.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rgraphviz_2.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rgraphviz_2.54.0.tgz vignettes: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.pdf, vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf vignetteTitles: A New Interface to Plot Graphs Using Rgraphviz, How To Plot A Graph Using Rgraphviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.R, vignettes/Rgraphviz/inst/doc/Rgraphviz.R dependsOnMe: biocGraph, BioMVCClass, CellNOptR, MineICA, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, maEndToEnd, dlsem, gridGraphviz importsMe: apComplex, biocGraph, bnem, chimeraviz, CytoML, DEGraph, EnrichDO, EnrichmentBrowser, flowWorkspace, GeneNetworkBuilder, GOstats, hyperdraw, KEGGgraph, mirIntegrator, MIRit, mnem, OncoSimulR, ontoProc, paircompviz, pathview, qpgraph, TRONCO, abn, agena.ai, BCDAG, BiDAG, bnpa, bnRep, ceg, CePa, classGraph, cogmapr, graphpcor, ontologyPlot, SEMgraph, stablespec, WayFindR suggestsMe: a4, altcdfenvs, annotate, Category, CNORfeeder, CNORfuzzy, DEGraph, flowCore, geneplotter, GlobalAncova, globaltest, GSEABase, MLP, NCIgraph, RBGL, rBiopaxParser, safe, SPIA, SRAdb, Streamer, topGO, ViSEAGO, vtpnet, NCIgraphData, SNAData, arulesViz, BayesNetBP, bnlearn, bnstruct, bsub, ChoR, CodeDepends, gbutils, GeneNet, HEMDAG, iTOP, kst, lava, loon, maGUI, micd, multiplex, netmeta, ParallelPC, pcalg, pchc, pks, psych, relations, rEMM, rPref, rSpectral, SCCI, sisal, textplot, tm, topologyGSA, tpc, unifDAG, zenplots dependencyCount: 10 Package: rGREAT Version: 2.11.0 Depends: R (>= 4.0.0), GenomicRanges, IRanges, methods Imports: graphics, rjson, GetoptLong (>= 0.0.9), RCurl, utils, stats, GlobalOptions, shiny, DT, GenomicFeatures, digest, GO.db, progress, circlize, AnnotationDbi, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, RColorBrewer, S4Vectors, GenomeInfoDb, foreach, doParallel, Rcpp LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, rmarkdown, BiocManager, org.Mm.eg.db, msigdbr, KEGGREST, reactome.db Enhances: BioMartGOGeneSets, UniProtKeywords License: MIT + file LICENSE MD5sum: 6b2f24cfad5728cfdae8a78d64d9974a NeedsCompilation: yes Title: GREAT Analysis - Functional Enrichment on Genomic Regions Description: GREAT (Genomic Regions Enrichment of Annotations Tool) is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm (the local GREAT analysis), also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions. biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing, WholeGenome, GenomeAnnotation, Coverage Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/rGREAT, http://great.stanford.edu/public/html/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGREAT git_branch: devel git_last_commit: 5a6bdb6 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/rGREAT_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rGREAT_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rGREAT_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rGREAT_2.11.0.tgz vignettes: vignettes/rGREAT/inst/doc/rGREAT.html vignetteTitles: The rGREAT package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 124 Package: RGSEA Version: 1.44.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: 907f598b5be82edd89806086374a4216 NeedsCompilation: no Title: Random Gene Set Enrichment Analysis Description: Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements. biocViews: GeneSetEnrichment, StatisticalMethod, Classification Author: Chengcheng Ma Maintainer: Chengcheng Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGSEA git_branch: RELEASE_3_22 git_last_commit: b670bf4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RGSEA_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RGSEA_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RGSEA_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RGSEA_1.44.0.tgz vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf vignetteTitles: Introduction to RGSEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGSEA/inst/doc/RGSEA.R dependencyCount: 6 Package: rgsepd Version: 1.42.0 Depends: R (>= 4.2.0), DESeq2, goseq (>= 1.28) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, AnnotationDbi Suggests: boot, tools, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: 0d403cecb5187fc6ad238cf02d7352ce NeedsCompilation: no Title: Gene Set Enrichment / Projection Displays Description: R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group. biocViews: ImmunoOncology, Software, DifferentialExpression, GeneSetEnrichment, RNASeq Author: Karl Stamm Maintainer: Karl Stamm VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rgsepd git_branch: RELEASE_3_22 git_last_commit: c033fd6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rgsepd_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rgsepd_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rgsepd_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rgsepd_1.42.0.tgz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R dependencyCount: 125 Package: rhdf5 Version: 2.54.0 Depends: R (>= 4.0.0), methods Imports: Rhdf5lib (>= 1.13.4), rhdf5filters (>= 1.15.5) LinkingTo: Rhdf5lib Suggests: bit64, BiocStyle, knitr, rmarkdown, testthat, bench, dplyr, ggplot2, mockery, BiocParallel License: Artistic-2.0 Archs: x64 MD5sum: f2ad2989d2e0a0ff97bc58c959fc5113 NeedsCompilation: yes Title: R Interface to HDF5 Description: This package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM. biocViews: Infrastructure, DataImport Author: Bernd Fischer [aut], Mike Smith [aut] (ORCID: , Maintainer from 2017 to 2025), Gregoire Pau [aut], Martin Morgan [ctb], Daniel van Twisk [ctb], Hugo Gruson [cre] (ORCID: ) Maintainer: Hugo Gruson URL: https://github.com/Huber-group-EMBL/rhdf5 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/rhdf5/issues git_url: https://git.bioconductor.org/packages/rhdf5 git_branch: RELEASE_3_22 git_last_commit: c7248dd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rhdf5_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rhdf5_2.53.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rhdf5_2.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rhdf5_2.54.0.tgz vignettes: vignettes/rhdf5/inst/doc/practical_tips.html, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.html, vignettes/rhdf5/inst/doc/rhdf5.html vignetteTitles: rhdf5 Practical Tips, Reading HDF5 Files In The Cloud, rhdf5 - HDF5 interface for R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/practical_tips.R, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.R, vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GSCA, h5mread, HiCBricks, LoomExperiment, MuData, octad importsMe: alabaster.base, alabaster.bumpy, alabaster.mae, alabaster.matrix, alabaster.ranges, alabaster.spatial, BayesSpace, BgeeCall, biomformat, bnbc, bsseq, chihaya, CiteFuse, cmapR, CoGAPS, CopyNumberPlots, cTRAP, cytomapper, diffHic, DropletUtils, epigraHMM, EventPointer, FRASER, GenomicScores, gep2pep, h5vc, HDF5Array, HicAggR, HiCcompare, HiCExperiment, HiCPotts, IONiseR, mariner, methodical, MOFA2, MoleculeExperiment, phantasus, plotgardener, ptairMS, PureCN, recountmethylation, ribor, scafari, scCB2, scMitoMut, scone, scRNAseqApp, signatureSearch, SpaceTrooper, SpliceWiz, SpotClean, SurfR, TENxIO, trackViewer, MafH5.gnomAD.v4.0.GRCh38, MethylSeqData, ptairData, scMultiome, signatureSearchData, TumourMethData, bioRad, ebvcube, file2meco, karyotapR, LOMAR, NEONiso, OmicFlow, rDataPipeline suggestsMe: anndataR, beachmat.hdf5, edgeR, HiCDOC, HiCParser, mia, pairedGSEA, phantasusLite, rhdf5filters, SCArray, scviR, slalom, SpatialFeatureExperiment, Spectra, SummarizedExperiment, tximport, Voyager, xcms, zellkonverter, ClustAssess, conos, CRMetrics, getRad, io, MplusAutomation, neonstore, neonUtilities, SignacX, SpatialDDLS dependencyCount: 3 Package: rhdf5client Version: 1.32.0 Depends: R (>= 3.6), methods, DelayedArray Imports: httr, rjson, utils, data.table Suggests: knitr, testthat, BiocStyle, DT, rmarkdown License: Artistic-2.0 MD5sum: e47adc6d64a1f404da24a1c2aca0a981 NeedsCompilation: yes Title: Access HDF5 content from HDF Scalable Data Service Description: This package provides functionality for reading data from HDF Scalable Data Service from within R. The HSDSArray function bridges from HSDS to the user via the DelayedArray interface. Bioconductor manages an open HSDS instance graciously provided by John Readey of the HDF Group. biocViews: DataImport, Software, Infrastructure Author: Samuela Pollack [aut], Shweta Gopaulakrishnan [aut], BJ Stubbs [aut], Alexey Sergushichev [aut, cre], Vincent Carey [aut] Maintainer: Alexey Sergushichev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rhdf5client git_branch: RELEASE_3_22 git_last_commit: 425507e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rhdf5client_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rhdf5client_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rhdf5client_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rhdf5client_1.32.0.tgz vignettes: vignettes/rhdf5client/inst/doc/delayed-array.html vignetteTitles: HSDSArray DelayedArray backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5client/inst/doc/delayed-array.R importsMe: phantasus, phantasusLite dependencyCount: 31 Package: rhdf5filters Version: 1.22.0 LinkingTo: Rhdf5lib Suggests: BiocStyle, knitr, rmarkdown, tinytest, rhdf5 (>= 2.47.7) License: BSD_2_clause + file LICENSE MD5sum: 6ad81aa9580ffdc3e26301793abc8507 NeedsCompilation: yes Title: HDF5 Compression Filters Description: Provides a collection of additional compression filters for HDF5 datasets. The package is intended to provide seemless integration with rhdf5, however the compiled filters can also be used with external applications. biocViews: Infrastructure, DataImport Author: Mike Smith [aut, ccp] (ORCID: ), Hugo Gruson [cre] (ORCID: ) Maintainer: Hugo Gruson URL: https://github.com/Huber-group-EMBL/rhdf5filters SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/rhdf5filters/issues git_url: https://git.bioconductor.org/packages/rhdf5filters git_branch: RELEASE_3_22 git_last_commit: 3465c24 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rhdf5filters_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rhdf5filters_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rhdf5filters_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rhdf5filters_1.22.0.tgz vignettes: vignettes/rhdf5filters/inst/doc/rhdf5filters.html vignetteTitles: HDF5 Compression Filters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rhdf5filters/inst/doc/rhdf5filters.R importsMe: h5mread, rhdf5 dependencyCount: 1 Package: Rhdf5lib Version: 1.32.0 Depends: R (>= 4.2.0) Suggests: BiocStyle, knitr, rmarkdown, tinytest, mockery License: Artistic-2.0 MD5sum: 3d99201552c1f32a3054e23f727daf28 NeedsCompilation: yes Title: hdf5 library as an R package Description: Provides C and C++ hdf5 libraries. biocViews: Infrastructure Author: Mike Smith [ctb] (ORCID: ), Hugo Gruson [cre] (ORCID: ), The HDF Group [cph] Maintainer: Hugo Gruson URL: https://github.com/Huber-group-EMBL/Rhdf5lib SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/Rhdf5lib/issues git_url: https://git.bioconductor.org/packages/Rhdf5lib git_branch: RELEASE_3_22 git_last_commit: f62ae28 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rhdf5lib_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rhdf5lib_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rhdf5lib_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rhdf5lib_1.32.0.tgz vignettes: vignettes/Rhdf5lib/inst/doc/downloadHDF5.html, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.html vignetteTitles: Creating this HDF5 distribution, Linking to Rhdf5lib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhdf5lib/inst/doc/downloadHDF5.R, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.R importsMe: epigraHMM, rhdf5 suggestsMe: mbkmeans linksToMe: alabaster.base, beachmat.hdf5, chihaya, CytoML, DropletUtils, epigraHMM, h5mread, mbkmeans, mzR, ncdfFlow, rhdf5, rhdf5filters, stPipe, smer dependencyCount: 0 Package: rhinotypeR Version: 1.4.0 Depends: R (>= 4.5.0) Imports: Biostrings, methods, MSA2dist, msa Suggests: knitr, rmarkdown, BiocManager, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: f33817476fdbd8ae878843aea4ffd010 NeedsCompilation: no Title: Rhinovirus genotyping Description: "rhinotypeR" is designed to automate the comparison of sequence data against prototype strains, streamlining the genotype assignment process. By implementing predefined pairwise distance thresholds, this package makes genotype assignment accessible to researchers and public health professionals. This tool enhances our epidemiological toolkit by enabling more efficient surveillance and analysis of rhinoviruses (RVs) and other viral pathogens with complex genomic landscapes. Additionally, "rhinotypeR" supports comprehensive visualization and analysis of single nucleotide polymorphisms (SNPs) and amino acid substitutions, facilitating in-depth genetic and evolutionary studies. biocViews: Sequencing, Genetics, Phylogenetics, Visualization, MultipleSequenceAlignment, MultipleComparison Author: Martha Luka [aut, cre] (ORCID: ), Ruth Nanjala [aut], Winfred Gatua [aut], Wafaa M. Rashed [aut], Olaitan Awe [aut] Maintainer: Martha Luka URL: https://github.com/omicscodeathon/rhinotypeR VignetteBuilder: knitr BugReports: https://github.com/omicscodeathon/rhinotypeR/issues git_url: https://git.bioconductor.org/packages/rhinotypeR git_branch: RELEASE_3_22 git_last_commit: 7f9c661 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rhinotypeR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rhinotypeR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rhinotypeR_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rhinotypeR_1.4.0.tgz vignettes: vignettes/rhinotypeR/inst/doc/rhinotypeR.html vignetteTitles: Introduction to rhinotypeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rhinotypeR/inst/doc/rhinotypeR.R dependencyCount: 57 Package: Rhisat2 Version: 1.26.0 Depends: R (>= 4.4.0) Imports: txdbmaker, SGSeq, GenomicRanges, methods, utils Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: 9daabe6380495bc82dafabd1a8c6c695 NeedsCompilation: yes Title: R Wrapper for HISAT2 Aligner Description: An R interface to the HISAT2 spliced short-read aligner by Kim et al. (2015). The package contains wrapper functions to create a genome index and to perform the read alignment to the generated index. biocViews: Alignment, Sequencing, SplicedAlignment Author: Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/Rhisat2 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rhisat2/issues git_url: https://git.bioconductor.org/packages/Rhisat2 git_branch: RELEASE_3_22 git_last_commit: 9f95fd4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rhisat2_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rhisat2_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rhisat2_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rhisat2_1.26.0.tgz vignettes: vignettes/Rhisat2/inst/doc/Rhisat2.html vignetteTitles: Rhisat2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rhisat2/inst/doc/Rhisat2.R importsMe: CircSeqAlignTk suggestsMe: eisaR, QuasR dependencyCount: 103 Package: Rhtslib Version: 3.6.0 Imports: tools Suggests: knitr, rmarkdown, BiocStyle License: LGPL (>= 2) MD5sum: e832528219ddfe8adb8c0f0ac4ba29bc NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.18 of the 'HTSlib' C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package="Rhtslib", "Rhtslib"). biocViews: DataImport, Sequencing Author: Nathaniel Hayden [led, aut], Martin Morgan [aut], Hervé Pagès [aut, cre], Tomas Kalibera [ctb], Jeroen Ooms [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/Rhtslib, http://www.htslib.org/ SystemRequirements: libbz2 & liblzma & libcurl (with header files), GNU make VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Rhtslib/issues git_url: https://git.bioconductor.org/packages/Rhtslib git_branch: RELEASE_3_22 git_last_commit: c4b7268 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rhtslib_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rhtslib_3.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rhtslib_3.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rhtslib_3.6.0.tgz vignettes: vignettes/Rhtslib/inst/doc/Rhtslib.html vignetteTitles: Motivation and use of Rhtslib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R importsMe: deepSNV, diffHic, maftools, mitoClone2, scPipe, stPipe linksToMe: bamsignals, csaw, deepSNV, DiffBind, diffHic, epialleleR, FLAMES, h5vc, iscream, maftools, methylKit, mitoClone2, podkat, QuasR, raer, Rsamtools, scPipe, ShortRead, stPipe, VariantAnnotation, jackalope dependencyCount: 1 Package: RiboCrypt Version: 1.16.0 Depends: R (>= 3.6.0), ORFik (>= 1.13.12) Imports: bslib, BiocGenerics, BiocParallel, Biostrings, ComplexHeatmap, cowplot, crosstalk, data.table, dplyr, DT, fst, Seqinfo, GenomicFeatures, GenomicRanges, ggplot2, grid, htmlwidgets, httr, IRanges, jsonlite, knitr, markdown, NGLVieweR, plotly, rlang, rclipboard, RCurl, rtracklayer, shiny, shinycssloaders, shinyhelper, shinyjs, shinyjqui, shinyWidgets, stringr, writexl Suggests: testthat, rmarkdown, BiocStyle, BSgenome, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: d3b8d637c442a03fb2fd534636573bd4 NeedsCompilation: no Title: Interactive visualization in genomics Description: R Package for interactive visualization and browsing NGS data. It contains a browser for both transcript and genomic coordinate view. In addition a QC and general metaplots are included, among others differential translation plots and gene expression plots. The package is still under development. biocViews: Software, Sequencing, RiboSeq, RNASeq, Author: Michal Swirski [aut, cre, cph], Haakon Tjeldnes [aut, ctb], Kornel Labun [ctb] Maintainer: Michal Swirski URL: https://github.com/m-swirski/RiboCrypt VignetteBuilder: knitr BugReports: https://github.com/m-swirski/RiboCrypt/issues git_url: https://git.bioconductor.org/packages/RiboCrypt git_branch: RELEASE_3_22 git_last_commit: e202b8e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RiboCrypt_1.16.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RiboCrypt_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RiboCrypt_1.16.0.tgz vignettes: vignettes/RiboCrypt/inst/doc/RiboCrypt_app_tutorial.html, vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.html vignetteTitles: RiboCrypt_app_tutorial.html, RiboCrypt_overview.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RiboCrypt/inst/doc/RiboCrypt_app_tutorial.R, vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.R dependencyCount: 184 Package: RiboDiPA Version: 1.18.0 Depends: R (>= 4.1), Rsamtools, GenomicFeatures, GenomicAlignments Imports: Rcpp (>= 1.0.2), graphics, stats, data.table, elitism, methods, S4Vectors, IRanges, GenomicRanges, matrixStats, reldist, doParallel, foreach, parallel, qvalue, DESeq2, ggplot2, BiocFileCache, BiocGenerics, txdbmaker LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL (>= 3) MD5sum: 6ffba567dfd33452827a0e8db3e04be8 NeedsCompilation: yes Title: Differential pattern analysis for Ribo-seq data Description: This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization. biocViews: RiboSeq, GeneExpression, GeneRegulation, DifferentialExpression, Sequencing, Coverage, Alignment, RNASeq, ImmunoOncology, QualityControl, DataImport, Software, Normalization Author: Keren Li [aut], Matt Hope [aut], Xiaozhong Wang [aut], Ji-Ping Wang [aut, cre] Maintainer: Ji-Ping Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboDiPA git_branch: RELEASE_3_22 git_last_commit: 44db22f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RiboDiPA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RiboDiPA_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RiboDiPA_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RiboDiPA_1.18.0.tgz vignettes: vignettes/RiboDiPA/inst/doc/RiboDiPA.html vignetteTitles: RiboDiPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RiboDiPA/inst/doc/RiboDiPA.R dependencyCount: 147 Package: RiboProfiling Version: 1.39.1 Depends: R (>= 3.5.0), Biostrings Imports: methods, BiocGenerics, Seqinfo, GenomicRanges, IRanges, reshape2, GenomicFeatures, grid, plyr, S4Vectors, GenomicAlignments, ggplot2, ggbio, Rsamtools, rtracklayer, data.table, sqldf Suggests: knitr, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, testthat, SummarizedExperiment License: GPL-3 MD5sum: 2c107c846c5b72cbb2f6633de49ceba6 NeedsCompilation: no Title: Ribosome Profiling Data Analysis: from BAM to Data Representation and Interpretation Description: Starting with a BAM file, this package provides the necessary functions for quality assessment, read start position recalibration, the counting of reads on CDS, 3'UTR, and 5'UTR, plotting of count data: pairs, log fold-change, codon frequency and coverage assessment, principal component analysis on codon coverage. biocViews: RiboSeq, Sequencing, Coverage, Alignment, QualityControl, Software, PrincipalComponent Author: Alexandra Popa Maintainer: A. Popa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboProfiling git_branch: devel git_last_commit: 0c82466 git_last_commit_date: 2025-07-31 Date/Publication: 2025-10-07 source.ver: src/contrib/RiboProfiling_1.39.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/RiboProfiling_1.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RiboProfiling_1.39.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RiboProfiling_1.39.1.tgz vignettes: vignettes/RiboProfiling/inst/doc/RiboProfiling.pdf vignetteTitles: Analysing Ribo-Seq data with the "RiboProfiling" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RiboProfiling/inst/doc/RiboProfiling.R dependencyCount: 140 Package: ribor Version: 1.22.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, hash, methods, rhdf5, rlang, stats, S4Vectors, tidyr, tools, yaml Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: f9aeaa433be4b1550ed02e6ef2e8a898 NeedsCompilation: no Title: An R Interface for Ribo Files Description: The ribor package provides an R Interface for .ribo files. It provides functionality to read the .ribo file, which is of HDF5 format, and performs common analyses on its contents. biocViews: Software, Infrastructure Author: Michael Geng [cre, aut], Hakan Ozadam [aut], Can Cenik [aut] Maintainer: Michael Geng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribor git_branch: RELEASE_3_22 git_last_commit: 65f2c78 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ribor_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ribor_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ribor_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ribor_1.22.0.tgz vignettes: vignettes/ribor/inst/doc/ribor.html vignetteTitles: A Walkthrough of RiboR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ribor/inst/doc/ribor.R dependencyCount: 44 Package: riboSeqR Version: 1.44.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, S4Vectors, baySeq, Seqinfo, seqLogo Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 Archs: x64 MD5sum: 0fe80fbb2f73f3da4f0075abb5aba111 NeedsCompilation: no Title: Analysis of sequencing data from ribosome profiling experiments Description: Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments. biocViews: Sequencing,Genetics,Visualization,RiboSeq Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/riboSeqR BugReports: https://github.com/samgg/riboSeqR/issues git_url: https://git.bioconductor.org/packages/riboSeqR git_branch: RELEASE_3_22 git_last_commit: d196728 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/riboSeqR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/riboSeqR_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/riboSeqR_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/riboSeqR_1.44.0.tgz vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf vignetteTitles: riboSeqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/riboSeqR/inst/doc/riboSeqR.R dependencyCount: 38 Package: ribosomeProfilingQC Version: 1.22.0 Depends: R (>= 4.0), GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, BSgenome, EDASeq, GenomicAlignments, GenomicFeatures, Seqinfo, GenomeInfoDb, IRanges, methods, motifStack, rtracklayer, Rsamtools, RUVSeq, Rsubread, S4Vectors, XVector, ggplot2, ggfittext, scales, ggrepel, utils, cluster, stats, graphics, grid, txdbmaker, ggExtra Suggests: RUnit, BiocStyle, knitr, BSgenome.Drerio.UCSC.danRer10, GenomeInfoDbData, edgeR, DESeq2, limma, ashr, testthat, rmarkdown, vsn, Biobase License: GPL (>=3) + file LICENSE Archs: x64 MD5sum: 2ac1a9862d92128ff831d9f9dd63e76f NeedsCompilation: no Title: Ribosome Profiling Quality Control Description: Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis. biocViews: RiboSeq, Sequencing, GeneRegulation, QualityControl, Visualization, Coverage Author: Jianhong Ou [aut, cre] (ORCID: ), Mariah Hoye [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribosomeProfilingQC git_branch: RELEASE_3_22 git_last_commit: d8ce3bd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ribosomeProfilingQC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ribosomeProfilingQC_1.21.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ribosomeProfilingQC_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ribosomeProfilingQC_1.22.0.tgz vignettes: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.html vignetteTitles: ribosomeProfilingQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.R dependencyCount: 177 Package: rifi Version: 1.14.0 Depends: R (>= 4.2) Imports: car, cowplot, doMC, parallel, dplyr, egg, foreach, ggplot2, graphics, grDevices, grid, methods, nls2, nnet, rlang, S4Vectors, scales, stats, stringr, SummarizedExperiment, tibble, rtracklayer, reshape2, utils Suggests: DescTools, devtools, knitr, rmarkdown, BiocStyle License: GPL-3 + file LICENSE MD5sum: 11aa0044615a5191f2d8aae2db2f9c8b NeedsCompilation: no Title: 'rifi' analyses data from rifampicin time series created by microarray or RNAseq Description: 'rifi' analyses data from rifampicin time series created by microarray or RNAseq. 'rifi' is a transcriptome data analysis tool for the holistic identification of transcription and decay associated processes. The decay constants and the delay of the onset of decay is fitted for each probe/bin. Subsequently, probes/bins of equal properties are combined into segments by dynamic programming, independent of a existing genome annotation. This allows to detect transcript segments of different stability or transcriptional events within one annotated gene. In addition to the classic decay constant/half-life analysis, 'rifi' detects processing sites, transcription pausing sites, internal transcription start sites in operons, sites of partial transcription termination in operons, identifies areas of likely transcriptional interference by the collision mechanism and gives an estimate of the transcription velocity. All data are integrated to give an estimate of continous transcriptional units, i.e. operons. Comprehensive output tables and visualizations of the full genome result and the individual fits for all probes/bins are produced. biocViews: RNASeq, DifferentialExpression, GeneRegulation, Transcriptomics, Regression, Microarray, Software Author: Loubna Youssar [aut, ctb], Walja Wanney [aut, ctb], Jens Georg [aut, cre] Maintainer: Jens Georg VignetteBuilder: knitr BugReports: https://github.com/CyanolabFreiburg/rifi git_url: https://git.bioconductor.org/packages/rifi git_branch: RELEASE_3_22 git_last_commit: ef65993 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rifi_1.14.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rifi_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rifi_1.14.0.tgz vignettes: vignettes/rifi/inst/doc/vignette.html vignetteTitles: Rifi for decay estimation,, based on high resolution microarray or RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rifi/inst/doc/vignette.R dependencyCount: 122 Package: rifiComparative Version: 1.10.0 Depends: R (>= 4.2) Imports: cowplot, doMC, parallel, dplyr, egg, foreach, ggplot2, ggrepel, graphics, grDevices, grid, methods, nnet, rlang, S4Vectors, scales, stats, stringr, tibble, rtracklayer, utils, writexl, DTA, LSD, reshape2, devtools, SummarizedExperiment Suggests: DescTools, knitr, rmarkdown, BiocStyle License: GPL-3 + file LICENSE MD5sum: f694b242e177775020e6bde9bb8aac81 NeedsCompilation: no Title: 'rifiComparative' compares the output of rifi from two different conditions. Description: 'rifiComparative' is a continuation of rifi package. It compares two conditions output of rifi using half-life and mRNA at time 0 segments. As an input for the segmentation, the difference between half-life of both condtions and log2FC of the mRNA at time 0 are used. The package provides segmentation, statistics, summary table, fragments visualization and some additional useful plots for further anaylsis. biocViews: RNASeq, DifferentialExpression, GeneRegulation, Transcriptomics, Microarray, Software Author: Loubna Youssar [aut, cre], Jens cre Georg [aut] Maintainer: Loubna Youssar VignetteBuilder: knitr BugReports: https://github.com/CyanolabFreiburg/rifiComparative git_url: https://git.bioconductor.org/packages/rifiComparative git_branch: RELEASE_3_22 git_last_commit: b07cf67 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rifiComparative_1.10.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rifiComparative_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rifiComparative_1.10.0.tgz vignettes: vignettes/rifiComparative/inst/doc/rifiComparative.html vignetteTitles: rifiComparative hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rifiComparative/inst/doc/rifiComparative.R dependencyCount: 167 Package: Rigraphlib Version: 1.2.0 LinkingTo: biocmake Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 53666ae46245494b96c36bdc8dd95daa NeedsCompilation: yes Title: igraph library as an R package Description: Vendors the igraph C source code and builds it into a static library. Other Bioconductor packages can link to libigraph.a in their own C/C++ code. This is intended for packages wrapping C/C++ libraries that depend on the igraph C library and cannot be easily adapted to use the igraph R package. biocViews: Clustering, GraphAndNetwork Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun URL: https://github.com/libscran/Rigraphlib VignetteBuilder: knitr BugReports: https://github.com/libscran/Rigraphlib/issues git_url: https://git.bioconductor.org/packages/Rigraphlib git_branch: RELEASE_3_22 git_last_commit: e14ac37 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rigraphlib_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rigraphlib_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rigraphlib_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rigraphlib_1.2.0.tgz vignettes: vignettes/Rigraphlib/inst/doc/userguide.html vignetteTitles: Using the igraph C library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rigraphlib/inst/doc/userguide.R importsMe: scrapper dependencyCount: 5 Package: rigvf Version: 1.2.0 Depends: R (>= 4.1.0) Imports: methods, httr2, rjsoncons, dplyr, tidyr, rlang, memoise, cachem, whisker, jsonlite, GenomicRanges, IRanges, Seqinfo Suggests: knitr, rmarkdown, testthat (>= 3.0.0), plyranges, plotgardener, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene License: MIT + file LICENSE MD5sum: 4ac8fabf1b6026c0d071de53f104b80f NeedsCompilation: no Title: R interface to the IGVF Catalog Description: The IGVF Catalog provides data on the impact of genomic variants on function. The `rigvf` package provides an interface to the IGVF Catalog, allowing easy integration with Bioconductor resources. biocViews: ThirdPartyClient, Annotation, VariantAnnotation, FunctionalGenomics, GeneRegulation, GenomicVariation, GeneTarget Author: Martin Morgan [aut] (ORCID: ), Michael Love [aut, cre] (ORCID: ), NIH NHGRI UM1HG012003 [fnd] Maintainer: Michael Love URL: https://IGVF.github.io/rigvf VignetteBuilder: knitr BugReports: https://github.com/IGVF/rigvf/issues git_url: https://git.bioconductor.org/packages/rigvf git_branch: RELEASE_3_22 git_last_commit: 790a647 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rigvf_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rigvf_1.1.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rigvf_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rigvf_1.2.0.tgz vignettes: vignettes/rigvf/inst/doc/rigvf.html vignetteTitles: Accessing data from the IGVF Catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rigvf/inst/doc/rigvf.R dependencyCount: 44 Package: RImmPort Version: 1.38.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 MD5sum: 3b590fbe45df9b3c080b22878f4369f3 NeedsCompilation: no Title: RImmPort: Enabling Ready-for-analysis Immunology Research Data Description: The RImmPort package simplifies access to ImmPort data for analysis in the R environment. It provides a standards-based interface to the ImmPort study data that is in a proprietary format. biocViews: BiomedicalInformatics, DataImport, DataRepresentation Author: Ravi Shankar Maintainer: Zicheng Hu , Ravi Shankar URL: http://bioconductor.org/packages/RImmPort/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RImmPort git_branch: RELEASE_3_22 git_last_commit: 0019565 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RImmPort_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RImmPort_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RImmPort_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RImmPort_1.38.0.tgz vignettes: vignettes/RImmPort/inst/doc/RImmPort_Article.pdf, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.pdf vignetteTitles: RImmPort: Enabling ready-for-analysis immunology research data, RImmPort: Quick Start Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RImmPort/inst/doc/RImmPort_Article.R, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.R dependencyCount: 41 Package: RITAN Version: 1.34.0 Depends: R (>= 4.0), Imports: graphics, methods, stats, utils, grid, gridExtra, reshape2, gplots, ggplot2, plotrix, RColorBrewer, STRINGdb, MCL, dynamicTreeCut, gsubfn, hash, png, sqldf, igraph, BgeeDB, knitr, RITANdata, GenomicFeatures, ensembldb, AnnotationFilter, EnsDb.Hsapiens.v86 Suggests: rmarkdown, BgeeDB License: file LICENSE MD5sum: 987c26b722e71827118f1740b30f5b0c NeedsCompilation: no Title: Rapid Integration of Term Annotation and Network resources Description: Tools for comprehensive gene set enrichment and extraction of multi-resource high confidence subnetworks. RITAN facilitates bioinformatic tasks for enabling network biology research. biocViews: QualityControl, Network, NetworkEnrichment, NetworkInference, GeneSetEnrichment, FunctionalGenomics, GraphAndNetwork Author: Michael Zimmermann [aut, cre] Maintainer: Michael Zimmermann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RITAN git_branch: RELEASE_3_22 git_last_commit: 0863028 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RITAN_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RITAN_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RITAN_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RITAN_1.34.0.tgz vignettes: vignettes/RITAN/inst/doc/choosing_resources.html, vignettes/RITAN/inst/doc/enrichment.html, vignettes/RITAN/inst/doc/multi_tissue_analysis.html, vignettes/RITAN/inst/doc/resource_relationships.html, vignettes/RITAN/inst/doc/subnetworks.html vignetteTitles: Choosing Resources, Enrichment Vignette, Multi-Tissue Analysis, Relationships Among Resources, Network Biology Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RITAN/inst/doc/choosing_resources.R, vignettes/RITAN/inst/doc/enrichment.R, vignettes/RITAN/inst/doc/multi_tissue_analysis.R, vignettes/RITAN/inst/doc/resource_relationships.R, vignettes/RITAN/inst/doc/subnetworks.R dependencyCount: 155 Package: RIVER Version: 1.34.0 Depends: R (>= 3.3.2) Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools License: GPL (>= 2) MD5sum: 19d9f5351623730c9d2912829bc50bea NeedsCompilation: no Title: R package for RIVER (RNA-Informed Variant Effect on Regulation) Description: An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples. biocViews: GeneExpression, GeneticVariability, SNP, Transcription, FunctionalPrediction, GeneRegulation, GenomicVariation, BiomedicalInformatics, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Bayesian, Clustering, TranscriptomeVariant, Regression Author: Yungil Kim [aut, cre], Alexis Battle [aut] Maintainer: Yungil Kim URL: https://github.com/ipw012/RIVER VignetteBuilder: knitr BugReports: https://github.com/ipw012/RIVER/issues git_url: https://git.bioconductor.org/packages/RIVER git_branch: RELEASE_3_22 git_last_commit: 88ba126 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RIVER_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RIVER_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RIVER_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RIVER_1.34.0.tgz vignettes: vignettes/RIVER/inst/doc/RIVER.html vignetteTitles: RIVER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIVER/inst/doc/RIVER.R dependencyCount: 37 Package: RJMCMCNucleosomes Version: 1.34.0 Depends: R (>= 3.5), IRanges, GenomicRanges Imports: Rcpp (>= 0.12.5), consensusSeekeR, BiocGenerics, Seqinfo, S4Vectors (>= 0.23.10), BiocParallel, stats, graphics, methods, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, nucleoSim, RUnit License: Artistic-2.0 MD5sum: 7cceb7000e7ca05018658dc0205a901a NeedsCompilation: yes Title: Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq) Description: This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling. biocViews: BiologicalQuestion, ChIPSeq, NucleosomePositioning, Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Pascal Belleau [aut], Rawane Samb [aut], Astrid Deschênes [cre, aut], Khader Khadraoui [aut], Lajmi Lakhal-Chaieb [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes SystemRequirements: Rcpp VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues git_url: https://git.bioconductor.org/packages/RJMCMCNucleosomes git_branch: RELEASE_3_22 git_last_commit: 91e1b58 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RJMCMCNucleosomes_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RJMCMCNucleosomes_1.33.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RJMCMCNucleosomes_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RJMCMCNucleosomes_1.34.0.tgz vignettes: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.html vignetteTitles: Nucleosome Positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.R dependencyCount: 67 Package: RLassoCox Version: 1.18.0 Depends: R (>= 4.1), glmnet Imports: Matrix, igraph, survival, stats Suggests: knitr License: Artistic-2.0 MD5sum: ecdfcf4ded245765b597bcf4340c5b85 NeedsCompilation: no Title: A reweighted Lasso-Cox by integrating gene interaction information Description: RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types. biocViews: Survival, Regression, GeneExpression, GenePrediction, Network Author: Wei Liu [cre, aut] (ORCID: ) Maintainer: Wei Liu VignetteBuilder: knitr BugReports: https://github.com/weiliu123/RLassoCox/issues git_url: https://git.bioconductor.org/packages/RLassoCox git_branch: RELEASE_3_22 git_last_commit: d1bd731 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RLassoCox_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RLassoCox_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RLassoCox_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RLassoCox_1.18.0.tgz vignettes: vignettes/RLassoCox/inst/doc/RLassoCox.pdf vignetteTitles: RLassoCox hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLassoCox/inst/doc/RLassoCox.R dependencyCount: 26 Package: RLMM Version: 1.72.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: 2520befae15506adab413f9cf5dc52d3 NeedsCompilation: no Title: A Genotype Calling Algorithm for Affymetrix SNP Arrays Description: A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now. biocViews: Microarray, OneChannel, SNP, GeneticVariability Author: Nusrat Rabbee , Gary Wong Maintainer: Nusrat Rabbee URL: http://www.stat.berkeley.edu/users/nrabbee/RLMM SystemRequirements: Internal files Xba.CQV, Xba.regions (or other regions file) git_url: https://git.bioconductor.org/packages/RLMM git_branch: RELEASE_3_22 git_last_commit: 5f16267 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RLMM_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RLMM_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RLMM_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RLMM_1.72.0.tgz vignettes: vignettes/RLMM/inst/doc/RLMM.pdf vignetteTitles: RLMM Doc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLMM/inst/doc/RLMM.R dependencyCount: 6 Package: Rmagpie Version: 1.66.0 Depends: R (>= 2.6.1), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), e1071, graphics, grDevices, kernlab, methods, pamr, stats, utils Suggests: xtable License: GPL (>= 3) MD5sum: 406664ee0a6ded7f009917ffc72855ca NeedsCompilation: no Title: MicroArray Gene-expression-based Program In Error rate estimation Description: Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes. biocViews: Microarray, Classification Author: Camille Maumet , with contributions from C. Ambroise J. Zhu Maintainer: Camille Maumet URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rmagpie git_branch: RELEASE_3_22 git_last_commit: de6a8a5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rmagpie_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rmagpie_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rmagpie_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rmagpie_1.66.0.tgz vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf vignetteTitles: Rmagpie Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmagpie/inst/doc/Magpie_examples.R dependencyCount: 20 Package: RMassBank Version: 3.20.0 Depends: R (>= 4.1.0), Rcpp Imports: assertthat, Biobase, ChemmineR, data.table, digest, dplyr, enviPat, glue, httr, httr2, logger, methods, MSnbase, mzR, purrr, R.utils, rcdk, readJDX, readr, rjson, S4Vectors, tibble, tidyselect, webchem, XML, yaml Suggests: BiocStyle, CAMERA, gplots, knitr, magick, rmarkdown, RMassBankData (>= 1.33.1), RUnit, xcms (>= 1.37.1) License: Artistic-2.0 MD5sum: e9a5a2fbc9bd1f161fd52151cacda73b NeedsCompilation: no Title: Workflow to process tandem MS files and build MassBank records Description: Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records. biocViews: ImmunoOncology, Bioinformatics, MassSpectrometry, Metabolomics, Software Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, Paul Stahlhofen, Tobias Schulze with contributions of Hendrik Treutler Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RMassBank git_branch: RELEASE_3_22 git_last_commit: 2056806 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RMassBank_3.20.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RMassBank_3.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RMassBank_3.20.0.tgz vignettes: vignettes/RMassBank/inst/doc/RMassBank.html, vignettes/RMassBank/inst/doc/RMassBankNonstandard.html vignetteTitles: RMassBank: The workflow by example, RMassBank: Non-standard usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R, vignettes/RMassBank/inst/doc/RMassBankNonstandard.R suggestsMe: RMassBankData dependencyCount: 168 Package: rmelting Version: 1.26.0 Depends: R (>= 3.6) Imports: Rdpack, rJava (>= 0.9-8) Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat License: GPL-2 | GPL-3 MD5sum: 21160716ca80a373639b5423e7ceaa3f NeedsCompilation: no Title: R Interface to MELTING 5 Description: R interface to the MELTING 5 program (https://www.ebi.ac.uk/biomodels/tools/melting/) to compute melting temperatures of nucleic acid duplexes along with other thermodynamic parameters. biocViews: BiomedicalInformatics, Cheminformatics, Author: J. Aravind [aut, cre] (ORCID: ), G. K. Krishna [aut], Bob Rudis [ctb] (melting5jars), Nicolas Le Novère [ctb] (MELTING 5 Java Library), Marine Dumousseau [ctb] (MELTING 5 Java Library), William John Gowers [ctb] (MELTING 5 Java Library) Maintainer: J. Aravind URL: https://github.com/aravind-j/rmelting, https://aravind-j.github.io/rmelting/ SystemRequirements: Java VignetteBuilder: knitr BugReports: https://github.com/aravind-j/rmelting/issues git_url: https://git.bioconductor.org/packages/rmelting git_branch: RELEASE_3_22 git_last_commit: 328b3ef git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rmelting_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rmelting_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rmelting_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rmelting_1.26.0.tgz vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: Rmmquant Version: 1.28.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, S4Vectors, GenomicRanges, SummarizedExperiment, devtools, TBX20BamSubset, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, DESeq2, apeglm, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: c30930265145992882bef84783d4c9be NeedsCompilation: yes Title: RNA-Seq multi-mapping Reads Quantification Tool Description: RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way. biocViews: GeneExpression, Transcription Author: Zytnicki Matthias [aut, cre] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rmmquant git_branch: RELEASE_3_22 git_last_commit: 60d48d5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rmmquant_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rmmquant_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rmmquant_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rmmquant_1.28.0.tgz vignettes: vignettes/Rmmquant/inst/doc/Rmmquant.html vignetteTitles: The Rmmquant package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmmquant/inst/doc/Rmmquant.R dependencyCount: 181 Package: rmspc Version: 1.16.0 Imports: processx, BiocManager, rtracklayer, stats, tools, methods, GenomicRanges, stringr Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: 1ee8564db93844a74bedf63ee1d4ec57 NeedsCompilation: no Title: Multiple Sample Peak Calling Description: The rmspc package runs MSPC (Multiple Sample Peak Calling) software using R. The analysis of ChIP-seq samples outputs a number of enriched regions (commonly known as "peaks"), each indicating a protein-DNA interaction or a specific chromatin modification. When replicate samples are analyzed, overlapping peaks are expected. This repeated evidence can therefore be used to locally lower the minimum significance required to accept a peak. MSPC uses combined evidence from replicated experiments to evaluate peak calling output, rescuing peaks, and reduce false positives. It takes any number of replicates as input and improves sensitivity and specificity of peak calling on each, and identifies consensus regions between the input samples. biocViews: ChIPSeq, Sequencing, ChipOnChip, DataImport, RNASeq Author: Vahid Jalili [aut], Marzia Angela Cremona [aut], Fernando Palluzzi [aut], Meriem Bahda [aut, cre] Maintainer: Meriem Bahda URL: https://genometric.github.io/MSPC/ SystemRequirements: .NET 9.0 VignetteBuilder: knitr BugReports: https://github.com/Genometric/MSPC/issues git_url: https://git.bioconductor.org/packages/rmspc git_branch: RELEASE_3_22 git_last_commit: 28a622e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rmspc_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rmspc_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rmspc_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rmspc_1.16.0.tgz vignettes: vignettes/rmspc/inst/doc/rmpsc.html vignetteTitles: User guide to the rmspc package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rmspc/inst/doc/rmpsc.R dependencyCount: 68 Package: RNAAgeCalc Version: 1.22.0 Depends: R (>= 3.6) Imports: ggplot2, recount, impute, AnnotationDbi, org.Hs.eg.db, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 Archs: x64 MD5sum: 63a42062ea615590a28f324882a539f3 NeedsCompilation: no Title: A multi-tissue transcriptional age calculator Description: It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data. biocViews: RNASeq,GeneExpression Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/RNAAgeCalc VignetteBuilder: knitr BugReports: https://github.com/reese3928/RNAAgeCalc/issues git_url: https://git.bioconductor.org/packages/RNAAgeCalc git_branch: RELEASE_3_22 git_last_commit: 5d0a53a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RNAAgeCalc_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAAgeCalc_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAAgeCalc_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAAgeCalc_1.22.0.tgz vignettes: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.html vignetteTitles: RNAAgeCalc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.R dependencyCount: 166 Package: rnaEditr Version: 1.20.0 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, BiocGenerics, Seqinfo, bumphunter, S4Vectors, stats, survival, logistf, plyr, corrplot Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 9562361989a497b38253e43de253e398 NeedsCompilation: no Title: Statistical analysis of RNA editing sites and hyper-editing regions Description: RNAeditr analyzes site-specific RNA editing events, as well as hyper-editing regions. The editing frequencies can be tested against binary, continuous or survival outcomes. Multiple covariate variables as well as interaction effects can also be incorporated in the statistical models. biocViews: GeneTarget, Epigenetics, DimensionReduction, FeatureExtraction, Regression, Survival, RNASeq Author: Lanyu Zhang [aut, cre], Gabriel Odom [aut], Tiago Silva [aut], Lissette Gomez [aut], Lily Wang [aut] Maintainer: Lanyu Zhang URL: https://github.com/TransBioInfoLab/rnaEditr VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/rnaEditr/issues git_url: https://git.bioconductor.org/packages/rnaEditr git_branch: RELEASE_3_22 git_last_commit: 326b028 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rnaEditr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rnaEditr_1.19.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rnaEditr_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rnaEditr_1.20.0.tgz vignettes: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.html vignetteTitles: Introduction to rnaEditr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.R dependencyCount: 137 Package: RNAmodR Version: 1.24.0 Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomicRanges, Modstrings Imports: methods, stats, grDevices, matrixStats, BiocGenerics, Biostrings (>= 2.57.2), BiocParallel, txdbmaker, GenomicFeatures, GenomicAlignments, Seqinfo, rtracklayer, Rsamtools, BSgenome, RColorBrewer, colorRamps, ggplot2, Gviz (>= 1.31.0), reshape2, graphics, ROCR Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data License: Artistic-2.0 MD5sum: 04944fa404d47c81463a9000874e6845 NeedsCompilation: no Title: Detection of post-transcriptional modifications in high throughput sequencing data Description: RNAmodR provides classes and workflows for loading/aggregation data from high througput sequencing aimed at detecting post-transcriptional modifications through analysis of specific patterns. In addition, utilities are provided to validate and visualize the results. The RNAmodR package provides a core functionality from which specific analysis strategies can be easily implemented as a seperate package. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR/issues git_url: https://git.bioconductor.org/packages/RNAmodR git_branch: RELEASE_3_22 git_last_commit: 683b357 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RNAmodR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAmodR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAmodR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAmodR_1.24.0.tgz vignettes: vignettes/RNAmodR/inst/doc/RNAmodR.creation.html, vignettes/RNAmodR/inst/doc/RNAmodR.html vignetteTitles: RNAmodR - creating new classes for a new detection strategy, RNAmodR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR/inst/doc/RNAmodR.creation.R, vignettes/RNAmodR/inst/doc/RNAmodR.R dependsOnMe: RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq dependencyCount: 162 Package: RNAmodR.AlkAnilineSeq Version: 1.24.0 Depends: R (>= 4.0), RNAmodR (>= 1.5.3) Imports: methods, S4Vectors, IRanges, BiocGenerics, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, Biostrings, Seqinfo, RNAmodR.Data License: Artistic-2.0 MD5sum: 0baf013000f8e1fea3adf49b112f04eb NeedsCompilation: no Title: Detection of m7G, m3C and D modification by AlkAnilineSeq Description: RNAmodR.AlkAnilineSeq implements the detection of m7G, m3C and D modifications on RNA from experimental data generated with the AlkAnilineSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.AlkAnilineSeq git_branch: RELEASE_3_22 git_last_commit: d4f3b89 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RNAmodR.AlkAnilineSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAmodR.AlkAnilineSeq_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAmodR.AlkAnilineSeq_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAmodR.AlkAnilineSeq_1.24.0.tgz vignettes: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.html vignetteTitles: RNAmodR.AlkAnilineSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.R suggestsMe: RNAmodR.ML dependencyCount: 163 Package: RNAmodR.ML Version: 1.24.0 Depends: R (>= 3.6), RNAmodR Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, stats, ranger Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data, RNAmodR.AlkAnilineSeq, GenomicFeatures, Rsamtools, rtracklayer, keras License: Artistic-2.0 MD5sum: 72e768541bfbae5f8a6e5dfa13accd99 NeedsCompilation: no Title: Detecting patterns of post-transcriptional modifications using machine learning Description: RNAmodR.ML extend the functionality of the RNAmodR package and classical detection strategies towards detection through machine learning models. RNAmodR.ML provides classes, functions and an example workflow to establish a detection stratedy, which can be packaged. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.ML VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.ML/issues git_url: https://git.bioconductor.org/packages/RNAmodR.ML git_branch: RELEASE_3_22 git_last_commit: 1a54329 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RNAmodR.ML_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAmodR.ML_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAmodR.ML_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAmodR.ML_1.24.0.tgz vignettes: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.html vignetteTitles: RNAmodR.ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.R dependencyCount: 164 Package: RNAmodR.RiboMethSeq Version: 1.24.0 Depends: R (>= 4.0), RNAmodR (>= 1.5.3) Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, Seqinfo, RNAmodR.Data License: Artistic-2.0 Archs: x64 MD5sum: f013efc8307983afa4f0f2043c2fb062 NeedsCompilation: no Title: Detection of 2'-O methylations by RiboMethSeq Description: RNAmodR.RiboMethSeq implements the detection of 2'-O methylations on RNA from experimental data generated with the RiboMethSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.RiboMethSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.RiboMethSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.RiboMethSeq git_branch: RELEASE_3_22 git_last_commit: 8156e42 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RNAmodR.RiboMethSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAmodR.RiboMethSeq_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAmodR.RiboMethSeq_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAmodR.RiboMethSeq_1.24.0.tgz vignettes: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.html vignetteTitles: RNAmodR.RiboMethSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.R dependencyCount: 163 Package: RNAsense Version: 1.24.0 Depends: R (>= 3.6) Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment, stats, utils, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: f77b7ae52bb06f4f1ec69cf6d863f805 NeedsCompilation: no Title: Analysis of Time-Resolved RNA-Seq Data Description: RNA-sense tool compares RNA-seq time curves in two experimental conditions, i.e. wild-type and mutant, and works in three steps. At Step 1, it builds expression profile for each transcript in one condition (i.e. wild-type) and tests if the transcript abundance grows or decays significantly. Dynamic transcripts are then sorted to non-overlapping groups (time profiles) by the time point of switch up or down. At Step 2, RNA-sense outputs the groups of differentially expressed transcripts, which are up- or downregulated in the mutant compared to the wild-type at each time point. At Step 3, Correlations (Fisher's exact test) between the outputs of Step 1 (switch up- and switch down- time profile groups) and the outputs of Step2 (differentially expressed transcript groups) are calculated. The results of the correlation analysis are printed as two-dimensional color plot, with time profiles and differential expression groups at y- and x-axis, respectively, and facilitates the biological interpretation of the data. biocViews: RNASeq, GeneExpression, DifferentialExpression Author: Marcus Rosenblatt [cre], Gao Meijang [aut], Helge Hass [aut], Daria Onichtchouk [aut] Maintainer: Marcus Rosenblatt VignetteBuilder: knitr BugReports: https://github.com/marcusrosenblatt/RNAsense git_url: https://git.bioconductor.org/packages/RNAsense git_branch: RELEASE_3_22 git_last_commit: 7827d43 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RNAsense_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAsense_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAsense_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAsense_1.24.0.tgz vignettes: vignettes/RNAsense/inst/doc/example.html vignetteTitles: Put the title of your vignette here hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAsense/inst/doc/example.R dependencyCount: 51 Package: rnaseqcomp Version: 1.40.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 83c1b04f775c91892a2629a3da5465b2 NeedsCompilation: no Title: Benchmarks for RNA-seq Quantification Pipelines Description: Several quantitative and visualized benchmarks for RNA-seq quantification pipelines. Two-condition quantifications for genes, transcripts, junctions or exons by each pipeline with necessary meta information should be organized into numeric matrices in order to proceed the evaluation. biocViews: RNASeq, Visualization, QualityControl Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/rnaseqcomp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqcomp git_branch: RELEASE_3_22 git_last_commit: 9cf1ec1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rnaseqcomp_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rnaseqcomp_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rnaseqcomp_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rnaseqcomp_1.40.0.tgz vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.html vignetteTitles: The rnaseqcomp user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.R dependencyCount: 2 Package: RNAseqCovarImpute Version: 1.8.0 Depends: R (>= 4.3.0) Imports: Biobase, BiocGenerics, BiocParallel, stats, limma, dplyr, magrittr, rlang, edgeR, foreach, mice Suggests: BiocStyle, knitr, PCAtools, rmarkdown, tidyr, stringr, testthat (>= 3.0.0) License: GPL-3 MD5sum: c393bd4177d5bab473ed5e53e4f8118a NeedsCompilation: no Title: Impute Covariate Data in RNA Sequencing Studies Description: The RNAseqCovarImpute package makes linear model analysis for RNA sequencing read counts compatible with multiple imputation (MI) of missing covariates. A major problem with implementing MI in RNA sequencing studies is that the outcome data must be included in the imputation prediction models to avoid bias. This is difficult in omics studies with high-dimensional data. The first method we developed in the RNAseqCovarImpute package surmounts the problem of high-dimensional outcome data by binning genes into smaller groups to analyze pseudo-independently. This method implements covariate MI in gene expression studies by 1) randomly binning genes into smaller groups, 2) creating M imputed datasets separately within each bin, where the imputation predictor matrix includes all covariates and the log counts per million (CPM) for the genes within each bin, 3) estimating gene expression changes using `limma::voom` followed by `limma::lmFit` functions, separately on each M imputed dataset within each gene bin, 4) un-binning the gene sets and stacking the M sets of model results before applying the `limma::squeezeVar` function to apply a variance shrinking Bayesian procedure to each M set of model results, 5) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 6) adjusting P-values for multiplicity to account for false discovery rate (FDR). A faster method uses principal component analysis (PCA) to avoid binning genes while still retaining outcome information in the MI models. Binning genes into smaller groups requires that the MI and limma-voom analysis is run many times (typically hundreds). The more computationally efficient MI PCA method implements covariate MI in gene expression studies by 1) performing PCA on the log CPM values for all genes using the Bioconductor `PCAtools` package, 2) creating M imputed datasets where the imputation predictor matrix includes all covariates and the optimum number of PCs to retain (e.g., based on Horn’s parallel analysis or the number of PCs that account for >80% explained variation), 3) conducting the standard limma-voom pipeline with the `voom` followed by `lmFit` followed by `eBayes` functions on each M imputed dataset, 4) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 5) adjusting P-values for multiplicity to account for false discovery rate (FDR). biocViews: RNASeq, GeneExpression, DifferentialExpression, Sequencing Author: Brennan Baker [aut, cre] (ORCID: ), Sheela Sathyanarayana [aut], Adam Szpiro [aut], James MacDonald [aut], Alison Paquette [aut] Maintainer: Brennan Baker URL: https://github.com/brennanhilton/RNAseqCovarImpute VignetteBuilder: knitr BugReports: https://github.com/brennanhilton/RNAseqCovarImpute/issues git_url: https://git.bioconductor.org/packages/RNAseqCovarImpute git_branch: RELEASE_3_22 git_last_commit: 2d5c9bb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RNAseqCovarImpute_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAseqCovarImpute_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAseqCovarImpute_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAseqCovarImpute_1.8.0.tgz vignettes: vignettes/RNAseqCovarImpute/inst/doc/Example_Data_for_RNAseqCovarImpute.html, vignettes/RNAseqCovarImpute/inst/doc/Impute_Covariate_Data_in_RNA_sequencing_Studies.html vignetteTitles: Example Data for RNAseqCovarImpute, Impute Covariate Data in RNA-sequencing Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAseqCovarImpute/inst/doc/Example_Data_for_RNAseqCovarImpute.R, vignettes/RNAseqCovarImpute/inst/doc/Impute_Covariate_Data_in_RNA_sequencing_Studies.R dependencyCount: 84 Package: RNASeqPower Version: 1.50.0 License: LGPL (>=2) MD5sum: ad7e1da228ef5f7da71f78779420dd0d NeedsCompilation: no Title: Sample size for RNAseq studies Description: RNA-seq, sample size biocViews: ImmunoOncology, RNASeq Author: Terry M Therneau [aut, cre], Hart Stephen [ctb] Maintainer: Terry M Therneau git_url: https://git.bioconductor.org/packages/RNASeqPower git_branch: RELEASE_3_22 git_last_commit: facb8f0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RNASeqPower_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNASeqPower_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNASeqPower_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNASeqPower_1.50.0.tgz vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf vignetteTitles: RNAseq samplesize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqPower/inst/doc/samplesize.R suggestsMe: DGEobj.utils dependencyCount: 0 Package: RnaSeqSampleSize Version: 2.20.0 Depends: R (>= 4.0.0), ggplot2, RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,methods,grDevices, graphics, stats, Rcpp (>= 0.11.2),recount,ggpubr,SummarizedExperiment,tidyr,dplyr,tidyselect,utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 4ca9945f8daf0693b52d3c07ad6951ad NeedsCompilation: yes Title: RnaSeqSampleSize Description: RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data. It controls FDR for multiple testing and utilizes the average read count and dispersion distributions from real data to estimate a more reliable sample size. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization. biocViews: ImmunoOncology, ExperimentalDesign, Sequencing, RNASeq, GeneExpression, DifferentialExpression Author: Shilin Zhao Developer [aut, cre], Chung-I Li [aut], Yan Guo [aut], Quanhu Sheng [aut], Yu Shyr [aut] Maintainer: Shilin Zhao Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RnaSeqSampleSize git_branch: RELEASE_3_22 git_last_commit: 6b8556b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RnaSeqSampleSize_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RnaSeqSampleSize_2.19.0.zip vignettes: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.pdf vignetteTitles: RnaSeqSampleSize: Sample size estimation by real data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.R dependencyCount: 205 Package: RnBeads Version: 2.28.0 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, grid, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr, reshape2 Imports: IRanges Suggests: Category, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RnBeads.hg19, RnBeads.mm9, RnBeads.hg38, XML, annotate, biomaRt, foreach, doParallel, ggbio, isva, mclust, mgcv, minfi, nlme, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, quadprog, rtracklayer, qvalue, sva, wateRmelon, wordcloud, qvalue, argparse, glmnet, IlluminaHumanMethylation450kanno.ilmn12.hg19, scales, missMethyl, impute, shiny, shinyjs, plotrix, hexbin, RUnit, MethylSeekR, sesame License: GPL-3 MD5sum: 45ae98147c76c9ffee9c9f8e9e9e9f20 NeedsCompilation: no Title: RnBeads Description: RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale. biocViews: DNAMethylation, MethylationArray, MethylSeq, Epigenetics, QualityControl, Preprocessing, BatchEffect, DifferentialMethylation, Sequencing, CpGIsland, ImmunoOncology, TwoChannel, DataImport Author: Yassen Assenov [aut], Christoph Bock [aut], Pavlo Lutsik [aut], Michael Scherer [aut], Fabian Mueller [aut, cre] Maintainer: Fabian Mueller git_url: https://git.bioconductor.org/packages/RnBeads git_branch: RELEASE_3_22 git_last_commit: bdda0ad git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RnBeads_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RnBeads_2.27.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RnBeads_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RnBeads_2.28.0.tgz vignettes: vignettes/RnBeads/inst/doc/RnBeads_Annotations.pdf, vignettes/RnBeads/inst/doc/RnBeads.pdf vignetteTitles: RnBeads Annotation, Comprehensive DNA Methylation Analysis with RnBeads hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnBeads/inst/doc/RnBeads_Annotations.R, vignettes/RnBeads/inst/doc/RnBeads.R dependsOnMe: MAGAR suggestsMe: RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5 dependencyCount: 169 Package: Rnits Version: 1.44.0 Depends: R (>= 3.6.0), Biobase, ggplot2, limma, methods Imports: affy, boot, impute, splines, graphics, qvalue, reshape2 Suggests: BiocStyle, knitr, GEOquery, stringr License: GPL-3 MD5sum: 68acbbfc0210ee45f9a1a181b0e3c983 NeedsCompilation: no Title: R Normalization and Inference of Time Series data Description: R/Bioconductor package for normalization, curve registration and inference in time course gene expression data. biocViews: GeneExpression, Microarray, TimeCourse, DifferentialExpression, Normalization Author: Dipen P. Sangurdekar Maintainer: Dipen P. Sangurdekar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rnits git_branch: RELEASE_3_22 git_last_commit: 083d512 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rnits_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rnits_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rnits_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rnits_1.44.0.tgz vignettes: vignettes/Rnits/inst/doc/Rnits-vignette.pdf vignetteTitles: R/Bioconductor package for normalization and differential expression inference in time series gene expression microarray data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rnits/inst/doc/Rnits-vignette.R dependencyCount: 42 Package: roar Version: 1.46.0 Depends: R (>= 3.0.1) Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, GenomicAlignments (>= 0.99.4), rtracklayer, GenomeInfoDb Suggests: RNAseqData.HNRNPC.bam.chr14, testthat License: GPL-3 MD5sum: 737e9acc4652a732c8893bf2c52e410a NeedsCompilation: no Title: Identify differential APA usage from RNA-seq alignments Description: Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments. biocViews: Sequencing, HighThroughputSequencing, RNAseq, Transcription Author: Elena Grassi Maintainer: Elena Grassi URL: https://github.com/vodkatad/roar/ git_url: https://git.bioconductor.org/packages/roar git_branch: RELEASE_3_22 git_last_commit: bb81790 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/roar_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/roar_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/roar_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/roar_1.46.0.tgz vignettes: vignettes/roar/inst/doc/roar.pdf vignetteTitles: Identify differential APA usage from RNA-seq alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roar/inst/doc/roar.R dependencyCount: 59 Package: roastgsa Version: 1.8.0 Depends: R (>= 4.3.0) Imports: parallel, grDevices, graphics, utils, stats, methods, grid, RColorBrewer, gplots, ggplot2, limma, Biobase Suggests: BiocStyle, knitr, rmarkdown, GSEABenchmarkeR, EnrichmentBrowser, preprocessCore, DESeq2 License: GPL-3 MD5sum: b77431a7ad8c5146899caee9872e601a NeedsCompilation: no Title: Rotation based gene set analysis Description: This package implements a variety of functions useful for gene set analysis using rotations to approximate the null distribution. It contributes with the implementation of seven test statistic scores that can be used with different goals and interpretations. Several functions are available to complement the statistical results with graphical representations. biocViews: Microarray, Preprocessing, Normalization, GeneExpression, Survival, Transcription, Sequencing, Transcriptomics, Bayesian, Clustering, Regression, RNASeq, MicroRNAArray, mRNAMicroarray, FunctionalGenomics, SystemsBiology, ImmunoOncology, DifferentialExpression, GeneSetEnrichment, BatchEffect, MultipleComparison, QualityControl, TimeCourse, Metabolomics, Proteomics, Epigenetics, Cheminformatics, ExonArray, OneChannel, TwoChannel, ProprietaryPlatforms, CellBiology, BiomedicalInformatics, AlternativeSplicing, DifferentialSplicing, DataImport, Pathways Author: Adria Caballe [aut, cre] (ORCID: ) Maintainer: Adria Caballe VignetteBuilder: knitr BugReports: https://github.com/adricaba/roastgsa/issues git_url: https://git.bioconductor.org/packages/roastgsa git_branch: RELEASE_3_22 git_last_commit: fee309a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/roastgsa_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/roastgsa_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/roastgsa_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/roastgsa_1.8.0.tgz vignettes: vignettes/roastgsa/inst/doc/roastgsaExample_genesetcollections.html, vignettes/roastgsa/inst/doc/roastgsaExample_main.html, vignettes/roastgsa/inst/doc/roastgsaExample_RNAseq.html vignetteTitles: roastgsa vignette (gene set collections), roastgsa vignette (main), roastgsa vignette (RNAseq) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roastgsa/inst/doc/roastgsaExample_genesetcollections.R, vignettes/roastgsa/inst/doc/roastgsaExample_main.R, vignettes/roastgsa/inst/doc/roastgsaExample_RNAseq.R dependencyCount: 33 Package: ROC Version: 1.86.0 Depends: R (>= 1.9.0), utils, methods Imports: knitr Suggests: rmarkdown, Biobase, BiocStyle License: Artistic-2.0 MD5sum: 60d8d355096003dcbd84e4814578fcf3 NeedsCompilation: yes Title: utilities for ROC, with microarray focus Description: Provide utilities for ROC, with microarray focus. biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: http://www.bioconductor.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ROC git_branch: RELEASE_3_22 git_last_commit: 08a8f6e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ROC_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ROC_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ROC_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ROC_1.86.0.tgz vignettes: vignettes/ROC/inst/doc/ROCnotes.html vignetteTitles: Notes on ROC package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: TCC, wateRmelon importsMe: clst suggestsMe: genefilter dependencyCount: 10 Package: ROCpAI Version: 1.22.0 Depends: boot, SummarizedExperiment, fission, knitr, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: f5ab64475cd0f8b60b966accb2027c96 NeedsCompilation: no Title: Receiver Operating Characteristic Partial Area Indexes for evaluating classifiers Description: The package analyzes the Curve ROC, identificates it among different types of Curve ROC and calculates the area under de curve through the method that is most accuracy. This package is able to standarizate proper and improper pAUC. biocViews: Software, StatisticalMethod, Classification Author: Juan-Pedro Garcia [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut] Maintainer: Juan-Pedro Garcia VignetteBuilder: knitr BugReports: https://github.com/juanpegarcia/ROCpAI/tree/master/issues git_url: https://git.bioconductor.org/packages/ROCpAI git_branch: RELEASE_3_22 git_last_commit: 0b013b7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ROCpAI_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ROCpAI_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ROCpAI_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ROCpAI_1.22.0.tgz vignettes: vignettes/ROCpAI/inst/doc/vignettes.html vignetteTitles: ROC Partial Area Indexes for evaluating classifiers hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROCpAI/inst/doc/vignettes.R dependencyCount: 32 Package: RolDE Version: 1.14.0 Depends: R (>= 4.2.0) Imports: stats, methods, ROTS, matrixStats, foreach, parallel, doParallel, doRNG, rngtools, SummarizedExperiment, nlme, qvalue, grDevices, graphics, utils Suggests: knitr, printr, rmarkdown, testthat License: GPL-3 MD5sum: bf38f20d7db5cf2d338b93565f320c68 NeedsCompilation: no Title: RolDE: Robust longitudinal Differential Expression Description: RolDE detects longitudinal differential expression between two conditions in noisy high-troughput data. Suitable even for data with a moderate amount of missing values.RolDE is a composite method, consisting of three independent modules with different approaches to detecting longitudinal differential expression. The combination of these diverse modules allows RolDE to robustly detect varying differences in longitudinal trends and expression levels in diverse data types and experimental settings. biocViews: StatisticalMethod, Software, TimeCourse, Regression, Proteomics, DifferentialExpression Author: Tommi Valikangas [aut], Medical Bioinformatics Centre [cre] Maintainer: Medical Bioinformatics Centre URL: https://github.com/elolab/RolDE VignetteBuilder: knitr BugReports: https://github.com/elolab/RolDE/issues git_url: https://git.bioconductor.org/packages/RolDE git_branch: RELEASE_3_22 git_last_commit: 8121fce git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RolDE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RolDE_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RolDE_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RolDE_1.14.0.tgz vignettes: vignettes/RolDE/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RolDE/inst/doc/Introduction.R dependencyCount: 76 Package: ROntoTools Version: 2.38.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: 9e1e1c71364c919eb081238f390fb572 NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis. biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sahar Ansari and Sorin Draghici Maintainer: Sorin Draghici git_url: https://git.bioconductor.org/packages/ROntoTools git_branch: RELEASE_3_22 git_last_commit: 5e9ce05 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ROntoTools_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ROntoTools_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ROntoTools_2.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ROntoTools_2.38.0.tgz vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf vignetteTitles: ROntoTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROntoTools/inst/doc/rontotools.R dependsOnMe: BLMA suggestsMe: RCPA dependencyCount: 33 Package: ropls Version: 1.42.0 Depends: R (>= 3.5.0) Imports: Biobase, ggplot2, graphics, grDevices, methods, plotly, stats, MultiAssayExperiment, MultiDataSet, SummarizedExperiment, utils Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, phenomis, rmarkdown, testthat License: CeCILL MD5sum: 97c13e9132ca5713916c1b302354067d NeedsCompilation: no Title: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data Description: Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). biocViews: Regression, Classification, PrincipalComponent, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry, ImmunoOncology Author: Etienne A. Thevenot [aut, cre] (ORCID: ) Maintainer: Etienne A. Thevenot URL: https://doi.org/10.1021/acs.jproteome.5b00354 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ropls git_branch: RELEASE_3_22 git_last_commit: 7e60063 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ropls_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ropls_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ropls_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ropls_1.42.0.tgz vignettes: vignettes/ropls/inst/doc/ropls-vignette.html vignetteTitles: ropls-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ropls/inst/doc/ropls-vignette.R importsMe: ASICS, biosigner, lipidr, MultiBaC, phenomis suggestsMe: autonomics, ptairMS, MetabolomicsBasics, readyomics dependencyCount: 97 Package: ROSeq Version: 1.22.0 Depends: R (>= 4.0) Imports: pbmcapply, edgeR, limma Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics License: GPL-3 MD5sum: 0d070f8bd69571615a526bea41ee9991 NeedsCompilation: no Title: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data Description: ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. ROSeq takes filtered and normalized read matrix and cell-annotation/condition as input and determines the differentially expressed genes between the contrasting groups of single cells. One of the input parameters is the number of cores to be used. biocViews: GeneExpression, DifferentialExpression, SingleCell Author: Krishan Gupta [aut, cre], Manan Lalit [aut], Aditya Biswas [aut], Abhik Ghosh [aut], Debarka Sengupta [aut] Maintainer: Krishan Gupta URL: https://github.com/krishan57gupta/ROSeq VignetteBuilder: knitr BugReports: https://github.com/krishan57gupta/ROSeq/issues git_url: https://git.bioconductor.org/packages/ROSeq git_branch: RELEASE_3_22 git_last_commit: 41d1eca git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ROSeq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ROSeq_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ROSeq_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ROSeq_1.22.0.tgz vignettes: vignettes/ROSeq/inst/doc/ROSeq.html vignetteTitles: ROSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROSeq/inst/doc/ROSeq.R dependencyCount: 13 Package: ROTS Version: 2.2.0 Depends: R (>= 3.6) Imports: Rcpp, stats, Biobase, methods, BiocParallel, lme4 LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) MD5sum: 51fedb785631d7be9ca9ebb352d05836 NeedsCompilation: yes Title: Reproducibility-Optimized Test Statistic Description: Calculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics, ImmunoOncology Author: Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/ROTS git_branch: RELEASE_3_22 git_last_commit: 214afc1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ROTS_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ROTS_2.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ROTS_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ROTS_2.2.0.tgz vignettes: vignettes/ROTS/inst/doc/ROTS.pdf vignetteTitles: ROTS: Reproducibility Optimized Test Statistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROTS/inst/doc/ROTS.R importsMe: PECA, PRONE, RolDE suggestsMe: LimROTS, wrProteo dependencyCount: 34 Package: RPA Version: 1.66.0 Depends: R (>= 3.1.1), affy, BiocGenerics, BiocStyle, methods, rmarkdown Imports: phyloseq Suggests: knitr, parallel License: BSD_2_clause + file LICENSE MD5sum: 84a6bdffe0f53d7484e212b2fb188076 NeedsCompilation: no Title: RPA: Robust Probabilistic Averaging for probe-level analysis Description: Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti [aut, cre] (ORCID: ) Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA VignetteBuilder: knitr BugReports: https://github.com/antagomir/RPA git_url: https://git.bioconductor.org/packages/RPA git_branch: RELEASE_3_22 git_last_commit: 38e23c1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RPA_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RPA_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RPA_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RPA_1.66.0.tgz vignettes: vignettes/RPA/inst/doc/RPA.html vignetteTitles: RPA R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs dependencyCount: 95 Package: rprimer Version: 1.14.0 Depends: R (>= 4.1) Imports: Biostrings, bslib, DT, ggplot2, IRanges, mathjaxr, methods, patchwork, reshape2, S4Vectors, shiny, shinycssloaders, shinyFeedback Suggests: BiocStyle, covr, kableExtra, knitr, rmarkdown, styler, testthat (>= 3.0.0) License: GPL-3 MD5sum: ef35d94051f88e635276ddcd43519f22 NeedsCompilation: no Title: Design Degenerate Oligos from a Multiple DNA Sequence Alignment Description: Functions, workflow, and a Shiny application for visualizing sequence conservation and designing degenerate primers, probes, and (RT)-(q/d)PCR assays from a multiple DNA sequence alignment. The results can be presented in data frame format and visualized as dashboard-like plots. For more information, please see the package vignette. biocViews: Alignment, ddPCR, Coverage, MultipleSequenceAlignment, SequenceMatching, qPCR Author: Sofia Persson [aut, cre] (ORCID: ) Maintainer: Sofia Persson URL: https://github.com/sofpn/rprimer VignetteBuilder: knitr BugReports: https://github.com/sofpn/rprimer/issues git_url: https://git.bioconductor.org/packages/rprimer git_branch: RELEASE_3_22 git_last_commit: d7001c1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rprimer_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rprimer_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rprimer_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rprimer_1.14.0.tgz vignettes: vignettes/rprimer/inst/doc/getting-started-with-rprimer.html vignetteTitles: Instructions for use hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rprimer/inst/doc/getting-started-with-rprimer.R dependencyCount: 75 Package: RProtoBufLib Version: 2.22.0 Suggests: knitr, rmarkdown License: BSD_3_clause MD5sum: d463fc87f6e56e53c1a5ec69865e77a4 NeedsCompilation: yes Title: C++ headers and static libraries of Protocol buffers Description: This package provides the headers and static library of Protocol buffers for other R packages to compile and link against. biocViews: Infrastructure Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RProtoBufLib git_branch: RELEASE_3_22 git_last_commit: 6416866 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RProtoBufLib_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RProtoBufLib_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RProtoBufLib_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RProtoBufLib_2.22.0.tgz vignettes: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.html vignetteTitles: Using RProtoBufLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.R importsMe: cytolib, flowWorkspace linksToMe: cytolib, CytoML, flowCore, flowWorkspace dependencyCount: 0 Package: rpx Version: 2.18.0 Depends: R (>= 3.5.0), methods Imports: BiocFileCache, jsonlite, xml2, RCurl, curl, utils Suggests: Biostrings, BiocStyle, testthat, knitr, tibble, rmarkdown License: GPL-2 MD5sum: b3263c46a08700cffcd67f248712968d NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DataImport, ThirdPartyClient Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/rpx VignetteBuilder: knitr BugReports: https://github.com/lgatto/rpx/issues git_url: https://git.bioconductor.org/packages/rpx git_branch: RELEASE_3_22 git_last_commit: 274a0b2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rpx_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rpx_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rpx_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rpx_2.17.0.tgz vignettes: vignettes/rpx/inst/doc/rpx.html vignetteTitles: An R interface to the ProteomeXchange repository hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R suggestsMe: MsExperiment, MSnbase, PSMatch, RforProteomics dependencyCount: 48 Package: Rqc Version: 1.44.0 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics (>= 0.25.1), Biostrings, IRanges, methods, S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid, reshape2, Rcpp (>= 0.11.6), biovizBase, shiny, Rsamtools, GenomicAlignments, GenomicFiles LinkingTo: Rcpp Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: 29247403c11a53bc488fc7c41c0e7b15 NeedsCompilation: yes Title: Quality Control Tool for High-Throughput Sequencing Data Description: Rqc is an optimised tool designed for quality control and assessment of high-throughput sequencing data. It performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics. biocViews: Sequencing, QualityControl, DataImport Author: Welliton Souza, Benilton Carvalho Maintainer: Welliton Souza URL: https://github.com/labbcb/Rqc VignetteBuilder: knitr BugReports: https://github.com/labbcb/Rqc/issues git_url: https://git.bioconductor.org/packages/Rqc git_branch: RELEASE_3_22 git_last_commit: e28c456 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rqc_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rqc_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rqc_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rqc_1.44.0.tgz vignettes: vignettes/Rqc/inst/doc/Rqc.html vignetteTitles: Using Rqc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R dependencyCount: 155 Package: rqt Version: 1.36.0 Depends: R (>= 3.4), SummarizedExperiment Imports: stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: 74faf9642c256183132f3fda6bf60ead NeedsCompilation: no Title: rqt: utilities for gene-level meta-analysis Description: Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, . biocViews: GenomeWideAssociation, Regression, Survival, PrincipalComponent, StatisticalMethod, Sequencing Author: Ilya Zhbannikov [aut, cre], Konstantin Arbeev [aut], Anatoliy Yashin [aut] Maintainer: Ilya Zhbannikov URL: https://github.com/izhbannikov/rqt VignetteBuilder: knitr BugReports: https://github.com/izhbannikov/rqt/issues git_url: https://git.bioconductor.org/packages/rqt git_branch: RELEASE_3_22 git_last_commit: 6ab0990 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rqt_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rqt_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rqt_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rqt_1.36.0.tgz vignettes: vignettes/rqt/inst/doc/rqt-vignette.html vignetteTitles: Tutorial for rqt package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqt/inst/doc/rqt-vignette.R dependencyCount: 151 Package: rqubic Version: 1.56.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 MD5sum: c40990a99b9d8d9afbfed9bc912c92aa NeedsCompilation: yes Title: Qualitative biclustering algorithm for expression data analysis in R Description: This package implements the QUBIC algorithm introduced by Li et al. for the qualitative biclustering with gene expression data. biocViews: Clustering Author: Jitao David Zhang [aut, cre, ctb] (ORCID: ) Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/rqubic git_branch: RELEASE_3_22 git_last_commit: 8629810 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rqubic_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rqubic_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rqubic_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rqubic_1.56.0.tgz vignettes: vignettes/rqubic/inst/doc/rqubic.pdf vignetteTitles: Qualitative Biclustering with Bioconductor Package rqubic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqubic/inst/doc/rqubic.R importsMe: miRSM dependencyCount: 48 Package: rRDP Version: 1.44.0 Depends: Biostrings (>= 2.26.2) Imports: rJava, utils Suggests: rRDPData, knitr, rmarkdown License: GPL-2 + file LICENSE Archs: x64 MD5sum: 0c204596aa38308b1012464818517f62 NeedsCompilation: no Title: Interface to the RDP Classifier Description: This package installs and interfaces the naive Bayesian classifier for 16S rRNA sequences developed by the Ribosomal Database Project (RDP). With this package the classifier trained with the standard training set can be used or a custom classifier can be trained. biocViews: Genetics, Sequencing, Infrastructure, Classification, Microbiome, ImmunoOncology, Alignment, SequenceMatching, DataImport, Bayesian Author: Michael Hahsler [aut, cre] (ORCID: ), Nagar Anurag [aut] Maintainer: Michael Hahsler URL: https://github.com/mhahsler/rRDP/ SystemRequirements: Java JDK 1.4 or higher VignetteBuilder: knitr BugReports: https://github.com/mhahsler/rRDP/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/rRDP git_branch: RELEASE_3_22 git_last_commit: 1dbe2d8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rRDP_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rRDP_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rRDP_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rRDP_1.44.0.tgz vignettes: vignettes/rRDP/inst/doc/rRDP.html vignetteTitles: rRDP: Interface to the RDP Classifier hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rRDP/inst/doc/rRDP.R dependsOnMe: rRDPData dependencyCount: 16 Package: RRHO Version: 1.50.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: 3ceb7a541f32ba9507d52688a16a27b5 NeedsCompilation: no Title: Inference on agreement between ordered lists Description: The package is aimed at inference on the amount of agreement in two sorted lists using the Rank-Rank Hypergeometric Overlap test. biocViews: Genetics, SequenceMatching, Microarray, Transcription Author: Jonathan Rosenblatt and Jason Stein Maintainer: Jonathan Rosenblatt git_url: https://git.bioconductor.org/packages/RRHO git_branch: RELEASE_3_22 git_last_commit: 86ba9fa git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RRHO_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RRHO_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RRHO_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RRHO_1.50.0.tgz vignettes: vignettes/RRHO/inst/doc/RRHO.pdf vignetteTitles: RRHO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RRHO/inst/doc/RRHO.R dependencyCount: 8 Package: rrvgo Version: 1.22.0 Imports: GOSemSim, AnnotationDbi, GO.db, pheatmap, ggplot2, ggrepel, treemap, tm, wordcloud, shiny, grDevices, grid, stats, methods, umap Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), shinydashboard, DT, plotly, heatmaply, magrittr, utils, clusterProfiler, DOSE, slam, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db License: GPL-3 MD5sum: be15a6d1bbe7d292a89709a5142e051a NeedsCompilation: no Title: Reduce + Visualize GO Description: Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity. biocViews: Annotation, Clustering, GO, Network, Pathways, Software Author: Sergi Sayols [aut, cre], Sara Elmeligy [ctb] Maintainer: Sergi Sayols URL: https://www.bioconductor.org/packages/rrvgo, https://ssayols.github.io/rrvgo/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rrvgo git_branch: RELEASE_3_22 git_last_commit: 76f2f43 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rrvgo_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rrvgo_1.21.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rrvgo_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rrvgo_1.22.0.tgz vignettes: vignettes/rrvgo/inst/doc/rrvgo.html vignetteTitles: Using rrvgo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rrvgo/inst/doc/rrvgo.R suggestsMe: genekitr, scDiffCom dependencyCount: 102 Package: Rsamtools Version: 2.26.0 Depends: R (>= 3.5.0), methods, Seqinfo, GenomicRanges (>= 1.61.1), Biostrings (>= 2.77.2) Imports: utils, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector (>= 0.19.7), bitops, BiocParallel, stats LinkingTo: Rhtslib (>= 3.3.1), S4Vectors, IRanges, XVector, Biostrings Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures, VariantAnnotation, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocStyle, knitr License: Artistic-2.0 | file LICENSE Archs: x64 MD5sum: 49e2932a8f1cb9515ef3cd8a79754c11 NeedsCompilation: yes Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import Description: This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files. biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl Author: Martin Morgan [aut], Hervé Pagès [aut], Valerie Obenchain [aut], Nathaniel Hayden [aut], Busayo Samuel [ctb] (Converted Rsamtools vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Rsamtools SystemRequirements: GNU make VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q BugReports: https://github.com/Bioconductor/Rsamtools/issues git_url: https://git.bioconductor.org/packages/Rsamtools git_branch: RELEASE_3_22 git_last_commit: ea99fb0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rsamtools_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rsamtools_2.25.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rsamtools_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rsamtools_2.26.0.tgz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.html vignetteTitles: An Introduction to Rsamtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R dependsOnMe: CODEX, CoverageView, esATAC, FRASER, GenomicAlignments, GenomicFiles, gmapR, HelloRanges, IntEREst, MEDIPS, methylPipe, MMDiff2, podkat, r3Cseq, RAIDS, RiboDiPA, SCOPE, SGSeq, ShortRead, SICtools, SNPhood, spiky, ssviz, strandCheckR, systemPipeR, TEQC, VariantAnnotation, wavClusteR, leeBamViews, TBX20BamSubset, sequencing, csawBook importsMe: alabaster.files, alabaster.vcf, AllelicImbalance, annmap, AnnotationHubData, appreci8R, ASpli, ATACseqQC, ATACseqTFEA, atena, BadRegionFinder, bambu, BBCAnalyzer, Bioc.gff, biovizBase, biscuiteer, breakpointR, BSgenome, CAGEr, casper, CellBarcode, cellbaseR, CexoR, cfdnakit, cfDNAPro, chimeraviz, ChIPexoQual, ChIPpeakAnno, ChromSCape, chromVAR, CircSeqAlignTk, CleanUpRNAseq, cn.mops, CNVfilteR, CNVPanelizer, CNVrd2, compEpiTools, consensusDE, CopyNumberPlots, CrispRVariants, crupR, csaw, CSSQ, DAMEfinder, Damsel, DegNorm, derfinder, DEXSeq, DiffBind, diffHic, DMRcaller, DNAfusion, easyRNASeq, EDASeq, ensembldb, epigraHMM, eudysbiome, extraChIPs, FilterFFPE, FLAMES, gcapc, gDNAx, genomation, GenomicAlignments, GenomicInteractions, GenomicPlot, ggbio, gmoviz, GOTHiC, GreyListChIP, GUIDEseq, Gviz, h5vc, icetea, INSPEcT, karyoploteR, magpie, MDTS, metagene2, metaseqR2, methylKit, mosaics, motifmatchr, MotifPeeker, msgbsR, NADfinder, NanoMethViz, nearBynding, nucleR, ORFik, panelcn.mops, PICB, plyranges, pram, PureCN, QDNAseq, qsea, QuasR, raer, ramwas, Rbowtie2, recoup, rfPred, RiboProfiling, riboSeqR, ribosomeProfilingQC, RNAmodR, Rqc, rtracklayer, scDblFinder, scPipe, scRNAseqApp, scruff, segmentSeq, seqsetvis, SimFFPE, sitadela, SplicingGraphs, srnadiff, tadar, TCseq, TFutils, tracktables, trackViewer, transcriptR, TRESS, tRNAscanImport, TVTB, UMI4Cats, uncoverappLib, VariantFiltering, VariantTools, VaSP, VCFArray, VplotR, ZygosityPredictor, chipseqDBData, gDNAinRNAseqData, LungCancerLines, MetaScope, raerdata, BIGr, GenoPop, hoardeR, iimi, MAAPER, NIPTeR, noisyr, PlasmaMutationDetector, revert, scPloidy, Signac, umiAnalyzer, VALERIE suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocGenerics, BiocParallel, biomvRCNS, BSgenomeForge, Chicago, cigarillo, ELViS, epivizrChart, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, gwascat, HIBAG, igvShiny, IRanges, iscream, ldblock, MOSim, MungeSumstats, omicsPrint, RNAmodR.ML, SeqArray, similaRpeak, Streamer, TENxIO, GeuvadisTranscriptExpr, NanoporeRNASeq, systemPipeRdata, chipseqDB, inDAGO, karyotapR, MoBPS, polyRAD, seqmagick dependencyCount: 28 Package: rsbml Version: 2.68.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 Archs: x64 MD5sum: b301cac4ade6fa8e411f970c9ab8311a NeedsCompilation: yes Title: R support for SBML, using libsbml Description: Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models. biocViews: GraphAndNetwork, Pathways, Network Author: Michael Lawrence Maintainer: Michael Lawrence URL: http://www.sbml.org SystemRequirements: libsbml (==5.10.2) git_url: https://git.bioconductor.org/packages/rsbml git_branch: RELEASE_3_22 git_last_commit: 8d17d9d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rsbml_2.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rsbml_2.67.0.zip vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rsbml/inst/doc/quick-start.R suggestsMe: piano, SBMLR dependencyCount: 8 Package: rScudo Version: 1.26.0 Depends: R (>= 3.6) Imports: methods, stats, igraph, stringr, grDevices, Biobase, S4Vectors, SummarizedExperiment, BiocGenerics Suggests: testthat, BiocStyle, knitr, rmarkdown, ALL, RCy3, caret, e1071, parallel, doParallel License: GPL-3 MD5sum: 35078a0274a9c73e634288d0e9b54a7e NeedsCompilation: no Title: Signature-based Clustering for Diagnostic Purposes Description: SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two samples is computed using a method similar to GSEA. The graph of samples can then be used to perform community clustering or to perform supervised classification of samples in a testing set. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Clustering, GraphAndNetwork, Network, Proteomics, Transcriptomics, SystemsBiology, FeatureExtraction Author: Matteo Ciciani [aut, cre], Thomas Cantore [aut], Enrica Colasurdo [ctb], Mario Lauria [ctb] Maintainer: Matteo Ciciani URL: https://github.com/Matteo-Ciciani/scudo VignetteBuilder: knitr BugReports: https://github.com/Matteo-Ciciani/scudo/issues git_url: https://git.bioconductor.org/packages/rScudo git_branch: RELEASE_3_22 git_last_commit: aed3af8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rScudo_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rScudo_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rScudo_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rScudo_1.26.0.tgz vignettes: vignettes/rScudo/inst/doc/rScudo-vignette.html vignetteTitles: Signature-based Clustering for Diagnostic Purposes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rScudo/inst/doc/rScudo-vignette.R dependencyCount: 36 Package: rsemmed Version: 1.20.0 Depends: R (>= 4.0), igraph Imports: methods, magrittr, stringr, dplyr Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 755968e81ab825f8deb8af8d098f48f8 NeedsCompilation: no Title: An interface to the Semantic MEDLINE database Description: A programmatic interface to the Semantic MEDLINE database. It provides functions for searching the database for concepts and finding paths between concepts. Path searching can also be tailored to user specifications, such as placing restrictions on concept types and the type of link between concepts. It also provides functions for summarizing and visualizing those paths. biocViews: Software, Annotation, Pathways, SystemsBiology Author: Leslie Myint [aut, cre] (ORCID: ) Maintainer: Leslie Myint URL: https://github.com/lmyint/rsemmed VignetteBuilder: knitr BugReports: https://github.com/lmyint/rsemmed/issues git_url: https://git.bioconductor.org/packages/rsemmed git_branch: RELEASE_3_22 git_last_commit: 2115f6d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rsemmed_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rsemmed_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rsemmed_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rsemmed_1.20.0.tgz vignettes: vignettes/rsemmed/inst/doc/rsemmed_user_guide.html vignetteTitles: rsemmed User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rsemmed/inst/doc/rsemmed_user_guide.R dependencyCount: 28 Package: RSeqAn Version: 1.30.0 Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 5a9a67861e1c0cf01d458914dea7cf8d NeedsCompilation: yes Title: R SeqAn Description: Headers and some wrapper functions from the SeqAn C++ library for ease of usage in R. biocViews: Infrastructure, Software Author: August Guang [aut, cre] Maintainer: August Guang VignetteBuilder: knitr BugReports: https://github.com/compbiocore/RSeqAn/issues git_url: https://git.bioconductor.org/packages/RSeqAn git_branch: RELEASE_3_22 git_last_commit: 67ea6d9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RSeqAn_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RSeqAn_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RSeqAn_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RSeqAn_1.30.0.tgz vignettes: vignettes/RSeqAn/inst/doc/first_example.html vignetteTitles: Introduction to Using RSeqAn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RSeqAn/inst/doc/first_example.R dependencyCount: 3 Package: Rsubread Version: 2.24.0 Imports: grDevices, stats, utils, Matrix License: GPL (>=3) MD5sum: 05f67cdf9ad3f67cc1572f716076c212 NeedsCompilation: yes Title: Mapping, quantification and variant analysis of sequencing data Description: Alignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads. biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq, SingleCell, GeneExpression, GeneRegulation, Genetics, ImmunoOncology, SNP, GeneticVariability, Preprocessing, QualityControl, GenomeAnnotation, GeneFusionDetection, IndelDetection, VariantAnnotation, VariantDetection, MultipleSequenceAlignment Author: Wei Shi, Yang Liao and Gordon K Smyth with contributions from Jenny Dai Maintainer: Wei Shi , Yang Liao and Gordon K Smyth URL: http://bioconductor.org/packages/Rsubread git_url: https://git.bioconductor.org/packages/Rsubread git_branch: RELEASE_3_22 git_last_commit: 4fb48d5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rsubread_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rsubread_2.23.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rsubread_2.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rsubread_2.24.0.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf, vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf vignetteTitles: Rsubread Vignette, SubreadUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R dependsOnMe: ExCluster importsMe: CleanUpRNAseq, Damsel, diffUTR, dupRadar, FRASER, ribosomeProfilingQC, scPipe, scruff, stPipe suggestsMe: autonomics, icetea, singleCellTK, SpliceWiz, tidybulk, MetaScope, inDAGO dependencyCount: 8 Package: RSVSim Version: 1.50.0 Depends: R (>= 3.5.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer, pwalign License: LGPL-3 Archs: x64 MD5sum: 3a5137a971814d21fb8b75a799070282 NeedsCompilation: no Title: RSVSim: an R/Bioconductor package for the simulation of structural variations Description: RSVSim is a package for the simulation of deletions, insertions, inversion, tandem-duplications and translocations of various sizes in any genome available as FASTA-file or BSgenome data package. SV breakpoints can be placed uniformly accross the whole genome, with a bias towards repeat regions and regions of high homology (for hg19) or at user-supplied coordinates. biocViews: Sequencing Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RSVSim git_branch: RELEASE_3_22 git_last_commit: 9b0b148 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RSVSim_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RSVSim_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RSVSim_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RSVSim_1.50.0.tgz vignettes: vignettes/RSVSim/inst/doc/vignette.pdf vignetteTitles: RSVSim: an R/Bioconductor package for the simulation of structural variations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RSVSim/inst/doc/vignette.R dependencyCount: 54 Package: rSWeeP Version: 1.22.0 Depends: foreach, doParallel, parallel, Biostrings, methods, utils Imports: tools, stringi, Suggests: Rtsne, ape, Seurat, knitr, rmarkdown, tictoc, BiocStyle, testthat (>= 3.0.0) License: GPL (>= 2) Archs: x64 MD5sum: 1215dca03f3106b27fea4e1bbd0edd37 NeedsCompilation: no Title: Spaced Words Projection (SWeeP) Description: "Spaced Words Projection (SWeeP)" is a method for representing biological sequences using vectors preserving inter-sequence comparability. Author: Camila Pereira Perico [com, cre, aut, cph] (ORCID: ), Danrley Rafael Fernandes [aut], Mariane Gonçalves Kulik [aut] (ORCID: ), Júlia Formighieri Varaschin [aut], Camilla Reginatto de Pierri [aut] (ORCID: ), Ricardo Assunção Vialle [aut] (ORCID: ), Roberto Tadeu Raittz [aut, cph] (ORCID: ) Maintainer: Camila P Perico VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rSWeeP git_branch: RELEASE_3_22 git_last_commit: a26c795 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rSWeeP_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rSWeeP_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rSWeeP_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rSWeeP_1.22.0.tgz vignettes: vignettes/rSWeeP/inst/doc/rSWeeP.html vignetteTitles: rSWeeP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rSWeeP/inst/doc/rSWeeP.R dependencyCount: 21 Package: RTCA Version: 1.62.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: 6dc361bdd0779a6f1db81bd1a52ee9f7 NeedsCompilation: no Title: Open-source toolkit to analyse data from xCELLigence System (RTCA) Description: Import, analyze and visualize data from Roche(R) xCELLigence RTCA systems. The package imports real-time cell electrical impedance data into R. As an alternative to commercial software shipped along the system, the Bioconductor package RTCA provides several unique transformation (normalization) strategies and various visualization tools. biocViews: ImmunoOncology, CellBasedAssays, Infrastructure, Visualization, TimeCourse Author: Jitao David Zhang Maintainer: Jitao David Zhang URL: http://code.google.com/p/xcelligence/,http://www.xcelligence.roche.com/,http://www.nextbiomotif.com/Home/scientific-programming git_url: https://git.bioconductor.org/packages/RTCA git_branch: RELEASE_3_22 git_last_commit: decee48 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RTCA_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTCA_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTCA_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTCA_1.62.0.tgz vignettes: vignettes/RTCA/inst/doc/aboutRTCA.pdf, vignettes/RTCA/inst/doc/RTCAtransformation.pdf vignetteTitles: Introduction to Data Analysis of the Roche xCELLigence System with RTCA Package, RTCAtransformation: Discussion of transformation methods of RTCA data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R dependencyCount: 9 Package: RTCGA Version: 1.40.0 Depends: R (>= 3.3.0) Imports: XML, RCurl, assertthat, stringi, rvest, data.table, xml2, dplyr, purrr, survival, survminer, ggplot2, ggthemes, viridis, knitr, scales, rmarkdown, htmltools Suggests: devtools, testthat, pander, Biobase, GenomicRanges, IRanges, S4Vectors, RTCGA.rnaseq, RTCGA.clinical, RTCGA.mutations, RTCGA.RPPA, RTCGA.mRNA, RTCGA.miRNASeq, RTCGA.methylation, RTCGA.CNV, magrittr, tidyr License: GPL-2 MD5sum: 5c52cf3976af5fc8032fc796fb54fa46 NeedsCompilation: no Title: The Cancer Genome Atlas Data Integration Description: The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use. biocViews: ImmunoOncology, Software, DataImport, DataRepresentation, Preprocessing, RNASeq, Survival, DNAMethylation, PrincipalComponent, Visualization Author: Marcin Kosinski [aut, cre], Przemyslaw Biecek [ctb], Witold Chodor [ctb] Maintainer: Marcin Kosinski URL: https://rtcga.github.io/RTCGA VignetteBuilder: knitr BugReports: https://github.com/RTCGA/RTCGA/issues git_url: https://git.bioconductor.org/packages/RTCGA git_branch: RELEASE_3_22 git_last_commit: 3684370 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RTCGA_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTCGA_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTCGA_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTCGA_1.40.0.tgz vignettes: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html vignetteTitles: Integrating TCGA Data - RTCGA Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCGA/inst/doc/RTCGA_Workflow.R dependsOnMe: RTCGA.clinical, RTCGA.CNV, RTCGA.methylation, RTCGA.miRNASeq, RTCGA.mRNA, RTCGA.mutations, RTCGA.PANCAN12, RTCGA.rnaseq, RTCGA.RPPA importsMe: TDbasedUFEadv dependencyCount: 130 Package: RTCGAToolbox Version: 2.40.0 Depends: R (>= 4.3.0) Imports: BiocGenerics, data.table, DelayedArray, GenomicRanges, Seqinfo, httr, methods, RaggedExperiment, RCurl, RJSONIO, rvest, S4Vectors, stats, stringr, SummarizedExperiment, TCGAutils, utils Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown License: GPL-2 Archs: x64 MD5sum: bf3d24722d96b9ca0331be85b5906308 NeedsCompilation: no Title: A new tool for exporting TCGA Firehose data Description: Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis. biocViews: DifferentialExpression, GeneExpression, Sequencing Author: Mehmet Samur [aut], Marcel Ramos [aut, cre] (ORCID: ), Ludwig Geistlinger [ctb] Maintainer: Marcel Ramos URL: http://mksamur.github.io/RTCGAToolbox/ VignetteBuilder: knitr BugReports: https://github.com/mksamur/RTCGAToolbox/issues git_url: https://git.bioconductor.org/packages/RTCGAToolbox git_branch: RELEASE_3_22 git_last_commit: 7f9df8a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RTCGAToolbox_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTCGAToolbox_2.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTCGAToolbox_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTCGAToolbox_2.40.0.tgz vignettes: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html vignetteTitles: RTCGAToolbox Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R importsMe: cBioPortalData suggestsMe: TCGAutils dependencyCount: 107 Package: RTN Version: 2.34.0 Depends: R (>= 3.6.3), methods, Imports: RedeR, minet, viper, mixtools, snow, stats, limma, data.table, IRanges, igraph, S4Vectors, SummarizedExperiment, car, pwr, pheatmap, grDevices, graphics, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: d2edeebfd7ba6fda112f75ac0e256ff3 NeedsCompilation: no Title: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons Description: A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons. biocViews: Transcription, Network, NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork, GeneSetEnrichment, GeneticVariability Author: Clarice Groeneveld [ctb], Gordon Robertson [ctb], Xin Wang [aut], Michael Fletcher [aut], Florian Markowetz [aut], Kerstin Meyer [aut], and Mauro Castro [aut] Maintainer: Mauro Castro URL: http://dx.doi.org/10.1038/ncomms3464 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTN git_branch: RELEASE_3_22 git_last_commit: 355296b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RTN_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTN_2.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTN_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTN_2.34.0.tgz vignettes: vignettes/RTN/inst/doc/RTN.html vignetteTitles: "RTN: reconstruction of transcriptional regulatory networks and analysis of regulons."" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R dependsOnMe: RTNduals, RTNsurvival, Fletcher2013b suggestsMe: geneplast dependencyCount: 135 Package: RTNduals Version: 1.34.0 Depends: R(>= 3.6.3), RTN(>= 2.14.1), methods Imports: graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: e10ed321bd661a0e24f78c0c99ff69cb NeedsCompilation: no Title: Analysis of co-regulation and inference of 'dual regulons' Description: RTNduals is a tool that searches for possible co-regulatory loops between regulon pairs generated by the RTN package. It compares the shared targets in order to infer 'dual regulons', a new concept that tests whether regulators can co-operate or compete in influencing targets. biocViews: GeneRegulation, GeneExpression, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Vinicius S. Chagas, Clarice S. Groeneveld, Gordon Robertson, Kerstin B. Meyer, Mauro A. A. Castro Maintainer: Mauro Castro , Clarice Groeneveld VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNduals git_branch: RELEASE_3_22 git_last_commit: 78c3b67 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RTNduals_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTNduals_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTNduals_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTNduals_1.34.0.tgz vignettes: vignettes/RTNduals/inst/doc/RTNduals.html vignetteTitles: "RTNduals: analysis of co-regulation and inference of dual regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNduals/inst/doc/RTNduals.R dependsOnMe: RTNsurvival dependencyCount: 136 Package: RTNsurvival Version: 1.34.0 Depends: R(>= 4.4.0), RTN(>= 2.29.1), RTNduals(>= 1.29.1), methods Imports: survival, RColorBrewer, grDevices, graphics, stats, utils, scales, data.table, egg, ggplot2, pheatmap, dunn.test Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: 27fdc9492bf8a4f7822a3e7b0268c293 NeedsCompilation: no Title: Survival analysis using transcriptional networks inferred by the RTN package Description: RTNsurvival is a tool for integrating regulons generated by the RTN package with survival information. For a given regulon, the 2-tailed GSEA approach computes a differential Enrichment Score (dES) for each individual sample, and the dES distribution of all samples is then used to assess the survival statistics for the cohort. There are two main survival analysis workflows: a Cox Proportional Hazards approach used to model regulons as predictors of survival time, and a Kaplan-Meier analysis assessing the stratification of a cohort based on the regulon activity. All plots can be fine-tuned to the user's specifications. biocViews: NetworkEnrichment, Survival, GeneRegulation, GeneSetEnrichment, NetworkInference, GraphAndNetwork Author: Clarice S. Groeneveld, Vinicius S. Chagas, Mauro A. A. Castro Maintainer: Clarice Groeneveld , Mauro A. A. Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNsurvival git_branch: RELEASE_3_22 git_last_commit: 3cf0aa1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RTNsurvival_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTNsurvival_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTNsurvival_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTNsurvival_1.34.0.tgz vignettes: vignettes/RTNsurvival/inst/doc/RTNsurvival.html vignetteTitles: "RTNsurvival: multivariate survival analysis using transcriptional networks and regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNsurvival/inst/doc/RTNsurvival.R dependencyCount: 140 Package: RTopper Version: 1.56.0 Depends: R (>= 2.12.0), Biobase Imports: limma, multtest Suggests: org.Hs.eg.db, KEGGREST, GO.db License: GPL (>= 3) + file LICENSE MD5sum: dc698d6757bc4f925ffc4ccdfc146d30 NeedsCompilation: no Title: This package is designed to perform Gene Set Analysis across multiple genomic platforms Description: the RTopper package is designed to perform and integrate gene set enrichment results across multiple genomic platforms. biocViews: Microarray Author: Luigi Marchionni , Svitlana Tyekucheva Maintainer: Luigi Marchionni git_url: https://git.bioconductor.org/packages/RTopper git_branch: RELEASE_3_22 git_last_commit: 161cf03 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RTopper_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTopper_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTopper_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTopper_1.56.0.tgz vignettes: vignettes/RTopper/inst/doc/RTopper.pdf vignetteTitles: RTopper user's manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTopper/inst/doc/RTopper.R dependencyCount: 18 Package: Rtpca Version: 1.20.0 Depends: R (>= 4.0.0), stats, dplyr, tidyr Imports: Biobase, methods, ggplot2, pROC, fdrtool, splines, utils, tibble Suggests: knitr, BiocStyle, TPP, testthat, rmarkdown License: GPL-3 Archs: x64 MD5sum: f0aab2c61773a43958b5362dc24b2805 NeedsCompilation: no Title: Thermal proximity co-aggregation with R Description: R package for performing thermal proximity co-aggregation analysis with thermal proteome profiling datasets to analyse protein complex assembly and (differential) protein-protein interactions across conditions. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], André Mateus [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rtpca git_branch: RELEASE_3_22 git_last_commit: 14fa3d8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rtpca_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rtpca_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rtpca_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rtpca_1.20.0.tgz vignettes: vignettes/Rtpca/inst/doc/Rtpca.html vignetteTitles: Introduction to Rtpca hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtpca/inst/doc/Rtpca.R dependencyCount: 42 Package: rtracklayer Version: 1.70.0 Depends: R (>= 3.5), methods, GenomicRanges (>= 1.37.2) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.35.3), S4Vectors (>= 0.23.18), IRanges (>= 2.13.13), XVector (>= 0.19.7), Seqinfo, Biostrings (>= 2.77.2), curl, httr, Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6), BiocIO, tools, restfulr (>= 0.0.13) LinkingTo: S4Vectors, IRanges, XVector Suggests: GenomeInfoDb, BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit License: Artistic-2.0 + file LICENSE Archs: x64 MD5sum: 0a9a0883305a887f70a1afe8d1a51544 NeedsCompilation: yes Title: R interface to genome annotation files and the UCSC genome browser Description: Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport. biocViews: Annotation,Visualization,DataImport Author: Michael Lawrence, Vince Carey, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/rtracklayer git_branch: RELEASE_3_22 git_last_commit: b6dc2ec git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rtracklayer_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rtracklayer_1.69.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rtracklayer_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rtracklayer_1.70.0.tgz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BSgenome, CAGEfightR, CoverageView, CSSQ, ExCluster, geneXtendeR, GenomicFiles, groHMM, HelloRanges, IdeoViz, MethylSeekR, ORFhunteR, r3Cseq, StructuralVariantAnnotation, svaNUMT, svaRetro, EatonEtAlChIPseq, liftOver, sequencing, csawBook, OSCA.intro importsMe: AnnotationHubData, annotatr, ATACseqQC, ATACseqTFEA, ballgown, bedbaser, BgeeCall, BindingSiteFinder, biscuiteer, BiSeq, BSgenomeForge, CAGEr, casper, CexoR, chipenrich, ChIPpeakAnno, ChIPseeker, ChromHeatMap, ChromSCape, circRNAprofiler, cliProfiler, CNEr, consensusSeekeR, conumee, crisprDesign, crupR, derfinder, DEScan2, diffHic, diffUTR, DMCFB, DMCHMM, dmrseq, DuplexDiscovereR, easylift, ELMER, enhancerHomologSearch, ensembldb, EpiCompare, epidecodeR, epigraHMM, epimutacions, esATAC, extraChIPs, fcScan, FindIT2, FLAMES, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, GenomicPlot, ggbio, gmapR, gmoviz, goseq, GOTHiC, GreyListChIP, gVenn, Gviz, HicAggR, HiCPotts, hicVennDiagram, HiTC, icetea, igvR, INSPEcT, IsoformSwitchAnalyzeR, karyoploteR, m6Aboost, magpie, maser, MEDIPS, metagene2, metaseqR2, methodical, methrix, methylKit, mist, mobileRNA, Moonlight2R, motifbreakR, MotifDb, MotifPeeker, multicrispr, MungeSumstats, NADfinder, nearBynding, normr, OGRE, OMICsPCA, ORFik, PAST, periodicDNA, plyranges, PMScanR, pram, primirTSS, proBAMr, PureCN, qsea, QuasR, raer, RCAS, recount, recount3, recoup, regioneR, REMP, RiboCrypt, RiboProfiling, ribosomeProfilingQC, rifi, rifiComparative, rmspc, RNAmodR, roar, scanMiRApp, scDblFinder, scPipe, scRNAseqApp, scruff, seqCAT, seqsetvis, sevenC, SGSeq, shinyepico, signeR, SigsPack, sitadela, SOMNiBUS, SpliceWiz, srnadiff, STADyUM, TEKRABber, TENET, TFBSTools, tidyCoverage, trackViewer, transcriptR, TRESS, tRNAscanImport, txcutr, txdbmaker, VariantAnnotation, VariantTools, wavClusteR, wiggleplotr, GenomicState, chipenrich.data, DMRcatedata, geneLenDataBase, NxtIRFdata, raerdata, spatialLIBD, seqpac, OSTA, crispRdesignR, GALLO, GencoDymo2, geneHapR, locuszoomr, PlasmaMutationDetector, tepr suggestsMe: alabaster.files, AnnotationHub, autonomics, BiocFileCache, biovizBase, BREW3R.r, bsseq, cicero, compEpiTools, CrispRVariants, crisprViz, DAMEfinder, DiffBind, DMRcaller, eisaR, epistack, epivizrChart, epivizrData, FRASER, G4SNVHunter, geneXtendeR, GenomicAlignments, GenomicDistributions, GenomicInteractionNodes, GenomicRanges, gwascat, HiCExperiment, HiContacts, igvShiny, InPAS, interactiveDisplay, linkSet, megadepth, methylumi, miRBaseConverter, motifTestR, MutationalPatterns, NanoMethViz, OrganismDbi, peakCombiner, PICB, pipeFrame, plotgardener, plyinteractions, pqsfinder, ProteoDisco, RcisTarget, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, RSVSim, similaRpeak, syntenet, systemPipeR, TAPseq, TCGAutils, transmogR, triplex, tRNAdbImport, TVTB, xcore, EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3, excluderanges, FDb.FANTOM4.promoters.hg19, fourDNData, GeuvadisTranscriptExpr, nanotubes, PasillaTranscriptExpr, systemPipeRdata, chipseqDB, gkmSVM, inDAGO, Rgff, RTIGER, Seurat, Signac dependencyCount: 56 Package: rTRM Version: 1.48.0 Depends: R (>= 2.10), igraph (>= 1.0) Imports: methods, AnnotationDbi, DBI, RSQLite Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db, org.Mm.eg.db, ggplot2, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: fef3bdf8af29066178a873061b7f719b NeedsCompilation: no Title: Identification of Transcriptional Regulatory Modules from Protein-Protein Interaction Networks Description: rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRM VignetteBuilder: knitr BugReports: https://github.com/ddiez/rTRM/issues git_url: https://git.bioconductor.org/packages/rTRM git_branch: RELEASE_3_22 git_last_commit: f220f64 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rTRM_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rTRM_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rTRM_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rTRM_1.48.0.tgz vignettes: vignettes/rTRM/inst/doc/Introduction.html vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRM/inst/doc/Introduction.R importsMe: rTRMui dependencyCount: 48 Package: rTRMui Version: 1.48.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: bbe727fecf3ba8c7800ff081481806e9 NeedsCompilation: no Title: A shiny user interface for rTRM Description: This package provides a web interface to compute transcriptional regulatory modules with rTRM. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork, GUI Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRMui BugReports: https://github.com/ddiez/rTRMui/issues git_url: https://git.bioconductor.org/packages/rTRMui git_branch: RELEASE_3_22 git_last_commit: 004f0b9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rTRMui_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rTRMui_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rTRMui_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rTRMui_1.48.0.tgz vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf vignetteTitles: Introduction to rTRMui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRMui/inst/doc/rTRMui.R dependencyCount: 102 Package: RUCova Version: 1.2.0 Depends: R (>= 4.4.0) Imports: dplyr, fastDummies, ggplot2, stringr, tibble, Matrix, ComplexHeatmap, grid, circlize, SingleCellExperiment, SummarizedExperiment, tidyverse, tidyr, magrittr, S4Vectors Suggests: knitr, rmarkdown, BiocManager, BiocStyle, remotes, ggpubr, ggcorrplot, ggh4x, testthat (>= 3.0.0) License: GPL-3 MD5sum: dce546b93e9a4dbbc51fbe2b9f53d279 NeedsCompilation: no Title: Removes unwanted covariance from mass cytometry data Description: Mass cytometry enables the simultaneous measurement of dozens of protein markers at the single-cell level, producing high dimensional datasets that provide deep insights into cellular heterogeneity and function. However, these datasets often contain unwanted covariance introduced by technical variations, such as differences in cell size, staining efficiency, and instrument-specific artifacts, which can obscure biological signals and complicate downstream analysis. This package addresses this challenge by implementing a robust framework of linear models designed to identify and remove these sources of unwanted covariance. By systematically modeling and correcting for technical noise, the package enhances the quality and interpretability of mass cytometry data, enabling researchers to focus on biologically relevant signals. biocViews: Software, SingleCell Author: Rosario Astaburuaga-García [aut, cre] (ORCID: ) Maintainer: Rosario Astaburuaga-García URL: https://github.com/molsysbio/RUCova SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/molsysbio/RUCova/issues git_url: https://git.bioconductor.org/packages/RUCova git_branch: RELEASE_3_22 git_last_commit: 2102db2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RUCova_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RUCova_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RUCova_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RUCova_1.2.0.tgz vignettes: vignettes/RUCova/inst/doc/RUCova.html vignetteTitles: Removing Unwanted Covariance in mass cytometry data with RUCova hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RUCova/inst/doc/RUCova.R dependencyCount: 140 Package: runibic Version: 1.32.0 Depends: R (>= 3.4.0), biclust, SummarizedExperiment Imports: Rcpp (>= 0.12.12), testthat, methods LinkingTo: Rcpp Suggests: knitr, rmarkdown, GEOquery, affy, airway, QUBIC License: MIT + file LICENSE MD5sum: beab7c81ce7cfea5d84f1637786b6a46 NeedsCompilation: yes Title: runibic: row-based biclustering algorithm for analysis of gene expression data in R Description: This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data. biocViews: Microarray, Clustering, GeneExpression, Sequencing, Coverage Author: Patryk Orzechowski, Artur Pańszczyk Maintainer: Patryk Orzechowski URL: http://github.com/athril/runibic SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: http://github.com/athril/runibic/issues git_url: https://git.bioconductor.org/packages/runibic git_branch: RELEASE_3_22 git_last_commit: f66aafc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/runibic_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/runibic_1.31.0.zip vignettes: vignettes/runibic/inst/doc/runibic.html vignetteTitles: runibic: UniBic in R Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: mosbi dependencyCount: 79 Package: RUVcorr Version: 1.42.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2, graphics Suggests: knitr, hgu133a2.db, rmarkdown License: GPL-2 MD5sum: 0216751b1151f524e08b372377b8c3ad NeedsCompilation: no Title: Removal of unwanted variation for gene-gene correlations and related analysis Description: RUVcorr allows to apply global removal of unwanted variation (ridged version of RUV) to real and simulated gene expression data. biocViews: GeneExpression, Normalization Author: Saskia Freytag Maintainer: Saskia Freytag VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RUVcorr git_branch: RELEASE_3_22 git_last_commit: e7ec9c7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RUVcorr_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RUVcorr_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RUVcorr_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RUVcorr_1.42.0.tgz vignettes: vignettes/RUVcorr/inst/doc/Vignette.html vignetteTitles: Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVcorr/inst/doc/Vignette.R dependencyCount: 42 Package: RUVnormalize Version: 1.44.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: 6e5b3de55b32da5aeac0a1c275fcb78b NeedsCompilation: no Title: RUV for normalization of expression array data Description: RUVnormalize is meant to remove unwanted variation from gene expression data when the factor of interest is not defined, e.g., to clean up a dataset for general use or to do any kind of unsupervised analysis. biocViews: StatisticalMethod, Normalization Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/RUVnormalize git_branch: RELEASE_3_22 git_last_commit: f0c7f18 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RUVnormalize_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RUVnormalize_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RUVnormalize_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RUVnormalize_1.44.0.tgz vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf vignetteTitles: RUVnormalize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVnormalize/inst/doc/RUVnormalize.R dependencyCount: 8 Package: RUVSeq Version: 1.44.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 MD5sum: 1c753cc63ee99705edbd2921048edc03 NeedsCompilation: no Title: Remove Unwanted Variation from RNA-Seq Data Description: This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, Software Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Lorena Pantano [ctb], Kamil Slowikowski [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/RUVSeq VignetteBuilder: knitr BugReports: https://github.com/drisso/RUVSeq/issues git_url: https://git.bioconductor.org/packages/RUVSeq git_branch: RELEASE_3_22 git_last_commit: a0bba0d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RUVSeq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RUVSeq_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RUVSeq_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RUVSeq_1.44.0.tgz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.html vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R dependsOnMe: octad, rnaseqGene importsMe: consensusDE, ribosomeProfilingQC, scone, standR suggestsMe: DEScan2, NanoTube, notame dependencyCount: 118 Package: Rvisdiff Version: 1.8.0 Depends: R (>= 4.5.0) Imports: edgeR, utils Suggests: knitr, rmarkdown, DESeq2, limma, SummarizedExperiment, airway, BiocStyle, matrixTests, BiocManager License: GPL-2 | GPL-3 MD5sum: a3b24503128b7069c5ad4749082d944b NeedsCompilation: no Title: Interactive Graphs for Differential Expression Description: Creates a muti-graph web page which allows the interactive exploration of differential analysis tests. The graphical web interface presents results as a table which is integrated with five interactive graphs: MA-plot, volcano plot, box plot, lines plot and cluster heatmap. Graphical aspect and information represented in the graphs can be customized by means of user controls. Final graphics can be exported as PNG format. biocViews: Software, Visualization, RNASeq, DataRepresentation, DifferentialExpression Author: Carlos Prieto [aut] (ORCID: ), David Barrios [cre, aut] (ORCID: ) Maintainer: David Barrios URL: https://github.com/BioinfoUSAL/Rvisdiff/ VignetteBuilder: knitr BugReports: https://github.com/BioinfoUSAL/Rvisdiff/issues/ git_url: https://git.bioconductor.org/packages/Rvisdiff git_branch: RELEASE_3_22 git_last_commit: 3c19cde git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Rvisdiff_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rvisdiff_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rvisdiff_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rvisdiff_1.8.0.tgz vignettes: vignettes/Rvisdiff/inst/doc/Rvisdiff.html vignetteTitles: Visualize Differential Expression results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rvisdiff/inst/doc/Rvisdiff.R dependencyCount: 11 Package: RVS Version: 1.32.0 Depends: R (>= 3.5.0) Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils, R.utils Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation License: GPL-2 MD5sum: 75f4503bd29be54cc4ce08924f4fc9ba NeedsCompilation: no Title: Computes estimates of the probability of related individuals sharing a rare variant Description: Rare Variant Sharing (RVS) implements tests of association and linkage between rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status, in family samples. The tests are based on probabilities of rare variant sharing by relatives under the null hypothesis of absence of linkage and association between the rare variants and the phenotype and apply to single variants or multiple variants in a region (e.g. gene-based test). biocViews: ImmunoOncology, Genetics, GenomeWideAssociation, VariantDetection, ExomeSeq, WholeGenome Author: Alexandre Bureau, Ingo Ruczinski, Samuel Younkin, Thomas Sherman Maintainer: Alexandre Bureau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RVS git_branch: RELEASE_3_22 git_last_commit: 93ba1e8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/RVS_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RVS_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RVS_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RVS_1.32.0.tgz vignettes: vignettes/RVS/inst/doc/RVS.html vignetteTitles: The RVS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RVS/inst/doc/RVS.R dependencyCount: 60 Package: rWikiPathways Version: 1.30.0 Imports: httr, utils, XML, rjson, data.table, RCurl, dplyr, tidyr, readr, stringr, purrr, lubridate Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 15c94218e3ba3150e697b85c36838379 NeedsCompilation: no Title: rWikiPathways - R client library for the WikiPathways API Description: Use this package to interface with the WikiPathways API. It provides programmatic access to WikiPathways content in multiple data and image formats, including official monthly release files and convenient GMT read/write functions. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network, Metabolomics Author: Egon Willighagen [aut, cre] (ORCID: ), Alex Pico [aut] (ORCID: ) Maintainer: Egon Willighagen URL: https://github.com/wikipathways/rWikiPathways VignetteBuilder: knitr BugReports: https://github.com/wikipathways/rWikiPathways/issues git_url: https://git.bioconductor.org/packages/rWikiPathways git_branch: RELEASE_3_22 git_last_commit: 1ca65ae git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/rWikiPathways_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rWikiPathways_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rWikiPathways_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rWikiPathways_1.30.0.tgz vignettes: vignettes/rWikiPathways/inst/doc/Overview.html, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.html vignetteTitles: 1. Overview, 4. Pathway Analysis, 2. rWikiPathways and BridgeDbR, 3. rWikiPathways and RCy3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rWikiPathways/inst/doc/Overview.R, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.R importsMe: RVA suggestsMe: TRONCO dependencyCount: 50 Package: S4Arrays Version: 1.10.0 Depends: R (>= 4.3.0), methods, Matrix, abind, BiocGenerics (>= 0.45.2), S4Vectors (>= 0.47.6), IRanges Imports: stats LinkingTo: S4Vectors Suggests: BiocParallel, SparseArray (>= 0.0.4), DelayedArray, HDF5Array, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 9587279662313a6d0b695a69c6992ffa NeedsCompilation: yes Title: Foundation of array-like containers in Bioconductor Description: The S4Arrays package defines the Array virtual class to be extended by other S4 classes that wish to implement a container with an array-like semantic. It also provides: (1) low-level functionality meant to help the developer of such container to implement basic operations like display, subsetting, or coercion of their array-like objects to an ordinary matrix or array, and (2) a framework that facilitates block processing of array-like objects (typically on-disk objects). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre] (ORCID: ), Jacques Serizay [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/S4Arrays VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/S4Arrays/issues git_url: https://git.bioconductor.org/packages/S4Arrays git_branch: RELEASE_3_22 git_last_commit: 79170b1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/S4Arrays_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/S4Arrays_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/S4Arrays_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/S4Arrays_1.10.0.tgz vignettes: vignettes/S4Arrays/inst/doc/S4Arrays_quick_overview.html vignetteTitles: A quick overview of the S4Arrays package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Arrays/inst/doc/S4Arrays_quick_overview.R dependsOnMe: DelayedArray, SparseArray importsMe: alabaster.matrix, DelayedTensor, dreamlet, FLAMES, GSVA, h5mread, HDF5Array, scran, scuttle, SummarizedExperiment suggestsMe: BiocGenerics dependencyCount: 14 Package: S4Vectors Version: 0.48.0 Depends: R (>= 4.0.0), methods, utils, stats, stats4, BiocGenerics (>= 0.53.2) Suggests: IRanges, GenomicRanges, SummarizedExperiment, Matrix, DelayedArray, ShortRead, graph, data.table, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 630961a4e2d96366a617dcc35f058105 NeedsCompilation: yes Title: Foundation of vector-like and list-like containers in Bioconductor Description: The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, Factor, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre], Michael Lawrence [aut], Patrick Aboyoun [aut], Aaron Lun [ctb], Beryl Kanali [ctb] (Converted vignettes from Sweave to RMarkdown) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/S4Vectors VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/S4Vectors/issues git_url: https://git.bioconductor.org/packages/S4Vectors git_branch: RELEASE_3_22 git_last_commit: c4f37f0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/S4Vectors_0.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/S4Vectors_0.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/S4Vectors_0.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/S4Vectors_0.48.0.tgz vignettes: vignettes/S4Vectors/inst/doc/RleTricks.html, vignettes/S4Vectors/inst/doc/S4QuickOverview.html, vignettes/S4Vectors/inst/doc/S4VectorsOverview.html vignetteTitles: Rle Tips and Tricks, A quick overview of the S4 class system, An Overview of the S4Vectors package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R, vignettes/S4Vectors/inst/doc/S4QuickOverview.R, vignettes/S4Vectors/inst/doc/S4VectorsOverview.R dependsOnMe: altcdfenvs, AnnotationHubData, ATACseqQC, bambu, bandle, betaHMM, Biostrings, BiSeq, BSgenome, bumphunter, Cardinal, CellMapper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, ChIPseqR, cigarillo, ClassifyR, cliProfiler, CODEX, CompoundDb, coseq, CSAR, CSSQ, DelayedArray, DelayedDataFrame, DESeq2, DEXSeq, DirichletMultinomial, DMCFB, DMCHMM, DMRcaller, ExperimentHubData, ExpressionAtlas, fCCAC, GA4GHclient, 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SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, bugphyzz, celldex, chipenrich.data, chipseqDBData, curatedMetagenomicData, curatedTCGAData, DNAZooData, DoReMiTra, DropletTestFiles, FlowSorted.Blood.EPIC, fourDNData, HCATonsilData, HighlyReplicatedRNASeq, HMP16SData, HMP2Data, homosapienDEE2CellScore, imcdatasets, leeBamViews, LegATo, MerfishData, MetaGxPancreas, MetaScope, MethylSeqData, MicrobiomeBenchmarkData, MouseGastrulationData, MouseThymusAgeing, pd.atdschip.tiling, scMultiome, scpdata, scRNAseq, sesameData, SimBenchData, SingleCellMultiModal, SomaticCancerAlterations, spatialLIBD, TransOmicsData, tuberculosis, GeoMxWorkflows, seqpac, crispRdesignR, DR.SC, driveR, genBaRcode, geno2proteo, HiCociety, hicream, hoardeR, imcExperiment, karyotapR, LoopRig, MetAlyzer, microbial, mikropml, multimedia, NIPTeR, PlasmaMutationDetector, restfulr, rliger, rnaCrosslinkOO, rsolr, scROSHI, Signac, SpatialDDLS, TaxaNorm, toxpiR suggestsMe: AlphaMissenseR, AlpsNMR, ANCOMBC, anndataR, BiocGenerics, CCAFE, chihaya, COTAN, dearseq, epiregulon.extra, epivizrChart, GeoTcgaData, globalSeq, GWASTools, GWENA, gypsum, iscream, koinar, maftools, martini, MicrobiotaProcess, MsQuality, MungeSumstats, RTCGA, scrapper, SpectraQL, SPOTlight, TFEA.ChIP, TFutils, XAItest, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, BioPlex, curatedAdipoChIP, curatedAdipoRNA, ObMiTi, xcoredata, dependentsimr, gkmSVM, grandR, inDAGO, LorMe, pmartR, polyRAD, pQTLdata, RCPA, Rgff, Seurat, SNPassoc, updog, valr linksToMe: Bioc.gff, Biostrings, cigarillo, CNEr, DECIPHER, DelayedArray, GenomicAlignments, GenomicRanges, h5mread, IRanges, kebabs, MatrixRider, pwalign, Rsamtools, rtracklayer, S4Arrays, ShortRead, SparseArray, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 7 Package: safe Version: 3.50.0 Depends: R (>= 2.4.0), AnnotationDbi, Biobase, methods, SparseM Suggests: GO.db, PFAM.db, reactome.db, hgu133a.db, breastCancerUPP, survival, foreach, doRNG, Rgraphviz, GOstats License: GPL (>= 2) MD5sum: e7d99c1e0d9dc19095a39ab64de81fea NeedsCompilation: no Title: Significance Analysis of Function and Expression Description: SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions. biocViews: DifferentialExpression, Pathways, GeneSetEnrichment, StatisticalMethod, Software Author: William T. Barry Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/safe git_branch: RELEASE_3_22 git_last_commit: 299aa5c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/safe_3.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/safe_3.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/safe_3.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/safe_3.50.0.tgz vignettes: vignettes/safe/inst/doc/SAFEmanual3.pdf vignetteTitles: SAFE manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/safe/inst/doc/SAFEmanual3.R importsMe: EGSEA, EnrichmentBrowser suggestsMe: ReporterScore dependencyCount: 44 Package: sagenhaft Version: 1.80.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, stats, utils License: GPL (>= 2) MD5sum: a3cbde485ac6d24b3014edfdc27e8e7b NeedsCompilation: no Title: Collection of functions for reading and comparing SAGE libraries Description: This package implements several functions useful for analysis of gene expression data by sequencing tags as done in SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction of a SAGE library from sequence files, sequence error correction, library comparison. Sequencing error correction is implementing using an Expectation Maximization Algorithm based on a Mixture Model of tag counts. biocViews: SAGE Author: Tim Beissbarth , with contributions from Gordon Smyth Maintainer: Tim Beissbarth URL: http://www.bioinf.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/sagenhaft git_branch: RELEASE_3_22 git_last_commit: 29966e0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sagenhaft_1.80.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sagenhaft_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sagenhaft_1.80.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sagenhaft_1.80.0.tgz vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf vignetteTitles: SAGEnhaft hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sagenhaft/inst/doc/SAGEnhaft.R dependencyCount: 5 Package: SAIGEgds Version: 2.10.0 Depends: R (>= 4.0.0), gdsfmt (>= 1.28.0), SeqArray (>= 1.49.6), Rcpp Imports: methods, stats, utils, Matrix, RcppParallel, SKAT, CompQuadForm, survey LinkingTo: Rcpp, RcppArmadillo, RcppParallel (>= 5.0.0) Suggests: parallel, markdown, rmarkdown, crayon, SNPRelate, RUnit, knitr, ggmanh, BiocGenerics License: GPL-3 MD5sum: b7255fbbf4ed713adacb114a0f4c3929 NeedsCompilation: yes Title: Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association Studies Description: Scalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests and set-based aggregate tests in large-scale Phenome-wide Association Studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the SAIGE R package (v0.45, Zhou et al. 2018 and Zhou et al. 2020), and it is extended to include the state-of-the-art ACAT-O set-based tests. Benchmarks show that SAIGEgds is significantly faster than the SAIGE R package. biocViews: Software, Genetics, StatisticalMethod, GenomeWideAssociation Author: Xiuwen Zheng [aut, cre] (ORCID: ), Wei Zhou [ctb] (the original author of the SAIGE R package), J. Wade Davis [ctb] Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SAIGEgds SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SAIGEgds git_branch: RELEASE_3_22 git_last_commit: 144fd63 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SAIGEgds_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SAIGEgds_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SAIGEgds_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SAIGEgds_2.10.0.tgz vignettes: vignettes/SAIGEgds/inst/doc/SAIGEgds.html vignetteTitles: SAIGEgds Tutorial (single variant tests) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAIGEgds/inst/doc/SAIGEgds.R dependencyCount: 38 Package: SamSPECTRAL Version: 1.64.0 Depends: R (>= 3.3.3) Imports: methods License: GPL (>= 2) Archs: x64 MD5sum: 740fa08adf003af4977f4711fbcfc8a1 NeedsCompilation: yes Title: Identifies cell population in flow cytometry data Description: Samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting "connected components" estimate biological cell populations in the data. See the vignette for more details on how to use this package, some illustrations, and simple examples. biocViews: FlowCytometry, CellBiology, Clustering, Cancer, FlowCytometry, StemCells, HIV, ImmunoOncology Author: Habil Zare and Parisa Shooshtari Maintainer: Habil git_url: https://git.bioconductor.org/packages/SamSPECTRAL git_branch: RELEASE_3_22 git_last_commit: 0c157f5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SamSPECTRAL_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SamSPECTRAL_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SamSPECTRAL_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SamSPECTRAL_1.64.0.tgz vignettes: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.pdf vignetteTitles: A modified spectral clustering method for clustering Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.R importsMe: ddPCRclust dependencyCount: 1 Package: sangeranalyseR Version: 1.19.0 Depends: R (>= 4.0.0), stringr, ape, Biostrings, pwalign, DECIPHER, parallel, reshape2, sangerseqR, gridExtra, shiny, shinydashboard, shinyjs, data.table, plotly, DT, zeallot, excelR, shinycssloaders, ggdendro, shinyWidgets, openxlsx, tools, rmarkdown (>= 2.9), knitr (>= 1.33), seqinr, BiocStyle, logger Suggests: testthat (>= 2.1.0) License: GPL-2 MD5sum: 6c63e13029a6557e619da005ad167af0 NeedsCompilation: no Title: sangeranalyseR: a suite of functions for the analysis of Sanger sequence data in R Description: This package builds on sangerseqR to allow users to create contigs from collections of Sanger sequencing reads. It provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. There is extensive online documentation, and the package can outputs detailed HTML reports, including chromatograms. biocViews: Genetics, Alignment, Sequencing, SangerSeq, Preprocessing, QualityControl, Visualization, GUI Author: Rob Lanfear , Kuan-Hao Chao Maintainer: Kuan-Hao Chao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangeranalyseR git_branch: devel git_last_commit: 5cc0f51 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/sangeranalyseR_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sangeranalyseR_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sangeranalyseR_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sangeranalyseR_1.19.0.tgz vignettes: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.html vignetteTitles: sangeranalyseR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.R dependencyCount: 116 Package: sangerseqR Version: 1.46.0 Depends: R (>= 3.5.0), Biostrings, pwalign, stringr Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: 3d441a924043980d2d7f04a70ba8c359 NeedsCompilation: no Title: Tools for Sanger Sequencing Data in R Description: This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms. biocViews: Sequencing, SNP, Visualization Author: Jonathon T. Hill, Bradley Demarest Maintainer: Jonathon Hill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangerseqR git_branch: RELEASE_3_22 git_last_commit: 650a2c8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sangerseqR_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sangerseqR_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sangerseqR_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sangerseqR_1.46.0.tgz vignettes: vignettes/sangerseqR/inst/doc/sangerseqRWalkthrough.html vignetteTitles: Using the sangerseqR package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sangerseqR/inst/doc/sangerseqRWalkthrough.R dependsOnMe: sangeranalyseR importsMe: scifer suggestsMe: CrispRVariants dependencyCount: 49 Package: SanityR Version: 1.0.0 Imports: Rcpp, BiocGenerics, BiocParallel, MatrixGenerics, methods, S4Vectors, scuttle, SingleCellExperiment, SummarizedExperiment LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), scater, Rtsne License: GPL (>= 3) Archs: x64 MD5sum: 9b901fe8e91f65d4c6b483488faf78fd NeedsCompilation: yes Title: R/Bioconductor interface to the Sanity model gene expression analysis Description: a Bayesian normalization procedure derived from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. biocViews: Software, GeneExpression, SingleCell, Normalization, Bayesian Author: Teo Sakel [aut, cre] (ORCID: ), MCIU/AEI [fnd] (ROR: , DOI: 10.13039/501100011033) Maintainer: Teo Sakel URL: https://github.com/TeoSakel/SanityR VignetteBuilder: knitr BugReports: https://github.com/TeoSakel/SanityR/issues git_url: https://git.bioconductor.org/packages/SanityR git_branch: RELEASE_3_22 git_last_commit: c64a735 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SanityR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SanityR_0.99.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SanityR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SanityR_1.0.0.tgz vignettes: vignettes/SanityR/inst/doc/SanityR.html vignetteTitles: Normalizing scRNA-seq data with Sanity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SanityR/inst/doc/SanityR.R dependencyCount: 40 Package: SANTA Version: 2.46.0 Depends: R (>= 4.1), igraph Imports: graphics, Matrix, methods, stats Suggests: BiocGenerics, BioNet, formatR, knitr, msm, org.Sc.sgd.db, markdown, rmarkdown, RUnit License: GPL (>= 2) MD5sum: 1795ad6587ae9f6ae70fc38bf690dae9 NeedsCompilation: yes Title: Spatial Analysis of Network Associations Description: This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode). biocViews: Network, NetworkEnrichment, Clustering Author: Alex Cornish [cre, aut] Maintainer: Alex Cornish VignetteBuilder: knitr BugReports: https://github.com/alexjcornish/SANTA git_url: https://git.bioconductor.org/packages/SANTA git_branch: RELEASE_3_22 git_last_commit: 1f8bb06 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SANTA_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SANTA_2.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SANTA_2.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SANTA_2.46.0.tgz vignettes: vignettes/SANTA/inst/doc/SANTA-vignette.html vignetteTitles: Introduction to SANTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R dependencyCount: 17 Package: sarks Version: 1.22.0 Depends: R (>= 4.0) Imports: rJava, Biostrings, IRanges, utils, stats, cluster, binom Suggests: RUnit, BiocGenerics, ggplot2 License: BSD_3_clause + file LICENSE MD5sum: fe237457efbe8bb60326339f7448a8a9 NeedsCompilation: no Title: Suffix Array Kernel Smoothing for discovery of correlative sequence motifs and multi-motif domains Description: Suffix Array Kernel Smoothing (see https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797), or SArKS, identifies sequence motifs whose presence correlates with numeric scores (such as differential expression statistics) assigned to the sequences (such as gene promoters). SArKS smooths over sequence similarity, quantified by location within a suffix array based on the full set of input sequences. A second round of smoothing over spatial proximity within sequences reveals multi-motif domains. Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing. biocViews: MotifDiscovery, GeneRegulation, GeneExpression, Transcriptomics, RNASeq, DifferentialExpression, FeatureExtraction Author: Dennis Wylie [aut, cre] (ORCID: ) Maintainer: Dennis Wylie URL: https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797, https://github.com/denniscwylie/sarks SystemRequirements: Java (>= 1.8) BugReports: https://github.com/denniscwylie/sarks/issues git_url: https://git.bioconductor.org/packages/sarks git_branch: RELEASE_3_22 git_last_commit: 1a0c6ad git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sarks_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sarks_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sarks_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sarks_1.22.0.tgz vignettes: vignettes/sarks/inst/doc/sarks-vignette.pdf vignetteTitles: sarks-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sarks/inst/doc/sarks-vignette.R dependencyCount: 18 Package: saseR Version: 1.6.0 Depends: R (>= 4.3.0) Imports: ASpli, BiocGenerics, BiocParallel, data.table, DESeq2, dplyr, edgeR, GenomicAlignments, GenomicFeatures, GenomicRanges, igraph, IRanges, limma, methods, MASS, MatrixGenerics, S4Vectors, stats, SummarizedExperiment, parallel, PRROC Suggests: rrcov, knitr, txdbmaker License: Artistic-2.0 MD5sum: 6a67cddf5b1ad6554ff3383b9c28aa1d NeedsCompilation: no Title: Scalable Aberrant Splicing and Expression Retrieval Description: saseR is a highly performant and fast framework for aberrant expression and splicing analyses. The main functions are: \itemize{ \item \code{\link{BamtoAspliCounts}} - Process BAM files to ASpli counts \item \code{\link{convertASpli}} - Get gene, bin or junction counts from ASpli SummarizedExperiment \item \code{\link{calculateOffsets}} - Create an offsets assays for aberrant expression or splicing analysis \item \code{\link{saseRfindEncodingDim}} - Estimate the optimal number of latent factors to include when estimating the mean expression \item \code{\link{saseRfit}} - Parameter estimation of the negative binomial distribution and compute p-values for aberrant expression and splicing } For information upon how to use these functions, check out our vignette at \url{https://github.com/statOmics/saseR/blob/main/vignettes/Vignette.Rmd} and the saseR paper: Segers, A. et al. (2023). Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. bioRxiv. \url{https://doi.org/10.1101/2023.06.29.547014}. biocViews: DifferentialExpression, DifferentialSplicing, Regression, GeneExpression, AlternativeSplicing, RNASeq, Sequencing, Software Author: Alexandre Segers [aut, cre], Jeroen Gilis [ctb], Mattias Van Heetvelde [ctb], Elfride De Baere [ctb], Lieven Clement [ctb] Maintainer: Alexandre Segers URL: https://github.com/statOmics/saseR, https://doi.org/10.1101/2023.06.29.547014 VignetteBuilder: knitr BugReports: https://github.com/statOmics/saseR/issues git_url: https://git.bioconductor.org/packages/saseR git_branch: RELEASE_3_22 git_last_commit: 8ee3ecc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/saseR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/saseR_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/saseR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/saseR_1.6.0.tgz vignettes: vignettes/saseR/inst/doc/saseR-vignette.html vignetteTitles: Main vignette: saseR analyses hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/saseR/inst/doc/saseR-vignette.R dependencyCount: 173 Package: satuRn Version: 1.18.0 Depends: R (>= 4.1) Imports: locfdr, SummarizedExperiment, BiocParallel, limma, pbapply, ggplot2, boot, Matrix, stats, methods, graphics Suggests: knitr, rmarkdown, testthat, covr, BiocStyle, AnnotationHub, ensembldb, edgeR, DEXSeq, stageR, DelayedArray License: Artistic-2.0 MD5sum: afbc10cccf2e00f352a83d6daa2b080a NeedsCompilation: no Title: Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications Description: satuRn provides a higly performant and scalable framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest. biocViews: Regression, ExperimentalDesign, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Jeroen Gilis [aut, cre], Kristoffer Vitting-Seerup [ctb], Koen Van den Berge [ctb], Lieven Clement [ctb] Maintainer: Jeroen Gilis URL: https://github.com/statOmics/satuRn VignetteBuilder: knitr BugReports: https://github.com/statOmics/satuRn/issues git_url: https://git.bioconductor.org/packages/satuRn git_branch: RELEASE_3_22 git_last_commit: 20071e7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/satuRn_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/satuRn_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/satuRn_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/satuRn_1.18.0.tgz vignettes: vignettes/satuRn/inst/doc/Vignette.html vignetteTitles: satuRn - vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/satuRn/inst/doc/Vignette.R dependsOnMe: IsoformSwitchAnalyzeR dependencyCount: 57 Package: SBGNview Version: 1.24.0 Depends: R (>= 3.6), pathview, SBGNview.data Imports: Rdpack, grDevices, methods, stats, utils, xml2, rsvg, igraph, rmarkdown, knitr, SummarizedExperiment, AnnotationDbi, httr, KEGGREST, bookdown Suggests: testthat, gage License: AGPL-3 MD5sum: d89a0860d0e35464ecfe9160cfc00cc4 NeedsCompilation: no Title: "SBGNview: Data Analysis, Integration and Visualization on SBGN Pathways" Description: SBGNview is a tool set for pathway based data visalization, integration and analysis. SBGNview is similar and complementary to the widely used Pathview, with the following key features: 1. Pathway definition by the widely adopted Systems Biology Graphical Notation (SBGN); 2. Supports multiple major pathway databases beyond KEGG (Reactome, MetaCyc, SMPDB, PANTHER, METACROP) and user defined pathways; 3. Covers 5,200 reference pathways and over 3,000 species by default; 4. Extensive graphics controls, including glyph and edge attributes, graph layout and sub-pathway highlight; 5. SBGN pathway data manipulation, processing, extraction and analysis. biocViews: GeneTarget, Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing, GeneTarget Author: Xiaoxi Dong*, Kovidh Vegesna*, Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/SBGNview VignetteBuilder: knitr BugReports: https://github.com/datapplab/SBGNview/issues git_url: https://git.bioconductor.org/packages/SBGNview git_branch: RELEASE_3_22 git_last_commit: b8c78f1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SBGNview_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SBGNview_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SBGNview_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SBGNview_1.24.0.tgz vignettes: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.html, vignettes/SBGNview/inst/doc/SBGNview.quick.start.html, vignettes/SBGNview/inst/doc/SBGNview.Vignette.html vignetteTitles: Pathway analysis using SBGNview gene set, Quick start SBGNview, SBGNview functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.R, vignettes/SBGNview/inst/doc/SBGNview.quick.start.R, vignettes/SBGNview/inst/doc/SBGNview.Vignette.R dependencyCount: 86 Package: SBMLR Version: 2.6.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: 8f9eaa66df0f88bb8a7fb090ae18d009 NeedsCompilation: no Title: SBML-R Interface and Analysis Tools Description: This package contains a systems biology markup language (SBML) interface to R. biocViews: GraphAndNetwork, Pathways, Network Author: Tomas Radivoyevitch, Vishak Venkateswaran Maintainer: Tomas Radivoyevitch URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html git_url: https://git.bioconductor.org/packages/SBMLR git_branch: RELEASE_3_22 git_last_commit: 1b8f18b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SBMLR_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SBMLR_2.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SBMLR_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SBMLR_2.6.0.tgz vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf vignetteTitles: Quick intro to SBMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBMLR/inst/doc/quick-start.R dependencyCount: 7 Package: SC3 Version: 1.38.0 Depends: R(>= 3.3) Imports: graphics, stats, utils, methods, e1071, parallel, foreach, doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8), ROCR, robustbase, rrcov, cluster, WriteXLS, Rcpp (>= 0.11.1), SummarizedExperiment, SingleCellExperiment, BiocGenerics, S4Vectors LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, mclust, scater, BiocStyle License: GPL-3 MD5sum: 144cd965a863ef7891ba2f469c428910 NeedsCompilation: yes Title: Single-Cell Consensus Clustering Description: A tool for unsupervised clustering and analysis of single cell RNA-Seq data. biocViews: ImmunoOncology, SingleCell, Software, Classification, Clustering, DimensionReduction, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, GUI, DifferentialExpression, Transcription Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/SC3 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/sc3/ git_url: https://git.bioconductor.org/packages/SC3 git_branch: RELEASE_3_22 git_last_commit: a9abb4a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SC3_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SC3_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SC3_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SC3_1.38.0.tgz vignettes: vignettes/SC3/inst/doc/SC3.html vignetteTitles: SC3 package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SC3/inst/doc/SC3.R importsMe: FEAST suggestsMe: InteractiveComplexHeatmap, scTreeViz, VAExprs dependencyCount: 92 Package: scafari Version: 1.0.0 Depends: R (>= 4.5.0) Imports: magrittr, shiny, shinycssloaders, DT, dplyr, waiter, ggplot2, tibble, stringr, reshape2, shinyjs, shinyBS, shinycustomloader, factoextra, markdown, plotly, ggbio, GenomicRanges, rhdf5, ComplexHeatmap, biomaRt, org.Hs.eg.db, SummarizedExperiment, SingleCellExperiment, S4Vectors, parallel, httr, jsonlite, scales, tidyr, txdbmaker, circlize, R.utils, dbscan, igraph, RANN Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: LGPL-3 MD5sum: 475d7a0ebee31f7bfb99672216a0e139 NeedsCompilation: no Title: Analysis of scDNA-seq data Description: Scafari is a Shiny application designed for the analysis of single-cell DNA sequencing (scDNA-seq) data provided in .h5 file format. The analysis process is structured into the four key steps "Sequencing", "Panel", "Variants", and "Explore Variants". It supports various analyses and visualizations. biocViews: Software, ShinyApps, SingleCell, Sequencing Author: Sophie Wind [aut, cre] (ORCID: ) Maintainer: Sophie Wind URL: https://github.com/sophiewind/scafari VignetteBuilder: knitr BugReports: https://github.com/sophiewind/scafari/issues git_url: https://git.bioconductor.org/packages/scafari git_branch: RELEASE_3_22 git_last_commit: 31af230 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scafari_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scafari_0.99.12.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scafari_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scafari_1.0.0.tgz vignettes: vignettes/scafari/inst/doc/scafari_vignette.html vignetteTitles: scafari_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scafari/inst/doc/scafari_vignette.R dependencyCount: 237 Package: Scale4C Version: 1.32.0 Depends: R (>= 3.5.0), smoothie, GenomicRanges, IRanges, SummarizedExperiment Imports: methods, grDevices, graphics, utils License: LGPL-3 MD5sum: 65f84f3f85f3ed8c32a3de763750e937 NeedsCompilation: no Title: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data Description: Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples. biocViews: Visualization, QualityControl, DataImport, Sequencing, Coverage Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Scale4C git_branch: RELEASE_3_22 git_last_commit: 6a8b6be git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Scale4C_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Scale4C_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Scale4C_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Scale4C_1.32.0.tgz vignettes: vignettes/Scale4C/inst/doc/vignette.pdf vignetteTitles: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Scale4C/inst/doc/vignette.R dependencyCount: 26 Package: ScaledMatrix Version: 1.18.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular, DelayedMatrixStats License: GPL-3 Archs: x64 MD5sum: 3a6e83465e35c036f7687e6216447d1a NeedsCompilation: no Title: Creating a DelayedMatrix of Scaled and Centered Values Description: Provides delayed computation of a matrix of scaled and centered values. The result is equivalent to using the scale() function but avoids explicit realization of a dense matrix during block processing. This permits greater efficiency in common operations, most notably matrix multiplication. biocViews: Software, DataRepresentation Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/ScaledMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/ScaledMatrix/issues git_url: https://git.bioconductor.org/packages/ScaledMatrix git_branch: RELEASE_3_22 git_last_commit: 2bcf86d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ScaledMatrix_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ScaledMatrix_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ScaledMatrix_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ScaledMatrix_1.18.0.tgz vignettes: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.html vignetteTitles: Using the ScaledMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.R importsMe: batchelor, BiocSingular, mumosa, scPCA suggestsMe: scran dependencyCount: 21 Package: SCAN.UPC Version: 2.52.0 Depends: R (>= 2.14.0), Biobase (>= 2.6.0), oligo, Biostrings, GEOquery, affy, affyio, foreach, sva Imports: utils, methods, MASS, tools, IRanges Suggests: pd.hg.u95a License: MIT MD5sum: 1365db0f1f73e73b39a85ccecf87a189 NeedsCompilation: no Title: Single-channel array normalization (SCAN) and Universal exPression Codes (UPC) Description: SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration. biocViews: ImmunoOncology, Software, Microarray, Preprocessing, RNASeq, TwoChannel, OneChannel Author: Stephen R. Piccolo and Andrea H. Bild and W. Evan Johnson Maintainer: Stephen R. Piccolo URL: http://bioconductor.org, http://jlab.bu.edu/software/scan-upc git_url: https://git.bioconductor.org/packages/SCAN.UPC git_branch: RELEASE_3_22 git_last_commit: 8c4e126 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SCAN.UPC_2.52.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCAN.UPC_2.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCAN.UPC_2.52.0.tgz vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.R dependencyCount: 116 Package: scanMiR Version: 1.16.0 Depends: R (>= 4.0) Imports: Biostrings, pwalign, GenomicRanges, IRanges, data.table, BiocParallel, methods, Seqinfo, S4Vectors, ggplot2, stats, stringi, utils, graphics, grid, seqLogo, cowplot Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: 3080a0d9d136a539d4f198a8f92c705f NeedsCompilation: no Title: scanMiR Description: A set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3' alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment). biocViews: miRNA, SequenceMatching, Alignment Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Michael Soutschek [aut], Fridolin Gross [aut] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiR git_branch: RELEASE_3_22 git_last_commit: fa9b00a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scanMiR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scanMiR_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scanMiR_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scanMiR_1.16.0.tgz vignettes: vignettes/scanMiR/inst/doc/Kdmodels.html, vignettes/scanMiR/inst/doc/scanning.html vignetteTitles: 2_Kdmodels, 1_scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scanMiR/inst/doc/Kdmodels.R, vignettes/scanMiR/inst/doc/scanning.R dependsOnMe: scanMiRApp importsMe: scanMiRData dependencyCount: 48 Package: scanMiRApp Version: 1.16.0 Depends: R (>= 4.0), scanMiR Imports: AnnotationDbi, AnnotationFilter, AnnotationHub, BiocParallel, Biostrings, data.table, digest, DT, ensembldb, fst, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, htmlwidgets, IRanges, Matrix, methods, plotly, rintrojs, rtracklayer, S4Vectors, scanMiRData, shiny, shinycssloaders, shinydashboard, shinyjqui, stats, utils, txdbmaker, waiter Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), shinytest, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-3 MD5sum: f85cd714bc4ae6f6eefd6af47cd6395b NeedsCompilation: no Title: scanMiR shiny application Description: A shiny interface to the scanMiR package. The application enables the scanning of transcripts and custom sequences for miRNA binding sites, the visualization of KdModels and binding results, as well as browsing predicted repression data. In addition contains the IndexedFst class for fast indexed reading of large GenomicRanges or data.frames, and some utilities for facilitating scans and identifying enriched miRNA-target pairs. biocViews: miRNA, SequenceMatching, GUI, ShinyApps Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Michael Soutschek [aut], Fridolin Gross [ctb] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiRApp git_branch: RELEASE_3_22 git_last_commit: bd87b89 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scanMiRApp_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scanMiRApp_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scanMiRApp_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scanMiRApp_1.16.0.tgz vignettes: vignettes/scanMiRApp/inst/doc/IndexedFST.html, vignettes/scanMiRApp/inst/doc/scanMiRApp.html vignetteTitles: IndexedFst, scanMiRApp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scanMiRApp/inst/doc/IndexedFST.R, vignettes/scanMiRApp/inst/doc/scanMiRApp.R dependencyCount: 157 Package: scAnnotatR Version: 1.16.0 Depends: R (>= 4.1), Seurat, SingleCellExperiment, SummarizedExperiment Imports: dplyr, ggplot2, caret, ROCR, pROC, data.tree, methods, stats, e1071, ape, kernlab, AnnotationHub, utils Suggests: knitr, rmarkdown, scRNAseq, testthat License: MIT + file LICENSE MD5sum: 33d8df90b18f3809cabdb866d8890c3d NeedsCompilation: no Title: Pretrained learning models for cell type prediction on single cell RNA-sequencing data Description: The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs. biocViews: SingleCell, Transcriptomics, GeneExpression, SupportVectorMachine, Classification, Software Author: Vy Nguyen [aut] (ORCID: ), Johannes Griss [cre] (ORCID: ) Maintainer: Johannes Griss URL: https://github.com/grisslab/scAnnotatR VignetteBuilder: knitr BugReports: https://github.com/grisslab/scAnnotatR/issues/new git_url: https://git.bioconductor.org/packages/scAnnotatR git_branch: RELEASE_3_22 git_last_commit: ebbbfcb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scAnnotatR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scAnnotatR_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scAnnotatR_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scAnnotatR_1.16.0.tgz vignettes: vignettes/scAnnotatR/inst/doc/classifying-cells.html, vignettes/scAnnotatR/inst/doc/training-basic-model.html, vignettes/scAnnotatR/inst/doc/training-child-model.html vignetteTitles: 1. Introduction to scAnnotatR, 2. Training basic model, 3. Training child model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scAnnotatR/inst/doc/classifying-cells.R, vignettes/scAnnotatR/inst/doc/training-basic-model.R, vignettes/scAnnotatR/inst/doc/training-child-model.R suggestsMe: scAnnotatR.models dependencyCount: 213 Package: SCANVIS Version: 1.23.0 Depends: R (>= 3.6) Imports: IRanges,plotrix,RCurl,rtracklayer Suggests: knitr, rmarkdown License: file LICENSE MD5sum: b53d2ceab450cdebeac2a93665b88320 NeedsCompilation: no Title: SCANVIS - a tool for SCoring, ANnotating and VISualizing splice junctions Description: SCANVIS is a set of annotation-dependent tools for analyzing splice junctions and their read support as predetermined by an alignment tool of choice (for example, STAR aligner). SCANVIS assesses each junction's relative read support (RRS) by relating to the context of local split reads aligning to annotated transcripts. SCANVIS also annotates each splice junction by indicating whether the junction is supported by annotation or not, and if not, what type of junction it is (e.g. exon skipping, alternative 5' or 3' events, Novel Exons). Unannotated junctions are also futher annotated by indicating whether it induces a frame shift or not. SCANVIS includes a visualization function to generate static sashimi-style plots depicting relative read support and number of split reads using arc thickness and arc heights, making it easy for users to spot well-supported junctions. These plots also clearly delineate unannotated junctions from annotated ones using designated color schemes, and users can also highlight splice junctions of choice. Variants and/or a read profile are also incoroporated into the plot if the user supplies variants in bed format and/or the BAM file. One further feature of the visualization function is that users can submit multiple samples of a certain disease or cohort to generate a single plot - this occurs via a "merge" function wherein junction details over multiple samples are merged to generate a single sashimi plot, which is useful when contrasting cohorots (eg. disease vs control). biocViews: Software,ResearchField,Transcriptomics,WorkflowStep,Annotation,Visualization Author: Phaedra Agius Maintainer: Phaedra Agius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCANVIS git_branch: devel git_last_commit: f253afc git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-08 source.ver: src/contrib/SCANVIS_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCANVIS_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCANVIS_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCANVIS_1.23.0.tgz vignettes: vignettes/SCANVIS/inst/doc/runningSCANVIS.pdf vignetteTitles: SCANVIS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SCANVIS/inst/doc/runningSCANVIS.R dependencyCount: 58 Package: SCArray Version: 1.18.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.36.0), methods, DelayedArray (>= 0.31.5) Imports: S4Vectors, utils, Matrix, SparseArray (>= 1.5.13), BiocParallel, DelayedMatrixStats, SummarizedExperiment, SingleCellExperiment, BiocSingular Suggests: BiocGenerics, scater, scuttle, uwot, RUnit, knitr, markdown, rmarkdown, rhdf5, HDF5Array License: GPL-3 Archs: x64 MD5sum: 2c9d7e5ca71397e5b137d501fa6d59a3 NeedsCompilation: yes Title: Large-scale single-cell omics data manipulation with GDS files Description: Provides large-scale single-cell omics data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language. biocViews: Infrastructure, DataRepresentation, DataImport, SingleCell, RNASeq Author: Xiuwen Zheng [aut, cre] (ORCID: ) Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SCArray VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCArray git_branch: RELEASE_3_22 git_last_commit: f7d874a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SCArray_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCArray_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCArray_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCArray_1.18.0.tgz vignettes: vignettes/SCArray/inst/doc/Overview.html, vignettes/SCArray/inst/doc/SCArray.html vignetteTitles: Overview, Single-cell RNA-seq data manipulation using GDS files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCArray/inst/doc/SCArray.R dependsOnMe: SCArray.sat dependencyCount: 46 Package: SCArray.sat Version: 1.9.0 Depends: methods, SCArray (>= 1.13.1), SeuratObject (>= 5.0), Seurat (>= 5.0) Imports: S4Vectors, utils, stats, BiocGenerics, BiocParallel, gdsfmt, DelayedArray, BiocSingular, SummarizedExperiment, Matrix Suggests: future, RUnit, knitr, markdown, rmarkdown, BiocStyle License: GPL-3 MD5sum: a29d06ebcb9a54982a9f22409f786a99 NeedsCompilation: no Title: Large-scale single-cell RNA-seq data analysis using GDS files and Seurat Description: Extends the Seurat classes and functions to support Genomic Data Structure (GDS) files as a DelayedArray backend for data representation. It relies on the implementation of GDS-based DelayedMatrix in the SCArray package to represent single cell RNA-seq data. The common optimized algorithms leveraging GDS-based and single cell-specific DelayedMatrix (SC_GDSMatrix) are implemented in the SCArray package. SCArray.sat introduces a new SCArrayAssay class (derived from the Seurat Assay), which wraps raw counts, normalized expressions and scaled data matrix based on GDS-specific DelayedMatrix. It is designed to integrate seamlessly with the Seurat package to provide common data analysis in the SeuratObject-based workflow. Compared with Seurat, SCArray.sat significantly reduces the memory usage without downsampling and can be applied to very large datasets. biocViews: DataRepresentation, DataImport, SingleCell, RNASeq Author: Xiuwen Zheng [aut, cre] (ORCID: ), Seurat contributors [ctb] (for the classes and methods defined in Seurat) Maintainer: Xiuwen Zheng VignetteBuilder: knitr BugReports: https://github.com/AbbVie-ComputationalGenomics/SCArray/issues git_url: https://git.bioconductor.org/packages/SCArray.sat git_branch: devel git_last_commit: cb8ac58 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/SCArray.sat_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCArray.sat_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCArray.sat_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCArray.sat_1.9.0.tgz vignettes: vignettes/SCArray.sat/inst/doc/SCArray.sat.html vignetteTitles: scRNA-seq data analysis with GDS files and Seurat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCArray.sat/inst/doc/SCArray.sat.R dependencyCount: 181 Package: scater Version: 1.38.0 Depends: SingleCellExperiment, scuttle, ggplot2 Imports: stats, utils, methods, Matrix, BiocGenerics, S4Vectors, SummarizedExperiment, MatrixGenerics, SparseArray, DelayedArray, beachmat, BiocNeighbors, BiocSingular, BiocParallel, rlang, ggbeeswarm, viridis, Rtsne, RColorBrewer, RcppML, uwot, pheatmap, ggrepel, ggrastr Suggests: BiocStyle, DelayedMatrixStats, snifter, densvis, cowplot, biomaRt, knitr, scRNAseq, robustbase, rmarkdown, testthat, Biobase, scattermore License: GPL-3 MD5sum: 913da599d06187e6f3de73f5a049ae21 NeedsCompilation: no Title: Single-Cell Analysis Toolkit for Gene Expression Data in R Description: A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage Author: Davis McCarthy [aut], Kieran Campbell [aut], Aaron Lun [aut, ctb], Quin Wills [aut], Vladimir Kiselev [ctb], Felix G.M. Ernst [ctb], Alan O'Callaghan [ctb, cre], Yun Peng [ctb], Leo Lahti [ctb] (ORCID: ), Tuomas Borman [ctb] (ORCID: ) Maintainer: Alan O'Callaghan URL: http://bioconductor.org/packages/scater/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/scater git_branch: RELEASE_3_22 git_last_commit: 64e2b5e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scater_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scater_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scater_1.38.0.tgz vignettes: vignettes/scater/inst/doc/overview.html vignetteTitles: Overview of scater functionality hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scater/inst/doc/overview.R dependsOnMe: chevreulProcess, netSmooth, omicsGMF, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: airpart, BayesSpace, blase, CATALYST, celda, CelliD, CellMixS, chevreulPlot, ChromSCape, clustSIGNAL, decontX, distinct, epiregulon.extra, FLAMES, M3Drop, MEB, mia, miaDash, miaViz, muscat, peco, pipeComp, RegionalST, scDblFinder, scDotPlot, scMerge, scTreeViz, scviR, shinyDSP, singleCellTK, SpaceTrooper, Spaniel, tricycle, VAExprs, DoReMiTra, spatialLIBD, OSTA, PRECAST suggestsMe: alabaster.sfe, anglemania, APL, Banksy, batchelor, bluster, ccImpute, CellTrails, Cepo, CiteFuse, concordexR, Coralysis, corral, crumblr, dittoSeq, DOtools, dreamlet, epiregulon, escheR, ExperimentSubset, ggsc, ggspavis, Glimma, HoloFoodR, HVP, Ibex, InteractiveComplexHeatmap, iSEE, iSEEfier, iSEEhex, iSEEpathways, iSEEtree, iSEEu, jazzPanda, MAST, mbkmeans, MGnifyR, miaTime, miloR, miQC, monocle, MOSim, MuData, mumosa, Nebulosa, raer, ReactomeGSA, SanityR, SC3, SCArray, scDiagnostics, scds, scGraphVerse, schex, scHOT, scLANE, scone, scp, scPipe, scran, scRepertoire, simPIC, SingleCellAlleleExperiment, sketchR, slalom, smartid, smoothclust, SpatialFeatureExperiment, speckle, splatter, SPOTlight, StabMap, standR, SuperCellCyto, SVP, tidySingleCellExperiment, tidySpatialExperiment, UCell, velociraptor, Voyager, waddR, curatedMetagenomicData, DuoClustering2018, HCAData, HCATonsilData, MerfishData, MouseAgingData, muscData, SingleCellMultiModal, TabulaMurisData, tuberculosis, simpleSingleCell, spicyWorkflow, Canek, ProFAST, SCdeconR, scellpam, SuperCell dependencyCount: 87 Package: scatterHatch Version: 1.16.0 Depends: R (>= 4.1) Imports: grid, ggplot2, plyr, spatstat.geom, stats, grDevices Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 60381d17cbd1948f70c1bd0788ca701e NeedsCompilation: no Title: Creates hatched patterns for scatterplots Description: The objective of this package is to efficiently create scatterplots where groups can be distinguished by color and texture. Visualizations in computational biology tend to have many groups making it difficult to distinguish between groups solely on color. Thus, this package is useful for increasing the accessibility of scatterplot visualizations to those with visual impairments such as color blindness. biocViews: Visualization, SingleCell, CellBiology, Software, Spatial Author: Atul Deshpande [aut, cre] (ORCID: ) Maintainer: Atul Deshpande URL: https://github.com/FertigLab/scatterHatch VignetteBuilder: knitr BugReports: https://github.com/FertigLab/scatterHatch/issues git_url: https://git.bioconductor.org/packages/scatterHatch git_branch: RELEASE_3_22 git_last_commit: b0c4185 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scatterHatch_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scatterHatch_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scatterHatch_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scatterHatch_1.16.0.tgz vignettes: vignettes/scatterHatch/inst/doc/vignette.html vignetteTitles: Creating a Scatterplot with Texture hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scatterHatch/inst/doc/vignette.R dependencyCount: 32 Package: scBFA Version: 1.24.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, Seurat, MASS, zinbwave, stats, copula, ggplot2, DESeq2, utils, grid, methods, Matrix Suggests: knitr, rmarkdown, testthat, Rtsne License: GPL-3 + file LICENSE MD5sum: 0f35d99977d537973edf5c4715738567 NeedsCompilation: no Title: A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq Description: This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis. biocViews: SingleCell, Transcriptomics, DimensionReduction,GeneExpression, ATACSeq, BatchEffect, KEGG, QualityControl Author: Ruoxin Li [aut, cre], Gerald Quon [aut] Maintainer: Ruoxin Li URL: https://github.com/ucdavis/quon-titative-biology/BFA VignetteBuilder: knitr BugReports: https://github.com/ucdavis/quon-titative-biology/BFA/issues git_url: https://git.bioconductor.org/packages/scBFA git_branch: RELEASE_3_22 git_last_commit: f8bf5ba git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scBFA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scBFA_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scBFA_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scBFA_1.24.0.tgz vignettes: vignettes/scBFA/inst/doc/vignette.html vignetteTitles: Gene Detection Analysis for scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scBFA/inst/doc/vignette.R dependencyCount: 201 Package: SCBN Version: 1.28.0 Depends: R (>= 3.5.0) Imports: stats Suggests: knitr,rmarkdown,BiocStyle,BiocManager License: GPL-2 MD5sum: 5bf854e32898d50cdc4d9207535b1509 NeedsCompilation: no Title: A statistical normalization method and differential expression analysis for RNA-seq data between different species Description: This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article 'A statistical normalization method and differential expression analysis for RNA-seq data between different species' by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication). biocViews: DifferentialExpression, GeneExpression, Normalization Author: Yan Zhou Maintainer: Yan Zhou <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCBN git_branch: RELEASE_3_22 git_last_commit: cdc443e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SCBN_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCBN_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCBN_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCBN_1.28.0.tgz vignettes: vignettes/SCBN/inst/doc/SCBN.html vignetteTitles: SCBN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCBN/inst/doc/SCBN.R importsMe: TEKRABber dependencyCount: 1 Package: scBubbletree Version: 1.11.0 Depends: R (>= 4.2.0) Imports: reshape2, BiocParallel, ape, scales, Seurat, ggplot2, ggtree, patchwork, proxy, methods, stats, base, utils, dplyr Suggests: BiocStyle, knitr, testthat, cluster, SingleCellExperiment License: GPL-3 + file LICENSE MD5sum: a69498e29f9a70a16677276c54912fc5 NeedsCompilation: no Title: Quantitative visual exploration of scRNA-seq data Description: scBubbletree is a quantitative method for the visual exploration of scRNA-seq data, preserving key biological properties such as local and global cell distances and cell density distributions across samples. It effectively resolves overplotting and enables the visualization of diverse cell attributes from multiomic single-cell experiments. Additionally, scBubbletree is user-friendly and integrates seamlessly with popular scRNA-seq analysis tools, facilitating comprehensive and intuitive data interpretation. biocViews: Visualization,Clustering, SingleCell,Transcriptomics,RNASeq Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski URL: https://github.com/snaketron/scBubbletree SystemRequirements: Python (>= 3.6), leidenalg (>= 0.8.2) VignetteBuilder: knitr BugReports: https://github.com/snaketron/scBubbletree/issues git_url: https://git.bioconductor.org/packages/scBubbletree git_branch: devel git_last_commit: b55a5b7 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/scBubbletree_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scBubbletree_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scBubbletree_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scBubbletree_1.11.0.tgz vignettes: vignettes/scBubbletree/inst/doc/User_manual.html vignetteTitles: User Manual: scBubbletree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scBubbletree/inst/doc/User_manual.R dependencyCount: 175 Package: scCB2 Version: 1.20.0 Depends: R (>= 3.6.0) Imports: SingleCellExperiment, SummarizedExperiment, Matrix, methods, utils, stats, edgeR, rhdf5, parallel, DropletUtils, doParallel, iterators, foreach, Seurat Suggests: testthat (>= 2.1.0), KernSmooth, beachmat, knitr, BiocStyle, rmarkdown License: GPL-3 Archs: x64 MD5sum: ca2a7f7d446ea4519cb658544bd89cc9 NeedsCompilation: yes Title: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data Description: scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled. biocViews: DataImport, RNASeq, SingleCell, Sequencing, GeneExpression, Transcriptomics, Preprocessing, Clustering Author: Zijian Ni [aut, cre], Shuyang Chen [ctb], Christina Kendziorski [ctb] Maintainer: Zijian Ni URL: https://github.com/zijianni/scCB2 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/zijianni/scCB2/issues git_url: https://git.bioconductor.org/packages/scCB2 git_branch: RELEASE_3_22 git_last_commit: 16b341e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scCB2_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scCB2_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scCB2_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scCB2_1.20.0.tgz vignettes: vignettes/scCB2/inst/doc/scCB2.html vignetteTitles: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scCB2/inst/doc/scCB2.R dependencyCount: 193 Package: scClassify Version: 1.22.0 Depends: R (>= 4.0) Imports: S4Vectors, limma, ggraph, igraph, methods, cluster, minpack.lm, mixtools, BiocParallel, proxy, proxyC, Matrix, ggplot2, hopach, diptest, mgcv, stats, graphics, statmod, Cepo Suggests: knitr, rmarkdown, BiocStyle, pkgdown License: GPL-3 MD5sum: ab6a829b02c74366aeef2337b813ff3c NeedsCompilation: no Title: scClassify: single-cell Hierarchical Classification Description: scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references. biocViews: SingleCell, GeneExpression, Classification Author: Yingxin Lin Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scClassify/issues git_url: https://git.bioconductor.org/packages/scClassify git_branch: RELEASE_3_22 git_last_commit: b85cf96 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scClassify_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scClassify_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scClassify_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scClassify_1.22.0.tgz vignettes: vignettes/scClassify/inst/doc/pretrainedModel.html, vignettes/scClassify/inst/doc/scClassify.html vignetteTitles: pretrainedModel, scClassify hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scClassify/inst/doc/pretrainedModel.R, vignettes/scClassify/inst/doc/scClassify.R dependencyCount: 153 Package: sccomp Version: 2.2.0 Depends: R (>= 4.3.0), instantiate (>= 0.2.3) Imports: stats, boot, utils, scales, lifecycle, rlang, tidyselect, magrittr, crayon, cli, fansi, dplyr, tidyr, purrr, tibble, ggplot2, ggrepel, patchwork, forcats, readr, stringr, glue, SingleCellExperiment Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), markdown, loo, prettydoc, SeuratObject, tidyseurat, tidySingleCellExperiment, bayesplot, posterior License: GPL-3 MD5sum: b3b8edc015140554b004cead29d3a764 NeedsCompilation: no Title: Differential Composition and Variability Analysis for Single-Cell Data Description: Comprehensive R package for differential composition and variability analysis in single-cell RNA sequencing, CyTOF, and microbiome data. Provides robust Bayesian modeling with outlier detection, random effects, and advanced statistical methods for cell type proportion analysis. Features include probabilistic outlier identification, mixed-effect modeling, differential variability testing, and comprehensive visualization tools. Perfect for cancer research, immunology, developmental biology, and single-cell genomics applications. biocViews: Bayesian, Regression, DifferentialExpression, SingleCell, Metagenomics, FlowCytometry, Spatial Author: Stefano Mangiola [aut, cre], Alexandra J. Roth-Schulze [aut], Marie Trussart [aut], Enrique Zozaya-Valdés [aut], Mengyao Ma [aut], Zijie Gao [aut], Alan F. Rubin [aut], Terence P. Speed [aut], Heejung Shim [aut], Anthony T. Papenfuss [aut] Maintainer: Stefano Mangiola URL: https://github.com/MangiolaLaboratory/sccomp SystemRequirements: CmdStan (https://mc-stan.org/users/interfaces/cmdstan), C++14 VignetteBuilder: knitr BugReports: https://github.com/MangiolaLaboratory/sccomp/issues git_url: https://git.bioconductor.org/packages/sccomp git_branch: RELEASE_3_22 git_last_commit: a805110 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sccomp_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sccomp_2.1.16.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sccomp_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sccomp_2.2.0.tgz vignettes: vignettes/sccomp/inst/doc/introduction.html vignetteTitles: sccomp: Differential Composition and Variability Analysis for Single-Cell Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sccomp/inst/doc/introduction.R dependencyCount: 75 Package: scDataviz Version: 1.20.0 Depends: R (>= 4.0), S4Vectors, SingleCellExperiment, Imports: ggplot2, ggrepel, flowCore, umap, Seurat, reshape2, scales, RColorBrewer, corrplot, stats, grDevices, graphics, utils, MASS, matrixStats, methods Suggests: PCAtools, cowplot, BiocGenerics, RUnit, knitr, kableExtra, rmarkdown License: GPL-3 Archs: x64 MD5sum: 6aad77038bd307cfbd2766d365832d52 NeedsCompilation: no Title: scDataviz: single cell dataviz and downstream analyses Description: In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a 'plug and play' feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can 'add on' features to these with ease. biocViews: SingleCell, ImmunoOncology, RNASeq, GeneExpression, Transcription, FlowCytometry, MassSpectrometry, DataImport Author: Kevin Blighe [aut, cre] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/scDataviz VignetteBuilder: knitr BugReports: https://github.com/kevinblighe/scDataviz/issues git_url: https://git.bioconductor.org/packages/scDataviz git_branch: RELEASE_3_22 git_last_commit: 6f0efdb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scDataviz_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scDataviz_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDataviz_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDataviz_1.20.0.tgz vignettes: vignettes/scDataviz/inst/doc/scDataviz.html vignetteTitles: scDataviz: single cell dataviz and downstream analyses hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDataviz/inst/doc/scDataviz.R dependencyCount: 172 Package: scDblFinder Version: 1.24.0 Depends: R (>= 4.0), SingleCellExperiment Imports: igraph, Matrix, BiocGenerics, BiocParallel, BiocNeighbors, BiocSingular, S4Vectors, SummarizedExperiment, scran, scater, scuttle, bluster, methods, DelayedArray, xgboost, stats, utils, MASS, IRanges, GenomicRanges, GenomeInfoDb, Rsamtools, rtracklayer Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, circlize, ComplexHeatmap, ggplot2, dplyr, viridisLite, mbkmeans License: GPL-3 + file LICENSE MD5sum: b0eb19c1043b6cfc23c534dc23d87156 NeedsCompilation: no Title: scDblFinder Description: The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, the new fast and comprehensive scDblFinder method, and a reimplementation of the Amulet detection method for single-cell ATAC-seq. biocViews: Preprocessing, SingleCell, RNASeq, ATACSeq Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Aaron Lun [ctb] Maintainer: Pierre-Luc Germain URL: https://github.com/plger/scDblFinder, https://plger.github.io/scDblFinder/ VignetteBuilder: knitr BugReports: https://github.com/plger/scDblFinder/issues git_url: https://git.bioconductor.org/packages/scDblFinder git_branch: RELEASE_3_22 git_last_commit: 0336756 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scDblFinder_1.24.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDblFinder_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDblFinder_1.24.0.tgz vignettes: vignettes/scDblFinder/inst/doc/computeDoubletDensity.html, vignettes/scDblFinder/inst/doc/findDoubletClusters.html, vignettes/scDblFinder/inst/doc/introduction.html, vignettes/scDblFinder/inst/doc/recoverDoublets.html, vignettes/scDblFinder/inst/doc/scATAC.html, vignettes/scDblFinder/inst/doc/scDblFinder.html vignetteTitles: 4_computeDoubletDensity, 3_findDoubletClusters, 1_introduction, 5_recoverDoublets, 6_scATAC, 2_scDblFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scDblFinder/inst/doc/computeDoubletDensity.R, vignettes/scDblFinder/inst/doc/findDoubletClusters.R, vignettes/scDblFinder/inst/doc/introduction.R, vignettes/scDblFinder/inst/doc/recoverDoublets.R, vignettes/scDblFinder/inst/doc/scATAC.R, vignettes/scDblFinder/inst/doc/scDblFinder.R dependsOnMe: OSCA.advanced importsMe: DOtools, singleCellTK dependencyCount: 124 Package: scDD Version: 1.34.0 Depends: R (>= 3.5.0) Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran Suggests: BiocStyle, knitr, gridExtra License: GPL-2 MD5sum: 12220348f699564f3847a52dd1535b7f NeedsCompilation: yes Title: Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions Description: This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions. biocViews: ImmunoOncology, Bayesian, Clustering, RNASeq, SingleCell, MultipleComparison, Visualization, DifferentialExpression Author: Keegan Korthauer [cre, aut] (ORCID: ) Maintainer: Keegan Korthauer URL: https://github.com/kdkorthauer/scDD VignetteBuilder: knitr BugReports: https://github.com/kdkorthauer/scDD/issues git_url: https://git.bioconductor.org/packages/scDD git_branch: RELEASE_3_22 git_last_commit: 924aec4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scDD_1.34.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDD_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDD_1.34.0.tgz vignettes: vignettes/scDD/inst/doc/scDD.pdf vignetteTitles: scDD Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDD/inst/doc/scDD.R suggestsMe: splatter dependencyCount: 117 Package: scDDboost Version: 1.12.0 Depends: R (>= 4.2), ggplot2 Imports: Rcpp (>= 0.12.11), RcppEigen (>= 0.3.2.9.0),EBSeq, BiocParallel, mclust, SingleCellExperiment, cluster, Oscope, SummarizedExperiment, stats, methods LinkingTo: Rcpp, RcppEigen, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: 183a5313a59539b63ba1ec63a53dd431 NeedsCompilation: yes Title: A compositional model to assess expression changes from single-cell rna-seq data Description: scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions. biocViews: SingleCell, Software, Clustering, Sequencing, GeneExpression, DifferentialExpression, Bayesian Author: Xiuyu Ma [cre, aut], Michael A. Newton [ctb] Maintainer: Xiuyu Ma URL: https://github.com/wiscstatman/scDDboost SystemRequirements: c++14 VignetteBuilder: knitr BugReports: https://github.com/wiscstatman/scDDboost/issues git_url: https://git.bioconductor.org/packages/scDDboost git_branch: RELEASE_3_22 git_last_commit: 2aff65c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scDDboost_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scDDboost_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDDboost_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDDboost_1.12.0.tgz vignettes: vignettes/scDDboost/inst/doc/scDDboost.html vignetteTitles: scDDboost Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDDboost/inst/doc/scDDboost.R dependencyCount: 82 Package: scde Version: 2.38.0 Depends: R (>= 3.0.0), flexmix Imports: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook, rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods, nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, cba, fastcluster, WGCNA, GO.db, org.Hs.eg.db, rmarkdown License: GPL-2 Archs: x64 MD5sum: cd1adc922007985e4c2362a5e52ce536 NeedsCompilation: yes Title: Single Cell Differential Expression Description: The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734). biocViews: ImmunoOncology, RNASeq, StatisticalMethod, DifferentialExpression, Bayesian, Transcription, Software Author: Peter Kharchenko [aut, cre], Jean Fan [aut], Evan Biederstedt [aut] Maintainer: Evan Biederstedt URL: http://pklab.med.harvard.edu/scde VignetteBuilder: knitr BugReports: https://github.com/hms-dbmi/scde/issues git_url: https://git.bioconductor.org/packages/scde git_branch: RELEASE_3_22 git_last_commit: fc608d5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scde_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scde_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scde_2.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scde_2.38.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: pagoda2 dependencyCount: 50 Package: scDesign3 Version: 1.8.0 Depends: R (>= 4.3.0) Imports: dplyr, tibble, stats, methods, mgcv, gamlss, gamlss.dist, SummarizedExperiment, SingleCellExperiment, mclust, mvtnorm, parallel, pbmcapply, umap, ggplot2, irlba, viridis, BiocParallel, matrixStats, Matrix, sparseMVN, coop Suggests: mvnfast, igraph, rvinecopulib, knitr, rmarkdown, testthat (>= 3.0.0), RefManageR, sessioninfo, BiocStyle License: MIT + file LICENSE MD5sum: f29fc18e792b67d65800c0783139a6ab NeedsCompilation: no Title: A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics Description: We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools. biocViews: Software, SingleCell, Sequencing, GeneExpression, Spatial Author: Dongyuan Song [aut, cre] (ORCID: ), Qingyang Wang [aut] (ORCID: ), Chenxin Jiang [aut] (ORCID: ) Maintainer: Dongyuan Song URL: https://github.com/SONGDONGYUAN1994/scDesign3 VignetteBuilder: knitr BugReports: https://github.com/SONGDONGYUAN1994/scDesign3/issues git_url: https://git.bioconductor.org/packages/scDesign3 git_branch: RELEASE_3_22 git_last_commit: 47185c0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scDesign3_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scDesign3_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDesign3_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDesign3_1.8.0.tgz vignettes: vignettes/scDesign3/inst/doc/scDesign3.html vignetteTitles: scDesign3-quickstart-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scDesign3/inst/doc/scDesign3.R dependencyCount: 89 Package: scDiagnostics Version: 1.4.0 Depends: R (>= 4.4.0) Imports: SingleCellExperiment, methods, isotree, FNN, igraph, ggplot2, GGally, ggridges, SummarizedExperiment, ranger, transport, cramer, rlang, bluster, scales, MASS, stringr, Matrix, grDevices Suggests: AUCell, BiocStyle, knitr, rmarkdown, scran, scRNAseq, SingleR, celldex, scuttle, scater, dplyr, ComplexHeatmap, grid, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 1559a795b0bd0949579398147160348e NeedsCompilation: no Title: Cell type annotation diagnostics Description: The scDiagnostics package provides diagnostic plots to assess the quality of cell type assignments from single cell gene expression profiles. The implemented functionality allows to assess the reliability of cell type annotations, investigate gene expression patterns, and explore relationships between different cell types in query and reference datasets allowing users to detect potential misalignments between reference and query datasets. The package also provides visualization capabilities for diagnostics purposes. biocViews: Annotation, Classification, Clustering, GeneExpression, RNASeq, SingleCell, Software, Transcriptomics Author: Anthony Christidis [aut, cre] (ORCID: ), Andrew Ghazi [aut], Smriti Chawla [aut], Nitesh Turaga [ctb], Ludwig Geistlinger [aut], Robert Gentleman [aut] Maintainer: Anthony Christidis URL: https://github.com/ccb-hms/scDiagnostics VignetteBuilder: knitr BugReports: https://github.com/ccb-hms/scDiagnostics/issues git_url: https://git.bioconductor.org/packages/scDiagnostics git_branch: RELEASE_3_22 git_last_commit: 7dca061 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scDiagnostics_1.4.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDiagnostics_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDiagnostics_1.4.0.tgz vignettes: vignettes/scDiagnostics/inst/doc/AnnotationAnomalies.html, vignettes/scDiagnostics/inst/doc/DatasetMarkerGeneAlignment.html, vignettes/scDiagnostics/inst/doc/scDiagnostics.html, vignettes/scDiagnostics/inst/doc/VisualizationTools.html vignetteTitles: 4. Detection and Analysis of Annotation Anomalies, 3. Evaluation of Dataset and Marker Gene Alignment, 1. Getting Started with scDiagnostics, 2. Visualization of Cell Type Annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDiagnostics/inst/doc/AnnotationAnomalies.R, vignettes/scDiagnostics/inst/doc/DatasetMarkerGeneAlignment.R, vignettes/scDiagnostics/inst/doc/scDiagnostics.R, vignettes/scDiagnostics/inst/doc/VisualizationTools.R dependencyCount: 89 Package: scDotPlot Version: 1.3.1 Depends: R (>= 4.4.0) Imports: aplot, BiocGenerics, cli, dplyr, ggplot2, ggsci, ggtree, grDevices, magrittr, purrr, rlang, scales, scater, Seurat, SingleCellExperiment, stats, stringr, tibble, tidyr Suggests: AnnotationDbi, BiocStyle, knitr, rmarkdown, scran, scRNAseq, scuttle, SeuratObject, testthat, vdiffr License: Artistic-2.0 Archs: x64 MD5sum: 95ecd162519f0515805bcc5f5449c5b4 NeedsCompilation: no Title: Cluster a Single-cell RNA-seq Dot Plot Description: Dot plots of single-cell RNA-seq data allow for an examination of the relationships between cell groupings (e.g. clusters) and marker gene expression. The scDotPlot package offers a unified approach to perform a hierarchical clustering analysis and add annotations to the columns and/or rows of a scRNA-seq dot plot. It works with SingleCellExperiment and Seurat objects as well as data frames. biocViews: Software, Visualization, DifferentialExpression, GeneExpression, Transcription, RNASeq, SingleCell, Sequencing, Clustering Author: Benjamin I Laufer [aut, cre], Brad A Friedman [aut] Maintainer: Benjamin I Laufer URL: https://github.com/ben-laufer/scDotPlot VignetteBuilder: knitr BugReports: https://github.com/ben-laufer/scDotPlot/issues git_url: https://git.bioconductor.org/packages/scDotPlot git_branch: devel git_last_commit: 4db1f01 git_last_commit_date: 2025-07-10 Date/Publication: 2025-10-07 source.ver: src/contrib/scDotPlot_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/scDotPlot_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDotPlot_1.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDotPlot_1.3.1.tgz vignettes: vignettes/scDotPlot/inst/doc/scDotPlot.html vignetteTitles: scDotPlot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDotPlot/inst/doc/scDotPlot.R dependencyCount: 206 Package: scds Version: 1.26.0 Depends: R (>= 3.6.0) Imports: Matrix, S4Vectors, SingleCellExperiment, SummarizedExperiment, xgboost, methods, stats, dplyr, pROC Suggests: BiocStyle, knitr, rsvd, Rtsne, scater, cowplot, rmarkdown License: MIT + file LICENSE MD5sum: 46f5f31829bf0bbb8efcb7dd9e14da28 NeedsCompilation: no Title: In-Silico Annotation of Doublets for Single Cell RNA Sequencing Data Description: In single cell RNA sequencing (scRNA-seq) data combinations of cells are sometimes considered a single cell (doublets). The scds package provides methods to annotate doublets in scRNA-seq data computationally. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Transcriptomics, GeneExpression, Sequencing, Software, Classification Author: Dennis Kostka [aut, cre], Bais Abha [aut] Maintainer: Dennis Kostka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scds git_branch: RELEASE_3_22 git_last_commit: 9b35025 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scds_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scds_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scds_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scds_1.26.0.tgz vignettes: vignettes/scds/inst/doc/scds.html vignetteTitles: Introduction to the scds package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scds/inst/doc/scds.R importsMe: singleCellTK suggestsMe: ExperimentSubset, muscData dependencyCount: 45 Package: SCFA Version: 1.20.0 Depends: R (>= 4.0) Imports: matrixStats, BiocParallel, torch (>= 0.3.0), coro, igraph, Matrix, cluster, psych, glmnet, RhpcBLASctl, stats, utils, methods, survival Suggests: knitr, rmarkdown, BiocStyle License: LGPL MD5sum: 90f1cf82e9e0790c2f59b6f814579450 NeedsCompilation: no Title: SCFA: Subtyping via Consensus Factor Analysis Description: Subtyping via Consensus Factor Analysis (SCFA) can efficiently remove noisy signals from consistent molecular patterns in multi-omics data. SCFA first uses an autoencoder to select only important features and then repeatedly performs factor analysis to represent the data with different numbers of factors. Using these representations, it can reliably identify cancer subtypes and accurately predict risk scores of patients. biocViews: Survival, Clustering, Classification Author: Duc Tran [aut, cre], Hung Nguyen [aut], Tin Nguyen [fnd] Maintainer: Duc Tran URL: https://github.com/duct317/SCFA VignetteBuilder: knitr BugReports: https://github.com/duct317/SCFA/issues git_url: https://git.bioconductor.org/packages/SCFA git_branch: RELEASE_3_22 git_last_commit: 00e97f9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SCFA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCFA_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCFA_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCFA_1.20.0.tgz vignettes: vignettes/SCFA/inst/doc/Example.html vignetteTitles: SCFA package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCFA/inst/doc/Example.R dependencyCount: 59 Package: scFeatureFilter Version: 1.30.0 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.3), ggplot2 (>= 2.1.0), magrittr (>= 1.5), rlang (>= 0.1.2), tibble (>= 1.3.4), stats, methods Suggests: testthat, knitr, rmarkdown, BiocStyle, SingleCellExperiment, SummarizedExperiment, scRNAseq, cowplot License: MIT + file LICENSE MD5sum: 471835c9e93a2d45d4d9572c230583e6 NeedsCompilation: no Title: A correlation-based method for quality filtering of single-cell RNAseq data Description: An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise. biocViews: ImmunoOncology, SingleCell, RNASeq, Preprocessing, GeneExpression Author: Angeles Arzalluz-Luque [aut], Guillaume Devailly [aut, cre], Anagha Joshi [aut] Maintainer: Guillaume Devailly VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scFeatureFilter git_branch: RELEASE_3_22 git_last_commit: ac63ad2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scFeatureFilter_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scFeatureFilter_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scFeatureFilter_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scFeatureFilter_1.30.0.tgz vignettes: vignettes/scFeatureFilter/inst/doc/Introduction.html vignetteTitles: Introduction to scFeatureFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scFeatureFilter/inst/doc/Introduction.R dependencyCount: 30 Package: scGPS Version: 1.24.0 Depends: R (>= 3.6), SummarizedExperiment, dynamicTreeCut, SingleCellExperiment Imports: glmnet (> 2.0), caret (>= 6.0), ggplot2 (>= 2.2.1), fastcluster, dplyr, Rcpp, RcppArmadillo, RcppParallel, grDevices, graphics, stats, utils, DESeq2, locfit LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: Matrix (>= 1.2), testthat, knitr, parallel, rmarkdown, RColorBrewer, ReactomePA, clusterProfiler, cowplot, org.Hs.eg.db, reshape2, xlsx, dendextend, networkD3, Rtsne, BiocParallel, e1071, WGCNA, devtools, DOSE License: GPL-3 Archs: x64 MD5sum: 7e49331f9853509630f65dda153c6d4d NeedsCompilation: yes Title: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation) Description: The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations. biocViews: SingleCell, Clustering, DataImport, Sequencing, Coverage Author: Quan Nguyen [aut, cre], Michael Thompson [aut], Anne Senabouth [aut] Maintainer: Quan Nguyen SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/IMB-Computational-Genomics-Lab/scGPS/issues git_url: https://git.bioconductor.org/packages/scGPS git_branch: RELEASE_3_22 git_last_commit: 2fe6e8f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scGPS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scGPS_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scGPS_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scGPS_1.24.0.tgz vignettes: vignettes/scGPS/inst/doc/vignette.html vignetteTitles: single cell Global fate Potential of Subpopulations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scGPS/inst/doc/vignette.R dependencyCount: 113 Package: scGraphVerse Version: 1.0.0 Depends: R (>= 4.5.0) Imports: BiocBaseUtils, BiocParallel (>= 1.30.0), doParallel, doRNG, GENIE3, Matrix, MultiAssayExperiment, SingleCellExperiment, SummarizedExperiment, distributions3, dplyr, grDevices, graphics, httr, igraph, jsonlite, methods, parallel, reticulate, tidyr, glmnet, MASS, utils, stats, S4Vectors, graph, mpath Suggests: AnnotationDbi, BiocStyle, clusterProfiler, DOSE, enrichplot, fmsb, ggplot2, ggraph, gridExtra, INetTool, org.Hs.eg.db, org.Mm.eg.db, patchwork, pROC, RColorBrewer, ReactomePA, rentrez, robin, scales, Seurat, STRINGdb, testthat (>= 3.0.0), knitr, rmarkdown, tidyverse, magick, celldex, SingleR, TENxPBMCData, scater, GenomeInfoDb, GenomicRanges, License: GPL-3 + file LICENSE MD5sum: 73f44ba6d1999491da440daa6f0fb82c NeedsCompilation: yes Title: scGraphVerse: A Gene Network Analysis Package Description: A package for inferring, comparing, and visualizing gene networks from single-cell RNA sequencing data. It integrates multiple methods (GENIE3, GRNBoost2, ZILGM, PCzinb, and JRF) for robust network inference, supports consensus building across methods or datasets, and provides tools for evaluating regulatory structure and community similarity. GRNBoost2 requires Python package 'arboreto' which can be installed using init_py(install_missing = TRUE). This package includes adapted functions from ZILGM (Park et al., 2021), JRF (Petralia et al., 2015), and learn2count (Nguyen et al. 2023) packages with proper attribution under GPL-2 license. biocViews: GeneRegulation, NetworkInference, SingleCell, RNASeq, Visualization, Software, GraphAndNetwork, GeneSetEnrichment, NetworkEnrichment, Pathways, Sequencing, Reactome, Network, KEGG Author: Francesco Cecere [aut, cre] (ORCID: ), Annamaria Carissimo [aut], Daniela De Canditiis [aut], Claudia Angelini [aut, fnd] Maintainer: Francesco Cecere URL: https://ngsFC.github.io/scGraphVerse SystemRequirements: Python (>= 3.6) and arboreto Python package for GRNBoost2 method. Use init_py(install_missing = TRUE) for automated installation. VignetteBuilder: knitr BugReports: https://github.com/ngsFC/scGraphVerse/issues git_url: https://git.bioconductor.org/packages/scGraphVerse git_branch: RELEASE_3_22 git_last_commit: 425f2dc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scGraphVerse_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scGraphVerse_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scGraphVerse_1.0.0.tgz vignettes: vignettes/scGraphVerse/inst/doc/case_study.html, vignettes/scGraphVerse/inst/doc/simulation_study.html vignetteTitles: scGraphVerse Case Study: B-cell GRN Reconstruction, scGraphVerse Simulation Study: Sim & GRN Reconstruction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scGraphVerse/inst/doc/case_study.R, vignettes/scGraphVerse/inst/doc/simulation_study.R dependencyCount: 105 Package: schex Version: 1.24.0 Depends: SingleCellExperiment (>= 1.7.4), ggplot2 (>= 3.2.1) Imports: hexbin, stats, methods, cluster, dplyr, entropy, ggforce, grid, rlang, concaveman Suggests: ggrepel, knitr, rmarkdown, testthat (>= 2.1.0), covr, TENxPBMCData, scater, Seurat, shinydashboard, iSEE, igraph, scran, tibble, scuttle License: GPL-3 Archs: x64 MD5sum: 679055bb0786e09fcbfa4978cb3258c8 NeedsCompilation: no Title: Hexbin plots for single cell omics data Description: Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley. biocViews: Software, Sequencing, SingleCell, DimensionReduction, Visualization, ImmunoOncology, DataImport Author: Saskia Freytag [aut, cre], Wancheng Tang [ctb], Zimo Peng [ctb], Jingxiu Huang [ctb] Maintainer: Saskia Freytag URL: https://github.com/SaskiaFreytag/schex VignetteBuilder: knitr BugReports: https://github.com/SaskiaFreytag/schex/issues git_url: https://git.bioconductor.org/packages/schex git_branch: RELEASE_3_22 git_last_commit: 6cbe3a6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/schex_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/schex_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/schex_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/schex_1.24.0.tgz vignettes: vignettes/schex/inst/doc/Seurat_to_SCE.html, vignettes/schex/inst/doc/using_schex.html vignetteTitles: Seurat_to_SCE, using_schex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/schex/inst/doc/Seurat_to_SCE.R, vignettes/schex/inst/doc/using_schex.R importsMe: scTensor, scTGIF dependencyCount: 74 Package: scHiCcompare Version: 1.2.0 Depends: R (>= 4.5.0) Imports: grDevices, graphics, stats, utils, dplyr, ggplot2, gtools, HiCcompare, lattice, mclust, mice, miceadds, ranger, rstatix, tidyr, rlang, data.table, BiocParallel Suggests: knitr, rmarkdown, testthat, BiocStyle, DT, gridExtra License: MIT + file LICENSE MD5sum: b72fe754df6b6d4a164116f1660c8715 NeedsCompilation: no Title: Differential Analysis of Single-cell Hi-C Data Description: This package provides functions for differential chromatin interaction analysis between two single-cell Hi-C data groups. It includes tools for imputation, normalization, and differential analysis of chromatin interactions. The package implements pooling techniques for imputation and offers methods to normalize and test for differential interactions across single-cell Hi-C datasets. biocViews: Software, SingleCell, HiC, Sequencing, Normalization Author: My Nguyen [aut, cre] (ORCID: ), Mikhail Dozmorov [aut] (ORCID: ) Maintainer: My Nguyen URL: https://github.com/dozmorovlab/ScHiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/ScHiCcompare/issues git_url: https://git.bioconductor.org/packages/scHiCcompare git_branch: RELEASE_3_22 git_last_commit: bb3c095 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scHiCcompare_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scHiCcompare_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scHiCcompare_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scHiCcompare_1.2.0.tgz vignettes: vignettes/scHiCcompare/inst/doc/ScHiCcompare.html vignetteTitles: Chromatin Differential Analysis of scHiC -scHiCcompare Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scHiCcompare/inst/doc/ScHiCcompare.R dependencyCount: 133 Package: scHOT Version: 1.22.0 Depends: R (>= 4.0) Imports: S4Vectors (>= 0.24.3), SingleCellExperiment, Matrix, SummarizedExperiment, IRanges, methods, stats, BiocParallel, reshape, ggplot2, igraph, grDevices, ggforce, graphics Suggests: knitr, markdown, rmarkdown, scater, scattermore, scales, matrixStats, deldir License: GPL-3 MD5sum: 17ed7872b84ee9e8b5f68cdb73776f83 NeedsCompilation: no Title: single-cell higher order testing Description: Single cell Higher Order Testing (scHOT) is an R package that facilitates testing changes in higher order structure of gene expression along either a developmental trajectory or across space. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data. biocViews: GeneExpression, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Shila Ghazanfar [aut, cre], Yingxin Lin [aut] Maintainer: Shila Ghazanfar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scHOT git_branch: RELEASE_3_22 git_last_commit: a61146b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scHOT_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scHOT_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scHOT_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scHOT_1.22.0.tgz vignettes: vignettes/scHOT/inst/doc/scHOT.html vignetteTitles: Getting started: scHOT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scHOT/inst/doc/scHOT.R dependencyCount: 66 Package: scider Version: 1.8.0 Depends: R (>= 4.3) Imports: SpatialExperiment, SummarizedExperiment, spatstat.geom, spatstat.explore, sf, lwgeom, SpatialPack, ggplot2, stats, pheatmap, plotly, shiny, igraph, janitor, knitr, methods, utils, isoband, S4Vectors, grDevices, dbscan, hexDensity, hexbin, uwot, SingleCellExperiment, BiocNeighbors, irlba Suggests: edgeR, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: ea2a5273e5bb79ece8de67b393503b05 NeedsCompilation: yes Title: Spatial cell-type inter-correlation by density in R Description: scider is an user-friendly R package providing functions to model the global density of cells in a slide of spatial transcriptomics data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. After modelling density, the package allows for serveral downstream analysis, including colocalization analysis, boundary detection analysis and differential density analysis. biocViews: Spatial, Transcriptomics Author: Mengbo Li [aut] (ORCID: ), Ning Liu [aut] (ORCID: ), Quoc Hoang Nguyen [aut] (ORCID: ), Yunshun Chen [aut, cre] (ORCID: ) Maintainer: Yunshun Chen URL: https://github.com/ChenLaboratory/scider, https://chenlaboratory.github.io/scider/ VignetteBuilder: knitr BugReports: https://github.com/ChenLaboratory/scider/issues git_url: https://git.bioconductor.org/packages/scider git_branch: RELEASE_3_22 git_last_commit: 2a5b6a6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scider_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scider_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scider_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scider_1.8.0.tgz vignettes: vignettes/scider/inst/doc/scider_userGuide.html vignetteTitles: scider_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scider/inst/doc/scider_userGuide.R importsMe: OSTA dependencyCount: 154 Package: scifer Version: 1.12.0 Imports: dplyr, rmarkdown, data.table, Biostrings, stats, plyr, knitr, ggplot2, gridExtra, DECIPHER, stringr, sangerseqR, kableExtra, tibble, scales, rlang, flowCore, methods, basilisk, basilisk.utils, reticulate, here, pwalign, utils Suggests: BiocBaseUtils, fs, BiocStyle, testthat (>= 3.0.0) Enhances: parallel License: MIT + file LICENSE Archs: x64 MD5sum: 7a7e15c7b409329806f5662e4d7c3988 NeedsCompilation: no Title: Scifer: Single-Cell Immunoglobulin Filtering of Sanger Sequences Description: Have you ever index sorted cells in a 96 or 384-well plate and then sequenced using Sanger sequencing? If so, you probably had some struggles to either check the electropherogram of each cell sequenced manually, or when you tried to identify which cell was sorted where after sequencing the plate. Scifer was developed to solve this issue by performing basic quality control of Sanger sequences and merging flow cytometry data from probed single-cell sorted B cells with sequencing data. scifer can export summary tables, 'fasta' files, electropherograms for visual inspection, and generate reports. biocViews: Preprocessing, QualityControl, SangerSeq, Sequencing, Software, FlowCytometry, SingleCell Author: Rodrigo Arcoverde Cerveira [aut, cre, cph] (ORCID: ), Marcel Martin [ctb], Matthew James Hinchcliff [ctb], Sebastian Ols [aut, dtc] (ORCID: ), Karin Loré [dtc, ths, fnd] (ORCID: ) Maintainer: Rodrigo Arcoverde Cerveira URL: https://github.com/rodrigarc/scifer VignetteBuilder: knitr BugReports: https://github.com/rodrigarc/scifer/issues git_url: https://git.bioconductor.org/packages/scifer git_branch: RELEASE_3_22 git_last_commit: e09cc12 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scifer_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scifer_1.11.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scifer_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scifer_1.12.0.tgz vignettes: vignettes/scifer/inst/doc/scifer_walkthrough.html vignetteTitles: Using scifer to filter single-cell sorted B cell receptor (BCR) sanger sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scifer/inst/doc/scifer_walkthrough.R dependencyCount: 104 Package: scLANE Version: 1.0.0 Depends: glm2, magrittr, R (>= 4.5.0) Imports: geeM, MASS, mpath, dplyr, stats, utils, withr, purrr, tidyr, furrr, doSNOW, gamlss, scales, future, Matrix, ggplot2, splines, foreach, glmmTMB, parallel, RcppEigen, bigstatsr, tidyselect, broom.mixed, Rcpp LinkingTo: Rcpp, RcppEigen Suggests: covr, grid, coop, uwot, scran, ggh4x, knitr, UCell, irlba, rlang, magick, igraph, scater, gtable, ggpubr, viridis, bluster, cluster, circlize, speedglm, rmarkdown, gridExtra, BiocStyle, slingshot, gprofiler2, GenomeInfoDb, BiocParallel, BiocGenerics, BiocNeighbors, ComplexHeatmap, Seurat (>= 5.0.0), testthat (>= 3.0.0), SingleCellExperiment, SummarizedExperiment License: MIT + file LICENSE MD5sum: d07c7c0fbccd31755c9f931a01d95a38 NeedsCompilation: yes Title: Model Gene Expression Dynamics with Spline-Based NB GLMs, GEEs, & GLMMs Description: Our scLANE model uses truncated power basis spline models to build flexible, interpretable models of single cell gene expression over pseudotime or latent time. The modeling architectures currently supported are Negative-binomial GLMs, GEEs, & GLMMs. Downstream analysis functionalities include model comparison, dynamic gene clustering, smoothed counts generation, gene set enrichment testing, & visualization. biocViews: RNASeq, Software, Clustering, TimeCourse, Sequencing, Regression, SingleCell, Visualization, GeneExpression, Transcriptomics, GeneSetEnrichment, DifferentialExpression Author: Jack R. Leary [aut, cre] (ORCID: ), Rhonda Bacher [ctb, fnd] (ORCID: ) Maintainer: Jack R. Leary URL: https://github.com/jr-leary7/scLANE VignetteBuilder: knitr BugReports: https://github.com/jr-leary7/scLANE/issues git_url: https://git.bioconductor.org/packages/scLANE git_branch: RELEASE_3_22 git_last_commit: 571c617 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scLANE_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scLANE_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scLANE_1.0.0.tgz vignettes: vignettes/scLANE/inst/doc/scLANE.html vignetteTitles: Interpretable Trajectory DE Testing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scLANE/inst/doc/scLANE.R dependencyCount: 106 Package: scmap Version: 1.32.0 Depends: R(>= 3.4) Imports: Biobase, SingleCellExperiment, SummarizedExperiment, BiocGenerics, S4Vectors, dplyr, reshape2, matrixStats, proxy, utils, googleVis, ggplot2, methods, stats, e1071, randomForest, Rcpp (>= 0.12.12) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 969bdeb217f427663dfe0a40addb5d73 NeedsCompilation: yes Title: A tool for unsupervised projection of single cell RNA-seq data Description: Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment. biocViews: ImmunoOncology, SingleCell, Software, Classification, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, Transcription, Sequencing, Preprocessing, GeneExpression, DataImport Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/scmap VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/scmap/ git_url: https://git.bioconductor.org/packages/scmap git_branch: RELEASE_3_22 git_last_commit: 8fee1fc git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scmap_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scmap_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scmap_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scmap_1.32.0.tgz vignettes: vignettes/scmap/inst/doc/scmap.html vignetteTitles: `scmap` package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scmap/inst/doc/scmap.R dependencyCount: 62 Package: scMerge Version: 1.26.0 Depends: R (>= 3.6.0) Imports: BiocParallel, BiocSingular, BiocNeighbors, cluster, DelayedArray, DelayedMatrixStats, distr, igraph, M3Drop (>= 1.9.4), proxyC, ruv, cvTools, scater, batchelor, scran, methods, S4Vectors (>= 0.23.19), SingleCellExperiment (>= 1.7.3), SummarizedExperiment Suggests: BiocStyle, covr, HDF5Array, knitr, Matrix, rmarkdown, scales, proxy, testthat, badger License: GPL-3 MD5sum: acb3b34d9cadaaa7540373231739b6ca NeedsCompilation: no Title: scMerge: Merging multiple batches of scRNA-seq data Description: Like all gene expression data, single-cell data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of single-cell data. biocViews: BatchEffect, GeneExpression, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Yingxin Lin [aut, cre], Kevin Wang [aut], Sydney Bioinformatics and Biometrics Group [fnd] Maintainer: Yingxin Lin URL: https://github.com/SydneyBioX/scMerge VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scMerge/issues git_url: https://git.bioconductor.org/packages/scMerge git_branch: RELEASE_3_22 git_last_commit: d9249c9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scMerge_1.26.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scMerge_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scMerge_1.26.0.tgz vignettes: vignettes/scMerge/inst/doc/scMerge.html, vignettes/scMerge/inst/doc/scMerge2.html vignetteTitles: scMerge, scMerge2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMerge/inst/doc/scMerge.R, vignettes/scMerge/inst/doc/scMerge2.R importsMe: singleCellTK suggestsMe: Cepo dependencyCount: 178 Package: scMET Version: 1.12.0 Depends: R (>= 4.2.0) Imports: methods, Rcpp (>= 1.0.0), RcppParallel (>= 5.0.1), rstan (>= 2.21.3), rstantools (>= 2.1.0), VGAM, data.table, MASS, logitnorm, ggplot2, matrixStats, assertthat, viridis, coda, BiocStyle, cowplot, stats, SummarizedExperiment, SingleCellExperiment, Matrix, dplyr, S4Vectors LinkingTo: BH (>= 1.66.0), Rcpp (>= 1.0.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.21.3), StanHeaders (>= 2.21.0.7) Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 23dffac930c2997413518896cab52a6d NeedsCompilation: yes Title: Bayesian modelling of cell-to-cell DNA methylation heterogeneity Description: High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation, DifferentialExpression, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, Bayesian, Sequencing, Coverage, SingleCell Author: Andreas C. Kapourani [aut, cre] (ORCID: ), John Riddell [ctb] Maintainer: Andreas C. Kapourani SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/andreaskapou/scMET/issues git_url: https://git.bioconductor.org/packages/scMET git_branch: RELEASE_3_22 git_last_commit: df8d5cb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scMET_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scMET_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scMET_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scMET_1.12.0.tgz vignettes: vignettes/scMET/inst/doc/scMET_vignette.html vignetteTitles: scMET analysis using synthetic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMET/inst/doc/scMET_vignette.R dependencyCount: 105 Package: scmeth Version: 1.30.0 Depends: R (>= 3.5.0) Imports: BiocGenerics, bsseq, AnnotationHub, Seqinfo, GenomicRanges, reshape2, stats, utils, BSgenome, DelayedArray (>= 0.5.15), annotatr, SummarizedExperiment (>= 1.5.6), GenomeInfoDb, Biostrings, DT, HDF5Array (>= 1.7.5) Suggests: knitr, rmarkdown, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, Biobase, ggplot2, ggthemes License: GPL-2 MD5sum: 1b8cc1431e735f4ef8fbf12682a03138 NeedsCompilation: no Title: Functions to conduct quality control analysis in methylation data Description: Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report. biocViews: DNAMethylation, QualityControl, Preprocessing, SingleCell, ImmunoOncology Author: Divy Kangeyan Maintainer: Divy Kangeyan VignetteBuilder: knitr BugReports: https://github.com/aryeelab/scmeth/issues git_url: https://git.bioconductor.org/packages/scmeth git_branch: RELEASE_3_22 git_last_commit: 0e01390 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scmeth_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scmeth_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scmeth_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scmeth_1.30.0.tgz vignettes: vignettes/scmeth/inst/doc/my-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scmeth/inst/doc/my-vignette.R suggestsMe: biscuiteer dependencyCount: 160 Package: scMitoMut Version: 1.6.0 Depends: R (>= 4.3.0) Imports: data.table, Rcpp, magrittr, plyr, stringr, utils, stats, methods, ggplot2, pheatmap, RColorBrewer, rhdf5, readr, parallel, grDevices LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, VGAM, R.utils License: Artistic-2.0 MD5sum: 85030b587fa894489f2de0aec90736fe NeedsCompilation: yes Title: Single-cell Mitochondrial Mutation Analysis Tool Description: This package is designed for calling lineage-informative mitochondrial mutations using single-cell sequencing data, such as scRNASeq and scATACSeq (preferably the latter due to RNA editing issues). It includes functions for mutation calling and visualization. Mutation calling is done using beta-binomial distribution. biocViews: Preprocessing, Sequencing, SingleCell Author: Wenjie Sun [cre, aut] (ORCID: ), Leila Perie [ctb] Maintainer: Wenjie Sun URL: http://github.com/wenjie1991/scMitoMut VignetteBuilder: knitr BugReports: https://github.com/wenjie1991/scMitoMut/issues git_url: https://git.bioconductor.org/packages/scMitoMut git_branch: RELEASE_3_22 git_last_commit: ed0def1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scMitoMut_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scMitoMut_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scMitoMut_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scMitoMut_1.6.0.tgz vignettes: vignettes/scMitoMut/inst/doc/Analysis_colon_cancer_dataset.html vignetteTitles: CRC_dataset_demo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMitoMut/inst/doc/Analysis_colon_cancer_dataset.R dependencyCount: 51 Package: scMultiSim Version: 1.6.0 Depends: R (>= 4.4.0) Imports: foreach, rlang, dplyr, ggplot2, Rtsne, ape, MASS, matrixStats, phytools, KernelKnn, gplots, zeallot, crayon, assertthat, igraph, methods, grDevices, graphics, stats, utils, markdown, SummarizedExperiment, BiocParallel Suggests: knitr, rmarkdown, roxygen2, shiny, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: f79f152a4f6928ed19494b247ed4667a NeedsCompilation: no Title: Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions Description: scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments. biocViews: SingleCell, Transcriptomics, GeneExpression, Sequencing, ExperimentalDesign Author: Hechen Li [aut, cre] (ORCID: ), Xiuwei Zhang [aut], Ziqi Zhang [aut], Michael Squires [aut] Maintainer: Hechen Li URL: https://zhanglabgt.github.io/scMultiSim/ VignetteBuilder: knitr BugReports: https://github.com/ZhangLabGT/scMultiSim/issues git_url: https://git.bioconductor.org/packages/scMultiSim git_branch: RELEASE_3_22 git_last_commit: 6add3c1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scMultiSim_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scMultiSim_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scMultiSim_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scMultiSim_1.6.0.tgz vignettes: vignettes/scMultiSim/inst/doc/basics.html, vignettes/scMultiSim/inst/doc/options.html, vignettes/scMultiSim/inst/doc/spatialCCI.html, vignettes/scMultiSim/inst/doc/workflow.html vignetteTitles: 2. Simulating Multimodal Single-cell Datasets, 4. Parameter Guide, 3. Simulating Spatial Cell-Cell Interactions, 1. Getting Started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMultiSim/inst/doc/basics.R, vignettes/scMultiSim/inst/doc/options.R, vignettes/scMultiSim/inst/doc/spatialCCI.R, vignettes/scMultiSim/inst/doc/workflow.R dependencyCount: 96 Package: SCnorm Version: 1.32.0 Depends: R (>= 3.4.0), Imports: SingleCellExperiment, SummarizedExperiment, stats, methods, graphics, grDevices, parallel, quantreg, cluster, moments, data.table, BiocParallel, S4Vectors, ggplot2, forcats, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL (>= 2) MD5sum: 28c3c72fe09eb62507e44409ba066893 NeedsCompilation: no Title: Normalization of single cell RNA-seq data Description: This package implements SCnorm — a method to normalize single-cell RNA-seq data. biocViews: Normalization, RNASeq, SingleCell, ImmunoOncology Author: Rhonda Bacher Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/SCnorm VignetteBuilder: knitr BugReports: https://github.com/rhondabacher/SCnorm/issues git_url: https://git.bioconductor.org/packages/SCnorm git_branch: RELEASE_3_22 git_last_commit: e86e3a7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SCnorm_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCnorm_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCnorm_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCnorm_1.32.0.tgz vignettes: vignettes/SCnorm/inst/doc/SCnorm.pdf vignetteTitles: SCnorm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCnorm/inst/doc/SCnorm.R dependencyCount: 67 Package: scone Version: 1.34.0 Depends: R (>= 3.4), methods, SummarizedExperiment Imports: graphics, stats, utils, aroma.light, BiocParallel, class, cluster, compositions, diptest, edgeR, fpc, gplots, grDevices, hexbin, limma, matrixStats, mixtools, RColorBrewer, boot, rhdf5, RUVSeq, rARPACK, MatrixGenerics, SingleCellExperiment, DelayedMatrixStats, sparseMatrixStats, SparseArray (>= 1.7.6) Suggests: BiocStyle, DT, ggplot2, knitr, miniUI, NMF, plotly, reshape2, rmarkdown, scran, scRNAseq, shiny, testthat, DelayedArray, visNetwork, doParallel, batchtools, splatter, scater, kableExtra, mclust, TENxPBMCData License: Artistic-2.0 MD5sum: 1ea3006362b45fdb099df517ce7e43a5 NeedsCompilation: no Title: Single Cell Overview of Normalized Expression data Description: SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses. biocViews: ImmunoOncology, Normalization, Preprocessing, QualityControl, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell, Coverage Author: Michael Cole [aut, cph], Davide Risso [aut, cre, cph], Matteo Borella [ctb], Chiara Romualdi [ctb] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/YosefLab/scone/issues git_url: https://git.bioconductor.org/packages/scone git_branch: RELEASE_3_22 git_last_commit: a2184ae git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scone_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scone_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scone_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scone_1.34.0.tgz vignettes: vignettes/scone/inst/doc/PsiNorm.html, vignettes/scone/inst/doc/sconeTutorial.html vignetteTitles: PsiNorm normalization, Introduction to SCONE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scone/inst/doc/PsiNorm.R, vignettes/scone/inst/doc/sconeTutorial.R dependencyCount: 184 Package: Sconify Version: 1.30.0 Depends: R (>= 3.5) Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils, stats, readr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 72c849de409101364ef2fca037d59903 NeedsCompilation: no Title: A toolkit for performing KNN-based statistics for flow and mass cytometry data Description: This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold. biocViews: ImmunoOncology, SingleCell, FlowCytometry, Software, MultipleComparison, Visualization Author: Tyler J Burns Maintainer: Tyler J Burns VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Sconify git_branch: RELEASE_3_22 git_last_commit: 615688e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Sconify_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Sconify_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Sconify_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Sconify_1.30.0.tgz vignettes: vignettes/Sconify/inst/doc/DataQuality.html, vignettes/Sconify/inst/doc/FindingIdealK.html, vignettes/Sconify/inst/doc/Step1.PreProcessing.html, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.html, vignettes/Sconify/inst/doc/Step3.PostProcessing.html vignetteTitles: Data Quality, Finding Ideal K, How to process FCS files for downstream use in R, General Scone Analysis, Final Post-Processing Steps for Scone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sconify/inst/doc/DataQuality.R, vignettes/Sconify/inst/doc/FindingIdealK.R, vignettes/Sconify/inst/doc/Step1.PreProcessing.R, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.R, vignettes/Sconify/inst/doc/Step3.PostProcessing.R dependencyCount: 54 Package: SCOPE Version: 1.22.0 Depends: R (>= 3.6.0), GenomicRanges, IRanges, Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19 Imports: stats, grDevices, graphics, utils, DescTools, RColorBrewer, gplots, foreach, parallel, doParallel, DNAcopy, BSgenome, Biostrings, BiocGenerics, S4Vectors Suggests: knitr, rmarkdown, WGSmapp, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, testthat (>= 2.1.0) License: GPL-2 MD5sum: ca2d65250746cde4abfafcd191d69d02 NeedsCompilation: no Title: A normalization and copy number estimation method for single-cell DNA sequencing Description: Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background. biocViews: SingleCell, Normalization, CopyNumberVariation, Sequencing, WholeGenome, Coverage, Alignment, QualityControl, DataImport, DNASeq Author: Rujin Wang, Danyu Lin, Yuchao Jiang Maintainer: Rujin Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCOPE git_branch: RELEASE_3_22 git_last_commit: a28af97 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SCOPE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCOPE_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCOPE_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCOPE_1.22.0.tgz vignettes: vignettes/SCOPE/inst/doc/SCOPE_vignette.html vignetteTitles: SCOPE: Single-cell Copy Number Estimation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCOPE/inst/doc/SCOPE_vignette.R dependencyCount: 113 Package: scoreInvHap Version: 1.32.0 Depends: R (>= 3.6.0) Imports: Biostrings, methods, snpStats, VariantAnnotation, GenomicRanges, BiocParallel, graphics, SummarizedExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown License: file LICENSE MD5sum: 84ddea0daf7df1669f0e1a1984ef2c09 NeedsCompilation: no Title: Get inversion status in predefined regions Description: scoreInvHap can get the samples' inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for 21 inversions. biocViews: SNP, Genetics, GenomicVariation Author: Carlos Ruiz [aut], Dolors Pelegrí [aut], Juan R. Gonzalez [aut, cre] Maintainer: Dolors Pelegri-Siso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scoreInvHap git_branch: RELEASE_3_22 git_last_commit: 3779f3d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scoreInvHap_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scoreInvHap_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scoreInvHap_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scoreInvHap_1.32.0.tgz vignettes: vignettes/scoreInvHap/inst/doc/scoreInvHap.html vignetteTitles: Inversion genotyping with scoreInvHap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scoreInvHap/inst/doc/scoreInvHap.R dependencyCount: 81 Package: scoup Version: 1.4.0 Depends: R (>= 4.4), Matrix Imports: Biostrings, methods Suggests: BiocManager, BiocStyle, bookdown, htmltools, knitr, testthat (>= 3.0.0), yaml License: GPL (>= 2) Archs: x64 MD5sum: 4b8ea8f3511fee1feab1fa41e0f855d9 NeedsCompilation: no Title: Simulate Codons with Darwinian Selection Modelled as an OU Process Description: An elaborate molecular evolutionary framework that facilitates straightforward simulation of codon genetic sequences subjected to different degrees and/or patterns of Darwinian selection. The model is built upon the fitness landscape paradigm of Sewall Wright, as popularised by the mutation-selection model of Halpern and Bruno. This enables realistic evolutionary process of living organisms to be reproducible seamlessly. For example, an Ornstein-Uhlenbeck fitness update algorithm is incorporated herein. Consequently, otherwise complex biological processes, such as the effect of the interplay between genetic drift and fitness landscape fluctuations on the inference of diversifying selection, may now be investigated with minimal effort. Frequency-dependent and stochastic fitness landscape update techniques are available. biocViews: Alignment, Classification, ComparativeGenomics, DataImport, Genetics, MathematicalBiology, ResearchField, Sequencing, SequenceMatching, Software, StatisticalMethod, WorkflowStep Author: Hassan Sadiq [aut, cre, cph] (ORCID: ) Maintainer: Hassan Sadiq URL: https://github.com/thsadiq/scoup VignetteBuilder: knitr BugReports: https://github.com/thsadiq/scoup/issues git_url: https://git.bioconductor.org/packages/scoup git_branch: RELEASE_3_22 git_last_commit: 8f36ed0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scoup_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scoup_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scoup_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scoup_1.4.0.tgz vignettes: vignettes/scoup/inst/doc/scoup.html vignetteTitles: scoup Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scoup/inst/doc/scoup.R dependencyCount: 18 Package: scp Version: 1.20.0 Depends: R (>= 4.3.0), QFeatures (>= 1.19.1) Imports: IHW, ggplot2, ggrepel, matrixStats, metapod, methods, MsCoreUtils, MultiAssayExperiment, nipals, RColorBrewer, S4Vectors, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, MsDataHub (>= 1.3.3), impute, knitr, patchwork, preprocessCore, rmarkdown, scater, scpdata, sva, testthat, vdiffr, vsn, uwot License: Artistic-2.0 MD5sum: c3ddf1116b60530b122829b2cffb9288 NeedsCompilation: no Title: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis Description: Utility functions for manipulating, processing, and analyzing mass spectrometry-based single-cell proteomics data. The package is an extension to the 'QFeatures' package and relies on 'SingleCellExpirement' to enable single-cell proteomics analyses. The package offers the user the functionality to process quantitative table (as generated by MaxQuant, Proteome Discoverer, and more) into data tables ready for downstream analysis and data visualization. biocViews: GeneExpression, Proteomics, SingleCell, MassSpectrometry, Preprocessing, CellBasedAssays Author: Christophe Vanderaa [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Léopold Guyot [ctb] Maintainer: Christophe Vanderaa URL: https://UCLouvain-CBIO.github.io/scp VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/scp/issues git_url: https://git.bioconductor.org/packages/scp git_branch: RELEASE_3_22 git_last_commit: 9c3cf13 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scp_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scp_1.19.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scp_1.20.0.tgz vignettes: vignettes/scp/inst/doc/advanced.html, vignettes/scp/inst/doc/QFeatures_nutshell.html, vignettes/scp/inst/doc/read_scp.html, vignettes/scp/inst/doc/reporting_missing_values.html, vignettes/scp/inst/doc/scp_data_modelling.html, vignettes/scp/inst/doc/scp.html vignetteTitles: Advanced usage of `scp`, QFeatures in a nutshell, Load data using readSCP, Reporting missing values, Single Cell Proteomics data modelling, Single Cell Proteomics data processing and analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scp/inst/doc/advanced.R, vignettes/scp/inst/doc/QFeatures_nutshell.R, vignettes/scp/inst/doc/read_scp.R, vignettes/scp/inst/doc/reporting_missing_values.R, vignettes/scp/inst/doc/scp_data_modelling.R, vignettes/scp/inst/doc/scp.R suggestsMe: scpdata dependencyCount: 108 Package: scPCA Version: 1.24.0 Depends: R (>= 4.0.0) Imports: stats, methods, assertthat, tibble, dplyr, purrr, stringr, Rdpack, matrixStats, BiocParallel, elasticnet, sparsepca, cluster, kernlab, origami, RSpectra, coop, Matrix, DelayedArray, ScaledMatrix, MatrixGenerics Suggests: DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter, SingleCellExperiment, microbenchmark License: MIT + file LICENSE MD5sum: bfc681b809d17502ae51fe69d2dc7785 NeedsCompilation: no Title: Sparse Contrastive Principal Component Analysis Description: A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA. biocViews: PrincipalComponent, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq Author: Philippe Boileau [aut, cre, cph] (ORCID: ), Nima Hejazi [aut] (ORCID: ), Sandrine Dudoit [ctb, ths] (ORCID: ) Maintainer: Philippe Boileau URL: https://github.com/PhilBoileau/scPCA VignetteBuilder: knitr BugReports: https://github.com/PhilBoileau/scPCA/issues git_url: https://git.bioconductor.org/packages/scPCA git_branch: RELEASE_3_22 git_last_commit: 49d6aeb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scPCA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scPCA_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scPCA_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scPCA_1.24.0.tgz vignettes: vignettes/scPCA/inst/doc/scpca_intro.html vignetteTitles: Sparse contrastive principal component analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scPCA/inst/doc/scpca_intro.R dependsOnMe: OSCA.advanced, OSCA.workflows dependencyCount: 70 Package: scPipe Version: 2.10.0 Depends: R (>= 4.2.0), SingleCellExperiment Imports: AnnotationDbi, basilisk, BiocGenerics, biomaRt, Biostrings, data.table, dplyr, DropletUtils, flexmix, GenomicRanges, GenomicAlignments, GGally, ggplot2, glue (>= 1.3.0), grDevices, graphics, hash, IRanges, magrittr, MASS, Matrix (>= 1.5.0), mclust, methods, MultiAssayExperiment, org.Hs.eg.db, org.Mm.eg.db, purrr, Rcpp (>= 0.11.3), reshape, reticulate, Rhtslib, rlang, robustbase, Rsamtools, Rsubread, rtracklayer, SummarizedExperiment, S4Vectors, scales, stats, stringr, tibble, tidyr, tools, utils, vctrs (>= 0.5.2) LinkingTo: Rcpp, Rhtslib (>= 1.13.1), testthat Suggests: BiocStyle, DT, GenomicFeatures, grid, igraph, kableExtra, knitr, locStra, plotly, rmarkdown, RColorBrewer, readr, reshape2, RANN, shiny, scater (>= 1.11.0), testthat, xml2, umap License: GPL (>= 2) MD5sum: 442ec8c24b2d0113819e358e83495e3d NeedsCompilation: yes Title: Pipeline for single cell multi-omic data pre-processing Description: A preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols. biocViews: ImmunoOncology, Software, Sequencing, RNASeq, GeneExpression, SingleCell, Visualization, SequenceMatching, Preprocessing, QualityControl, GenomeAnnotation, DataImport Author: Luyi Tian [aut], Shian Su [aut, cre], Shalin Naik [ctb], Shani Amarasinghe [aut], Oliver Voogd [aut], Phil Yang [aut], Matthew Ritchie [ctb] Maintainer: Shian Su URL: https://github.com/LuyiTian/scPipe SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/LuyiTian/scPipe git_url: https://git.bioconductor.org/packages/scPipe git_branch: RELEASE_3_22 git_last_commit: c021029 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scPipe_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scPipe_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scPipe_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scPipe_2.10.0.tgz vignettes: vignettes/scPipe/inst/doc/scPipe_atac_tutorial.html, vignettes/scPipe/inst/doc/scPipe_tutorial.html vignetteTitles: scPipe: a flexible data preprocessing pipeline for scATAC-seq data, scPipe: a flexible data preprocessing pipeline for 3' end scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scPipe/inst/doc/scPipe_atac_tutorial.R, vignettes/scPipe/inst/doc/scPipe_tutorial.R importsMe: stPipe dependencyCount: 167 Package: scran Version: 1.38.0 Depends: SingleCellExperiment, scuttle Imports: SummarizedExperiment, S4Vectors, BiocGenerics, BiocParallel, Rcpp, stats, methods, utils, Matrix, edgeR, limma, igraph, statmod, MatrixGenerics, S4Arrays, DelayedArray, BiocSingular, bluster, metapod, dqrng, beachmat LinkingTo: Rcpp, beachmat, BH, dqrng, scuttle Suggests: testthat, BiocStyle, knitr, rmarkdown, DelayedMatrixStats, HDF5Array, scRNAseq, dynamicTreeCut, ResidualMatrix, ScaledMatrix, DESeq2, pheatmap, scater License: GPL-3 MD5sum: f299210b2363b0abef5555215275890c NeedsCompilation: yes Title: Methods for Single-Cell RNA-Seq Data Analysis Description: Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb] Maintainer: Aaron Lun URL: https://github.com/MarioniLab/scran/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/MarioniLab/scran/issues git_url: https://git.bioconductor.org/packages/scran git_branch: RELEASE_3_22 git_last_commit: 50cb1ba git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scran_1.38.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scran_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scran_1.38.0.tgz vignettes: vignettes/scran/inst/doc/scran.html vignetteTitles: Using scran to analyze scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scran/inst/doc/scran.R dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: BASiCS, BASiCStan, BatchQC, BayesSpace, BioTIP, celda, chevreulPlot, chevreulProcess, ChromSCape, CiteFuse, Coralysis, DeconvoBuddies, Dino, epiregulon, epiregulon.extra, FLAMES, MOSim, MPAC, msImpute, mumosa, pipeComp, scDblFinder, scDD, scMerge, scTreeViz, singleCellTK, Spaniel, SpaNorm, OSTA, mixhvg, SpatialDDLS suggestsMe: anglemania, APL, Banksy, batchelor, blase, bluster, CellTrails, clusterExperiment, decontX, dittoSeq, DOtools, escape, escheR, ExperimentSubset, ggsc, ggspavis, Glimma, glmGamPoi, GSVA, iSEEu, jazzPanda, miloR, Nebulosa, nnSVG, raer, ReactomeGSA, scDiagnostics, scDotPlot, schex, scLANE, scone, scuttle, simPIC, SingleCellAlleleExperiment, sketchR, smoothclust, splatter, SPOTlight, StabMap, SuperCellCyto, SVP, tidySingleCellExperiment, tpSVG, transformGamPoi, TSCAN, velociraptor, Voyager, HCAData, SingleCellMultiModal, TabulaMurisData, simpleSingleCell, Canek, SCdeconR dependencyCount: 62 Package: scrapper Version: 1.4.0 Imports: methods, Rcpp, beachmat (>= 2.25.1), DelayedArray, BiocNeighbors (>= 1.99.0), Rigraphlib, parallel LinkingTo: Rcpp, assorthead (>= 1.3.10), beachmat, BiocNeighbors Suggests: testthat, knitr, rmarkdown, BiocStyle, MatrixGenerics, sparseMatrixStats, Matrix, S4Vectors, SummarizedExperiment, SingleCellExperiment, scRNAseq, igraph License: MIT + file LICENSE Archs: x64 MD5sum: 933c284316f24726c27cb57c7d19276b NeedsCompilation: yes Title: Bindings to C++ Libraries for Single-Cell Analysis Description: Implements R bindings to C++ code for analyzing single-cell (expression) data, mostly from various libscran libraries. Each function performs an individual step in the single-cell analysis workflow, ranging from quality control to clustering and marker detection. It is mostly intended for other Bioconductor package developers to build more user-friendly end-to-end workflows. biocViews: Normalization, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, QualityControl, DifferentialExpression, FeatureExtraction, PrincipalComponent, Clustering Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scrapper git_branch: RELEASE_3_22 git_last_commit: 4de467a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scrapper_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scrapper_1.3.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scrapper_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scrapper_1.4.0.tgz vignettes: vignettes/scrapper/inst/doc/userguide.html vignetteTitles: Using scrapper to analyze single-cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scrapper/inst/doc/userguide.R dependsOnMe: OSCA.advanced, OSCA.basic, SingleRBook importsMe: epiregulon suggestsMe: Coralysis, SingleR, OSTA dependencyCount: 30 Package: scReClassify Version: 1.16.0 Depends: R (>= 4.1) Imports: randomForest, e1071, stats, SummarizedExperiment, SingleCellExperiment, methods Suggests: testthat, knitr, BiocStyle, rmarkdown, DT, mclust, dplyr License: GPL-3 + file LICENSE MD5sum: 79e52d3dd6b41ba0a92a830754dc8822 NeedsCompilation: no Title: scReClassify: post hoc cell type classification of single-cell RNA-seq data Description: A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier. biocViews: Software, Transcriptomics, SingleCell, Classification, SupportVectorMachine Author: Pengyi Yang [aut] (ORCID: ), Taiyun Kim [aut, cre] (ORCID: ) Maintainer: Taiyun Kim URL: https://github.com/SydneyBioX/scReClassify, http://www.bioconductor.org/packages/release/bioc/html/scReClassify.html VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scReClassify/issues git_url: https://git.bioconductor.org/packages/scReClassify git_branch: RELEASE_3_22 git_last_commit: d6212d3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scReClassify_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scReClassify_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scReClassify_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scReClassify_1.16.0.tgz vignettes: vignettes/scReClassify/inst/doc/scReClassify.html vignetteTitles: An introduction to scReClassify package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scReClassify/inst/doc/scReClassify.R dependencyCount: 31 Package: scRecover Version: 1.26.0 Depends: R (>= 3.4.0) Imports: stats, utils, methods, graphics, doParallel, foreach, parallel, penalized, kernlab, rsvd, Matrix (>= 1.2-14), MASS (>= 7.3-45), pscl (>= 1.4.9), bbmle (>= 1.0.18), gamlss (>= 4.4-0), preseqR (>= 4.0.0), SAVER (>= 1.1.1), BiocParallel (>= 1.12.0) Suggests: knitr, rmarkdown, SingleCellExperiment, testthat License: GPL MD5sum: e77ed02eeae08128930f2ce3119d68d7 NeedsCompilation: no Title: scRecover for imputation of single-cell RNA-seq data Description: scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged, since there are both dropout zeros and real zeros in scRNA-seq data. By combination with scImpute, SAVER and MAGIC, scRecover not only detects dropout and real zeros at higher accuracy, but also improve the downstream clustering and visualization results. biocViews: GeneExpression, SingleCell, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao, Xuegong Zhang Maintainer: Zhun Miao URL: https://miaozhun.github.io/scRecover VignetteBuilder: knitr BugReports: https://github.com/miaozhun/scRecover/issues git_url: https://git.bioconductor.org/packages/scRecover git_branch: RELEASE_3_22 git_last_commit: 4850584 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scRecover_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scRecover_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scRecover_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scRecover_1.26.0.tgz vignettes: vignettes/scRecover/inst/doc/scRecover.html vignetteTitles: scRecover hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRecover/inst/doc/scRecover.R dependencyCount: 46 Package: screenCounter Version: 1.10.0 Depends: S4Vectors, SummarizedExperiment Imports: Rcpp, BiocParallel LinkingTo: Rcpp Suggests: BiocGenerics, Biostrings, BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: ebea2e3b51669c39384510061dbbdfb1 NeedsCompilation: yes Title: Counting Reads in High-Throughput Sequencing Screens Description: Provides functions for counting reads from high-throughput sequencing screen data (e.g., CRISPR, shRNA) to quantify barcode abundance. Currently supports single barcodes in single- or paired-end data, and combinatorial barcodes in paired-end data. biocViews: CRISPR, Alignment, FunctionalGenomics, FunctionalPrediction Author: Aaron Lun [aut, cre] (ORCID: ) Maintainer: Aaron Lun URL: https://github.com/crisprVerse/screenCounter SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/screenCounter/issues git_url: https://git.bioconductor.org/packages/screenCounter git_branch: RELEASE_3_22 git_last_commit: 008d705 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/screenCounter_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/screenCounter_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/screenCounter_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/screenCounter_1.10.0.tgz vignettes: vignettes/screenCounter/inst/doc/counting.html vignetteTitles: Counting barcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/screenCounter/inst/doc/counting.R dependencyCount: 36 Package: scRepertoire Version: 2.5.8 Depends: ggplot2, R (>= 4.0) Imports: dplyr, evmix, ggalluvial, ggdendro, ggraph, grDevices, igraph, immApex, iNEXT, Matrix, quantreg, Rcpp, rjson, rlang, S4Vectors, SeuratObject, SingleCellExperiment, SummarizedExperiment, tidygraph, purrr, lifecycle, methods LinkingTo: Rcpp Suggests: BiocManager, BiocStyle, circlize, knitr, Peptides, rmarkdown, scales, scater, Seurat, spelling, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 7b355990615bf0b6b86ffc1290d39cd7 NeedsCompilation: yes Title: A toolkit for single-cell immune receptor profiling Description: scRepertoire is a toolkit for processing and analyzing single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R single-cell workflows. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing Author: Nick Borcherding [aut, cre], Qile Yang [aut], Ksenia Safina [aut], Justin Reimertz [ctb] Maintainer: Nick Borcherding URL: https://www.borch.dev/uploads/scRepertoire/ VignetteBuilder: knitr BugReports: https://github.com/BorchLab/scRepertoire/issues git_url: https://git.bioconductor.org/packages/scRepertoire git_branch: devel git_last_commit: 74605e1 git_last_commit_date: 2025-10-17 Date/Publication: 2025-10-17 source.ver: src/contrib/scRepertoire_2.5.8.tar.gz win.binary.ver: bin/windows/contrib/4.5/scRepertoire_2.5.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scRepertoire_2.5.8.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scRepertoire_2.5.8.tgz vignettes: vignettes/scRepertoire/inst/doc/vignette.html vignetteTitles: Using scRepertoire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scRepertoire/inst/doc/vignette.R importsMe: Ibex suggestsMe: immApex dependencyCount: 112 Package: scRNAseqApp Version: 1.10.0 Depends: R (>= 4.3.0) Imports: bibtex, bslib, circlize, ComplexHeatmap, colourpicker, data.table, DBI, DT, fs, GenomicRanges, GenomeInfoDb, ggdendro, ggforce, ggplot2, ggrepel, ggridges, grDevices, grid, gridExtra, htmltools, IRanges, jsonlite, Matrix, magrittr, methods, patchwork, plotly, RColorBrewer, RefManageR, reshape2, rhdf5, Rsamtools, RSQLite, rtracklayer, S4Vectors, scales, scrypt, Seurat, SeuratObject, shiny, shinyhelper, shinymanager, slingshot, SingleCellExperiment, sortable, stats, tools, xfun, xml2, utils Suggests: rmarkdown, knitr, testthat, BiocStyle, shinytest2 Enhances: celldex, future, SingleR, SummarizedExperiment, tricycle License: GPL-3 MD5sum: 71059e301391c286ffbb13b02a7d7290 NeedsCompilation: no Title: A single-cell RNAseq Shiny app-package Description: The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously, facilitating comprehensive analysis. Additionally, it includes a user management system to regulate database accessibility for different users. biocViews: Visualization, SingleCell, RNASeq Author: Jianhong Ou [aut, cre] (ORCID: ) Maintainer: Jianhong Ou URL: https://github.com/jianhong/scRNAseqApp VignetteBuilder: knitr BugReports: https://github.com/jianhong/scRNAseqApp/issues git_url: https://git.bioconductor.org/packages/scRNAseqApp git_branch: RELEASE_3_22 git_last_commit: 799772d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scRNAseqApp_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scRNAseqApp_1.9.10.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scRNAseqApp_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scRNAseqApp_1.10.0.tgz vignettes: vignettes/scRNAseqApp/inst/doc/scRNAseqApp.html vignetteTitles: scRNAseqApp Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRNAseqApp/inst/doc/scRNAseqApp.R dependencyCount: 236 Package: scruff Version: 1.28.0 Depends: R (>= 4.0) Imports: data.table, GenomicAlignments, GenomicFeatures, txdbmaker, GenomicRanges, Rsamtools, ShortRead, parallel, plyr, BiocGenerics, BiocParallel, S4Vectors, AnnotationDbi, Biostrings, methods, ggplot2, ggthemes, scales, GenomeInfoDb, stringdist, ggbio, rtracklayer, SingleCellExperiment, SummarizedExperiment, Rsubread, parallelly, patchwork Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: bab300439a1f254f5b9b92871537368a NeedsCompilation: no Title: Single Cell RNA-Seq UMI Filtering Facilitator (scruff) Description: A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics. biocViews: Software, Technology, Sequencing, Alignment, RNASeq, SingleCell, WorkflowStep, Preprocessing, QualityControl, Visualization, ImmunoOncology Author: Zhe Wang [aut, cre], Junming Hu [aut], Joshua Campbell [aut] Maintainer: Zhe Wang VignetteBuilder: knitr BugReports: https://github.com/campbio/scruff/issues git_url: https://git.bioconductor.org/packages/scruff git_branch: RELEASE_3_22 git_last_commit: 085152e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scruff_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scruff_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scruff_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scruff_1.28.0.tgz vignettes: vignettes/scruff/inst/doc/scruff.html vignetteTitles: Process Single Cell RNA-Seq reads using scruff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scruff/inst/doc/scruff.R dependencyCount: 168 Package: scry Version: 1.22.0 Depends: R (>= 4.0), stats, methods Imports: DelayedArray, glmpca (>= 0.2.0), Matrix, SingleCellExperiment, SummarizedExperiment, BiocSingular Suggests: BiocGenerics, covr, DuoClustering2018, ggplot2, HDF5Array, knitr, markdown, rmarkdown, TENxPBMCData, testthat License: Artistic-2.0 Archs: x64 MD5sum: e434e17e36985262c078329f977206a6 NeedsCompilation: no Title: Small-Count Analysis Methods for High-Dimensional Data Description: Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq. biocViews: DimensionReduction, GeneExpression, Normalization, PrincipalComponent, RNASeq, Software, Sequencing, SingleCell, Transcriptomics Author: Kelly Street [aut, cre], F. William Townes [aut, cph], Davide Risso [aut], Stephanie Hicks [aut] Maintainer: Kelly Street URL: https://bioconductor.org/packages/scry.html VignetteBuilder: knitr BugReports: https://github.com/kstreet13/scry/issues git_url: https://git.bioconductor.org/packages/scry git_branch: RELEASE_3_22 git_last_commit: a608ad1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scry_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scry_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scry_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scry_1.22.0.tgz vignettes: vignettes/scry/inst/doc/bigdata.html, vignettes/scry/inst/doc/scry.html vignetteTitles: Scry Methods For Larger Datasets, Overview of Scry Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scry/inst/doc/bigdata.R, vignettes/scry/inst/doc/scry.R importsMe: BatchSVG dependencyCount: 45 Package: scShapes Version: 1.16.0 Depends: R (>= 4.1) Imports: Matrix, stats, methods, pscl, VGAM, dgof, BiocParallel, MASS, emdbook, magrittr, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: c54b86970ce5d735cec61978d0c8c3c8 NeedsCompilation: yes Title: A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data Description: We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic. biocViews: RNASeq, SingleCell, MultipleComparison, GeneExpression Author: Malindrie Dharmaratne [cre, aut] (ORCID: ) Maintainer: Malindrie Dharmaratne URL: https://github.com/Malindrie/scShapes VignetteBuilder: knitr BugReports: https://github.com/Malindrie/scShapes/issues git_url: https://git.bioconductor.org/packages/scShapes git_branch: RELEASE_3_22 git_last_commit: 8def801 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scShapes_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scShapes_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scShapes_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scShapes_1.16.0.tgz vignettes: vignettes/scShapes/inst/doc/vignette_scShapes.html vignetteTitles: The vignette for running scShapes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scShapes/inst/doc/vignette_scShapes.R dependencyCount: 34 Package: scTensor Version: 2.20.0 Depends: R (>= 4.1.0) Imports: methods, RSQLite, igraph, S4Vectors, plotly, reactome.db, AnnotationDbi, SummarizedExperiment, SingleCellExperiment, nnTensor (>= 1.1.5), ccTensor (>= 1.0.2), rTensor (>= 1.4.8), abind, plotrix, heatmaply, tagcloud, rmarkdown, BiocStyle, knitr, AnnotationHub, MeSHDbi (>= 1.29.2), grDevices, graphics, stats, utils, outliers, Category, meshr (>= 1.99.1), GOstats, ReactomePA, DOSE, crayon, checkmate, BiocManager, visNetwork, schex, ggplot2 Suggests: testthat, LRBaseDbi, Seurat, scTGIF, Homo.sapiens License: Artistic-2.0 MD5sum: 88c503b37223f892186470ac2386e108 NeedsCompilation: no Title: Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition Description: The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor. biocViews: DimensionReduction, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTensor git_branch: RELEASE_3_22 git_last_commit: 11f50bb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scTensor_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scTensor_2.19.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scTensor_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scTensor_2.20.0.tgz vignettes: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.html, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.html, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.html, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.html, vignettes/scTensor/inst/doc/scTensor.html vignetteTitles: scTensor: 1. Data format and ID conversion, scTensor: 2. Interpretation of HTML report, scTensor: 3. Simulation of CCI, scTensor: 4. Reanalysis of the results of scTensor, scTensor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.R, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.R, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.R, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.R, vignettes/scTensor/inst/doc/scTensor.R dependencyCount: 242 Package: scTGIF Version: 1.24.0 Depends: R (>= 3.6.0) Imports: GSEABase, Biobase, SingleCellExperiment, BiocStyle, plotly, tagcloud, rmarkdown, Rcpp, grDevices, graphics, utils, knitr, S4Vectors, SummarizedExperiment, RColorBrewer, nnTensor, methods, scales, msigdbr, schex, tibble, ggplot2, igraph Suggests: testthat License: Artistic-2.0 MD5sum: 5fdca889ea02ad7fc01ed1ae97804070 NeedsCompilation: no Title: Cell type annotation for unannotated single-cell RNA-Seq data Description: scTGIF connects the cells and the related gene functions without cell type label. biocViews: DimensionReduction, QualityControl, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTGIF git_branch: RELEASE_3_22 git_last_commit: 30812b8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scTGIF_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scTGIF_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scTGIF_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scTGIF_1.24.0.tgz vignettes: vignettes/scTGIF/inst/doc/scTGIF.html vignetteTitles: scTGIF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTGIF/inst/doc/scTGIF.R suggestsMe: scTensor dependencyCount: 142 Package: scTHI Version: 1.22.0 Depends: R (>= 4.0) Imports: BiocParallel, Rtsne, grDevices, graphics, stats Suggests: scTHI.data, knitr, rmarkdown, BiocStyle License: GPL-2 Archs: x64 MD5sum: 489ceb578ff112dbc89bf8fee6d33509 NeedsCompilation: no Title: Indentification of significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data Description: scTHI is an R package to identify active pairs of ligand-receptors from single cells in order to study,among others, tumor-host interactions. scTHI contains a set of signatures to classify cells from the tumor microenvironment. biocViews: Software,SingleCell Author: Francesca Pia Caruso [aut], Michele Ceccarelli [aut, cre] Maintainer: Michele Ceccarelli VignetteBuilder: knitr BugReports: https://github.com/miccec/scTHI/issues git_url: https://git.bioconductor.org/packages/scTHI git_branch: RELEASE_3_22 git_last_commit: f8c5d18 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scTHI_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scTHI_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scTHI_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scTHI_1.22.0.tgz vignettes: vignettes/scTHI/inst/doc/vignette.html vignetteTitles: Using scTHI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTHI/inst/doc/vignette.R dependencyCount: 17 Package: scTreeViz Version: 1.16.0 Depends: R (>= 4.0), methods, epivizr, SummarizedExperiment Imports: data.table, S4Vectors, digest, Matrix, Rtsne, httr, igraph, clustree, scran, sys, epivizrData, epivizrServer, ggraph, scater, Seurat, SingleCellExperiment, ggplot2, stats, utils Suggests: knitr, BiocStyle, testthat, SC3, scRNAseq, rmarkdown, msd16s, metagenomeSeq, epivizrStandalone, GenomeInfoDb License: Artistic-2.0 MD5sum: 3e87803ff4f93ed7504849d3b05223eb NeedsCompilation: no Title: R/Bioconductor package to interactively explore and visualize single cell RNA-seq datasets with hierarhical annotations Description: scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters. biocViews: Visualization, Infrastructure, GUI, SingleCell Author: Jayaram Kancherla [aut, cre], Hector Corrada Bravo [aut], Kazi Tasnim Zinat [aut], Stephanie Hicks [aut] Maintainer: Jayaram Kancherla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTreeViz git_branch: RELEASE_3_22 git_last_commit: 0c746f3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/scTreeViz_1.16.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scTreeViz_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scTreeViz_1.16.0.tgz vignettes: vignettes/scTreeViz/inst/doc/ExploreTreeViz.html vignetteTitles: Explore Data using scTreeViz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTreeViz/inst/doc/ExploreTreeViz.R dependencyCount: 244 Package: scuttle Version: 1.19.0 Depends: SingleCellExperiment Imports: methods, utils, stats, Matrix, Rcpp, BiocGenerics, S4Vectors, BiocParallel, GenomicRanges, SummarizedExperiment, S4Arrays, MatrixGenerics, SparseArray, DelayedArray, beachmat LinkingTo: Rcpp, beachmat Suggests: BiocStyle, knitr, scRNAseq, rmarkdown, testthat, sparseMatrixStats, DelayedMatrixStats, scran License: GPL-3 MD5sum: 207a1869cc9aaeeb9785f250159b7d80 NeedsCompilation: yes Title: Single-Cell RNA-Seq Analysis Utilities Description: Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport Author: Aaron Lun [aut, cre], Davis McCarthy [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scuttle git_branch: devel git_last_commit: bdcc1f8 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/scuttle_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scuttle_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scuttle_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scuttle_1.20.0.tgz vignettes: vignettes/scuttle/inst/doc/misc.html, vignettes/scuttle/inst/doc/norm.html, vignettes/scuttle/inst/doc/qc.html vignetteTitles: 3. Other functions, 2. Normalization, 1. Quality control hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scuttle/inst/doc/misc.R, vignettes/scuttle/inst/doc/norm.R, vignettes/scuttle/inst/doc/qc.R dependsOnMe: omicsGMF, scater, scran, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: BASiCS, BASiCStan, batchelor, chevreulPlot, chevreulProcess, DESpace, DropletUtils, epiregulon, FLAMES, imcRtools, mia, miaDash, mumosa, muscat, SanityR, scDblFinder, simPIC, singleCellTK, SpaceTrooper, splatter, SplineDV, spoon, velociraptor, spatialLIBD, OSTA, mixhvg, SpatialDDLS suggestsMe: Banksy, bluster, CSOA, dreamlet, epiregulon.extra, escheR, ggsc, GSVA, iSEEde, iSEEfier, iSEEpathways, mastR, miloR, raer, ReactomeGSA, SCArray, scDiagnostics, scDotPlot, schex, SingleCellAlleleExperiment, SingleR, sketchR, smoothclust, SpotSweeper, SVP, tpSVG, TSCAN, HCAData, MouseThymusAgeing, scCustomize linksToMe: DropletUtils, scran dependencyCount: 39 Package: scviR Version: 1.10.0 Depends: R (>= 4.3), basilisk, shiny, SingleCellExperiment Imports: reticulate, BiocFileCache, utils, pheatmap, SummarizedExperiment, S4Vectors, limma, scater, stats, MatrixGenerics Suggests: knitr, testthat, reshape2, ggplot2, rhdf5, BiocStyle License: Artistic-2.0 MD5sum: 382d62ef63bdaddba74b45cb24488746 NeedsCompilation: no Title: experimental inferface from R to scvi-tools Description: This package defines interfaces from R to scvi-tools. A vignette works through the totalVI tutorial for analyzing CITE-seq data. Another vignette compares outputs of Chapter 12 of the OSCA book with analogous outputs based on totalVI quantifications. Future work will address other components of scvi-tools, with a focus on building understanding of probabilistic methods based on variational autoencoders. biocViews: Infrastructure, SingleCell, DataImport Author: Vincent Carey [aut, cre] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/scviR VignetteBuilder: knitr BugReports: https://github.com/vjcitn/scviR/issues git_url: https://git.bioconductor.org/packages/scviR git_branch: RELEASE_3_22 git_last_commit: 76c9278 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-30 source.ver: src/contrib/scviR_1.10.0.tar.gz vignettes: vignettes/scviR/inst/doc/citeseq_tut.html, vignettes/scviR/inst/doc/compch12.html, vignettes/scviR/inst/doc/new_citeseq.html, vignettes/scviR/inst/doc/scviR.html vignetteTitles: scvi-tools CITE-seq tutorial in R,, using serialized tutorial components, Comparing totalVI and OSCA book CITE-seq analyses, CITE-seq setup for scviR,, 2025, scviR: an R package interfacing Bioconductor and scvi-tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scviR/inst/doc/citeseq_tut.R, vignettes/scviR/inst/doc/compch12.R, vignettes/scviR/inst/doc/new_citeseq.R, vignettes/scviR/inst/doc/scviR.R dependencyCount: 140 Package: SDAMS Version: 1.30.0 Depends: R(>= 3.5), SummarizedExperiment Imports: trust, qvalue, methods, stats, utils Suggests: testthat License: GPL Archs: x64 MD5sum: ebf90ba6ac6d254a21db0ddce008491c NeedsCompilation: no Title: Differential Abundant/Expression Analysis for Metabolomics, Proteomics and single-cell RNA sequencing Data Description: This Package utilizes a Semi-parametric Differential Abundance/expression analysis (SDA) method for metabolomics and proteomics data from mass spectrometry as well as single-cell RNA sequencing data. SDA is able to robustly handle non-normally distributed data and provides a clear quantification of the effect size. biocViews: ImmunoOncology, DifferentialExpression, Metabolomics, Proteomics, MassSpectrometry, SingleCell Author: Yuntong Li , Chi Wang , Li Chen Maintainer: Yuntong Li git_url: https://git.bioconductor.org/packages/SDAMS git_branch: RELEASE_3_22 git_last_commit: 9d30a5b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SDAMS_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SDAMS_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SDAMS_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SDAMS_1.30.0.tgz vignettes: vignettes/SDAMS/inst/doc/SDAMS.pdf vignetteTitles: SDAMS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SDAMS/inst/doc/SDAMS.R dependencyCount: 50 Package: seahtrue Version: 1.4.0 Depends: R (>= 4.2.0) Imports: dplyr (>= 1.1.2), readxl (>= 1.4.1), logger (>= 0.2.2), tidyxl (>= 1.0.8), purrr (>= 0.3.5), tidyr (>= 1.3.0), lubridate (>= 1.8.0), stringr (>= 1.4.1), tibble (>= 3.1.8), validate (>= 1.1.1), rlang (>= 1.0.0), glue (>= 1.6.2), cli (>= 3.4.1), janitor (>= 2.2.0), ggplot2 (>= 3.5.0), RColorBrewer (>= 1.1.3), colorspace (>= 2.1.0), forcats (>= 1.0.0), ggridges (>= 0.5.6), readr (>= 2.1.5), scales (>= 1.3.0) Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle License: Artistic-2.0 MD5sum: ed93e5c132f65f367b5a796a5469eff7 NeedsCompilation: no Title: Seahtrue revives XF data for structured data analysis Description: Seahtrue organizes oxygen consumption and extracellular acidification analysis data from experiments performed on an XF analyzer into structured nested tibbles.This allows for detailed processing of raw data and advanced data visualization and statistics. Seahtrue introduces an open and reproducible way to analyze these XF experiments. It uses file paths to .xlsx files. These .xlsx files are supplied by the userand are generated by the user in the Wave software from Agilent from the assay result files (.asyr). The .xlsx file contains different sheets of important data for the experiment; 1. Assay Information - Details about how the experiment was set up. 2. Rate Data - Information about the OCR and ECAR rates. 3. Raw Data - The original raw data collected during the experiment. 4. Calibration Data - Data related to calibrating the instrument. Seahtrue focuses on getting the specific data needed for analysis. Once this data is extracted, it is prepared for calculations through preprocessing. To make sure everything is accurate, both the initial data and the preprocessed data go through thorough checks. biocViews: CellBasedAssays, FunctionalPrediction, DataRepresentation, DataImport, CellBiology, Cheminformatics, Metabolomics, MicrotitrePlateAssay, Visualization, QualityControl, BatchEffect, ExperimentalDesign, Preprocessing, GO Author: Vincent de Boer [cre, aut] (ORCID: ), Gerwin Smits [aut], Xiang Zhang [aut] Maintainer: Vincent de Boer URL: https://vcjdeboer.github.io/seahtrue/ VignetteBuilder: knitr BugReports: https://vcjdeboer.github.io/seahtrue/issues git_url: https://git.bioconductor.org/packages/seahtrue git_branch: RELEASE_3_22 git_last_commit: 5959d37 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/seahtrue_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seahtrue_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seahtrue_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seahtrue_1.4.0.tgz vignettes: vignettes/seahtrue/inst/doc/seahtrue.html vignetteTitles: Introduction to Seahtrue hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seahtrue/inst/doc/seahtrue.R dependencyCount: 63 Package: sechm Version: 1.18.0 Depends: R (>= 4.0), SummarizedExperiment, ComplexHeatmap Imports: S4Vectors, seriation, circlize, methods, randomcoloR, stats, grid, grDevices, matrixStats Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 4ad5c2ff062dd3253e5f4295c8438624 NeedsCompilation: no Title: sechm: Complex Heatmaps from a SummarizedExperiment Description: sechm provides a simple interface between SummarizedExperiment objects and the ComplexHeatmap package. It enables plotting annotated heatmaps from SE objects, with easy access to rowData and colData columns, and implements a number of features to make the generation of heatmaps easier and more flexible. These functionalities used to be part of the SEtools package. biocViews: GeneExpression, Visualization Author: Pierre-Luc Germain [cre, aut] (ORCID: ) Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/sechm git_url: https://git.bioconductor.org/packages/sechm git_branch: RELEASE_3_22 git_last_commit: 635c3e6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sechm_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sechm_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sechm_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sechm_1.18.0.tgz vignettes: vignettes/sechm/inst/doc/sechm.html vignetteTitles: sechm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sechm/inst/doc/sechm.R dependsOnMe: SEtools dependencyCount: 74 Package: segmenter Version: 1.16.0 Depends: R (>= 4.1) Imports: ChIPseeker, GenomicRanges, SummarizedExperiment, IRanges, S4Vectors, bamsignals, ComplexHeatmap, graphics, stats, utils, methods, chromhmmData Suggests: testthat, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg18.knownGene, Gviz License: GPL-3 Archs: x64 MD5sum: 7d0b7cf0f5bfa6a58951b3c8c24a6d2f NeedsCompilation: no Title: Perform Chromatin Segmentation Analysis in R by Calling ChromHMM Description: Chromatin segmentation analysis transforms ChIP-seq data into signals over the genome. The latter represents the observed states in a multivariate Markov model to predict the chromatin's underlying states. ChromHMM, written in Java, integrates histone modification datasets to learn the chromatin states de-novo. The goal of this package is to call chromHMM from within R, capture the output files in an S4 object and interface to other relevant Bioconductor analysis tools. In addition, segmenter provides functions to test, select and visualize the output of the segmentation. biocViews: Software, HistoneModification Author: Mahmoud Ahmed [aut, cre] (ORCID: ) Maintainer: Mahmoud Ahmed VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/segmenter/issues git_url: https://git.bioconductor.org/packages/segmenter git_branch: RELEASE_3_22 git_last_commit: c192571 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/segmenter_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/segmenter_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/segmenter_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/segmenter_1.16.0.tgz vignettes: vignettes/segmenter/inst/doc/segmenter.html vignetteTitles: Chromatin Segmentation Analysis Using segmenter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmenter/inst/doc/segmenter.R dependencyCount: 178 Package: segmentSeq Version: 2.44.0 Depends: R (>= 3.5.0), methods, baySeq (>= 2.9.0), S4Vectors, parallel, GenomicRanges, ShortRead, stats Imports: Rsamtools, IRanges, Seqinfo, graphics, grDevices, utils, abind Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: fa1fd0141d5f921d069166af3b6053d3 NeedsCompilation: no Title: Methods for identifying small RNA loci from high-throughput sequencing data Description: High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery. biocViews: MultipleComparison, Sequencing, Alignment, DifferentialExpression, QualityControl, DataImport Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/segmentSeq VignetteBuilder: knitr BugReports: https://github.com/samgg/segmentSeq/issues git_url: https://git.bioconductor.org/packages/segmentSeq git_branch: RELEASE_3_22 git_last_commit: cab2d96 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/segmentSeq_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/segmentSeq_2.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/segmentSeq_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/segmentSeq_2.44.0.tgz vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.html, vignettes/segmentSeq/inst/doc/segmentSeq.html vignetteTitles: segmentsSeq: Methylation locus identification, segmentSeq: small RNA locus detection hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmentSeq/inst/doc/methylationAnalysis.R, vignettes/segmentSeq/inst/doc/segmentSeq.R dependencyCount: 59 Package: selectKSigs Version: 1.22.0 Depends: R(>= 3.6) Imports: HiLDA, magrittr, gtools, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, dplyr, tidyr License: GPL-3 MD5sum: 49db9e7f0fd1a4b3130402cf244babd8 NeedsCompilation: yes Title: Selecting the number of mutational signatures using a perplexity-based measure and cross-validation Description: A package to suggest the number of mutational signatures in a collection of somatic mutations using calculating the cross-validated perplexity score. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Clustering Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/selectKSigs VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/selectKSigs git_url: https://git.bioconductor.org/packages/selectKSigs git_branch: RELEASE_3_22 git_last_commit: 9b94859 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/selectKSigs_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/selectKSigs_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/selectKSigs_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/selectKSigs_1.22.0.tgz vignettes: vignettes/selectKSigs/inst/doc/selectKSigs.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/selectKSigs/inst/doc/selectKSigs.R dependencyCount: 109 Package: SELEX Version: 1.42.0 Depends: rJava (>= 0.5-0), Biostrings (>= 2.26.0) Imports: stats, utils License: GPL (>=2) MD5sum: 4346656d912130571322810ef311f8d3 NeedsCompilation: no Title: Functions for analyzing SELEX-seq data Description: Tools for quantifying DNA binding specificities based on SELEX-seq data. biocViews: Software, MotifDiscovery, MotifAnnotation, GeneRegulation, Transcription Author: Chaitanya Rastogi, Dahong Liu, Lucas Melo, and Harmen J. Bussemaker Maintainer: Harmen J. Bussemaker URL: https://bussemakerlab.org/site/software/ SystemRequirements: Java (>= 1.5) git_url: https://git.bioconductor.org/packages/SELEX git_branch: RELEASE_3_22 git_last_commit: 9d010d3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SELEX_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SELEX_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SELEX_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SELEX_1.42.0.tgz vignettes: vignettes/SELEX/inst/doc/SELEX.pdf vignetteTitles: Motif Discovery with SELEX-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SELEX/inst/doc/SELEX.R dependencyCount: 16 Package: SemDist Version: 1.44.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: d29b679207b1a0323b3ce122f731c696 NeedsCompilation: no Title: Information Accretion-based Function Predictor Evaluation Description: This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor. biocViews: Classification, Annotation, GO, Software Author: Ian Gonzalez and Wyatt Clark Maintainer: Ian Gonzalez URL: http://github.com/iangonzalez/SemDist git_url: https://git.bioconductor.org/packages/SemDist git_branch: RELEASE_3_22 git_last_commit: d765069 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SemDist_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SemDist_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SemDist_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SemDist_1.44.0.tgz vignettes: vignettes/SemDist/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SemDist/inst/doc/introduction.R dependencyCount: 47 Package: semisup Version: 1.34.0 Depends: R (>= 3.0.0) Imports: VGAM Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: ad032c7d45ec0acb1d0cbea9ebd440fe NeedsCompilation: no Title: Semi-Supervised Mixture Model Description: Implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis. biocViews: SNP, GenomicVariation, SomaticMutation, Genetics, Classification, Clustering, DNASeq, Microarray, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/semisup VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/semisup/issues git_url: https://git.bioconductor.org/packages/semisup git_branch: RELEASE_3_22 git_last_commit: 0a83e29 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/semisup_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/semisup_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/semisup_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/semisup_1.34.0.tgz vignettes: vignettes/semisup/inst/doc/semisup.pdf, vignettes/semisup/inst/doc/article.html, vignettes/semisup/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/semisup/inst/doc/semisup.R dependencyCount: 5 Package: seq.hotSPOT Version: 1.10.0 Depends: R (>= 3.5.0) Imports: R.utils, hash, stats, base, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 79f7b92c162c2829b337f26918347ddc NeedsCompilation: no Title: Targeted sequencing panel design based on mutation hotspots Description: seq.hotSPOT provides a resource for designing effective sequencing panels to help improve mutation capture efficacy for ultradeep sequencing projects. Using SNV datasets, this package designs custom panels for any tissue of interest and identify the genomic regions likely to contain the most mutations. Establishing efficient targeted sequencing panels can allow researchers to study mutation burden in tissues at high depth without the economic burden of whole-exome or whole-genome sequencing. This tool was developed to make high-depth sequencing panels to study low-frequency clonal mutations in clinically normal and cancerous tissues. biocViews: Software, Technology, Sequencing, DNASeq, WholeGenome Author: Sydney Grant [aut, cre], Lei Wei [aut], Gyorgy Paragh [aut] Maintainer: Sydney Grant URL: https://github.com/sydney-grant/seq.hotSPOT VignetteBuilder: knitr BugReports: https://github.com/sydney-grant/seq.hotSPOT/issues git_url: https://git.bioconductor.org/packages/seq.hotSPOT git_branch: RELEASE_3_22 git_last_commit: 435a980 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/seq.hotSPOT_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seq.hotSPOT_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seq.hotSPOT_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seq.hotSPOT_1.10.0.tgz vignettes: vignettes/seq.hotSPOT/inst/doc/hotSPOT-vignette.html vignetteTitles: hotSPOT-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seq.hotSPOT/inst/doc/hotSPOT-vignette.R dependencyCount: 9 Package: SeqArray Version: 1.50.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.31.1) Imports: methods, parallel, digest, S4Vectors, IRanges, GenomicRanges, Seqinfo, Biostrings LinkingTo: gdsfmt Suggests: Biobase, BiocGenerics, BiocParallel, RUnit, Rcpp, SNPRelate, crayon, knitr, markdown, rmarkdown, Rsamtools, VariantAnnotation License: GPL-3 MD5sum: 17ed44f3115358c0a416fa85db1c8a3b NeedsCompilation: yes Title: Data management of large-scale whole-genome sequence variant calls using GDS files Description: Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. biocViews: Infrastructure, DataRepresentation, Sequencing, Genetics Author: Xiuwen Zheng [aut, cre] (ORCID: ), Stephanie Gogarten [aut], David Levine [ctb], Cathy Laurie [ctb] Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/SeqArray VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/SeqArray/issues git_url: https://git.bioconductor.org/packages/SeqArray git_branch: RELEASE_3_22 git_last_commit: e6376f0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SeqArray_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SeqArray_1.49.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SeqArray_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SeqArray_1.50.0.tgz vignettes: vignettes/SeqArray/inst/doc/OverviewSlides.html, vignettes/SeqArray/inst/doc/SeqArray.html, vignettes/SeqArray/inst/doc/SeqArrayTutorial.html vignetteTitles: SeqArray Overview, R Integration, SeqArray Data Format and Access hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/SeqArray.R, vignettes/SeqArray/inst/doc/SeqArrayTutorial.R dependsOnMe: GBScleanR, SAIGEgds, SeqVarTools importsMe: GDSArray, GENESIS, ggmanh, VariantExperiment suggestsMe: DelayedDataFrame, HIBAG, VCFArray, GMMAT, MAGEE dependencyCount: 19 Package: seqCAT Version: 1.32.0 Depends: R (>= 3.6), GenomicRanges (>= 1.26.4), VariantAnnotation(>= 1.20.3) Imports: dplyr (>= 0.5.0), GenomeInfoDb (>= 1.13.4), ggplot2 (>= 2.2.1), grid (>= 3.5.0), IRanges (>= 2.8.2), methods, rtracklayer, rlang, scales (>= 0.4.1), S4Vectors (>= 0.12.2), stats, SummarizedExperiment (>= 1.4.0), tidyr (>= 0.6.1), utils Suggests: knitr, BiocStyle, rmarkdown, testthat, BiocManager License: MIT + file LICENCE Archs: x64 MD5sum: b6b92f1b329b62c28b5b763a5ce55e58 NeedsCompilation: no Title: High Throughput Sequencing Cell Authentication Toolkit Description: The seqCAT package uses variant calling data (in the form of VCF files) from high throughput sequencing technologies to authenticate and validate the source, function and characteristics of biological samples used in scientific endeavours. biocViews: Coverage, GenomicVariation, Sequencing, VariantAnnotation Author: Erik Fasterius [aut, cre] Maintainer: Erik Fasterius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqCAT git_branch: RELEASE_3_22 git_last_commit: ccf04ac git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/seqCAT_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seqCAT_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seqCAT_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seqCAT_1.32.0.tgz vignettes: vignettes/seqCAT/inst/doc/seqCAT.html vignetteTitles: seqCAT: The High Throughput Sequencing Cell Authentication Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCAT/inst/doc/seqCAT.R dependencyCount: 100 Package: seqcombo Version: 1.32.0 Depends: R (>= 3.4.0) Imports: ggplot2, grid, igraph, utils, yulab.utils Suggests: emojifont, knitr, rmarkdown, prettydoc, tibble License: Artistic-2.0 MD5sum: 3e2cc6891f7cb249d6734c5d9d53a683 NeedsCompilation: no Title: Visualization Tool for Genetic Reassortment Description: Provides useful functions for visualizing virus reassortment events. biocViews: Alignment, Software, Visualization Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/seqcombo/issues git_url: https://git.bioconductor.org/packages/seqcombo git_branch: RELEASE_3_22 git_last_commit: 32009df git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/seqcombo_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seqcombo_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seqcombo_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seqcombo_1.32.0.tgz vignettes: vignettes/seqcombo/inst/doc/seqcombo.html vignetteTitles: Reassortment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqcombo/inst/doc/seqcombo.R dependencyCount: 33 Package: SeqGate Version: 1.20.0 Depends: S4Vectors, SummarizedExperiment, GenomicRanges Imports: stats, methods, BiocManager Suggests: testthat (>= 3.0.0), edgeR, BiocStyle, knitr, rmarkdown License: GPL (>= 2.0) MD5sum: dd059e023184083b6b2fa15e0a2a6619 NeedsCompilation: no Title: Filtering of Lowly Expressed Features Description: Filtering of lowly expressed features (e.g. genes) is a common step before performing statistical analysis, but an arbitrary threshold is generally chosen. SeqGate implements a method that rationalize this step by the analysis of the distibution of counts in replicate samples. The gate is the threshold above which sequenced features can be considered as confidently quantified. biocViews: DifferentialExpression, GeneExpression, Transcriptomics, Sequencing, RNASeq Author: Christelle Reynès [aut], Stéphanie Rialle [aut, cre] Maintainer: Stéphanie Rialle VignetteBuilder: knitr BugReports: https://github.com/srialle/SeqGate/issues git_url: https://git.bioconductor.org/packages/SeqGate git_branch: RELEASE_3_22 git_last_commit: b9fa51c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SeqGate_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SeqGate_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SeqGate_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SeqGate_1.20.0.tgz vignettes: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.html vignetteTitles: SeqGate: Filter lowly expressed features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.R dependencyCount: 26 Package: SeqGSEA Version: 1.50.0 Depends: Biobase, doParallel, DESeq2 Imports: methods, biomaRt Suggests: GenomicRanges License: GPL (>= 3) MD5sum: 95e52f97cfd49accc6cddfcfafb71453 NeedsCompilation: no Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing Description: The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively. biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression, DifferentialExpression, DifferentialSplicing, ImmunoOncology Author: Xi Wang Maintainer: Xi Wang git_url: https://git.bioconductor.org/packages/SeqGSEA git_branch: RELEASE_3_22 git_last_commit: 03ba6a6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SeqGSEA_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SeqGSEA_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SeqGSEA_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SeqGSEA_1.50.0.tgz vignettes: vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf vignetteTitles: Gene set enrichment analysis of RNA-Seq data with the SeqGSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGSEA/inst/doc/SeqGSEA.R dependencyCount: 100 Package: Seqinfo Version: 1.0.0 Depends: methods, BiocGenerics Imports: stats, S4Vectors (>= 0.47.6), IRanges Suggests: GenomeInfoDb, GenomicRanges, BSgenome, GenomicFeatures, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Celegans.UCSC.ce2, RUnit, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 10585f976146cbcf32a4ce7d46a18709 NeedsCompilation: no Title: A simple S4 class for storing basic information about a collection of genomic sequences Description: The Seqinfo class stores the names, lengths, circularity flags, and genomes for a particular collection of sequences. These sequences are typically the chromosomes and/or scaffolds of a specific genome assembly of a given organism. Seqinfo objects are rarely used as standalone objects. Instead, they are used as part of higher-level objects to represent their seqinfo() component. Examples of such higher-level objects are GRanges, RangedSummarizedExperiment, VCF, GAlignments, etc... defined in other Bioconductor infrastructure packages. biocViews: Infrastructure, DataRepresentation, GenomeAssembly, Annotation, GenomeAnnotation Author: Hervé Pagès [aut, cre] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/Seqinfo VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Seqinfo/issues git_url: https://git.bioconductor.org/packages/Seqinfo git_branch: RELEASE_3_22 git_last_commit: 9fc5a61 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Seqinfo_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Seqinfo_0.99.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Seqinfo_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Seqinfo_1.0.0.tgz vignettes: vignettes/Seqinfo/inst/doc/Seqinfo.html vignetteTitles: The Seqinfo package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Seqinfo/inst/doc/Seqinfo.R dependsOnMe: Biostrings, BSgenome, BSgenomeForge, bumphunter, CSAR, extraChIPs, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, groHMM, HelloRanges, OrganismDbi, Rsamtools, txdbmaker, VariantAnnotation importsMe: alabaster.ranges, AnnotationHubData, annotatr, ATACseqTFEA, atena, ballgown, Bioc.gff, biovizBase, BiSeq, bnbc, bsseq, CAGEfightR, CAGEr, casper, cBioPortalData, CexoR, chipenrich, ChIPexoQual, chromVAR, cleanUpdTSeq, CleanUpRNAseq, cn.mops, CNEr, Cogito, compEpiTools, consensusSeekeR, conumee, crisprBowtie, crisprBwa, crisprDesign, CRISPRseek, crisprShiny, crisprViz, crupR, csaw, DAMEfinder, decompTumor2Sig, DegCre, demuxSNP, derfinder, derfinderPlot, DEScan2, DEWSeq, DMRcaller, DMRcate, DMRScan, dmrseq, DominoEffect, easyRNASeq, ELMER, enhancerHomologSearch, ensembldb, EpiCompare, epigraHMM, EpiMix, EpiTxDb, epivizrData, epivizrStandalone, esATAC, FindIT2, FLAMES, G4SNVHunter, GA4GHclient, GA4GHshiny, gcapc, gDNAx, geneAttribution, genomation, GenomAutomorphism, genomeIntervals, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicOZone, GenomicPlot, GenomicScores, geomeTriD, ggbio, gmoviz, goseq, GOTHiC, GreyListChIP, Gviz, gwascat, heatmaps, HicAggR, HiCBricks, HiCDOC, HiCExperiment, HiCParser, hicVennDiagram, HiTC, InPAS, INSPEcT, InteractionSet, IsoformSwitchAnalyzeR, IVAS, karyoploteR, ldblock, maser, metaseqR2, methInheritSim, methylKit, methylPipe, methylSig, minfi, MinimumDistance, mobileRNA, monaLisa, mosaics, motifmatchr, MotifPeeker, motifTestR, MouseFM, msgbsR, multicrispr, MutationalPatterns, myvariant, nearBynding, nucleR, nullranges, OGRE, OMICsPCA, Organism.dplyr, panelcn.mops, peakCombiner, periodicDNA, PICB, pipeFrame, plyinteractions, plyranges, podkat, pram, prebs, ProteoDisco, PureCN, QDNAseq, qpgraph, qsea, QuasR, r3Cseq, raer, RaggedExperiment, ramr, recoup, regioneR, regionReport, REMP, rfPred, RgnTX, RiboCrypt, RiboProfiling, riboSeqR, ribosomeProfilingQC, rigvf, RJMCMCNucleosomes, rnaEditr, RNAmodR, RTCGAToolbox, rtracklayer, scanMiR, scmeth, segmentSeq, SeqArray, seqsetvis, sesame, sevenC, SGSeq, ShortRead, SingleMoleculeFootprinting, sitadela, SomaticSignatures, SplicingGraphs, SPLINTER, srnadiff, strandCheckR, SummarizedExperiment, svaNUMT, svaRetro, tadar, TCGAutils, TEKRABber, TENxIO, TEQC, TFBSTools, trackViewer, transmogR, tRNAscanImport, TVTB, tximeta, VariantFiltering, VplotR, YAPSA, GenomicState, grasp2db, sesameData suggestsMe: AlphaMissenseR, AnnotationHub, RAIDS, RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, TFEA.ChIP, TFutils dependencyCount: 9 Package: seqLogo Version: 1.76.0 Depends: R (>= 4.2), methods, grid Imports: stats4, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat License: LGPL (>= 2) MD5sum: 64249344e682f195c85ed539413135a4 NeedsCompilation: no Title: Sequence logos for DNA sequence alignments Description: seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990). biocViews: SequenceMatching Author: Oliver Bembom [aut], Robert Ivanek [aut, cre] (ORCID: ) Maintainer: Robert Ivanek VignetteBuilder: knitr BugReports: https://github.com/ivanek/seqLogo/issues git_url: https://git.bioconductor.org/packages/seqLogo git_branch: RELEASE_3_22 git_last_commit: 5d40dcb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/seqLogo_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seqLogo_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seqLogo_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seqLogo_1.76.0.tgz vignettes: vignettes/seqLogo/inst/doc/seqLogo.html vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R dependsOnMe: generegulation importsMe: IntEREst, PWMEnrich, RCAS, riboSeqR, scanMiR, SPLINTER, TENET, TFBSTools, kmeRtone suggestsMe: BCRANK, DiffLogo, igvR, MAGAR, motifcounter, MotifDb, PMScanR, universalmotif dependencyCount: 4 Package: seqPattern Version: 1.42.0 Depends: methods, R (>= 2.15.0) Imports: Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix Suggests: BSgenome.Drerio.UCSC.danRer7, CAGEr, RUnit, BiocGenerics, BiocStyle Enhances: parallel License: GPL-3 MD5sum: e56f0663a24b852cc5b2d31c1bfc0419 NeedsCompilation: no Title: Visualising oligonucleotide patterns and motif occurrences across a set of sorted sequences Description: Visualising oligonucleotide patterns and sequence motifs occurrences across a large set of sequences centred at a common reference point and sorted by a user defined feature. biocViews: Visualization, SequenceMatching Author: Vanja Haberle Maintainer: Vanja Haberle git_url: https://git.bioconductor.org/packages/seqPattern git_branch: RELEASE_3_22 git_last_commit: 0101858 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/seqPattern_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seqPattern_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seqPattern_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seqPattern_1.42.0.tgz vignettes: vignettes/seqPattern/inst/doc/seqPattern.pdf vignetteTitles: seqPattern hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqPattern/inst/doc/seqPattern.R importsMe: genomation dependencyCount: 18 Package: seqsetvis Version: 1.30.0 Depends: R (>= 4.3), ggplot2 Imports: cowplot, data.table, eulerr, Seqinfo, GenomicAlignments, GenomicRanges, ggplotify, grDevices, grid, IRanges, limma, methods, pbapply, pbmcapply, png, RColorBrewer, Rsamtools, rtracklayer, S4Vectors, scales, stats, UpSetR Suggests: BiocFileCache, BiocManager, BiocStyle, ChIPpeakAnno, GenomeInfoDb, covr, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: f38a9a677cc1d5ac5b43b3edef39f4c1 NeedsCompilation: no Title: Set Based Visualizations for Next-Gen Sequencing Data Description: seqsetvis enables the visualization and analysis of sets of genomic sites in next gen sequencing data. Although seqsetvis was designed for the comparison of mulitple ChIP-seq samples, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files pileups from bam file). seqsetvis has multiple functions for fetching data from regions into a tidy format for analysis in data.table or tidyverse and visualization via ggplot2. biocViews: Software, ChIPSeq, MultipleComparison, Sequencing, Visualization Author: Joseph R Boyd [aut, cre] (ORCID: ) Maintainer: Joseph R Boyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqsetvis git_branch: RELEASE_3_22 git_last_commit: 079b602 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/seqsetvis_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seqsetvis_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seqsetvis_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seqsetvis_1.30.0.tgz vignettes: vignettes/seqsetvis/inst/doc/seqsetvis_overview.html vignetteTitles: Overview and Use Cases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/seqsetvis/inst/doc/seqsetvis_overview.R dependencyCount: 94 Package: SeqSQC Version: 1.32.0 Depends: R (>= 3.4), ExperimentHub (>= 1.3.7), SNPRelate (>= 1.10.2) Imports: e1071, GenomicRanges, gdsfmt, ggplot2, GGally, IRanges, methods, plotly, RColorBrewer, reshape2, rmarkdown, S4Vectors, stats, utils Suggests: BiocStyle, knitr, testthat License: GPL-3 Archs: x64 MD5sum: d008cfbb39378bea42b381f27906d855 NeedsCompilation: no Title: A bioconductor package for sample quality check with next generation sequencing data Description: The SeqSQC is designed to identify problematic samples in NGS data, including samples with gender mismatch, contamination, cryptic relatedness, and population outlier. biocViews: Experiment Data, Homo_sapiens_Data, Sequencing Data, Project1000genomes, Genome Author: Qian Liu [aut, cre] Maintainer: Qian Liu URL: https://github.com/Liubuntu/SeqSQC VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/SeqSQC/issues git_url: https://git.bioconductor.org/packages/SeqSQC git_branch: RELEASE_3_22 git_last_commit: a5a9f88 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SeqSQC_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SeqSQC_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SeqSQC_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SeqSQC_1.32.0.tgz vignettes: vignettes/SeqSQC/inst/doc/vignette.html vignetteTitles: Sample Quality Check for Next-Generation Sequencing Data with SeqSQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqSQC/inst/doc/vignette.R dependencyCount: 115 Package: SeqVarTools Version: 1.48.0 Depends: SeqArray Imports: grDevices, graphics, stats, methods, Biobase, BiocGenerics, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, logistf, Matrix, data.table, Suggests: BiocStyle, RUnit, stringr License: GPL-3 MD5sum: 6d5bc4b643890fe1f6ba8bb6290b4733 NeedsCompilation: no Title: Tools for variant data Description: An interface to the fast-access storage format for VCF data provided in SeqArray, with tools for common operations and analysis. biocViews: SNP, GeneticVariability, Sequencing, Genetics Author: Stephanie M. Gogarten, Xiuwen Zheng, Adrienne Stilp Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/SeqVarTools git_url: https://git.bioconductor.org/packages/SeqVarTools git_branch: RELEASE_3_22 git_last_commit: cefddfd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SeqVarTools_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SeqVarTools_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SeqVarTools_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SeqVarTools_1.48.0.tgz vignettes: vignettes/SeqVarTools/inst/doc/Iterators.pdf, vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Iterators in SeqVarTools, Introduction to SeqVarTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqVarTools/inst/doc/Iterators.R, vignettes/SeqVarTools/inst/doc/SeqVarTools.R importsMe: GENESIS suggestsMe: GMMAT, MAGEE dependencyCount: 89 Package: SEraster Version: 1.2.0 Depends: R (>= 4.5.0) Imports: BiocParallel, ggplot2, Matrix, methods, rearrr, sf, SpatialExperiment, SummarizedExperiment Suggests: CooccurrenceAffinity, nnSVG, testthat (>= 3.0.0), knitr, rmarkdown, BiocManager, remotes License: GPL-3 MD5sum: 83f9d8f731b370316c3597fbd9d6646d NeedsCompilation: no Title: Rasterization Preprocessing Framework for Scalable Spatial Omics Data Analysis Description: SEraster is a rasterization preprocessing framework that aggregates cellular information into spatial pixels to reduce resource requirements for spatial omics data analysis. SEraster reduces the number of spatial points in spatial omics datasets for downstream analysis through a process of rasterization where single cells’ gene expression or cell-type labels are aggregated into equally sized pixels based on a user-defined resolution. SEraster is built on an R/Bioconductor S4 class called SpatialExperiment. SEraster can be incorporated with other packages to conduct downstream analyses for spatial omics datasets, such as detecting spatially variable genes. biocViews: Software, Spatial, GeneExpression, Transcriptomics, SingleCell, Preprocessing Author: Gohta Aihara [aut, cre] (ORCID: ), Mayling Chen [aut] (ORCID: ), Lyla Atta [aut] (ORCID: ), Jean Fan [aut, rev] (ORCID: ) Maintainer: Gohta Aihara URL: https://github.com/JEFworks-Lab/SEraster VignetteBuilder: knitr BugReports: https://github.com/JEFworks-Lab/SEraster/issues git_url: https://git.bioconductor.org/packages/SEraster git_branch: RELEASE_3_22 git_last_commit: 804b492 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SEraster_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SEraster_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SEraster_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SEraster_1.2.0.tgz vignettes: vignettes/SEraster/inst/doc/getting-started-with-SEraster.html vignetteTitles: Getting Started With SEraster hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEraster/inst/doc/getting-started-with-SEraster.R dependencyCount: 99 Package: sesame Version: 1.28.0 Depends: R (>= 4.5.0), sesameData Imports: graphics, BiocParallel, utils, methods, stringr, readr, tibble, MASS, wheatmap (>= 0.2.0), GenomicRanges (>= 1.61.1), IRanges, grid, preprocessCore, S4Vectors, ggplot2, BiocFileCache, Seqinfo, stats, SummarizedExperiment (>= 1.39.1), dplyr, reshape2 Suggests: scales, BiocManager, GenomeInfoDb, knitr, DNAcopy, e1071, randomForest, RPMM, rmarkdown, testthat, tidyr, BiocStyle, ggrepel, grDevices, KernSmooth, pals License: MIT + file LICENSE MD5sum: 6663fcb092fb71752b3438a37039344f NeedsCompilation: no Title: SEnsible Step-wise Analysis of DNA MEthylation BeadChips Description: Tools For analyzing Illumina Infinium DNA methylation arrays. SeSAMe provides utilities to support analyses of multiple generations of Infinium DNA methylation BeadChips, including preprocessing, quality control, visualization and inference. SeSAMe features accurate detection calling, intelligent inference of ethnicity, sex and advanced quality control routines. biocViews: DNAMethylation, MethylationArray, Preprocessing, QualityControl Author: Wanding Zhou [aut, cre] (ORCID: ), Wubin Ding [ctb], David Goldberg [ctb], Ethan Moyer [ctb], Bret Barnes [ctb], Timothy Triche [ctb], Hui Shen [aut] Maintainer: Wanding Zhou URL: https://github.com/zwdzwd/sesame VignetteBuilder: knitr BugReports: https://github.com/zwdzwd/sesame/issues git_url: https://git.bioconductor.org/packages/sesame git_branch: RELEASE_3_22 git_last_commit: f0f05cb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sesame_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sesame_1.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sesame_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sesame_1.28.0.tgz vignettes: vignettes/sesame/inst/doc/inferences.html, vignettes/sesame/inst/doc/modeling.html, vignettes/sesame/inst/doc/nonhuman.html, vignettes/sesame/inst/doc/QC.html, vignettes/sesame/inst/doc/sesame.html vignetteTitles: "4. Data Inference", 3. Modeling, 2. Non-human Array, 1. Quality Control, "0. Basic Usage" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sesame/inst/doc/inferences.R, vignettes/sesame/inst/doc/modeling.R, vignettes/sesame/inst/doc/nonhuman.R, vignettes/sesame/inst/doc/QC.R, vignettes/sesame/inst/doc/sesame.R importsMe: MethReg, TENET, CytoMethIC suggestsMe: knowYourCG, RnBeads, TCGAbiolinks, sesameData dependencyCount: 109 Package: SETA Version: 1.0.0 Depends: R (>= 4.5.0) Imports: dplyr, MASS, Matrix, SingleCellExperiment (>= 1.30.1), stats, tidygraph, rlang, utils Suggests: BiocStyle, caret, glmnet, corrplot, ggplot2, ggraph, knitr, methods, patchwork, reshape2, rmarkdown, SeuratObject, Seurat, SummarizedExperiment, TabulaMurisSenisData, tidyr, tidytext, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 11a7e6b41f6adbd163427414f26869a8 NeedsCompilation: no Title: Single Cell Ecological Taxonomic Analysis Description: Tools for compositional and other sample-level ecological analyses and visualizations tailored for single-cell RNA-seq data. SETA includes functions for taxonomizing celltypes, normalizing data, performing statistical tests, and visualizing results. Several tutorials are included to guide users and introduce them to key concepts. SETA is meant to teach users about statistical concepts underlying ecological analysis methods so they can apply them to their own single-cell data. biocViews: SingleCell, Transcriptomics, RNASeq, GeneExpression, StatisticalMethod, DimensionReduction, Visualization, Normalization, DataRepresentation, SystemsBiology Author: Kyle Kimler [aut, cre] (ORCID: ), Marc Elosua-Bayes [aut] Maintainer: Kyle Kimler URL: https://github.com/kkimler/SETA VignetteBuilder: knitr BugReports: https://github.com/kkimler/SETA/issues git_url: https://git.bioconductor.org/packages/SETA git_branch: RELEASE_3_22 git_last_commit: 2e28769 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SETA_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SETA_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SETA_1.0.0.tgz vignettes: vignettes/SETA/inst/doc/comparing_samples.html, vignettes/SETA/inst/doc/introductory_vignette.html, vignettes/SETA/inst/doc/reference_frames.html vignetteTitles: Comparing samples with SETA, Introduction to SETA ecological transforms and sample-level latent spaces, Multi-Resolution Compositional Analysis in scRNA-seq: Reference Frames with SETA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SETA/inst/doc/comparing_samples.R, vignettes/SETA/inst/doc/introductory_vignette.R, vignettes/SETA/inst/doc/reference_frames.R dependencyCount: 48 Package: SEtools Version: 1.24.0 Depends: R (>= 4.0), SummarizedExperiment, sechm Imports: BiocParallel, Matrix, DESeq2, S4Vectors, data.table, edgeR, openxlsx, pheatmap, stats, circlize, methods, sva Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: GPL MD5sum: 2f279d30c7e36fe020dc3b4d7e95730f NeedsCompilation: no Title: SEtools: tools for working with SummarizedExperiment Description: This includes a set of convenience functions for working with the SummarizedExperiment class. Note that plotting functions historically in this package have been moved to the sechm package (see vignette for details). biocViews: GeneExpression Author: Pierre-Luc Germain [cre, aut] (ORCID: ) Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/SEtools git_url: https://git.bioconductor.org/packages/SEtools git_branch: RELEASE_3_22 git_last_commit: 06b3955 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SEtools_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SEtools_1.23.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SEtools_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SEtools_1.24.0.tgz vignettes: vignettes/SEtools/inst/doc/SEtools.html vignetteTitles: SEtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEtools/inst/doc/SEtools.R dependencyCount: 122 Package: sevenbridges Version: 1.40.0 Depends: methods, utils, stats Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors, docopt, curl, uuid, data.table Suggests: knitr, rmarkdown, testthat, readr License: Apache License 2.0 | file LICENSE MD5sum: 9d15413e5cc0409dacb74cbf7a05776b NeedsCompilation: no Title: Seven Bridges Platform API Client and Common Workflow Language Tool Builder in R Description: R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms. biocViews: Software, DataImport, ThirdPartyClient Author: Phil Webster [aut, cre], Soner Koc [aut] (ORCID: ), Nan Xiao [aut], Tengfei Yin [aut], Dusan Randjelovic [ctb], Emile Young [ctb], Velsera [cph, fnd] Maintainer: Phil Webster URL: https://www.sevenbridges.com, https://sbg.github.io/sevenbridges-r/, https://github.com/sbg/sevenbridges-r VignetteBuilder: knitr BugReports: https://github.com/sbg/sevenbridges-r/issues git_url: https://git.bioconductor.org/packages/sevenbridges git_branch: RELEASE_3_22 git_last_commit: 49577ad git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sevenbridges_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sevenbridges_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sevenbridges_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sevenbridges_1.40.0.tgz vignettes: vignettes/sevenbridges/inst/doc/api.html, vignettes/sevenbridges/inst/doc/apps.html, vignettes/sevenbridges/inst/doc/bioc-workflow.html, vignettes/sevenbridges/inst/doc/cgc-datasets.html, vignettes/sevenbridges/inst/doc/docker.html, vignettes/sevenbridges/inst/doc/rstudio.html vignetteTitles: Complete Guide for Seven Bridges API R Client, Describe and Execute CWL Tools/Workflows in R, Master Tutorial: Use R for Cancer Genomics Cloud, Find Data on CGC via Data Browser and Datasets API, Creating Your Docker Container and Command Line Interface (with docopt), IDE Container: RStudio Server,, Shiny Server,, and More hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sevenbridges/inst/doc/api.R, vignettes/sevenbridges/inst/doc/apps.R, vignettes/sevenbridges/inst/doc/bioc-workflow.R, vignettes/sevenbridges/inst/doc/cgc-datasets.R, vignettes/sevenbridges/inst/doc/docker.R, vignettes/sevenbridges/inst/doc/rstudio.R dependencyCount: 31 Package: sevenC Version: 1.30.0 Depends: R (>= 3.5), InteractionSet (>= 1.2.0) Imports: rtracklayer (>= 1.34.1), BiocGenerics (>= 0.22.0), Seqinfo, GenomicRanges (>= 1.28.5), IRanges (>= 2.10.3), S4Vectors (>= 0.14.4), readr (>= 1.1.0), purrr (>= 0.2.2), data.table (>= 1.10.4), boot (>= 1.3-20), methods (>= 3.4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicInteractions, covr License: GPL-3 MD5sum: 90e869a15e4b6922bf24bf8c5a1e852d NeedsCompilation: no Title: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs Description: Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes. biocViews: DNA3DStructure, ChIPchip, Coverage, DataImport, Epigenetics, FunctionalGenomics, Classification, Regression, ChIPSeq, HiC, Annotation Author: Jonas Ibn-Salem [aut, cre] Maintainer: Jonas Ibn-Salem URL: https://github.com/ibn-salem/sevenC VignetteBuilder: knitr BugReports: https://github.com/ibn-salem/sevenC/issues git_url: https://git.bioconductor.org/packages/sevenC git_branch: RELEASE_3_22 git_last_commit: c38ab75 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sevenC_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sevenC_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sevenC_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sevenC_1.30.0.tgz vignettes: vignettes/sevenC/inst/doc/sevenC.html vignetteTitles: Introduction to sevenC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sevenC/inst/doc/sevenC.R dependencyCount: 83 Package: SGSeq Version: 1.44.0 Depends: R (>= 4.0), IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), Rsamtools (>= 1.31.2), SummarizedExperiment, methods Imports: AnnotationDbi, BiocGenerics (>= 0.31.5), Biostrings (>= 2.47.6), GenomicAlignments (>= 1.15.7), GenomicFeatures (>= 1.31.5), GenomeInfoDb, RUnit, S4Vectors (>= 0.23.19), Seqinfo, grDevices, graphics, igraph, parallel, rtracklayer (>= 1.39.7), stats Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown License: Artistic-2.0 MD5sum: 8b90d50a2861de62d29a0dfb1cc4f984 NeedsCompilation: no Title: Splice event prediction and quantification from RNA-seq data Description: SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. biocViews: AlternativeSplicing, ImmunoOncology, RNASeq, Transcription Author: Leonard Goldstein [cre, aut] Maintainer: Leonard Goldstein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SGSeq git_branch: RELEASE_3_22 git_last_commit: 2a90d1b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SGSeq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SGSeq_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SGSeq_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SGSeq_1.44.0.tgz vignettes: vignettes/SGSeq/inst/doc/SGSeq.html vignetteTitles: SGSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R dependsOnMe: EventPointer importsMe: Rhisat2 suggestsMe: FRASER dependencyCount: 81 Package: SharedObject Version: 1.24.0 Depends: R (>= 3.6.0) Imports: Rcpp, methods, stats, BiocGenerics LinkingTo: BH, Rcpp Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 725707e5bf6c3d4d10911da3103c3318 NeedsCompilation: yes Title: Sharing R objects across multiple R processes without memory duplication Description: This package is developed for facilitating parallel computing in R. It is capable to create an R object in the shared memory space and share the data across multiple R processes. It avoids the overhead of memory dulplication and data transfer, which make sharing big data object across many clusters possible. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/SharedObject/issues git_url: https://git.bioconductor.org/packages/SharedObject git_branch: RELEASE_3_22 git_last_commit: e673c87 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SharedObject_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SharedObject_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SharedObject_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SharedObject_1.24.0.tgz vignettes: vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.html, vignettes/SharedObject/inst/doc/quick_start_guide.html vignetteTitles: quickStartChinese, quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.R, vignettes/SharedObject/inst/doc/quick_start_guide.R importsMe: NewWave suggestsMe: ClustAssess dependencyCount: 8 Package: shinybiocloader Version: 1.0.0 Depends: htmltools Imports: shiny Suggests: shinydashboard, tinytest, quarto License: Artistic-2.0 MD5sum: 0b06a967937afcdf8a1d852998311aa7 NeedsCompilation: no Title: Use a Shiny Bioconductor CSS loader Description: Add a Bioconductor themed CSS loader to your shiny app. It is based on the shinycustomloader R package. Use a spinning Bioconductor note loader to enhance your shiny app loading screen. This package is intended for developer use. biocViews: Software, Infrastructure, GUI Author: Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/shinybiocloader SystemRequirements: quarto VignetteBuilder: quarto BugReports: https://github.com/Bioconductor/shinybiocloader/issues git_url: https://git.bioconductor.org/packages/shinybiocloader git_branch: RELEASE_3_22 git_last_commit: 80a07b2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/shinybiocloader_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/shinybiocloader_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/shinybiocloader_1.0.0.tgz vignettes: vignettes/shinybiocloader/inst/doc/shinybiocloader.html vignetteTitles: shinybiocloader.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinybiocloader/inst/doc/shinybiocloader.R importsMe: BiocHubsShiny dependencyCount: 36 Package: shinyDSP Version: 1.2.0 Depends: R (>= 4.5) Imports: AnnotationHub, BiocGenerics, bsicons, bslib, circlize, ComplexHeatmap, cowplot, dplyr, DT, edgeR, ExperimentHub, ggplot2, ggpubr, ggrepel, grDevices, grid, htmltools, limma, magrittr, pals, readr, S4Vectors, scales, scater, shiny, shinycssloaders, shinyjs, shinyvalidate, shinyWidgets, SingleCellExperiment, standR, stats, stringr, SummarizedExperiment, tibble, tidyr, utils, withr Suggests: BiocStyle, knitr, rmarkdown, shinytest2, spelling, svglite, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: d56b59df0e6d4241e022dc0be765aa10 NeedsCompilation: no Title: A Shiny App For Visualizing Nanostring GeoMx DSP Data Description: This package is a Shiny app for interactively analyzing and visualizing Nanostring GeoMX Whole Transcriptome Atlas data. Users have the option of exploring a sample data to explore this app's functionality. Regions of interest (ROIs) can be filtered based on any user-provided metadata. Upon taking two or more groups of interest, all pairwise and ANOVA-like testing are automatically performed. Available ouputs include PCA, Volcano plots, tables and heatmaps. Aesthetics of each output are highly customizable. biocViews: DifferentialExpression, GeneExpression, ShinyApps, Spatial, Transcriptomics Author: Seung J. Kim [aut, cre] (ORCID: ), Marco Mura [aut, fnd] Maintainer: Seung J. Kim URL: https://github.com/kimsjune/shinyDSP, http://joonkim.ca/shinyDSP/ VignetteBuilder: knitr BugReports: https://github.com/kimsjune/shinyDSP/issues git_url: https://git.bioconductor.org/packages/shinyDSP git_branch: RELEASE_3_22 git_last_commit: 867bc12 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/shinyDSP_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/shinyDSP_1.1.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/shinyDSP_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/shinyDSP_1.2.0.tgz vignettes: vignettes/shinyDSP/inst/doc/shinyDSP_secondary.html, vignettes/shinyDSP/inst/doc/shinyDSP.html vignetteTitles: shinyDSP_secondary, shinyDSP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/shinyDSP/inst/doc/shinyDSP_secondary.R, vignettes/shinyDSP/inst/doc/shinyDSP.R dependencyCount: 253 Package: shinyepico Version: 1.18.0 Depends: R (>= 4.3.0) Imports: DT (>= 0.15.0), data.table (>= 1.13.0), doParallel (>= 1.0.0), dplyr (>= 1.0.9), foreach (>= 1.5.0), GenomicRanges (>= 1.38.0), ggplot2 (>= 3.3.0), gplots (>= 3.0.0), heatmaply (>= 1.1.0), limma (>= 3.42.0), minfi (>= 1.32.0), plotly (>= 4.9.2), reshape2 (>= 1.4.0), rlang (>= 1.0.2), rmarkdown (>= 2.3.0), rtracklayer (>= 1.46.0), shiny (>= 1.5.0), shinyWidgets (>= 0.5.0), shinycssloaders (>= 0.3.0), shinyjs (>= 1.1.0), shinythemes (>= 1.1.0), statmod (>= 1.4.0), tidyr (>= 1.2.0), zip (>= 2.1.0) Suggests: knitr (>= 1.30.0), mCSEA (>= 1.10.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, testthat, minfiData, BiocStyle License: AGPL-3 + file LICENSE MD5sum: 4a6d146107775f8839a659384a0733af NeedsCompilation: no Title: ShinyÉPICo Description: ShinyÉPICo is a graphical pipeline to analyze Illumina DNA methylation arrays (450k or EPIC). It allows to calculate differentially methylated positions and differentially methylated regions in a user-friendly interface. Moreover, it includes several options to export the results and obtain files to perform downstream analysis. biocViews: DifferentialMethylation,DNAMethylation,Microarray,Preprocessing,QualityControl Author: Octavio Morante-Palacios [cre, aut] Maintainer: Octavio Morante-Palacios URL: https://github.com/omorante/shiny_epico VignetteBuilder: knitr BugReports: https://github.com/omorante/shiny_epico/issues git_url: https://git.bioconductor.org/packages/shinyepico git_branch: RELEASE_3_22 git_last_commit: d61e0ee git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/shinyepico_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/shinyepico_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/shinyepico_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/shinyepico_1.18.0.tgz vignettes: vignettes/shinyepico/inst/doc/shiny_epico.html vignetteTitles: shinyepico hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/shinyepico/inst/doc/shiny_epico.R dependencyCount: 209 Package: shinyMethyl Version: 1.46.0 Imports: Biobase, BiocGenerics, graphics, grDevices, htmltools, MatrixGenerics, methods, minfi, RColorBrewer, shiny, stats, utils Suggests: shinyMethylData, minfiData, BiocStyle, knitr, testthat License: Artistic-2.0 Archs: x64 MD5sum: e69e2edf57a08a016efb3bad517c2450 NeedsCompilation: no Title: Interactive visualization for Illumina methylation arrays Description: Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl, MethylationArray Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/shinyMethyl VignetteBuilder: knitr BugReports: https://github.com/Jfortin1/shinyMethyl git_url: https://git.bioconductor.org/packages/shinyMethyl git_branch: RELEASE_3_22 git_last_commit: 3cd20a1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/shinyMethyl_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/shinyMethyl_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/shinyMethyl_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/shinyMethyl_1.46.0.tgz vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.html vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K methylation arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinyMethyl/inst/doc/shinyMethyl.R dependencyCount: 157 Package: ShortRead Version: 1.68.0 Depends: BiocGenerics (>= 0.23.3), BiocParallel, Biostrings (>= 2.47.6), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6) Imports: Biobase, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Seqinfo, GenomicRanges (>= 1.31.8), pwalign, hwriter, methods, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi, knitr License: Artistic-2.0 Archs: x64 MD5sum: 43070d34a1b5f924c71600ad8dcc965b NeedsCompilation: yes Title: FASTQ input and manipulation Description: This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. biocViews: DataImport, Sequencing, QualityControl Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Michael Lawrence [ctb], Simon Anders [ctb], Rohit Satyam [ctb] (Converted Overview.Rnw vignette from Sweave to RMarkdown / HTML.), J Wokaty [ctb] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/ShortRead, https://github.com/Bioconductor/ShortRead, https://support.bioconductor.org/tag/ShortRead VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ShortRead/issues git_url: https://git.bioconductor.org/packages/ShortRead git_branch: RELEASE_3_22 git_last_commit: df90c87 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ShortRead_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ShortRead_1.67.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ShortRead_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ShortRead_1.68.0.tgz vignettes: vignettes/ShortRead/inst/doc/Overview.html vignetteTitles: An introduction to ShortRead hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ShortRead/inst/doc/Overview.R dependsOnMe: chipseq, EDASeq, esATAC, OTUbase, Rqc, segmentSeq, systemPipeR, EatonEtAlChIPseq, NestLink, sequencing importsMe: basecallQC, BEAT, CellBarcode, chipseq, ChIPseqR, ChIPsim, CircSeqAlignTk, dada2, easyRNASeq, FastqCleaner, FLAMES, GOTHiC, icetea, IONiseR, nucleR, QuasR, RSVSim, scruff, UMI4Cats, seqpac, DBTC, genBaRcode, rsahmi suggestsMe: BiocParallel, CSAR, DspikeIn, GenomicAlignments, Rsamtools, S4Vectors, HiCDataLymphoblast, systemPipeRdata, yeastRNASeq, demulticoder, inDAGO dependencyCount: 53 Package: SIAMCAT Version: 2.14.0 Depends: R (>= 4.2.0), mlr3, phyloseq Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase, gridExtra, LiblineaR, matrixStats, methods, pROC, PRROC, RColorBrewer, scales, stats, stringr, utils, infotheo, progress, corrplot, lmerTest, mlr3learners, mlr3tuning, paradox, lgr Suggests: BiocStyle, testthat, knitr, rmarkdown, tidyverse, ggpubr License: GPL-3 MD5sum: 8d6a88d2d609fe8cdb36f952a58ccba1 NeedsCompilation: no Title: Statistical Inference of Associations between Microbial Communities And host phenoTypes Description: Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots). biocViews: ImmunoOncology, Metagenomics, Classification, Microbiome, Sequencing, Preprocessing, Clustering, FeatureExtraction, GeneticVariability, MultipleComparison,Regression Author: Konrad Zych [aut] (ORCID: ), Jakob Wirbel [aut, cre] (ORCID: ), Georg Zeller [aut] (ORCID: ), Morgan Essex [ctb], Nicolai Karcher [ctb], Kersten Breuer [ctb] Maintainer: Jakob Wirbel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIAMCAT git_branch: RELEASE_3_22 git_last_commit: cf500c9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SIAMCAT_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SIAMCAT_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SIAMCAT_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SIAMCAT_2.14.0.tgz vignettes: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.html vignetteTitles: SIAMCAT confounder example, SIAMCAT holdout testing, SIAMCAT meta-analysis, SIAMCAT ML pitfalls, SIAMCAT input, SIAMCAT basic vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.R dependencyCount: 115 Package: SICtools Version: 1.40.0 Depends: R (>= 3.0.0), methods, Rsamtools (>= 1.18.1), doParallel (>= 1.0.8), Biostrings (>= 2.32.1), stringr (>= 0.6.2), matrixStats (>= 0.10.0), plyr (>= 1.8.3), GenomicRanges (>= 1.22.4), IRanges (>= 2.4.8) Suggests: knitr, RUnit, BiocGenerics License: GPL (>=2) MD5sum: fe3c75c00d4faddddbc376b3d03c4f80 NeedsCompilation: yes Title: Find SNV/Indel differences between two bam files with near relationship Description: This package is to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison thourgh each base position across the genome region of interest. The difference is inferred by fisher test and euclidean distance, the input of which is the base count (A,T,G,C) in a given position and read counts for indels that span no less than 2bp on both sides of indel region. biocViews: Alignment, Sequencing, Coverage, SequenceMatching, QualityControl, DataImport, Software, SNP, VariantDetection Author: Xiaobin Xing, Wu Wei Maintainer: Xiaobin Xing VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SICtools git_branch: RELEASE_3_22 git_last_commit: 88c4203 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SICtools_1.40.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SICtools_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SICtools_1.40.0.tgz vignettes: vignettes/SICtools/inst/doc/SICtools.pdf vignetteTitles: Using SICtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SICtools/inst/doc/SICtools.R dependencyCount: 43 Package: SigCheck Version: 2.42.0 Depends: R (>= 4.0.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: 799e2c9a68fbfd2a1171ec6f578f5204 NeedsCompilation: no Title: Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata Description: While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata. biocViews: GeneExpression, Classification, GeneSetEnrichment Author: Rory Stark and Justin Norden Maintainer: Rory Stark git_url: https://git.bioconductor.org/packages/SigCheck git_branch: RELEASE_3_22 git_last_commit: 5d4b11a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SigCheck_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SigCheck_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SigCheck_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SigCheck_2.42.0.tgz vignettes: vignettes/SigCheck/inst/doc/SigCheck.pdf vignetteTitles: Checking gene expression signatures against random and known signatures with SigCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigCheck/inst/doc/SigCheck.R dependencyCount: 132 Package: sigFeature Version: 1.28.0 Depends: R (>= 3.5.0) Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL (>= 2) MD5sum: c2b374cc6d6d25db5b84f2e3110563a3 NeedsCompilation: no Title: sigFeature: Significant feature selection using SVM-RFE & t-statistic Description: This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy. biocViews: FeatureExtraction, GeneExpression, Microarray, Transcription, mRNAMicroarray, GenePrediction, Normalization, Classification, SupportVectorMachine Author: Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb] Maintainer: Pijush Das Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sigFeature git_branch: RELEASE_3_22 git_last_commit: f80d75f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sigFeature_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sigFeature_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sigFeature_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sigFeature_1.28.0.tgz vignettes: vignettes/sigFeature/inst/doc/vignettes.html vignetteTitles: sigFeature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigFeature/inst/doc/vignettes.R dependencyCount: 65 Package: siggenes Version: 1.84.0 Depends: Biobase, multtest, splines, methods Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5) Suggests: affy, annotate, genefilter, KernSmooth License: LGPL (>= 2) MD5sum: 00278bafdec2d2b9ab674d674c852a40 NeedsCompilation: no Title: Multiple Testing using SAM and Efron's Empirical Bayes Approaches Description: Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). biocViews: MultipleComparison, Microarray, GeneExpression, SNP, ExonArray, DifferentialExpression Author: Holger Schwender Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/siggenes git_branch: RELEASE_3_22 git_last_commit: bc03145 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/siggenes_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/siggenes_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/siggenes_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/siggenes_1.84.0.tgz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf, vignettes/siggenes/inst/doc/siggenesRnews.pdf, vignettes/siggenes/inst/doc/identify.sam.html, vignettes/siggenes/inst/doc/plot.ebam.html, vignettes/siggenes/inst/doc/plot.finda0.html, vignettes/siggenes/inst/doc/plot.sam.html, vignettes/siggenes/inst/doc/print.ebam.html, vignettes/siggenes/inst/doc/print.finda0.html, vignettes/siggenes/inst/doc/print.sam.html, vignettes/siggenes/inst/doc/summary.ebam.html, vignettes/siggenes/inst/doc/summary.sam.html vignetteTitles: siggenes Manual, siggenesRnews.pdf, identify.sam.html, plot.ebam.html, plot.finda0.html, plot.sam.html, print.ebam.html, print.finda0.html, print.sam.html, summary.ebam.html, summary.sam.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/siggenes/inst/doc/siggenes.R dependsOnMe: KCsmart importsMe: minfi, trio, XDE, DeSousa2013, NPFD suggestsMe: logicFS dependencyCount: 17 Package: sights Version: 1.36.0 Depends: R(>= 3.3) Imports: MASS(>= 7.3), qvalue(>= 2.2), ggplot2(>= 2.0), reshape2(>= 1.4), lattice(>= 0.2), stats(>= 3.3) Suggests: testthat, knitr, rmarkdown, ggthemes, gridExtra, xlsx License: GPL-3 | file LICENSE MD5sum: b26520731847bc7171755b5e232e5040 NeedsCompilation: no Title: Statistics and dIagnostic Graphs for HTS Description: SIGHTS is a suite of normalization methods, statistical tests, and diagnostic graphical tools for high throughput screening (HTS) assays. HTS assays use microtitre plates to screen large libraries of compounds for their biological, chemical, or biochemical activity. biocViews: ImmunoOncology, CellBasedAssays, MicrotitrePlateAssay, Normalization, MultipleComparison, Preprocessing, QualityControl, BatchEffect, Visualization Author: Elika Garg [aut, cre], Carl Murie [aut], Heydar Ensha [ctb], Robert Nadon [aut] Maintainer: Elika Garg URL: https://eg-r.github.io/sights/ VignetteBuilder: knitr BugReports: https://github.com/eg-r/sights/issues git_url: https://git.bioconductor.org/packages/sights git_branch: RELEASE_3_22 git_last_commit: 9287ac1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sights_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sights_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sights_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sights_1.36.0.tgz vignettes: vignettes/sights/inst/doc/sights.html vignetteTitles: Using **SIGHTS** R-package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sights/inst/doc/sights.R dependencyCount: 33 Package: signatureSearch Version: 1.24.0 Depends: R(>= 4.2.0), Rcpp, SummarizedExperiment, org.Hs.eg.db Imports: AnnotationDbi, ggplot2, data.table, ExperimentHub, HDF5Array, magrittr, RSQLite, dplyr, fgsea, scales, methods, qvalue, stats, utils, reshape2, visNetwork, BiocParallel, fastmatch, reactome.db, Matrix, clusterProfiler, readr, DOSE, rhdf5, GSEABase, DelayedArray, BiocGenerics, tibble LinkingTo: Rcpp Suggests: knitr, testthat, rmarkdown, BiocStyle, signatureSearchData, DT License: Artistic-2.0 MD5sum: 311f78dda1d746a24c5350b383f7a0ae NeedsCompilation: yes Title: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis Description: This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. biocViews: Software, GeneExpression, GO, KEGG, NetworkEnrichment, Sequencing, Coverage, DifferentialExpression Author: Yuzhu Duan [aut], Brendan Gongol [cre, aut], Thomas Girke [aut] Maintainer: Brendan Gongol URL: https://github.com/yduan004/signatureSearch/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/yduan004/signatureSearch/issues git_url: https://git.bioconductor.org/packages/signatureSearch git_branch: RELEASE_3_22 git_last_commit: ce6ab95 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/signatureSearch_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/signatureSearch_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/signatureSearch_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/signatureSearch_1.24.0.tgz vignettes: vignettes/signatureSearch/inst/doc/signatureSearch.html vignetteTitles: signatureSearch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signatureSearch/inst/doc/signatureSearch.R dependsOnMe: DFD dependencyCount: 174 Package: signeR Version: 2.12.0 Depends: R (>= 4.1.0), NMF Imports: BiocGenerics, Biostrings, class, grDevices, GenomeInfoDb, GenomicRanges, IRanges, nloptr, methods, stats, utils, PMCMRplus, parallel, pvclust, ppclust, clue, survival, maxstat, future, VGAM, MASS, kknn, glmnet, e1071, randomForest, ada, future.apply, ggplot2, pROC, pheatmap, RColorBrewer, listenv, reshape2, scales, survminer, dplyr, ggpubr, cowplot, tibble, readr, shiny, shinydashboard, shinycssloaders, shinyWidgets, bsplus, DT, magrittr, tidyr, BiocFileCache, proxy, rtracklayer, BSgenome, broom, VariantAnnotation LinkingTo: Rcpp, RcppArmadillo (>= 0.7.100) Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, rmarkdown License: GPL-3 Archs: x64 MD5sum: 7872383e48eed0b6f753bfdee9f32d7e NeedsCompilation: yes Title: Empirical Bayesian approach to mutational signature discovery Description: The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variation (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided. biocViews: GenomicVariation, SomaticMutation, StatisticalMethod, Visualization Author: Rafael Rosales, Rodrigo Drummond, Renan Valieris, Alexandre Defelicibus, Israel Tojal da Silva Maintainer: Renan Valieris URL: https://github.com/TojalLab/signeR SystemRequirements: C++14 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/signeR git_branch: RELEASE_3_22 git_last_commit: 7b4a6ab git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/signeR_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/signeR_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/signeR_2.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/signeR_2.12.0.tgz vignettes: vignettes/signeR/inst/doc/signeR-vignette.html, vignettes/signeR/inst/doc/signeRFlow.html vignetteTitles: signeR, signeRFlow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R, vignettes/signeR/inst/doc/signeRFlow.R dependencyCount: 241 Package: signifinder Version: 1.12.0 Depends: R (>= 4.4.0) Imports: AnnotationDbi, BiocGenerics, ComplexHeatmap, consensusOV, cowplot, DGEobj.utils, dplyr, ensembldb, ggplot2, ggridges, GSVA, IRanges, magrittr, matrixStats, maxstat, methods, openair, org.Hs.eg.db, patchwork, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, SpatialExperiment, stats, scales, SummarizedExperiment, survival, survminer, viridis Suggests: BiocStyle, edgeR, grid, kableExtra, knitr, limma, testthat (>= 3.0.0) License: AGPL-3 MD5sum: 9753d8ce51f66cd2b0a54abdeb47060a NeedsCompilation: no Title: Collection and implementation of public transcriptional cancer signatures Description: signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures coming from public literature, based on gene expression values, and return single-sample (-cell/-spot) scores. Currently, signifinder collects more than 70 distinct signatures, relating to multiple tumors and multiple cancer processes. biocViews: GeneExpression, GeneTarget, ImmunoOncology, BiomedicalInformatics, RNASeq, Microarray, ReportWriting, Visualization, SingleCell, Spatial, GeneSignaling Author: Stefania Pirrotta [cre, aut] (ORCID: ), Enrica Calura [aut] (ORCID: ) Maintainer: Stefania Pirrotta URL: https://github.com/CaluraLab/signifinder VignetteBuilder: knitr BugReports: https://github.com/CaluraLab/signifinder/issues git_url: https://git.bioconductor.org/packages/signifinder git_branch: RELEASE_3_22 git_last_commit: 4cc4ec4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/signifinder_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/signifinder_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/signifinder_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/signifinder_1.12.0.tgz vignettes: vignettes/signifinder/inst/doc/signifinder.html vignetteTitles: signifinder vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signifinder/inst/doc/signifinder.R dependencyCount: 259 Package: SigsPack Version: 1.24.0 Depends: R (>= 3.6) Imports: quadprog (>= 1.5-5), methods, Biobase, BSgenome (>= 1.46.0), VariantAnnotation (>= 1.24.5), Biostrings, GenomeInfoDb, GenomicRanges, rtracklayer, SummarizedExperiment, graphics, stats, utils Suggests: IRanges, BSgenome.Hsapiens.UCSC.hg19, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 880aacbc132508e05bd4fb184a206f92 NeedsCompilation: no Title: Mutational Signature Estimation for Single Samples Description: Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues. biocViews: SomaticMutation, SNP, VariantAnnotation, BiomedicalInformatics, DNASeq Author: Franziska Schumann Maintainer: Franziska Schumann URL: https://github.com/bihealth/SigsPack VignetteBuilder: knitr BugReports: https://github.com/bihealth/SigsPack/issues git_url: https://git.bioconductor.org/packages/SigsPack git_branch: RELEASE_3_22 git_last_commit: 13120a5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SigsPack_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SigsPack_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SigsPack_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SigsPack_1.24.0.tgz vignettes: vignettes/SigsPack/inst/doc/SigsPack.html vignetteTitles: Introduction to SigsPack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigsPack/inst/doc/SigsPack.R dependencyCount: 81 Package: sigsquared Version: 1.42.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: 0cb8e3d7673ce095f76ee87c7ef069ff NeedsCompilation: no Title: Gene signature generation for functionally validated signaling pathways Description: By leveraging statistical properties (log-rank test for survival) of patient cohorts defined by binary thresholds, poor-prognosis patients are identified by the sigsquared package via optimization over a cost function reducing type I and II error. Author: UnJin Lee Maintainer: UnJin Lee git_url: https://git.bioconductor.org/packages/sigsquared git_branch: RELEASE_3_22 git_last_commit: c9bfb9e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sigsquared_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sigsquared_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sigsquared_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sigsquared_1.42.0.tgz vignettes: vignettes/sigsquared/inst/doc/sigsquared.pdf vignetteTitles: SigSquared hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigsquared/inst/doc/sigsquared.R dependencyCount: 13 Package: SIM Version: 1.80.0 Depends: R (>= 3.5), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) MD5sum: ce518fb3260b74abae74be697121a380 NeedsCompilation: yes Title: Integrated Analysis on two human genomic datasets Description: Finds associations between two human genomic datasets. biocViews: Microarray, Visualization Author: Renee X. de Menezes and Judith M. Boer Maintainer: Renee X. de Menezes git_url: https://git.bioconductor.org/packages/SIM git_branch: RELEASE_3_22 git_last_commit: 14fb293 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SIM_1.80.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SIM_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SIM_1.80.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SIM_1.80.0.tgz vignettes: vignettes/SIM/inst/doc/SIM.pdf vignetteTitles: SIM vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIM/inst/doc/SIM.R dependencyCount: 57 Package: SIMAT Version: 1.42.0 Depends: R (>= 3.5.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 6c531f0502668e0337c5c3b868ffe86a NeedsCompilation: no Title: GC-SIM-MS data processing and alaysis tool Description: This package provides a pipeline for analysis of GC-MS data acquired in selected ion monitoring (SIM) mode. The tool also provides a guidance in choosing appropriate fragments for the targets of interest by using an optimization algorithm. This is done by considering overlapping peaks from a provided library by the user. biocViews: ImmunoOncology, Software, Metabolomics, MassSpectrometry Author: M. R. Nezami Ranjbar Maintainer: M. R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html git_url: https://git.bioconductor.org/packages/SIMAT git_branch: RELEASE_3_22 git_last_commit: 84133b6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SIMAT_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SIMAT_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SIMAT_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SIMAT_1.42.0.tgz vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf vignetteTitles: SIMAT Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMAT/inst/doc/SIMAT-vignette.R dependencyCount: 36 Package: SimBu Version: 1.12.0 Imports: basilisk, BiocParallel, data.table, dplyr, ggplot2, tools, Matrix (>= 1.3.3), methods, phyloseq, proxyC, RColorBrewer, RCurl, reticulate, sparseMatrixStats, SummarizedExperiment, tidyr Suggests: curl, knitr, matrixStats, rmarkdown, Seurat (>= 5.0.0), SeuratObject (>= 5.0.0), testthat (>= 3.0.0) License: GPL-3 + file LICENSE Archs: x64 MD5sum: 8b69b6ccf70a48388ae9682cbf04f57c NeedsCompilation: no Title: Simulate Bulk RNA-seq Datasets from Single-Cell Datasets Description: SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations. biocViews: Software, RNASeq, SingleCell Author: Alexander Dietrich [aut, cre] Maintainer: Alexander Dietrich URL: https://github.com/omnideconv/SimBu VignetteBuilder: knitr BugReports: https://github.com/omnideconv/SimBu/issues git_url: https://git.bioconductor.org/packages/SimBu git_branch: RELEASE_3_22 git_last_commit: c0333a2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SimBu_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SimBu_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SimBu_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SimBu_1.12.0.tgz vignettes: vignettes/SimBu/inst/doc/sfaira_vignette.html, vignettes/SimBu/inst/doc/SimBu.html, vignettes/SimBu/inst/doc/simulator_input_output.html, vignettes/SimBu/inst/doc/simulator_scaling_factors.html vignetteTitles: Public Data Integration using Sfaira, Getting started, Inputs and Outputs, Introducing mRNA bias into simulations with scaling factors hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SimBu/inst/doc/sfaira_vignette.R, vignettes/SimBu/inst/doc/SimBu.R, vignettes/SimBu/inst/doc/simulator_input_output.R, vignettes/SimBu/inst/doc/simulator_scaling_factors.R dependencyCount: 105 Package: SIMD Version: 1.28.0 Depends: R (>= 3.5.0) Imports: edgeR, statmod, methylMnM, stats, utils Suggests: BiocStyle, knitr,rmarkdown License: GPL-3 Archs: x64 MD5sum: 30a83e5d9e6d04ec49d4946543e1eded NeedsCompilation: yes Title: Statistical Inferences with MeDIP-seq Data (SIMD) to infer the methylation level for each CpG site Description: This package provides a inferential analysis method for detecting differentially expressed CpG sites in MeDIP-seq data. It uses statistical framework and EM algorithm, to identify differentially expressed CpG sites. The methods on this package are described in the article 'Methylation-level Inferences and Detection of Differential Methylation with Medip-seq Data' by Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang and Xiyan Yang (2018, pending publication). biocViews: ImmunoOncology, DifferentialMethylation,SingleCell, DifferentialExpression Author: Yan Zhou Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIMD git_branch: RELEASE_3_22 git_last_commit: 0910665 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SIMD_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SIMD_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SIMD_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SIMD_1.28.0.tgz vignettes: vignettes/SIMD/inst/doc/SIMD.html vignetteTitles: SIMD Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMD/inst/doc/SIMD.R dependencyCount: 12 Package: SimFFPE Version: 1.22.0 Depends: Biostrings Imports: dplyr, foreach, doParallel, truncnorm, GenomicRanges, IRanges, Rsamtools, parallel, graphics, stats, utils, methods Suggests: BiocStyle License: LGPL-3 MD5sum: 95f649560ed144f440d2e3ec146e6fda NeedsCompilation: no Title: NGS Read Simulator for FFPE Tissue Description: The NGS (Next-Generation Sequencing) reads from FFPE (Formalin-Fixed Paraffin-Embedded) samples contain numerous artifact chimeric reads (ACRS), which can lead to false positive structural variant calls. These ACRs are derived from the combination of two single-stranded DNA (ss-DNA) fragments with short reverse complementary regions (SRCRs). This package simulates these artifact chimeric reads as well as normal reads for FFPE samples on the whole genome / several chromosomes / large regions. biocViews: Sequencing, Alignment, MultipleComparison, SequenceMatching, DataImport Author: Lanying Wei [aut, cre] (ORCID: ) Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/SimFFPE git_branch: RELEASE_3_22 git_last_commit: 9123bff git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SimFFPE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SimFFPE_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SimFFPE_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SimFFPE_1.22.0.tgz vignettes: vignettes/SimFFPE/inst/doc/SimFFPE.pdf vignetteTitles: An introduction to SimFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimFFPE/inst/doc/SimFFPE.R dependencyCount: 47 Package: similaRpeak Version: 1.42.0 Depends: R6 (>= 2.0) Imports: stats Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments, rtracklayer, rmarkdown, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 6f26cfd6d30be5f40b4a7713a9529bc7 NeedsCompilation: no Title: Metrics to estimate a level of similarity between two ChIP-Seq profiles Description: This package calculates metrics which quantify the level of similarity between ChIP-Seq profiles. More specifically, the package implements six pseudometrics specialized in pattern similarity detection in ChIP-Seq profiles. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, DifferentialExpression Author: Astrid Deschênes [cre, aut], Elsa Bernatchez [aut], Charles Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb [aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/similaRpeak VignetteBuilder: knitr BugReports: https://github.com/adeschen/similaRpeak/issues git_url: https://git.bioconductor.org/packages/similaRpeak git_branch: RELEASE_3_22 git_last_commit: c364d6b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/similaRpeak_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/similaRpeak_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/similaRpeak_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/similaRpeak_1.42.0.tgz vignettes: vignettes/similaRpeak/inst/doc/similaRpeak.html vignetteTitles: Similarity between two ChIP-Seq profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R dependencyCount: 2 Package: SIMLR Version: 1.36.0 Depends: R (>= 4.1.0), Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra LinkingTo: Rcpp Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph License: file LICENSE MD5sum: bca8d0d03f294e42313254d533ab1441 NeedsCompilation: yes Title: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR) Description: Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. biocViews: ImmunoOncology, Clustering, GeneExpression, Sequencing, SingleCell Author: Daniele Ramazzotti [aut] (ORCID: ), Bo Wang [aut], Luca De Sano [cre, aut] (ORCID: ), Serafim Batzoglou [ctb] Maintainer: Luca De Sano URL: https://github.com/BatzoglouLabSU/SIMLR VignetteBuilder: knitr BugReports: https://github.com/BatzoglouLabSU/SIMLR git_url: https://git.bioconductor.org/packages/SIMLR git_branch: RELEASE_3_22 git_last_commit: bf45dbb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SIMLR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SIMLR_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SIMLR_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SIMLR_1.36.0.tgz vignettes: vignettes/SIMLR/inst/doc/v1_introduction.html, vignettes/SIMLR/inst/doc/v2_running_SIMLR.html vignetteTitles: Introduction, Running SIMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SIMLR/inst/doc/v1_introduction.R, vignettes/SIMLR/inst/doc/v2_running_SIMLR.R dependencyCount: 14 Package: simona Version: 1.8.0 Depends: R (>= 4.1.0) Imports: methods, Rcpp, matrixStats, GetoptLong, grid, GlobalOptions, igraph, Polychrome, S4Vectors, xml2 (>= 1.3.3), circlize, ComplexHeatmap, grDevices, stats, utils, shiny, fastmatch LinkingTo: Rcpp Suggests: knitr, testthat, BiocManager, GO.db, org.Hs.eg.db, proxyC, AnnotationDbi, Matrix, DiagrammeR, ragg, png, InteractiveComplexHeatmap, UniProtKeywords, simplifyEnrichment, AnnotationHub, jsonlite License: MIT + file LICENSE MD5sum: 0c51f46170cba5a52d34e043d149b747 NeedsCompilation: yes Title: Semantic Similarity on Bio-Ontologies Description: This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis. biocViews: Software, Annotation, GO, BiomedicalInformatics Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/simona SystemRequirements: Perl, Java VignetteBuilder: knitr BugReports: https://github.com/jokergoo/simona/issues git_url: https://git.bioconductor.org/packages/simona git_branch: RELEASE_3_22 git_last_commit: e220e94 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/simona_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/simona_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/simona_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/simona_1.8.0.tgz vignettes: vignettes/simona/inst/doc/simona.html vignetteTitles: The simona package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: simplifyEnrichment dependencyCount: 70 Package: simPIC Version: 1.6.0 Depends: R (>= 4.5.0), SingleCellExperiment Imports: BiocGenerics, checkmate (>= 2.0.0), fitdistrplus, matrixStats, actuar, Matrix, stats, SummarizedExperiment, rlang, S4Vectors, methods, scales, scuttle, edgeR, withr Suggests: ggplot2 (>= 3.4.0), knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), scater, scran, magick License: GPL-3 MD5sum: d6236631a12030e4cc18cec24bf8205d NeedsCompilation: no Title: Flexible simulation of paired-insertion counts for single-cell ATAC-sequencing data Description: simPIC is a package for simulating single-cell ATAC-seq count data. It provides a user-friendly, well documented interface for data simulation. Functions are provided for parameter estimation, realistic scATAC-seq data simulation, and comparing real and simulated datasets. biocViews: SingleCell, ATACSeq, Software, Sequencing, ImmunoOncology, DataImport Author: Sagrika Chugh [aut, cre] (ORCID: ), Heejung Shim [aut], Davis McCarthy [aut] Maintainer: Sagrika Chugh URL: https://github.com/sagrikachugh/simPIC VignetteBuilder: knitr BugReports: https://github.com/sagrikachugh/simPIC/issues git_url: https://git.bioconductor.org/packages/simPIC git_branch: RELEASE_3_22 git_last_commit: c272c6f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/simPIC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/simPIC_1.5.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/simPIC_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/simPIC_1.6.0.tgz vignettes: vignettes/simPIC/inst/doc/vignette.html vignetteTitles: simPIC: simulating single-cell ATAC-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simPIC/inst/doc/vignette.R dependencyCount: 63 Package: simpleSeg Version: 1.12.0 Depends: R (>= 3.5.0) Imports: BiocParallel, EBImage, terra, stats, spatstat.geom, S4Vectors, grDevices, SummarizedExperiment, methods, cytomapper Suggests: BiocStyle, testthat (>= 3.0.0), knitr, ggplot2 License: GPL-3 MD5sum: ddc125d9681411dea80eea0c45dafde0 NeedsCompilation: no Title: A package to perform simple cell segmentation Description: Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained from multiple images. biocViews: Classification, Survival, SingleCell, Normalization, Spatial Author: Nicolas Canete [aut], Alexander Nicholls [aut], Ellis Patrick [aut, cre] Maintainer: Ellis Patrick URL: https://sydneybiox.github.io/simpleSeg/ https://github.com/SydneyBioX/simpleSeg VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/simpleSeg/issues git_url: https://git.bioconductor.org/packages/simpleSeg git_branch: RELEASE_3_22 git_last_commit: 2b63ec0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/simpleSeg_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/simpleSeg_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/simpleSeg_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/simpleSeg_1.12.0.tgz vignettes: vignettes/simpleSeg/inst/doc/simpleSeg.html vignetteTitles: "Introduction to simpleSeg" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleSeg/inst/doc/simpleSeg.R importsMe: lisaClust, spicyR suggestsMe: spicyWorkflow dependencyCount: 145 Package: simplifyEnrichment Version: 2.4.0 Depends: R (>= 4.1.0) Imports: simona, ComplexHeatmap (>= 2.7.4), grid, circlize, GetoptLong, digest, tm, GO.db, AnnotationDbi, slam, methods, clue, grDevices, stats, utils, cluster (>= 1.14.2), colorspace, GlobalOptions (>= 0.1.0) Suggests: knitr, ggplot2, cowplot, mclust, apcluster, MCL, dbscan, igraph, gridExtra, dynamicTreeCut, testthat, gridGraphics, flexclust, BiocManager, InteractiveComplexHeatmap (>= 0.99.11), shiny, shinydashboard, cola, hu6800.db, rmarkdown, genefilter, gridtext, fpc License: MIT + file LICENSE MD5sum: b0fa13663c93d6f396bffcac32a2e381 NeedsCompilation: no Title: Simplify Functional Enrichment Results Description: A new clustering algorithm, "binary cut", for clustering similarity matrices of functional terms is implemeted in this package. It also provides functions for visualizing, summarizing and comparing the clusterings. biocViews: Software, Visualization, GO, Clustering, GeneSetEnrichment Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/simplifyEnrichment, https://simplifyEnrichment.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/simplifyEnrichment git_branch: RELEASE_3_22 git_last_commit: 366f2df git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/simplifyEnrichment_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/simplifyEnrichment_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/simplifyEnrichment_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/simplifyEnrichment_2.4.0.tgz vignettes: vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.html vignetteTitles: The simplifyEnrichment package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: cola, InteractiveComplexHeatmap, simona, scITD dependencyCount: 93 Package: sincell Version: 1.42.0 Depends: R (>= 3.0.2), igraph Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2, reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster, statmod LinkingTo: Rcpp Suggests: BiocStyle, knitr, biomaRt, stringr, monocle License: GPL (>= 2) MD5sum: 402ff93b1a81c97de306937c5bd7b3a5 NeedsCompilation: yes Title: R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data Description: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology Author: Miguel Julia , Amalio Telenti , Antonio Rausell Maintainer: Miguel Julia , Antonio Rausell URL: http://bioconductor.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sincell git_branch: RELEASE_3_22 git_last_commit: 3e5b589 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sincell_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sincell_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sincell_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sincell_1.42.0.tgz vignettes: vignettes/sincell/inst/doc/sincell-vignette.pdf vignetteTitles: Sincell: Analysis of cell state hierarchies from single-cell RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sincell/inst/doc/sincell-vignette.R dependencyCount: 51 Package: SingleCellAlleleExperiment Version: 1.6.0 Depends: R (>= 4.4.0), SingleCellExperiment Imports: SummarizedExperiment, BiocParallel, DelayedArray, methods, utils, Matrix, S4Vectors, stats Suggests: scaeData, knitr, rmarkdown, BiocStyle, scran, scater, scuttle, ggplot2, patchwork, org.Hs.eg.db, AnnotationDbi, DropletUtils, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 628a8174c066c033795bebbf4917ac13 NeedsCompilation: no Title: S4 Class for Single Cell Data with Allele and Functional Levels for Immune Genes Description: Defines a S4 class that is based on SingleCellExperiment. In addition to the usual gene layer the object can also store data for immune genes such as HLAs, Igs and KIRs at allele and functional level. The package is part of a workflow named single-cell ImmunoGenomic Diversity (scIGD), that firstly incorporates allele-aware quantification data for immune genes. This new data can then be used with the here implemented data structure and functionalities for further data handling and data analysis. biocViews: DataRepresentation, Infrastructure, SingleCell, Transcriptomics, GeneExpression, Genetics, ImmunoOncology, DataImport Author: Jonas Schuck [aut, cre] (ORCID: ), Ahmad Al Ajami [aut] (ORCID: ), Federico Marini [aut] (ORCID: ), Katharina Imkeller [aut] (ORCID: ) Maintainer: Jonas Schuck URL: https://github.com/AGImkeller/SingleCellAlleleExperiment VignetteBuilder: knitr BugReports: https://github.com/AGImkeller/SingleCellAlleleExperiment/issues git_url: https://git.bioconductor.org/packages/SingleCellAlleleExperiment git_branch: RELEASE_3_22 git_last_commit: f2f4799 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SingleCellAlleleExperiment_1.6.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SingleCellAlleleExperiment_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SingleCellAlleleExperiment_1.6.0.tgz vignettes: vignettes/SingleCellAlleleExperiment/inst/doc/scae_intro.html vignetteTitles: An introduction to the SingleCellAlleleExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SingleCellAlleleExperiment/inst/doc/scae_intro.R suggestsMe: scaeData dependencyCount: 36 Package: SingleCellExperiment Version: 1.32.0 Depends: SummarizedExperiment Imports: methods, utils, stats, S4Vectors, BiocGenerics, GenomicRanges, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, scRNAseq (>= 2.9.1), Rtsne License: GPL-3 MD5sum: f7f2c21bd508f443e7d3e8211c4fd32f NeedsCompilation: no Title: S4 Classes for Single Cell Data Description: Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Aaron Lun [aut, cph], Davide Risso [aut, cre, cph], Keegan Korthauer [ctb], Kevin Rue-Albrecht [ctb], Luke Zappia [ctb] (ORCID: , github: lazappi) Maintainer: Davide Risso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellExperiment git_branch: RELEASE_3_22 git_last_commit: db7133e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SingleCellExperiment_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SingleCellExperiment_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SingleCellExperiment_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SingleCellExperiment_1.32.0.tgz vignettes: vignettes/SingleCellExperiment/inst/doc/apply.html, vignettes/SingleCellExperiment/inst/doc/devel.html, vignettes/SingleCellExperiment/inst/doc/intro.html vignetteTitles: 2. Applying over a SingleCellExperiment object, 3. Developing around the SingleCellExperiment class, 1. An introduction to the SingleCellExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellExperiment/inst/doc/apply.R, vignettes/SingleCellExperiment/inst/doc/devel.R, vignettes/SingleCellExperiment/inst/doc/intro.R dependsOnMe: alabaster.sce, BASiCS, batchelor, BayesSpace, CATALYST, celda, CellBench, CelliD, CellTrails, CHETAH, chevreulPlot, chevreulProcess, clusterExperiment, cydar, cytomapper, DeeDeeExperiment, demuxSNP, dreamlet, DropletUtils, epiregulon, epiregulon.extra, ExperimentSubset, iSEE, iSEEhub, iSEEindex, LoomExperiment, MAST, mia, mumosa, omicsGMF, POWSC, scAnnotatR, scater, scDblFinder, scGPS, schex, scPipe, scran, scuttle, scviR, simPIC, SingleCellAlleleExperiment, singleCellTK, SiPSiC, SpatialExperiment, splatter, switchde, TENxIO, tidySingleCellExperiment, TrajectoryUtils, TreeSummarizedExperiment, tricycle, TSCAN, zinbwave, HCAData, imcdatasets, MouseAgingData, MouseGastrulationData, MouseThymusAgeing, muscData, scATAC.Explorer, scMultiome, scRNAseq, STexampleData, TENxBrainData, TENxPBMCData, TMExplorer, WeberDivechaLCdata, OSCA.intro, DIscBIO, imcExperiment, karyotapR importsMe: ADImpute, aggregateBioVar, airpart, alabaster.sfe, anansi, anglemania, APL, ASURAT, Banksy, BASiCStan, bayNorm, blase, BUSseq, CARDspa, CatsCradle, ccfindR, ccImpute, CDI, CellMixS, Cepo, ChromSCape, CiteFuse, ClusterFoldSimilarity, clustifyr, clustSIGNAL, CoGAPS, concordexR, condiments, Coralysis, corral, COTAN, crumblr, CTexploreR, CuratedAtlasQueryR, cytoviewer, decontX, DeconvoBuddies, DifferentialRegulation, Dino, distinct, dittoSeq, DOtools, escheR, EWCE, FEAST, fishpond, FLAMES, ggsc, ggspavis, glmGamPoi, GloScope, GSVA, HIPPO, Ibex, ILoReg, imcRtools, immApex, infercnv, iSEEfier, iSEEtree, iSEEu, lemur, lisaClust, looking4clusters, mastR, mbkmeans, MEB, MetaNeighbor, miaDash, miaTime, miaViz, miloR, miQC, mist, MPAC, MuData, muscat, Nebulosa, netSmooth, NewWave, nnSVG, partCNV, peco, pipeComp, projectR, raer, RCSL, RegionalST, RUCova, SanityR, SC3, scafari, SCArray, scBFA, scCB2, sccomp, scDD, scDDboost, scDesign3, scDiagnostics, scDotPlot, scds, scGraphVerse, scHOT, scider, scmap, scMerge, scMET, SCnorm, scone, scp, scReClassify, scRepertoire, scRNAseqApp, scruff, scry, scTensor, scTGIF, scTreeViz, SETA, shinyDSP, slalom, slingshot, sosta, Spaniel, SpaNorm, SpatialExperimentIO, SpatialFeatureExperiment, speckle, spicyR, SplineDV, SPOTlight, SpotSweeper, SPsimSeq, standR, Statial, stPipe, SVP, tidySpatialExperiment, tpSVG, tradeSeq, treekoR, UCell, VAExprs, VDJdive, velociraptor, VisiumIO, visiumStitched, Voyager, waddR, xCell2, XeniumIO, xenLite, zellkonverter, HCATonsilData, MerfishData, raerdata, scpdata, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, mikropml, mixhvg, nebula, scROSHI, SpatialDDLS suggestsMe: ANCOMBC, anndataR, cellxgenedp, CTdata, DEsingle, dominoSignal, escape, FuseSOM, genomicInstability, GEOquery, HDF5Array, HVP, InteractiveComplexHeatmap, jazzPanda, M3Drop, MOFA2, MOSim, ontoProc, phenopath, progeny, QFeatures, ReactomeGSA, scBubbletree, scFeatureFilter, scLANE, scPCA, scrapper, scRecover, SingleR, sketchR, SummarizedExperiment, SuperCellCyto, TREG, updateObject, dorothea, DuoClustering2018, GSE103322, microbiomeDataSets, TabulaMurisData, simpleSingleCell, Canek, clustree, CytoSimplex, dyngen, harmony, Platypus, RaceID, rliger, SCdeconR, SCORPIUS, Seurat, singleCellHaystack, SuperCell, tidydr dependencyCount: 25 Package: SingleCellSignalR Version: 2.0.0 Depends: R (>= 4.5) Imports: stats, utils, methods, ggplot2, matrixTests, matrixStats, foreach, BulkSignalR Suggests: knitr, markdown, rmarkdown License: CeCILL | file LICENSE MD5sum: 1206326a4bc78b88841ddf029950e3dd NeedsCompilation: no Title: Cell Signalling Using Single-Cell RNA-seq or Proteomics Data Description: Inference of ligand-receptor (L-R) interactions from single-cell expression (transcriptomics/proteomics) data. SingleCellSignalR v2 inferences rely on the statistical model we introduced in the BulkSignalR package as well as the original SingleCellSignalR LR-score (both are available). SingleCellSignalR v2 can be regarded as a wrapper to BulkSignalR fundamental classes. This also enables v2 users to work with any species, whereas only Mus musculus & Homo sapiens were available before in SingleCellSignalR v1. biocViews: Network, RNASeq, Software, Proteomics, Transcriptomics ,SingleCell, NetworkInference Author: Jacques Colinge [aut] (ORCID: ), Jean-Philippe Villemin [cre] (ORCID: ) Maintainer: Jean-Philippe Villemin URL: https://github.com/jcolinge/SingleCellSignalR VignetteBuilder: knitr BugReports: https://github.com/jcolinge/SingleCellSignalR/issues git_url: https://git.bioconductor.org/packages/SingleCellSignalR git_branch: RELEASE_3_22 git_last_commit: 27f381b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SingleCellSignalR_2.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SingleCellSignalR_2.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SingleCellSignalR_2.0.0.tgz vignettes: vignettes/SingleCellSignalR/inst/doc/SingleCellSignalR-Main.html vignetteTitles: SingleCellSignalR-Main hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellSignalR/inst/doc/SingleCellSignalR-Main.R suggestsMe: tidySingleCellExperiment dependencyCount: 199 Package: singleCellTK Version: 2.20.0 Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment, DelayedArray, Biobase Imports: ape, anndata, AnnotationHub, batchelor, BiocParallel, celldex, colourpicker, colorspace, cowplot, cluster, ComplexHeatmap, data.table, DelayedMatrixStats, DESeq2, dplyr, DT, ExperimentHub, ensembldb, fields, ggplot2, ggplotify, ggrepel, ggtree, gridExtra, grid, GSVA (>= 1.50.0), GSVAdata, igraph, KernSmooth, limma, MAST, Matrix (>= 1.6-1), matrixStats, methods, msigdbr, multtest, plotly, plyr, ROCR, Rtsne, S4Vectors, scater, scMerge (>= 1.2.0), scran, Seurat (>= 3.1.3), shiny, shinyjs, SingleR, stringr, SoupX, sva, reshape2, shinyalert, circlize, enrichR (>= 3.2), celda, shinycssloaders, DropletUtils, scds (>= 1.2.0), reticulate (>= 1.14), tools, tximport, tidyr, eds, withr, GSEABase, R.utils, zinbwave, scRNAseq (>= 2.0.2), TENxPBMCData, yaml, rmarkdown, magrittr, scDblFinder, metap, VAM (>= 0.5.3), tibble, rlang, TSCAN, TrajectoryUtils, scuttle, utils, stats, zellkonverter, lifecycle Suggests: testthat, Rsubread, BiocStyle, knitr, lintr, spelling, org.Mm.eg.db, kableExtra, shinythemes, shinyBS, shinyjqui, shinyWidgets, shinyFiles, BiocGenerics, RColorBrewer, fastmap (>= 1.1.0), harmony, SeuratObject, optparse License: MIT + file LICENSE MD5sum: db09f69f54fff77bbebebad4810a8d8f NeedsCompilation: no Title: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data Description: The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk. biocViews: SingleCell, GeneExpression, DifferentialExpression, Alignment, Clustering, ImmunoOncology, BatchEffect, Normalization, QualityControl, DataImport, GUI Author: Yichen Wang [aut] (ORCID: ), Irzam Sarfraz [aut] (ORCID: ), Rui Hong [aut], Yusuke Koga [aut], Salam Alabdullatif [aut], Nida Pervaiz [aut], David Jenkins [aut] (ORCID: ), Vidya Akavoor [aut], Xinyun Cao [aut], Shruthi Bandyadka [aut], Anastasia Leshchyk [aut], Tyler Faits [aut], Mohammed Muzamil Khan [aut], Zhe Wang [aut], W. Evan Johnson [aut] (ORCID: ), Ming Liu [aut], Joshua David Campbell [aut, cre] (ORCID: ) Maintainer: Joshua David Campbell URL: https://www.camplab.net/sctk/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/singleCellTK/issues git_url: https://git.bioconductor.org/packages/singleCellTK git_branch: RELEASE_3_22 git_last_commit: e2bff7b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/singleCellTK_2.20.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/singleCellTK_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/singleCellTK_2.20.0.tgz vignettes: vignettes/singleCellTK/inst/doc/singleCellTK.html vignetteTitles: 1. Introduction to singleCellTK hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/singleCellTK/inst/doc/singleCellTK.R suggestsMe: celda dependencyCount: 395 Package: SingleMoleculeFootprinting Version: 2.4.0 Depends: R (>= 4.4.0) Imports: BiocGenerics, Biostrings, BSgenome, cluster, dplyr, Seqinfo, GenomicRanges, ggpointdensity, ggplot2, ggrepel, grDevices, IRanges, magrittr, Matrix, methods, miscTools, parallel, parallelDist, patchwork, plyranges, qs, QuasR, RColorBrewer, rlang, S4Vectors, stats, stringr, tibble, tidyr, utils, viridis Suggests: BSgenome.Mmusculus.UCSC.mm10, devtools, ExperimentHub, knitr, qs, rmarkdown, readr, rrapply, SingleMoleculeFootprintingData, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: 5889f66060ee690f7c81cd3c65565234 NeedsCompilation: no Title: Analysis tools for Single Molecule Footprinting (SMF) data Description: SingleMoleculeFootprinting provides functions to analyze Single Molecule Footprinting (SMF) data. Following the workflow exemplified in its vignette, the user will be able to perform basic data analysis of SMF data with minimal coding effort. Starting from an aligned bam file, we show how to perform quality controls over sequencing libraries, extract methylation information at the single molecule level accounting for the two possible kind of SMF experiments (single enzyme or double enzyme), classify single molecules based on their patterns of molecular occupancy, plot SMF information at a given genomic location. biocViews: DNAMethylation, Coverage, NucleosomePositioning, DataRepresentation, Epigenetics, MethylSeq, QualityControl, Sequencing Author: Guido Barzaghi [aut, cre] (ORCID: ), Arnaud Krebs [aut] (ORCID: ), Mike Smith [ctb] (ORCID: ) Maintainer: Guido Barzaghi URL: https://www.bioconductor.org/packages/release/bioc/html/SingleMoleculeFootprinting.html VignetteBuilder: knitr BugReports: https://github.com/Krebslabrep/SingleMoleculeFootprinting/issues git_url: https://git.bioconductor.org/packages/SingleMoleculeFootprinting git_branch: RELEASE_3_22 git_last_commit: a905f25 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SingleMoleculeFootprinting_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SingleMoleculeFootprinting_2.3.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SingleMoleculeFootprinting_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SingleMoleculeFootprinting_2.4.0.tgz vignettes: vignettes/SingleMoleculeFootprinting/inst/doc/FootprintCharter.html, vignettes/SingleMoleculeFootprinting/inst/doc/methylation_calling_and_QCs.html, vignettes/SingleMoleculeFootprinting/inst/doc/single_molecule_sorting_by_TF.html vignetteTitles: FootprintCharter.html, methylation_calling_and_QCs.html, single_molecule_sorting_by_TF.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleMoleculeFootprinting/inst/doc/FootprintCharter.R, vignettes/SingleMoleculeFootprinting/inst/doc/methylation_calling_and_QCs.R, vignettes/SingleMoleculeFootprinting/inst/doc/single_molecule_sorting_by_TF.R dependencyCount: 140 Package: SingleR Version: 2.12.0 Depends: SummarizedExperiment Imports: methods, Matrix, S4Vectors, DelayedArray, DelayedMatrixStats, BiocParallel, BiocNeighbors, stats, utils, Rcpp, beachmat (>= 2.25.1) LinkingTo: Rcpp, beachmat, assorthead (>= 1.3.5), BiocNeighbors Suggests: testthat, knitr, rmarkdown, BiocStyle, BiocGenerics, SingleCellExperiment, scuttle, scrapper (>= 1.3.14), scRNAseq, ggplot2, pheatmap, grDevices, gridExtra, viridis, celldex License: GPL-3 MD5sum: 12622389366f03fd039efce486105774 NeedsCompilation: yes Title: Reference-Based Single-Cell RNA-Seq Annotation Description: Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. biocViews: Software, SingleCell, GeneExpression, Transcriptomics, Classification, Clustering, Annotation Author: Dvir Aran [aut, cph], Aaron Lun [ctb, cre], Daniel Bunis [ctb], Jared Andrews [ctb], Friederike Dündar [ctb] Maintainer: Aaron Lun URL: https://github.com/SingleR-inc/SingleR SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SingleR-inc/SingleR/issues git_url: https://git.bioconductor.org/packages/SingleR git_branch: RELEASE_3_22 git_last_commit: 39ca8d4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SingleR_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SingleR_2.11.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SingleR_2.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SingleR_2.12.0.tgz vignettes: vignettes/SingleR/inst/doc/SingleR.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleR/inst/doc/SingleR.R dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: singleCellTK, OSTA, scPipeline suggestsMe: Coralysis, scDiagnostics, scGraphVerse, sketchR, tidySingleCellExperiment dependencyCount: 41 Package: singscore Version: 1.30.0 Depends: R (>= 3.6) Imports: methods, stats, graphics, ggplot2, grDevices, ggrepel, GSEABase, plotly, tidyr, plyr, magrittr, reshape, edgeR, RColorBrewer, Biobase, BiocParallel, SummarizedExperiment, matrixStats, reshape2, S4Vectors Suggests: pkgdown, BiocStyle, hexbin, knitr, rmarkdown, testthat, covr License: GPL-3 Archs: x64 MD5sum: 99908987c09e9c88712de251f1ad536d NeedsCompilation: no Title: Rank-based single-sample gene set scoring method Description: A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Dharmesh D. Bhuva [aut] (ORCID: ), Ruqian Lyu [aut, ctb], Momeneh Foroutan [aut, ctb] (ORCID: ), Malvika Kharbanda [aut, cre] (ORCID: ) Maintainer: Malvika Kharbanda URL: https://davislaboratory.github.io/singscore VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/singscore/issues git_url: https://git.bioconductor.org/packages/singscore git_branch: RELEASE_3_22 git_last_commit: 5bc304d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/singscore_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/singscore_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/singscore_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/singscore_1.30.0.tgz vignettes: vignettes/singscore/inst/doc/singscore.html vignetteTitles: Single sample scoring hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/singscore/inst/doc/singscore.R importsMe: pathMED, TBSignatureProfiler, xCell2, clustermole suggestsMe: mastR, vissE, msigdb dependencyCount: 121 Package: SiPSiC Version: 1.10.0 Depends: Matrix, SingleCellExperiment Suggests: knitr, rmarkdown, BiocStyle License: file LICENSE MD5sum: c4b99fa346fcb2f3ab9ddd547b5a77ee NeedsCompilation: no Title: Calculate Pathway Scores for Each Cell in scRNA-Seq Data Description: Infer biological pathway activity of cells from single-cell RNA-sequencing data by calculating a pathway score for each cell (pathway genes are specified by the user). It is recommended to have the data in Transcripts-Per-Million (TPM) or Counts-Per-Million (CPM) units for best results. Scores may change when adding cells to or removing cells off the data. SiPSiC stands for Single Pathway analysis in Single Cells. biocViews: Software, DifferentialExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, Transcriptomics, RNASeq, SingleCell, Transcription, Sequencing, ImmunoOncology, DataImport Author: Daniel Davis [aut, cre] (ORCID: ), Yotam Drier [aut] Maintainer: Daniel Davis URL: https://www.genome.org/cgi/doi/10.1101/gr.278431.123 VignetteBuilder: knitr BugReports: https://github.com/DanielDavis12/SiPSiC/issues git_url: https://git.bioconductor.org/packages/SiPSiC git_branch: RELEASE_3_22 git_last_commit: 1909d2a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SiPSiC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SiPSiC_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SiPSiC_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SiPSiC_1.10.0.tgz vignettes: vignettes/SiPSiC/inst/doc/SiPSiC.html vignetteTitles: Infer Biological Pathway Activity from Single-Cell RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SiPSiC/inst/doc/SiPSiC.R dependencyCount: 26 Package: sitadela Version: 1.18.0 Depends: R (>= 4.1.0) Imports: Biobase, BiocGenerics, biomaRt, Biostrings, Seqinfo, GenomicFeatures, GenomicRanges, IRanges, methods, parallel, Rsamtools, RSQLite, rtracklayer, S4Vectors, tools, txdbmaker, utils Suggests: GenomeInfoDb, BiocStyle, BSgenome, knitr, rmarkdown, RMySQL, RUnit License: Artistic-2.0 Archs: x64 MD5sum: ad2387bed3f6aff91b1cb284afeb71a6 NeedsCompilation: no Title: An R package for the easy provision of simple but complete tab-delimited genomic annotation from a variety of sources and organisms Description: Provides an interface to build a unified database of genomic annotations and their coordinates (gene, transcript and exon levels). It is aimed to be used when simple tab-delimited annotations (or simple GRanges objects) are required instead of the more complex annotation Bioconductor packages. Also useful when combinatorial annotation elements are reuired, such as RefSeq coordinates with Ensembl biotypes. Finally, it can download, construct and handle annotations with versioned genes and transcripts (where available, e.g. RefSeq and latest Ensembl). This is particularly useful in precision medicine applications where the latter must be reported. biocViews: Software, WorkflowStep, RNASeq, Transcription, Sequencing, Transcriptomics, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, AlternativeSplicing, DataImport, ChIPSeq Author: Panagiotis Moulos [aut, cre] Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/sitadela VignetteBuilder: knitr BugReports: https://github.com/pmoulos/sitadela/issues git_url: https://git.bioconductor.org/packages/sitadela git_branch: RELEASE_3_22 git_last_commit: 84fdb44 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sitadela_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sitadela_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sitadela_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sitadela_1.18.0.tgz vignettes: vignettes/sitadela/inst/doc/sitadela.html vignetteTitles: Building a simple annotation database hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sitadela/inst/doc/sitadela.R dependencyCount: 100 Package: Site2Target Version: 1.2.0 Depends: R (>= 4.4) Imports: S4Vectors, stats, utils, BiocGenerics, GenomeInfoDb, MASS, IRanges, GenomicRanges Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-2 MD5sum: 050fbe495fc48b8eedc3017b0db79f9c NeedsCompilation: no Title: An R package to associate peaks and target genes Description: Statistics implemented for both peak-wise and gene-wise associations. In peak-wise associations, the p-value of the target genes of a given set of peaks are calculated. Negative binomial or Poisson distributions can be used for modeling the unweighted peaks targets and log-nromal can be used to model the weighted peaks. In gene-wise associations a table consisting of a set of genes, mapped to specific peaks, is generated using the given rules. biocViews: Annotation, ChIPSeq, Software, Epigenetics, GeneExpression, GeneTarget Author: Peyman Zarrineh [cre, aut] (ORCID: ) Maintainer: Peyman Zarrineh VignetteBuilder: knitr BugReports: https://github.com/fls-bioinformatics-core/Site2Target/issues git_url: https://git.bioconductor.org/packages/Site2Target git_branch: RELEASE_3_22 git_last_commit: 902e93a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Site2Target_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Site2Target_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Site2Target_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Site2Target_1.2.0.tgz vignettes: vignettes/Site2Target/inst/doc/Site2Target.html vignetteTitles: Site2Target hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Site2Target/inst/doc/Site2Target.R dependencyCount: 24 Package: sitePath Version: 1.25.1 Depends: R (>= 4.2) Imports: RColorBrewer, Rcpp, ape, aplot, ggplot2, ggrepel, ggtree, graphics, grDevices, gridExtra, methods, parallel, seqinr, stats, tidytree, utils LinkingTo: Rcpp Suggests: BiocStyle, devtools, knitr, magick, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 5fd524d32bd0c9fb957f3d0119aef8f6 NeedsCompilation: yes Title: Phylogeny-based sequence clustering with site polymorphism Description: Using site polymorphism is one of the ways to cluster DNA/protein sequences but it is possible for the sequences with the same polymorphism on a single site to be genetically distant. This package is aimed at clustering sequences using site polymorphism and their corresponding phylogenetic trees. By considering their location on the tree, only the structurally adjacent sequences will be clustered. However, the adjacent sequences may not necessarily have the same polymorphism. So a branch-and-bound like algorithm is used to minimize the entropy representing the purity of site polymorphism of each cluster. biocViews: Alignment, MultipleSequenceAlignment, Phylogenetics, SNP, Software Author: Chengyang Ji [aut, cre, cph] (ORCID: ), Hangyu Zhou [ths], Aiping Wu [ths] Maintainer: Chengyang Ji URL: https://wuaipinglab.github.io/sitePath/ VignetteBuilder: knitr BugReports: https://github.com/wuaipinglab/sitePath/issues git_url: https://git.bioconductor.org/packages/sitePath git_branch: devel git_last_commit: 9ca37dc git_last_commit_date: 2025-09-17 Date/Publication: 2025-10-07 source.ver: src/contrib/sitePath_1.25.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/sitePath_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sitePath_1.25.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sitePath_1.25.1.tgz vignettes: vignettes/sitePath/inst/doc/sitePath.html vignetteTitles: An introduction to sitePath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sitePath/inst/doc/sitePath.R dependencyCount: 88 Package: sizepower Version: 1.80.0 Depends: stats License: LGPL Archs: x64 MD5sum: 2f7becc1e00815f39c059f2856d7ee4d NeedsCompilation: no Title: Sample Size and Power Calculation in Micorarray Studies Description: This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice. biocViews: Microarray Author: Weiliang Qiu and Mei-Ling Ting Lee and George Alex Whitmore Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/sizepower git_branch: RELEASE_3_22 git_last_commit: 259a02f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sizepower_1.80.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sizepower_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sizepower_1.80.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sizepower_1.80.0.tgz vignettes: vignettes/sizepower/inst/doc/sizepower.pdf vignetteTitles: Sample Size and Power Calculation in Microarray Studies Using the \Rpackage{sizepower} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sizepower/inst/doc/sizepower.R dependencyCount: 1 Package: sketchR Version: 1.6.0 Imports: basilisk, Biobase, DelayedArray, dplyr, ggplot2, methods, reticulate, rlang, scales, stats Suggests: rmarkdown, knitr, testthat (>= 3.0.0), TENxPBMCData, scuttle, scran, scater, SingleR, celldex, cowplot, SummarizedExperiment, beachmat.hdf5, BiocStyle, BiocManager, SingleCellExperiment, snifter, uwot, bluster, class License: MIT + file LICENSE MD5sum: 5af7266a9caee8bee657c50aba65f1ab NeedsCompilation: no Title: An R interface for python subsampling/sketching algorithms Description: Provides an R interface for various subsampling algorithms implemented in python packages. Currently, interfaces to the geosketch and scSampler python packages are implemented. In addition it also provides diagnostic plots to evaluate the subsampling. biocViews: SingleCell Author: Charlotte Soneson [aut, cre] (ORCID: ), Michael Stadler [aut] (ORCID: ), Friedrich Miescher Institute for Biomedical Research [cph] Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/sketchR VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/sketchR/issues git_url: https://git.bioconductor.org/packages/sketchR git_branch: RELEASE_3_22 git_last_commit: b2a0713 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sketchR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sketchR_1.5.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sketchR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sketchR_1.6.0.tgz vignettes: vignettes/sketchR/inst/doc/sketching_workflows.html, vignettes/sketchR/inst/doc/sketchR.html vignetteTitles: sketching_workflows, sketchR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sketchR/inst/doc/sketching_workflows.R, vignettes/sketchR/inst/doc/sketchR.R dependencyCount: 57 Package: skewr Version: 1.42.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 Archs: x64 MD5sum: 0e7e1a3b6d9d37b4b69d8e2e6c0c2565 NeedsCompilation: no Title: Visualize Intensities Produced by Illumina's Human Methylation 450k BeadChip Description: The skewr package is a tool for visualizing the output of the Illumina Human Methylation 450k BeadChip to aid in quality control. It creates a panel of nine plots. Six of the plots represent the density of either the methylated intensity or the unmethylated intensity given by one of three subsets of the 485,577 total probes. These subsets include Type I-red, Type I-green, and Type II.The remaining three distributions give the density of the Beta-values for these same three subsets. Each of the nine plots optionally displays the distributions of the "rs" SNP probes and the probes associated with imprinted genes as series of 'tick' marks located above the x-axis. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl Author: Ryan Putney [cre, aut], Steven Eschrich [aut], Anders Berglund [aut] Maintainer: Ryan Putney VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/skewr git_branch: RELEASE_3_22 git_last_commit: 37202c3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/skewr_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/skewr_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/skewr_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/skewr_1.42.0.tgz vignettes: vignettes/skewr/inst/doc/skewr.pdf vignetteTitles: An Introduction to the skewr Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/skewr/inst/doc/skewr.R dependencyCount: 175 Package: slalom Version: 1.32.0 Depends: R (>= 4.0) Imports: Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase, methods, rsvd, SingleCellExperiment, SummarizedExperiment, stats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: BiocStyle, knitr, rhdf5, rmarkdown, scater, testthat License: GPL-2 MD5sum: cea932853b71cc56efb7c9b787b2e061 NeedsCompilation: yes Title: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data Description: slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. The method uses Bayesian factor analysis with a latent variable model to identify active pathways (selected by the user, e.g. KEGG pathways) that explain variation in a single-cell RNA-seq dataset. This an R/C++ implementation of the f-scLVM Python package. See the publication describing the method at https://doi.org/10.1186/s13059-017-1334-8. biocViews: ImmunoOncology, SingleCell, RNASeq, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, Reactome, KEGG Author: Florian Buettner [aut], Naruemon Pratanwanich [aut], Davis McCarthy [aut, cre], John Marioni [aut], Oliver Stegle [aut] Maintainer: Davis McCarthy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/slalom git_branch: RELEASE_3_22 git_last_commit: 712aadd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/slalom_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/slalom_1.31.0.zip vignettes: vignettes/slalom/inst/doc/vignette.html vignetteTitles: Introduction to slalom hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slalom/inst/doc/vignette.R dependencyCount: 74 Package: slingshot Version: 2.18.0 Depends: R (>= 4.0), princurve (>= 2.0.4), stats, TrajectoryUtils Imports: graphics, grDevices, igraph, matrixStats, methods, S4Vectors, SingleCellExperiment, SummarizedExperiment Suggests: BiocGenerics, BiocStyle, clusterExperiment, DelayedMatrixStats, knitr, mclust, mgcv, RColorBrewer, rgl, rmarkdown, testthat, uwot, covr License: Artistic-2.0 MD5sum: 0658996f7ae4e9d001c204e73f14afb3 NeedsCompilation: no Title: Tools for ordering single-cell sequencing Description: Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction. biocViews: Clustering, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, Sequencing, SingleCell, Transcriptomics, Visualization Author: Kelly Street [aut, cre, cph], Davide Risso [aut], Diya Das [aut], Sandrine Dudoit [ths], Koen Van den Berge [ctb], Robrecht Cannoodt [ctb] (ORCID: , github: rcannood) Maintainer: Kelly Street VignetteBuilder: knitr BugReports: https://github.com/kstreet13/slingshot/issues git_url: https://git.bioconductor.org/packages/slingshot git_branch: RELEASE_3_22 git_last_commit: 1c825f7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/slingshot_2.18.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/slingshot_2.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/slingshot_2.18.0.tgz vignettes: vignettes/slingshot/inst/doc/conditionsVignette.html, vignettes/slingshot/inst/doc/vignette.html vignetteTitles: Differential Topology: Comparing Conditions along a Trajectory, Slingshot: Trajectory Inference for Single-Cell Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slingshot/inst/doc/conditionsVignette.R, vignettes/slingshot/inst/doc/vignette.R dependsOnMe: OSCA.advanced importsMe: condiments, scRNAseqApp, tradeSeq suggestsMe: blase, scLANE, Platypus, RaceID dependencyCount: 38 Package: SLqPCR Version: 1.76.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: c4dbb4de74a7431ed96e2a11d6d6b130 NeedsCompilation: no Title: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH Description: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH biocViews: MicrotitrePlateAssay, qPCR Author: Matthias Kohl Maintainer: Matthias Kohl git_url: https://git.bioconductor.org/packages/SLqPCR git_branch: RELEASE_3_22 git_last_commit: 367fb97 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SLqPCR_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SLqPCR_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SLqPCR_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SLqPCR_1.76.0.tgz vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf vignetteTitles: SLqPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLqPCR/inst/doc/SLqPCR.R dependencyCount: 1 Package: SMAD Version: 1.26.0 Depends: R (>= 3.6.0), RcppAlgos Imports: magrittr (>= 1.5), dplyr, stats, tidyr, utils, Rcpp (>= 1.0.0) LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: d6614c15134fe16767b3037f029eab73 NeedsCompilation: yes Title: Statistical Modelling of AP-MS Data (SMAD) Description: Assigning probability scores to protein interactions captured in affinity purification mass spectrometry (AP-MS) expriments to infer protein-protein interactions. The output would facilitate non-specific background removal as contaminants are commonly found in AP-MS data. biocViews: MassSpectrometry, Proteomics, Software Author: Qingzhou Zhang [aut, cre] Maintainer: Qingzhou Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SMAD git_branch: RELEASE_3_22 git_last_commit: 8072203 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SMAD_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SMAD_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SMAD_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SMAD_1.26.0.tgz vignettes: vignettes/SMAD/inst/doc/quickstart.html vignetteTitles: SMAD Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SMAD/inst/doc/quickstart.R dependencyCount: 29 Package: smartid Version: 1.6.0 Depends: R (>= 4.4) Imports: dplyr, ggplot2, graphics, Matrix, mclust, methods, mixtools, sparseMatrixStats, stats, SummarizedExperiment, tidyr, utils Suggests: BiocStyle, dbscan, ggpubr, knitr, rmarkdown, scater, splatter, testthat (>= 3.0.0), tidytext, UpSetR License: MIT + file LICENSE MD5sum: 0c3412e095f7b2d281940ffd3a16667b NeedsCompilation: no Title: Scoring and Marker Selection Method Based on Modified TF-IDF Description: This package enables automated selection of group specific signature, especially for rare population. The package is developed for generating specifc lists of signature genes based on Term Frequency-Inverse Document Frequency (TF-IDF) modified methods. It can also be used as a new gene-set scoring method or data transformation method. Multiple visualization functions are implemented in this package. biocViews: Software, GeneExpression, Transcriptomics Author: Jinjin Chen [aut, cre] (ORCID: ) Maintainer: Jinjin Chen URL: https://davislaboratory.github.io/smartid VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/smartid/issues git_url: https://git.bioconductor.org/packages/smartid git_branch: RELEASE_3_22 git_last_commit: d405774 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/smartid_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/smartid_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/smartid_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/smartid_1.6.0.tgz vignettes: vignettes/smartid/inst/doc/smartid_Demo.html vignetteTitles: smartid: Scoring and MARker selection method based on modified Tf-IDf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/smartid/inst/doc/smartid_Demo.R dependencyCount: 97 Package: SmartPhos Version: 1.0.0 Depends: R (>= 4.4.0) Imports: MultiAssayExperiment, SummarizedExperiment, data.table, shiny, shinythemes, shinyjs, shinyBS, shinyWidgets, parallel, DT, tools, stats, ggplot2, plotly, ggbeeswarm, pheatmap, grid, XML, MsCoreUtils, imputeLCMD, missForest, limma, proDA, decoupleR, piano, BiocParallel, doParallel, doRNG, e1071, magrittr, matrixStats, rlang, stringr, tibble, dplyr, tidyr, Biobase, vsn, factoextra, cowplot Suggests: knitr, BiocStyle, PhosR, testthat License: GPL-3 MD5sum: bf38e14741c3dd2ead81ab7fe58dda8a NeedsCompilation: no Title: A phosphoproteomics data analysis package with an interactive ShinyApp Description: To facilitate and streamline phosphoproteomics data analysis, we developed SmartPhos, an R package for the pre-processing, quality control, and exploratory analysis of phosphoproteomics data generated by MaxQuant and Spectronaut. The package can be used either through the R command line or through an interactive ShinyApp called SmartPhos Explorer. The package contains methods such as normalization and normalization correction, transformation, imputation, batch effect correction, PCA, heatmap, differential expression, time-series clustering, gene set enrichment analysis, and kinase activity inference. biocViews: Visualization, ShinyApps, GUI, QualityControl, Proteomics, DifferentialExpression, Normalization, Preprocessing, GeneSetEnrichment, Clustering, GeneExpression, MassSpectrometry, BatchEffect Author: Shubham Agrawal [aut, cre] (ORCID: ), Junyan Lu [aut] (ORCID: ) Maintainer: Shubham Agrawal URL: https://lu-group-ukhd.github.io/SmartPhos/ VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SmartPhos/issues git_url: https://git.bioconductor.org/packages/SmartPhos git_branch: RELEASE_3_22 git_last_commit: a7a52e6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SmartPhos_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SmartPhos_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SmartPhos_1.0.0.tgz vignettes: vignettes/SmartPhos/inst/doc/SmartPhos_Shiny.html, vignettes/SmartPhos/inst/doc/SmartPhos.html vignetteTitles: "Introduction to Shiny App", "Introduction to SmartPhos" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SmartPhos/inst/doc/SmartPhos_Shiny.R, vignettes/SmartPhos/inst/doc/SmartPhos.R dependencyCount: 215 Package: SMITE Version: 1.38.0 Depends: R (>= 3.5), GenomicRanges Imports: scales, plyr, Hmisc, AnnotationDbi, org.Hs.eg.db, ggplot2, reactome.db, KEGGREST, BioNet, goseq, methods, IRanges, igraph, Biobase,tools, S4Vectors, geneLenDataBase, grDevices, graphics, stats, utils Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 06b9ed4b810388ce3f55c08faf203f17 NeedsCompilation: no Title: Significance-based Modules Integrating the Transcriptome and Epigenome Description: This package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules. biocViews: ImmunoOncology, DifferentialMethylation, DifferentialExpression, SystemsBiology, NetworkEnrichment,GenomeAnnotation,Network, Sequencing, RNASeq, Coverage Author: Neil Ari Wijetunga, Andrew Damon Johnston, John Murray Greally Maintainer: Neil Ari Wijetunga , Andrew Damon Johnston URL: https://github.com/GreallyLab/SMITE VignetteBuilder: knitr BugReports: https://github.com/GreallyLab/SMITE/issues git_url: https://git.bioconductor.org/packages/SMITE git_branch: RELEASE_3_22 git_last_commit: 184b926 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SMITE_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SMITE_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SMITE_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SMITE_1.38.0.tgz vignettes: vignettes/SMITE/inst/doc/SMITE.pdf vignetteTitles: SMITE Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMITE/inst/doc/SMITE.R dependencyCount: 152 Package: smoothclust Version: 1.6.0 Depends: R (>= 4.4.0) Imports: SpatialExperiment, SummarizedExperiment, BiocNeighbors, Matrix, methods, utils Suggests: BiocStyle, knitr, STexampleData, scuttle, scran, scater, ggspavis, testthat License: MIT + file LICENSE MD5sum: 78350707e08a122ba63a4de9505b0873 NeedsCompilation: no Title: smoothclust Description: Method for identification of spatial domains and spatially-aware clustering in spatial transcriptomics data. The method generates spatial domains with smooth boundaries by smoothing gene expression profiles across neighboring spatial locations, followed by unsupervised clustering. Spatial domains consisting of consistent mixtures of cell types may then be further investigated by applying cell type compositional analyses or differential analyses. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Clustering Author: Lukas M. Weber [aut, cre] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/smoothclust VignetteBuilder: knitr BugReports: https://github.com/lmweber/smoothclust/issues git_url: https://git.bioconductor.org/packages/smoothclust git_branch: RELEASE_3_22 git_last_commit: da7fe79 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/smoothclust_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/smoothclust_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/smoothclust_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/smoothclust_1.6.0.tgz vignettes: vignettes/smoothclust/inst/doc/smoothclust.html vignetteTitles: Smoothclust Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/smoothclust/inst/doc/smoothclust.R dependencyCount: 69 Package: smoppix Version: 1.2.0 Depends: R (>= 4.4.0) Imports: spatstat.geom(>= 3.2.0),spatstat.random,methods,BiocParallel,SummarizedExperiment,SpatialExperiment,scam,Rdpack,stats,utils,extraDistr,lmerTest,lme4,ggplot2,graphics,grDevices,Rcpp (>= 1.0.11),spatstat.model,openxlsx,Rfast LinkingTo: Rcpp Suggests: testthat,rmarkdown,knitr,DropletUtils,polyCub,RImageJROI,sp,ape,htmltools,funkycells,glmnet,doParallel License: GPL-2 MD5sum: 8b12f4a5097a8e17c6b231289f037436 NeedsCompilation: yes Title: Analyze Single Molecule Spatial Omics Data Using the Probabilistic Index Description: Test for univariate and bivariate spatial patterns in spatial omics data with single-molecule resolution. The tests implemented allow for analysis of nested designs and are automatically calibrated to different biological specimens. Tests for aggregation, colocalization, gradients and vicinity to cell edge or centroid are provided. biocViews: Transcriptomics, Spatial, SingleCell Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel URL: https://github.com/sthawinke/smoppix VignetteBuilder: knitr BugReports: https://github.com/sthawinke/smoppix/issues git_url: https://git.bioconductor.org/packages/smoppix git_branch: RELEASE_3_22 git_last_commit: ff312ee git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/smoppix_1.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/smoppix_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/smoppix_1.2.0.tgz vignettes: vignettes/smoppix/inst/doc/smoppixVignette.html vignetteTitles: Vignette of the smoppix package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/smoppix/inst/doc/smoppixVignette.R dependencyCount: 120 Package: SNAGEE Version: 1.50.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: e532f75e1f86bc1115152898f4c47cbc NeedsCompilation: no Title: Signal-to-Noise applied to Gene Expression Experiments Description: Signal-to-Noise applied to Gene Expression Experiments. Signal-to-noise ratios can be used as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set. biocViews: Microarray, OneChannel, TwoChannel, QualityControl Author: David Venet Maintainer: David Venet URL: http://bioconductor.org/ git_url: https://git.bioconductor.org/packages/SNAGEE git_branch: RELEASE_3_22 git_last_commit: 4d53bd8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SNAGEE_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SNAGEE_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SNAGEE_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SNAGEE_1.50.0.tgz vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf vignetteTitles: SNAGEE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNAGEE/inst/doc/SNAGEE.R suggestsMe: SNAGEEdata dependencyCount: 1 Package: snapcount Version: 1.22.0 Depends: R (>= 4.0.0) Imports: R6, httr, rlang, purrr, jsonlite, assertthat, data.table, Matrix, magrittr, methods, stringr, stats, IRanges, GenomicRanges, SummarizedExperiment Suggests: BiocManager, bit64, covr, knitcitations, knitr (>= 1.6), devtools, BiocStyle (>= 2.5.19), rmarkdown (>= 0.9.5), testthat (>= 2.1.0) License: MIT + file LICENSE Archs: x64 MD5sum: aee964c8dd0d790bc2105d0a9fdd0dc1 NeedsCompilation: no Title: R/Bioconductor Package for interfacing with Snaptron for rapid querying of expression counts Description: snapcount is a client interface to the Snaptron webservices which support querying by gene name or genomic region. Results include raw expression counts derived from alignment of RNA-seq samples and/or various summarized measures of expression across one or more regions/genes per-sample (e.g. percent spliced in). biocViews: Coverage, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Rone Charles [aut, cre] Maintainer: Rone Charles URL: https://github.com/langmead-lab/snapcount VignetteBuilder: knitr BugReports: https://github.com/langmead-lab/snapcount/issues git_url: https://git.bioconductor.org/packages/snapcount git_branch: RELEASE_3_22 git_last_commit: d7ccab9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/snapcount_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/snapcount_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/snapcount_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/snapcount_1.22.0.tgz vignettes: vignettes/snapcount/inst/doc/snapcount_vignette.html vignetteTitles: snapcount quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/snapcount/inst/doc/snapcount_vignette.R dependencyCount: 44 Package: snifter Version: 1.20.0 Depends: R (>= 4.0.0) Imports: basilisk, reticulate, irlba, stats, assertthat Suggests: knitr, rmarkdown, BiocStyle, ggplot2, testthat (>= 3.0.0) License: GPL-3 MD5sum: 1d6fbcaf665deca86c743068da613725 NeedsCompilation: no Title: R wrapper for the python openTSNE library Description: Provides an R wrapper for the implementation of FI-tSNE from the python package openTNSE. See Poličar et al. (2019) and the algorithm described by Linderman et al. (2018) . biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Aaron Lun [aut] Maintainer: Alan O'Callaghan URL: https://bioconductor.org/packages/snifter VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/snifter/issues git_url: https://git.bioconductor.org/packages/snifter git_branch: RELEASE_3_22 git_last_commit: 38ed1de git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/snifter_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/snifter_1.19.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/snifter_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/snifter_1.20.0.tgz vignettes: vignettes/snifter/inst/doc/snifter.html vignetteTitles: Introduction to snifter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snifter/inst/doc/snifter.R dependsOnMe: OSCA.advanced suggestsMe: scater, sketchR dependencyCount: 25 Package: snm Version: 1.58.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: 51e04ebc06391fe63778b8932e954d91 NeedsCompilation: no Title: Supervised Normalization of Microarrays Description: SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest. biocViews: Microarray, OneChannel, TwoChannel, MultiChannel, DifferentialExpression, ExonArray, GeneExpression, Transcription, MultipleComparison, Preprocessing, QualityControl Author: Brig Mecham and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/snm git_branch: RELEASE_3_22 git_last_commit: 23a1e16 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/snm_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/snm_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/snm_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/snm_1.58.0.tgz vignettes: vignettes/snm/inst/doc/snm.pdf vignetteTitles: snm Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snm/inst/doc/snm.R importsMe: ExpressionNormalizationWorkflow dependencyCount: 23 Package: SNPediaR Version: 1.36.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: ed5f828c5a57b673ff2ce4afb3860bf2 NeedsCompilation: no Title: Query data from SNPedia Description: SNPediaR provides some tools for downloading and parsing data from the SNPedia web site . The implemented functions allow users to import the wiki text available in SNPedia pages and to extract the most relevant information out of them. If some information in the downloaded pages is not automatically processed by the library functions, users can easily implement their own parsers to access it in an efficient way. biocViews: SNP, VariantAnnotation Author: David Montaner [aut, cre] Maintainer: David Montaner URL: https://github.com/genometra/SNPediaR VignetteBuilder: knitr BugReports: https://github.com/genometra/SNPediaR/issues git_url: https://git.bioconductor.org/packages/SNPediaR git_branch: RELEASE_3_22 git_last_commit: d984760 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SNPediaR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SNPediaR_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SNPediaR_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SNPediaR_1.36.0.tgz vignettes: vignettes/SNPediaR/inst/doc/SNPediaR.html vignetteTitles: SNPediaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPediaR/inst/doc/SNPediaR.R dependencyCount: 4 Package: SNPhood Version: 1.40.0 Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb (>= 1.34.8), BiocParallel, VariantAnnotation, BiocGenerics, IRanges, methods, SummarizedExperiment, RColorBrewer, Biostrings, grDevices, gridExtra, stats, grid, utils, reshape2, scales, S4Vectors Suggests: BiocStyle, knitr, pryr, rmarkdown, SNPhoodData, corrplot License: LGPL (>= 3) MD5sum: f8d08e6b5690ea74549d864304f13415 NeedsCompilation: no Title: SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data Description: To date, thousands of single nucleotide polymorphisms (SNPs) have been found to be associated with complex traits and diseases. However, the vast majority of these disease-associated SNPs lie in the non-coding part of the genome, and are likely to affect regulatory elements, such as enhancers and promoters, rather than function of a protein. Thus, to understand the molecular mechanisms underlying genetic traits and diseases, it becomes increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin environment or transcription factor (TF) binding. Towards this aim, we developed SNPhood, a user-friendly *Bioconductor* R package to investigate and visualize the local neighborhood of a set of SNPs of interest for NGS data such as chromatin marks or transcription factor binding sites from ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. Finally, SNPhood can be employed for determining, investigating, and visualizing allele-specific binding patterns around the SNPs of interest. biocViews: Software Author: Christian Arnold [aut, cre], Pooja Bhat [aut], Judith Zaugg [aut] Maintainer: Christian Arnold URL: https://bioconductor.org/packages/SNPhood VignetteBuilder: knitr BugReports: mailto: git_url: https://git.bioconductor.org/packages/SNPhood git_branch: RELEASE_3_22 git_last_commit: 60c689c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SNPhood_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SNPhood_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SNPhood_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SNPhood_1.40.0.tgz vignettes: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.html, vignettes/SNPhood/inst/doc/workflow.html vignetteTitles: Introduction and Methodological Details, Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.R, vignettes/SNPhood/inst/doc/workflow.R dependencyCount: 104 Package: SNPRelate Version: 1.44.0 Depends: R (>= 2.15), gdsfmt (>= 1.8.3) Imports: methods, RhpcBLASctl LinkingTo: gdsfmt Suggests: parallel, Matrix, RUnit, knitr, markdown, rmarkdown, MASS, BiocGenerics Enhances: SeqArray (>= 1.12.0) License: GPL-3 MD5sum: a927303486250424348465a8e05f4bdf NeedsCompilation: yes Title: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data Description: Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data. biocViews: Infrastructure, Genetics, StatisticalMethod, PrincipalComponent Author: Xiuwen Zheng [aut, cre, cph] (ORCID: ), Stephanie Gogarten [ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] (ORCID: ) Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/SNPRelate VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/SNPRelate/issues git_url: https://git.bioconductor.org/packages/SNPRelate git_branch: RELEASE_3_22 git_last_commit: 50237e4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SNPRelate_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SNPRelate_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SNPRelate_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SNPRelate_1.44.0.tgz vignettes: vignettes/SNPRelate/inst/doc/SNPRelate.html vignetteTitles: SNPRelate Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelate.R dependsOnMe: RAIDS, SeqSQC importsMe: CNVRanger, GDSArray, GENESIS, gwasurvivr, VariantExperiment, EthSEQ, gwid, simplePHENOTYPES, snplinkage suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray dependencyCount: 3 Package: snpStats Version: 1.60.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics Suggests: hexbin License: GPL-3 Archs: x64 MD5sum: edcd52705c16690e1b44b50918b5adc4 NeedsCompilation: yes Title: SnpMatrix and XSnpMatrix classes and methods Description: Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. biocViews: Microarray, SNP, GeneticVariability Author: David Clayton Maintainer: David Clayton git_url: https://git.bioconductor.org/packages/snpStats git_branch: RELEASE_3_22 git_last_commit: 34f8800 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/snpStats_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/snpStats_1.59.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/snpStats_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/snpStats_1.60.0.tgz vignettes: vignettes/snpStats/inst/doc/data-input-vignette.pdf, vignettes/snpStats/inst/doc/differences.pdf, vignettes/snpStats/inst/doc/Fst-vignette.pdf, vignettes/snpStats/inst/doc/imputation-vignette.pdf, vignettes/snpStats/inst/doc/ld-vignette.pdf, vignettes/snpStats/inst/doc/pca-vignette.pdf, vignettes/snpStats/inst/doc/snpStats-vignette.pdf, vignettes/snpStats/inst/doc/tdt-vignette.pdf vignetteTitles: Data input, snpMatrix-differences, Fst, Imputation and meta-analysis, LD statistics, Principal components analysis, snpStats introduction, TDT tests hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snpStats/inst/doc/data-input-vignette.R, vignettes/snpStats/inst/doc/Fst-vignette.R, vignettes/snpStats/inst/doc/imputation-vignette.R, vignettes/snpStats/inst/doc/ld-vignette.R, vignettes/snpStats/inst/doc/pca-vignette.R, vignettes/snpStats/inst/doc/snpStats-vignette.R, vignettes/snpStats/inst/doc/tdt-vignette.R dependsOnMe: MAGAR importsMe: cardelino, DExMA, gwascat, martini, RVS, scoreInvHap, dartR.base, GenomicTools.fileHandler, gpcp, GWASbyCluster, PhenotypeSimulator, TriadSim suggestsMe: crlmm, GenomicFiles, GWASTools, ldblock, omicRexposome, omicsPrint, VariantAnnotation, adjclust, dartR, dartR.popgen, genio, pegas, RcppDPR, statgenGWAS dependencyCount: 12 Package: SomaticSignatures Version: 2.46.0 Depends: R (>= 3.5.0), VariantAnnotation, GenomicRanges, NMF Imports: S4Vectors, IRanges, Seqinfo, Biostrings, ggplot2, ggbio, reshape2, NMF, pcaMethods, Biobase, methods, proxy Suggests: testthat, knitr, parallel, GenomeInfoDb, BSgenome.Hsapiens.1000genomes.hs37d5, SomaticCancerAlterations, ggdendro, fastICA, sva License: MIT + file LICENSE MD5sum: d222d5398115f3955867403f7dffe455 NeedsCompilation: no Title: Somatic Signatures Description: The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms. biocViews: Sequencing, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod Author: Julian Gehring Maintainer: Julian Gehring URL: https://github.com/juliangehring/SomaticSignatures VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/SomaticSignatures git_branch: RELEASE_3_22 git_last_commit: 7058eda git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SomaticSignatures_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SomaticSignatures_2.45.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SomaticSignatures_2.46.0.tgz vignettes: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html vignetteTitles: SomaticSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R importsMe: YAPSA dependencyCount: 146 Package: SOMNiBUS Version: 1.18.0 Depends: R (>= 4.1.0) Imports: Matrix, mgcv, stats, VGAM, IRanges, GenomeInfoDb, GenomicRanges, rtracklayer, S4Vectors, BiocManager, annotatr, yaml, utils, bsseq, reshape2, data.table, ggplot2, tidyr, Suggests: BiocStyle, covr, devtools, dplyr, knitr, magick, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, License: MIT + file LICENSE MD5sum: 26292a7809cdd709c9db7737c983371b NeedsCompilation: no Title: Smooth modeling of bisulfite sequencing Description: This package aims to analyse count-based methylation data on predefined genomic regions, such as those obtained by targeted sequencing, and thus to identify differentially methylated regions (DMRs) that are associated with phenotypes or traits. The method is built a rich flexible model that allows for the effects, on the methylation levels, of multiple covariates to vary smoothly along genomic regions. At the same time, this method also allows for sequencing errors and can adjust for variability in cell type mixture. biocViews: DNAMethylation, Regression, Epigenetics, DifferentialMethylation, Sequencing, FunctionalPrediction Author: Kaiqiong Zhao [aut], Kathleen Klein [cre], Audrey Lemaçon [ctb, ctr], Simon Laurin-Lemay [ctb, ctr], My Intelligent Machines Inc. [ctr], Celia Greenwood [ths, aut] Maintainer: Kathleen Klein URL: https://github.com/kaiqiong/SOMNiBUS VignetteBuilder: knitr BugReports: https://github.com/kaiqiong/SOMNiBUS/issues git_url: https://git.bioconductor.org/packages/SOMNiBUS git_branch: RELEASE_3_22 git_last_commit: 9c4cdd6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SOMNiBUS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SOMNiBUS_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SOMNiBUS_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SOMNiBUS_1.18.0.tgz vignettes: vignettes/SOMNiBUS/inst/doc/SOMNiBUS.html vignetteTitles: Analyzing Targeted Bisulfite Sequencing data with SOMNiBUS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SOMNiBUS/inst/doc/SOMNiBUS.R dependencyCount: 143 Package: sosta Version: 1.2.0 Depends: R (>= 4.4.0) Imports: terra, sf, smoothr, spatstat.explore, spatstat.geom, SpatialExperiment, SingleCellExperiment, dplyr, ggplot2, patchwork, SummarizedExperiment, stats, rlang, parallel, EBImage, spatstat.random, S4Vectors Suggests: knitr, rmarkdown, BiocStyle, ExperimentHub, lme4, lmerTest, ggfortify, tidyr, testthat (>= 3.0.0) License: GPL (>= 3) + file LICENSE MD5sum: d9d0043b396ca23f0f194190391f6c78 NeedsCompilation: no Title: A package for the analysis of anatomical tissue structures in spatial omics data Description: sosta (Spatial Omics STructure Analysis) is a package for analyzing spatial omics data to explore tissue organization at the anatomical structure level. It reconstructs anatomically relevant structures based on molecular features or cell types. It further calculates a range of metrics at the structure level to quantitatively describe tissue architecture. The package is designed to integrate with other packages for the analysis of spatial omics data. biocViews: Software, Spatial, Transcriptomics, Visualization Author: Samuel Gunz [aut, cre] (ORCID: ), Mark D. Robinson [aut, fnd] Maintainer: Samuel Gunz URL: https://github.com/sgunz/sosta, https://sgunz.github.io/sosta/ VignetteBuilder: knitr BugReports: https://github.com/sgunz/sosta/issues git_url: https://git.bioconductor.org/packages/sosta git_branch: RELEASE_3_22 git_last_commit: ecee469 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sosta_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sosta_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sosta_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sosta_1.2.0.tgz vignettes: vignettes/sosta/inst/doc/ImcDiabetesIsletsVignette.html, vignettes/sosta/inst/doc/StructureReconstructionVignette.html vignetteTitles: Reconstruction and analysis of pancreatic islets from IMC data, Overview of sosta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sosta/inst/doc/ImcDiabetesIsletsVignette.R, vignettes/sosta/inst/doc/StructureReconstructionVignette.R importsMe: OSTA dependencyCount: 128 Package: SpaceMarkers Version: 2.0.0 Depends: R (>= 4.4.0) Imports: matrixStats, matrixTests, rstatix, spatstat.explore, spatstat.geom, ape, hdf5r, nanoparquet, jsonlite, Matrix, qvalue, stats, utils, methods, ggplot2, reshape2, RColorBrewer, circlize, mixtools, dplyr, readbitmap, rlang, effsize, viridis Suggests: data.table, devtools, knitr, cowplot, rjson, rmarkdown, BiocStyle, testthat (>= 3.0.0), CoGAPS, ComplexHeatmap Enhances: BiocParallel License: MIT + file LICENSE MD5sum: a04fbd44004811af5b25c55a1234d7d3 NeedsCompilation: no Title: Spatial Interaction Markers Description: Spatial transcriptomic technologies have helped to resolve the connection between gene expression and the 2D orientation of tissues relative to each other. However, the limited single-cell resolution makes it difficult to highlight the most important molecular interactions in these tissues. SpaceMarkers, R/Bioconductor software, can help to find molecular interactions, by identifying genes associated with latent space interactions in spatial transcriptomics. biocViews: SingleCell, GeneExpression, Software, Spatial, Transcriptomics Author: Atul Deshpande [aut, cre] (ORCID: ), Ludmila Danilova [ctb], Dmitrijs Lvovs [ctb] (ORCID: ) Maintainer: Atul Deshpande URL: https://github.com/DeshpandeLab/SpaceMarkers VignetteBuilder: knitr BugReports: https://github.com/DeshpandeLab/SpaceMarkers/issues git_url: https://git.bioconductor.org/packages/SpaceMarkers git_branch: RELEASE_3_22 git_last_commit: 275cd1c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpaceMarkers_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpaceMarkers_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpaceMarkers_2.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpaceMarkers_2.0.0.tgz vignettes: vignettes/SpaceMarkers/inst/doc/SpaceMarkers_vignette.html vignetteTitles: Inferring Immune Interactions in Breast Cancer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpaceMarkers/inst/doc/SpaceMarkers_vignette.R dependencyCount: 142 Package: SpaceTrooper Version: 1.0.0 Depends: R (>= 4.4.0), SpatialExperiment Imports: DropletUtils, S4Vectors, SummarizedExperiment, arrow, data.table, dplyr, e1071, ggplot2, ggpubr, robustbase, scater, scuttle, sf, sfheaders, cowplot, glmnet, rhdf5, methods, rlang, SpatialExperimentIO Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), withr, viridis License: MIT + file LICENSE MD5sum: a59731d10459bb64d298aa6e445e2d20 NeedsCompilation: no Title: SpaceTrooper performs Quality Control analysis of Image-Based spatial Description: SpaceTrooper performs Quality Control analysis using data driven GLM models of Image-Based spatial data, providing exploration plots, QC metrics computation, outlier detection. It implements a GLM strategy for the detection of low quality cells in imaging-based spatial data (Transcriptomics and Proteomics). It additionally implements several plots for the visualization of imaging based polygons through the ggplot2 package. biocViews: Software, Transcriptomics, GeneExpression, QualityControl, Spatial, SingleCell, DataImport, ImmunoOncology Author: Dario Righelli [aut, cre] (ORCID: ), Benedetta Banzi [aut], Oriana Romano [ctb], Matteo Merchionni [ctb], Mattia Forcato [ctb], Silvio Bicciato [aut], Davide Risso [ctb] Maintainer: Dario Righelli URL: https://github.com/drighelli/SpaceTrooper VignetteBuilder: knitr BugReports: https://github.com/drighelli/SpaceTrooper/issues git_url: https://git.bioconductor.org/packages/SpaceTrooper git_branch: RELEASE_3_22 git_last_commit: 88c0696 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpaceTrooper_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpaceTrooper_0.99.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpaceTrooper_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpaceTrooper_1.0.0.tgz vignettes: vignettes/SpaceTrooper/inst/doc/introduction.html, vignettes/SpaceTrooper/inst/doc/loading_intro.html, vignettes/SpaceTrooper/inst/doc/proteinQC.html vignetteTitles: introduction.html, Loading CosMx and Xenium data with SpaceTrooper, Spatial Data Quality Control on Protein data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpaceTrooper/inst/doc/introduction.R, vignettes/SpaceTrooper/inst/doc/loading_intro.R, vignettes/SpaceTrooper/inst/doc/proteinQC.R importsMe: OSTA dependencyCount: 189 Package: spacexr Version: 1.2.0 Depends: R (>= 4.5.0) Imports: ggplot2, Matrix, parallel, quadprog, httr, methods, memoise, BiocParallel, BiocFileCache, SummarizedExperiment, scatterpie, SpatialExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 3) MD5sum: c0fd0162e1c4df16dc96878674084e99 NeedsCompilation: no Title: SpatialeXpressionR: Cell Type Identification in Spatial Transcriptomics Description: Spatial-eXpression-R (spacexr) is a package for analyzing cell types in spatial transcriptomics data. This implementation is a fork of the spacexr GitHub repo (https://github.com/dmcable/spacexr), adapted to work with Bioconductor objects. The original package implements two statistical methods: RCTD for learning cell types and CSIDE for inferring cell type-specific differential expression. Currently, this fork only implements RCTD, which learns cell type profiles from annotated RNA sequencing (RNA-seq) reference data and uses these profiles to identify cell types in spatial transcriptomic pixels while accounting for platform-specific effects. Future releases will include an implementation of CSIDE. biocViews: GeneExpression, DifferentialExpression, SingleCell, RNASeq, Software, Spatial, Transcriptomics Author: Dylan Cable [aut], Rafael Irizarry [aut] (ORCID: ), Gabriel Grajeda [cre] (ORCID: ), Fannie and John Hertz Foundation [fnd] Maintainer: Gabriel Grajeda URL: https://github.com/ggrajeda/spacexr VignetteBuilder: knitr BugReports: https://github.com/ggrajeda/spacexr/issues git_url: https://git.bioconductor.org/packages/spacexr git_branch: RELEASE_3_22 git_last_commit: b6d81f6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spacexr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spacexr_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spacexr_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spacexr_1.2.0.tgz vignettes: vignettes/spacexr/inst/doc/rctd-tutorial.html vignetteTitles: rctd-tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spacexr/inst/doc/rctd-tutorial.R importsMe: OSTA dependencyCount: 100 Package: Spaniel Version: 1.24.0 Depends: R (>= 4.0) Imports: Seurat, SingleCellExperiment, SummarizedExperiment, dplyr, methods, ggplot2, scater (>= 1.13), scran, igraph, shiny, jpeg, magrittr, utils, S4Vectors, DropletUtils, jsonlite, png Suggests: knitr, rmarkdown, testthat, devtools License: MIT + file LICENSE MD5sum: 596cd2434a4d0587b66480751b9d367d NeedsCompilation: no Title: Spatial Transcriptomics Analysis Description: Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. Spaniel can import data from either the original Spatial Transcriptomics system or 10X Visium technology. The package contains functions to create a SingleCellExperiment Seurat object and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage, Clustering Author: Rachel Queen [aut, cre] Maintainer: Rachel Queen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Spaniel git_branch: RELEASE_3_22 git_last_commit: 5171341 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Spaniel_1.24.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Spaniel_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Spaniel_1.24.0.tgz vignettes: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.html vignetteTitles: Spaniel 10X Visium hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.R dependencyCount: 210 Package: SpaNorm Version: 1.4.0 Depends: R (>= 4.4) Imports: edgeR, ggplot2, Matrix, matrixStats, methods, rlang, scran, SeuratObject, SingleCellExperiment, SpatialExperiment, stats, SummarizedExperiment, S4Vectors, utils Suggests: testthat (>= 3.0.0), knitr, rmarkdown, prettydoc, pkgdown, covr, BiocStyle, Seurat, patchwork, ggforce, ggnewscale License: GPL (>= 3) MD5sum: c0d37183bccf9e1609f7b29b85c4443e NeedsCompilation: no Title: Spatially-aware normalisation for spatial transcriptomics data Description: This package implements the spatially aware library size normalisation algorithm, SpaNorm. SpaNorm normalises out library size effects while retaining biology through the modelling of smooth functions for each effect. Normalisation is performed in a gene- and cell-/spot- specific manner, yielding library size adjusted data. biocViews: Software, GeneExpression, Transcriptomics, Spatial, CellBiology Author: Dharmesh D. Bhuva [aut, cre] (ORCID: ), Agus Salim [aut] (ORCID: ), Ahmed Mohamed [aut] (ORCID: ) Maintainer: Dharmesh D. Bhuva URL: https://bhuvad.github.io/SpaNorm VignetteBuilder: knitr BugReports: https://github.com/bhuvad/SpaNorm/issues git_url: https://git.bioconductor.org/packages/SpaNorm git_branch: RELEASE_3_22 git_last_commit: 3627d0d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpaNorm_1.4.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpaNorm_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpaNorm_1.4.0.tgz vignettes: vignettes/SpaNorm/inst/doc/SpaNorm.html vignetteTitles: SpaNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpaNorm/inst/doc/SpaNorm.R dependencyCount: 116 Package: spARI Version: 1.0.0 Depends: R (>= 4.1.0) Imports: Rcpp, stats, Matrix, SpatialExperiment, SummarizedExperiment, BiocParallel (>= 1.0) LinkingTo: Rcpp Suggests: FNN, knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: 4f589ea3f82e287291800fe8aff0212f NeedsCompilation: yes Title: Spatially Aware Adjusted Rand Index for Evaluating Spatial Transcritpomics Clustering Description: The R package used in the manuscript "Spatially Aware Adjusted Rand Index for Evaluating Spatial Transcritpomics Clustering". biocViews: Clustering, DataImport, GeneExpression, Transcriptomics, Spatial, Software Author: Yinqiao Yan [aut, cre], Xiangnan Feng [aut, fnd], Xiangyu Luo [aut, fnd] Maintainer: Yinqiao Yan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/spARI git_branch: RELEASE_3_22 git_last_commit: 4a3384b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spARI_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spARI_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spARI_1.0.0.tgz vignettes: vignettes/spARI/inst/doc/spARI.html vignetteTitles: An Introduction to spARI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spARI/inst/doc/spARI.R dependencyCount: 76 Package: sparrow Version: 1.16.0 Depends: R (>= 4.1.0) Imports: babelgene (>= 21.4), BiocGenerics, BiocParallel, BiocSet, checkmate, circlize, ComplexHeatmap (>= 2.0), data.table (>= 1.10.4), DelayedMatrixStats, edgeR (>= 3.18.1), ggplot2 (>= 2.2.0), graphics, grDevices, GSEABase, irlba, limma, Matrix, methods, plotly (>= 4.9.0), stats, utils, viridis Suggests: AnnotationDbi, BiasedUrn, Biobase (>= 2.24.0), BiocStyle, DESeq2, dplyr, dtplyr, fgsea, GSVA, GO.db, goseq, hexbin, KernSmooth, knitr, magrittr, matrixStats, msigdbr (>= 10.0), orthogene, PANTHER.db (>= 1.0.3), R.utils, reactome.db, rmarkdown, SummarizedExperiment, statmod, stringr, testthat, webshot License: MIT + file LICENSE Archs: x64 MD5sum: 5cdef0a1f6392a6aaaaf9835c9259927 NeedsCompilation: no Title: Take command of set enrichment analyses through a unified interface Description: Provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package. biocViews: GeneSetEnrichment, Pathways Author: Steve Lianoglou [aut, cre] (ORCID: ), Arkadiusz Gladki [ctb], Aratus Informatics, LLC [fnd] (2023+), Denali Therapeutics [fnd] (2018-2022), Genentech [fnd] (2014 - 2017) Maintainer: Steve Lianoglou URL: https://github.com/lianos/sparrow VignetteBuilder: knitr BugReports: https://github.com/lianos/sparrow/issues git_url: https://git.bioconductor.org/packages/sparrow git_branch: RELEASE_3_22 git_last_commit: fc1b95c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sparrow_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sparrow_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sparrow_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sparrow_1.16.0.tgz vignettes: vignettes/sparrow/inst/doc/sparrow.html vignetteTitles: Performing gene set enrichment analyses with sparrow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparrow/inst/doc/sparrow.R suggestsMe: gCrisprTools dependencyCount: 139 Package: SparseArray Version: 1.10.0 Depends: R (>= 4.3.0), methods, Matrix, BiocGenerics (>= 0.43.1), MatrixGenerics (>= 1.11.1), S4Vectors (>= 0.43.2), S4Arrays (>= 1.9.3) Imports: utils, stats, matrixStats, IRanges, XVector LinkingTo: S4Vectors, IRanges, XVector Suggests: HDF5Array, ExperimentHub, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 6b7c0a251a9e19bfa4dd7702f351a11c NeedsCompilation: yes Title: High-performance sparse data representation and manipulation in R Description: The SparseArray package provides array-like containers for efficient in-memory representation of multidimensional sparse data in R (arrays and matrices). The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data: the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they suppport most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre] (ORCID: ), Vince Carey [fnd] (ORCID: ), Rafael A. Irizarry [fnd] (ORCID: ), Jacques Serizay [ctb] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/SparseArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SparseArray/issues git_url: https://git.bioconductor.org/packages/SparseArray git_branch: RELEASE_3_22 git_last_commit: b793c60 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SparseArray_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SparseArray_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SparseArray_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SparseArray_1.10.0.tgz vignettes: vignettes/SparseArray/inst/doc/SparseArray_objects.html vignetteTitles: SparseArray objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SparseArray/inst/doc/SparseArray_objects.R dependsOnMe: DelayedArray, DelayedRandomArray, h5mread, HDF5Array, TileDBArray importsMe: alabaster.matrix, batchelor, beachmat, DelayedMatrixStats, DelayedTensor, dreamlet, DropletUtils, glmGamPoi, GSVA, SCArray, scater, scone, scuttle, TSCAN, zellkonverter, scRNAseq, IDLFM suggestsMe: BiocGenerics, MatrixGenerics, S4Arrays, SummarizedExperiment dependencyCount: 19 Package: sparseMatrixStats Version: 1.22.0 Depends: MatrixGenerics (>= 1.5.3) Imports: Rcpp, Matrix, matrixStats (>= 0.60.0), methods LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), knitr, bench, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 0911afd4b58595f5b43aa39120a702e0 NeedsCompilation: yes Title: Summary Statistics for Rows and Columns of Sparse Matrices Description: High performance functions for row and column operations on sparse matrices. For example: col / rowMeans2, col / rowMedians, col / rowVars etc. Currently, the optimizations are limited to data in the column sparse format. This package is inspired by the matrixStats package by Henrik Bengtsson. biocViews: Infrastructure, Software, DataRepresentation Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/sparseMatrixStats SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/const-ae/sparseMatrixStats/issues git_url: https://git.bioconductor.org/packages/sparseMatrixStats git_branch: RELEASE_3_22 git_last_commit: 3acf348 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sparseMatrixStats_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sparseMatrixStats_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sparseMatrixStats_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sparseMatrixStats_1.22.0.tgz vignettes: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.html vignetteTitles: sparseMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.R importsMe: atena, ccImpute, concordexR, Coralysis, DelayedMatrixStats, dreamlet, GSVA, scone, SimBu, smartid, SplineDV, SPOTlight, adjclust, CRMetrics, GrabSVG, modelSelection, mombf, scBSP suggestsMe: APL, blase, MatrixGenerics, miloR, scPCA, scrapper, scuttle, SpatialFeatureExperiment, StabMap, zinbwave, singleCellHaystack dependencyCount: 11 Package: sparsenetgls Version: 1.28.0 Depends: R (>= 4.0.0), Matrix, MASS Imports: methods, glmnet, huge, stats, graphics, utils Suggests: testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>= 5.0.0) License: GPL-3 MD5sum: 97dff0db5136bbc47d77997b7b3e2e03 NeedsCompilation: no Title: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression Description: The package provides methods of combining the graph structure learning and generalized least squares regression to improve the regression estimation. The main function sparsenetgls() provides solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the sandwich estimators in generalized least squares (gls) regression. This package also provides functions for assessing a Gaussian graphical model which uses the penalized approach. It uses Receiver Operative Characteristics curve as a visualization tool in the assessment. biocViews: ImmunoOncology, GraphAndNetwork,Regression,Metabolomics,CopyNumberVariation,MassSpectrometry,Proteomics,Software,Visualization Author: Irene Zeng [aut, cre], Thomas Lumley [ctb] Maintainer: Irene Zeng SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparsenetgls git_branch: RELEASE_3_22 git_last_commit: ea9f507 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sparsenetgls_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sparsenetgls_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sparsenetgls_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sparsenetgls_1.28.0.tgz vignettes: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.html vignetteTitles: Introduction to sparsenetgls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.R dependencyCount: 28 Package: SparseSignatures Version: 2.20.0 Depends: R (>= 4.1.0), NMF Imports: nnlasso, nnls, parallel, data.table, Biostrings, GenomicRanges, IRanges, BSgenome, GenomeInfoDb, ggplot2, gridExtra, reshape2, RhpcBLASctl Suggests: BiocGenerics, BSgenome.Hsapiens.1000genomes.hs37d5, BiocStyle, testthat, knitr, License: file LICENSE MD5sum: c38e7899144bb44e1f136377d64dce4c NeedsCompilation: no Title: SparseSignatures Description: Point mutations occurring in a genome can be divided into 96 categories based on the base being mutated, the base it is mutated into and its two flanking bases. Therefore, for any patient, it is possible to represent all the point mutations occurring in that patient's tumor as a vector of length 96, where each element represents the count of mutations for a given category in the patient. A mutational signature represents the pattern of mutations produced by a mutagen or mutagenic process inside the cell. Each signature can also be represented by a vector of length 96, where each element represents the probability that this particular mutagenic process generates a mutation of the 96 above mentioned categories. In this R package, we provide a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Avantika Lal [aut], Keli Liu [ctb], Luca De Sano [cre, aut] (ORCID: ), Robert Tibshirani [ctb], Arend Sidow [aut] Maintainer: Luca De Sano URL: https://github.com/danro9685/SparseSignatures VignetteBuilder: knitr BugReports: https://github.com/danro9685/SparseSignatures git_url: https://git.bioconductor.org/packages/SparseSignatures git_branch: RELEASE_3_22 git_last_commit: 9b8de11 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SparseSignatures_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SparseSignatures_2.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SparseSignatures_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SparseSignatures_2.20.0.tgz vignettes: vignettes/SparseSignatures/inst/doc/v1_introduction.html, vignettes/SparseSignatures/inst/doc/v2_using_the_package.html vignetteTitles: v1_introduction.html, v2_using_the_package.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SparseSignatures/inst/doc/v2_using_the_package.R dependencyCount: 97 Package: spaSim Version: 1.12.0 Depends: R (>= 4.2.0) Imports: ggplot2, methods, stats, dplyr, spatstat.geom, spatstat.random, SpatialExperiment, SummarizedExperiment, RANN Suggests: RefManageR, BiocStyle, knitr, testthat (>= 3.0.0), sessioninfo, rmarkdown, markdown License: Artistic-2.0 MD5sum: 6c30e16703f70395822a2f3d4fdda86a NeedsCompilation: no Title: Spatial point data simulator for tissue images Description: A suite of functions for simulating spatial patterns of cells in tissue images. Output images are multitype point data in SingleCellExperiment format. Each point represents a cell, with its 2D locations and cell type. Potential cell patterns include background cells, tumour/immune cell clusters, immune rings, and blood/lymphatic vessels. biocViews: StatisticalMethod, Spatial, BiomedicalInformatics Author: Yuzhou Feng [aut, cre] (ORCID: ), Anna Trigos [aut] (ORCID: ) Maintainer: Yuzhou Feng URL: https://trigosteam.github.io/spaSim/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/spaSim git_url: https://git.bioconductor.org/packages/spaSim git_branch: RELEASE_3_22 git_last_commit: a381de3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spaSim_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spaSim_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spaSim_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spaSim_1.12.0.tgz vignettes: vignettes/spaSim/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spaSim/inst/doc/vignette.R dependencyCount: 84 Package: SpatialCPie Version: 1.26.0 Depends: R (>= 3.6) Imports: colorspace (>= 1.3-2), data.table (>= 1.12.2), digest (>= 0.6.21), dplyr (>= 0.7.6), ggforce (>= 0.3.0), ggiraph (>= 0.5.0), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), grid (>= 3.5.1), igraph (>= 1.2.2), lpSolve (>= 5.6.13), methods (>= 3.5.0), purrr (>= 0.2.5), readr (>= 1.1.1), rlang (>= 0.2.2), shiny (>= 1.1.0), shinycssloaders (>= 0.2.0), shinyjs (>= 1.0), shinyWidgets (>= 0.4.8), stats (>= 3.6.0), SummarizedExperiment (>= 1.10.1), tibble (>= 1.4.2), tidyr (>= 0.8.1), tidyselect (>= 0.2.4), tools (>= 3.6.0), utils (>= 3.5.0), zeallot (>= 0.1.0) Suggests: BiocStyle (>= 2.8.2), jpeg (>= 0.1-8), knitr (>= 1.20), rmarkdown (>= 1.10), testthat (>= 2.0.0) License: MIT + file LICENSE MD5sum: 3b716d43a2964e6fdd63e4f0d843f02c NeedsCompilation: no Title: Cluster analysis of Spatial Transcriptomics data Description: SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues. biocViews: Transcriptomics, Clustering, RNASeq, Software Author: Joseph Bergenstraahle [aut, cre] Maintainer: Joseph Bergenstraahle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialCPie git_branch: RELEASE_3_22 git_last_commit: 07c0bdf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpatialCPie_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpatialCPie_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpatialCPie_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpatialCPie_1.26.0.tgz vignettes: vignettes/SpatialCPie/inst/doc/SpatialCPie.html vignetteTitles: SpatialCPie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialCPie/inst/doc/SpatialCPie.R dependencyCount: 113 Package: spatialDE Version: 1.16.0 Depends: R (>= 4.3) Imports: reticulate, basilisk (>= 1.9.10), checkmate, stats, SpatialExperiment, methods, SummarizedExperiment, Matrix, ggplot2, ggrepel, scales, gridExtra Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 242079d1326e3005997eda8e3d56bc23 NeedsCompilation: no Title: R wrapper for SpatialDE Description: SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk. biocViews: Software, Transcriptomics Author: Davide Corso [aut] (ORCID: ), Milan Malfait [aut] (ORCID: ), Lambda Moses [aut] (ORCID: ), Gabriele Sales [cre] Maintainer: Gabriele Sales URL: https://github.com/sales-lab/spatialDE, https://bioconductor.org/packages/spatialDE/ VignetteBuilder: knitr BugReports: https://github.com/sales-lab/spatialDE/issues git_url: https://git.bioconductor.org/packages/spatialDE git_branch: RELEASE_3_22 git_last_commit: dfd143b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spatialDE_1.16.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spatialDE_1.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spatialDE_1.15.1.tgz vignettes: vignettes/spatialDE/inst/doc/spatialDE.html vignetteTitles: Introduction to spatialDE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spatialDE/inst/doc/spatialDE.R dependencyCount: 89 Package: SpatialDecon Version: 1.20.0 Depends: R (>= 4.0.0) Imports: grDevices, stats, utils, graphics, SeuratObject, Biobase, GeomxTools, repmis, methods, Matrix, logNormReg (>= 0.4) Suggests: testthat, knitr, rmarkdown, qpdf, Seurat License: MIT + file LICENSE MD5sum: ef45d9aaedeb0e2f67187c5a1ae267e2 NeedsCompilation: no Title: Deconvolution of mixed cells from spatial and/or bulk gene expression data Description: Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data. biocViews: ImmunoOncology, FeatureExtraction, GeneExpression, Transcriptomics, Spatial Author: Maddy Griswold [cre, aut], Patrick Danaher [aut] Maintainer: Maddy Griswold VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/SpatialDecon/issues git_url: https://git.bioconductor.org/packages/SpatialDecon git_branch: RELEASE_3_22 git_last_commit: 3cc3c50 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpatialDecon_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpatialDecon_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpatialDecon_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpatialDecon_1.20.0.tgz vignettes: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.html, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.html vignetteTitles: Use of SpatialDecon in a large GeoMx dataset with GeomxTools, Use of SpatialDecon in a small GeoMx dataet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.R, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.R suggestsMe: GeomxTools dependencyCount: 135 Package: SpatialExperiment Version: 1.20.0 Depends: R (>= 4.1.0), methods, SingleCellExperiment Imports: rjson, grDevices, magick, utils, S4Vectors, SummarizedExperiment, BiocGenerics, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle, BumpyMatrix, DropletUtils, VisiumIO License: GPL-3 MD5sum: 7fd5877f5b4cfa9b0ec02d50ca7840f1 NeedsCompilation: no Title: S4 Class for Spatially Resolved -omics Data Description: Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform. biocViews: DataRepresentation, DataImport, Infrastructure, ImmunoOncology, GeneExpression, Transcriptomics, SingleCell, Spatial Author: Dario Righelli [aut, cre] (ORCID: ), Davide Risso [aut] (ORCID: ), Helena L. Crowell [aut] (ORCID: ), Lukas M. Weber [aut] (ORCID: ), Nicholas J. Eagles [ctb] Maintainer: Dario Righelli URL: https://github.com/drighelli/SpatialExperiment VignetteBuilder: knitr BugReports: https://github.com/drighelli/SpatialExperiment/issues git_url: https://git.bioconductor.org/packages/SpatialExperiment git_branch: RELEASE_3_22 git_last_commit: dfdda19 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpatialExperiment_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpatialExperiment_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpatialExperiment_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpatialExperiment_1.20.0.tgz vignettes: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html vignetteTitles: Introduction to the SpatialExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.R dependsOnMe: alabaster.spatial, clustSIGNAL, ExperimentSubset, imcRtools, SpaceTrooper, SPIAT, tidySpatialExperiment, visiumStitched, imcdatasets, MerfishData, MouseGastrulationData, spatialLIBD, STexampleData, TENxVisiumData, VectraPolarisData, WeberDivechaLCdata importsMe: Banksy, BulkSignalR, CARDspa, CatsCradle, concordexR, CTSV, cytomapper, DESpace, escheR, FLAMES, ggspavis, GSVA, hoodscanR, lisaClust, MoleculeExperiment, nnSVG, poem, scider, SEraster, signifinder, smoothclust, sosta, spacexr, SpaNorm, spARI, spaSim, spatialDE, SpatialExperimentIO, spatialFDA, SpatialFeatureExperiment, spatialSimGP, spicyR, spoon, SpotClean, SpotSweeper, standR, Statial, stJoincount, stPipe, SVP, tpSVG, VisiumIO, Voyager, XeniumIO, xenLite, HCATonsilData, SingleCellMultiModal, SubcellularSpatialData, TENxXeniumData, OSTA, SpatialDDLS suggestsMe: GeomxTools, ggsc, SPOTlight, zellkonverter, muSpaData dependencyCount: 66 Package: SpatialExperimentIO Version: 1.2.0 Depends: R (>= 4.1.0) Imports: DropletUtils, SpatialExperiment, SingleCellExperiment, methods, data.table, arrow, purrr, S4Vectors Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: Artistic-2.0 MD5sum: 106b460e315b4dd91c41466b2707aeb9 NeedsCompilation: no Title: Read in Xenium, CosMx, MERSCOPE or STARmapPLUS data as SpatialExperiment object Description: Read in imaging-based spatial transcriptomics technology data. Current available modules are for Xenium by 10X Genomics, CosMx by Nanostring, MERSCOPE by Vizgen, or STARmapPLUS from Broad Institute. You can choose to read the data in as a SpatialExperiment or a SingleCellExperiment object. biocViews: DataRepresentation, DataImport, Infrastructure, Transcriptomics, SingleCell, Spatial, GeneExpression Author: Yixing E. Dong [aut, cre] (ORCID: ) Maintainer: Yixing E. Dong URL: https://github.com/estellad/SpatialExperimentIO VignetteBuilder: knitr BugReports: https://github.com/estellad/SpatialExperimentIO/issues git_url: https://git.bioconductor.org/packages/SpatialExperimentIO git_branch: RELEASE_3_22 git_last_commit: 811da54 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpatialExperimentIO_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpatialExperimentIO_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpatialExperimentIO_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpatialExperimentIO_1.2.0.tgz vignettes: vignettes/SpatialExperimentIO/inst/doc/SpatialExperimentIO.html vignetteTitles: SpatialExperimentIO Reader Package Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialExperimentIO/inst/doc/SpatialExperimentIO.R importsMe: SpaceTrooper, OSTA suggestsMe: OSTA.data dependencyCount: 99 Package: spatialFDA Version: 1.2.0 Depends: R (>= 4.3.0) Imports: dplyr, ggplot2, parallel, patchwork, purrr, refund, SpatialExperiment, spatstat.explore, spatstat.geom, SummarizedExperiment, methods, stats, fda, tidyr, graphics, ExperimentHub, scales, S4Vectors Suggests: stringr, knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), mgcv License: GPL (>= 3) + file LICENSE MD5sum: 1804e0f18209031aad8986c7d058ed4c NeedsCompilation: no Title: A Tool for Spatial Multi-sample Comparisons Description: spatialFDA is a package to calculate spatial statistics metrics. The package takes a SpatialExperiment object and calculates spatial statistics metrics using the package spatstat. Then it compares the resulting functions across samples/conditions using functional additive models as implemented in the package refund. Furthermore, it provides exploratory visualisations using functional principal component analysis, as well implemented in refund. biocViews: Software, Spatial, Transcriptomics Author: Martin Emons [aut, cre] (ORCID: ), Samuel Gunz [aut] (ORCID: ), Fabian Scheipl [aut] (ORCID: ), Mark D. Robinson [aut, fnd] (ORCID: ) Maintainer: Martin Emons URL: https://github.com/mjemons/spatialFDA VignetteBuilder: knitr BugReports: https://github.com/mjemons/spatialFDA/issues git_url: https://git.bioconductor.org/packages/spatialFDA git_branch: RELEASE_3_22 git_last_commit: 6860169 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spatialFDA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spatialFDA_1.1.8.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spatialFDA_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spatialFDA_1.2.0.tgz vignettes: vignettes/spatialFDA/inst/doc/DiabetesIsletExample.html, vignettes/spatialFDA/inst/doc/DiabetesIsletExampleBrief.html vignetteTitles: Functional Data Analysis of Spatial Metrics, Overview Functional Data Analysis of Spatial Metrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spatialFDA/inst/doc/DiabetesIsletExample.R, vignettes/spatialFDA/inst/doc/DiabetesIsletExampleBrief.R importsMe: OSTA dependencyCount: 141 Package: SpatialFeatureExperiment Version: 1.11.1 Depends: R (>= 4.3.0) Imports: Biobase, BiocGenerics (>= 0.51.2), BiocNeighbors, BiocParallel, data.table, DropletUtils, EBImage, grDevices, lifecycle, Matrix, methods, rjson, rlang, S4Vectors, sf, sfheaders, SingleCellExperiment, SpatialExperiment, spatialreg, spdep (>= 1.1-7), SummarizedExperiment, stats, terra, utils, zeallot Suggests: arrow, BiocStyle, dplyr, gmp, knitr, RBioFormats, rhdf5, rmarkdown, scater, sfarrow, SFEData (>= 1.5.3), Seurat, SeuratObject, sparseMatrixStats, testthat (>= 3.0.0), tidyr, VisiumIO, Voyager (>= 1.7.2), withr, xml2 License: Artistic-2.0 MD5sum: 301053a9ecbe9639bcfea490b9f15aa0 NeedsCompilation: no Title: Integrating SpatialExperiment with Simple Features in sf Description: A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used. biocViews: DataRepresentation, Transcriptomics, Spatial Author: Lambda Moses [aut, cre] (ORCID: ), Alik Huseynov [aut] (ORCID: ), Lior Pachter [aut, ths] (ORCID: ) Maintainer: Lambda Moses URL: https://github.com/pachterlab/SpatialFeatureExperiment VignetteBuilder: knitr BugReports: https://github.com/pachterlab/SpatialFeatureExperiment/issues git_url: https://git.bioconductor.org/packages/SpatialFeatureExperiment git_branch: devel git_last_commit: 80353da git_last_commit_date: 2025-09-25 Date/Publication: 2025-10-07 source.ver: src/contrib/SpatialFeatureExperiment_1.11.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpatialFeatureExperiment_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpatialFeatureExperiment_1.11.1.tgz vignettes: vignettes/SpatialFeatureExperiment/inst/doc/SFE.html vignetteTitles: Introduction to the SpatialFeatureExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialFeatureExperiment/inst/doc/SFE.R dependsOnMe: alabaster.sfe, Voyager importsMe: TENxXeniumData, OSTA suggestsMe: concordexR, jazzPanda, xenLite, SFEData dependencyCount: 153 Package: SpatialOmicsOverlay Version: 1.10.0 Depends: R (>= 4.1.0) Imports: S4Vectors, Biobase, base64enc, EBImage, ggplot2, XML, scattermore, dplyr, pbapply, data.table, readxl, magick, grDevices, stringr, plotrix, GeomxTools, BiocFileCache, stats, utils, methods, ggtext, tools, RBioFormats Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), stringi, qpdf, pheatmap, viridis, cowplot, vdiffr, sf License: MIT MD5sum: dee308061f23efe94a97a52c873ed7aa NeedsCompilation: no Title: Spatial Overlay for Omic Data from Nanostring GeoMx Data Description: Tools for NanoString Technologies GeoMx Technology. Package to easily graph on top of an OME-TIFF image. Plotting annotations can range from tissue segment to gene expression. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, ProprietaryPlatforms, RNASeq, Spatial, DataRepresentation, Visualization Author: Maddy Griswold [cre, aut], Megan Vandenberg [ctb], Stephanie Zimmerman [ctb] Maintainer: Maddy Griswold VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialOmicsOverlay git_branch: RELEASE_3_22 git_last_commit: edcd7be git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpatialOmicsOverlay_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpatialOmicsOverlay_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpatialOmicsOverlay_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpatialOmicsOverlay_1.10.0.tgz vignettes: vignettes/SpatialOmicsOverlay/inst/doc/SpatialOmicsOverlay.html vignetteTitles: Introduction to SpatialOmicsOverlay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialOmicsOverlay/inst/doc/SpatialOmicsOverlay.R dependencyCount: 160 Package: spatialSimGP Version: 1.4.0 Depends: R (>= 4.4) Imports: SpatialExperiment, MASS, SummarizedExperiment Suggests: testthat (>= 3.0.0), STexampleData, ggplot2, knitr License: MIT + file LICENSE MD5sum: 8efa078139b7784baebaa998f0d7684e NeedsCompilation: no Title: Simulate Spatial Transcriptomics Data with the Mean-variance Relationship Description: This packages simulates spatial transcriptomics data with the mean- variance relationship using a Gaussian Process model per gene. biocViews: Spatial, Transcriptomics, GeneExpression Author: Kinnary Shah [aut, cre] (ORCID: ), Boyi Guo [aut] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Kinnary Shah URL: https://github.com/kinnaryshah/spatialSimGP VignetteBuilder: knitr BugReports: https://github.com/kinnaryshah/spatialSimGP/issues git_url: https://git.bioconductor.org/packages/spatialSimGP git_branch: RELEASE_3_22 git_last_commit: e777034 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spatialSimGP_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spatialSimGP_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spatialSimGP_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spatialSimGP_1.4.0.tgz vignettes: vignettes/spatialSimGP/inst/doc/spatialSimGP.html vignetteTitles: spatialSimGP Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spatialSimGP/inst/doc/spatialSimGP.R dependencyCount: 68 Package: speckle Version: 1.10.0 Depends: R (>= 4.2.0) Imports: limma, edgeR, SingleCellExperiment, Seurat, ggplot2, methods, stats, grDevices, graphics Suggests: BiocStyle, knitr, rmarkdown, statmod, CellBench, scater, patchwork, jsonlite, vdiffr, testthat (>= 3.0.0) License: GPL-3 MD5sum: f91ab799bb43a70090125a1394c50e8b NeedsCompilation: no Title: Statistical methods for analysing single cell RNA-seq data Description: The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions. biocViews: SingleCell, RNASeq, Regression, GeneExpression Author: Belinda Phipson [aut, cre] Maintainer: Belinda Phipson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/speckle git_branch: RELEASE_3_22 git_last_commit: 71ee725 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/speckle_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/speckle_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/speckle_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/speckle_1.10.0.tgz vignettes: vignettes/speckle/inst/doc/speckle.html vignetteTitles: speckle: statistical methods for analysing single cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/speckle/inst/doc/speckle.R dependencyCount: 170 Package: specL Version: 1.44.0 Depends: R (>= 4.1), DBI (>= 0.5), methods (>= 3.3), protViz (>= 0.7), RSQLite (>= 1.1), seqinr (>= 3.3) Suggests: BiocGenerics, BiocStyle (>= 2.2), knitr (>= 1.15), rmarkdown, RUnit (>= 0.4) License: GPL-3 MD5sum: 6747073259fa57a151ff47c72b02a3e6 NeedsCompilation: no Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: provides a functions for generating spectra libraries that can be used for MRM SRM MS workflows in proteomics. The package provides a BiblioSpec reader, a function which can add the protein information using a FASTA formatted amino acid file, and an export method for using the created library in the Spectronaut software. The package is developed, tested and used at the Functional Genomics Center Zurich . biocViews: MassSpectrometry, Proteomics Author: Christian Panse [aut, cre] (ORCID: ), Jonas Grossmann [aut] (ORCID: ), Christian Trachsel [aut], Witold E. Wolski [ctb] Maintainer: Christian Panse URL: http://bioconductor.org/packages/specL/ VignetteBuilder: knitr BugReports: https://github.com/fgcz/specL/issues git_url: https://git.bioconductor.org/packages/specL git_branch: RELEASE_3_22 git_last_commit: bb8652f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/specL_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/specL_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/specL_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/specL_1.44.0.tgz vignettes: vignettes/specL/inst/doc/report.html, vignettes/specL/inst/doc/specL.html vignetteTitles: Automatic specL Workflow, Introduction to specL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/report.R, vignettes/specL/inst/doc/specL.R suggestsMe: msqc1, NestLink dependencyCount: 33 Package: SpeCond Version: 1.64.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: 7bbe568e6c85db8df0fc2f5b67333ac1 NeedsCompilation: no Title: Condition specific detection from expression data Description: This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, ReportWriting Author: Florence Cavalli Maintainer: Florence Cavalli git_url: https://git.bioconductor.org/packages/SpeCond git_branch: RELEASE_3_22 git_last_commit: 32c7308 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpeCond_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpeCond_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpeCond_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpeCond_1.64.0.tgz vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf vignetteTitles: SpeCond hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpeCond/inst/doc/SpeCond.R dependencyCount: 18 Package: Spectra Version: 1.19.11 Depends: R (>= 4.1.0), S4Vectors, BiocParallel Imports: ProtGenerics (>= 1.39.2), methods, IRanges, MsCoreUtils (>= 1.7.5), graphics, grDevices, stats, tools, utils, fs, BiocGenerics, MetaboCoreUtils Suggests: testthat, knitr (>= 1.1.0), msdata (>= 0.19.3), roxygen2, BiocStyle (>= 2.5.19), mzR (>= 2.19.6), rhdf5 (>= 2.32.0), rmarkdown, vdiffr (>= 1.0.0), msentropy, patrick License: Artistic-2.0 MD5sum: b222ff860334177e191ea2ececaa0344 NeedsCompilation: no Title: Spectra Infrastructure for Mass Spectrometry Data Description: The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Jan Stanstrup [ctb] (ORCID: ), Nir Shahaf [ctb], Mar Garcia-Aloy [ctb] (ORCID: ), Guillaume Deflandre [ctb] (ORCID: ), Ahlam Mentag [ctb] (ORCID: ) Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/Spectra VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/Spectra/issues git_url: https://git.bioconductor.org/packages/Spectra git_branch: devel git_last_commit: dcc0e56 git_last_commit_date: 2025-10-20 Date/Publication: 2025-10-20 source.ver: src/contrib/Spectra_1.19.11.tar.gz win.binary.ver: bin/windows/contrib/4.5/Spectra_1.19.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Spectra_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Spectra_1.20.0.tgz vignettes: vignettes/Spectra/inst/doc/MsBackend.html, vignettes/Spectra/inst/doc/Spectra-large-scale.html, vignettes/Spectra/inst/doc/Spectra.html vignetteTitles: Creating new `MsBackend` class, Large-scale data handling and processing with Spectra, Description and usage of Spectra object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Spectra/inst/doc/MsBackend.R, vignettes/Spectra/inst/doc/Spectra-large-scale.R, vignettes/Spectra/inst/doc/Spectra.R dependsOnMe: hdxmsqc, MetCirc, MsBackendMassbank, MsBackendMetaboLights, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql importsMe: Chromatograms, CompoundDb, MetaboAnnotation, MsExperiment, MsQuality, PSMatch, SpectraQL, SpectriPy, xcms suggestsMe: koinar, MetNet, MsDataHub, MSnbase, RaMS dependencyCount: 29 Package: SpectralTAD Version: 1.26.0 Depends: R (>= 3.6) Imports: dplyr, PRIMME, cluster, Matrix, parallel, BiocParallel, magrittr, HiCcompare, GenomicRanges, utils Suggests: BiocCheck, BiocManager, BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr License: MIT + file LICENSE Archs: x64 MD5sum: 7572c1ce3bcf85a9afb4f8c1a471c49b NeedsCompilation: no Title: SpectralTAD: Hierarchical TAD detection using spectral clustering Description: SpectralTAD is an R package designed to identify Topologically Associated Domains (TADs) from Hi-C contact matrices. It uses a modified version of spectral clustering that uses a sliding window to quickly detect TADs. The function works on a range of different formats of contact matrices and returns a bed file of TAD coordinates. The method does not require users to adjust any parameters to work and gives them control over the number of hierarchical levels to be returned. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Mikhail Dozmorov [aut, cre] (ORCID: ), Kellen Cresswell [aut], John Stansfield [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/SpectralTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/SpectralTAD/issues git_url: https://git.bioconductor.org/packages/SpectralTAD git_branch: RELEASE_3_22 git_last_commit: 1a597b5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpectralTAD_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpectralTAD_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpectralTAD_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpectralTAD_1.26.0.tgz vignettes: vignettes/SpectralTAD/inst/doc/SpectralTAD.html vignetteTitles: SpectralTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpectralTAD/inst/doc/SpectralTAD.R suggestsMe: TADCompare dependencyCount: 74 Package: SpectraQL Version: 1.4.0 Depends: R (>= 4.4.0), ProtGenerics (>= 1.25.1) Imports: Spectra (>= 1.5.6), MsCoreUtils, methods Suggests: testthat, msdata (>= 0.19.3), roxygen2, rmarkdown, knitr, S4Vectors, BiocStyle, mzR License: Artistic-2.0 MD5sum: 95d15caed36939d1b1c81b57068d0139 NeedsCompilation: no Title: MassQL support for Spectra Description: The Mass Spec Query Language (MassQL) is a domain-specific language enabling to express a query and retrieve mass spectrometry (MS) data in a more natural and understandable way for MS users. It is inspired by SQL and is by design programming language agnostic. The SpectraQL package adds support for the MassQL query language to R, in particular to MS data represented by Spectra objects. Users can thus apply MassQL expressions to analyze and retrieve specific data from Spectra objects. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: Johannes Rainer [aut, cre] (ORCID: ), Andrea Vicini [aut], Sebastian Gibb [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/SpectraQL VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/SpectraQL/issues git_url: https://git.bioconductor.org/packages/SpectraQL git_branch: RELEASE_3_22 git_last_commit: 9390ac1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpectraQL_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpectraQL_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpectraQL_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpectraQL_1.4.0.tgz vignettes: vignettes/SpectraQL/inst/doc/SpectraQL.html vignetteTitles: Mass Spec Query Language Support to the Spectra Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpectraQL/inst/doc/SpectraQL.R dependencyCount: 30 Package: SpectriPy Version: 1.0.0 Depends: R (>= 4.4.0), reticulate (>= 1.42.0) Imports: Spectra (>= 1.19.9), IRanges, S4Vectors, MsCoreUtils, ProtGenerics, methods Suggests: testthat, quarto, MsBackendMgf, MsDataHub, mzR, knitr, BiocStyle License: Artistic-2.0 MD5sum: c94df7804227ced8f7cbda9c65f9c321 NeedsCompilation: no Title: Enhancing Cross-Language Mass Spectrometry Data Analysis with R and Python Description: The SpectriPy package allows integration of Python-based MS analysis code with the Spectra package. Spectra objects can be converted into Python MS data structures. In addition, SpectriPy integrates and wraps the similarity scoring and processing/filtering functions from the Python matchms package into R. biocViews: Infrastructure, Metabolomics, MassSpectrometry, Proteomics Author: Michael Witting [aut] (ORCID: ), Johannes Rainer [aut, cre] (ORCID: ), Carolin Huber [aut] (ORCID: ), Helge Hecht [ctb] (ORCID: ), Marilyn De Graeve [aut] (ORCID: ), Wout Bittremieux [aut] (ORCID: ), Thomas Naake [aut] (ORCID: ), Victor Chrone [ctb] (ORCID: ), Matthias Anagho-Mattanovich [ctb] (ORCID: ), Pierre Marchal [ctb] (ORCID: ), Philippine Louail [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/SpectriPy SystemRequirements: python (>= 3.12), pandoc, quarto VignetteBuilder: quarto BugReports: https://github.com/RforMassSpectrometry/SpectriPy/issues git_url: https://git.bioconductor.org/packages/SpectriPy git_branch: RELEASE_3_22 git_last_commit: 017658a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpectriPy_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpectriPy_0.99.8.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpectriPy_1.0.0.tgz vignettes: vignettes/SpectriPy/inst/doc/detailed-installation-configuration.html, vignettes/SpectriPy/inst/doc/SpectriPy.html vignetteTitles: detailed-installation-configuration.html, SpectriPy.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpectriPy/inst/doc/detailed-installation-configuration.R, vignettes/SpectriPy/inst/doc/SpectriPy.R dependencyCount: 42 Package: SPEM Version: 1.50.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: a404866ec003fb00e96ad86919029b46 NeedsCompilation: no Title: S-system parameter estimation method Description: This package can optimize the parameter in S-system models given time series data biocViews: Network, NetworkInference, Software Author: Xinyi YANG Developer, Jennifer E. DENT Developer and Christine NARDINI Supervisor Maintainer: Xinyi YANG git_url: https://git.bioconductor.org/packages/SPEM git_branch: RELEASE_3_22 git_last_commit: d70b1ef git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SPEM_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPEM_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPEM_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPEM_1.50.0.tgz vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf vignetteTitles: Vignette for SPEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPEM/inst/doc/SPEM-package.R importsMe: TMixClust dependencyCount: 21 Package: SPIA Version: 2.62.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: 1802e51209ffb1246566b257ac862796 NeedsCompilation: no Title: Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations Description: This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. biocViews: Microarray, GraphAndNetwork Author: Adi Laurentiu Tarca , Purvesh Kathri and Sorin Draghici Maintainer: Adi Laurentiu Tarca URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1 git_url: https://git.bioconductor.org/packages/SPIA git_branch: RELEASE_3_22 git_last_commit: eaac9a0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SPIA_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPIA_2.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPIA_2.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPIA_2.62.0.tgz vignettes: vignettes/SPIA/inst/doc/SPIA.pdf vignetteTitles: SPIA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SPIA/inst/doc/SPIA.R importsMe: EnrichmentBrowser suggestsMe: graphite, KEGGgraph dependencyCount: 15 Package: SPIAT Version: 1.12.0 Depends: R (>= 4.2.0), SpatialExperiment (>= 1.8.0) Imports: apcluster (>= 1.4.7), ggplot2 (>= 3.2.1), gridExtra (>= 2.3), gtools (>= 3.8.1), reshape2 (>= 1.4.3), dplyr (>= 0.8.3), RANN (>= 2.6.1), pracma (>= 2.2.5), dbscan (>= 1.1-5), mmand (>= 1.5.4), tibble (>= 2.1.3), grDevices, stats, utils, vroom, dittoSeq, spatstat.geom, methods, spatstat.explore, raster, sp, SummarizedExperiment, rlang Suggests: BiocStyle, plotly (>= 4.9.0), knitr, rmarkdown, pkgdown, testthat, graphics, alphahull, Rtsne, umap, ComplexHeatmap, elsa License: Artistic-2.0 + file LICENSE Archs: x64 MD5sum: 4f45c03ccbefdd090b008454530c2089 NeedsCompilation: no Title: Spatial Image Analysis of Tissues Description: SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues) is an R package with a suite of data processing, quality control, visualization and data analysis tools. SPIAT is compatible with data generated from single-cell spatial proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler). SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes. SPIAT includes six analysis modules that allow visualization, calculation of cell colocalization, categorization of the immune microenvironment relative to tumor areas, analysis of cellular neighborhoods, and the quantification of spatial heterogeneity, providing a comprehensive toolkit for spatial data analysis. biocViews: BiomedicalInformatics, CellBiology, Spatial, Clustering, DataImport, ImmunoOncology, QualityControl, SingleCell, Software, Visualization Author: Anna Trigos [aut] (ORCID: ), Yuzhou Feng [aut, cre] (ORCID: ), Tianpei Yang [aut], Mabel Li [aut], John Zhu [aut], Volkan Ozcoban [aut], Maria Doyle [aut] Maintainer: Yuzhou Feng URL: https://trigosteam.github.io/SPIAT/ VignetteBuilder: knitr BugReports: https://github.com/trigosteam/SPIAT/issues git_url: https://git.bioconductor.org/packages/SPIAT git_branch: RELEASE_3_22 git_last_commit: 60e740a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SPIAT_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPIAT_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPIAT_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPIAT_1.12.0.tgz vignettes: vignettes/SPIAT/inst/doc/basic_analysis.html, vignettes/SPIAT/inst/doc/cell-colocalisation.html, vignettes/SPIAT/inst/doc/data_reading-formatting.html, vignettes/SPIAT/inst/doc/neighborhood.html, vignettes/SPIAT/inst/doc/quality-control_visualisation.html, vignettes/SPIAT/inst/doc/spatial-heterogeneity.html, vignettes/SPIAT/inst/doc/SPIAT.html, vignettes/SPIAT/inst/doc/tissue-structure.html vignetteTitles: Basic analyses with SPIAT, Quantifying cell colocalisation with SPIAT, Reading in data and data formatting in SPIAT, Identifying cellular neighborhood with SPIAT, Quality control and visualisation with SPIAT, Spatial heterogeneity with SPIAT, Overview of the SPIAT package, Characterising tissue structure with SPIAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SPIAT/inst/doc/basic_analysis.R, vignettes/SPIAT/inst/doc/cell-colocalisation.R, vignettes/SPIAT/inst/doc/data_reading-formatting.R, vignettes/SPIAT/inst/doc/neighborhood.R, vignettes/SPIAT/inst/doc/quality-control_visualisation.R, vignettes/SPIAT/inst/doc/spatial-heterogeneity.R, vignettes/SPIAT/inst/doc/SPIAT.R, vignettes/SPIAT/inst/doc/tissue-structure.R dependencyCount: 112 Package: SPICEY Version: 1.0.0 Depends: R (>= 4.5.0), utils, stats, grDevices Imports: GenomicRanges, GenomicFeatures, AnnotationDbi, S4Vectors, ggplot2, dplyr, tidyr, tibble, GenomeInfoDb, scales, cowplot Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: facd0aacc1572a4d87306339d24f6d9b NeedsCompilation: no Title: Calculates cell type specificity from single cell data Description: SPICEY (SPecificity Index for Coding and Epigenetic activitY) is an R package designed to quantify cell-type specificity in single-cell transcriptomic and epigenomic data, particularly scRNA-seq and scATAC-seq. It introduces two complementary indices: the Gene Expression Tissue Specificity Index (GETSI) and the Regulatory Element Tissue Specificity Index (RETSI), both based on entropy to provide continuous, interpretable measures of specificity. By integrating gene expression and chromatin accessibility, SPICEY enables standardized analysis of cell-type-specific regulatory programs across diverse tissues and conditions. biocViews: Transcriptomics, Epigenetics, SingleCell, DifferentialExpression, DifferentialPeakCalling, GeneRegulation, GeneTarget, GeneExpression, Transcription Author: Georgina Fuentes-Páez [aut, cre] (ORCID: ), Nacho Molina [aut], Mireia Ramos-Rodriguez [aut], Lorenzo Pasquali [aut], Ministerio de Ciencia e Innovación Spain [fnd] (program: FPI Fellowship) Maintainer: Georgina Fuentes-Páez URL: https://georginafp.github.io/SPICEY VignetteBuilder: knitr BugReports: https://github.com/georginafp/SPICEY/issues git_url: https://git.bioconductor.org/packages/SPICEY git_branch: RELEASE_3_22 git_last_commit: 625bc2e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SPICEY_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPICEY_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPICEY_1.0.0.tgz vignettes: vignettes/SPICEY/inst/doc/SPICEY.html vignetteTitles: Measuring tissue specificity from single cell data with SPICEY hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPICEY/inst/doc/SPICEY.R dependencyCount: 99 Package: spicyR Version: 1.22.0 Depends: R (>= 4.1) Imports: BiocParallel, ClassifyR, S4Vectors, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, cli, concaveman, coxme, data.table, dplyr, ggforce, ggh4x, ggnewscale, ggplot2, ggthemes, grDevices, lifecycle, lmerTest, magrittr, methods, pheatmap, rlang, scales, scam, simpleSeg, spatstat.explore, spatstat.geom, stats, survival, tibble, tidyr Suggests: SpatialDatasets, BiocStyle, knitr, rmarkdown, pkgdown, imcRtools, testthat (>= 3.0.0) License: GPL (>=2) MD5sum: ded9d03ccf996c164f819e886d0fc939 NeedsCompilation: no Title: Spatial analysis of in situ cytometry data Description: The spicyR package provides a framework for performing inference on changes in spatial relationships between pairs of cell types for cell-resolution spatial omics technologies. spicyR consists of three primary steps: (i) summarizing the degree of spatial localization between pairs of cell types for each image; (ii) modelling the variability in localization summary statistics as a function of cell counts and (iii) testing for changes in spatial localizations associated with a response variable. biocViews: SingleCell, CellBasedAssays, Spatial Author: Nicolas Canete [aut], Ellis Patrick [aut, cre], Nicholas Robertson [ctb], Alex Qin [ctb], Farhan Ameen [ctb], Shreya Rao [ctb] Maintainer: Ellis Patrick URL: https://ellispatrick.github.io/spicyR/ https://github.com/SydneyBioX/spicyR, https://sydneybiox.github.io/spicyR/ VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/spicyR/issues git_url: https://git.bioconductor.org/packages/spicyR git_branch: RELEASE_3_22 git_last_commit: a906b18 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spicyR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spicyR_1.21.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spicyR_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spicyR_1.22.0.tgz vignettes: vignettes/spicyR/inst/doc/spicyR.html vignetteTitles: "Spatial Linear and Mixed-Effects Modelling with spicy" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spicyR/inst/doc/spicyR.R importsMe: lisaClust, OSTA suggestsMe: Statial, spicyWorkflow dependencyCount: 234 Package: spikeLI Version: 2.70.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: 8ee55371b7e195c216bdd219f988aa98 NeedsCompilation: no Title: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool Description: SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006). biocViews: Microarray, QualityControl Author: Delphine Baillon, Paul Leclercq , Sarah Ternisien, Thomas Heim, Enrico Carlon Maintainer: Enrico Carlon git_url: https://git.bioconductor.org/packages/spikeLI git_branch: RELEASE_3_22 git_last_commit: 57a08b7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spikeLI_2.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spikeLI_2.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spikeLI_2.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spikeLI_2.70.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: spiky Version: 1.16.0 Depends: Rsamtools, GenomicRanges, R (>= 3.6.0) Imports: stats, scales, bamlss, methods, tools, IRanges, Biostrings, GenomicAlignments, BlandAltmanLeh, GenomeInfoDb, BSgenome, S4Vectors, graphics, ggplot2, utils Suggests: covr, testthat, rmarkdown, markdown, knitr, devtools, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg38.masked, BiocManager License: GPL-2 MD5sum: 889ac584b49e2f08e80e9c21e81ee3f3 NeedsCompilation: no Title: Spike-in calibration for cell-free MeDIP Description: spiky implements methods and model generation for cfMeDIP (cell-free methylated DNA immunoprecipitation) with spike-in controls. CfMeDIP is an enrichment protocol which avoids destructive conversion of scarce template, making it ideal as a "liquid biopsy," but creating certain challenges in comparing results across specimens, subjects, and experiments. The use of synthetic spike-in standard oligos allows diagnostics performed with cfMeDIP to quantitatively compare samples across subjects, experiments, and time points in both relative and absolute terms. biocViews: DifferentialMethylation, DNAMethylation, Normalization, Preprocessing, QualityControl, Sequencing Author: Samantha Wilson [aut], Lauren Harmon [aut], Tim Triche [aut, cre] Maintainer: Tim Triche URL: https://github.com/trichelab/spiky VignetteBuilder: knitr BugReports: https://github.com/trichelab/spiky/issues git_url: https://git.bioconductor.org/packages/spiky git_branch: RELEASE_3_22 git_last_commit: e4c8b36 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spiky_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spiky_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spiky_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spiky_1.16.0.tgz vignettes: vignettes/spiky/inst/doc/spiky_vignette.html vignetteTitles: Spiky: Analysing cfMeDIP-seq data with spike-in controls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spiky/inst/doc/spiky_vignette.R dependencyCount: 88 Package: spillR Version: 1.6.0 Depends: R (>= 4.3.0), SummarizedExperiment, CATALYST Imports: dplyr, tibble, tidyselect, stats, ggplot2, tidyr, spatstat.univar, S4Vectors, parallel Suggests: knitr, rmarkdown, cowplot, testthat (>= 3.0.0), BiocStyle, hexbin License: LGPL-3 MD5sum: 03e7905e9ade01b70aae06b5def9bed3 NeedsCompilation: no Title: Spillover Compensation in Mass Cytometry Data Description: Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. We implement our method using expectation-maximization to fit the mixture model. biocViews: FlowCytometry, ImmunoOncology, MassSpectrometry, Preprocessing, SingleCell, Software, StatisticalMethod, Visualization, Regression Author: Marco Guazzini [aut, cre] (ORCID: ), Alexander G. Reisach [aut] (ORCID: ), Sebastian Weichwald [aut] (ORCID: ), Christof Seiler [aut] (ORCID: ) Maintainer: Marco Guazzini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/spillR git_branch: RELEASE_3_22 git_last_commit: 401c8dd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spillR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spillR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spillR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spillR_1.6.0.tgz vignettes: vignettes/spillR/inst/doc/spillR-vignette.html vignetteTitles: spillR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spillR/inst/doc/spillR-vignette.R dependencyCount: 176 Package: spkTools Version: 1.66.0 Depends: R (>= 2.7.0), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), graphics, grDevices, gtools, methods, RColorBrewer, stats, utils Suggests: xtable License: GPL (>= 2) MD5sum: 1e489060e20484196652f04d3f2dd083 NeedsCompilation: no Title: Methods for Spike-in Arrays Description: The package contains functions that can be used to compare expression measures on different array platforms. biocViews: Software, Technology, Microarray Author: Matthew N McCall , Rafael A Irizarry Maintainer: Matthew N McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/spkTools git_branch: RELEASE_3_22 git_last_commit: 4f539d9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spkTools_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spkTools_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spkTools_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spkTools_1.66.0.tgz vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf vignetteTitles: spkTools: Spike-in Data Analysis and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spkTools/inst/doc/spkDoc.R dependencyCount: 10 Package: splatter Version: 1.34.0 Depends: R (>= 4.0), SingleCellExperiment Imports: BiocGenerics, BiocParallel, checkmate (>= 2.0.0), crayon, edgeR, fitdistrplus, grDevices, locfit, matrixStats, methods, rlang, S4Vectors, scuttle, stats, SummarizedExperiment, utils, withr Suggests: BASiCS (>= 1.7.10), BiocManager, BiocSingular, BiocStyle, Biostrings, covr, cowplot, GenomeInfoDb, GenomicRanges, ggplot2 (>= 3.4.0), igraph, IRanges, knitr, limSolve, lme4, magick, mfa, phenopath, preprocessCore, progress, pscl, rmarkdown, scales, scater (>= 1.15.16), scDD, scran, SparseDC, spelling, testthat, VariantAnnotation, zinbwave License: GPL-3 + file LICENSE MD5sum: e3e5c9b4277dad03889ec5b32330ad1f NeedsCompilation: no Title: Simple Simulation of Single-cell RNA Sequencing Data Description: Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets. biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, Software, ImmunoOncology Author: Luke Zappia [aut, cre] (ORCID: , GitHub: lazappi), Belinda Phipson [aut] (ORCID: , GitHub: bphipson), Christina Azodi [ctb] (ORCID: , GitHub: azodichr), Alicia Oshlack [aut] (ORCID: ) Maintainer: Luke Zappia URL: https://bioconductor.org/packages/splatter/, https://github.com/Oshlack/splatter, http://oshlacklab.com/splatter/ VignetteBuilder: knitr BugReports: https://github.com/Oshlack/splatter/issues git_url: https://git.bioconductor.org/packages/splatter git_branch: RELEASE_3_22 git_last_commit: e9a1ee1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/splatter_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/splatter_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/splatter_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/splatter_1.34.0.tgz vignettes: vignettes/splatter/inst/doc/splat_params.html, vignettes/splatter/inst/doc/splatPop.html, vignettes/splatter/inst/doc/splatter.html vignetteTitles: Splat simulation parameters, splatPop simulation, An introduction to the Splatter package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/splatter/inst/doc/splat_params.R, vignettes/splatter/inst/doc/splatPop.R, vignettes/splatter/inst/doc/splatter.R suggestsMe: anglemania, ccImpute, HVP, mastR, NewWave, scone, scPCA, smartid, scellpam dependencyCount: 53 Package: SpliceWiz Version: 1.11.0 Depends: R (>= 3.5.0), NxtIRFdata Imports: ompBAM, methods, stats, utils, tools, parallel, scales, magrittr, Rcpp (>= 1.0.5), data.table, fst, ggplot2, AnnotationHub, RSQLite, BiocFileCache, BiocGenerics, BiocParallel, Biostrings, BSgenome, DelayedArray, DelayedMatrixStats, genefilter, GenomeInfoDb, GenomicRanges, HDF5Array, h5mread, htmltools, IRanges, patchwork, pheatmap, progress, plotly, R.utils, rhdf5, rtracklayer, SummarizedExperiment, S4Vectors, shiny, shinyFiles, shinyWidgets, shinydashboard, stringi, rhandsontable, DT, grDevices, heatmaply, matrixStats, RColorBrewer, rvest, httr LinkingTo: ompBAM, Rcpp, RcppProgress Suggests: knitr, rmarkdown, crayon, splines, testthat (>= 3.0.0), DESeq2, limma, DoubleExpSeq, edgeR, DBI, GO.db, AnnotationDbi, fgsea, Rsubread License: MIT + file LICENSE Archs: x64 MD5sum: 31b9a6af8b538540785e61ef8ade4ec0 NeedsCompilation: yes Title: interactive analysis and visualization of alternative splicing in R Description: The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across conditions. SpliceWiz features a shiny-based GUI facilitating interactive data exploration of results including gene ontology enrichment. It is performance optimized with multi-threaded processing of BAM files and a new COV file format for fast recall of sequencing coverage. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization. biocViews: Software, Transcriptomics, RNASeq, AlternativeSplicing, Coverage, DifferentialSplicing, DifferentialExpression, GUI, Sequencing Author: Alex Chit Hei Wong [aut, cre, cph], Ulf Schmitz [ctb], William Ritchie [cph] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/SpliceWiz SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/SpliceWiz git_branch: devel git_last_commit: ff5b222 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/SpliceWiz_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpliceWiz_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpliceWiz_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpliceWiz_1.12.0.tgz vignettes: vignettes/SpliceWiz/inst/doc/SW_Cookbook.html, vignettes/SpliceWiz/inst/doc/SW_QuickStart.html vignetteTitles: SpliceWiz: the cookbook, SpliceWiz: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpliceWiz/inst/doc/SW_Cookbook.R, vignettes/SpliceWiz/inst/doc/SW_QuickStart.R dependencyCount: 196 Package: SplicingFactory Version: 1.18.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, methods, stats Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr License: GPL-3 + file LICENSE Archs: x64 MD5sum: 9cf58796357dd2eb8862684ad9ea6744 NeedsCompilation: no Title: Splicing Diversity Analysis for Transcriptome Data Description: The SplicingFactory R package uses transcript-level expression values to analyze splicing diversity based on various statistical measures, like Shannon entropy or the Gini index. These measures can quantify transcript isoform diversity within samples or between conditions. Additionally, the package analyzes the isoform diversity data, looking for significant changes between conditions. biocViews: Transcriptomics, RNASeq, DifferentialSplicing, AlternativeSplicing, TranscriptomeVariant Author: Peter A. Szikora [aut], Tamas Por [aut], Endre Sebestyen [aut, cre] (ORCID: ) Maintainer: Endre Sebestyen URL: https://github.com/esebesty/SplicingFactory VignetteBuilder: knitr BugReports: https://github.com/esebesty/SplicingFactory/issues git_url: https://git.bioconductor.org/packages/SplicingFactory git_branch: RELEASE_3_22 git_last_commit: 419a965 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SplicingFactory_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SplicingFactory_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SplicingFactory_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SplicingFactory_1.18.0.tgz vignettes: vignettes/SplicingFactory/inst/doc/SplicingFactory.html vignetteTitles: SplicingFactory hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SplicingFactory/inst/doc/SplicingFactory.R dependencyCount: 25 Package: SplicingGraphs Version: 1.49.1 Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, graphics, igraph, BiocGenerics, S4Vectors (>= 0.17.5), BiocParallel, IRanges (>= 2.21.2), Seqinfo, GenomicRanges (>= 1.23.21), Rsamtools, graph Suggests: igraph, Gviz, txdbmaker, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: 690f6cd4f96e876b833a6174683f1617 NeedsCompilation: no Title: Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them Description: This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways. biocViews: Genetics, Annotation, DataRepresentation, Visualization, Sequencing, RNASeq, GeneExpression, AlternativeSplicing, Transcription, ImmunoOncology Author: D. Bindreither, M. Carlson, M. Morgan, H. Pagès Maintainer: H. Pagès URL: https://bioconductor.org/packages/SplicingGraphs BugReports: https://github.com/Bioconductor/SplicingGraphs/issues git_url: https://git.bioconductor.org/packages/SplicingGraphs git_branch: devel git_last_commit: c006477 git_last_commit_date: 2025-07-24 Date/Publication: 2025-10-07 source.ver: src/contrib/SplicingGraphs_1.49.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/SplicingGraphs_1.49.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SplicingGraphs_1.49.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SplicingGraphs_1.49.1.tgz vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf vignetteTitles: Splicing graphs and RNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.R dependencyCount: 80 Package: SplineDV Version: 1.1.1 Depends: R (>= 3.5.0) Imports: plotly, dplyr, scuttle, methods, Biobase, BiocGenerics, S4Vectors, sparseMatrixStats, SingleCellExperiment, SummarizedExperiment, Matrix (>= 1.6.4), utils Suggests: knitr, DelayedMatrixStats, rmarkdown, BiocStyle, ggplot2, ggpubr, MASS, scales, scRNAseq, testthat (>= 3.0.0) License: GPL-2 MD5sum: 3ad56524c4008edd3baac807ec223ce5 NeedsCompilation: no Title: Differential Variability (DV) analysis for single-cell RNA sequencing data. (e.g. Identify Differentially Variable Genes across two experimental conditions) Description: A spline based scRNA-seq method for identifying differentially variable (DV) genes across two experimental conditions. Spline-DV constructs a 3D spline from 3 key gene statistics: mean expression, coefficient of variance, and dropout rate. This is done for both conditions. The 3D spline provides the “expected” behavior of genes in each condition. The distance of the observed mean, CV and dropout rate of each gene from the expected 3D spline is used to measure variability. As the final step, the spline-DV method compares the variabilities of each condition to identify differentially variable (DV) genes. biocViews: Software, SingleCell, Sequencing, DifferentialExpression, RNASeq, GeneExpression, Transcriptomics, FeatureExtraction Author: Shreyan Gupta [aut, cre] (ORCID: ), James Cai [aut] (ORCID: ) Maintainer: Shreyan Gupta URL: https://github.com/Xenon8778/SplineDV VignetteBuilder: knitr BugReports: https://github.com/Xenon8778/SplineDV/issues git_url: https://git.bioconductor.org/packages/SplineDV git_branch: devel git_last_commit: 16d71a5 git_last_commit_date: 2025-05-01 Date/Publication: 2025-10-07 source.ver: src/contrib/SplineDV_1.1.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/SplineDV_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SplineDV_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SplineDV_1.2.0.tgz vignettes: vignettes/SplineDV/inst/doc/SplineDV.html vignetteTitles: Introduction to Spline-DV hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SplineDV/inst/doc/SplineDV.R dependencyCount: 102 Package: splineTimeR Version: 1.38.0 Depends: R (>= 3.3), Biobase, igraph, limma, GSEABase, gtools, splines, GeneNet (>= 1.2.13), longitudinal (>= 1.1.12), FIs Suggests: knitr License: GPL-3 MD5sum: 60fa55f8d732c45a1c10ab63829150c4 NeedsCompilation: no Title: Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction Description: This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks. biocViews: GeneExpression, DifferentialExpression, TimeCourse, Regression, GeneSetEnrichment, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Agata Michna Maintainer: Herbert Braselmann , Martin Selmansberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splineTimeR git_branch: RELEASE_3_22 git_last_commit: 2ba6766 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/splineTimeR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/splineTimeR_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/splineTimeR_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/splineTimeR_1.38.0.tgz vignettes: vignettes/splineTimeR/inst/doc/splineTimeR.pdf vignetteTitles: splineTimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splineTimeR/inst/doc/splineTimeR.R dependencyCount: 62 Package: SPLINTER Version: 1.36.0 Depends: R (>= 3.6.0), grDevices, stats Imports: graphics, ggplot2, seqLogo, Biostrings, pwalign, biomaRt, GenomicAlignments, GenomicRanges, GenomicFeatures, Gviz, IRanges, S4Vectors, Seqinfo, utils, plyr,stringr, methods, BSgenome.Mmusculus.UCSC.mm9, googleVis Suggests: txdbmaker, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 4e8812456fd9fb12cf893e9c7b99e488 NeedsCompilation: no Title: Splice Interpreter of Transcripts Description: Provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments. biocViews: ImmunoOncology, GeneExpression, RNASeq, Visualization, AlternativeSplicing Author: Diana Low [aut, cre] Maintainer: Diana Low URL: https://github.com/dianalow/SPLINTER/ VignetteBuilder: knitr BugReports: https://github.com/dianalow/SPLINTER/issues git_url: https://git.bioconductor.org/packages/SPLINTER git_branch: RELEASE_3_22 git_last_commit: 00c62ac git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SPLINTER_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPLINTER_1.35.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPLINTER_1.36.0.tgz vignettes: vignettes/SPLINTER/inst/doc/vignette.pdf vignetteTitles: SPLINTER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPLINTER/inst/doc/vignette.R dependencyCount: 157 Package: splots Version: 1.76.0 Imports: grid, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown, assertthat, HD2013SGI, dplyr, ggplot2 License: LGPL MD5sum: b6895bfb6c13b3f70c4945656b73279b NeedsCompilation: no Title: Visualization of high-throughput assays in microtitre plate or slide format Description: This package is here to support legacy usages of it, but it should not be used for new code development. It provides a single function, plotScreen, for visualising data in microtitre plate or slide format. As a better alternative for such functionality, please consider the platetools package on CRAN (https://cran.r-project.org/package=platetools and https://github.com/Swarchal/platetools), or ggplot2 (geom_raster, facet_wrap) as exemplified in the vignette of this package. biocViews: Visualization, Sequencing, MicrotitrePlateAssay Author: Wolfgang Huber, Oleg Sklyar Maintainer: Wolfgang Huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splots git_branch: RELEASE_3_22 git_last_commit: 27ca6c3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/splots_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/splots_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/splots_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/splots_1.76.0.tgz vignettes: vignettes/splots/inst/doc/splots.html vignetteTitles: splots: visualization of data from assays in microtitre plate or slide format hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splots/inst/doc/splots.R dependsOnMe: HD2013SGI dependencyCount: 2 Package: SPONGE Version: 1.32.0 Depends: R (>= 3.6) Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table, MASS, expm, gRbase, glmnet, igraph, iterators, tidyverse, caret, dplyr, biomaRt, randomForest, ggridges, cvms, ComplexHeatmap, ggplot2, MetBrewer, rlang, tnet, ggpubr, stringr, tidyr Suggests: testthat, knitr, rmarkdown, visNetwork, ggrepel, gridExtra, digest, doParallel, bigmemory, GSVA License: GPL (>=3) MD5sum: b9743643891a7b5a0b9991532783b69b NeedsCompilation: no Title: Sparse Partial Correlations On Gene Expression Description: This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape. biocViews: GeneExpression, Transcription, GeneRegulation, NetworkInference, Transcriptomics, SystemsBiology, Regression, RandomForest, MachineLearning Author: Markus List [aut, cre] (ORCID: ), Markus Hoffmann [aut] (ORCID: ), Lena Strasser [aut] (ORCID: ) Maintainer: Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPONGE git_branch: RELEASE_3_22 git_last_commit: f1371d0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SPONGE_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPONGE_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPONGE_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPONGE_1.32.0.tgz vignettes: vignettes/SPONGE/inst/doc/SPONGE.html, vignettes/SPONGE/inst/doc/spongEffects.html vignetteTitles: SPONGE vignette, spongEffects vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPONGE/inst/doc/SPONGE.R, vignettes/SPONGE/inst/doc/spongEffects.R importsMe: miRspongeR suggestsMe: mirTarRnaSeq dependencyCount: 226 Package: spoon Version: 1.5.0 Depends: R (>= 4.4) Imports: SpatialExperiment, BRISC, nnSVG, BiocParallel, Matrix, methods, SummarizedExperiment, stats, utils, scuttle Suggests: testthat, STexampleData, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 97dc5648af92f4e59b3d4151ecac1697 NeedsCompilation: no Title: Address the Mean-variance Relationship in Spatial Transcriptomics Data Description: This package addresses the mean-variance relationship in spatially resolved transcriptomics data. Precision weights are generated for individual observations using Empirical Bayes techniques. These weights are used to rescale the data and covariates, which are then used as input in spatially variable gene detection tools. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Preprocessing Author: Kinnary Shah [aut, cre] (ORCID: ), Boyi Guo [aut] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Kinnary Shah URL: https://github.com/kinnaryshah/spoon VignetteBuilder: knitr BugReports: https://github.com/kinnaryshah/spoon/issues git_url: https://git.bioconductor.org/packages/spoon git_branch: devel git_last_commit: a142b22 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/spoon_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spoon_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spoon_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spoon_1.6.0.tgz vignettes: vignettes/spoon/inst/doc/spoon.html vignetteTitles: spoon Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spoon/inst/doc/spoon.R dependencyCount: 85 Package: SpotClean Version: 1.12.0 Depends: R (>= 4.2.0), Imports: stats, methods, utils, dplyr, S4Vectors, SummarizedExperiment, SpatialExperiment, Matrix, rhdf5, ggplot2, grid, readbitmap, rjson, tibble, viridis, grDevices, RColorBrewer, Seurat, rlang Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, R.utils, spelling License: GPL-3 MD5sum: 40659d863304eab3837bc6035cb061e4 NeedsCompilation: yes Title: SpotClean adjusts for spot swapping in spatial transcriptomics data Description: SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses. biocViews: DataImport, RNASeq, Sequencing, GeneExpression, Spatial, SingleCell, Transcriptomics, Preprocessing Author: Zijian Ni [aut, cre] (ORCID: ), Christina Kendziorski [ctb] Maintainer: Zijian Ni URL: https://github.com/zijianni/SpotClean VignetteBuilder: knitr BugReports: https://github.com/zijianni/SpotClean/issues git_url: https://git.bioconductor.org/packages/SpotClean git_branch: RELEASE_3_22 git_last_commit: b7f5f4b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpotClean_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpotClean_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpotClean_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpotClean_1.12.0.tgz vignettes: vignettes/SpotClean/inst/doc/SpotClean.html vignetteTitles: SpotClean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpotClean/inst/doc/SpotClean.R dependencyCount: 187 Package: SPOTlight Version: 1.14.0 Depends: R (>= 4.5.0) Imports: ggplot2, Matrix, SingleCellExperiment, sparseMatrixStats, stats LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, colorBlindness, DelayedArray, DropletUtils, ExperimentHub, ggcorrplot, grDevices, grid, igraph, jpeg, knitr, methods, png, rmarkdown, scater, scatterpie, scran, SpatialExperiment, SummarizedExperiment, S4Vectors, TabulaMurisSenisData, TENxVisiumData, testthat License: GPL-3 MD5sum: 164db14c5ac2b60011ca384c6864fcee NeedsCompilation: yes Title: `SPOTlight`: Spatial Transcriptomics Deconvolution Description: `SPOTlight` provides a method to deconvolute spatial transcriptomics spots using a seeded NMF approach along with visualization tools to assess the results. Spatially resolved gene expression profiles are key to understand tissue organization and function. However, novel spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). biocViews: SingleCell, Spatial, StatisticalMethod Author: Marc Elosua-Bayes [aut, cre], Zachary DeBruine [aut], Helena L. Crowell [aut] Maintainer: Marc Elosua-Bayes URL: https://github.com/MarcElosua/SPOTlight VignetteBuilder: knitr BugReports: https://github.com/MarcElosua/SPOTlight/issues git_url: https://git.bioconductor.org/packages/SPOTlight git_branch: RELEASE_3_22 git_last_commit: fb783eb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SPOTlight_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPOTlight_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPOTlight_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPOTlight_1.14.0.tgz vignettes: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.html vignetteTitles: "SPOTlight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.R dependencyCount: 45 Package: SpotSweeper Version: 1.6.0 Depends: R (>= 4.4.0) Imports: SpatialExperiment, SummarizedExperiment, BiocNeighbors, SingleCellExperiment, stats, escheR, MASS, ggplot2, spatialEco, grDevices, BiocParallel Suggests: knitr, BiocStyle, rmarkdown, scuttle, STexampleData, ggpubr, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: b50df82025286e70a6958a770ddd2d66 NeedsCompilation: no Title: Spatially-aware quality control for spatial transcriptomics Description: Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts. biocViews: Software, Spatial, Transcriptomics, QualityControl, GeneExpression, Author: Michael Totty [aut, cre] (ORCID: ), Stephanie Hicks [aut] (ORCID: ), Boyi Guo [aut] (ORCID: ) Maintainer: Michael Totty URL: https://github.com/MicTott/SpotSweeper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/SpotSweeper git_url: https://git.bioconductor.org/packages/SpotSweeper git_branch: RELEASE_3_22 git_last_commit: 2a9e4a7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SpotSweeper_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpotSweeper_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpotSweeper_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpotSweeper_1.6.0.tgz vignettes: vignettes/SpotSweeper/inst/doc/getting_started.html vignetteTitles: Getting Started with `SpotSweeper` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpotSweeper/inst/doc/getting_started.R importsMe: OSTA dependencyCount: 100 Package: spqn Version: 1.22.0 Depends: R (>= 4.0), ggplot2, ggridges, SummarizedExperiment, BiocGenerics Imports: graphics, stats, utils, matrixStats Suggests: BiocStyle, knitr, rmarkdown, tools, spqnData (>= 0.99.3), RUnit License: Artistic-2.0 Archs: x64 MD5sum: 405a81e67b52d572f5b64a6f849dfabf NeedsCompilation: no Title: Spatial quantile normalization Description: The spqn package implements spatial quantile normalization (SpQN). This method was developed to remove a mean-correlation relationship in correlation matrices built from gene expression data. It can serve as pre-processing step prior to a co-expression analysis. biocViews: NetworkInference, GraphAndNetwork, Normalization Author: Yi Wang [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Yi Wang URL: https://github.com/hansenlab/spqn VignetteBuilder: knitr BugReports: https://github.com/hansenlab/spqn/issues git_url: https://git.bioconductor.org/packages/spqn git_branch: RELEASE_3_22 git_last_commit: 1495b7b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/spqn_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spqn_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spqn_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spqn_1.22.0.tgz vignettes: vignettes/spqn/inst/doc/spqn.html vignetteTitles: spqn User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spqn/inst/doc/spqn.R dependencyCount: 42 Package: SPsimSeq Version: 1.20.0 Depends: R (>= 4.0) Imports: stats, methods, SingleCellExperiment, fitdistrplus, graphics, edgeR, Hmisc, WGCNA, limma, mvtnorm, phyloseq, utils Suggests: knitr, rmarkdown, LSD, testthat, BiocStyle License: GPL-2 MD5sum: 837923eb2530fef3bb9bf9a3dade99cd NeedsCompilation: no Title: Semi-parametric simulation tool for bulk and single-cell RNA sequencing data Description: SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size. biocViews: GeneExpression, RNASeq, SingleCell, Sequencing, DNASeq Author: Alemu Takele Assefa [aut], Olivier Thas [ths], Joris Meys [cre], Stijn Hawinkel [aut] Maintainer: Joris Meys URL: https://github.com/CenterForStatistics-UGent/SPsimSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPsimSeq git_branch: RELEASE_3_22 git_last_commit: bfb4e62 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SPsimSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPsimSeq_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPsimSeq_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPsimSeq_1.20.0.tgz vignettes: vignettes/SPsimSeq/inst/doc/SPsimSeq.html vignetteTitles: Manual for the SPsimSeq package: semi-parametric simulation for bulk and single cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPsimSeq/inst/doc/SPsimSeq.R importsMe: SurfR dependencyCount: 137 Package: squallms Version: 1.4.0 Depends: R (>= 4.1.0) Imports: xcms, MSnbase, MsExperiment, RaMS, dplyr, tidyr, tibble, ggplot2, shiny, plotly, data.table, caret, stats, graphics, utils, keys Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: ab6a0402658be8d45597021d3c9551fc NeedsCompilation: no Title: Speedy quality assurance via lasso labeling for LC-MS data Description: squallms is a Bioconductor R package that implements a "semi-labeled" approach to untargeted mass spectrometry data. It pulls in raw data from mass-spec files to calculate several metrics that are then used to label MS features in bulk as high or low quality. These metrics of peak quality are then passed to a simple logistic model that produces a fully-labeled dataset suitable for downstream analysis. biocViews: MassSpectrometry, Metabolomics, Proteomics, Lipidomics, ShinyApps, Classification, Clustering, FeatureExtraction, PrincipalComponent, Regression, Preprocessing, QualityControl, Visualization Author: William Kumler [aut, cre, cph] (ORCID: ) Maintainer: William Kumler URL: https://github.com/wkumler/squallms VignetteBuilder: knitr BugReports: https://github.com/wkumler/squallms/issues git_url: https://git.bioconductor.org/packages/squallms git_branch: RELEASE_3_22 git_last_commit: b2083b7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/squallms_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/squallms_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/squallms_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/squallms_1.4.0.tgz vignettes: vignettes/squallms/inst/doc/intro_to_squallms.html vignetteTitles: Introduction to squallms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/squallms/inst/doc/intro_to_squallms.R dependencyCount: 182 Package: sRACIPE Version: 2.2.0 Depends: R (>= 3.6.0),SummarizedExperiment, methods, Rcpp Imports: ggplot2, reshape2, MASS, RColorBrewer, gridExtra,visNetwork, gplots, umap, htmlwidgets, S4Vectors, BiocGenerics, grDevices, stats, utils, graphics, doFuture, doRNG, future, foreach LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, tinytest License: MIT + file LICENSE MD5sum: 39bfb1e0f1acd7e23261fdb54475f489 NeedsCompilation: yes Title: Systems biology tool to simulate gene regulatory circuits Description: sRACIPE implements a randomization-based method for gene circuit modeling. It allows us to study the effect of both the gene expression noise and the parametric variation on any gene regulatory circuit (GRC) using only its topology, and simulates an ensemble of models with random kinetic parameters at multiple noise levels. Statistical analysis of the generated gene expressions reveals the basin of attraction and stability of various phenotypic states and their changes associated with intrinsic and extrinsic noises. sRACIPE provides a holistic picture to evaluate the effects of both the stochastic nature of cellular processes and the parametric variation. biocViews: ResearchField, SystemsBiology, MathematicalBiology, GeneExpression, GeneRegulation, GeneTarget Author: Mingyang Lu [aut, cre] (ORCID: ), Vivek Kohar [aut], Aidan Tillman [aut], Daniel Ramirez [aut] Maintainer: Mingyang Lu URL: https://github.com/lusystemsbio/sRACIPE, https://geneex.jax.org/, https://vivekkohar.github.io/sRACIPE/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sRACIPE git_branch: RELEASE_3_22 git_last_commit: 26d0307 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sRACIPE_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sRACIPE_2.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sRACIPE_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sRACIPE_2.2.0.tgz vignettes: vignettes/sRACIPE/inst/doc/sRACIPE.html vignetteTitles: A systems biology tool for gene regulatory circuit simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sRACIPE/inst/doc/sRACIPE.R dependencyCount: 100 Package: SRAdb Version: 1.72.0 Depends: RSQLite, graph, RCurl Imports: R.utils Suggests: Rgraphviz License: Artistic-2.0 Archs: x64 MD5sum: 97a17b17f135fce3e1b8161ef38bf5ef NeedsCompilation: no Title: A compilation of metadata from NCBI SRA and tools Description: The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. biocViews: Infrastructure, Sequencing, DataImport Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu BugReports: https://github.com/zhujack/SRAdb/issues/new git_url: https://git.bioconductor.org/packages/SRAdb git_branch: RELEASE_3_22 git_last_commit: e1054a1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SRAdb_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SRAdb_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SRAdb_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SRAdb_1.72.0.tgz vignettes: vignettes/SRAdb/inst/doc/SRAdb.pdf vignetteTitles: Using SRAdb to Query the Sequence Read Archive hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SRAdb/inst/doc/SRAdb.R dependencyCount: 30 Package: srnadiff Version: 1.30.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), stats, methods, S4Vectors, Seqinfo, rtracklayer, SummarizedExperiment, IRanges, GenomicRanges, DESeq2, edgeR, Rsamtools, GenomicFeatures, GenomicAlignments, grDevices, Gviz, BiocParallel, BiocManager, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocManager, BiocStyle License: GPL-3 MD5sum: f6052c1090a0d0c5805f26a5a069988e NeedsCompilation: yes Title: Finding differentially expressed unannotated genomic regions from RNA-seq data Description: srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. It reads BAM files as input, and outputs a list differentially regions, together with the adjusted p-values. biocViews: ImmunoOncology, GeneExpression, Coverage, SmallRNA, Epigenetics, StatisticalMethod, Preprocessing, DifferentialExpression Author: Zytnicki Matthias [aut, cre], Gonzalez Ignacio [aut] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/srnadiff git_branch: RELEASE_3_22 git_last_commit: 809d1c9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/srnadiff_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/srnadiff_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/srnadiff_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/srnadiff_1.30.0.tgz vignettes: vignettes/srnadiff/inst/doc/srnadiff.html vignetteTitles: The srnadiff package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/srnadiff/inst/doc/srnadiff.R dependencyCount: 161 Package: sscu Version: 2.40.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: f79f1b25301babd29c159d5eaede1e00 NeedsCompilation: no Title: Strength of Selected Codon Usage Description: The package calculates the indexes for selective stength in codon usage in bacteria species. (1) The package can calculate the strength of selected codon usage bias (sscu, also named as s_index) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on four pairs of codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. (2) The package can detect the strength of translational accuracy selection by Akashi's test. The test tabulating all codons into four categories with the feature as conserved/variable amino acids and optimal/non-optimal codons. (3) Optimal codon lists (selected codons) can be calculated by either op_highly function (by using the highly expressed genes compared with all genes to identify optimal codons), or op_corre_CodonW/op_corre_NCprime function (by correlative method developed by Hershberg & Petrov). Users will have a list of optimal codons for further analysis, such as input to the Akashi's test. (4) The detailed codon usage information, such as RSCU value, number of optimal codons in the highly/all gene set, as well as the genomic gc3 value, can be calculate by the optimal_codon_statistics and genomic_gc3 function. (5) Furthermore, we added one test function low_frequency_op in the package. The function try to find the low frequency optimal codons, among all the optimal codons identified by the op_highly function. biocViews: Genetics, GeneExpression, WholeGenome Author: Yu Sun Maintainer: Yu Sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sscu git_branch: RELEASE_3_22 git_last_commit: 76c9295 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sscu_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sscu_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sscu_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sscu_2.40.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 26 Package: sSeq Version: 1.48.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) Archs: x64 MD5sum: cde7d3af53719112ed351349c074f62d NeedsCompilation: no Title: Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size Description: The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution. biocViews: ImmunoOncology, RNASeq Author: Danni Yu , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu git_url: https://git.bioconductor.org/packages/sSeq git_branch: RELEASE_3_22 git_last_commit: 6eece88 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sSeq_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sSeq_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sSeq_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sSeq_1.48.0.tgz vignettes: vignettes/sSeq/inst/doc/sSeq.pdf vignetteTitles: sSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSeq/inst/doc/sSeq.R importsMe: MLSeq suggestsMe: NBLDA dependencyCount: 3 Package: ssize Version: 1.84.0 Depends: gdata, xtable License: LGPL MD5sum: a543e5a6bf0f6a0a632d958dcd4326c7 NeedsCompilation: no Title: Estimate Microarray Sample Size Description: Functions for computing and displaying sample size information for gene expression arrays. biocViews: Microarray, DifferentialExpression Author: Gregory R. Warnes, Peng Liu, and Fasheng Li Maintainer: Gregory R. Warnes git_url: https://git.bioconductor.org/packages/ssize git_branch: RELEASE_3_22 git_last_commit: eca0982 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ssize_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ssize_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ssize_1.84.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ssize_1.84.0.tgz vignettes: vignettes/ssize/inst/doc/ssize.pdf vignetteTitles: Sample Size Estimation for Microarray Experiments Using the \code{ssize} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssize/inst/doc/ssize.R suggestsMe: maGUI dependencyCount: 6 Package: sSNAPPY Version: 1.14.0 Depends: R (>= 4.3.0), ggplot2 Imports: dplyr (>= 1.1), magrittr, rlang, stats, graphite, tibble, ggraph, igraph, reshape2, org.Hs.eg.db, SummarizedExperiment, edgeR, methods, ggforce, pheatmap, utils, stringr, gtools, tidyr Suggests: BiocManager, BiocStyle, colorspace, cowplot, DT, htmltools, knitr, pander, patchwork, rmarkdown, spelling, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: e25b30c096a7963df9faa09296d28bf0 NeedsCompilation: no Title: Single Sample directioNAl Pathway Perturbation analYsis Description: A single sample pathway perturbation testing method for RNA-seq data. The method propagates changes in gene expression down gene-set topologies to compute single-sample directional pathway perturbation scores that reflect potential direction of change. Perturbation scores can be used to test significance of pathway perturbation at both individual-sample and treatment levels. biocViews: Software, GeneExpression, GeneSetEnrichment, GeneSignaling Author: Wenjun Liu [aut, cre] (ORCID: ), Stephen Pederson [aut] (ORCID: ) Maintainer: Wenjun Liu URL: https://wenjun-liu.github.io/sSNAPPY/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Wenjun-Liu/sSNAPPY/issues git_url: https://git.bioconductor.org/packages/sSNAPPY git_branch: RELEASE_3_22 git_last_commit: 5d40009 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sSNAPPY_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sSNAPPY_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sSNAPPY_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sSNAPPY_1.14.0.tgz vignettes: vignettes/sSNAPPY/inst/doc/sSNAPPY.html vignetteTitles: Single Sample Directional Pathway Perturbation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSNAPPY/inst/doc/sSNAPPY.R dependencyCount: 103 Package: ssPATHS Version: 1.24.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: ROCR, dml, MESS Suggests: ggplot2, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: e62f574c85c22d183c3a4da6e0d2d795 NeedsCompilation: no Title: ssPATHS: Single Sample PATHway Score Description: This package generates pathway scores from expression data for single samples after training on a reference cohort. The score is generated by taking the expression of a gene set (pathway) from a reference cohort and performing linear discriminant analysis to distinguish samples in the cohort that have the pathway augmented and not. The separating hyperplane is then used to score new samples. biocViews: Software, GeneExpression, BiomedicalInformatics, RNASeq, Pathways, Transcriptomics, DimensionReduction, Classification Author: Natalie R. Davidson Maintainer: Natalie R. Davidson git_url: https://git.bioconductor.org/packages/ssPATHS git_branch: RELEASE_3_22 git_last_commit: 951f8cf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ssPATHS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ssPATHS_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ssPATHS_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ssPATHS_1.24.0.tgz vignettes: vignettes/ssPATHS/inst/doc/ssPATHS.pdf vignetteTitles: Using ssPATHS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ssPATHS/inst/doc/ssPATHS.R dependencyCount: 129 Package: ssrch Version: 1.26.0 Depends: R (>= 3.6), methods Imports: shiny, DT, utils Suggests: knitr, testthat, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 6c26de376b946072927440870fd3058b NeedsCompilation: no Title: a simple search engine Description: Demonstrate tokenization and a search gadget for collections of CSV files. biocViews: Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssrch git_branch: RELEASE_3_22 git_last_commit: e264286 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ssrch_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ssrch_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ssrch_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ssrch_1.26.0.tgz vignettes: vignettes/ssrch/inst/doc/ssrch.html vignetteTitles: ssrch: small search engine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssrch/inst/doc/ssrch.R dependencyCount: 47 Package: ssviz Version: 1.44.0 Depends: R (>= 3.5.0), methods, Rsamtools, Biostrings, reshape, ggplot2, RColorBrewer, stats Suggests: knitr License: GPL-2 Archs: x64 MD5sum: 7f778a55a1fa18e2d981c902394de650 NeedsCompilation: no Title: A small RNA-seq visualizer and analysis toolkit Description: Small RNA sequencing viewer biocViews: ImmunoOncology, Sequencing,RNASeq,Visualization,MultipleComparison,Genetics Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssviz git_branch: RELEASE_3_22 git_last_commit: e7f05f5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ssviz_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ssviz_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ssviz_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ssviz_1.44.0.tgz vignettes: vignettes/ssviz/inst/doc/ssviz.pdf vignetteTitles: ssviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssviz/inst/doc/ssviz.R dependencyCount: 49 Package: StabMap Version: 1.4.0 Depends: R (>= 4.4.0), Imports: igraph, slam, BiocNeighbors, Matrix, MASS, abind, SummarizedExperiment, methods, MatrixGenerics, BiocGenerics, BiocSingular, BiocParallel Suggests: scran, scater, knitr, UpSetR, gridExtra, SingleCellMultiModal, BiocStyle, magrittr, testthat (>= 3.0.0), purrr, sparseMatrixStats License: GPL-2 MD5sum: 6931bf1c774fe77824d8a9a091ab10ba NeedsCompilation: no Title: Stabilised mosaic single cell data integration using unshared features Description: StabMap performs single cell mosaic data integration by first building a mosaic data topology, and for each reference dataset, traverses the topology to project and predict data onto a common embedding. Mosaic data should be provided in a list format, with all relevant features included in the data matrices within each list object. The output of stabMap is a joint low-dimensional embedding taking into account all available relevant features. Expression imputation can also be performed using the StabMap embedding and any of the original data matrices for given reference and query cell lists. biocViews: SingleCell, DimensionReduction, Software Author: Shila Ghazanfar [aut, cre, ctb], Aiden Jin [ctb], Nicholas Robertson [ctb] Maintainer: Shila Ghazanfar URL: https://sydneybiox.github.io/StabMap, https://sydneybiox.github.io/StabMap/ VignetteBuilder: knitr BugReports: https://github.com/sydneybiox/StabMap/issues git_url: https://git.bioconductor.org/packages/StabMap git_branch: RELEASE_3_22 git_last_commit: 82229fe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/StabMap_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/StabMap_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/StabMap_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/StabMap_1.4.0.tgz vignettes: vignettes/StabMap/inst/doc/stabMap_PBMC_Multiome.html vignetteTitles: Mosaic single cell data integration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StabMap/inst/doc/stabMap_PBMC_Multiome.R dependencyCount: 53 Package: STADyUM Version: 1.0.0 Depends: R (>= 4.5.0) Imports: GenomicRanges, IRanges, S4Vectors, methods, tibble, dplyr, ggplot2, progress, plyranges, GenomeInfoDb, Rcpp, data.table, purrr, rtracklayer, tidyr, rlang, MASS LinkingTo: Rcpp Suggests: testthat (>= 3.0.0), knitr, rmarkdown, devtools License: MIT + file LICENSE MD5sum: 185682ac5661f37b0c743b83749148ab NeedsCompilation: yes Title: Statistical Transcriptome Analysis under a Dynamic Unified Model Description: STADyUM is a package with functionality for analyzing nascent RNA read counts to infer transcription rates. This includes utilities for processing experimental nascent RNA read counts as well as for simulating PRO-seq data. Rates such as initiation, pause release and landing pad occupancy are estimated from either synthetic or experimental data. There are also options for varying pause sites and including steric hindrance of initiation in the model. biocViews: StatisticalMethod, Transcriptomics, Transcription, Sequencing Author: Yixin Zhao [aut] (ORCID: ), Lingjie Liu [aut] (ORCID: ), Rebecca Hassett [aut, cre] (ORCID: ) Maintainer: Rebecca Hassett URL: https://github.com/rhassett-cshl/STADyUM VignetteBuilder: knitr BugReports: https://github.com/rhassett-cshl/STADyUM git_url: https://git.bioconductor.org/packages/STADyUM git_branch: RELEASE_3_22 git_last_commit: 55e9577 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/STADyUM_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/STADyUM_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/STADyUM_1.0.0.tgz vignettes: vignettes/STADyUM/inst/doc/STADyUM.html vignetteTitles: STADyUM: Simulating and Analyzing Transcription Dynamics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/STADyUM/inst/doc/STADyUM.R dependencyCount: 92 Package: stageR Version: 1.32.0 Depends: R (>= 3.4), SummarizedExperiment Imports: methods, stats Suggests: knitr, rmarkdown, BiocStyle, methods, Biobase, edgeR, limma, DEXSeq, testthat License: GNU General Public License version 3 MD5sum: 451c67225b5e0a339074c66bbdd15838 NeedsCompilation: no Title: stageR: stage-wise analysis of high throughput gene expression data in R Description: The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0 biocViews: Software, StatisticalMethod Author: Koen Van den Berge and Lieven Clement Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stageR git_branch: RELEASE_3_22 git_last_commit: f65931c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/stageR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/stageR_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/stageR_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/stageR_1.32.0.tgz vignettes: vignettes/stageR/inst/doc/stageRVignette.html vignetteTitles: stageR: stage-wise analysis of high-throughput gene expression data in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stageR/inst/doc/stageRVignette.R dependsOnMe: rnaseqDTU suggestsMe: MethReg, muscat, satuRn dependencyCount: 25 Package: standR Version: 1.14.0 Depends: R (>= 4.1) Imports: dplyr, SpatialExperiment (>= 1.5.2), SummarizedExperiment, SingleCellExperiment, edgeR, rlang, readr, tibble, ggplot2, tidyr, ruv, limma, patchwork, S4Vectors, Biobase, BiocGenerics, grDevices, stats, methods, ggalluvial, mclustcomp, RUVSeq Suggests: knitr, ExperimentHub, rmarkdown, scater, uwot, ggpubr, ggrepel, cluster, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 90f01f2cb1f0734293c92095b92a4044 NeedsCompilation: no Title: Spatial transcriptome analyses of Nanostring's DSP data in R Description: standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations. biocViews: Spatial, Transcriptomics, GeneExpression, DifferentialExpression, QualityControl, Normalization, ExperimentHubSoftware Author: Ning Liu [aut, cre] (ORCID: ), Dharmesh D Bhuva [aut] (ORCID: ), Ahmed Mohamed [aut] Maintainer: Ning Liu URL: https://github.com/DavisLaboratory/standR VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/standR/issues git_url: https://git.bioconductor.org/packages/standR git_branch: RELEASE_3_22 git_last_commit: 40f29f9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/standR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/standR_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/standR_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/standR_1.14.0.tgz vignettes: vignettes/standR/inst/doc/Quick_start.html vignetteTitles: standR_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/standR/inst/doc/Quick_start.R importsMe: shinyDSP dependencyCount: 143 Package: STATegRa Version: 1.46.0 Depends: R (>= 2.10) Imports: Biobase, gridExtra, ggplot2, methods, stats, grid, MASS, calibrate, gplots, edgeR, limma, foreach, affy Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>= 1.3), roxygen2, doSNOW License: GPL-2 Archs: x64 MD5sum: 25c63d5735f60b75975524f4e7e29524 NeedsCompilation: no Title: Classes and methods for multi-omics data integration Description: Classes and tools for multi-omics data integration. biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: STATegra Consortia Maintainer: David Gomez-Cabrero , Núria Planell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STATegRa git_branch: RELEASE_3_22 git_last_commit: 5d45aa5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/STATegRa_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/STATegRa_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/STATegRa_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/STATegRa_1.46.0.tgz vignettes: vignettes/STATegRa/inst/doc/STATegRa.html vignetteTitles: STATegRa User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R dependencyCount: 45 Package: Statial Version: 1.12.0 Depends: R (>= 4.1.0) Imports: BiocParallel, spatstat.geom, concaveman, data.table, spatstat.explore, dplyr, tidyr, SingleCellExperiment, tibble, stringr, tidyselect, ggplot2, methods, stats, SummarizedExperiment, S4Vectors, plotly, purrr, ranger, magrittr, limma, SpatialExperiment, cluster, treekoR, edgeR Suggests: BiocStyle, knitr, testthat (>= 3.0.0), ClassifyR, spicyR, ggsurvfit, lisaClust, survival License: GPL-3 MD5sum: ac6c1e781f74f8483975fe987a9ade14 NeedsCompilation: no Title: A package to identify changes in cell state relative to spatial associations Description: Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with. biocViews: SingleCell, Spatial, Classification Author: Farhan Ameen [aut, cre], Sourish Iyengar [aut], Alex Qin [aut], Shila Ghazanfar [aut], Ellis Patrick [aut] Maintainer: Farhan Ameen URL: https://sydneybiox.github.io/Statial https://github.com/SydneyBioX/Statial/issues VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/Statial/issues git_url: https://git.bioconductor.org/packages/Statial git_branch: RELEASE_3_22 git_last_commit: 7de3cdd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Statial_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Statial_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Statial_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Statial_1.12.0.tgz vignettes: vignettes/Statial/inst/doc/Statial.html vignetteTitles: "Introduction to Statial" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Statial/inst/doc/Statial.R importsMe: OSTA suggestsMe: spicyWorkflow dependencyCount: 228 Package: statTarget Version: 1.40.0 Depends: R (>= 3.6.0) Imports: randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats, pls,impute Suggests: testthat, BiocStyle, knitr, rmarkdown License: LGPL (>= 3) Archs: x64 MD5sum: 9ab231c1c58ef0031fed598a94149abd NeedsCompilation: no Title: Statistical Analysis of Molecular Profiles Description: A streamlined tool provides a graphical user interface for quality control based signal drift correction (QC-RFSC), integration of data from multi-batch MS-based experiments, and the comprehensive statistical analysis in metabolomics and proteomics. biocViews: ImmunoOncology, Metabolomics, Proteomics, Machine Learning, Lipidomics, MassSpectrometry, QualityControl, Normalization, QC-RFSC, ComBat, DifferentialExpression, BatchEffect, Visualization, MultipleComparison,Preprocessing, Software Author: Hemi Luan Maintainer: Hemi Luan URL: https://stattarget.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/statTarget git_branch: RELEASE_3_22 git_last_commit: 4a61542 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/statTarget_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/statTarget_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/statTarget_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/statTarget_1.40.0.tgz vignettes: vignettes/statTarget/inst/doc/Combat.html, vignettes/statTarget/inst/doc/pathway_analysis.html, vignettes/statTarget/inst/doc/statTarget.html vignetteTitles: QC_free approach with Combat method, statTarget2 for pathway analysis, statTarget2 On using the Graphical User Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/statTarget/inst/doc/Combat.R, vignettes/statTarget/inst/doc/pathway_analysis.R, vignettes/statTarget/inst/doc/statTarget.R dependencyCount: 26 Package: stepNorm Version: 1.82.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: 153668ada779e1deeccf46331be16c78 NeedsCompilation: no Title: Stepwise normalization functions for cDNA microarrays Description: Stepwise normalization functions for cDNA microarray data. biocViews: Microarray, TwoChannel, Preprocessing Author: Yuanyuan Xiao , Yee Hwa (Jean) Yang Maintainer: Yuanyuan Xiao URL: http://www.biostat.ucsf.edu/jean/ git_url: https://git.bioconductor.org/packages/stepNorm git_branch: RELEASE_3_22 git_last_commit: 8e8d3d5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/stepNorm_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/stepNorm_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/stepNorm_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/stepNorm_1.82.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 9 Package: stJoincount Version: 1.12.0 Depends: R (>= 4.2.0) Imports: graphics, stats, dplyr, magrittr, sp, raster, spdep, ggplot2, pheatmap, grDevices, Seurat, SpatialExperiment, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 1cf8d134fb86fd6ebab658c5de71911e NeedsCompilation: no Title: stJoincount - Join count statistic for quantifying spatial correlation between clusters Description: stJoincount facilitates the application of join count analysis to spatial transcriptomic data generated from the 10x Genomics Visium platform. This tool first converts a labeled spatial tissue map into a raster object, in which each spatial feature is represented by a pixel coded by label assignment. This process includes automatic calculation of optimal raster resolution and extent for the sample. A neighbors list is then created from the rasterized sample, in which adjacent and diagonal neighbors for each pixel are identified. After adding binary spatial weights to the neighbors list, a multi-categorical join count analysis is performed to tabulate "joins" between all possible combinations of label pairs. The function returns the observed join counts, the expected count under conditions of spatial randomness, and the variance calculated under non-free sampling. The z-score is then calculated as the difference between observed and expected counts, divided by the square root of the variance. biocViews: Transcriptomics, Clustering, Spatial, BiocViews, Software Author: Jiarong Song [cre, aut] (ORCID: ), Rania Bassiouni [aut], David Craig [aut] Maintainer: Jiarong Song URL: https://github.com/Nina-Song/stJoincount VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stJoincount git_branch: RELEASE_3_22 git_last_commit: e320f30 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/stJoincount_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/stJoincount_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/stJoincount_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/stJoincount_1.12.0.tgz vignettes: vignettes/stJoincount/inst/doc/stJoincount-vignette.html vignetteTitles: Introduction to stJoincount hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/stJoincount/inst/doc/stJoincount-vignette.R dependencyCount: 193 Package: stPipe Version: 1.0.0 Depends: R (>= 4.5.0) Imports: basilisk, data.table, DropletUtils, dplyr, ggplot2, methods, pbmcapply, reticulate, rmarkdown, Rcpp, Rhtslib, Rsubread, Rtsne, Seurat, SeuratObject, scPipe, shiny, SummarizedExperiment, SingleCellExperiment, SpatialExperiment, stats, umap, yaml LinkingTo: Rcpp, Rhdf5lib, testthat, Rhtslib Suggests: knitr, plotly, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: cf9e53dcf0a422d2a278a0aa7dd85cac NeedsCompilation: yes Title: Upstream pre-processing for Sequencing-Based Spatial Transcriptomics Description: This package serves as an upstream pipeline for pre-processing sequencing-based spatial transcriptomics data. Functions includes FASTQ trimming, BAM file reformatting, index building, spatial barcode detection, demultiplexing, gene count matrix generation with UMI deduplication, QC, and revelant visualization. Config is an essential input for most of the functions which aims to improve reproducibility. biocViews: ImmunoOncology, Software, Sequencing, RNASeq, GeneExpression, SingleCell, Visualization, SequenceMatching, Preprocessing, QualityControl, GenomeAnnotation, DataImport, Spatial, Transcriptomics, Clustering Author: Yang Xu [aut, cre] (ORCID: ), Callum Sargeant [aut], Shian Su [aut], Luyi Tian [aut], Yunshun Chen [ctb], Matthew Ritchie [ctb, fnd] Maintainer: Yang Xu URL: https://github.com/mritchielab/stPipe SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/mritchielab/stPipe/issues/new git_url: https://git.bioconductor.org/packages/stPipe git_branch: RELEASE_3_22 git_last_commit: f234c12 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/stPipe_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/stPipe_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/stPipe_1.0.0.tgz vignettes: vignettes/stPipe/inst/doc/stPipe-vignette.html vignetteTitles: stPipe: A flexible and streamlined pipeline for processing sequencing-based spatial transcriptomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stPipe/inst/doc/stPipe-vignette.R dependencyCount: 253 Package: strandCheckR Version: 1.28.0 Depends: ggplot2 (>= 4.0.0), Rsamtools, S4Vectors Imports: BiocGenerics, dplyr, Seqinfo, GenomicAlignments, GenomicRanges, gridExtra, IRanges, grid, methods, reshape2, rlang, stats, stringr, TxDb.Hsapiens.UCSC.hg38.knownGene, tidyselect Suggests: BiocStyle, knitr, magrittr, rmarkdown, testthat License: GPL (>= 2) MD5sum: dbecd6ac62e0a40730905acad638c54d NeedsCompilation: no Title: Calculate strandness information of a bam file Description: This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering. biocViews: RNASeq, Alignment, QualityControl, Coverage, ImmunoOncology Author: Thu-Hien To [aut, cre], Stevie Pederson [aut] (ORCID: ) Maintainer: Thu-Hien To URL: https://github.com/UofABioinformaticsHub/strandCheckR VignetteBuilder: knitr BugReports: https://github.com/UofABioinformaticsHub/strandCheckR/issues git_url: https://git.bioconductor.org/packages/strandCheckR git_branch: RELEASE_3_22 git_last_commit: 01a9239 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/strandCheckR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/strandCheckR_1.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/strandCheckR_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/strandCheckR_1.28.0.tgz vignettes: vignettes/strandCheckR/inst/doc/strandCheckR.html vignetteTitles: An Introduction To strandCheckR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/strandCheckR/inst/doc/strandCheckR.R dependencyCount: 99 Package: Streamer Version: 1.56.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 MD5sum: 05dcb9e83ab64032e82a40063eeecade NeedsCompilation: yes Title: Enabling stream processing of large files Description: Large data files can be difficult to work with in R, where data generally resides in memory. This package encourages a style of programming where data is 'streamed' from disk into R via a `producer' and through a series of `consumers' that, typically reduce the original data to a manageable size. The package provides useful Producer and Consumer stream components for operations such as data input, sampling, indexing, and transformation; see package?Streamer for details. biocViews: Infrastructure, DataImport Author: Martin Morgan, Nishant Gopalakrishnan Maintainer: Martin Morgan PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/Streamer git_branch: RELEASE_3_22 git_last_commit: d98ca40 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Streamer_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Streamer_1.55.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Streamer_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Streamer_1.56.0.tgz vignettes: vignettes/Streamer/inst/doc/Streamer.pdf vignetteTitles: Streamer: A simple example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Streamer/inst/doc/Streamer.R dependencyCount: 11 Package: STRINGdb Version: 2.22.0 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, httr, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: edb5a92f1cf55c5929bb60b4500d6b17 NeedsCompilation: no Title: STRINGdb - Protein-Protein Interaction Networks and Functional Enrichment Analysis Description: The STRINGdb package provides a R interface to the STRING protein-protein interactions database (https://string-db.org). biocViews: Network Author: Andrea Franceschini Maintainer: Damian Szklarczyk git_url: https://git.bioconductor.org/packages/STRINGdb git_branch: RELEASE_3_22 git_last_commit: 94848b5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/STRINGdb_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/STRINGdb_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/STRINGdb_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/STRINGdb_2.22.0.tgz vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf vignetteTitles: STRINGdb Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STRINGdb/inst/doc/STRINGdb.R dependsOnMe: PPInfer importsMe: IMMAN, RITAN, TDbasedUFEadv, XINA, crosstalkr, DeSciDe suggestsMe: epiNEM, GeneNetworkBuilder, martini, netSmooth, PCAN, scGraphVerse, protti dependencyCount: 50 Package: Structstrings Version: 1.26.0 Depends: R (>= 4.0), S4Vectors (>= 0.47.2), IRanges (>= 2.23.9), Biostrings (>= 2.57.2) Imports: methods, BiocGenerics, XVector, stringr, stringi, crayon, grDevices LinkingTo: IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, tRNAscanImport, BiocStyle License: Artistic-2.0 MD5sum: ec5a0b925561379849b72465ae3c9639 NeedsCompilation: yes Title: Implementation of the dot bracket annotations with Biostrings Description: The Structstrings package implements the widely used dot bracket annotation for storing base pairing information in structured RNA. Structstrings uses the infrastructure provided by the Biostrings package and derives the DotBracketString and related classes from the BString class. From these, base pair tables can be produced for in depth analysis. In addition, the loop indices of the base pairs can be retrieved as well. For better efficiency, information conversion is implemented in C, inspired to a large extend by the ViennaRNA package. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software, Alignment, SequenceMatching Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/Structstrings VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Structstrings/issues git_url: https://git.bioconductor.org/packages/Structstrings git_branch: RELEASE_3_22 git_last_commit: bb58b12 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Structstrings_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Structstrings_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Structstrings_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Structstrings_1.26.0.tgz vignettes: vignettes/Structstrings/inst/doc/Structstrings.html vignetteTitles: Structstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Structstrings/inst/doc/Structstrings.R dependsOnMe: tRNA, tRNAdbImport importsMe: tRNAscanImport dependencyCount: 23 Package: StructuralVariantAnnotation Version: 1.26.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, BiocGenerics, R (>= 4.1.0) Imports: assertthat, Biostrings, pwalign, stringr, dplyr, methods, rlang, GenomicFeatures, IRanges, S4Vectors, SummarizedExperiment, GenomeInfoDb, Suggests: ggplot2, devtools, testthat (>= 2.1.0), roxygen2, rmarkdown, tidyverse, knitr, ggbio, biovizBase, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, License: GPL-3 + file LICENSE MD5sum: b1f4bb3505e2ebbfa49e92c274eb2ff8 NeedsCompilation: no Title: Variant annotations for structural variants Description: StructuralVariantAnnotation provides a framework for analysis of structural variants within the Bioconductor ecosystem. This package contains contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Daniel Cameron [aut, cre] (ORCID: ), Ruining Dong [aut] (ORCID: ) Maintainer: Daniel Cameron VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/StructuralVariantAnnotation git_branch: RELEASE_3_22 git_last_commit: 328c319 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/StructuralVariantAnnotation_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/StructuralVariantAnnotation_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/StructuralVariantAnnotation_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/StructuralVariantAnnotation_1.26.0.tgz vignettes: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.html vignetteTitles: Structural Variant Annotation Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.R dependsOnMe: svaNUMT, svaRetro dependencyCount: 91 Package: SubCellBarCode Version: 1.26.0 Depends: R (>= 3.6) Imports: Rtsne, scatterplot3d, caret, e1071, ggplot2, gridExtra, networkD3, ggrepel, graphics, stats, org.Hs.eg.db, AnnotationDbi Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 963acc66d310fadeb7fb08293f37fba3 NeedsCompilation: no Title: SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome Description: Mass-Spectrometry based spatial proteomics have enabled the proteome-wide mapping of protein subcellular localization (Orre et al. 2019, Molecular Cell). SubCellBarCode R package robustly classifies proteins into corresponding subcellular localization. biocViews: Proteomics, MassSpectrometry, Classification Author: Taner Arslan Maintainer: Taner Arslan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SubCellBarCode git_branch: RELEASE_3_22 git_last_commit: 97a1a22 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SubCellBarCode_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SubCellBarCode_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SubCellBarCode_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SubCellBarCode_1.26.0.tgz vignettes: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.html vignetteTitles: SubCellBarCode R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.R dependencyCount: 134 Package: subSeq Version: 1.40.0 Depends: R (>= 3.2) Imports: data.table, dplyr, tidyr, ggplot2, magrittr, qvalue (>= 1.99), digest, Biobase Suggests: limma, edgeR, DESeq2, DEXSeq (>= 1.9.7), testthat, knitr License: MIT + file LICENSE MD5sum: 22ae067f91810e156902519ca1cd40aa NeedsCompilation: no Title: Subsampling of high-throughput sequencing count data Description: Subsampling of high throughput sequencing count data for use in experiment design and analysis. biocViews: ImmunoOncology, Sequencing, Transcription, RNASeq, GeneExpression, DifferentialExpression Author: David Robinson, John D. Storey, with contributions from Andrew J. Bass Maintainer: Andrew J. Bass , John D. Storey URL: http://github.com/StoreyLab/subSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/subSeq git_branch: RELEASE_3_22 git_last_commit: 47bc0af git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/subSeq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/subSeq_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/subSeq_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/subSeq_1.40.0.tgz vignettes: vignettes/subSeq/inst/doc/subSeq.pdf vignetteTitles: subSeq Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/subSeq/inst/doc/subSeq.R dependencyCount: 45 Package: SUITOR Version: 1.12.0 Depends: R (>= 4.2.0) Imports: stats, utils, graphics, ggplot2, BiocParallel Suggests: devtools, MutationalPatterns, RUnit, BiocManager, BiocGenerics, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: f8f5f1d3712f8f1d2e56bc0b20892b80 NeedsCompilation: yes Title: Selecting the number of mutational signatures through cross-validation Description: An unsupervised cross-validation method to select the optimal number of mutational signatures. A data set of mutational counts is split into training and validation data.Signatures are estimated in the training data and then used to predict the mutations in the validation data. biocViews: Genetics, Software, SomaticMutation Author: DongHyuk Lee [aut], Bin Zhu [aut], Bill Wheeler [cre] Maintainer: Bill Wheeler VignetteBuilder: knitr BugReports: https://github.com/wheelerb/SUITOR/issues git_url: https://git.bioconductor.org/packages/SUITOR git_branch: RELEASE_3_22 git_last_commit: c64d23d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SUITOR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SUITOR_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SUITOR_1.12.0.tgz vignettes: vignettes/SUITOR/inst/doc/vignette.pdf vignetteTitles: SUITOR: selecting the number of mutational signatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SUITOR/inst/doc/vignette.R dependencyCount: 32 Package: SummarizedExperiment Version: 1.40.0 Depends: R (>= 4.0.0), methods, MatrixGenerics (>= 1.1.3), GenomicRanges (>= 1.61.4), Biobase Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.51.3), S4Vectors (>= 0.33.7), IRanges (>= 2.23.9), Seqinfo, S4Arrays (>= 1.1.1), DelayedArray (>= 0.31.12) Suggests: GenomeInfoDb (>= 1.45.5), rhdf5, HDF5Array (>= 1.7.5), annotate, AnnotationDbi, GenomicFeatures, SparseArray, SingleCellExperiment, TxDb.Hsapiens.UCSC.hg19.knownGene, hgu95av2.db, airway (>= 1.15.1), BiocStyle, knitr, rmarkdown, RUnit, testthat, digest License: Artistic-2.0 Archs: x64 MD5sum: f983c78496375a418ccd4853f966d9a8 NeedsCompilation: no Title: A container (S4 class) for matrix-like assays Description: The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: Martin Morgan [aut], Valerie Obenchain [aut], Jim Hester [aut], Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/SummarizedExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SummarizedExperiment/issues git_url: https://git.bioconductor.org/packages/SummarizedExperiment git_branch: RELEASE_3_22 git_last_commit: 469a2de git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SummarizedExperiment_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SummarizedExperiment_1.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SummarizedExperiment_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SummarizedExperiment_1.40.0.tgz vignettes: vignettes/SummarizedExperiment/inst/doc/Extensions.html, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html vignetteTitles: 2. Extending the SummarizedExperiment class, 1. 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Informeasure, InteractiveComplexHeatmap, interactiveDisplay, iscream, knowYourCG, MatrixGenerics, mitology, MOFA2, MSnbase, pathMED, pathwayPCA, philr, podkat, PSMatch, RiboProfiling, Rvisdiff, S4Vectors, scFeatureFilter, scLANE, scrapper, semisup, SETA, sketchR, sparrow, SPOTlight, svaNUMT, svaRetro, systemPipeShiny, updateObject, biotmleData, curatedAdipoArray, curatedTBData, dorothea, DuoClustering2018, gDRtestData, GSE103322, multiWGCNAdata, pRolocdata, RforProteomics, SBGNview.data, tissueTreg, CAGEWorkflow, Canek, ClusterGVis, clustree, conos, CytoSimplex, dependentsimr, dyngen, file2meco, ggpicrust2, lfc, MiscMetabar, parafac4microbiome, polyRAD, RaceID, rliger, scStability, seqgendiff, Seurat, Signac, singleCellHaystack, speakeasyR, SuperCell, teal.slice, tidydr, volcano3D dependencyCount: 24 Package: Summix Version: 2.16.0 Depends: R (>= 4.3) Imports: dplyr, nloptr, magrittr, methods, tibble, tidyselect, BEDASSLE, scales, visNetwork, randomcoloR Suggests: rmarkdown, markdown, knitr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 682491fe14c9c743b2d49c5daa28e968 NeedsCompilation: no Title: Summix2: A suite of methods to estimate, adjust, and leverage substructure in genetic summary data Description: This package contains the Summix2 method for estimating and adjusting for substructure in genetic summary allele frequency data. The function summix() estimates reference group proportions using a mixture model. The adjAF() function produces adjusted allele frequencies for an observed group with reference group proportions matching a target individual or sample. The summix_local() function estimates local ancestry mixture proportions and performs selection scans in genetic summary data. biocViews: StatisticalMethod, WholeGenome, Genetics Author: Audrey Hendricks [cre], Price Adelle [aut], Stoneman Haley [aut] Maintainer: Audrey Hendricks VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Summix/issues git_url: https://git.bioconductor.org/packages/Summix git_branch: RELEASE_3_22 git_last_commit: f4f477f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Summix_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Summix_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Summix_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Summix_2.16.0.tgz vignettes: vignettes/Summix/inst/doc/Summix.html vignetteTitles: Summix.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Summix/inst/doc/Summix.R dependencyCount: 73 Package: SuperCellCyto Version: 1.0.0 Imports: SuperCell, data.table, Matrix, BiocParallel Suggests: flowCore, knitr, rmarkdown, usethis, testthat (>= 3.0.0), BiocSingular, bluster, scater, scran, Seurat, SingleCellExperiment, BiocStyle, magick, qs2 License: GPL-3 + file LICENSE MD5sum: fb05de279bc8406b0b71abe45633b143 NeedsCompilation: no Title: SuperCell For Cytometry Data Description: SuperCellCyto provides the ability to summarise cytometry data into supercells by merging together cells that are similar in their marker expressions using the SuperCell package. biocViews: CellBiology, FlowCytometry, Software, SingleCell Author: Givanna Putri [aut, cre] (ORCID: ), George Howitt [aut], Felix Marsh-Wakefield [aut], Thomas Ashhurst [aut], Belinda Phipson [aut] Maintainer: Givanna Putri URL: https://phipsonlab.github.io/SuperCellCyto/ VignetteBuilder: knitr BugReports: https://github.com/phipsonlab/SuperCellCyto/issues git_url: https://git.bioconductor.org/packages/SuperCellCyto git_branch: RELEASE_3_22 git_last_commit: 1e49305 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SuperCellCyto_1.0.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SuperCellCyto_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SuperCellCyto_1.0.0.tgz vignettes: vignettes/SuperCellCyto/inst/doc/how_to_prepare_data.html, vignettes/SuperCellCyto/inst/doc/interoperability_with_sce.html, vignettes/SuperCellCyto/inst/doc/interoperability_with_seurat.html, vignettes/SuperCellCyto/inst/doc/SuperCellCyto.html, vignettes/SuperCellCyto/inst/doc/using_supercellcyto_for_stratified_summarising.html vignetteTitles: how_to_prepare_data, Using SuperCellCyto with Single-Cell Based Objects, interoperability_with_seurat, How to create supercells, using-runsupercellcyto-for-stratified-summarising hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SuperCellCyto/inst/doc/how_to_prepare_data.R, vignettes/SuperCellCyto/inst/doc/interoperability_with_sce.R, vignettes/SuperCellCyto/inst/doc/interoperability_with_seurat.R, vignettes/SuperCellCyto/inst/doc/SuperCellCyto.R, vignettes/SuperCellCyto/inst/doc/using_supercellcyto_for_stratified_summarising.R dependencyCount: 159 Package: supersigs Version: 1.18.0 Depends: R (>= 4.1) Imports: assertthat, caret, dplyr, tidyr, rsample, methods, rlang, utils, Biostrings, stats, SummarizedExperiment Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, ggplot2, testthat, VariantAnnotation License: GPL-3 MD5sum: 4278053b0e66c626f6a8b926c94529b5 NeedsCompilation: no Title: Supervised mutational signatures Description: Generate SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife). biocViews: FeatureExtraction, Classification, Regression, Sequencing, WholeGenome, SomaticMutation Author: Albert Kuo [aut, cre] (ORCID: ), Yifan Zhang [aut], Bahman Afsari [aut], Cristian Tomasetti [aut] Maintainer: Albert Kuo URL: https://tomasettilab.github.io/supersigs/ VignetteBuilder: knitr BugReports: https://github.com/TomasettiLab/supersigs/issues git_url: https://git.bioconductor.org/packages/supersigs git_branch: RELEASE_3_22 git_last_commit: 44913f5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/supersigs_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/supersigs_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/supersigs_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/supersigs_1.18.0.tgz vignettes: vignettes/supersigs/inst/doc/supersigs.html vignetteTitles: Using supersigs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supersigs/inst/doc/supersigs.R dependencyCount: 104 Package: surfaltr Version: 1.16.0 Depends: R (>= 4.0) Imports: dplyr (>= 1.0.6), biomaRt (>= 2.46.0), protr (>= 1.6-2), seqinr (>= 4.2-5), ggplot2 (>= 3.3.2), utils (>= 2.10.1), stringr (>= 1.4.0), Biostrings (>= 2.58.0),readr (>= 1.4.0), httr (>= 1.4.2), testthat(>= 3.0.0), xml2(>= 1.3.2), msa (>= 1.22.0), methods (>= 4.0.3) Suggests: knitr, rmarkdown, devtools, kableExtra License: MIT + file LICENSE MD5sum: cf4efdefb67e298d8f7423e5eb3c9b85 NeedsCompilation: no Title: Rapid Comparison of Surface Protein Isoform Membrane Topologies Through surfaltr Description: Cell surface proteins form a major fraction of the druggable proteome and can be used for tissue-specific delivery of oligonucleotide/cell-based therapeutics. Alternatively spliced surface protein isoforms have been shown to differ in their subcellular localization and/or their transmembrane (TM) topology. Surface proteins are hydrophobic and remain difficult to study thereby necessitating the use of TM topology prediction methods such as TMHMM and Phobius. However, there exists a need for bioinformatic approaches to streamline batch processing of isoforms for comparing and visualizing topologies. To address this gap, we have developed an R package, surfaltr. It pairs inputted isoforms, either known alternatively spliced or novel, with their APPRIS annotated principal counterparts, predicts their TM topologies using TMHMM or Phobius, and generates a customizable graphical output. Further, surfaltr facilitates the prioritization of biologically diverse isoform pairs through the incorporation of three different ranking metrics and through protein alignment functions. Citations for programs mentioned here can be found in the vignette. biocViews: Software, Visualization, DataRepresentation, SplicedAlignment, Alignment, MultipleSequenceAlignment, MultipleComparison Author: Pooja Gangras [aut, cre] (ORCID: ), Aditi Merchant [aut] Maintainer: Pooja Gangras VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/surfaltr git_branch: RELEASE_3_22 git_last_commit: b68ffe3 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/surfaltr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/surfaltr_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/surfaltr_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/surfaltr_1.16.0.tgz vignettes: vignettes/surfaltr/inst/doc/surfaltr_vignette.html vignetteTitles: surfaltr_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/surfaltr/inst/doc/surfaltr_vignette.R dependencyCount: 105 Package: SurfR Version: 1.6.0 Depends: R (>= 4.4.0) Imports: httr, BiocFileCache, SPsimSeq, DESeq2, edgeR, openxlsx, stringr, rhdf5, ggplot2, ggrepel, stats, magrittr, assertr, tidyr, dplyr, TCGAbiolinks, biomaRt, metaRNASeq, scales, venn, gridExtra, SummarizedExperiment, knitr, rjson, grDevices, graphics, curl, utils Suggests: BiocStyle, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: 2ac834086b2d94e4e08b16920d212cca NeedsCompilation: no Title: Surface Protein Prediction and Identification Description: Identify Surface Protein coding genes from a list of candidates. Systematically download data from GEO and TCGA or use your own data. Perform DGE on bulk RNAseq data. Perform Meta-analysis. Descriptive enrichment analysis and plots. biocViews: Software, Sequencing, RNASeq, GeneExpression, Transcription, DifferentialExpression, PrincipalComponent, GeneSetEnrichment, Pathways, BatchEffect, FunctionalGenomics, Visualization, DataImport, FunctionalPrediction, GenePrediction, GO Author: Aurora Maurizio [aut, cre] (ORCID: ), Anna Sofia Tascini [aut, ctb] (ORCID: ) Maintainer: Aurora Maurizio URL: https://github.com/auroramaurizio/SurfR VignetteBuilder: knitr BugReports: https://github.com/auroramaurizio/SurfR/issues git_url: https://git.bioconductor.org/packages/SurfR git_branch: RELEASE_3_22 git_last_commit: 6bb52a0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SurfR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SurfR_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SurfR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SurfR_1.6.0.tgz vignettes: vignettes/SurfR/inst/doc/Intro_to_SurfR.html vignetteTitles: Introduction to SurfR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SurfR/inst/doc/Intro_to_SurfR.R dependencyCount: 183 Package: survClust Version: 1.4.0 Depends: R (>= 3.5.0) Imports: Rcpp, MultiAssayExperiment, pdist, survival LinkingTo: Rcpp Suggests: knitr, testthat (>= 3.0.0), gplots, htmltools, BiocParallel License: MIT + file LICENSE MD5sum: df9608806f9b2f71051c623664359d85 NeedsCompilation: yes Title: Identification Of Clinically Relevant Genomic Subtypes Using Outcome Weighted Learning Description: survClust is an outcome weighted integrative clustering algorithm used to classify multi-omic samples on their available time to event information. The resulting clusters are cross-validated to avoid over overfitting and output classification of samples that are molecularly distinct and clinically meaningful. It takes in binary (mutation) as well as continuous data (other omic types). biocViews: Software, Clustering, Survival, Classification Author: Arshi Arora [aut, cre] (ORCID: ) Maintainer: Arshi Arora URL: https://github.com/arorarshi/survClust VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/survClust git_url: https://git.bioconductor.org/packages/survClust git_branch: RELEASE_3_22 git_last_commit: b744e5d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/survClust_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/survClust_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/survClust_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/survClust_1.4.0.tgz vignettes: vignettes/survClust/inst/doc/survClust_vignette.html vignetteTitles: An introduction to survClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/survClust/inst/doc/survClust_vignette.R dependencyCount: 50 Package: survcomp Version: 1.60.0 Depends: survival, prodlim, R (>= 3.4) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta, stats, graphics Suggests: Hmisc, clinfun, xtable, Biobase, BiocManager License: Artistic-2.0 MD5sum: 185ad641790d78106f07126e206bd4f0 NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: Assessment and Comparison for Performance of Risk Prediction (Survival) Models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains [aut, cre], Markus Schroeder [aut], Catharina Olsen [aut], Christos Sotiriou [aut], Gianluca Bontempi [aut], John Quackenbush [aut], Samuel Branders [aut], Zhaleh Safikhani [aut] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/ git_url: https://git.bioconductor.org/packages/survcomp git_branch: RELEASE_3_22 git_last_commit: 3577a35 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/survcomp_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/survcomp_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/survcomp_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/survcomp_1.60.0.tgz vignettes: vignettes/survcomp/inst/doc/survcomp.pdf vignetteTitles: SurvComp: a package for performance assessment and comparison for survival analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survcomp/inst/doc/survcomp.R dependsOnMe: genefu importsMe: metaseqR2, PDATK, Coxmos, E2E, FLORAL, pencal, plsRcox, SIGN suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 54 Package: survtype Version: 1.26.0 Depends: SummarizedExperiment, pheatmap, survival, survminer, clustvarsel, stats, utils Suggests: maftools, scales, knitr, rmarkdown License: Artistic-2.0 MD5sum: 19509f6cd7dae409148815b946758376 NeedsCompilation: no Title: Subtype Identification with Survival Data Description: Subtypes are defined as groups of samples that have distinct molecular and clinical features. Genomic data can be analyzed for discovering patient subtypes, associated with clinical data, especially for survival information. This package is aimed to identify subtypes that are both clinically relevant and biologically meaningful. biocViews: Software, StatisticalMethod, GeneExpression, Survival, Clustering, Sequencing, Coverage Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/survtype git_branch: RELEASE_3_22 git_last_commit: 5edb8f1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/survtype_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/survtype_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/survtype_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/survtype_1.26.0.tgz vignettes: vignettes/survtype/inst/doc/survtype.html vignetteTitles: survtype hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survtype/inst/doc/survtype.R dependencyCount: 129 Package: sva Version: 3.58.0 Depends: R (>= 3.2), mgcv, genefilter, BiocParallel Imports: matrixStats, stats, graphics, utils, limma, edgeR Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 MD5sum: 4c609856bfb95385557c693a6c8e0b7f NeedsCompilation: yes Title: Surrogate Variable Analysis Description: The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics). biocViews: ImmunoOncology, Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , Yuqing Zhang , John D. Storey , Leonardo Collado Torres Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson git_url: https://git.bioconductor.org/packages/sva git_branch: RELEASE_3_22 git_last_commit: 32d0f19 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/sva_3.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sva_3.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sva_3.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sva_3.58.0.tgz vignettes: vignettes/sva/inst/doc/sva.pdf vignetteTitles: sva tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sva/inst/doc/sva.R dependsOnMe: DeMixT, IsoformSwitchAnalyzeR, SCAN.UPC, rnaseqGene, bapred, leapp, SmartSVA importsMe: ASSIGN, ballgown, BatchQC, BERT, BioNERO, bnbc, bnem, DaMiRseq, debrowser, DExMA, doppelgangR, edge, HarmonizR, KnowSeq, MatrixQCvis, MBECS, MSPrep, omicRexposome, PAA, pairedGSEA, POMA, PROPS, qsmooth, qsvaR, SEtools, singleCellTK, DeSousa2013, ExpressionNormalizationWorkflow, causalBatch, cinaR, dSVA, scITD, seqgendiff, TransProR suggestsMe: compcodeR, GSVA, Harman, iasva, randRotation, RnBeads, scp, SomaticSignatures, TBSignatureProfiler, TCGAbiolinks, tidybulk, curatedBladderData, curatedOvarianData, curatedTBData, FieldEffectCrc, CAGEWorkflow, DGEobj.utils, DRomics, SuperLearner dependencyCount: 69 Package: svaNUMT Version: 1.16.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, StructuralVariantAnnotation, BiocGenerics, Biostrings, R (>= 4.0) Imports: assertthat, stringr, dplyr, methods, rlang, S4Vectors, Seqinfo, GenomeInfoDb, GenomicFeatures, pwalign Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, ggplot2, devtools, testthat (>= 2.1.0), roxygen2, knitr, readr, plyranges, circlize, IRanges, SummarizedExperiment, rmarkdown License: GPL-3 + file LICENSE Archs: x64 MD5sum: 1e4d323f14924846427b6ed4b6596e96 NeedsCompilation: no Title: NUMT detection from structural variant calls Description: svaNUMT contains functions for detecting NUMT events from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies NUMTs by nuclear-mitochondrial breakend junctions. The main function reports candidate NUMTs if there is a pair of valid insertion sites found on the nuclear genome within a certain distance threshold. The candidate NUMTs are reported by events. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Ruining Dong [aut, cre] (ORCID: ) Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaNUMT/issues git_url: https://git.bioconductor.org/packages/svaNUMT git_branch: RELEASE_3_22 git_last_commit: 87701e2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/svaNUMT_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/svaNUMT_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/svaNUMT_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/svaNUMT_1.16.0.tgz vignettes: vignettes/svaNUMT/inst/doc/svaNUMT.html vignetteTitles: svaNUMT Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/svaNUMT/inst/doc/svaNUMT.R dependencyCount: 92 Package: svaRetro Version: 1.15.1 Depends: GenomicRanges, rtracklayer, BiocGenerics, StructuralVariantAnnotation, R (>= 4.0) Imports: VariantAnnotation, assertthat, Biostrings, stringr, dplyr, methods, rlang, S4Vectors, Seqinfo, GenomeInfoDb, GenomicFeatures, utils Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, ggplot2, devtools, testthat (>= 2.1.0), roxygen2, knitr, BiocStyle, plyranges, circlize, tictoc, IRanges, stats, SummarizedExperiment, rmarkdown License: GPL-3 + file LICENSE Archs: x64 MD5sum: b471a5413823400ba168b8d6f167e66f NeedsCompilation: no Title: Retrotransposed transcript detection from structural variants Description: svaRetro contains functions for detecting retrotransposed transcripts (RTs) from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies RTs by exon-exon junctions and insertion sites. The candidate RTs are reported by events and annotated with information of the inserted transcripts. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation, Coverage, VariantDetection Author: Ruining Dong [aut, cre] (ORCID: ) Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaRetro/issues git_url: https://git.bioconductor.org/packages/svaRetro git_branch: devel git_last_commit: 238534b git_last_commit_date: 2025-07-22 Date/Publication: 2025-10-07 source.ver: src/contrib/svaRetro_1.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/svaRetro_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/svaRetro_1.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/svaRetro_1.15.1.tgz vignettes: vignettes/svaRetro/inst/doc/svaRetro.html vignetteTitles: svaRetro Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/svaRetro/inst/doc/svaRetro.R dependencyCount: 92 Package: SVMDO Version: 1.10.0 Depends: R(>= 4.4), shiny (>= 1.7.4) Imports: shinyFiles (>= 0.9.3), shinytitle (>= 0.1.0), golem (>= 0.3.5), nortest (>= 1.0-4), e1071 (>= 1.7-12), BSDA (>= 1.2.1), data.table (>= 1.14.6), sjmisc (>= 2.8.9), klaR (>= 1.7-1), caTools (>= 1.18.2), caret (>= 6.0-93), survival (>= 3.4-0), DT (>= 0.33.0), DOSE (>= 3.24.2), AnnotationDbi (>= 1.60.0), org.Hs.eg.db (>= 3.16.0), dplyr (>= 1.0.10), SummarizedExperiment (>= 1.28.0), grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.1.6) License: GPL-3 MD5sum: 6a5480329c0036e1e35dde10197db64c NeedsCompilation: no Title: Identification of Tumor-Discriminating mRNA Signatures via Support Vector Machines Supported by Disease Ontology Description: It is an easy-to-use GUI using disease information for detecting tumor/normal sample discriminating gene sets from differentially expressed genes. Our approach is based on an iterative algorithm filtering genes with disease ontology enrichment analysis and wilk and wilks lambda criterion connected to SVM classification model construction. Along with gene set extraction, SVMDO also provides individual prognostic marker detection. The algorithm is designed for FPKM and RPKM normalized RNA-Seq transcriptome datasets. biocViews: GeneSetEnrichment, DifferentialExpression, GUI, Classification, RNASeq, Transcriptomics, Survival Author: Mustafa Erhan Ozer [aut, cre] (ORCID: ), Pemra Ozbek Sarica [aut], Kazim Yalcin Arga [aut] Maintainer: Mustafa Erhan Ozer VignetteBuilder: knitr BugReports: https://github.com/robogeno/SVMDO/issues git_url: https://git.bioconductor.org/packages/SVMDO git_branch: RELEASE_3_22 git_last_commit: 86871da git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SVMDO_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SVMDO_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SVMDO_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SVMDO_1.10.0.tgz vignettes: vignettes/SVMDO/inst/doc/SVMDO_guide.html vignetteTitles: SVMDO-Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SVMDO/inst/doc/SVMDO_guide.R dependencyCount: 196 Package: SVP Version: 1.1.1 Depends: R (>= 4.1.0) Imports: Rcpp, RcppParallel, methods, cli, dplyr, rlang, S4Vectors, SummarizedExperiment, SingleCellExperiment, SpatialExperiment, BiocGenerics, BiocParallel, fastmatch, pracma, stats, withr, Matrix, DelayedMatrixStats, deldir, utils, BiocNeighbors, ggplot2, ggstar, ggtree, ggfun LinkingTo: Rcpp, RcppArmadillo (>= 14.0), RcppParallel, RcppEigen, dqrng Suggests: rmarkdown, prettydoc, broman, RSpectra, BiasedUrn, knitr, ks, igraph, testthat (>= 3.0.0), scuttle, magrittr, DropletUtils, tibble, tidyr, harmony, aplot, scales, ggsc, scatterpie, scran, scater, STexampleData, ape License: GPL-3 MD5sum: e6658f68f069bbdb8b83228cc9747868 NeedsCompilation: yes Title: Predicting cell states and their variability in single-cell or spatial omics data Description: SVP uses the distance between cells and cells, features and features, cells and features in the space of MCA to build nearest neighbor graph, then uses random walk with restart algorithm to calculate the activity score of gene sets (such as cell marker genes, kegg pathway, go ontology, gene modules, transcription factor or miRNA target sets, reactome pathway, ...), which is then further weighted using the hypergeometric test results from the original expression matrix. To detect the spatially or single cell variable gene sets or (other features) and the spatial colocalization between the features accurately, SVP provides some global and local spatial autocorrelation method to identify the spatial variable features. SVP is developed based on SingleCellExperiment class, which can be interoperable with the existing computing ecosystem. biocViews: SingleCell, Software, Spatial, Transcriptomics, GeneTarget, GeneExpression, GeneSetEnrichment, Transcription, GO, KEGG Author: Shuangbin Xu [aut, cre] (ORCID: ), Guangchuang Yu [aut, ctb] (ORCID: ) Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/SVP SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/SVP/issues git_url: https://git.bioconductor.org/packages/SVP git_branch: devel git_last_commit: 9c5839c git_last_commit_date: 2025-10-14 Date/Publication: 2025-10-15 source.ver: src/contrib/SVP_1.1.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/SVP_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SVP_1.1.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SVP_1.1.1.tgz vignettes: vignettes/SVP/inst/doc/SVP.html vignetteTitles: SVP Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SVP/inst/doc/SVP.R dependencyCount: 135 Package: SWATH2stats Version: 1.40.0 Depends: R(>= 2.10.0) Imports: data.table, reshape2, ggplot2, stats, grDevices, graphics, utils, biomaRt, methods Suggests: testthat, knitr, rmarkdown Enhances: MSstats, PECA, aLFQ License: GPL-3 MD5sum: e763dc400431a6c32f5c4d38fb340cf4 NeedsCompilation: no Title: Transform and Filter SWATH Data for Statistical Packages Description: This package is intended to transform SWATH data from the OpenSWATH software into a format readable by other statistics packages while performing filtering, annotation and FDR estimation. biocViews: Proteomics, Annotation, ExperimentalDesign, Preprocessing, MassSpectrometry, ImmunoOncology Author: Peter Blattmann [aut, cre] Moritz Heusel [aut] Ruedi Aebersold [aut] Maintainer: Peter Blattmann URL: https://peterblattmann.github.io/SWATH2stats/ VignetteBuilder: knitr BugReports: https://github.com/peterblattmann/SWATH2stats git_url: https://git.bioconductor.org/packages/SWATH2stats git_branch: RELEASE_3_22 git_last_commit: 63159bf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SWATH2stats_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SWATH2stats_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SWATH2stats_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SWATH2stats_1.40.0.tgz vignettes: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.pdf, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.pdf vignetteTitles: SWATH2stats example script, SWATH2stats package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.R, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.R dependencyCount: 78 Package: SwathXtend Version: 2.32.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: 800006ec81916e4ccd97e582bafd0dc8 NeedsCompilation: no Title: SWATH extended library generation and statistical data analysis Description: Contains utility functions for integrating spectral libraries for SWATH and statistical data analysis for SWATH generated data. biocViews: Software Author: J WU and D Pascovici Maintainer: Jemma Wu git_url: https://git.bioconductor.org/packages/SwathXtend git_branch: RELEASE_3_22 git_last_commit: 2ae0cff git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SwathXtend_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SwathXtend_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SwathXtend_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SwathXtend_2.32.0.tgz vignettes: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.pdf vignetteTitles: SwathXtend hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.R dependencyCount: 21 Package: swfdr Version: 1.36.0 Depends: R (>= 3.4) Imports: methods, splines, stats4, stats Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat License: GPL (>= 3) MD5sum: 8650ba83d14511bc8b636d272b8f9fca NeedsCompilation: no Title: Estimation of the science-wise false discovery rate and the false discovery rate conditional on covariates Description: This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the false discovery rate conditional on covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ. biocViews: MultipleComparison, StatisticalMethod, Software Author: Jeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka Maintainer: Simina M. Boca , Jeffrey T. Leek URL: https://github.com/leekgroup/swfdr VignetteBuilder: knitr BugReports: https://github.com/leekgroup/swfdr/issues git_url: https://git.bioconductor.org/packages/swfdr git_branch: RELEASE_3_22 git_last_commit: f1e5cfe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/swfdr_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/swfdr_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/swfdr_1.36.0.tgz vignettes: vignettes/swfdr/inst/doc/swfdrQ.pdf, vignettes/swfdr/inst/doc/swfdrTutorial.pdf vignetteTitles: Computing covariate-adjusted q-values, Tutorial for swfdr package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/swfdr/inst/doc/swfdrQ.R, vignettes/swfdr/inst/doc/swfdrTutorial.R dependencyCount: 4 Package: switchBox Version: 1.46.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 MD5sum: 8c872376b938ea347fac905c5eb65cf8 NeedsCompilation: yes Title: Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm Description: The package offer different classifiers based on comparisons of pair of features (TSP), using various decision rules (e.g., majority wins principle). biocViews: Software, StatisticalMethod, Classification Author: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara Maintainer: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara git_url: https://git.bioconductor.org/packages/switchBox git_branch: RELEASE_3_22 git_last_commit: db4c33d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/switchBox_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/switchBox_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/switchBox_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/switchBox_1.46.0.tgz vignettes: vignettes/switchBox/inst/doc/switchBox.pdf vignetteTitles: Working with the switchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchBox/inst/doc/switchBox.R importsMe: PDATK suggestsMe: multiclassPairs dependencyCount: 10 Package: switchde Version: 1.36.0 Depends: R (>= 3.4), SingleCellExperiment Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr License: GPL (>= 2) MD5sum: e0f986580592d24d6a20897f1b081817 NeedsCompilation: no Title: Switch-like differential expression across single-cell trajectories Description: Inference and detection of switch-like differential expression across single-cell RNA-seq trajectories. biocViews: ImmunoOncology, Software, Transcriptomics, GeneExpression, RNASeq, Regression, DifferentialExpression, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell URL: https://github.com/kieranrcampbell/switchde VignetteBuilder: knitr BugReports: https://github.com/kieranrcampbell/switchde git_url: https://git.bioconductor.org/packages/switchde git_branch: RELEASE_3_22 git_last_commit: c431044 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/switchde_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/switchde_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/switchde_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/switchde_1.36.0.tgz vignettes: vignettes/switchde/inst/doc/switchde_vignette.html vignetteTitles: An overview of the switchde package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchde/inst/doc/switchde_vignette.R dependencyCount: 49 Package: synapsis Version: 1.16.0 Depends: R (>= 4.1) Imports: EBImage, stats, utils, graphics Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2, tidyverse, BiocStyle License: MIT + file LICENSE MD5sum: e5d51d7dae2820d5a151af23a4ba9291 NeedsCompilation: no Title: An R package to automate the analysis of double-strand break repair during meiosis Description: Synapsis is a Bioconductor software package for automated (unbiased and reproducible) analysis of meiotic immunofluorescence datasets. The primary functions of the software can i) identify cells in meiotic prophase that are labelled by a synaptonemal complex axis or central element protein, ii) isolate individual synaptonemal complexes and measure their physical length, iii) quantify foci and co-localise them with synaptonemal complexes, iv) measure interference between synaptonemal complex-associated foci. The software has applications that extend to multiple species and to the analysis of other proteins that label meiotic prophase chromosomes. The software converts meiotic immunofluorescence images into R data frames that are compatible with machine learning methods. Given a set of microscopy images of meiotic spread slides, synapsis crops images around individual single cells, counts colocalising foci on strands on a per cell basis, and measures the distance between foci on any given strand. biocViews: Software, SingleCell Author: Lucy McNeill [aut, cre, cph] (ORCID: ), Wayne Crismani [rev, ctb] (ORCID: ) Maintainer: Lucy McNeill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapsis git_branch: RELEASE_3_22 git_last_commit: 184fc99 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/synapsis_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/synapsis_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/synapsis_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/synapsis_1.16.0.tgz vignettes: vignettes/synapsis/inst/doc/synapsis_tutorial.html vignetteTitles: Using-synapsis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/synapsis/inst/doc/synapsis_tutorial.R dependencyCount: 46 Package: synergyfinder Version: 3.18.0 Depends: R (>= 4.0.0) Imports: drc (>= 3.0-1), reshape2 (>= 1.4.4), tidyverse (>= 1.3.0), dplyr (>= 1.0.3), tidyr (>= 1.1.2), purrr (>= 0.3.4), furrr (>= 0.2.2), ggplot2 (>= 3.3.3), ggforce (>= 0.3.2), grid (>= 4.0.2), vegan (>= 2.5-7), gstat (>= 2.0-6), sp (>= 1.4-5), methods (>= 4.0.2), SpatialExtremes (>= 2.0-9), ggrepel (>= 0.9.1), kriging (>= 1.1), plotly (>= 4.9.3), stringr (>= 1.4.0), future (>= 1.21.0), mice (>= 3.13.0), lattice (>= 0.20-41), nleqslv (>= 3.3.2), stats (>= 4.0.2), graphics (>= 4.0.2), grDevices (>= 4.0.2), magrittr (>= 2.0.1), pbapply (>= 1.4-3), metR (>= 0.9.1) Suggests: knitr, rmarkdown License: Mozilla Public License 2.0 MD5sum: db490f3964047a1802d35ca6dfb933c4 NeedsCompilation: no Title: Calculate and Visualize Synergy Scores for Drug Combinations Description: Efficient implementations for analyzing pre-clinical multiple drug combination datasets. It provides efficient implementations for 1.the popular synergy scoring models, including HSA, Loewe, Bliss, and ZIP to quantify the degree of drug combination synergy; 2. higher order drug combination data analysis and synergy landscape visualization for unlimited number of drugs in a combination; 3. statistical analysis of drug combination synergy and sensitivity with confidence intervals and p-values; 4. synergy barometer for harmonizing multiple synergy scoring methods to provide a consensus metric of synergy; 5. evaluation of synergy and sensitivity simultaneously to provide an unbiased interpretation of the clinical potential of the drug combinations. Based on this package, we also provide a web application (http://www.synergyfinder.org) for users who prefer graphical user interface. biocViews: Software, StatisticalMethod Author: Shuyu Zheng [aut, cre], Jing Tang [aut] Maintainer: Shuyu Zheng URL: http://www.synergyfinder.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synergyfinder git_branch: RELEASE_3_22 git_last_commit: 6dcbcb8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/synergyfinder_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/synergyfinder_3.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/synergyfinder_3.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/synergyfinder_3.18.0.tgz vignettes: vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.html vignetteTitles: User tutorial of the SynergyFinder Plus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.R dependencyCount: 207 Package: SynExtend Version: 1.22.0 Depends: R (>= 4.5.0), DECIPHER (>= 2.28.0) Imports: methods, Biostrings, S4Vectors, IRanges, utils, stats, parallel, graphics, grDevices, RSQLite, DBI Suggests: BiocStyle, knitr, igraph, markdown, rmarkdown License: GPL-3 MD5sum: 48e649c20b0c5e11e8aeff40757245a3 NeedsCompilation: yes Title: Tools for Comparative Genomics Description: A multitude of tools for comparative genomics, focused on large-scale analyses of biological data. SynExtend includes tools for working with syntenic data, clustering massive network structures, and estimating functional relationships among genes. biocViews: Genetics, Clustering, ComparativeGenomics, DataImport Author: Nicholas Cooley [aut, cre] (ORCID: ), Aidan Lakshman [aut, ctb] (ORCID: ), Adelle Fernando [ctb], Erik Wright [aut] Maintainer: Nicholas Cooley URL: https://github.com/npcooley/SynExtend VignetteBuilder: knitr BugReports: https://github.com/npcooley/SynExtend/issues/new/ git_url: https://git.bioconductor.org/packages/SynExtend git_branch: RELEASE_3_22 git_last_commit: 9df95cb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SynExtend_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SynExtend_1.21.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SynExtend_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SynExtend_1.22.0.tgz vignettes: vignettes/SynExtend/inst/doc/UsingSynExtend.html vignetteTitles: UsingSynExtend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynExtend/inst/doc/UsingSynExtend.R dependencyCount: 33 Package: synlet Version: 2.10.0 Depends: R (>= 3.5.0) Imports: data.table, ggplot2, grDevices, magrittr, methods, patchwork, RankProd, RColorBrewer, stats, utils Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-3 MD5sum: 119f47c9ab21aa82b642ca8ef8f8fe18 NeedsCompilation: no Title: Hits Selection for Synthetic Lethal RNAi Screen Data Description: Select hits from synthetic lethal RNAi screen data. For example, there are two identical celllines except one gene is knocked-down in one cellline. The interest is to find genes that lead to stronger lethal effect when they are knocked-down further by siRNA. Quality control and various visualisation tools are implemented. Four different algorithms could be used to pick up the interesting hits. This package is designed based on 384 wells plates, but may apply to other platforms with proper configuration. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization, FeatureExtraction Author: Chunxuan Shao [aut, cre] Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synlet git_branch: RELEASE_3_22 git_last_commit: efe839e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/synlet_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/synlet_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/synlet_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/synlet_2.10.0.tgz vignettes: vignettes/synlet/inst/doc/synlet-vignette.html vignetteTitles: A working Demo for synlet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R dependencyCount: 28 Package: SynMut Version: 1.26.0 Imports: seqinr, methods, Biostrings, stringr, BiocGenerics Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc, glue License: GPL-2 MD5sum: 7d07da4d98f56db1ffedf164a680fce5 NeedsCompilation: no Title: SynMut: Designing Synonymously Mutated Sequences with Different Genomic Signatures Description: There are increasing demands on designing virus mutants with specific dinucleotide or codon composition. This tool can take both dinucleotide preference and/or codon usage bias into account while designing mutants. It is a powerful tool for in silico designs of DNA sequence mutants. biocViews: SequenceMatching, ExperimentalDesign, Preprocessing Author: Haogao Gu [aut, cre], Leo L.M. Poon [led] Maintainer: Haogao Gu URL: https://github.com/Koohoko/SynMut VignetteBuilder: knitr BugReports: https://github.com/Koohoko/SynMut/issues git_url: https://git.bioconductor.org/packages/SynMut git_branch: RELEASE_3_22 git_last_commit: 910fcab git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/SynMut_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SynMut_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SynMut_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SynMut_1.26.0.tgz vignettes: vignettes/SynMut/inst/doc/SynMut.html vignetteTitles: SynMut: Designing Synonymous Mutants for DNA Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynMut/inst/doc/SynMut.R dependencyCount: 34 Package: syntenet Version: 1.11.2 Depends: R (>= 4.2) Imports: Rcpp (>= 1.0.8), BiocParallel, GenomicRanges, rlang, Biostrings, utils, methods, igraph, stats, grDevices, RColorBrewer, pheatmap, ggplot2, ggnetwork, intergraph LinkingTo: Rcpp, testthat Suggests: rtracklayer, BiocStyle, ggtree, labdsv, covr, knitr, rmarkdown, testthat (>= 3.0.0), xml2, networkD3 License: GPL-3 MD5sum: 3f05c20c3c1ee82e929f32d80237444e NeedsCompilation: yes Title: Inference And Analysis Of Synteny Networks Description: syntenet can be used to infer synteny networks from whole-genome protein sequences and analyze them. Anchor pairs are detected with the MCScanX algorithm, which was ported to this package with the Rcpp framework for R and C++ integration. Anchor pairs from synteny analyses are treated as an undirected unweighted graph (i.e., a synteny network), and users can perform: i. network clustering; ii. phylogenomic profiling (by identifying which species contain which clusters) and; iii. microsynteny-based phylogeny reconstruction with maximum likelihood. biocViews: Software, NetworkInference, FunctionalGenomics, ComparativeGenomics, Phylogenetics, SystemsBiology, GraphAndNetwork, WholeGenome, Network Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Tao Zhao [aut] (ORCID: ), Kristian K Ullrich [aut] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/syntenet VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/syntenet git_url: https://git.bioconductor.org/packages/syntenet git_branch: devel git_last_commit: 1607c7f git_last_commit_date: 2025-07-23 Date/Publication: 2025-10-07 source.ver: src/contrib/syntenet_1.11.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/syntenet_1.11.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/syntenet_1.11.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/syntenet_1.11.2.tgz vignettes: vignettes/syntenet/inst/doc/vignette_01_inference_and_analysis_of_synteny_networks.html, vignettes/syntenet/inst/doc/vignette_02_synteny_detection_with_syntenet.html vignetteTitles: Inference and analysis of synteny networks, syntenet as a synteny detection tool hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/syntenet/inst/doc/vignette_01_inference_and_analysis_of_synteny_networks.R, vignettes/syntenet/inst/doc/vignette_02_synteny_detection_with_syntenet.R importsMe: doubletrouble dependencyCount: 76 Package: systemPipeR Version: 2.16.0 Depends: Rsamtools (>= 1.31.2), Biostrings, ShortRead (>= 1.37.1), methods Imports: GenomicRanges, SummarizedExperiment, ggplot2, yaml, stringr, magrittr, S4Vectors, crayon, BiocGenerics, htmlwidgets Suggests: BiocStyle, knitr, rmarkdown, systemPipeRdata, GenomicAlignments, grid, dplyr, testthat, rjson, annotate, AnnotationDbi, kableExtra, GO.db, GenomeInfoDb, DT, rtracklayer, limma, edgeR, DESeq2, IRanges, batchtools, GenomicFeatures, txdbmaker, GenomeInfoDbData, VariantAnnotation (>= 1.25.11) License: Artistic-2.0 MD5sum: 9582ca25522e862d819ca753a30e5e3f NeedsCompilation: no Title: systemPipeR: Workflow Environment for Data Analysis and Report Generation Description: systemPipeR is a multipurpose data analysis workflow environment that unifies R with command-line tools. It enables scientists to analyze many types of large- or small-scale data on local or distributed computer systems with a high level of reproducibility, scalability and portability. At its core is a command-line interface (CLI) that adopts the Common Workflow Language (CWL). This design allows users to choose for each analysis step the optimal R or command-line software. It supports both end-to-end and partial execution of workflows with built-in restart functionalities. Efficient management of complex analysis tasks is accomplished by a flexible workflow control container class. Handling of large numbers of input samples and experimental designs is facilitated by consistent sample annotation mechanisms. As a multi-purpose workflow toolkit, systemPipeR enables users to run existing workflows, customize them or design entirely new ones while taking advantage of widely adopted data structures within the Bioconductor ecosystem. Another important core functionality is the generation of reproducible scientific analysis and technical reports. For result interpretation, systemPipeR offers a wide range of plotting functionality, while an associated Shiny App offers many useful functionalities for interactive result exploration. The vignettes linked from this page include (1) a general introduction, (2) a description of technical details, and (3) a collection of workflow templates. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage, GeneSetEnrichment, Alignment, QualityControl, ImmunoOncology, ReportWriting, WorkflowStep, WorkflowManagement Author: Thomas Girke Maintainer: Thomas Girke URL: https://github.com/tgirke/systemPipeR SystemRequirements: systemPipeR can be used to run external command-line software (e.g. short read aligners), but the corresponding tool needs to be installed on a system. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeR git_branch: RELEASE_3_22 git_last_commit: 4c55692 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/systemPipeR_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/systemPipeR_2.15.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/systemPipeR_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/systemPipeR_2.16.0.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html, vignettes/systemPipeR/inst/doc/systemPipeR.html vignetteTitles: systemPipeR: Workflow Templates, Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.R, vignettes/systemPipeR/inst/doc/systemPipeR.R importsMe: DiffBind suggestsMe: systemPipeShiny, systemPipeTools, systemPipeRdata dependencyCount: 94 Package: systemPipeShiny Version: 1.20.0 Depends: R (>= 4.0.0), shiny (>= 1.6.0), spsUtil (>= 0.2.2), spsComps (>= 0.3.3), drawer (>= 0.2) Imports: DT, assertthat, bsplus, crayon, dplyr, ggplot2, htmltools, glue, magrittr, methods, plotly, rlang, rstudioapi, shinyAce, shinyFiles, shinyWidgets, shinydashboard, shinydashboardPlus (>= 2.0.0), shinyjqui, shinyjs, shinytoastr, stringr, stats, styler, tibble, utils, vroom (>= 1.3.1), yaml, R6, RSQLite, openssl Suggests: testthat, BiocStyle, knitr, rmarkdown, systemPipeR (>= 2.12.0), systemPipeRdata (>= 2.10.0), rhandsontable, zip, callr, pushbar, fs, readr, R.utils, DESeq2, SummarizedExperiment, glmpca, pheatmap, grid, ape, Rtsne, UpSetR, tidyr, esquisse (>= 1.1.0), cicerone License: GPL (>= 3) MD5sum: 08b81a626f9cf4fadd11a30bb012ce6d NeedsCompilation: no Title: systemPipeShiny: An Interactive Framework for Workflow Management and Visualization Description: systemPipeShiny (SPS) extends the widely used systemPipeR (SPR) workflow environment with a versatile graphical user interface provided by a Shiny App. This allows non-R users, such as experimentalists, to run many systemPipeR’s workflow designs, control, and visualization functionalities interactively without requiring knowledge of R. Most importantly, SPS has been designed as a general purpose framework for interacting with other R packages in an intuitive manner. Like most Shiny Apps, SPS can be used on both local computers as well as centralized server-based deployments that can be accessed remotely as a public web service for using SPR’s functionalities with community and/or private data. The framework can integrate many core packages from the R/Bioconductor ecosystem. Examples of SPS’ current functionalities include: (a) interactive creation of experimental designs and metadata using an easy to use tabular editor or file uploader; (b) visualization of workflow topologies combined with auto-generation of R Markdown preview for interactively designed workflows; (d) access to a wide range of data processing routines; (e) and an extendable set of visualization functionalities. Complex visual results can be managed on a 'Canvas Workbench’ allowing users to organize and to compare plots in an efficient manner combined with a session snapshot feature to continue work at a later time. The present suite of pre-configured visualization examples. The modular design of SPR makes it easy to design custom functions without any knowledge of Shiny, as well as extending the environment in the future with contributions from the community. biocViews: ShinyApps, Infrastructure, DataImport, Sequencing, QualityControl, ReportWriting, ExperimentalDesign, Clustering Author: Le Zhang [aut, cre], Daniela Cassol [aut], Ponmathi Ramasamy [aut], Jianhai Zhang [aut], Gordon Mosher [aut], Thomas Girke [aut] Maintainer: Le Zhang URL: https://systempipe.org/sps, https://github.com/systemPipeR/systemPipeShiny VignetteBuilder: knitr BugReports: https://github.com/systemPipeR/systemPipeShiny/issues git_url: https://git.bioconductor.org/packages/systemPipeShiny git_branch: RELEASE_3_22 git_last_commit: b13844c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/systemPipeShiny_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/systemPipeShiny_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/systemPipeShiny_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/systemPipeShiny_1.20.0.tgz vignettes: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.html vignetteTitles: systemPipeShiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.R dependencyCount: 113 Package: systemPipeTools Version: 1.17.0 Imports: DESeq2, GGally, Rtsne, SummarizedExperiment, ape, dplyr, ggplot2, ggrepel, ggtree, glmpca, pheatmap, plotly, tibble, magrittr, DT, stats Suggests: systemPipeR, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), BiocGenerics, Biostrings, methods License: Artistic-2.0 MD5sum: 6df9f372a79383547d31cec7c8e37d04 NeedsCompilation: no Title: Tools for data visualization Description: systemPipeTools package extends the widely used systemPipeR (SPR) workflow environment with an enhanced toolkit for data visualization, including utilities to automate the data visualizaton for analysis of differentially expressed genes (DEGs). systemPipeTools provides data transformation and data exploration functions via scatterplots, hierarchical clustering heatMaps, principal component analysis, multidimensional scaling, generalized principal components, t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and volcano plots. All these utilities can be integrated with the modular design of the systemPipeR environment that allows users to easily substitute any of these features and/or custom with alternatives. biocViews: Infrastructure, DataImport, Sequencing, QualityControl, ReportWriting, ExperimentalDesign, Clustering, DifferentialExpression, MultidimensionalScaling, PrincipalComponent Author: Daniela Cassol [aut, cre], Ponmathi Ramasamy [aut], Le Zhang [aut], Thomas Girke [aut] Maintainer: Daniela Cassol VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeTools git_branch: devel git_last_commit: 83b911f git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/systemPipeTools_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/systemPipeTools_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/systemPipeTools_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/systemPipeTools_1.17.0.tgz vignettes: vignettes/systemPipeTools/inst/doc/systemPipeTools.html vignetteTitles: systemPipeTools: Data Visualizations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeTools/inst/doc/systemPipeTools.R dependencyCount: 130 Package: tadar Version: 1.8.0 Depends: GenomicRanges, ggplot2, R (>= 4.4.0) Imports: BiocGenerics, Seqinfo, Gviz, IRanges, lifecycle, MatrixGenerics, methods, rlang, Rsamtools, S4Vectors, stats, VariantAnnotation Suggests: BiocStyle, covr, knitr, limma, rmarkdown, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: da3ea954dbdf22e5adb34342fc4fff56 NeedsCompilation: no Title: Transcriptome Analysis of Differential Allelic Representation Description: This package provides functions to standardise the analysis of Differential Allelic Representation (DAR). DAR compromises the integrity of Differential Expression analysis results as it can bias expression, influencing the classification of genes (or transcripts) as being differentially expressed. DAR analysis results in an easy-to-interpret value between 0 and 1 for each genetic feature of interest, where 0 represents identical allelic representation and 1 represents complete diversity. This metric can be used to identify features prone to false-positive calls in Differential Expression analysis, and can be leveraged with statistical methods to alleviate the impact of such artefacts on RNA-seq data. biocViews: Sequencing, RNASeq, SNP, GenomicVariation, VariantAnnotation, DifferentialExpression Author: Lachlan Baer [aut, cre] (ORCID: ), Stevie Pederson [aut] (ORCID: ) Maintainer: Lachlan Baer URL: https://github.com/baerlachlan/tadar VignetteBuilder: knitr BugReports: https://github.com/baerlachlan/tadar/issues git_url: https://git.bioconductor.org/packages/tadar git_branch: RELEASE_3_22 git_last_commit: bdb4556 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tadar_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tadar_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tadar_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tadar_1.8.0.tgz vignettes: vignettes/tadar/inst/doc/dar.html vignetteTitles: DAR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tadar/inst/doc/dar.R dependencyCount: 152 Package: TADCompare Version: 1.20.0 Depends: R (>= 4.0) Imports: dplyr, PRIMME, cluster, Matrix, magrittr, HiCcompare, ggplot2, tidyr, ggpubr, RColorBrewer, reshape2, cowplot Suggests: BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr, pheatmap, SpectralTAD, magick, qpdf License: MIT + file LICENSE MD5sum: c52cfcc8a1f7746d89574cc96673c02a NeedsCompilation: no Title: TADCompare: Identification and characterization of differential TADs Description: TADCompare is an R package designed to identify and characterize differential Topologically Associated Domains (TADs) between multiple Hi-C contact matrices. It contains functions for finding differential TADs between two datasets, finding differential TADs over time and identifying consensus TADs across multiple matrices. It takes all of the main types of HiC input and returns simple, comprehensive, easy to analyze results. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Mikhail Dozmorov [aut, cre] (ORCID: ), Kellen Cresswell [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/TADCompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/TADCompare/issues git_url: https://git.bioconductor.org/packages/TADCompare git_branch: RELEASE_3_22 git_last_commit: da26eff git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TADCompare_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TADCompare_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TADCompare_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TADCompare_1.20.0.tgz vignettes: vignettes/TADCompare/inst/doc/Input_Data.html, vignettes/TADCompare/inst/doc/Ontology_Analysis.html, vignettes/TADCompare/inst/doc/TADCompare.html vignetteTitles: Input data formats, Gene Ontology Enrichment Analysis, TAD comparison between two conditions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TADCompare/inst/doc/Input_Data.R, vignettes/TADCompare/inst/doc/Ontology_Analysis.R, vignettes/TADCompare/inst/doc/TADCompare.R dependencyCount: 113 Package: tanggle Version: 1.15.3 Depends: R (>= 4.1), ggplot2 (>= 3.0.0), ggtree Imports: ape (>= 5.0), phangorn (>= 2.12), rlang, utils, methods, dplyr Suggests: tinytest, BiocStyle, ggimage, knitr, rmarkdown License: Artistic-2.0 MD5sum: 550051d850e706e17587ad3715be9064 NeedsCompilation: no Title: Visualization of Phylogenetic Networks Description: Offers functions for plotting split (or implicit) networks (unrooted, undirected) and explicit networks (rooted, directed) with reticulations extending. 'ggtree' and using functions from 'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017] to allow the visualization of phylogenetic networks using the 'ggplot2' syntax. It offers an alternative to the plot functions already available in 'ape' Paradis and Schliep (2019) and 'phangorn' Schliep (2011) . biocViews: Software, Visualization, Phylogenetics, Alignment, Clustering, MultipleSequenceAlignment, DataImport Author: Klaus Schliep [aut, cre] (ORCID: ), Marta Vidal-Garcia [aut], Claudia Solis-Lemus [aut] (ORCID: ), Leann Biancani [aut], Eren Ada [aut], L. Francisco Henao Diaz [aut], Guangchuang Yu [ctb], Joshua Justison [aut] Maintainer: Klaus Schliep URL: https://klausvigo.github.io/tanggle/, https://github.com/KlausVigo/tanggle VignetteBuilder: knitr BugReports: https://github.com/KlausVigo/tanggle/issues git_url: https://git.bioconductor.org/packages/tanggle git_branch: devel git_last_commit: 217e91a git_last_commit_date: 2025-10-22 Date/Publication: 2025-10-23 source.ver: src/contrib/tanggle_1.15.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/tanggle_1.15.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tanggle_1.15.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tanggle_1.15.3.tgz vignettes: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.html, vignettes/tanggle/inst/doc/tanggle_vignette.html vignetteTitles: ***tanggle***: Visualización de redes filogenéticas con *ggplot2*, ***tanggle***: Visualization of phylogenetic networks in a *ggplot2* framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.R, vignettes/tanggle/inst/doc/tanggle_vignette.R dependencyCount: 84 Package: TAPseq Version: 1.22.0 Depends: R (>= 4.0.0) Imports: methods, GenomicAlignments, GenomicRanges, IRanges, BiocGenerics, S4Vectors (>= 0.20.1), GenomeInfoDb, BSgenome, GenomicFeatures, Biostrings, dplyr, tidyr, BiocParallel Suggests: testthat, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, ggplot2, Seurat, glmnet, cowplot, Matrix, rtracklayer, BiocStyle License: MIT + file LICENSE MD5sum: 1f4f61f36dbd61484e75abfe48c9d245 NeedsCompilation: no Title: Targeted scRNA-seq primer design for TAP-seq Description: Design primers for targeted single-cell RNA-seq used by TAP-seq. Create sequence templates for target gene panels and design gene-specific primers using Primer3. Potential off-targets can be estimated with BLAST. Requires working installations of Primer3 and BLASTn. biocViews: SingleCell, Sequencing, Technology, CRISPR, PooledScreens Author: Andreas R. Gschwind [aut, cre] (ORCID: ), Lars Velten [aut] (ORCID: ), Lars M. Steinmetz [aut] Maintainer: Andreas R. Gschwind URL: https://github.com/argschwind/TAPseq SystemRequirements: Primer3 (>= 2.5.0), BLAST+ (>=2.6.0) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TAPseq git_branch: RELEASE_3_22 git_last_commit: d0aad39 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TAPseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TAPseq_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TAPseq_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TAPseq_1.22.0.tgz vignettes: vignettes/TAPseq/inst/doc/tapseq_primer_design.html, vignettes/TAPseq/inst/doc/tapseq_target_genes.html vignetteTitles: TAP-seq primer design workflow, Select target genes for TAP-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TAPseq/inst/doc/tapseq_primer_design.R, vignettes/TAPseq/inst/doc/tapseq_target_genes.R dependencyCount: 90 Package: target Version: 1.24.0 Depends: R (>= 3.6) Imports: BiocGenerics, GenomicRanges, IRanges, matrixStats, methods, stats, graphics, shiny Suggests: testthat (>= 2.1.0), knitr, rmarkdown, shinytest, shinyBS, covr License: GPL-3 MD5sum: 60c562f5c0f03842875b5c7f6f6aa9db NeedsCompilation: no Title: Predict Combined Function of Transcription Factors Description: Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) . Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data. biocViews: Software, StatisticalMethod, Transcription Author: Mahmoud Ahmed [aut, cre] Maintainer: Mahmoud Ahmed URL: https://github.com/MahShaaban/target VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/target/issues git_url: https://git.bioconductor.org/packages/target git_branch: RELEASE_3_22 git_last_commit: a11af87 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/target_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/target_1.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/target_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/target_1.24.0.tgz vignettes: vignettes/target/inst/doc/extend-target.html, vignettes/target/inst/doc/target.html vignetteTitles: Using target to predict combined binding, Using the target package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/target/inst/doc/extend-target.R, vignettes/target/inst/doc/target.R dependencyCount: 44 Package: TargetDecoy Version: 1.16.0 Depends: R (>= 4.1) Imports: ggplot2, ggpubr, methods, miniUI, mzID, mzR, shiny, stats Suggests: BiocStyle, knitr, msdata, sessioninfo, rmarkdown, gridExtra, testthat (>= 3.0.0), covr License: Artistic-2.0 MD5sum: 6394b49486fa80cca8d2dd49be7aa57d NeedsCompilation: no Title: Diagnostic Plots to Evaluate the Target Decoy Approach Description: A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum is then assigned to the theoretical peptide with the best score, which is also referred to as the peptide to spectrum match (PSM). It is of course crucial for the downstream analysis to evaluate the quality of these matches. Therefore False Discovery Rate (FDR) control is used to return a reliable list PSMs. The FDR, however, requires a good characterisation of the score distribution of PSMs that are matched to the wrong peptide (bad target hits). In proteomics, the target decoy approach (TDA) is typically used for this purpose. The TDA method matches the spectra to a database of real (targets) and nonsense peptides (decoys). A popular approach to generate these decoys is to reverse the target database. Hence, all the PSMs that match to a decoy are known to be bad hits and the distribution of their scores are used to estimate the distribution of the bad scoring target PSMs. A crucial assumption of the TDA is that the decoy PSM hits have similar properties as bad target hits so that the decoy PSM scores are a good simulation of the target PSM scores. Users, however, typically do not evaluate these assumptions. To this end we developed TargetDecoy to generate diagnostic plots to evaluate the quality of the target decoy method. biocViews: MassSpectrometry, Proteomics, QualityControl, Software, Visualization Author: Elke Debrie [aut, cre], Lieven Clement [aut] (ORCID: ), Milan Malfait [aut] (ORCID: ) Maintainer: Elke Debrie URL: https://www.bioconductor.org/packages/TargetDecoy, https://statomics.github.io/TargetDecoy/, https://github.com/statOmics/TargetDecoy/ VignetteBuilder: knitr BugReports: https://github.com/statOmics/TargetDecoy/issues git_url: https://git.bioconductor.org/packages/TargetDecoy git_branch: RELEASE_3_22 git_last_commit: 612ab7d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TargetDecoy_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TargetDecoy_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TargetDecoy_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TargetDecoy_1.16.0.tgz vignettes: vignettes/TargetDecoy/inst/doc/TargetDecoy.html vignetteTitles: Introduction to TargetDecoy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetDecoy/inst/doc/TargetDecoy.R dependencyCount: 114 Package: TargetScore Version: 1.48.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 4a9ead8a97577086a52660386e3b6262 NeedsCompilation: no Title: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information Description: Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html git_url: https://git.bioconductor.org/packages/TargetScore git_branch: RELEASE_3_22 git_last_commit: 9d2ed75 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TargetScore_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TargetScore_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TargetScore_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TargetScore_1.48.0.tgz vignettes: vignettes/TargetScore/inst/doc/TargetScore.pdf vignetteTitles: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetScore/inst/doc/TargetScore.R suggestsMe: TargetScoreData dependencyCount: 9 Package: TargetSearch Version: 2.12.0 Imports: graphics, grDevices, methods, ncdf4, stats, utils, assertthat Suggests: TargetSearchData, BiocStyle, knitr, tinytest License: GPL (>= 2) MD5sum: 78737af4f5a2a2cc3d1681b7d6b24bee NeedsCompilation: yes Title: A package for the analysis of GC-MS metabolite profiling data Description: This packages provides a flexible, fast and accurate method for targeted pre-processing of GC-MS data. The user provides a (often very large) set of GC chromatograms and a metabolite library of targets. The package will automatically search those targets in the chromatograms resulting in a data matrix that can be used for further data analysis. biocViews: MassSpectrometry, Preprocessing, DecisionTree, ImmunoOncology Author: Alvaro Cuadros-Inostroza [aut, cre], Jan Lisec [aut], Henning Redestig [aut], Matt Hannah [aut] Maintainer: Alvaro Cuadros-Inostroza URL: https://github.com/acinostroza/TargetSearch VignetteBuilder: knitr BugReports: https://github.com/acinostroza/TargetSearch/issues git_url: https://git.bioconductor.org/packages/TargetSearch git_branch: RELEASE_3_22 git_last_commit: ab9658a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TargetSearch_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TargetSearch_2.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TargetSearch_2.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TargetSearch_2.12.0.tgz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction extra, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RetentionIndexCorrection.R, vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R dependencyCount: 8 Package: TaxSEA Version: 1.2.0 Depends: R (>= 4.5.0) Imports: stats, utils Suggests: BiocStyle, bugsigdbr, fgsea, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 55144a99c1d3135064ed21646f85b9e3 NeedsCompilation: no Title: Taxon Set Enrichment Analysis Description: TaxSEA is an R package for Taxon Set Enrichment Analysis, which utilises a Kolmogorov-Smirnov test analyses to investigate differential abundance analysis output for whether there are alternations in a-priori defined sets of taxa from public databases (BugSigDB, MiMeDB, GutMGene, mBodyMap, BacDive and GMRepoV2) and collated from the literature. TaxSEA takes as input a list of taxonomic identifiers (e.g. species names, NCBI IDs etc.) and a rank (E.g. fold change, correlation coefficient). TaxSEA be applied to any microbiota taxonomic profiling technology (array-based, 16S rRNA gene sequencing, shotgun metagenomics & metatranscriptomics etc.) and enables researchers to rapidly contextualize their findings within the broader literature to accelerate interpretation of results. biocViews: Microbiome, Metagenomics, Sequencing, GeneSetEnrichment, RNASeq Author: Feargal Ryan [aut, cre] (ORCID: ) Maintainer: Feargal Ryan URL: https://github.com/feargalr/taxsea VignetteBuilder: knitr BugReports: https://github.com/feargalr/taxsea/issues git_url: https://git.bioconductor.org/packages/TaxSEA git_branch: RELEASE_3_22 git_last_commit: e59c6e8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TaxSEA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TaxSEA_1.1.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TaxSEA_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TaxSEA_1.2.0.tgz vignettes: vignettes/TaxSEA/inst/doc/TaxSEA.html vignetteTitles: TaxSEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TaxSEA/inst/doc/TaxSEA.R dependencyCount: 2 Package: TBSignatureProfiler Version: 1.22.0 Depends: R (>= 4.4.0) Imports: ASSIGN (>= 1.23.1), BiocParallel, ComplexHeatmap, DESeq2, DT, edgeR, gdata, ggplot2, glmnet, GSVA (>= 1.51.3), HGNChelper, magrittr, methods, pROC, RColorBrewer, reshape2, ROCit, S4Vectors, singscore, stats, SummarizedExperiment, tibble Suggests: BiocStyle, caret, circlize, class, covr, dplyr, e1071, impute, knitr, lintr, MASS, plyr, randomForest, rmarkdown, shiny, spelling, sva, testthat License: MIT + file LICENSE MD5sum: 97bc1d8d297ab078181bfa486f6f1ed2 NeedsCompilation: no Title: Profile RNA-Seq Data Using TB Pathway Signatures Description: Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates known signatures and provides computational tools to enlist their usage on other datasets. The TBSignatureProfiler makes it easy to profile RNA-Seq data using these signatures and includes common signature profiling tools including ASSIGN, GSVA, and ssGSEA. Original models for some gene signatures are also available. A shiny app provides some functionality alongside for detailed command line accessibility. biocViews: GeneExpression, DifferentialExpression Author: Kiloni Quiles [cre] (ORCID: ), Aubrey R. Odom [aut, dtm] (ORCID: ), David Jenkins [aut, org] (ORCID: ), Xutao Wang [aut], Yue Zhao [ctb] (ORCID: ), Christian Love [ctb], W. Evan Johnson [aut] Maintainer: Kiloni Quiles URL: https://github.com/wejlab/TBSignatureProfiler, https://wejlab.github.io/TBSignatureProfiler-docs/ VignetteBuilder: knitr BugReports: https://github.com/wejlab/TBSignatureProfiler/issues git_url: https://git.bioconductor.org/packages/TBSignatureProfiler git_branch: RELEASE_3_22 git_last_commit: 376376a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TBSignatureProfiler_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TBSignatureProfiler_1.21.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TBSignatureProfiler_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TBSignatureProfiler_1.22.0.tgz vignettes: vignettes/TBSignatureProfiler/inst/doc/tbspVignetteT.html vignetteTitles: "Introduction to the TBSignatureProfiler" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TBSignatureProfiler/inst/doc/tbspVignetteT.R suggestsMe: LegATo dependencyCount: 181 Package: TCC Version: 1.50.0 Depends: R (>= 3.0), methods, DESeq2, edgeR, ROC Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: fcb6a810fd7067fa13aa930a01a9992b NeedsCompilation: no Title: TCC: Differential expression analysis for tag count data with robust normalization strategies Description: This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages. biocViews: ImmunoOncology, Sequencing, DifferentialExpression, RNASeq Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer: Jianqiang Sun , Tomoaki Nishiyama git_url: https://git.bioconductor.org/packages/TCC git_branch: RELEASE_3_22 git_last_commit: 0697a1a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TCC_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TCC_1.49.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TCC_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TCC_1.50.0.tgz vignettes: vignettes/TCC/inst/doc/TCC.pdf vignetteTitles: TCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCC/inst/doc/TCC.R suggestsMe: compcodeR dependencyCount: 64 Package: TCGAbiolinks Version: 2.37.3 Depends: R (>= 4.1.0) Imports: downloader (>= 0.4), grDevices, biomaRt, dplyr, graphics, tibble, GenomicRanges, XML (>= 3.98.0), data.table, jsonlite (>= 1.0.0), plyr, knitr, methods, ggplot2, stringr (>= 1.0.0), IRanges, rvest (>= 0.3.0), stats, utils, S4Vectors, R.utils, SummarizedExperiment (>= 1.4.0), TCGAbiolinksGUI.data (>= 1.15.1), readr, tools, tidyr, purrr, xml2, httr (>= 1.2.1) Suggests: jpeg, png, BiocStyle, rmarkdown, devtools, maftools, parmigene, c3net, minet, Biobase, affy, testthat, sesame, AnnotationHub, ExperimentHub, pathview, clusterProfiler, Seurat, ComplexHeatmap, circlize, ConsensusClusterPlus, igraph, limma, edgeR, sva, EDASeq, survminer, genefilter, gridExtra, survival, doParallel, parallel, ggrepel (>= 0.6.3), scales, grid, DT License: GPL (>= 3) MD5sum: d1e9cec8a3eb4d663274a49b6d9975ec NeedsCompilation: no Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data Description: The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Sequencing, Survival, Software Author: Antonio Colaprico, Tiago Chedraoui Silva, Catharina Olsen, Luciano Garofano, Davide Garolini, Claudia Cava, Thais Sabedot, Tathiane Malta, Stefano M. Pagnotta, Isabella Castiglioni, Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr Maintainer: Tiago Chedraoui Silva , Antonio Colaprico URL: https://github.com/BioinformaticsFMRP/TCGAbiolinks VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues git_url: https://git.bioconductor.org/packages/TCGAbiolinks git_branch: devel git_last_commit: 3eab81f git_last_commit_date: 2025-10-07 Date/Publication: 2025-10-08 source.ver: src/contrib/TCGAbiolinks_2.37.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/TCGAbiolinks_2.37.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TCGAbiolinks_2.37.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TCGAbiolinks_2.37.3.tgz vignettes: vignettes/TCGAbiolinks/inst/doc/analysis.html, vignettes/TCGAbiolinks/inst/doc/casestudy.html, vignettes/TCGAbiolinks/inst/doc/classifiers.html, vignettes/TCGAbiolinks/inst/doc/clinical.html, vignettes/TCGAbiolinks/inst/doc/download_prepare.html, vignettes/TCGAbiolinks/inst/doc/extension.html, vignettes/TCGAbiolinks/inst/doc/index.html, vignettes/TCGAbiolinks/inst/doc/mutation.html, vignettes/TCGAbiolinks/inst/doc/query.html, vignettes/TCGAbiolinks/inst/doc/stemness_score.html, vignettes/TCGAbiolinks/inst/doc/subtypes.html vignetteTitles: 7. Analyzing and visualizing TCGA data, 8. Case Studies, 10. Classifiers, "4. Clinical data", "3. Downloading and preparing files for analysis", "10. TCGAbiolinks_Extension", "1. Introduction", "5. Mutation data", "2. Searching GDC database", 11. Stemness score, 6. Compilation of TCGA molecular subtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinks/inst/doc/analysis.R, vignettes/TCGAbiolinks/inst/doc/casestudy.R, vignettes/TCGAbiolinks/inst/doc/classifiers.R, vignettes/TCGAbiolinks/inst/doc/clinical.R, vignettes/TCGAbiolinks/inst/doc/download_prepare.R, vignettes/TCGAbiolinks/inst/doc/extension.R, vignettes/TCGAbiolinks/inst/doc/index.R, vignettes/TCGAbiolinks/inst/doc/mutation.R, vignettes/TCGAbiolinks/inst/doc/query.R, vignettes/TCGAbiolinks/inst/doc/stemness_score.R, vignettes/TCGAbiolinks/inst/doc/subtypes.R importsMe: CBN2Path, ELMER, MoonlightR, SurfR, TENET, CureAuxSP suggestsMe: GeoTcgaData, musicatk dependencyCount: 105 Package: TCGAutils Version: 1.29.5 Depends: R (>= 4.5.0) Imports: AnnotationDbi, BiocGenerics, BiocBaseUtils, GenomeInfoDb, GenomicFeatures, GenomicRanges, GenomicDataCommons, IRanges, methods, MultiAssayExperiment, RaggedExperiment, rvest, S4Vectors, Seqinfo, stats, stringr, SummarizedExperiment, utils, xml2 Suggests: AnnotationHub, BiocStyle, curatedTCGAData, ComplexHeatmap, devtools, dplyr, httr, IlluminaHumanMethylation450kanno.ilmn12.hg19, impute, knitr, magrittr, org.Hs.eg.db, RColorBrewer, readr, rmarkdown, RTCGAToolbox, rtracklayer, R.utils, testthat, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 0ae92fd9ed392c48313d01767e128508 NeedsCompilation: no Title: TCGA utility functions for data management Description: A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. Exported functions include those that import data from flat files into Bioconductor objects, convert row annotations, and identifier translation via the GDC API. biocViews: Software, WorkflowStep, Preprocessing, DataImport Author: Marcel Ramos [aut, cre] (ORCID: ), Lucas Schiffer [aut], Sean Davis [ctb], Levi Waldron [aut] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TCGAutils/issues git_url: https://git.bioconductor.org/packages/TCGAutils git_branch: devel git_last_commit: bafe4ce git_last_commit_date: 2025-07-02 Date/Publication: 2025-10-07 source.ver: src/contrib/TCGAutils_1.29.5.tar.gz win.binary.ver: bin/windows/contrib/4.5/TCGAutils_1.29.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TCGAutils_1.29.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TCGAutils_1.29.5.tgz vignettes: vignettes/TCGAutils/inst/doc/TCGAutils.html vignetteTitles: TCGAutils Essentials hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAutils/inst/doc/TCGAutils.R importsMe: cBioPortalData, glmSparseNet, RTCGAToolbox, terraTCGAdata suggestsMe: CNVRanger, curatedTCGAData dependencyCount: 104 Package: TCseq Version: 1.34.0 Depends: R (>= 3.4) Imports: edgeR, BiocGenerics, reshape2, GenomicRanges, IRanges, SummarizedExperiment, GenomicAlignments, Rsamtools, e1071, cluster, ggplot2, grid, grDevices, stats, utils, methods, locfit Suggests: testthat License: GPL (>= 2) Archs: x64 MD5sum: 11da5adde96cbbc30b4cd68b57999ea1 NeedsCompilation: no Title: Time course sequencing data analysis Description: Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of the temporal patterns of time course data. biocViews: Epigenetics, TimeCourse, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, Clustering, Visualization Author: Mengjun Wu , Lei Gu Maintainer: Mengjun Wu git_url: https://git.bioconductor.org/packages/TCseq git_branch: RELEASE_3_22 git_last_commit: 406875c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TCseq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TCseq_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TCseq_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TCseq_1.34.0.tgz vignettes: vignettes/TCseq/inst/doc/TCseq.pdf vignetteTitles: TCseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCseq/inst/doc/TCseq.R suggestsMe: ClusterGVis dependencyCount: 73 Package: TDbasedUFE Version: 1.10.0 Imports: GenomicRanges, rTensor, readr, methods, MOFAdata, tximport, tximportData, graphics, stats, utils, shiny Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 0a5fd60cf378ef9fba2bc4b67e8f01b6 NeedsCompilation: no Title: Tensor Decomposition Based Unsupervised Feature Extraction Description: This is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. It can perform unsupervised feature extraction. It uses tensor decomposition. It is applicable to gene expression, DNA methylation, and histone modification etc. It can perform multiomics analysis. It is also potentially applicable to single cell omics data sets. biocViews: GeneExpression, FeatureExtraction, MethylationArray, SingleCell Author: Y-h. Taguchi [aut, cre] (ORCID: ) Maintainer: Y-h. Taguchi URL: https://github.com/tagtag/TDbasedUFE VignetteBuilder: knitr BugReports: https://github.com/tagtag/TDbasedUFE/issues git_url: https://git.bioconductor.org/packages/TDbasedUFE git_branch: RELEASE_3_22 git_last_commit: e69824b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TDbasedUFE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TDbasedUFE_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TDbasedUFE_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TDbasedUFE_1.10.0.tgz vignettes: vignettes/TDbasedUFE/inst/doc/QuickStart.html, vignettes/TDbasedUFE/inst/doc/TDbasedUFE.html vignetteTitles: QuickStart, TDbasedUFE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDbasedUFE/inst/doc/QuickStart.R, vignettes/TDbasedUFE/inst/doc/TDbasedUFE.R importsMe: TDbasedUFEadv dependencyCount: 64 Package: TDbasedUFEadv Version: 1.10.0 Imports: TDbasedUFE, Biobase, GenomicRanges, utils, rTensor, methods, graphics, RTCGA, stats, enrichplot, DOSE, STRINGdb, enrichR, hash, shiny Suggests: knitr, rmarkdown, testthat (>= 3.0.0), RTCGA.rnaseq, RTCGA.clinical, BiocStyle, MOFAdata License: GPL-3 MD5sum: e3fe3512766690fcc6a6b15d1f12a5a0 NeedsCompilation: no Title: Advanced package of tensor decomposition based unsupervised feature extraction Description: This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality. biocViews: GeneExpression, FeatureExtraction, MethylationArray, SingleCell, Software Author: Y-h. Taguchi [aut, cre] (ORCID: ) Maintainer: Y-h. Taguchi URL: https://github.com/tagtag/TDbasedUFEadv VignetteBuilder: knitr BugReports: https://github.com/tagtag/TDbasedUFEadv/issues git_url: https://git.bioconductor.org/packages/TDbasedUFEadv git_branch: RELEASE_3_22 git_last_commit: 3045ab4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TDbasedUFEadv_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TDbasedUFEadv_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TDbasedUFEadv_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TDbasedUFEadv_1.10.0.tgz vignettes: vignettes/TDbasedUFEadv/inst/doc/Enrichment.html, vignettes/TDbasedUFEadv/inst/doc/Explanation_of_TDbasedUFEadv.html, vignettes/TDbasedUFEadv/inst/doc/How_to_use_TDbasedUFEadv.html vignetteTitles: Enrichment, Explanation of TDbasedUFEadv, How to use TDbasedUFEadv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDbasedUFEadv/inst/doc/Enrichment.R, vignettes/TDbasedUFEadv/inst/doc/Explanation_of_TDbasedUFEadv.R, vignettes/TDbasedUFEadv/inst/doc/How_to_use_TDbasedUFEadv.R dependencyCount: 226 Package: TEKRABber Version: 1.14.0 Depends: R (>= 4.3) Imports: apeglm, biomaRt, dplyr, doParallel, DESeq2, foreach, Seqinfo, magrittr, Rcpp (>= 1.0.7), rtracklayer, SCBN, stats, utils LinkingTo: Rcpp Suggests: BiocStyle, GenomeInfoDb, bslib, ggplot2, ggpubr, plotly, rmarkdown, shiny, knitr, testthat (>= 3.0.0) License: LGPL (>=3) MD5sum: 70744ee52ed659516786710a414c4e24 NeedsCompilation: yes Title: An R package estimates the correlations of orthologs and transposable elements between two species Description: TEKRABber is made to provide a user-friendly pipeline for comparing orthologs and transposable elements (TEs) between two species. It considers the orthology confidence between two species from BioMart to normalize expression counts and detect differentially expressed orthologs/TEs. Then it provides one to one correlation analysis for desired orthologs and TEs. There is also an app function to have a first insight on the result. Users can prepare orthologs/TEs RNA-seq expression data by their own preference to run TEKRABber following the data structure mentioned in the vignettes. biocViews: DifferentialExpression, Normalization, Transcription, GeneExpression Author: Yao-Chung Chen [aut, cre] (ORCID: ), Katja Nowick [aut] (ORCID: ) Maintainer: Yao-Chung Chen URL: https://github.com/ferygood/TEKRABber VignetteBuilder: knitr BugReports: https://github.com/ferygood/TEKRABber/issues git_url: https://git.bioconductor.org/packages/TEKRABber git_branch: RELEASE_3_22 git_last_commit: 3191b35 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TEKRABber_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TEKRABber_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TEKRABber_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TEKRABber_1.14.0.tgz vignettes: vignettes/TEKRABber/inst/doc/TEKRABber.html vignetteTitles: TEKRABber hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEKRABber/inst/doc/TEKRABber.R dependencyCount: 124 Package: TENET Version: 1.2.0 Depends: R (>= 4.5) Imports: graphics, grDevices, stats, utils, tools, S4Vectors, GenomicRanges, IRanges, parallel, pastecs, ggplot2 (>= 4.0), RCircos, survival, BSgenome.Hsapiens.UCSC.hg38, seqLogo, Biostrings, matlab, TCGAbiolinks, methods, R.utils, MultiAssayExperiment, SummarizedExperiment, sesame, sesameData, AnnotationHub, ExperimentHub, TENET.ExperimentHub, rtracklayer, MotifDb, BAMMtools, survminer Suggests: TENET.AnnotationHub, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 115f720e964f4f4ee861637009138bb4 NeedsCompilation: no Title: R package for TENET (Tracing regulatory Element Networks using Epigenetic Traits) to identify key transcription factors Description: TENET identifies key transcription factors (TFs) and regulatory elements (REs) linked to a specific cell type by finding significantly correlated differences in gene expression and RE DNA methylation between case and control input datasets, and identifying the top genes by number of significant RE DNA methylation site links. It also includes many tools for visualization and analysis of the results, including plots displaying and comparing methylation and expression data and methylation site link counts, survival analysis, TF motif searching in the vicinity of linked RE DNA methylation sites, custom TAD and peak overlap analysis, and UCSC Genome Browser track file generation. A utility function is also provided to download methylation, expression, and patient survival data from The Cancer Genome Atlas (TCGA) for use in TENET or other analyses. biocViews: Software, BiomedicalInformatics, CellBiology, Genetics, Epigenetics, MultipleComparison, GeneExpression, DifferentialExpression, DNAMethylation, DifferentialMethylation, MethylationArray, Sequencing, MethylSeq, RNASeq, FunctionalGenomics, GeneRegulation, GeneTarget, HistoneModification, Transcription, Transcriptomics, Survival, Visualization Author: Rhie Lab at the University of Southern California [cre], Daniel Mullen [aut] (ORCID: ), Zexun Wu [aut] (ORCID: ), Ethan Nelson-Moore [aut] (ORCID: ), Suhn Rhie [aut] (ORCID: ) Maintainer: Rhie Lab at the University of Southern California URL: https://github.com/rhielab/TENET VignetteBuilder: knitr BugReports: https://github.com/rhielab/TENET/issues git_url: https://git.bioconductor.org/packages/TENET git_branch: RELEASE_3_22 git_last_commit: b0a4fcf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TENET_1.2.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TENET_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TENET_1.2.0.tgz vignettes: vignettes/TENET/inst/doc/TENET_vignette.html vignetteTitles: Using TENET hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TENET/inst/doc/TENET_vignette.R suggestsMe: TENET.AnnotationHub, TENET.ExperimentHub dependencyCount: 206 Package: TENxIO Version: 1.12.0 Depends: R (>= 4.5.0), SingleCellExperiment, SummarizedExperiment Imports: BiocBaseUtils, BiocGenerics, BiocIO, Seqinfo, GenomicRanges, HDF5Array, Matrix, MatrixGenerics, methods, RCurl, readr, rhdf5, R.utils, S4Vectors, utils Suggests: BiocStyle, DropletTestFiles, ExperimentHub, knitr, RaggedExperiment (>= 1.33.3), rmarkdown, Rsamtools, tinytest License: Artistic-2.0 MD5sum: 2028666dbdcb4c0c4d2ef34258b56477 NeedsCompilation: no Title: Import methods for 10X Genomics files Description: Provides a structured S4 approach to importing data files from the 10X pipelines. It mainly supports Single Cell Multiome ATAC + Gene Expression data among other data types. The main Bioconductor data representations used are SingleCellExperiment and RaggedExperiment. biocViews: Software, Infrastructure, DataImport, SingleCell Author: Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/waldronlab/TENxIO VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TENxIO/issues git_url: https://git.bioconductor.org/packages/TENxIO git_branch: RELEASE_3_22 git_last_commit: 4adee52 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TENxIO_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TENxIO_1.11.5.zip vignettes: vignettes/TENxIO/inst/doc/TENxIO.html vignetteTitles: TENxIO Quick Start Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TENxIO/inst/doc/TENxIO.R dependsOnMe: VisiumIO, XeniumIO importsMe: xenLite dependencyCount: 62 Package: tenXplore Version: 1.32.0 Depends: R (>= 4.0), shiny Imports: methods, ontoProc (>= 0.99.7), SummarizedExperiment, AnnotationDbi, matrixStats, org.Mm.eg.db, stats, utils, BiocFileCache Suggests: org.Hs.eg.db, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 7c1d77358d110c96feb5efed0c7ad1f9 NeedsCompilation: no Title: ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics Description: Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics. biocViews: ImmunoOncology, DimensionReduction, PrincipalComponent, Transcriptomics, SingleCell Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tenXplore git_branch: RELEASE_3_22 git_last_commit: e8cff89 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tenXplore_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tenXplore_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tenXplore_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tenXplore_1.32.0.tgz vignettes: vignettes/tenXplore/inst/doc/tenXplore.html vignetteTitles: tenXplore: ontology for scRNA-seq,, applied to 10x 1.3 million neurons hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tenXplore/inst/doc/tenXplore.R dependencyCount: 125 Package: TEQC Version: 4.32.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: S4Vectors, Seqinfo, GenomicRanges, Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: ac404d3eba653ada22de7b3a247e2363 NeedsCompilation: no Title: Quality control for target capture experiments Description: Target capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on. biocViews: QualityControl, Microarray, Sequencing, Genetics Author: M. Hummel, S. Bonnin, E. Lowy, G. Roma Maintainer: Sarah Bonnin git_url: https://git.bioconductor.org/packages/TEQC git_branch: RELEASE_3_22 git_last_commit: 536787c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TEQC_4.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TEQC_4.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TEQC_4.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TEQC_4.32.0.tgz vignettes: vignettes/TEQC/inst/doc/TEQC.pdf vignetteTitles: TEQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEQC/inst/doc/TEQC.R dependencyCount: 31 Package: terapadog Version: 1.2.0 Imports: DESeq2, KEGGREST, stats, utils, dplyr, plotly, htmlwidgets, biomaRt, methods Suggests: apeglm, BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 1a3d9e53ef2ac3dc29c66f24efedf536 NeedsCompilation: no Title: Translational Efficiency Regulation Analysis using the PADOG Method Description: This package performs a Gene Set Analysis with the approach adopted by PADOG on the genes that are reported as translationally regulated (ie. exhibit a significant change in TE) by the DeltaTE package. It can be used on its own to see the impact of translation regulation on gene sets, but it is also integrated as an additional analysis method within ReactomeGSA, where results are further contextualised in terms of pathways and directionality of the change. biocViews: RiboSeq, Transcriptomics, GeneSetEnrichment, GeneRegulation, Reactome, Software Author: Gionmattia Carancini [cre, aut] (ORCID: ) Maintainer: Gionmattia Carancini URL: https://github.com/Gionmattia/terapadog VignetteBuilder: knitr BugReports: https://github.com/Gionmattia/terapadog/issues git_url: https://git.bioconductor.org/packages/terapadog git_branch: RELEASE_3_22 git_last_commit: c641090 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/terapadog_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/terapadog_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/terapadog_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/terapadog_1.2.0.tgz vignettes: vignettes/terapadog/inst/doc/terapadog_vignette.html vignetteTitles: terapadog: Translational Efficiency Regulation Analysis & Pathway Analysis with Down-weighting of Overlapping Genes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/terapadog/inst/doc/terapadog_vignette.R dependencyCount: 120 Package: ternarynet Version: 1.54.0 Depends: R (>= 4.0) Imports: utils, igraph, methods, graphics, stats, BiocParallel Suggests: testthat Enhances: Rmpi, snow License: GPL (>= 2) MD5sum: 6771731fe4217f001a7f3dd33810df72 NeedsCompilation: yes Title: Ternary Network Estimation Description: Gene-regulatory network (GRN) modeling seeks to infer dependencies between genes and thereby provide insight into the regulatory relationships that exist within a cell. This package provides a computational Bayesian approach to GRN estimation from perturbation experiments using a ternary network model, in which gene expression is discretized into one of 3 states: up, unchanged, or down). The ternarynet package includes a parallel implementation of the replica exchange Monte Carlo algorithm for fitting network models, using MPI. biocViews: Software, CellBiology, GraphAndNetwork, Network, Bayesian Author: Matthew N. McCall , Anthony Almudevar , David Burton , Harry Stern Maintainer: McCall N. Matthew git_url: https://git.bioconductor.org/packages/ternarynet git_branch: RELEASE_3_22 git_last_commit: 091c254 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ternarynet_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ternarynet_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ternarynet_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ternarynet_1.54.0.tgz vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf vignetteTitles: ternarynet: A Computational Bayesian Approach to Ternary Network Estimation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R dependencyCount: 26 Package: terraTCGAdata Version: 1.14.0 Depends: AnVILGCP, MultiAssayExperiment Imports: AnVIL, BiocFileCache, dplyr, GenomicRanges, methods, RaggedExperiment, readr, S4Vectors, stats, tidyr, TCGAutils, utils Suggests: AnVILBase, knitr, rmarkdown, BiocStyle, withr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 9786eccb40fcd3a369932b4eecd056d4 NeedsCompilation: no Title: OpenAccess TCGA Data on Terra as MultiAssayExperiment Description: Leverage the existing open access TCGA data on Terra with well-established Bioconductor infrastructure. Make use of the Terra data model without learning its complexities. With a few functions, you can copy / download and generate a MultiAssayExperiment from the TCGA example workspaces provided by Terra. biocViews: Software, Infrastructure, DataImport Author: Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/waldronlab/terraTCGAdata VignetteBuilder: knitr BugReports: https://github.com/waldronlab/terraTCGAdata/issues git_url: https://git.bioconductor.org/packages/terraTCGAdata git_branch: RELEASE_3_22 git_last_commit: eba5301 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/terraTCGAdata_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/terraTCGAdata_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/terraTCGAdata_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/terraTCGAdata_1.14.0.tgz vignettes: vignettes/terraTCGAdata/inst/doc/terraTCGAdata.html vignetteTitles: Obtain Terra TCGA data as MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/terraTCGAdata/inst/doc/terraTCGAdata.R dependencyCount: 142 Package: TFARM Version: 1.32.0 Depends: R (>= 3.5.0) Imports: arules, fields, GenomicRanges, graphics, stringr, methods, stats, gplots Suggests: BiocStyle, knitr, plyr License: Artistic-2.0 MD5sum: 4b84c8dbdc992ed7776c1024c2e6816a NeedsCompilation: no Title: Transcription Factors Association Rules Miner Description: It searches for relevant associations of transcription factors with a transcription factor target, in specific genomic regions. It also allows to evaluate the Importance Index distribution of transcription factors (and combinations of transcription factors) in association rules. biocViews: BiologicalQuestion, Infrastructure, StatisticalMethod, Transcription Author: Liuba Nausicaa Martino, Alice Parodi, Gaia Ceddia, Piercesare Secchi, Stefano Campaner, Marco Masseroli Maintainer: Liuba Nausicaa Martino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFARM git_branch: RELEASE_3_22 git_last_commit: a23b7f5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TFARM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TFARM_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TFARM_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TFARM_1.32.0.tgz vignettes: vignettes/TFARM/inst/doc/TFARM.pdf vignetteTitles: Transcription Factor Association Rule Miner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFARM/inst/doc/TFARM.R dependencyCount: 37 Package: TFBSTools Version: 1.48.0 Depends: R (>= 3.2.2) Imports: Biobase(>= 2.28), Biostrings(>= 2.36.4), pwalign, BiocGenerics(>= 0.14.0), BiocParallel(>= 1.2.21), BSgenome(>= 1.36.3), caTools(>= 1.17.1), DirichletMultinomial(>= 1.10.0), Seqinfo, GenomicRanges(>= 1.20.6), gtools(>= 3.5.0), grid, IRanges(>= 2.2.7), methods, DBI (>= 0.6), RSQLite(>= 1.0.0), rtracklayer(>= 1.28.10), seqLogo(>= 1.34.0), S4Vectors(>= 0.9.25), TFMPvalue(>= 0.0.5), XML(>= 3.98-1.3), XVector(>= 0.8.0), parallel Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11), testthat, JASPAR2016(>= 1.0.0), JASPAR2018(>= 1.0.0), rmarkdown License: GPL-2 MD5sum: 72bfbf0b475f92b0d3a22a62062979e1 NeedsCompilation: yes Title: Software Package for Transcription Factor Binding Site (TFBS) Analysis Description: TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software. biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery, Transcription, Alignment Author: Ge Tan [aut, cre] Maintainer: Ge Tan URL: https://github.com/ge11232002/TFBSTools VignetteBuilder: knitr BugReports: https://github.com/ge11232002/TFBSTools/issues git_url: https://git.bioconductor.org/packages/TFBSTools git_branch: RELEASE_3_22 git_last_commit: 77e9a0e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TFBSTools_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TFBSTools_1.47.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TFBSTools_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TFBSTools_1.48.0.tgz vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.html vignetteTitles: Transcription factor binding site (TFBS) analysis with the "TFBSTools" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R importsMe: ATACseqTFEA, chromVAR, esATAC, MatrixRider, MethReg, monaLisa, motifmatchr, motifStack, primirTSS suggestsMe: enhancerHomologSearch, GRaNIE, MAGAR, pageRank, universalmotif, JASPAR2018, JASPAR2020, JASPAR2022, CAGEWorkflow, Signac dependencyCount: 80 Package: TFEA.ChIP Version: 1.30.0 Depends: R (>= 4.2.0) Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, GenomicRanges, grDevices, dplyr, stats, utils, R.utils, methods, org.Hs.eg.db, org.Mm.eg.db, rlang, ExperimentHub Suggests: knitr, rmarkdown, BiocStyle, S4Vectors, Seqinfo, meta, plotly, scales, tidyr, purrr, tibble, ggplot2, DESeq2, edgeR, limma, babelgene, BiocGenerics, ggrepel, rcompanion, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi, RColorBrewer, RUnit, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 05872e28e67d7a6f2f6fab965037d1d9 NeedsCompilation: no Title: TFEA.ChIP, a Tool Kit for Transcription Factor Enrichment Description: Package to analyze transcription factor enrichment in a gene set using data from ChIP-Seq experiments. biocViews: Transcription, GeneRegulation, GeneSetEnrichment, Transcriptomics, Sequencing, ChIPSeq, RNASeq, ImmunoOncology, GeneExpression, ChipOnChip Author: Yosra Berrouayel [aut, cre] (ORCID: ), Laura Puente-Santamaria [aut], Luis del Peso [aut] (ORCID: ) Maintainer: Yosra Berrouayel URL: https://github.com/yberda/TFEA.ChIP VignetteBuilder: knitr, BiocStyle BugReports: https://github.com/yberda/TFEA.ChIP/issues git_url: https://git.bioconductor.org/packages/TFEA.ChIP git_branch: RELEASE_3_22 git_last_commit: 228e5ca git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TFEA.ChIP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TFEA.ChIP_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TFEA.ChIP_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TFEA.ChIP_1.30.0.tgz vignettes: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.html vignetteTitles: TFEA.ChIP: a tool kit for transcription factor enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.R suggestsMe: ChIPDBData dependencyCount: 107 Package: TFHAZ Version: 1.32.0 Depends: R (>= 3.5.0) Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils, IRanges, methods, ORFik Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 5ade4736ba5a87a95e7ae547da21dfff NeedsCompilation: no Title: Transcription Factor High Accumulation Zones Description: It finds trascription factor (TF) high accumulation DNA zones, i.e., regions along the genome where there is a high presence of different transcription factors. Starting from a dataset containing the genomic positions of TF binding regions, for each base of the selected chromosome the accumulation of TFs is computed. Three different types of accumulation (TF, region and base accumulation) are available, together with the possibility of considering, in the single base accumulation computing, the TFs present not only in that single base, but also in its neighborhood, within a window of a given width. Two different methods for the search of TF high accumulation DNA zones, called "binding regions" and "overlaps", are available. In addition, some functions are provided in order to analyze, visualize and compare results obtained with different input parameters. biocViews: Software, BiologicalQuestion, Transcription, ChIPSeq, Coverage Author: Alberto Marchesi, Silvia Cascianelli, Marco Masseroli Maintainer: Gaia Ceddia VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFHAZ git_branch: RELEASE_3_22 git_last_commit: c615f9e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TFHAZ_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TFHAZ_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TFHAZ_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TFHAZ_1.32.0.tgz vignettes: vignettes/TFHAZ/inst/doc/TFHAZ.html vignetteTitles: TFHAZ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFHAZ/inst/doc/TFHAZ.R dependencyCount: 138 Package: TFutils Version: 1.30.0 Depends: R (>= 4.1.0) Imports: methods, dplyr, magrittr, miniUI, shiny, Rsamtools, GSEABase, rjson, BiocFileCache, DT, httr, readxl, AnnotationDbi, org.Hs.eg.db, utils, GenomicFiles, SummarizedExperiment Suggests: knitr, data.table, testthat, AnnotationFilter, Biobase, GenomicFeatures, GenomicRanges, Gviz, IRanges, S4Vectors, EnsDb.Hsapiens.v75, BiocParallel, BiocStyle, GO.db, Seqinfo, UpSetR, ggplot2, png, gwascat, MotifDb, motifStack, RColorBrewer, rmarkdown License: Artistic-2.0 MD5sum: c03324d65d80759a19fa645f46947e80 NeedsCompilation: no Title: TFutils Description: This package helps users to work with TF metadata from various sources. Significant catalogs of TFs and classifications thereof are made available. Tools for working with motif scans are also provided. biocViews: Transcriptomics Author: Vincent Carey [aut, cre], Shweta Gopaulakrishnan [aut] Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFutils git_branch: RELEASE_3_22 git_last_commit: fc98f4d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TFutils_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TFutils_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TFutils_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TFutils_1.30.0.tgz vignettes: vignettes/TFutils/inst/doc/fimo16.html, vignettes/TFutils/inst/doc/TFutils.html vignetteTitles: A note on fimo16, TFutils -- representing TFBS and TF target sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFutils/inst/doc/fimo16.R, vignettes/TFutils/inst/doc/TFutils.R dependencyCount: 136 Package: tidybulk Version: 2.0.0 Depends: R (>= 4.4.0), ttservice (>= 0.3.6) Imports: tibble, dplyr (>= 1.1.0), magrittr, tidyr, stringr, rlang, purrr, tidyselect, stats, parallel, utils, lifecycle, scales, ggplot2, SummarizedExperiment, GenomicRanges, methods, S4Vectors, crayon, Matrix Suggests: BiocStyle, testthat, vctrs, AnnotationDbi, BiocManager, Rsubread, e1071, edgeR, limma, org.Hs.eg.db, org.Mm.eg.db, sva, GGally, knitr, qpdf, covr, Seurat, KernSmooth, Rtsne, widyr, clusterProfiler, msigdbr, DESeq2, broom, survival, boot, betareg, tidyHeatmap, pasilla, ggrepel, devtools, functional, survminer, tidySummarizedExperiment, markdown, uwot, matrixStats, preprocessCore, igraph, EGSEA, IRanges, here, glmmSeq, pbapply, pbmcapply, lme4, glmmTMB, MASS, pkgconfig, enrichplot, patchwork, airway License: GPL-3 MD5sum: 93f69a3f44fa16a2efbc8048761bc3c3 NeedsCompilation: no Title: Brings transcriptomics to the tidyverse Description: This is a collection of utility functions that allow to perform exploration of and calculations to RNA sequencing data, in a modular, pipe-friendly and tidy fashion. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre], Maria Doyle [ctb] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidybulk VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidybulk/issues git_url: https://git.bioconductor.org/packages/tidybulk git_branch: RELEASE_3_22 git_last_commit: ade4539 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tidybulk_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidybulk_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidybulk_2.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidybulk_2.0.0.tgz vignettes: vignettes/tidybulk/inst/doc/comparison_coding.html, vignettes/tidybulk/inst/doc/introduction.html vignetteTitles: Side-by-side comparison with standard interfaces, Overview of the tidybulk package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidybulk/inst/doc/comparison_coding.R, vignettes/tidybulk/inst/doc/introduction.R suggestsMe: tidyomics dependencyCount: 91 Package: tidyCoverage Version: 1.6.0 Depends: R (>= 4.3.0), SummarizedExperiment Imports: S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, BiocParallel, BiocIO, rtracklayer, methods, tidyr, ggplot2, dplyr, fansi, pillar, rlang, scales, cli, purrr, vctrs, stats Suggests: tidySummarizedExperiment, plyranges, TxDb.Hsapiens.UCSC.hg19.knownGene, AnnotationHub, GenomicFeatures, BiocStyle, hues, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: f9093ac01e5a92a7b1fa84fbe937fc23 NeedsCompilation: no Title: Extract and aggregate genomic coverage over features of interest Description: `tidyCoverage` framework enables tidy manipulation of collections of genomic tracks and features using `tidySummarizedExperiment` methods. It facilitates the extraction, aggregation and visualization of genomic coverage over individual or thousands of genomic loci, relying on `CoverageExperiment` and `AggregatedCoverage` classes. This accelerates the integration of genomic track data in genomic analysis workflows. biocViews: Software, Sequencing, Coverage, Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://github.com/js2264/tidyCoverage VignetteBuilder: knitr BugReports: https://github.com/js2264/tidyCoverage/issues git_url: https://git.bioconductor.org/packages/tidyCoverage git_branch: RELEASE_3_22 git_last_commit: 56ab248 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tidyCoverage_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidyCoverage_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidyCoverage_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidyCoverage_1.6.0.tgz vignettes: vignettes/tidyCoverage/inst/doc/tidyCoverage.html vignetteTitles: Introduction to tidyCoverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidyCoverage/inst/doc/tidyCoverage.R dependencyCount: 86 Package: tidyFlowCore Version: 1.4.0 Depends: R (>= 4.3) Imports: Biobase, dplyr, flowCore, ggplot2, methods, purrr, rlang, stats, stringr, tibble, tidyr Suggests: BiocStyle, HDCytoData, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 100ee8c99b68d8bbbee82bc6f95be414 NeedsCompilation: no Title: tidyFlowCore: Bringing flowCore to the tidyverse Description: tidyFlowCore bridges the gap between flow cytometry analysis using the flowCore Bioconductor package and the tidy data principles advocated by the tidyverse. It provides a suite of dplyr-, ggplot2-, and tidyr-like verbs specifically designed for working with flowFrame and flowSet objects as if they were tibbles; however, your data remain flowCore data structures under this layer of abstraction. tidyFlowCore enables intuitive and streamlined analysis workflows that can leverage both the Bioconductor and tidyverse ecosystems for cytometry data. biocViews: SingleCell, FlowCytometry, Infrastructure Author: Timothy Keyes [cre] (ORCID: ), Kara Davis [rth, own], Garry Nolan [rth, own] Maintainer: Timothy Keyes URL: https://github.com/keyes-timothy/tidyFlowCore, https://keyes-timothy.github.io/tidyFlowCore/ VignetteBuilder: knitr BugReports: https://github.com/keyes-timothy/tidyFlowCore/issues git_url: https://git.bioconductor.org/packages/tidyFlowCore git_branch: RELEASE_3_22 git_last_commit: 1366b28 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tidyFlowCore_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidyFlowCore_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidyFlowCore_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidyFlowCore_1.4.0.tgz vignettes: vignettes/tidyFlowCore/inst/doc/tidyFlowCore.html vignetteTitles: tidyFlowCore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidyFlowCore/inst/doc/tidyFlowCore.R dependencyCount: 47 Package: tidyomics Version: 1.6.0 Depends: R (>= 4.2) Imports: tidySummarizedExperiment, tidySingleCellExperiment, tidySpatialExperiment, tidyseurat, plyranges, purrr, rlang, stringr, cli, Suggests: utils, tidyr, dplyr, tibble, ggplot2, mockr (>= 0.2.0), knitr (>= 1.41), rmarkdown (>= 2.20), testthat (>= 3.1.6), nullranges, tidybulk, plyinteractions License: MIT + file LICENSE Archs: x64 MD5sum: b7e55839fd2d85090f51794ba1fe6456 NeedsCompilation: no Title: Easily install and load the tidyomics ecosystem Description: The tidyomics ecosystem is a set of packages for ’omic data analysis that work together in harmony; they share common data representations and API design, consistent with the tidyverse ecosystem. The tidyomics package is designed to make it easy to install and load core packages from the tidyomics ecosystem with a single command. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] (ORCID: ), Michael Love [aut] (ORCID: ), William Hutchison [aut] (ORCID: ) Maintainer: Stefano Mangiola URL: https://github.com/tidyomics/tidyomics VignetteBuilder: knitr BugReports: https://github.com/tidyomics/tidyomics/issues git_url: https://git.bioconductor.org/packages/tidyomics git_branch: RELEASE_3_22 git_last_commit: 3cf2429 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tidyomics_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidyomics_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidyomics_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidyomics_1.6.0.tgz vignettes: vignettes/tidyomics/inst/doc/loading-tidyomics.html vignetteTitles: Loading the tidyomics ecosystem hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidyomics/inst/doc/loading-tidyomics.R dependencyCount: 207 Package: tidysbml Version: 1.4.0 Depends: R (>= 4.4.0) Imports: xml2, methods Suggests: rmarkdown, knitr, BiocStyle, biomaRt, RCy3, testthat (>= 3.0.0) License: CC BY 4.0 MD5sum: 28a4f2f55d752b04bf63780ef7b716b5 NeedsCompilation: no Title: Extract SBML's data into dataframes Description: Starting from one SBML file, it extracts information from each listOfCompartments, listOfSpecies and listOfReactions element by saving them into data frames. Each table provides one row for each entity (i.e. either compartment, species, reaction or speciesReference) and one set of columns for the attributes, one column for the content of the 'notes' subelement and one set of columns for the content of the 'annotation' subelement. biocViews: GraphAndNetwork, Network, Pathways, Software Author: Veronica Paparozzi [aut, cre] (ORCID: ), Christine Nardini [aut] (ORCID: ) Maintainer: Veronica Paparozzi URL: https://github.com/veronicapaparozzi/tidysbml VignetteBuilder: knitr BugReports: https://github.com/veronicapaparozzi/tidysbml/issues git_url: https://git.bioconductor.org/packages/tidysbml git_branch: RELEASE_3_22 git_last_commit: 7d0a0cf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tidysbml_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidysbml_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidysbml_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidysbml_1.4.0.tgz vignettes: vignettes/tidysbml/inst/doc/tidysbml-introduction.html vignetteTitles: Introduction to the tidysbml package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidysbml/inst/doc/tidysbml-introduction.R dependencyCount: 5 Package: tidySingleCellExperiment Version: 1.20.0 Depends: R (>= 4.4.0), SingleCellExperiment, ttservice (>= 0.4.0) Imports: dplyr, tidyr, SummarizedExperiment, tibble, ggplot2, magrittr, rlang, purrr, pkgconfig, lifecycle, methods, utils, S4Vectors, tidyselect, ellipsis, vctrs, pillar, stringr, cli, fansi, Matrix, stats Suggests: BiocStyle, testthat, knitr, markdown, rmarkdown, SingleCellSignalR, SingleR, scater, scran, tidyHeatmap, igraph, GGally, uwot, celldex, dittoSeq, plotly, rbibutils, prettydoc License: GPL-3 MD5sum: cdf550a06d84b89347840567e5fcc111 NeedsCompilation: no Title: Brings SingleCellExperiment to the Tidyverse Description: 'tidySingleCellExperiment' is an adapter that abstracts the 'SingleCellExperiment' container in the form of a 'tibble'. This allows *tidy* data manipulation, nesting, and plotting. For example, a 'tidySingleCellExperiment' is directly compatible with functions from 'tidyverse' packages `dplyr` and `tidyr`, as well as plotting with `ggplot2` and `plotly`. In addition, the package provides various utility functions specific to single-cell omics data analysis (e.g., aggregation of cell-level data to pseudobulks). biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, SingleCell, GeneExpression, Normalization, Clustering, QualityControl, Sequencing Author: Stefano Mangiola [aut, cre] (ORCID: ) Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidySingleCellExperiment VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidySingleCellExperiment/issues git_url: https://git.bioconductor.org/packages/tidySingleCellExperiment git_branch: RELEASE_3_22 git_last_commit: fe23184 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tidySingleCellExperiment_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidySingleCellExperiment_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidySingleCellExperiment_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidySingleCellExperiment_1.20.0.tgz vignettes: vignettes/tidySingleCellExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySingleCellExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySingleCellExperiment/inst/doc/introduction.R dependsOnMe: tidySpatialExperiment importsMe: tidyomics suggestsMe: CuratedAtlasQueryR, sccomp, spicyWorkflow dependencyCount: 92 Package: tidySpatialExperiment Version: 1.5.1 Depends: R (>= 4.3.0), SpatialExperiment, tidySingleCellExperiment Imports: ttservice, SummarizedExperiment, SingleCellExperiment, BiocGenerics, Matrix, S4Vectors, methods, utils, pkgconfig, tibble, dplyr, tidyr, ggplot2 (>= 4.0.0), plotly, rlang, purrr, stringr, vctrs, tidyselect, pillar, cli, fansi, lifecycle, magick, tidygate (>= 1.0.13), shiny Suggests: BiocStyle, testthat, knitr, markdown, scater, igraph, cowplot, DropletUtils, tidySummarizedExperiment License: GPL (>= 3) MD5sum: 53f81a98b8d47bdef191b79cc0a8e222 NeedsCompilation: no Title: SpatialExperiment with tidy principles Description: tidySpatialExperiment provides a bridge between the SpatialExperiment package and the tidyverse ecosystem. It creates an invisible layer that allows you to interact with a SpatialExperiment object as if it were a tibble; enabling the use of functions from dplyr, tidyr, ggplot2 and plotly. But, underneath, your data remains a SpatialExperiment object. biocViews: Infrastructure, RNASeq, GeneExpression, Sequencing, Spatial, Transcriptomics, SingleCell Author: William Hutchison [aut, cre] (ORCID: ), Stefano Mangiola [aut] Maintainer: William Hutchison URL: https://github.com/william-hutchison/tidySpatialExperiment, https://william-hutchison.github.io/tidySpatialExperiment/ VignetteBuilder: knitr BugReports: https://github.com/william-hutchison/tidySpatialExperiment/issues git_url: https://git.bioconductor.org/packages/tidySpatialExperiment git_branch: devel git_last_commit: 8909c54 git_last_commit_date: 2025-10-21 Date/Publication: 2025-10-23 source.ver: src/contrib/tidySpatialExperiment_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidySpatialExperiment_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidySpatialExperiment_1.5.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidySpatialExperiment_1.5.1.tgz vignettes: vignettes/tidySpatialExperiment/inst/doc/overview.html vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySpatialExperiment/inst/doc/overview.R importsMe: tidyomics dependencyCount: 114 Package: tidySummarizedExperiment Version: 1.20.0 Depends: R (>= 4.3.0), SummarizedExperiment, ttservice (>= 0.5.0) Imports: dplyr, tibble (>= 3.0.4), magrittr, tidyr, ggplot2, rlang, purrr, lifecycle, methods, utils, S4Vectors, tidyselect, ellipsis, vctrs, pillar, stringr, cli, fansi, stats, pkgconfig, plyxp Suggests: BiocStyle, testthat, knitr, markdown, rmarkdown, plotly, rbibutils, prettydoc, airway License: GPL-3 MD5sum: 49000e0ea9fd0e361a85a3994b228e15 NeedsCompilation: no Title: Brings SummarizedExperiment to the Tidyverse Description: The tidySummarizedExperiment package provides a set of tools for creating and manipulating tidy data representations of SummarizedExperiment objects. SummarizedExperiment is a widely used data structure in bioinformatics for storing high-throughput genomic data, such as gene expression or DNA sequencing data. The tidySummarizedExperiment package introduces a tidy framework for working with SummarizedExperiment objects. It allows users to convert their data into a tidy format, where each observation is a row and each variable is a column. This tidy representation simplifies data manipulation, integration with other tidyverse packages, and enables seamless integration with the broader ecosystem of tidy tools for data analysis. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidySummarizedExperiment VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidySummarizedExperiment/issues git_url: https://git.bioconductor.org/packages/tidySummarizedExperiment git_branch: RELEASE_3_22 git_last_commit: 9479c4e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tidySummarizedExperiment_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidySummarizedExperiment_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidySummarizedExperiment_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidySummarizedExperiment_1.20.0.tgz vignettes: vignettes/tidySummarizedExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySummarizedExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySummarizedExperiment/inst/doc/introduction.R importsMe: tidyomics suggestsMe: tidybulk, tidyCoverage, tidySpatialExperiment dependencyCount: 92 Package: tigre Version: 1.64.0 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, grDevices, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle, BiocManager License: AGPL-3 MD5sum: 8be2cbddb5aba2b3ad3aa3936e3ac8f6 NeedsCompilation: yes Title: Transcription factor Inference through Gaussian process Reconstruction of Expression Description: The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF. biocViews: Microarray, TimeCourse, GeneExpression, Transcription, GeneRegulation, NetworkInference, Bayesian Author: Antti Honkela, Pei Gao, Jonatan Ropponen, Miika-Petteri Matikainen, Magnus Rattray, Neil D. Lawrence Maintainer: Antti Honkela URL: https://github.com/ahonkela/tigre BugReports: https://github.com/ahonkela/tigre/issues git_url: https://git.bioconductor.org/packages/tigre git_branch: RELEASE_3_22 git_last_commit: 0b46268 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tigre_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tigre_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tigre_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tigre_1.64.0.tgz vignettes: vignettes/tigre/inst/doc/tigre.pdf vignetteTitles: tigre User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tigre/inst/doc/tigre.R dependencyCount: 51 Package: TileDBArray Version: 1.20.0 Depends: SparseArray (>= 1.5.20), DelayedArray (>= 0.31.7) Imports: methods, tiledb, S4Vectors Suggests: knitr, Matrix, rmarkdown, BiocStyle, BiocParallel, testthat License: MIT + file LICENSE MD5sum: 5a7f89ce1f75c9ed43700abd46bc55ee NeedsCompilation: no Title: Using TileDB as a DelayedArray Backend Description: Implements a DelayedArray backend for reading and writing dense or sparse arrays in the TileDB format. The resulting TileDBArrays are compatible with all Bioconductor pipelines that can accept DelayedArray instances. biocViews: DataRepresentation, Infrastructure, Software Author: Aaron Lun [aut, cre], Genentech, Inc. [cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/TileDBArray VignetteBuilder: knitr BugReports: https://github.com/LTLA/TileDBArray git_url: https://git.bioconductor.org/packages/TileDBArray git_branch: RELEASE_3_22 git_last_commit: e9b1538 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TileDBArray_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TileDBArray_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TileDBArray_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TileDBArray_1.20.0.tgz vignettes: vignettes/TileDBArray/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TileDBArray/inst/doc/userguide.R importsMe: beachmat.tiledb dependencyCount: 33 Package: tilingArray Version: 1.88.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 MD5sum: 11883b2dd1afec4a761f63a675fa6d66 NeedsCompilation: yes Title: Transcript mapping with high-density oligonucleotide tiling arrays Description: The package provides functionality that can be useful for the analysis of high-density tiling microarray data (such as from Affymetrix genechips) for measuring transcript abundance and architecture. The main functionalities of the package are: 1. the class 'segmentation' for representing partitionings of a linear series of data; 2. the function 'segment' for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact; 3. the function 'confint' for calculating confidence intervals using the strucchange package; 4. the function 'plotAlongChrom' for generating pretty plots; 5. the function 'normalizeByReference' for probe-sequence dependent response adjustment from a (set of) reference hybridizations. biocViews: Microarray, OneChannel, Preprocessing, Visualization Author: Wolfgang Huber, Zhenyu Xu, Joern Toedling with contributions from Matt Ritchie Maintainer: Zhenyu Xu git_url: https://git.bioconductor.org/packages/tilingArray git_branch: RELEASE_3_22 git_last_commit: 14b8616 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tilingArray_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tilingArray_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tilingArray_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tilingArray_1.88.0.tgz vignettes: vignettes/tilingArray/inst/doc/assessNorm.pdf, vignettes/tilingArray/inst/doc/costMatrix.pdf, vignettes/tilingArray/inst/doc/findsegments.pdf, vignettes/tilingArray/inst/doc/plotAlongChrom.pdf, vignettes/tilingArray/inst/doc/segmentation.pdf vignetteTitles: Normalisation with the normalizeByReference function in the tilingArray package, Supplement. Calculation of the cost matrix, Introduction to using the segment function to fit a piecewise constant curve, Introduction to the plotAlongChrom function, Segmentation demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tilingArray/inst/doc/findsegments.R, vignettes/tilingArray/inst/doc/plotAlongChrom.R dependsOnMe: davidTiling importsMe: ADaCGH2 dependencyCount: 75 Package: timecourse Version: 1.82.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 832c65adbbaf086c9762ee6314e276fa NeedsCompilation: no Title: Statistical Analysis for Developmental Microarray Time Course Data Description: Functions for data analysis and graphical displays for developmental microarray time course data. biocViews: Microarray, TimeCourse, DifferentialExpression Author: Yu Chuan Tai Maintainer: Yu Chuan Tai URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/timecourse git_branch: RELEASE_3_22 git_last_commit: c4ca002 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/timecourse_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/timecourse_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/timecourse_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/timecourse_1.82.0.tgz vignettes: vignettes/timecourse/inst/doc/timecourse.pdf vignetteTitles: timecourse manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timecourse/inst/doc/timecourse.R dependencyCount: 12 Package: timeOmics Version: 1.22.0 Depends: mixOmics, R (>= 4.0) Imports: dplyr, tidyr, tibble, purrr, magrittr, ggplot2, stringr, ggrepel, lmtest, plyr, checkmate Suggests: BiocStyle, knitr, rmarkdown, testthat, snow, tidyverse, igraph, gplots License: GPL-3 MD5sum: 3199a96560cb1501a16ac36a456c0574 NeedsCompilation: no Title: Time-Course Multi-Omics data integration Description: timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step. biocViews: Clustering,FeatureExtraction,TimeCourse,DimensionReduction,Software, Sequencing, Microarray, Metabolomics, Metagenomics, Proteomics, Classification, Regression, ImmunoOncology, GenePrediction, MultipleComparison Author: Antoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le Cao [aut], Arnaud Droit [aut] Maintainer: Antoine Bodein VignetteBuilder: knitr BugReports: https://github.com/abodein/timeOmics/issues git_url: https://git.bioconductor.org/packages/timeOmics git_branch: RELEASE_3_22 git_last_commit: 07e290a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/timeOmics_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/timeOmics_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/timeOmics_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/timeOmics_1.22.0.tgz vignettes: vignettes/timeOmics/inst/doc/vignette.html vignetteTitles: timeOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timeOmics/inst/doc/vignette.R dependencyCount: 90 Package: timescape Version: 1.34.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), stringr (>= 1.0.0), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 4e538868afa918598ea47aa3f7a73fc4 NeedsCompilation: no Title: Patient Clonal Timescapes Description: TimeScape is an automated tool for navigating temporal clonal evolution data. The key attributes of this implementation involve the enumeration of clones, their evolutionary relationships and their shifting dynamics over time. TimeScape requires two inputs: (i) the clonal phylogeny and (ii) the clonal prevalences. Optionally, TimeScape accepts a data table of targeted mutations observed in each clone and their allele prevalences over time. The output is the TimeScape plot showing clonal prevalence vertically, time horizontally, and the plot height optionally encoding tumour volume during tumour-shrinking events. At each sampling time point (denoted by a faint white line), the height of each clone accurately reflects its proportionate prevalence. These prevalences form the anchors for bezier curves that visually represent the dynamic transitions between time points. biocViews: Visualization, BiomedicalInformatics Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/timescape git_branch: RELEASE_3_22 git_last_commit: 5a69ef1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/timescape_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/timescape_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/timescape_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/timescape_1.34.0.tgz vignettes: vignettes/timescape/inst/doc/timescape_vignette.html vignetteTitles: TimeScape vignette hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timescape/inst/doc/timescape_vignette.R dependencyCount: 46 Package: TIN Version: 1.42.0 Depends: R (>= 2.12.0), data.table, impute, aroma.affymetrix Imports: WGCNA, squash, stringr Suggests: knitr, aroma.light, affxparser, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 9334766a92dce9fbe73c19ca8a296a2c NeedsCompilation: no Title: Transcriptome instability analysis Description: The TIN package implements a set of tools for transcriptome instability analysis based on exon expression profiles. Deviating exon usage is studied in the context of splicing factors to analyse to what degree transcriptome instability is correlated to splicing factor expression. In the transcriptome instability correlation analysis, the data is compared to both random permutations of alternative splicing scores and expression of random gene sets. biocViews: ExonArray, Microarray, GeneExpression, AlternativeSplicing, Genetics, DifferentialSplicing Author: Bjarne Johannessen, Anita Sveen and Rolf I. Skotheim Maintainer: Bjarne Johannessen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TIN git_branch: RELEASE_3_22 git_last_commit: de4c17a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TIN_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TIN_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TIN_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TIN_1.42.0.tgz vignettes: vignettes/TIN/inst/doc/TIN.pdf vignetteTitles: Introduction to the TIN package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TIN/inst/doc/TIN.R dependencyCount: 125 Package: TissueEnrich Version: 1.30.0 Depends: R (>= 3.5), ggplot2 (>= 2.2.1), SummarizedExperiment (>= 1.6.5), GSEABase (>= 1.38.2) Imports: dplyr (>= 0.7.3), tidyr (>= 0.8.0), stats Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 700cb058e6d65612861257e019493517 NeedsCompilation: no Title: Tissue-specific gene enrichment analysis Description: The TissueEnrich package is used to calculate enrichment of tissue-specific genes in a set of input genes. For example, the user can input the most highly expressed genes from RNA-Seq data, or gene co-expression modules to determine which tissue-specific genes are enriched in those datasets. Tissue-specific genes were defined by processing RNA-Seq data from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx (Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using the algorithm from the HPA (Uhlén et al. 2015).The hypergeometric test is being used to determine if the tissue-specific genes are enriched among the input genes. Along with tissue-specific gene enrichment, the TissueEnrich package can also be used to define tissue-specific genes from expression datasets provided by the user, which can then be used to calculate tissue-specific gene enrichments. biocViews: GeneSetEnrichment, GeneExpression, Sequencing Author: Ashish Jain [aut, cre], Geetu Tuteja [aut] Maintainer: Ashish Jain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TissueEnrich git_branch: RELEASE_3_22 git_last_commit: a378680 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TissueEnrich_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TissueEnrich_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TissueEnrich_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TissueEnrich_1.30.0.tgz vignettes: vignettes/TissueEnrich/inst/doc/TissueEnrich.html vignetteTitles: TissueEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TissueEnrich/inst/doc/TissueEnrich.R dependencyCount: 79 Package: tkWidgets Version: 1.88.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: 40d66dc82bf369331ddeb768a2e6667c NeedsCompilation: no Title: R based tk widgets Description: Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. biocViews: Infrastructure Author: J. Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/tkWidgets git_branch: RELEASE_3_22 git_last_commit: 598ba4c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tkWidgets_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tkWidgets_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tkWidgets_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tkWidgets_1.88.0.tgz vignettes: vignettes/tkWidgets/inst/doc/importWizard.pdf, vignettes/tkWidgets/inst/doc/tkWidgets.pdf vignetteTitles: tkWidgets importWizard, tkWidgets contents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tkWidgets/inst/doc/importWizard.R, vignettes/tkWidgets/inst/doc/tkWidgets.R importsMe: Mfuzz, OLINgui suggestsMe: affy, annotate, Biobase, genefilter, marray dependencyCount: 6 Package: tLOH Version: 1.18.0 Depends: R (>= 4.2) Imports: scales, stats, utils, ggplot2, data.table, purrr, dplyr, VariantAnnotation, GenomicRanges, MatrixGenerics, bestNormalize, depmixS4, naniar, stringr Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: c7bf7ba6b5490983a6eaec2d41e68ae3 NeedsCompilation: no Title: Assessment of evidence for LOH in spatial transcriptomics pre-processed data using Bayes factor calculations Description: tLOH, or transcriptomicsLOH, assesses evidence for loss of heterozygosity (LOH) in pre-processed spatial transcriptomics data. This tool requires spatial transcriptomics cluster and allele count information at likely heterozygous single-nucleotide polymorphism (SNP) positions in VCF format. Bayes factors are calculated at each SNP to determine likelihood of potential loss of heterozygosity event. Two plotting functions are included to visualize allele fraction and aggregated Bayes factor per chromosome. Data generated with the 10X Genomics Visium Spatial Gene Expression platform must be pre-processed to obtain an individual sample VCF with columns for each cluster. Required fields are allele depth (AD) with counts for reference/alternative alleles and read depth (DP). biocViews: CopyNumberVariation, Transcription, SNP, GeneExpression, Transcriptomics Author: Michelle Webb [cre, aut], David Craig [aut] Maintainer: Michelle Webb URL: https://github.com/USCDTG/tLOH VignetteBuilder: knitr BugReports: https://github.com/USCDTG/tLOH/issues git_url: https://git.bioconductor.org/packages/tLOH git_branch: RELEASE_3_22 git_last_commit: 35f04e6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tLOH_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tLOH_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tLOH_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tLOH_1.18.0.tgz vignettes: vignettes/tLOH/inst/doc/tLOH_vignette.html vignetteTitles: tLOH hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tLOH/inst/doc/tLOH_vignette.R dependencyCount: 162 Package: TMixClust Version: 1.32.0 Depends: R (>= 3.4) Imports: gss, mvtnorm, stats, zoo, cluster, utils, BiocParallel, flexclust, grDevices, graphics, Biobase, SPEM Suggests: rmarkdown, knitr, BiocStyle, testthat License: GPL (>=2) MD5sum: f1bafb85d05cbc68c30673b9c9ea6422 NeedsCompilation: no Title: Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines Description: Implementation of a clustering method for time series gene expression data based on mixed-effects models with Gaussian variables and non-parametric cubic splines estimation. The method can robustly account for the high levels of noise present in typical gene expression time series datasets. biocViews: Software, StatisticalMethod, Clustering, TimeCourse, GeneExpression Author: Monica Golumbeanu Maintainer: Monica Golumbeanu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TMixClust git_branch: RELEASE_3_22 git_last_commit: b2d1cbb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TMixClust_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TMixClust_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TMixClust_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TMixClust_1.32.0.tgz vignettes: vignettes/TMixClust/inst/doc/TMixClust.pdf vignetteTitles: Clustering time series gene expression data with TMixClust hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TMixClust/inst/doc/TMixClust.R dependencyCount: 42 Package: TMSig Version: 1.4.0 Depends: R (>= 4.4.0), limma Imports: circlize, ComplexHeatmap, data.table, grDevices, grid, GSEABase, Matrix, methods, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: dde139c71de6c3881678dd5caba521f2 NeedsCompilation: no Title: Tools for Molecular Signatures Description: The TMSig package contains tools to prepare, analyze, and visualize named lists of sets, with an emphasis on molecular signatures (such as gene or kinase sets). It includes fast, memory efficient functions to construct sparse incidence and similarity matrices and filter, cluster, invert, and decompose sets. Additionally, bubble heatmaps can be created to visualize the results of any differential or molecular signatures analysis. biocViews: Clustering, GeneSetEnrichment, GraphAndNetwork, Pathways, Visualization Author: Tyler Sagendorf [aut, cre] (ORCID: ), Di Wu [ctb], Gordon Smyth [ctb] Maintainer: Tyler Sagendorf URL: https://github.com/EMSL-Computing/TMSig VignetteBuilder: knitr BugReports: https://github.com/EMSL-Computing/TMSig/issues git_url: https://git.bioconductor.org/packages/TMSig git_branch: RELEASE_3_22 git_last_commit: c174939 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TMSig_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TMSig_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TMSig_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TMSig_1.4.0.tgz vignettes: vignettes/TMSig/inst/doc/TMSig.html vignetteTitles: An Introduction to TMSig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TMSig/inst/doc/TMSig.R dependencyCount: 71 Package: TnT Version: 1.32.0 Depends: R (>= 3.4), GenomicRanges Imports: methods, stats, utils, grDevices, htmlwidgets, jsonlite, data.table, Biobase, GenomeInfoDb, IRanges, S4Vectors, knitr Suggests: GenomicFeatures, shiny, BiocManager, rmarkdown, testthat License: AGPL-3 Archs: x64 MD5sum: 5b43ded6f6eeb06fbb192f162e696d06 NeedsCompilation: no Title: Interactive Visualization for Genomic Features Description: A R interface to the TnT javascript library (https://github.com/ tntvis) to provide interactive and flexible visualization of track-based genomic data. biocViews: Infrastructure, Visualization Author: Jialin Ma [cre, aut], Miguel Pignatelli [aut], Toby Hocking [aut] Maintainer: Jialin Ma URL: https://github.com/Marlin-Na/TnT VignetteBuilder: knitr BugReports: https://github.com/Marlin-Na/TnT/issues git_url: https://git.bioconductor.org/packages/TnT git_branch: RELEASE_3_22 git_last_commit: 0724934 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TnT_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TnT_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TnT_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TnT_1.32.0.tgz vignettes: vignettes/TnT/inst/doc/introduction.html vignetteTitles: Introduction to TnT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TnT/inst/doc/introduction.R dependencyCount: 49 Package: TOAST Version: 1.24.0 Depends: R (>= 3.6), EpiDISH, limma, nnls, quadprog Imports: stats, methods, SummarizedExperiment, corpcor, doParallel, parallel, ggplot2, tidyr, GGally Suggests: BiocStyle, knitr, rmarkdown, gplots, matrixStats, Matrix License: GPL-2 MD5sum: fbe09b44a5966151093a80210da23641 NeedsCompilation: no Title: Tools for the analysis of heterogeneous tissues Description: This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge. biocViews: DNAMethylation, GeneExpression, DifferentialExpression, DifferentialMethylation, Microarray, GeneTarget, Epigenetics, MethylationArray Author: Ziyi Li and Weiwei Zhang and Luxiao Chen and Hao Wu Maintainer: Ziyi Li VignetteBuilder: knitr BugReports: https://github.com/ziyili20/TOAST/issues git_url: https://git.bioconductor.org/packages/TOAST git_branch: RELEASE_3_22 git_last_commit: a147119 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TOAST_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TOAST_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TOAST_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TOAST_1.24.0.tgz vignettes: vignettes/TOAST/inst/doc/TOAST.html vignetteTitles: The TOAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TOAST/inst/doc/TOAST.R importsMe: MICSQTL, RegionalST dependencyCount: 78 Package: tomoda Version: 1.20.0 Depends: R (>= 4.0.0) Imports: methods, stats, grDevices, reshape2, Rtsne, umap, RColorBrewer, ggplot2, ggrepel, SummarizedExperiment Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: 1be5ecf23d06fd5b0fd6081c027e73ec NeedsCompilation: no Title: Tomo-seq data analysis Description: This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots. biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics, Spatial, Clustering, Visualization Author: Wendao Liu [aut, cre] (ORCID: ) Maintainer: Wendao Liu URL: https://github.com/liuwd15/tomoda VignetteBuilder: knitr BugReports: https://github.com/liuwd15/tomoda/issues git_url: https://git.bioconductor.org/packages/tomoda git_branch: RELEASE_3_22 git_last_commit: d70e226 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tomoda_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tomoda_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tomoda_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tomoda_1.20.0.tgz vignettes: vignettes/tomoda/inst/doc/tomoda.html vignetteTitles: tomoda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tomoda/inst/doc/tomoda.R dependencyCount: 62 Package: tomoseqr Version: 1.14.0 Depends: R (>= 4.2) Imports: grDevices, graphics, animation, tibble, dplyr, stringr, purrr, methods, shiny, BiocFileCache, readr, tools, plotly, ggplot2 Suggests: rmarkdown, knitr, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 92f12c334a4d8e0dbec5a42b9081e0d5 NeedsCompilation: no Title: R Package for Analyzing Tomo-seq Data Description: `tomoseqr` is an R package for analyzing Tomo-seq data. Tomo-seq is a genome-wide RNA tomography method that combines combining high-throughput RNA sequencing with cryosectioning for spatially resolved transcriptomics. `tomoseqr` reconstructs 3D expression patterns from tomo-seq data and visualizes the reconstructed 3D expression patterns. biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics, Spatial, Visualization, Software Author: Ryosuke Matsuzawa [aut, cre] (ORCID: ) Maintainer: Ryosuke Matsuzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tomoseqr git_branch: RELEASE_3_22 git_last_commit: be3bd9b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tomoseqr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tomoseqr_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tomoseqr_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tomoseqr_1.14.0.tgz vignettes: vignettes/tomoseqr/inst/doc/tomoseqr.html vignetteTitles: tomoseqr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tomoseqr/inst/doc/tomoseqr.R dependencyCount: 96 Package: TOP Version: 1.10.0 Depends: R (>= 4.1.0) Imports: assertthat, caret, ClassifyR, directPA, doParallel, dplyr, ggnewscale, ggplot2, ggraph, ggrepel, ggthemes, glmnet, Hmisc, igraph, latex2exp, limma, magrittr, methods, plotly, pROC, purrr, reshape2, stats, stringr, survival, tibble, tidygraph, tidyr, statmod Suggests: knitr, rmarkdown, BiocStyle, Biobase, curatedOvarianData, ggbeeswarm, ggsci, survminer, tidyverse License: GPL-3 MD5sum: 30b4b46eff1355765c1aec74fd2aa625 NeedsCompilation: no Title: TOP Constructs Transferable Model Across Gene Expression Platforms Description: TOP constructs a transferable model across gene expression platforms for prospective experiments. Such a transferable model can be trained to make predictions on independent validation data with an accuracy that is similar to a re-substituted model. The TOP procedure also has the flexibility to be adapted to suit the most common clinical response variables, including linear response, binomial and Cox PH models. biocViews: Software, Survival, GeneExpression Author: Harry Robertson [aut, cre] (ORCID: ), Nicholas Robertson [aut] Maintainer: Harry Robertson URL: https://github.com/Harry25R/TOP VignetteBuilder: knitr BugReports: https://github.com/Harry25R/TOP/issues git_url: https://git.bioconductor.org/packages/TOP git_branch: RELEASE_3_22 git_last_commit: 37ed69c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TOP_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TOP_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TOP_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TOP_1.10.0.tgz vignettes: vignettes/TOP/inst/doc/BuildingATOPModel.html vignetteTitles: "Introduction to TOP" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TOP/inst/doc/BuildingATOPModel.R suggestsMe: ClassifyR dependencyCount: 219 Package: topconfects Version: 1.26.0 Depends: R (>= 3.6.0) Imports: methods, utils, stats, assertthat, ggplot2, scales, grid, grDevices Suggests: limma, edgeR, statmod, DESeq2, ashr, NBPSeq, dplyr, testthat, reshape2, tidyr, readr, org.At.tair.db, AnnotationDbi, knitr, rmarkdown, BiocStyle License: LGPL-2.1 | file LICENSE MD5sum: a0ede0781f7bd87b3050db8e4c448177 NeedsCompilation: no Title: Top Confident Effect Sizes Description: Rank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is an alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, RNASeq, mRNAMicroarray, Regression, MultipleComparison Author: Paul Harrison [aut, cre] (ORCID: ) Maintainer: Paul Harrison URL: https://github.com/pfh/topconfects VignetteBuilder: knitr BugReports: https://github.com/pfh/topconfects/issues git_url: https://git.bioconductor.org/packages/topconfects git_branch: RELEASE_3_22 git_last_commit: c2d1793 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/topconfects_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/topconfects_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/topconfects_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/topconfects_1.26.0.tgz vignettes: vignettes/topconfects/inst/doc/an_overview.html, vignettes/topconfects/inst/doc/fold_change.html vignetteTitles: An overview of topconfects, Confident fold change hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/topconfects/inst/doc/an_overview.R, vignettes/topconfects/inst/doc/fold_change.R importsMe: GeoTcgaData, weitrix dependencyCount: 24 Package: topdownr Version: 1.32.0 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.20.0), ProtGenerics (>= 1.10.0), Biostrings (>= 2.42.1), S4Vectors (>= 0.12.2) Imports: grDevices, stats, tools, utils, Biobase, Matrix (>= 1.4-2), MSnbase (>= 2.33.5), PSMatch (>= 1.11.4), ggplot2 (>= 2.2.1), mzR (>= 2.27.5) Suggests: topdownrdata (>= 0.2), knitr, rmarkdown, ranger, testthat, BiocStyle, xml2 License: GPL (>= 3) MD5sum: 6e859a080dfafbf8bded40adeb97c600 NeedsCompilation: no Title: Investigation of Fragmentation Conditions in Top-Down Proteomics Description: The topdownr package allows automatic and systemic investigation of fragment conditions. It creates Thermo Orbitrap Fusion Lumos method files to test hundreds of fragmentation conditions. Additionally it provides functions to analyse and process the generated MS data and determine the best conditions to maximise overall fragment coverage. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, Coverage Author: Sebastian Gibb [aut, cre] (ORCID: ), Pavel Shliaha [aut] (ORCID: ), Ole Nørregaard Jensen [aut] (ORCID: ) Maintainer: Sebastian Gibb URL: https://codeberg.org/sgibb/topdownr/ VignetteBuilder: knitr BugReports: https://codeberg.org/sgibb/topdownr/issues/ git_url: https://git.bioconductor.org/packages/topdownr git_branch: RELEASE_3_22 git_last_commit: 7bbdc4a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/topdownr_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/topdownr_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/topdownr_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/topdownr_1.32.0.tgz vignettes: vignettes/topdownr/inst/doc/analysis.html, vignettes/topdownr/inst/doc/data-generation.html vignetteTitles: Fragmentation Analysis with topdownr, Data Generation for topdownr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topdownr/inst/doc/analysis.R, vignettes/topdownr/inst/doc/data-generation.R dependsOnMe: topdownrdata dependencyCount: 133 Package: topGO Version: 2.62.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.6), graph (>= 1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi (>= 1.7.19), SparseM (>= 0.73) Imports: lattice, matrixStats, DBI Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, multtest, Rgraphviz, globaltest, knitr, BiocStyle, rmarkdown License: LGPL MD5sum: e6650a60ec5a6fe0df59bc15b3115c46 NeedsCompilation: no Title: Enrichment Analysis for Gene Ontology Description: topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. biocViews: GeneExpression, Transcriptomics, GeneSetEnrichment, GO, Annotation, Pathways, SystemsBiology, Microarray, Sequencing, Visualization, Software Author: Adrian Alexa [aut], Jörg Rahnenführer [aut], Federico Marini [cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/topGO VignetteBuilder: knitr BugReports: https://github.com/federicomarini/topGO/issues git_url: https://git.bioconductor.org/packages/topGO git_branch: RELEASE_3_22 git_last_commit: 07c1375 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/topGO_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/topGO_2.61.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/topGO_2.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/topGO_2.62.0.tgz vignettes: vignettes/topGO/inst/doc/topGO_manual.html vignetteTitles: Gene set enrichment analysis with topGO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topGO/inst/doc/topGO_manual.R dependsOnMe: BgeeDB, compEpiTools, EGSEA, ideal, moanin, tRanslatome, maEndToEnd importsMe: APL, cellity, consICA, GRaNIE, mosdef, OmaDB, pcaExplorer, transcriptogramer, ViSEAGO, ExpHunterSuite suggestsMe: DeeDeeExperiment, fenr, FGNet, geva, IntramiRExploreR, miRNAtap dependencyCount: 49 Package: ToxicoGx Version: 2.14.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, BiocGenerics, S4Vectors, Biobase, BiocParallel, ggplot2, tibble, dplyr, caTools, downloader, magrittr, methods, reshape2, tidyr, data.table, assertthat, scales, graphics, grDevices, parallel, stats, utils, limma, jsonlite Suggests: rmarkdown, testthat, BiocStyle, knitr, tinytex, devtools, PharmacoGx, xtable, markdown License: MIT + file LICENSE Archs: x64 MD5sum: 71b5f2beb4aaa8e53fedf69d88127591 NeedsCompilation: no Title: Analysis of Large-Scale Toxico-Genomic Data Description: Contains a set of functions to perform large-scale analysis of toxicogenomic data, providing a standardized data structure to hold information relevant to annotation, visualization and statistical analysis of toxicogenomic data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software Author: Sisira Nair [aut], Esther Yoo [aut], Christopher Eeles [aut], Amy Tang [aut], Nehme El-Hachem [aut], Petr Smirnov [aut], Jermiah Joseph [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ToxicoGx git_branch: RELEASE_3_22 git_last_commit: c4f8032 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ToxicoGx_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ToxicoGx_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ToxicoGx_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ToxicoGx_2.14.0.tgz vignettes: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.html vignetteTitles: ToxicoGx: An R Platform for Integrated Toxicogenomics Data Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.R dependencyCount: 132 Package: TPP Version: 3.38.0 Depends: R (>= 3.4), Biobase, dplyr, magrittr, tidyr Imports: biobroom, broom, data.table, doParallel, foreach, futile.logger, ggplot2, grDevices, gridExtra, grid, knitr, limma, MASS, mefa, nls2, openxlsx (>= 2.4.0), parallel, plyr, purrr, RColorBrewer, RCurl, reshape2, rmarkdown, splines, stats, stringr, tibble, utils, VennDiagram, VGAM Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 406823fbce30a105dcdab7c5f096509d NeedsCompilation: no Title: Analyze thermal proteome profiling (TPP) experiments Description: Analyze thermal proteome profiling (TPP) experiments with varying temperatures (TR) or compound concentrations (CCR). biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Dorothee Childs, Nils Kurzawa, Holger Franken, Carola Doce, Mikhail Savitski and Wolfgang Huber Maintainer: Dorothee Childs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TPP git_branch: RELEASE_3_22 git_last_commit: 958aced git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TPP_3.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TPP_3.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TPP_3.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TPP_3.38.0.tgz vignettes: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.pdf, vignettes/TPP/inst/doc/TPP_introduction_1D.pdf, vignettes/TPP/inst/doc/TPP_introduction_2D.pdf vignetteTitles: TPP_introduction_NPARC, TPP_introduction_1D, TPP_introduction_2D hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.R, vignettes/TPP/inst/doc/TPP_introduction_1D.R, vignettes/TPP/inst/doc/TPP_introduction_2D.R suggestsMe: Rtpca dependencyCount: 90 Package: TPP2D Version: 1.26.0 Depends: R (>= 3.6.0), stats, utils, dplyr, methods Imports: ggplot2, tidyr, foreach, doParallel, openxlsx, stringr, RCurl, parallel, MASS, BiocParallel, limma Suggests: knitr, testthat, rmarkdown, BiocStyle License: GPL-3 MD5sum: 4323e330802052aa6454cd78545a717c NeedsCompilation: no Title: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP) Description: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP), Performs an FDR-controlled analysis of 2D-TPP experiments by functional analysis of dose-response curves across temperatures. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], Holger Franken [aut], Simon Anders [aut], Wolfgang Huber [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa URL: http://bioconductor.org/packages/TPP2D VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/TPP2D git_branch: RELEASE_3_22 git_last_commit: 41578f0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TPP2D_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TPP2D_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TPP2D_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TPP2D_1.26.0.tgz vignettes: vignettes/TPP2D/inst/doc/TPP2D.html vignetteTitles: Introduction to TPP2D for 2D-TPP analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP2D/inst/doc/TPP2D.R dependencyCount: 56 Package: tpSVG Version: 1.6.0 Depends: mgcv, R (>= 4.4) Imports: stats, BiocParallel, MatrixGenerics, methods, SingleCellExperiment, SummarizedExperiment, SpatialExperiment Suggests: BiocStyle, knitr, nnSVG, rmarkdown, scran, scuttle, STexampleData, escheR, ggpubr, colorspace, BumpyMatrix, sessioninfo, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 3cd5e9bc0cdb9d8e003e2708008f1e82 NeedsCompilation: no Title: Thin plate models to detect spatially variable genes Description: The goal of `tpSVG` is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, `tpSVG` introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to currently available count-based model for spatially resolved data analysis, the `tpSVG` models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis. biocViews: Spatial, Transcriptomics, GeneExpression, Software, StatisticalMethod, DimensionReduction, Regression, Preprocessing Author: Boyi Guo [aut, cre] (ORCID: ), Lukas M. Weber [ctb] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Boyi Guo URL: https://github.com/boyiguo1/tpSVG VignetteBuilder: knitr BugReports: https://github.com/boyiguo1/tpSVG/issues git_url: https://git.bioconductor.org/packages/tpSVG git_branch: RELEASE_3_22 git_last_commit: a7202a9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tpSVG_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tpSVG_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tpSVG_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tpSVG_1.6.0.tgz vignettes: vignettes/tpSVG/inst/doc/intro_to_tpSVG.html vignetteTitles: intro_to_tpSVG hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tpSVG/inst/doc/intro_to_tpSVG.R dependencyCount: 79 Package: tracktables Version: 1.44.0 Depends: R (>= 3.5.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: cd76fab5a9496abeef230d43a7cac50d NeedsCompilation: no Title: Build IGV tracks and HTML reports Description: Methods to create complex IGV genome browser sessions and dynamic IGV reports in HTML pages. biocViews: Sequencing, ReportWriting Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tracktables git_branch: RELEASE_3_22 git_last_commit: a3c4c40 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tracktables_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tracktables_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tracktables_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tracktables_1.44.0.tgz vignettes: vignettes/tracktables/inst/doc/tracktables.pdf vignetteTitles: Creating IGV HTML reports with tracktables hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tracktables/inst/doc/tracktables.R dependencyCount: 45 Package: trackViewer Version: 1.46.0 Depends: R (>= 3.5.0), grDevices, methods, GenomicRanges, grid Imports: Seqinfo, GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz, Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools, IRanges, AnnotationDbi, grImport, htmlwidgets, InteractionSet, utils, rhdf5, strawr, txdbmaker Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, org.Hs.eg.db, BiocStyle, knitr, VariantAnnotation, httr, htmltools, rmarkdown, motifStack License: GPL (>= 2) MD5sum: cc92a216b090cb16ec4f3a7bd84c6b1d NeedsCompilation: no Title: A R/Bioconductor package with web interface for drawing elegant interactive tracks or lollipop plot to facilitate integrated analysis of multi-omics data Description: Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data. biocViews: Visualization Author: Jianhong Ou [aut, cre] (ORCID: ), Julie Lihua Zhu [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/trackViewer git_branch: RELEASE_3_22 git_last_commit: 7f9ba3a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/trackViewer_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/trackViewer_1.45.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/trackViewer_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/trackViewer_1.46.0.tgz vignettes: vignettes/trackViewer/inst/doc/changeTracksStyles.html, vignettes/trackViewer/inst/doc/dandelionPlot.html, vignettes/trackViewer/inst/doc/lollipopPlot.html, vignettes/trackViewer/inst/doc/plotInteractionData.html, vignettes/trackViewer/inst/doc/trackViewer.html vignetteTitles: trackViewer Vignette: change the track styles, trackViewer Vignette: dandelionPlot, trackViewer Vignette: lollipopPlot, trackViewer Vignette: plot interaction data, trackViewer Vignette: overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trackViewer/inst/doc/changeTracksStyles.R, vignettes/trackViewer/inst/doc/dandelionPlot.R, vignettes/trackViewer/inst/doc/lollipopPlot.R, vignettes/trackViewer/inst/doc/plotInteractionData.R, vignettes/trackViewer/inst/doc/trackViewer.R importsMe: geomeTriD, NADfinder suggestsMe: ATACseqQC, ChIPpeakAnno dependencyCount: 159 Package: tradeSeq Version: 1.24.0 Depends: R (>= 3.6) Imports: mgcv, edgeR, SingleCellExperiment, SummarizedExperiment, slingshot, magrittr, RColorBrewer, BiocParallel, Biobase, pbapply, igraph, ggplot2, princurve, methods, S4Vectors, tibble, Matrix, TrajectoryUtils, viridis, matrixStats, MASS Suggests: knitr, rmarkdown, testthat, covr, clusterExperiment, DelayedMatrixStats License: MIT + file LICENSE MD5sum: 69077852ddc95efc94df4af1cec84938 NeedsCompilation: no Title: trajectory-based differential expression analysis for sequencing data Description: tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM. biocViews: Clustering, Regression, TimeCourse, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Koen Van den Berge [aut], Hector Roux de Bezieux [aut, cre] (ORCID: ), Kelly Street [aut, ctb], Lieven Clement [aut, ctb], Sandrine Dudoit [ctb] Maintainer: Hector Roux de Bezieux URL: https://statomics.github.io/tradeSeq/index.html VignetteBuilder: knitr BugReports: https://github.com/statOmics/tradeSeq/issues git_url: https://git.bioconductor.org/packages/tradeSeq git_branch: RELEASE_3_22 git_last_commit: 7eea99e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tradeSeq_1.24.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tradeSeq_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tradeSeq_1.24.0.tgz vignettes: vignettes/tradeSeq/inst/doc/fitGAM.html, vignettes/tradeSeq/inst/doc/Monocle.html, vignettes/tradeSeq/inst/doc/multipleConditions.html, vignettes/tradeSeq/inst/doc/tradeSeq.html vignetteTitles: More details on working with fitGAM, Monocle + tradeSeq, Differential expression across conditions, The tradeSeq workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tradeSeq/inst/doc/fitGAM.R, vignettes/tradeSeq/inst/doc/Monocle.R, vignettes/tradeSeq/inst/doc/tradeSeq.R dependsOnMe: OSCA.advanced suggestsMe: blase dependencyCount: 73 Package: TrajectoryGeometry Version: 1.18.0 Depends: R (>= 4.1) Imports: pracma, rgl, ggplot2, stats, methods Suggests: dplyr, knitr, RColorBrewer, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: dea072bd3fb8bc622b3080168adfbe70 NeedsCompilation: no Title: This Package Discovers Directionality in Time and Pseudo-times Series of Gene Expression Patterns Description: Given a time series or pseudo-times series of gene expression data, we might wish to know: Do the changes in gene expression in these data exhibit directionality? Are there turning points in this directionality. Do different subsets of the data move in different directions? This package uses spherical geometry to probe these sorts of questions. In particular, if we are looking at (say) the first n dimensions of the PCA of gene expression, directionality can be detected as the clustering of points on the (n-1)-dimensional sphere. biocViews: BiologicalQuestion, StatisticalMethod, GeneExpression, SingleCell Author: Michael Shapiro [aut, cre] (ORCID: ) Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TrajectoryGeometry git_branch: RELEASE_3_22 git_last_commit: ac9a5f5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TrajectoryGeometry_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TrajectoryGeometry_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TrajectoryGeometry_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TrajectoryGeometry_1.18.0.tgz vignettes: vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.html, vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.html vignetteTitles: SingleCellTrajectoryAnalysis, TrajectoryGeometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.R, vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.R dependencyCount: 48 Package: TrajectoryUtils Version: 1.18.0 Depends: SingleCellExperiment Imports: methods, stats, Matrix, igraph, S4Vectors, SummarizedExperiment Suggests: BiocNeighbors, DelayedArray, DelayedMatrixStats, BiocParallel, testthat, knitr, BiocStyle, rmarkdown License: GPL-3 Archs: x64 MD5sum: d4cb46f56f7e01346b264db570f65811 NeedsCompilation: no Title: Single-Cell Trajectory Analysis Utilities Description: Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results. biocViews: GeneExpression, SingleCell Author: Aaron Lun [aut, cre], Kelly Street [aut] Maintainer: Aaron Lun URL: https://bioconductor.org/packages/TrajectoryUtils VignetteBuilder: knitr BugReports: https://github.com/LTLA/TrajectoryUtils/issues git_url: https://git.bioconductor.org/packages/TrajectoryUtils git_branch: RELEASE_3_22 git_last_commit: 4cf65d4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TrajectoryUtils_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TrajectoryUtils_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TrajectoryUtils_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TrajectoryUtils_1.18.0.tgz vignettes: vignettes/TrajectoryUtils/inst/doc/overview.html vignetteTitles: Trajectory utilities hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TrajectoryUtils/inst/doc/overview.R dependsOnMe: slingshot, TSCAN importsMe: condiments, singleCellTK, tradeSeq dependencyCount: 35 Package: transcriptogramer Version: 1.32.0 Depends: R (>= 3.4), methods Imports: biomaRt, data.table, doSNOW, foreach, ggplot2, graphics, grDevices, igraph, limma, parallel, progress, RedeR, snow, stats, tidyr, topGO Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 697d55047d065daefe9f4b9f1f213929 NeedsCompilation: no Title: Transcriptional analysis based on transcriptograms Description: R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expressions are altered. biocViews: Software, Network, Visualization, SystemsBiology, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Clustering, DifferentialExpression, Microarray, RNASeq, Transcription, ImmunoOncology Author: Diego Morais [aut, cre], Rodrigo Dalmolin [aut] Maintainer: Diego Morais URL: https://github.com/arthurvinx/transcriptogramer SystemRequirements: Java Runtime Environment (>= 6) VignetteBuilder: knitr BugReports: https://github.com/arthurvinx/transcriptogramer/issues git_url: https://git.bioconductor.org/packages/transcriptogramer git_branch: RELEASE_3_22 git_last_commit: 148c879 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/transcriptogramer_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transcriptogramer_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/transcriptogramer_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/transcriptogramer_1.32.0.tgz vignettes: vignettes/transcriptogramer/inst/doc/transcriptogramer.html vignetteTitles: The transcriptogramer user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptogramer/inst/doc/transcriptogramer.R dependencyCount: 92 Package: transcriptR Version: 1.37.0 Depends: R (>= 3.5.0), methods Imports: BiocGenerics, caret, chipseq, e1071, GenomicAlignments, GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2, graphics, grDevices, IRanges (>= 2.11.15), pROC, reshape2, Rsamtools, rtracklayer, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg19.knownGene, testthat License: GPL-3 Archs: x64 MD5sum: 605f1bc1f673a52eec9a870d0c419e37 NeedsCompilation: no Title: An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification Description: The differences in the RNA types being sequenced have an impact on the resulting sequencing profiles. mRNA-seq data is enriched with reads derived from exons, while GRO-, nucRNA- and chrRNA-seq demonstrate a substantial broader coverage of both exonic and intronic regions. The presence of intronic reads in GRO-seq type of data makes it possible to use it to computationally identify and quantify all de novo continuous regions of transcription distributed across the genome. This type of data, however, is more challenging to interpret and less common practice compared to mRNA-seq. One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. The R package transcriptR combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample. biocViews: ImmunoOncology, Transcription, Software, Sequencing, RNASeq, Coverage Author: Armen R. Karapetyan Maintainer: Armen R. Karapetyan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transcriptR git_branch: devel git_last_commit: cdc425b git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/transcriptR_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transcriptR_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/transcriptR_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/transcriptR_1.37.0.tgz vignettes: vignettes/transcriptR/inst/doc/transcriptR.html vignetteTitles: transcriptR: an integrative tool for ChIP- and RNA-seq based primary transcripts detection and quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptR/inst/doc/transcriptR.R dependencyCount: 149 Package: transformGamPoi Version: 1.16.0 Imports: glmGamPoi, DelayedArray, Matrix, MatrixGenerics, SummarizedExperiment, HDF5Array, methods, utils, Rcpp LinkingTo: Rcpp Suggests: testthat, TENxPBMCData, scran, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 3227309caf303bc9b2ef129f1d48be0f NeedsCompilation: yes Title: Variance Stabilizing Transformation for Gamma-Poisson Models Description: Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals). biocViews: SingleCell, Normalization, Preprocessing, Regression Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/transformGamPoi VignetteBuilder: knitr BugReports: https://github.com/const-ae/transformGamPoi/issues git_url: https://git.bioconductor.org/packages/transformGamPoi git_branch: RELEASE_3_22 git_last_commit: aca099c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/transformGamPoi_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transformGamPoi_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/transformGamPoi_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/transformGamPoi_1.16.0.tgz vignettes: vignettes/transformGamPoi/inst/doc/transformGamPoi.html vignetteTitles: glmGamPoi Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transformGamPoi/inst/doc/transformGamPoi.R dependencyCount: 44 Package: transite Version: 1.28.0 Depends: R (>= 3.5) Imports: BiocGenerics (>= 0.26.0), Biostrings (>= 2.48.0), dplyr (>= 0.7.6), GenomicRanges (>= 1.32.6), ggplot2 (>= 3.0.0), grDevices, gridExtra (>= 2.3), methods, parallel, Rcpp (>= 1.0.4.8), scales (>= 1.0.0), stats, TFMPvalue (>= 0.0.8), stringr (>= 1.5.1), utils LinkingTo: Rcpp (>= 1.0.4.8) Suggests: knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 24ad350af945833c21961947911711f6 NeedsCompilation: yes Title: RNA-binding protein motif analysis Description: transite is a computational method that allows comprehensive analysis of the regulatory role of RNA-binding proteins in various cellular processes by leveraging preexisting gene expression data and current knowledge of binding preferences of RNA-binding proteins. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, mRNAMicroarray, Genetics, GeneSetEnrichment Author: Konstantin Krismer [aut, cre, cph] (ORCID: ), Anna Gattinger [aut] (ORCID: ), Michael Yaffe [ths, cph] (ORCID: ), Ian Cannell [ths] (ORCID: ) Maintainer: Konstantin Krismer URL: https://transite.mit.edu SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transite git_branch: RELEASE_3_22 git_last_commit: 9a7574c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/transite_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transite_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/transite_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/transite_1.28.0.tgz vignettes: vignettes/transite/inst/doc/spma.html vignetteTitles: Spectrum Motif Analysis (SPMA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/transite/inst/doc/spma.R dependencyCount: 46 Package: tRanslatome Version: 1.48.0 Depends: R (>= 2.15.0), methods, limma, anota, DESeq2, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 MD5sum: c4fa470be0e80264fbc7adc4099ff3c7 NeedsCompilation: no Title: Comparison between multiple levels of gene expression Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression, DifferentialExpression, Microarray, HighThroughputSequencing, QualityControl, GO, MultipleComparisons, Bioinformatics Author: Toma Tebaldi, Erik Dassi, Galena Kostoska Maintainer: Toma Tebaldi , Erik Dassi git_url: https://git.bioconductor.org/packages/tRanslatome git_branch: RELEASE_3_22 git_last_commit: 4f15a1c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tRanslatome_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tRanslatome_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tRanslatome_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tRanslatome_1.48.0.tgz vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf vignetteTitles: tRanslatome hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tRanslatome/inst/doc/tRanslatome_package.R dependencyCount: 114 Package: transmogR Version: 1.6.0 Depends: R (>= 4.1.0), Biostrings, GenomicRanges Imports: BSgenome, data.table, Seqinfo, GenomicFeatures, ggplot2 (>= 4.0.0), IRanges, jsonlite, matrixStats, methods, parallel, patchwork, scales, stats, S4Vectors, SummarizedExperiment, VariantAnnotation Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, edgeR, extraChIPs, InteractionSet, knitr, readr, rmarkdown, rtracklayer, SimpleUpset, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 25e9c7eec410553920880ac6bf891ea4 NeedsCompilation: yes Title: Modify a set of reference sequences using a set of variants Description: transmogR provides the tools needed to crate a new reference genome or reference transcriptome, using a set of variants. Variants can be any combination of SNPs, Insertions and Deletions. The intended use-case is to enable creation of variant-modified reference transcriptomes for incorporation into transcriptomic pseudo-alignment workflows, such as salmon. biocViews: Alignment, GenomicVariation, Sequencing, TranscriptomeVariant, VariantAnnotation Author: Stevie Pederson [aut, cre] (ORCID: ) Maintainer: Stevie Pederson URL: https://github.com/smped/transmogR VignetteBuilder: knitr BugReports: https://github.com/smped/transmogR/issues git_url: https://git.bioconductor.org/packages/transmogR git_branch: RELEASE_3_22 git_last_commit: 7065211 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/transmogR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transmogR_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/transmogR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/transmogR_1.6.0.tgz vignettes: vignettes/transmogR/inst/doc/creating_a_new_reference.html vignetteTitles: Creating a Variant-Modified Reference hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transmogR/inst/doc/creating_a_new_reference.R dependencyCount: 90 Package: transomics2cytoscape Version: 1.20.0 Imports: RCy3, KEGGREST, dplyr, purrr, tibble, pbapply Suggests: testthat, roxygen2, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 5853dc9a9ec5ef0119e927db779757c4 NeedsCompilation: no Title: A tool set for 3D Trans-Omic network visualization with Cytoscape Description: transomics2cytoscape generates a file for 3D transomics visualization by providing input that specifies the IDs of multiple KEGG pathway layers, their corresponding Z-axis heights, and an input that represents the edges between the pathway layers. The edges are used, for example, to describe the relationships between kinase on a pathway and enzyme on another pathway. This package automates creation of a transomics network as shown in the figure in Yugi.2014 (https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape automation (https://doi.org/10.1186/s13059-019-1758-4). biocViews: Network, Software, Pathways, DataImport, KEGG Author: Kozo Nishida [aut, cre] (ORCID: ), Katsuyuki Yugi [aut] (ORCID: ) Maintainer: Kozo Nishida SystemRequirements: Cytoscape >= 3.10.0 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transomics2cytoscape git_branch: RELEASE_3_22 git_last_commit: a8fc930 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/transomics2cytoscape_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transomics2cytoscape_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/transomics2cytoscape_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/transomics2cytoscape_1.20.0.tgz vignettes: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.html vignetteTitles: transomics2cytoscape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.R dependencyCount: 66 Package: traseR Version: 1.40.0 Depends: R (>= 3.5.0), GenomicRanges, IRanges, BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL MD5sum: 03bf364ab6b2f0b9d333785b353b7930 NeedsCompilation: no Title: GWAS trait-associated SNP enrichment analyses in genomic intervals Description: traseR performs GWAS trait-associated SNP enrichment analyses in genomic intervals using different hypothesis testing approaches, also provides various functionalities to explore and visualize the results. biocViews: Genetics,Sequencing, Coverage, Alignment, QualityControl, DataImport Author: Li Chen, Zhaohui S.Qin Maintainer: li chen git_url: https://git.bioconductor.org/packages/traseR git_branch: RELEASE_3_22 git_last_commit: f737293 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/traseR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/traseR_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/traseR_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/traseR_1.40.0.tgz vignettes: vignettes/traseR/inst/doc/traseR.pdf vignetteTitles: Perform GWAS trait-associated SNP enrichment analyses in genomic intervals hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/traseR/inst/doc/traseR.R dependencyCount: 59 Package: TreeAndLeaf Version: 1.22.0 Depends: R(>= 4.0) Imports: RedeR(>= 1.40.4), igraph, ape Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, stringr, geneplast, ggtree, ggplot2, dplyr, dendextend, RColorBrewer License: Artistic-2.0 MD5sum: 337e3e29934f58c6eb8072bfa9db3542 NeedsCompilation: no Title: Displaying binary trees with focus on dendrogram leaves Description: The TreeAndLeaf package combines unrooted and force-directed graph algorithms in order to layout binary trees, aiming to represent multiple layers of information onto dendrogram leaves. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Leonardo W. Kume, Luis E. A. Rizzardi, Milena A. Cardoso, Mauro A. A. Castro Maintainer: Milena A. Cardoso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeAndLeaf git_branch: RELEASE_3_22 git_last_commit: 1198a67 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TreeAndLeaf_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TreeAndLeaf_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TreeAndLeaf_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TreeAndLeaf_1.22.0.tgz vignettes: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.html vignetteTitles: TreeAndLeaf: an graph layout to dendrograms. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.R suggestsMe: RedeR dependencyCount: 29 Package: treeclimbR Version: 1.5.1 Depends: R (>= 4.4.0) Imports: TreeSummarizedExperiment (>= 1.99.0), edgeR, methods, SummarizedExperiment, S4Vectors, dirmult, dplyr, tibble, tidyr, ape, diffcyt, ggnewscale, ggplot2 (>= 3.4.0), viridis, ggtree, stats, utils, rlang Suggests: knitr, rmarkdown, scales, testthat (>= 3.0.0), BiocStyle, GenomeInfoDb License: Artistic-2.0 Archs: x64 MD5sum: 4cdbed6b8229cf2b151dc8a5dcbf2fe8 NeedsCompilation: no Title: An algorithm to find optimal signal levels in a tree Description: The arrangement of hypotheses in a hierarchical structure appears in many research fields and often indicates different resolutions at which data can be viewed. This raises the question of which resolution level the signal should best be interpreted on. treeclimbR provides a flexible method to select optimal resolution levels (potentially different levels in different parts of the tree), rather than cutting the tree at an arbitrary level. treeclimbR uses a tuning parameter to generate candidate resolutions and from these selects the optimal one. biocViews: StatisticalMethod, CellBasedAssays Author: Ruizhu Huang [aut] (ORCID: ), Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/treeclimbR VignetteBuilder: knitr BugReports: https://github.com/csoneson/treeclimbR/issues git_url: https://git.bioconductor.org/packages/treeclimbR git_branch: devel git_last_commit: 88e3b05 git_last_commit_date: 2025-07-18 Date/Publication: 2025-10-07 source.ver: src/contrib/treeclimbR_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/treeclimbR_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/treeclimbR_1.5.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/treeclimbR_1.5.1.tgz vignettes: vignettes/treeclimbR/inst/doc/treeclimbR.html vignetteTitles: treeclimbR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treeclimbR/inst/doc/treeclimbR.R dependencyCount: 185 Package: treeio Version: 1.33.0 Depends: R (>= 4.1.0) Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, stats, tibble, tidytree (>= 0.4.5), utils, yulab.utils (>= 0.1.6) Suggests: Biostrings, cli, ggplot2, ggtree, igraph, knitr, rmarkdown, phangorn, prettydoc, purrr, testthat, tidyr, vroom, xml2, yaml License: Artistic-2.0 MD5sum: cf3db88250672f5ccc8861ab9eb960bc NeedsCompilation: no Title: Base Classes and Functions for Phylogenetic Tree Input and Output Description: 'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats. biocViews: Software, Annotation, Clustering, DataImport, DataRepresentation, Alignment, MultipleSequenceAlignment, Phylogenetics Author: Guangchuang Yu [aut, cre] (ORCID: ), Tommy Tsan-Yuk Lam [ctb, ths], Shuangbin Xu [ctb] (ORCID: ), Bradley Jones [ctb], Casey Dunn [ctb], Tyler Bradley [ctb], Konstantinos Geles [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-tree-data/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/treeio/issues git_url: https://git.bioconductor.org/packages/treeio git_branch: devel git_last_commit: dc61491 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/treeio_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/treeio_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/treeio_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/treeio_1.33.0.tgz vignettes: vignettes/treeio/inst/doc/treeio.html vignetteTitles: treeio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treeio/inst/doc/treeio.R importsMe: ggtree, lefser, MicrobiotaProcess, TreeSummarizedExperiment, geneplast.data, BioVizSeq, dowser, EvoPhylo, RevGadgets, shinyTempSignal suggestsMe: ggtreeDendro, ggtreeExtra, rfaRm, FossilSim, idiogramFISH, MetaNet, nosoi, treestructure dependencyCount: 39 Package: treekoR Version: 1.17.0 Depends: R (>= 4.1) Imports: stats, utils, tidyr, dplyr, data.table, ggiraph, ggplot2, hopach, ape, ggtree, patchwork, SingleCellExperiment, diffcyt, edgeR, lme4, multcomp Suggests: knitr, rmarkdown, BiocStyle, CATALYST, testthat (>= 3.0.0) License: GPL-3 MD5sum: 441930c27d6bf0848a2bd35c73051608 NeedsCompilation: no Title: Cytometry Cluster Hierarchy and Cellular-to-phenotype Associations Description: treekoR is a novel framework that aims to utilise the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points. These associations are aimed to recapitulate the nested proportions prevalent in workflows inovlving manual gating, which are often overlooked in workflows using automatic clustering to identify cell populations. We developed treekoR to: Derive a hierarchical tree structure of cell clusters; quantify a cell types as a proportion relative to all cells in a sample (%total), and, as the proportion relative to a parent population (%parent); perform significance testing using the calculated proportions; and provide an interactive html visualisation to help highlight key results. biocViews: Clustering, DifferentialExpression, FlowCytometry, ImmunoOncology, MassSpectrometry, SingleCell, Software, StatisticalMethod, Visualization Author: Adam Chan [aut, cre], Ellis Patrick [ctb] Maintainer: Adam Chan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/treekoR git_branch: devel git_last_commit: 66405c8 git_last_commit_date: 2025-04-15 Date/Publication: 2025-10-07 source.ver: src/contrib/treekoR_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/treekoR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/treekoR_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/treekoR_1.17.0.tgz vignettes: vignettes/treekoR/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treekoR/inst/doc/vignette.R importsMe: Statial suggestsMe: spicyWorkflow dependencyCount: 177 Package: TreeSummarizedExperiment Version: 2.17.1 Depends: R(>= 3.6.0), SingleCellExperiment, S4Vectors (>= 0.23.18), Biostrings Imports: methods, BiocGenerics, utils, ape, rlang, dplyr, SummarizedExperiment, BiocParallel, IRanges, treeio Suggests: ggtree, ggplot2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: ad8693acb33085728470aca357d1b92d NeedsCompilation: no Title: TreeSummarizedExperiment: a S4 Class for Data with Tree Structures Description: TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data. biocViews: DataRepresentation, Infrastructure Author: Ruizhu Huang [aut, cre] (ORCID: ), Felix G.M. Ernst [ctb] (ORCID: ), Mark Robinson [ctb] (ORCID: ), Tuomas Borman [ctb] (ORCID: ) Maintainer: Ruizhu Huang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeSummarizedExperiment git_branch: devel git_last_commit: daaf32f git_last_commit_date: 2025-07-22 Date/Publication: 2025-10-07 source.ver: src/contrib/TreeSummarizedExperiment_2.17.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/TreeSummarizedExperiment_2.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TreeSummarizedExperiment_2.17.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TreeSummarizedExperiment_2.17.1.tgz vignettes: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.html, vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.html vignetteTitles: 1. Introduction to TreeSE, 2. Combine TSEs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.R, vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.R dependsOnMe: ExperimentSubset, HoloFoodR, MGnifyR, mia, miaSim, miaViz, curatedMetagenomicData, MicrobiomeBenchmarkData, microbiomeDataSets importsMe: anansi, DspikeIn, iSEEtree, maaslin3, miaDash, miaTime, PLSDAbatch, treeclimbR, mikropml suggestsMe: ANCOMBC, dar, philr, LegATo, file2meco, parafac4microbiome dependencyCount: 67 Package: TREG Version: 1.14.0 Depends: R (>= 4.2), SummarizedExperiment Imports: Matrix, purrr, rafalib Suggests: BiocFileCache, BiocStyle, dplyr, ggplot2, knitr, pheatmap, sessioninfo, RefManageR, rmarkdown, testthat (>= 3.0.0), tibble, tidyr, SingleCellExperiment License: Artistic-2.0 Archs: x64 MD5sum: 8c9b08fd0c5c2f39a96872bb2fea85e6 NeedsCompilation: no Title: Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data Description: RNA abundance and cell size parameters could improve RNA-seq deconvolution algorithms to more accurately estimate cell type proportions given the different cell type transcription activity levels. A Total RNA Expression Gene (TREG) can facilitate estimating total RNA content using single molecule fluorescent in situ hybridization (smFISH). We developed a data-driven approach using a measure of expression invariance to find candidate TREGs in postmortem human brain single nucleus RNA-seq. This R package implements the method for identifying candidate TREGs from snRNA-seq data. biocViews: Software, SingleCell, RNASeq, GeneExpression, Transcriptomics, Transcription, Sequencing Author: Louise Huuki-Myers [aut, cre] (ORCID: ), Leonardo Collado-Torres [ctb] (ORCID: ) Maintainer: Louise Huuki-Myers URL: https://github.com/LieberInstitute/TREG, http://research.libd.org/TREG/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/TREG git_url: https://git.bioconductor.org/packages/TREG git_branch: RELEASE_3_22 git_last_commit: 75764ce git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TREG_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TREG_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TREG_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TREG_1.14.0.tgz vignettes: vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.html vignetteTitles: How to find Total RNA Expression Genes (TREGs) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.R dependencyCount: 35 Package: Trendy Version: 1.32.0 Depends: R (>= 3.4) Imports: stats, utils, graphics, grDevices, segmented, gplots, parallel, magrittr, BiocParallel, DT, S4Vectors, SummarizedExperiment, methods, shiny, shinyFiles Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL-3 MD5sum: 0912b55df1bab8dede2864693a8bd01f NeedsCompilation: no Title: Breakpoint analysis of time-course expression data Description: Trendy implements segmented (or breakpoint) regression models to estimate breakpoints which represent changes in expression for each feature/gene in high throughput data with ordered conditions. biocViews: TimeCourse, RNASeq, Regression, ImmunoOncology Author: Rhonda Bacher and Ning Leng Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/Trendy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Trendy git_branch: RELEASE_3_22 git_last_commit: a0843fb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Trendy_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Trendy_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Trendy_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Trendy_1.32.0.tgz vignettes: vignettes/Trendy/inst/doc/Trendy_vignette.pdf vignetteTitles: Trendy Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Trendy/inst/doc/Trendy_vignette.R dependencyCount: 90 Package: TRESS Version: 1.16.0 Depends: R (>= 4.1.0), parallel, S4Vectors Imports: utils, rtracklayer, Matrix, matrixStats, stats, methods, graphics, GenomicRanges, GenomicFeatures, IRanges, Rsamtools, AnnotationDbi Suggests: knitr, rmarkdown,BiocStyle License: GPL-3 + file LICENSE MD5sum: 9a56170182b69654ecb084bb2b054d48 NeedsCompilation: no Title: Toolbox for mRNA epigenetics sequencing analysis Description: This package is devoted to analyzing MeRIP-seq data. Current functionalities include 1. detect transcriptome wide m6A methylation regions 2. detect transcriptome wide differential m6A methylation regions. biocViews: Epigenetics, RNASeq, PeakDetection, DifferentialMethylation Author: Zhenxing Guo [aut, cre], Hao Wu [ctb] Maintainer: Zhenxing Guo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TRESS git_branch: RELEASE_3_22 git_last_commit: b61dd31 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TRESS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TRESS_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TRESS_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TRESS_1.16.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: magpie dependencyCount: 76 Package: tricycle Version: 1.18.0 Depends: R (>= 4.0), SingleCellExperiment Imports: methods, circular, ggplot2, ggnewscale, AnnotationDbi, scater, GenomicRanges, IRanges, S4Vectors, scattermore, dplyr, RColorBrewer, grDevices, stats, SummarizedExperiment, utils Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, CircStats, cowplot, htmltools, Seurat, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 Archs: x64 MD5sum: 9891a877bbe515c0eb22a0a7f286455d NeedsCompilation: no Title: tricycle: Transferable Representation and Inference of cell cycle Description: The package contains functions to infer and visualize cell cycle process using Single Cell RNASeq data. It exploits the idea of transfer learning, projecting new data to the previous learned biologically interpretable space. We provide a pre-learned cell cycle space, which could be used to infer cell cycle time of human and mouse single cell samples. In addition, we also offer functions to visualize cell cycle time on different embeddings and functions to build new reference. biocViews: SingleCell, Software, Transcriptomics, RNASeq, Transcription, BiologicalQuestion, DimensionReduction, ImmunoOncology Author: Shijie Zheng [aut, cre] Maintainer: Shijie Zheng URL: https://github.com/hansenlab/tricycle VignetteBuilder: knitr BugReports: https://github.com/hansenlab/tricycle/issues git_url: https://git.bioconductor.org/packages/tricycle git_branch: RELEASE_3_22 git_last_commit: 4fdcfb2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tricycle_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tricycle_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tricycle_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tricycle_1.18.0.tgz vignettes: vignettes/tricycle/inst/doc/tricycle.html vignetteTitles: tricycle: Transferable Representation and Inference of Cell Cycle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tricycle/inst/doc/tricycle.R dependencyCount: 119 Package: TrIdent Version: 1.2.0 Depends: R (>= 4.2.0) Imports: graphics, utils, stats, dplyr, ggplot2, patchwork, stringr, tidyr, roll Suggests: BiocStyle, knitr, rmarkdown, kableExtra, testthat (>= 3.0.0) License: GPL-2 MD5sum: dd0145715cf011ca98e1a30d8cabee78 NeedsCompilation: no Title: TrIdent - Transduction Identification Description: The `TrIdent` R package automates the analysis of transductomics data by detecting, classifying, and characterizing read coverage patterns associated with potential transduction events. Transductomics is a DNA sequencing-based method for the detection and characterization of transduction events in pure cultures and complex communities. Transductomics relies on mapping sequencing reads from a viral-like particle (VLP)-fraction of a sample to contigs assembled from the metagenome (whole-community) of the same sample. Reads from bacterial DNA carried by VLPs will map back to the bacterial contigs of origin creating read coverage patterns indicative of ongoing transduction. biocViews: Coverage, Metagenomics, PatternLogic, Classification, Sequencing Author: Jessie Maier [aut, cre] (ORCID: ), Jorden Rabasco [aut, ctb] (ORCID: ), Craig Gin [aut] (ORCID: ), Benjamin Callahan [aut] (ORCID: ), Manuel Kleiner [aut, ths] (ORCID: ) Maintainer: Jessie Maier URL: https://github.com/jlmaier12/TrIdent, https://jlmaier12.github.io/TrIdent/ VignetteBuilder: knitr BugReports: https://github.com/jlmaier12/TrIdent/issues git_url: https://git.bioconductor.org/packages/TrIdent git_branch: RELEASE_3_22 git_last_commit: 31b416e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TrIdent_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TrIdent_1.1.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TrIdent_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TrIdent_1.2.0.tgz vignettes: vignettes/TrIdent/inst/doc/TrIdent-vignette.html vignetteTitles: TrIdent hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TrIdent/inst/doc/TrIdent-vignette.R dependencyCount: 41 Package: trio Version: 3.48.0 Depends: R (>= 3.0.1) Imports: grDevices, graphics, methods, stats, survival, utils, siggenes, LogicReg (>= 1.6.1), data.table Suggests: haplo.stats, mcbiopi, splines, logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 MD5sum: dc9530c8bf67c2ef9ce805bd38c86686 NeedsCompilation: no Title: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies Description: Testing SNPs and SNP interactions with a genotypic TDT. This package furthermore contains functions for computing pairwise values of LD measures and for identifying LD blocks, as well as functions for setting up matched case pseudo-control genotype data for case-parent trios in order to run trio logic regression, for imputing missing genotypes in trios, for simulating case-parent trios with disease risk dependent on SNP interaction, and for power and sample size calculation in trio data. biocViews: SNP, GeneticVariability, Microarray, Genetics Author: Holger Schwender, Qing Li, Philipp Berger, Christoph Neumann, Margaret Taub, Ingo Ruczinski Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/trio git_branch: RELEASE_3_22 git_last_commit: 2a3434d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/trio_3.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/trio_3.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/trio_3.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/trio_3.48.0.tgz vignettes: vignettes/trio/inst/doc/trio.pdf vignetteTitles: Trio Logic Regression and genotypic TDT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trio/inst/doc/trio.R dependencyCount: 20 Package: triplex Version: 1.50.0 Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 2.5.27), XVector (>= 0.11.6), Biostrings (>= 2.39.10) Imports: methods, grid, GenomicRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer License: BSD_2_clause + file LICENSE MD5sum: 4be06eac78c450795679436b0c42201a NeedsCompilation: yes Title: Search and visualize intramolecular triplex-forming sequences in DNA Description: This package provides functions for identification and visualization of potential intramolecular triplex patterns in DNA sequence. The main functionality is to detect the positions of subsequences capable of folding into an intramolecular triplex (H-DNA) in a much larger sequence. The potential H-DNA (triplexes) should be made of as many cannonical nucleotide triplets as possible. The package includes visualization showing the exact base-pairing in 1D, 2D or 3D. biocViews: SequenceMatching, GeneRegulation Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with contributions from Daniel Kopecek Maintainer: Jiri Hon URL: http://www.fi.muni.cz/~lexa/triplex/ git_url: https://git.bioconductor.org/packages/triplex git_branch: RELEASE_3_22 git_last_commit: 29318c0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/triplex_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/triplex_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/triplex_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/triplex_1.50.0.tgz vignettes: vignettes/triplex/inst/doc/triplex.pdf vignetteTitles: Triplex User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/triplex/inst/doc/triplex.R dependencyCount: 17 Package: tripr Version: 1.16.0 Depends: R (>= 4.1.0), shiny (>= 1.6.0), shinyBS Imports: shinyjs, shinyFiles, plyr, data.table, DT, stringr, stringdist, plot3D, gridExtra, RColorBrewer, plotly, dplyr, config (>= 0.3.1), golem (>= 0.3.1), methods, grDevices, graphics, stats, utils, vegan Suggests: BiocGenerics, shinycssloaders, tidyverse, BiocManager, Biostrings, xtable, rlist, motifStack, knitr, rmarkdown, testthat (>= 3.0.0), fs, BiocStyle, RefManageR, biocthis, pryr Enhances: parallel License: MIT + file LICENSE MD5sum: d948175f3207e1c0d1694dc03c5ba763 NeedsCompilation: no Title: T-cell Receptor/Immunoglobulin Profiler (TRIP) Description: TRIP is a software framework that provides analytics services on antigen receptor (B cell receptor immunoglobulin, BcR IG | T cell receptor, TR) gene sequence data. It is a web application written in R Shiny. It takes as input the output files of the IMGT/HighV-Quest tool. Users can select to analyze the data from each of the input samples separately, or the combined data files from all samples and visualize the results accordingly. biocViews: BatchEffect, MultipleComparison, GeneExpression, ImmunoOncology, TargetedResequencing Author: Maria Th. Kotouza [aut], Katerina Gemenetzi [aut], Chrysi Galigalidou [aut], Elisavet Vlachonikola [aut], Nikolaos Pechlivanis [cre], Andreas Agathangelidis [aut], Raphael Sandaltzopoulos [aut], Pericles A. Mitkas [aut], Kostas Stamatopoulos [aut], Anastasia Chatzidimitriou [aut], Fotis E. Psomopoulos [aut], Iason Ofeidis [aut], Aspasia Orfanou [aut] Maintainer: Nikolaos Pechlivanis URL: https://github.com/BiodataAnalysisGroup/tripr VignetteBuilder: knitr BugReports: https://github.com/BiodataAnalysisGroup/tripr/issues git_url: https://git.bioconductor.org/packages/tripr git_branch: RELEASE_3_22 git_last_commit: 1de3618 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tripr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tripr_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tripr_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tripr_1.16.0.tgz vignettes: vignettes/tripr/inst/doc/tripr_guide.html vignetteTitles: tripr User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tripr/inst/doc/tripr_guide.R dependencyCount: 101 Package: tRNA Version: 1.28.0 Depends: R (>= 3.5), GenomicRanges, Structstrings Imports: stringr, S4Vectors, methods, BiocGenerics, IRanges, XVector, Biostrings, Modstrings, ggplot2, scales Suggests: knitr, rmarkdown, testthat, BiocStyle, tRNAscanImport License: GPL-3 + file LICENSE MD5sum: 32713831f6dd0f504849f29e7f6848b2 NeedsCompilation: no Title: Analyzing tRNA sequences and structures Description: The tRNA package allows tRNA sequences and structures to be accessed and used for subsetting. In addition, it provides visualization tools to compare feature parameters of multiple tRNA sets and correlate them to additional data. The tRNA package uses GRanges objects as inputs requiring only few additional column data sets. biocViews: Software, Visualization Author: Felix GM Ernst [aut, cre] (ORCID: ) Maintainer: Felix GM Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNA/issues git_url: https://git.bioconductor.org/packages/tRNA git_branch: RELEASE_3_22 git_last_commit: 3266a22 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tRNA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tRNA_1.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tRNA_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tRNA_1.28.0.tgz vignettes: vignettes/tRNA/inst/doc/tRNA.html vignetteTitles: tRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNA/inst/doc/tRNA.R dependsOnMe: tRNAdbImport, tRNAscanImport dependencyCount: 38 Package: tRNAdbImport Version: 1.28.0 Depends: R (>= 3.6), GenomicRanges, Modstrings, Structstrings, tRNA Imports: Biostrings, stringr, httr2, xml2, S4Vectors, methods, IRanges, utils Suggests: BiocGenerics, knitr, rmarkdown, testthat, httptest, BiocStyle, rtracklayer License: GPL-3 + file LICENSE MD5sum: d9559efa314daf2c51252eb38e0dcc01 NeedsCompilation: no Title: Importing from tRNAdb and mitotRNAdb as GRanges objects Description: tRNAdbImport imports the entries of the tRNAdb and mtRNAdb (http://trna.bioinf.uni-leipzig.de) as GRanges object. biocViews: Software, Visualization, DataImport Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAdbImport/issues git_url: https://git.bioconductor.org/packages/tRNAdbImport git_branch: RELEASE_3_22 git_last_commit: 73aeefe git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tRNAdbImport_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tRNAdbImport_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tRNAdbImport_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tRNAdbImport_1.28.0.tgz vignettes: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.html vignetteTitles: tRNAdbImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.R importsMe: EpiTxDb dependencyCount: 46 Package: tRNAscanImport Version: 1.30.0 Depends: R (>= 3.5), GenomicRanges, tRNA Imports: methods, stringr, BiocGenerics, Biostrings, Structstrings, S4Vectors, IRanges, XVector, Seqinfo, rtracklayer, BSgenome, Rsamtools Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2, BSgenome.Scerevisiae.UCSC.sacCer3 License: GPL-3 + file LICENSE MD5sum: a4c9d68670eeb63d00c87e480f76a51b NeedsCompilation: no Title: Importing a tRNAscan-SE result file as GRanges object Description: The package imports the result of tRNAscan-SE as a GRanges object. biocViews: Software, DataImport, WorkflowStep, Preprocessing, Visualization Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/tRNAscanImport VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAscanImport/issues git_url: https://git.bioconductor.org/packages/tRNAscanImport git_branch: RELEASE_3_22 git_last_commit: ac13675 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tRNAscanImport_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tRNAscanImport_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tRNAscanImport_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tRNAscanImport_1.30.0.tgz vignettes: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.html vignetteTitles: tRNAscanImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.R suggestsMe: Structstrings, tRNA dependencyCount: 79 Package: TRONCO Version: 2.42.0 Depends: R (>= 4.1.0), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, grDevices, graphics, stats, utils, methods Suggests: BiocGenerics, BiocStyle, testthat, knitr, rWikiPathways, magick License: GPL-3 MD5sum: 14a8f30f24e214a246582c708a18bb64 NeedsCompilation: no Title: TRONCO, an R package for TRanslational ONCOlogy Description: The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC). biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork, SomaticMutation, NetworkInference, Network, Clustering, DataImport, SingleCell, ImmunoOncology Author: Marco Antoniotti [ctb], Giulio Caravagna [aut], Luca De Sano [cre, aut] (ORCID: ), Alex Graudenzi [aut], Giancarlo Mauri [ctb], Bud Mishra [ctb], Daniele Ramazzotti [aut] (ORCID: ) Maintainer: Luca De Sano URL: https://sites.google.com/site/troncopackage/ VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/TRONCO git_url: https://git.bioconductor.org/packages/TRONCO git_branch: RELEASE_3_22 git_last_commit: f575cac git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TRONCO_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TRONCO_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TRONCO_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TRONCO_2.42.0.tgz vignettes: vignettes/TRONCO/inst/doc/f1_introduction.html, vignettes/TRONCO/inst/doc/f2_loading_data.html, vignettes/TRONCO/inst/doc/f3_data_visualization.html, vignettes/TRONCO/inst/doc/f4_data_manipulation.html, vignettes/TRONCO/inst/doc/f5_model_inference.html, vignettes/TRONCO/inst/doc/f6_post_reconstruction.html, vignettes/TRONCO/inst/doc/f7_import_export.html vignetteTitles: f1_introduction.html, Loading data, Data visualization, Data manipulation, Model inference, Post reconstruction, Import/export from other tools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TRONCO/inst/doc/f1_introduction.R, vignettes/TRONCO/inst/doc/f2_loading_data.R, vignettes/TRONCO/inst/doc/f3_data_visualization.R, vignettes/TRONCO/inst/doc/f4_data_manipulation.R, vignettes/TRONCO/inst/doc/f5_model_inference.R, vignettes/TRONCO/inst/doc/f6_post_reconstruction.R, vignettes/TRONCO/inst/doc/f7_import_export.R dependencyCount: 47 Package: TSAR Version: 1.8.0 Depends: R (>= 4.3.0) Imports: dplyr (>= 1.0.7), ggplot2 (>= 3.3.5), ggpubr (>= 0.4.0), magrittr (>= 2.0.3), mgcv (>= 1.8.38), readxl (>= 1.4.0), stringr (>= 1.4.0), tidyr (>= 1.1.4), utils (>= 4.3.1), shiny (>= 1.7.4.1), plotly (>= 4.10.2), shinyjs (>= 2.1.0), jsonlite (>= 1.8.7), rhandsontable (>= 0.3.8), openxlsx (>= 4.2.5.2), shinyWidgets (>= 0.7.6), minpack.lm (>= 1.2.3) Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 MD5sum: cd25a686bcaef3a95327f10e98be7893 NeedsCompilation: no Title: Thermal Shift Analysis in R Description: This package automates analysis workflow for Thermal Shift Analysis (TSA) data. Processing, analyzing, and visualizing data through both shiny applications and command lines. Package aims to simplify data analysis and offer front to end workflow, from raw data to multiple trial analysis. biocViews: Software, ShinyApps, Visualization, qPCR Author: Xinlin Gao [aut, cre] (ORCID: ), William M. McFadden [aut, fnd] (ORCID: ), Stefan G. Sarafianos [fnd, aut, ths] (ORCID: ) Maintainer: Xinlin Gao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSAR git_branch: RELEASE_3_22 git_last_commit: 2730de0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TSAR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TSAR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TSAR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TSAR_1.8.0.tgz vignettes: vignettes/TSAR/inst/doc/FAQ_assistance.html, vignettes/TSAR/inst/doc/TSAR_Package_Structure.html, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Command.html, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Shiny.html vignetteTitles: Frequently Asked Questions, TSAR Package Structure, TSAR Workflow by Command, TSAR Workflow by Shiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSAR/inst/doc/FAQ_assistance.R, vignettes/TSAR/inst/doc/TSAR_Package_Structure.R, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Command.R, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Shiny.R dependencyCount: 130 Package: TSCAN Version: 1.48.0 Depends: R (>= 4.4.0), SingleCellExperiment, TrajectoryUtils Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots, methods, stats, Matrix, SummarizedExperiment, SparseArray (>= 1.5.23), DelayedArray (>= 0.31.9), S4Vectors Suggests: knitr, testthat, scuttle, scran, metapod, BiocParallel, BiocNeighbors, batchelor License: GPL(>=2) MD5sum: 12beec0b6b4ab87246c8610977565665 NeedsCompilation: no Title: Tools for Single-Cell Analysis Description: Provides methods to perform trajectory analysis based on a minimum spanning tree constructed from cluster centroids. Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji [aut, cre], Hongkai Ji [aut], Aaron Lun [ctb] Maintainer: Zhicheng Ji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSCAN git_branch: RELEASE_3_22 git_last_commit: 7f095e0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TSCAN_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TSCAN_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TSCAN_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TSCAN_1.48.0.tgz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R dependsOnMe: OSCA.advanced, OSCA.multisample importsMe: FEAST, singleCellTK, DIscBIO suggestsMe: condiments dependencyCount: 82 Package: ttgsea Version: 1.18.0 Depends: keras Imports: tm, text2vec, tokenizers, textstem, stopwords, data.table, purrr, DiagrammeR, stats Suggests: fgsea, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 72f1057b4789fda5ea3631e2ea315e47 NeedsCompilation: no Title: Tokenizing Text of Gene Set Enrichment Analysis Description: Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ttgsea git_branch: RELEASE_3_22 git_last_commit: 2003726 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ttgsea_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ttgsea_1.17.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ttgsea_1.18.0.tgz vignettes: vignettes/ttgsea/inst/doc/ttgsea.html vignetteTitles: ttgsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ttgsea/inst/doc/ttgsea.R importsMe: DeepPINCS, GenProSeq dependencyCount: 122 Package: TTMap Version: 1.32.0 Depends: rgl, colorRamps Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment, Biobase Suggests: BiocStyle, airway License: GPL-2 MD5sum: 3cf4cbfe89f53b4abbac14cb137e9b36 NeedsCompilation: no Title: Two-Tier Mapper: a clustering tool based on topological data analysis Description: TTMap is a clustering method that groups together samples with the same deviation in comparison to a control group. It is specially useful when the data is small. It is parameter free. biocViews: Software, Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Rachel Jeitziner Maintainer: Rachel Jeitziner git_url: https://git.bioconductor.org/packages/TTMap git_branch: RELEASE_3_22 git_last_commit: f7f5f32 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TTMap_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TTMap_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TTMap_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TTMap_1.32.0.tgz vignettes: vignettes/TTMap/inst/doc/TTMap.pdf vignetteTitles: Manual for the TTMap library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TTMap/inst/doc/TTMap.R dependencyCount: 55 Package: TurboNorm Version: 1.58.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata, hgu95av2cdf License: LGPL MD5sum: 51184c8b4661217ed80766eba2e0ceef NeedsCompilation: yes Title: A fast scatterplot smoother suitable for microarray normalization Description: A fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, DNAMethylation, CpGIsland, MethylationArray, Normalization Author: Maarten van Iterson and Chantal van Leeuwen Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html git_url: https://git.bioconductor.org/packages/TurboNorm git_branch: RELEASE_3_22 git_last_commit: cadb0c6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TurboNorm_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TurboNorm_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TurboNorm_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TurboNorm_1.58.0.tgz vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf vignetteTitles: TurboNorm Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TurboNorm/inst/doc/turbonorm.R dependencyCount: 18 Package: TVTB Version: 1.36.0 Depends: R (>= 3.4), methods, utils, stats Imports: AnnotationFilter, BiocGenerics (>= 0.25.1), BiocParallel, Biostrings, ensembldb, Seqinfo, GenomicRanges, GGally, ggplot2, Gviz, limma, IRanges (>= 2.21.6), reshape2, Rsamtools, S4Vectors (>= 0.25.14), SummarizedExperiment, VariantAnnotation (>= 1.19.9) Suggests: EnsDb.Hsapiens.v75 (>= 0.99.7), shiny (>= 0.13.2.9005), DT (>= 0.1.67), rtracklayer, BiocStyle (>= 2.5.19), knitr (>= 1.12), rmarkdown, testthat, covr, pander License: Artistic-2.0 MD5sum: 49fe345e9d262ae772e6924c25fd5498 NeedsCompilation: no Title: TVTB: The VCF Tool Box Description: The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE). biocViews: Software, Genetics, GeneticVariability, GenomicVariation, DataRepresentation, GUI, Genetics, DNASeq, WholeGenome, Visualization, MultipleComparison, DataImport, VariantAnnotation, Sequencing, Coverage, Alignment, SequenceMatching Author: Kevin Rue-Albrecht [aut, cre] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/TVTB VignetteBuilder: knitr BugReports: https://github.com/kevinrue/TVTB/issues git_url: https://git.bioconductor.org/packages/TVTB git_branch: RELEASE_3_22 git_last_commit: f5471e1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/TVTB_1.36.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TVTB_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TVTB_1.36.0.tgz vignettes: vignettes/TVTB/inst/doc/Introduction.html, vignettes/TVTB/inst/doc/tSVE.html, vignettes/TVTB/inst/doc/VcfFilterRules.html vignetteTitles: Introduction to TVTB, The Shiny Variant Explorer, VCF filter rules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TVTB/inst/doc/Introduction.R, vignettes/TVTB/inst/doc/tSVE.R, vignettes/TVTB/inst/doc/VcfFilterRules.R dependencyCount: 160 Package: tweeDEseq Version: 1.56.0 Depends: R (>= 4.3.0) Imports: Rcpp (>= 1.0.10), MASS, limma, edgeR, parallel, cqn, grDevices, graphics, stats, utils LinkingTo: Rcpp Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) MD5sum: 1167a03817f09340c31fd77201969c75 NeedsCompilation: yes Title: RNA-seq data analysis using the Poisson-Tweedie family of distributions Description: Differential expression analysis of RNA-seq using the Poisson-Tweedie (PT) family of distributions. PT distributions are described by a mean, a dispersion and a shape parameter and include Poisson and NB distributions, among others, as particular cases. An important feature of this family is that, while the Negative Binomial (NB) distribution only allows a quadratic mean-variance relationship, the PT distributions generalizes this relationship to any orde. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, Sequencing, RNASeq, DNASeq Author: Dolors Pelegri-Siso [aut, cre] (ORCID: ), Juan R. Gonzalez [aut] (ORCID: ), Mikel Esnaola [aut], Robert Castelo [aut] Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/tweeDEseq/ BugReports: https://github.com/isglobal-brge/tweeDEseq/issues git_url: https://git.bioconductor.org/packages/tweeDEseq git_branch: RELEASE_3_22 git_last_commit: 07f346b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tweeDEseq_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tweeDEseq_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tweeDEseq_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tweeDEseq_1.56.0.tgz vignettes: vignettes/tweeDEseq/inst/doc/tweeDEseq.pdf vignetteTitles: tweeDEseq: analysis of RNA-seq data using the Poisson-Tweedie family of distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tweeDEseq/inst/doc/tweeDEseq.R importsMe: ptmixed dependencyCount: 23 Package: twilight Version: 1.86.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, splines, stats Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2) License: GPL (>= 2) MD5sum: a4c454c17157d9538f6d09ea3ba77480 NeedsCompilation: yes Title: Estimation of local false discovery rate Description: In a typical microarray setting with gene expression data observed under two conditions, the local false discovery rate describes the probability that a gene is not differentially expressed between the two conditions given its corrresponding observed score or p-value level. The resulting curve of p-values versus local false discovery rate offers an insight into the twilight zone between clear differential and clear non-differential gene expression. Package 'twilight' contains two main functions: Function twilight.pval performs a two-condition test on differences in means for a given input matrix or expression set and computes permutation based p-values. Function twilight performs a stochastic downhill search to estimate local false discovery rates and effect size distributions. The package further provides means to filter for permutations that describe the null distribution correctly. Using filtered permutations, the influence of hidden confounders could be diminished. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Stefanie Senger [cre, aut] (ORCID: ) Maintainer: Stefanie Senger URL: http://compdiag.molgen.mpg.de/software/twilight.shtml git_url: https://git.bioconductor.org/packages/twilight git_branch: RELEASE_3_22 git_last_commit: 4db5592 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/twilight_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/twilight_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/twilight_1.86.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/twilight_1.86.0.tgz vignettes: vignettes/twilight/inst/doc/tr_2004_01.pdf vignetteTitles: Estimation of Local False Discovery Rates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twilight/inst/doc/tr_2004_01.R dependsOnMe: OrderedList dependencyCount: 9 Package: twoddpcr Version: 1.34.0 Depends: R (>= 3.4) Imports: class, ggplot2, hexbin, methods, scales, shiny, stats, utils, RColorBrewer, S4Vectors Suggests: devtools, knitr, reshape2, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 6eedee4c8ebbcfb8ba3a7347e1dcffba NeedsCompilation: no Title: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules Description: The twoddpcr package takes Droplet Digital PCR (ddPCR) droplet amplitude data from Bio-Rad's QuantaSoft and can classify the droplets. A summary of the positive/negative droplet counts can be generated, which can then be used to estimate the number of molecules using the Poisson distribution. This is the first open source package that facilitates the automatic classification of general two channel ddPCR data. Previous work includes 'definetherain' (Jones et al., 2014) and 'ddpcRquant' (Trypsteen et al., 2015) which both handle one channel ddPCR experiments only. The 'ddpcr' package available on CRAN (Attali et al., 2016) supports automatic gating of a specific class of two channel ddPCR experiments only. biocViews: ddPCR, Software, Classification Author: Anthony Chiu [aut, cre] Maintainer: Anthony Chiu URL: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/ VignetteBuilder: knitr BugReports: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/issues/ git_url: https://git.bioconductor.org/packages/twoddpcr git_branch: RELEASE_3_22 git_last_commit: 75a6d44 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/twoddpcr_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/twoddpcr_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/twoddpcr_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/twoddpcr_1.34.0.tgz vignettes: vignettes/twoddpcr/inst/doc/twoddpcr.html vignetteTitles: twoddpcr: A package for Droplet Digital PCR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twoddpcr/inst/doc/twoddpcr.R dependencyCount: 55 Package: txcutr Version: 1.16.0 Depends: R (>= 4.5.0) Imports: AnnotationDbi, GenomicFeatures, txdbmaker, IRanges, GenomicRanges, BiocGenerics, Biostrings, S4Vectors, rtracklayer, BiocParallel, stats, methods, utils Suggests: RefManageR, BiocStyle, knitr, sessioninfo, rmarkdown, testthat (>= 3.0.0), TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, BSgenome.Scerevisiae.UCSC.sacCer3, GenomeInfoDbData License: GPL-3 MD5sum: 1391b32317473ba2bbcc82a02c71fb9a NeedsCompilation: no Title: Transcriptome CUTteR Description: Various mRNA sequencing library preparation methods generate sequencing reads specifically from the transcript ends. Analyses that focus on quantification of isoform usage from such data can be aided by using truncated versions of transcriptome annotations, both at the alignment or pseudo-alignment stage, as well as in downstream analysis. This package implements some convenience methods for readily generating such truncated annotations and their corresponding sequences. biocViews: Alignment, Annotation, RNASeq, Sequencing, Transcriptomics Author: Mervin Fansler [aut, cre] (ORCID: ) Maintainer: Mervin Fansler URL: https://github.com/mfansler/txcutr VignetteBuilder: knitr BugReports: https://github.com/mfansler/txcutr/issues git_url: https://git.bioconductor.org/packages/txcutr git_branch: RELEASE_3_22 git_last_commit: c2ea2a8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/txcutr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/txcutr_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/txcutr_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/txcutr_1.16.0.tgz vignettes: vignettes/txcutr/inst/doc/intro.html vignetteTitles: Introduction to txcutr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/txcutr/inst/doc/intro.R dependencyCount: 100 Package: txdbmaker Version: 1.6.0 Depends: BiocGenerics, S4Vectors (>= 0.47.6), Seqinfo, GenomicRanges (>= 1.61.1), GenomicFeatures (>= 1.61.4) Imports: methods, utils, stats, tools, httr, rjson, DBI, RSQLite (>= 2.0), IRanges, UCSC.utils, GenomeInfoDb, AnnotationDbi, Biobase, BiocIO, rtracklayer, biomaRt (>= 2.59.1) Suggests: RMariaDB, ensembldb, GenomeInfoDbData, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 26fd51d57cfe04d1272e8e62bb183cc9 NeedsCompilation: no Title: Tools for making TxDb objects from genomic annotations Description: A set of tools for making TxDb objects from genomic annotations from various sources (e.g. UCSC, Ensembl, and GFF files). These tools allow the user to download the genomic locations of transcripts, exons, and CDS, for a given assembly, and to import them in a TxDb object. TxDb objects are implemented in the GenomicFeatures package, together with flexible methods for extracting the desired features in convenient formats. biocViews: Infrastructure, DataImport, Annotation, GenomeAnnotation, GenomeAssembly, Genetics, Sequencing Author: H. Pagès [aut, cre], M. Carlson [aut], P. Aboyoun [aut], S. Falcon [aut], M. Morgan [aut], R. Castelo [ctb], M. Lawrence [ctb], J. MacDonald [ctb], M. Ramos [ctb], S. Saini [ctb], L. Shepherd [ctb] Maintainer: H. Pagès URL: https://bioconductor.org/packages/txdbmaker VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/txdbmaker/issues git_url: https://git.bioconductor.org/packages/txdbmaker git_branch: RELEASE_3_22 git_last_commit: eed71e0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/txdbmaker_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/txdbmaker_1.5.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/txdbmaker_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/txdbmaker_1.6.0.tgz vignettes: vignettes/txdbmaker/inst/doc/txdbmaker.html vignetteTitles: Making TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/txdbmaker/inst/doc/txdbmaker.R dependsOnMe: mygene importsMe: ASpli, BgeeCall, crisprDesign, crisprViz, DegNorm, ELViS, EpiTxDb, GenomicPlot, IntEREst, metaseqR2, ORFik, OUTRIDER, proActiv, proBAMr, QuasR, RCAS, recoup, Rhisat2, RiboDiPA, ribosomeProfilingQC, RNAmodR, scafari, scanMiRApp, scruff, sitadela, trackViewer, txcutr, tximeta, GenomicState, geneLenDataBase suggestsMe: AnnotationHub, BindingSiteFinder, Bioc.gff, bumphunter, BUSpaRse, DEXSeq, doubletrouble, eisaR, GenomicFeatures, GenomicRanges, OrganismDbi, raer, recount, saseR, SplicingGraphs, SPLINTER, systemPipeR dependencyCount: 99 Package: tximeta Version: 1.28.0 Depends: R (>= 4.1.0) Imports: SummarizedExperiment (>= 1.39.1), tximport, jsonlite, S4Vectors, IRanges, GenomicRanges (>= 1.61.1), AnnotationDbi, GenomicFeatures, txdbmaker, ensembldb, BiocFileCache, AnnotationHub, Biostrings, tibble, Seqinfo, tools, utils, methods, Matrix Suggests: knitr, rmarkdown, testthat, tximportData (>= 1.37.5), org.Dm.eg.db, DESeq2, edgeR, limma, devtools, macrophage License: GPL-2 MD5sum: 9fc2e33fd4986a3396cb642e4748ce92 NeedsCompilation: no Title: Transcript Quantification Import with Automatic Metadata Description: Transcript quantification import from Salmon and other quantifiers with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility. biocViews: Annotation, GenomeAnnotation, DataImport, Preprocessing, RNASeq, SingleCell, Transcriptomics, Transcription, GeneExpression, FunctionalGenomics, ReproducibleResearch, ReportWriting, ImmunoOncology Author: Michael Love [aut, cre], Charlotte Soneson [aut, ctb], Peter Hickey [aut, ctb], Rob Patro [aut, ctb], NIH NHGRI [fnd], CZI [fnd] Maintainer: Michael Love URL: https://github.com/thelovelab/tximeta VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/tximeta git_branch: RELEASE_3_22 git_last_commit: 4479514 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tximeta_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tximeta_1.27.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tximeta_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tximeta_1.28.0.tgz vignettes: vignettes/tximeta/inst/doc/tximeta.html vignetteTitles: tximeta: transcript quantification import with automatic metadata hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximeta/inst/doc/tximeta.R dependsOnMe: rnaseqGene importsMe: IsoformSwitchAnalyzeR suggestsMe: DESeq2, fishpond, fluentGenomics dependencyCount: 109 Package: tximport Version: 1.38.0 Imports: utils, stats, methods Suggests: knitr, rmarkdown, testthat, tximportData, TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), arrow, limma, edgeR, DESeq2 (>= 1.11.6), rhdf5, jsonlite, matrixStats, Matrix, eds License: LGPL (>=2) Archs: x64 MD5sum: 08b18d4e64142c563d26f90220fd5b25 NeedsCompilation: no Title: Import and summarize transcript-level estimates for transcript- and gene-level analysis Description: Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts. biocViews: DataImport, Preprocessing, RNASeq, Transcriptomics, Transcription, GeneExpression, ImmunoOncology Author: Michael Love [cre,aut], Charlotte Soneson [aut], Mark Robinson [aut], Rob Patro [ctb], Andrew Parker Morgan [ctb], Ryan C. Thompson [ctb], Matt Shirley [ctb], Avi Srivastava [ctb] Maintainer: Michael Love URL: https://github.com/thelovelab/tximport VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximport git_branch: RELEASE_3_22 git_last_commit: 452dc1a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/tximport_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tximport_1.37.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tximport_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tximport_1.38.0.tgz vignettes: vignettes/tximport/inst/doc/tximport.html vignetteTitles: Importing transcript abundance datasets with tximport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximport/inst/doc/tximport.R importsMe: alevinQC, BgeeCall, CleanUpRNAseq, DifferentialRegulation, EventPointer, IsoformSwitchAnalyzeR, singleCellTK, TDbasedUFE, tximeta, ExpHunterSuite, cpam suggestsMe: BANDITS, DESeq2, variancePartition dependencyCount: 3 Package: UCell Version: 2.14.0 Depends: R(>= 4.3.0) Imports: methods, data.table(>= 1.13.6), Matrix, stats, BiocParallel, BiocNeighbors, SingleCellExperiment, SummarizedExperiment Suggests: scater, scRNAseq, reshape2, patchwork, ggplot2, BiocStyle, Seurat(>= 5.0.0), SeuratObject(>= 5.0.0), knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: c7e5305d8821b2fc2fa2cdccd9848636 NeedsCompilation: no Title: Rank-based signature enrichment analysis for single-cell data Description: UCell is a package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with SingleCellExperiment and Seurat objects. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, GeneExpression, CellBasedAssays Author: Massimo Andreatta [aut, cre] (ORCID: ), Santiago Carmona [aut] (ORCID: ) Maintainer: Massimo Andreatta URL: https://github.com/carmonalab/UCell VignetteBuilder: knitr BugReports: https://github.com/carmonalab/UCell/issues git_url: https://git.bioconductor.org/packages/UCell git_branch: RELEASE_3_22 git_last_commit: 4d11456 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/UCell_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/UCell_2.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/UCell_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/UCell_2.14.0.tgz vignettes: vignettes/UCell/inst/doc/UCell_parameters.html, vignettes/UCell/inst/doc/UCell_sce.html, vignettes/UCell/inst/doc/UCell_Seurat.html, vignettes/UCell/inst/doc/UCell_vignette_basic.html vignetteTitles: 4. Some important parameters for UCell, 2. Using UCell with SingleCellExperiment, 3. Using UCell with Seurat, 1. Gene signature scoring with UCell hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/UCell/inst/doc/UCell_parameters.R, vignettes/UCell/inst/doc/UCell_sce.R, vignettes/UCell/inst/doc/UCell_Seurat.R, vignettes/UCell/inst/doc/UCell_vignette_basic.R importsMe: scGate suggestsMe: escape, scLANE, SCpubr dependencyCount: 40 Package: UCSC.utils Version: 1.6.0 Imports: methods, stats, httr, jsonlite, S4Vectors (>= 0.47.6) Suggests: DBI, RMariaDB, GenomeInfoDb, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 88d9dd1d3ab8c5597926f4da7a3fabf4 NeedsCompilation: no Title: Low-level utilities to retrieve data from the UCSC Genome Browser Description: A set of low-level utilities to retrieve data from the UCSC Genome Browser. Most functions in the package access the data via the UCSC REST API but some of them query the UCSC MySQL server directly. Note that the primary purpose of the package is to support higher-level functionalities implemented in downstream packages like GenomeInfoDb or txdbmaker. biocViews: Infrastructure, GenomeAssembly, Annotation, GenomeAnnotation, DataImport Author: Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/UCSC.utils VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/UCSC.utils/issues git_url: https://git.bioconductor.org/packages/UCSC.utils git_branch: RELEASE_3_22 git_last_commit: a6d3175 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/UCSC.utils_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/UCSC.utils_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/UCSC.utils_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/UCSC.utils_1.6.0.tgz vignettes: vignettes/UCSC.utils/inst/doc/UCSC.utils.html vignetteTitles: The UCSC.utils package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UCSC.utils/inst/doc/UCSC.utils.R importsMe: GenomeInfoDb, txdbmaker dependencyCount: 17 Package: Ularcirc Version: 1.28.0 Depends: R (>= 3.4.0) Imports: AnnotationHub, AnnotationDbi, BiocGenerics, Biostrings, BSgenome, data.table (>= 1.9.4), DT, GenomicFeatures, GenomeInfoDb, GenomeInfoDbData, GenomicAlignments, GenomicRanges, ggplot2, ggrepel, gsubfn, moments, Organism.dplyr, plotgardener,R.utils, S4Vectors, shiny, shinydashboard, shinyFiles, shinyjs, yaml Suggests: BSgenome.Hsapiens.UCSC.hg38, BiocStyle, httpuv, knitr, org.Hs.eg.db, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: a96dd9dad6695dd158963002c27f6c17 NeedsCompilation: no Title: Shiny app for canonical and back splicing analysis (i.e. circular and mRNA analysis) Description: Ularcirc reads in STAR aligned splice junction files and provides visualisation and analysis tools for splicing analysis. Users can assess backsplice junctions and forward canonical junctions. biocViews: DataRepresentation,Visualization, Genetics, Sequencing, Annotation, Coverage, AlternativeSplicing, DifferentialSplicing Author: David Humphreys [aut, cre] Maintainer: David Humphreys VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Ularcirc git_branch: RELEASE_3_22 git_last_commit: e6f04b4 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Ularcirc_1.28.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Ularcirc_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Ularcirc_1.28.0.tgz vignettes: vignettes/Ularcirc/inst/doc/Ularcirc.html vignetteTitles: Ularcirc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Ularcirc/inst/doc/Ularcirc.R dependencyCount: 158 Package: UMI4Cats Version: 1.20.0 Depends: R (>= 4.1.0), SummarizedExperiment Imports: magick, cowplot, scales, GenomicRanges, ShortRead, zoo, ggplot2, reshape2, regioneR, IRanges, S4Vectors, dplyr, BSgenome, Biostrings, DESeq2, R.utils, Rsamtools, stringr, Rbowtie2, methods, GenomeInfoDb, GenomicAlignments, RColorBrewer, utils, grDevices, stats, annotate, rlang, GenomicFeatures, BiocFileCache, rappdirs, fda, BiocGenerics Suggests: knitr, rmarkdown, BiocStyle, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, tidyr, testthat License: Artistic-2.0 MD5sum: 3fe8d0d790a4827f808460cb5919df31 NeedsCompilation: no Title: UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data Description: UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers (UMIs) that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions. This package allows processing of UMI-4C data, starting from FastQ files provided by the sequencing facility. It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay. biocViews: QualityControl, Preprocessing, Alignment, Normalization, Visualization, Sequencing, Coverage Author: Mireia Ramos-Rodriguez [aut, cre] (ORCID: ), Marc Subirana-Granes [aut], Lorenzo Pasquali [aut] Maintainer: Mireia Ramos-Rodriguez URL: https://github.com/Pasquali-lab/UMI4Cats VignetteBuilder: knitr BugReports: https://github.com/Pasquali-lab/UMI4Cats/issues git_url: https://git.bioconductor.org/packages/UMI4Cats git_branch: RELEASE_3_22 git_last_commit: ddc6b2d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/UMI4Cats_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/UMI4Cats_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/UMI4Cats_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/UMI4Cats_1.20.0.tgz vignettes: vignettes/UMI4Cats/inst/doc/UMI4Cats.html vignetteTitles: Analyzing UMI-4C data with UMI4Cats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UMI4Cats/inst/doc/UMI4Cats.R dependencyCount: 149 Package: uncoverappLib Version: 1.20.0 Imports: markdown, shiny, shinyjs, shinyBS, shinyWidgets,shinycssloaders, DT, Gviz, Homo.sapiens, openxlsx, condformat, stringr, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, BiocFileCache,rappdirs, TxDb.Hsapiens.UCSC.hg19.knownGene, rlist, utils,S4Vectors, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v86, OrganismDbi, processx, Rsamtools, GenomicRanges Suggests: BiocStyle, knitr, testthat, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: c809e2ccc3c39badf60cf5617ae910be NeedsCompilation: no Title: Interactive graphical application for clinical assessment of sequence coverage at the base-pair level Description: a Shiny application containing a suite of graphical and statistical tools to support clinical assessment of low coverage regions.It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. uncoverAPP provides a statisticl summary of coverage given target file or genes name. biocViews: Software, Visualization, Annotation, Coverage Author: Emanuela Iovino [cre, aut], Tommaso Pippucci [aut] Maintainer: Emanuela Iovino URL: https://github.com/Manuelaio/uncoverappLib VignetteBuilder: knitr BugReports: https://github.com/Manuelaio/uncoverappLib/issues git_url: https://git.bioconductor.org/packages/uncoverappLib git_branch: RELEASE_3_22 git_last_commit: e5e7acf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/uncoverappLib_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/uncoverappLib_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/uncoverappLib_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/uncoverappLib_1.20.0.tgz vignettes: vignettes/uncoverappLib/inst/doc/uncoverappLib.html vignetteTitles: uncoverappLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/uncoverappLib/inst/doc/uncoverappLib.R dependencyCount: 185 Package: UNDO Version: 1.52.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 Archs: x64 MD5sum: b8b1a38445eeaee922678cf1ea314ba5 NeedsCompilation: no Title: Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions Description: UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge. biocViews: Software Author: Niya Wang Maintainer: Niya Wang git_url: https://git.bioconductor.org/packages/UNDO git_branch: RELEASE_3_22 git_last_commit: 6244b5b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/UNDO_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/UNDO_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/UNDO_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/UNDO_1.52.0.tgz vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf vignetteTitles: UNDO Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R dependencyCount: 11 Package: unifiedWMWqPCR Version: 1.46.0 Depends: methods Imports: BiocGenerics, limma, stats, graphics License: GPL (>=2) MD5sum: ea2b269c62cbf3e6ed9047f044312fba NeedsCompilation: no Title: Unified Wilcoxon-Mann Whitney Test for testing differential expression in qPCR data Description: This packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data. biocViews: DifferentialExpression, GeneExpression, MicrotitrePlateAssay, MultipleComparison, QualityControl, Software, Visualization, qPCR Author: Jan R. De Neve & Joris Meys Maintainer: Joris Meys git_url: https://git.bioconductor.org/packages/unifiedWMWqPCR git_branch: RELEASE_3_22 git_last_commit: f29f417 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/unifiedWMWqPCR_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/unifiedWMWqPCR_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/unifiedWMWqPCR_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/unifiedWMWqPCR_1.46.0.tgz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: Using unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R dependencyCount: 9 Package: UniProt.ws Version: 2.50.0 Depends: R (>= 4.5.0) Imports: AnnotationDbi, BiocFileCache, BiocBaseUtils, BiocGenerics, httr2, jsonlite, methods, progress, rjsoncons, rlang, utils Suggests: BiocStyle, knitr, rmarkdown, tinytest License: Artistic-2.0 MD5sum: 6633c7099e896a0223452485690932aa NeedsCompilation: no Title: R Interface to UniProt Web Services Description: The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. This package provides a collection of functions for retrieving, processing, and re-packaging UniProt web services. The package makes use of UniProt's modernized REST API and allows mapping of identifiers accross different databases. biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta Author: Marc Carlson [aut], Csaba Ortutay [ctb], Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/UniProt.ws VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/UniProt.ws/issues git_url: https://git.bioconductor.org/packages/UniProt.ws git_branch: RELEASE_3_22 git_last_commit: d7b37e1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/UniProt.ws_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/UniProt.ws_2.49.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/UniProt.ws_2.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/UniProt.ws_2.50.0.tgz vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.html vignetteTitles: UniProt.ws: A package for retrieving data from the UniProt web service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UniProt.ws/inst/doc/UniProt.ws.R importsMe: dagLogo, drugTargetInteractions, ginmappeR, immunogenViewer suggestsMe: autonomics, cleaver, qPLEXanalyzer dependencyCount: 64 Package: Uniquorn Version: 2.30.0 Depends: R (>= 3.5) Imports: stringr, R.utils, WriteXLS, stats, doParallel, foreach, GenomicRanges, IRanges, VariantAnnotation, data.table Suggests: testthat, knitr, rmarkdown, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: 48578c77d285cd47190229118bd92a67 NeedsCompilation: no Title: Identification of cancer cell lines based on their weighted mutational/ variational fingerprint Description: 'Uniquorn' enables users to identify cancer cell lines. Cancer cell line misidentification and cross-contamination reprents a significant challenge for cancer researchers. The identification is vital and in the frame of this package based on the locations/ loci of somatic and germline mutations/ variations. The input format is vcf/ vcf.gz and the files have to contain a single cancer cell line sample (i.e. a single member/genotype/gt column in the vcf file). biocViews: ImmunoOncology, StatisticalMethod, WholeGenome, ExomeSeq Author: Raik Otto Maintainer: Raik Otto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Uniquorn git_branch: RELEASE_3_22 git_last_commit: cf421c9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Uniquorn_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Uniquorn_2.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Uniquorn_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Uniquorn_2.30.0.tgz vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html vignetteTitles: Uniquorn vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 89 Package: universalmotif Version: 1.27.4 Depends: R (>= 4.1.0) Imports: methods, stats, utils, MASS, ggplot2, yaml, IRanges, Rcpp, Biostrings, BiocGenerics, S4Vectors, rlang, grid, MatrixGenerics LinkingTo: Rcpp, RcppThread Suggests: spelling, knitr, bookdown, TFBSTools, rmarkdown, MotifDb, testthat, BiocParallel, seqLogo, motifStack, dplyr, ape, ggtree, processx, ggseqlogo, cowplot, GenomicRanges, ggbio Enhances: PWMEnrich, rGADEM License: GPL-3 MD5sum: 788ecae19529b2b2f6610ff466a78ee4 NeedsCompilation: yes Title: Import, Modify, and Export Motifs with R Description: Allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others. biocViews: MotifAnnotation, MotifDiscovery, DataImport, GeneRegulation Author: Benjamin Jean-Marie Tremblay [aut, cre] (ORCID: ), Spencer Nystrom [ctb] (ORCID: ) Maintainer: Benjamin Jean-Marie Tremblay URL: https://bioconductor.org/packages/universalmotif/ VignetteBuilder: knitr BugReports: https://github.com/bjmt/universalmotif/issues git_url: https://git.bioconductor.org/packages/universalmotif git_branch: devel git_last_commit: 26a42b3 git_last_commit_date: 2025-09-29 Date/Publication: 2025-10-07 source.ver: src/contrib/universalmotif_1.27.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/universalmotif_1.27.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/universalmotif_1.27.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/universalmotif_1.27.4.tgz vignettes: vignettes/universalmotif/inst/doc/Introduction.pdf, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.pdf, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.pdf, vignettes/universalmotif/inst/doc/MotifManipulation.pdf, vignettes/universalmotif/inst/doc/SequenceSearches.pdf vignetteTitles: Introduction to "universalmotif", Introduction to sequence motifs, Motif comparisons and P-values, Motif import,, export,, and manipulation, Sequence manipulation and scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/universalmotif/inst/doc/Introduction.R, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.R, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.R, vignettes/universalmotif/inst/doc/MotifManipulation.R, vignettes/universalmotif/inst/doc/SequenceSearches.R importsMe: ChIPpeakAnno, circRNAprofiler, memes, MotifPeeker dependencyCount: 38 Package: updateObject Version: 1.14.0 Depends: R (>= 4.2.0), methods, BiocGenerics (>= 0.51.1), S4Vectors Imports: utils, digest Suggests: GenomicRanges, SummarizedExperiment, InteractionSet, SingleCellExperiment, MultiAssayExperiment, BiSeq, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 3d70050f1d12fc76fb1cb870ecc8b842 NeedsCompilation: no Title: Find/fix old serialized S4 instances Description: A set of tools built around updateObject() to work with old serialized S4 instances. The package is primarily useful to package maintainers who want to update the serialized S4 instances included in their package. This is still work-in-progress. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/updateObject SystemRequirements: git VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/updateObject/issues git_url: https://git.bioconductor.org/packages/updateObject git_branch: RELEASE_3_22 git_last_commit: f166607 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/updateObject_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/updateObject_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/updateObject_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/updateObject_1.14.0.tgz vignettes: vignettes/updateObject/inst/doc/updateObject.html vignetteTitles: A quick introduction to the updateObject package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/updateObject/inst/doc/updateObject.R dependencyCount: 9 Package: UPDhmm Version: 1.6.0 Depends: R (>= 4.1.0) Imports: HMM, utils, VariantAnnotation, GenomicRanges, S4Vectors, IRanges, SummarizedExperiment, Biobase, stats, BiocParallel, GenomeInfoDb Suggests: knitr, testthat (>= 2.1.0), BiocStyle, rmarkdown, markdown, karyoploteR, regioneR, dplyr, BiocManager License: MIT + file LICENSE MD5sum: 2652e051f2c1b2637a7ab5ec20215f0a NeedsCompilation: no Title: Detecting Uniparental Disomy through NGS trio data Description: Uniparental disomy (UPD) is a genetic condition where an individual inherits both copies of a chromosome or part of it from one parent, rather than one copy from each parent. This package contains a HMM for detecting UPDs through HTS (High Throughput Sequencing) data from trio assays. By analyzing the genotypes in the trio, the model infers a hidden state (normal, father isodisomy, mother isodisomy, father heterodisomy and mother heterodisomy). biocViews: Software, HiddenMarkovModel, Genetics Author: Marta Sevilla [aut, cre] (ORCID: ), Carlos Ruiz-Arenas [aut] (ORCID: ) Maintainer: Marta Sevilla URL: https://github.com/martasevilla/UPDhmm VignetteBuilder: knitr BugReports: https://github.com/martasevilla/UPDhmm/issues git_url: https://git.bioconductor.org/packages/UPDhmm git_branch: RELEASE_3_22 git_last_commit: 0af356a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/UPDhmm_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/UPDhmm_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/UPDhmm_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/UPDhmm_1.6.0.tgz vignettes: vignettes/UPDhmm/inst/doc/preprocessing.html, vignettes/UPDhmm/inst/doc/UPDhmm.html vignetteTitles: VCF Preprocessing User Guide, UPDhmm User Guide: From Detection to Postprocessing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/UPDhmm/inst/doc/preprocessing.R, vignettes/UPDhmm/inst/doc/UPDhmm.R dependencyCount: 81 Package: uSORT Version: 1.36.0 Depends: R (>= 3.3.0), tcltk Imports: igraph, Matrix, RANN, RSpectra, VGAM, gplots, parallel, plyr, methods, cluster, Biobase, fpc, BiocGenerics, monocle, grDevices, graphics, stats, utils Suggests: knitr, RUnit, testthat, ggplot2 License: Artistic-2.0 MD5sum: dfddf4a0bfac1ced9b919387d457767b NeedsCompilation: no Title: uSORT: A self-refining ordering pipeline for gene selection Description: This package is designed to uncover the intrinsic cell progression path from single-cell RNA-seq data. It incorporates data pre-processing, preliminary PCA gene selection, preliminary cell ordering, feature selection, refined cell ordering, and post-analysis interpretation and visualization. biocViews: ImmunoOncology, RNASeq, GUI, CellBiology, DNASeq Author: Mai Chan Lau, Hao Chen, Jinmiao Chen Maintainer: Hao Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/uSORT git_branch: RELEASE_3_22 git_last_commit: a4638f8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/uSORT_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/uSORT_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/uSORT_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/uSORT_1.36.0.tgz vignettes: vignettes/uSORT/inst/doc/uSORT_quick_start.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/uSORT/inst/doc/uSORT_quick_start.R dependencyCount: 92 Package: VAExprs Version: 1.16.0 Depends: keras, mclust Imports: SingleCellExperiment, SummarizedExperiment, tensorflow, scater, CatEncoders, DeepPINCS, purrr, DiagrammeR, stats Suggests: SC3, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: e4ca49608662dbea882c9623311bd15a NeedsCompilation: no Title: Generating Samples of Gene Expression Data with Variational Autoencoders Description: A fundamental problem in biomedical research is the low number of observations, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. By augmenting a few real observations with artificially generated samples, their analysis could lead to more robust and higher reproducible. One possible solution to the problem is the use of generative models, which are statistical models of data that attempt to capture the entire probability distribution from the observations. Using the variational autoencoder (VAE), a well-known deep generative model, this package is aimed to generate samples with gene expression data, especially for single-cell RNA-seq data. Furthermore, the VAE can use conditioning to produce specific cell types or subpopulations. The conditional VAE (CVAE) allows us to create targeted samples rather than completely random ones. biocViews: Software, GeneExpression, SingleCell Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VAExprs git_branch: RELEASE_3_22 git_last_commit: 1b7ae7b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VAExprs_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VAExprs_1.15.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VAExprs_1.16.0.tgz vignettes: vignettes/VAExprs/inst/doc/VAExprs.html vignetteTitles: VAExprs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VAExprs/inst/doc/VAExprs.R suggestsMe: GenProSeq dependencyCount: 202 Package: VanillaICE Version: 1.72.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.27.6), SummarizedExperiment (>= 1.5.3) Imports: MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges (>= 1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb (>= 1.11.4), crlmm, tools, stats, utils, BSgenome.Hsapiens.UCSC.hg18 Suggests: RUnit, human610quadv1bCrlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 MD5sum: 986b373b7dfc8b96c3345e4f6a0f5514 NeedsCompilation: yes Title: A Hidden Markov Model for high throughput genotyping arrays Description: Hidden Markov Models for characterizing chromosomal alteration in high throughput SNP arrays. biocViews: CopyNumberVariation Author: Robert Scharpf [aut, cre] Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/VanillaICE git_branch: RELEASE_3_22 git_last_commit: c1e8f15 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VanillaICE_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VanillaICE_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VanillaICE_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VanillaICE_1.72.0.tgz vignettes: vignettes/VanillaICE/inst/doc/crlmmDownstream.pdf, vignettes/VanillaICE/inst/doc/VanillaICE.pdf vignetteTitles: crlmmDownstream, VanillaICE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VanillaICE/inst/doc/crlmmDownstream.R, vignettes/VanillaICE/inst/doc/VanillaICE.R dependsOnMe: MinimumDistance suggestsMe: oligoClasses dependencyCount: 96 Package: VarCon Version: 1.18.0 Depends: Biostrings, BSgenome, GenomicRanges, R (>= 4.1) Imports: methods, stats, IRanges, shiny, shinycssloaders, shinyFiles, ggplot2 Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: e3f1579c85a76a99d058c61d7f735f84 NeedsCompilation: no Title: VarCon: an R package for retrieving neighboring nucleotides of an SNV Description: VarCon is an R package which converts the positional information from the annotation of an single nucleotide variation (SNV) (either referring to the coding sequence or the reference genomic sequence). It retrieves the genomic reference sequence around the position of the single nucleotide variation. To asses, whether the SNV could potentially influence binding of splicing regulatory proteins VarCon calcualtes the HEXplorer score as an estimation. Besides, VarCon additionally reports splice site strengths of splice sites within the retrieved genomic sequence and any changes due to the SNV. biocViews: FunctionalGenomics, AlternativeSplicing Author: Johannes Ptok [aut, cre] Maintainer: Johannes Ptok VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VarCon git_branch: RELEASE_3_22 git_last_commit: 5d3f112 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VarCon_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VarCon_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VarCon_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VarCon_1.18.0.tgz vignettes: vignettes/VarCon/inst/doc/VarCon.html vignetteTitles: Analysing SNVs with VarCon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VarCon/inst/doc/VarCon.R dependencyCount: 101 Package: variancePartition Version: 1.40.0 Depends: R (>= 4.3.0), ggplot2, limma (>= 3.62.2), BiocParallel Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, Matrix (>= 1.4.0), iterators, gplots, corpcor, matrixStats, RhpcBLASctl, reshape2, gtools, remaCor (>= 0.0.15), fANCOVA, aod, scales, Rdpack, rlang, lme4 (>= 1.1.33), grDevices, graphics, Biobase, methods, utils, stats Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, RUnit, cowplot, Rfast, zenith, statmod, BiocGenerics, r2glmm, readr License: GPL-2 MD5sum: c1461f06b4b3e0425aa63acd9360093e NeedsCompilation: no Title: Quantify and interpret drivers of variation in multilevel gene expression experiments Description: Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures. biocViews: RNASeq, GeneExpression, GeneSetEnrichment, DifferentialExpression, BatchEffect, QualityControl, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Preprocessing, Microarray, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] (ORCID: ) Maintainer: Gabriel E. Hoffman URL: http://bioconductor.org/packages/variancePartition, https://DiseaseNeuroGenomics.github.io/variancePartition VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeuroGenomics/variancePartition/issues git_url: https://git.bioconductor.org/packages/variancePartition git_branch: RELEASE_3_22 git_last_commit: 1ff4f97 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/variancePartition_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/variancePartition_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/variancePartition_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/variancePartition_1.40.0.tgz vignettes: vignettes/variancePartition/inst/doc/additional_visualization.html, vignettes/variancePartition/inst/doc/dream.html, vignettes/variancePartition/inst/doc/errors.html, vignettes/variancePartition/inst/doc/FAQ.html, vignettes/variancePartition/inst/doc/mvtests.html, vignettes/variancePartition/inst/doc/rnd_effects.html, vignettes/variancePartition/inst/doc/variancePartition.html vignetteTitles: 2) Additional visualizations, 4) dream: differential expression testing with repeated measures designs, 5) Error handling, 6) Frequently asked questions, 7) Multivariate tests, 3) Theory and practice of random effects and REML, 1) Variance partitioning analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variancePartition/inst/doc/additional_visualization.R, vignettes/variancePartition/inst/doc/dream.R, vignettes/variancePartition/inst/doc/errors.R, vignettes/variancePartition/inst/doc/FAQ.R, vignettes/variancePartition/inst/doc/mvtests.R, vignettes/variancePartition/inst/doc/rnd_effects.R, vignettes/variancePartition/inst/doc/variancePartition.R dependsOnMe: dreamlet importsMe: crumblr, muscat, zenith suggestsMe: GRaNIE dependencyCount: 92 Package: VariantAnnotation Version: 1.56.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), MatrixGenerics, Seqinfo, GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), Rsamtools (>= 2.25.1) Imports: utils, DBI, Biobase, S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), XVector (>= 0.29.2), Biostrings (>= 2.77.2), AnnotationDbi (>= 1.27.9), rtracklayer (>= 1.69.1), BSgenome (>= 1.77.1), GenomicFeatures (>= 1.61.4), curl LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib (>= 2.99.0) Suggests: GenomeInfoDb, RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle, knitr, magick, jsonlite, httr, rjsoncons License: Artistic-2.0 MD5sum: d775bb0b91c65a871bcf107844e0da9f NeedsCompilation: yes Title: Annotation of Genetic Variants Description: Annotate variants, compute amino acid coding changes, predict coding outcomes. biocViews: DataImport, Sequencing, SNP, Annotation, Genetics, VariantAnnotation Author: Valerie Oberchain [aut], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer SystemRequirements: GNU make VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q git_url: https://git.bioconductor.org/packages/VariantAnnotation git_branch: RELEASE_3_22 git_last_commit: dc8ecd1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VariantAnnotation_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VariantAnnotation_1.55.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VariantAnnotation_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VariantAnnotation_1.56.0.tgz vignettes: vignettes/VariantAnnotation/inst/doc/ensemblVEP.html, vignettes/VariantAnnotation/inst/doc/filterVcf.html, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.html vignetteTitles: ensemblVEP: using the REST API with Bioconductor, 2. Using filterVcf to Select Variants from VCF Files, 1. Introduction to VariantAnnotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantAnnotation/inst/doc/ensemblVEP.R, vignettes/VariantAnnotation/inst/doc/filterVcf.R, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R dependsOnMe: alabaster.vcf, CNVrd2, deepSNV, demuxSNP, HelloRanges, myvariant, PureCN, RareVariantVis, seqCAT, SomaticSignatures, StructuralVariantAnnotation, svaNUMT, VariantFiltering, VariantTools, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, VariantToolsData, annotation, sequencing, variants, PlasmaMutationDetector importsMe: AllelicImbalance, appreci8R, BadRegionFinder, BBCAnalyzer, biovizBase, biscuiteer, cardelino, CCAFE, CNVfilteR, CopyNumberPlots, crisprDesign, DAMEfinder, decompTumor2Sig, DominoEffect, fcScan, G4SNVHunter, GA4GHclient, GenomicFiles, ggbio, gmapR, gwascat, gwasurvivr, icetea, igvR, karyoploteR, katdetectr, lineagespot, motifbreakR, MungeSumstats, musicatk, MutationalPatterns, ProteoDisco, RAIDS, scoreInvHap, signeR, SigsPack, SNPhood, svaRetro, tadar, tLOH, transmogR, TVTB, Uniquorn, UPDhmm, VCFArray, YAPSA, ZygosityPredictor, COSMIC.67, gpcp suggestsMe: alabaster.files, AnnotationHub, BiocParallel, cellbaseR, CrispRVariants, epialleleR, GenomicDataCommons, GenomicRanges, GenomicScores, GWASTools, igvShiny, ldblock, omicsPrint, podkat, Rsamtools, RVS, SeqArray, splatter, supersigs, systemPipeR, trackViewer, trio, vtpnet, AshkenazimSonChr21, GeuvadisTranscriptExpr, ldsep, MoBPS, polyRAD, SNPassoc, updog dependencyCount: 77 Package: VariantExperiment Version: 1.24.0 Depends: R (>= 3.6.0), S4Vectors (>= 0.21.24), SummarizedExperiment (>= 1.13.0), GenomicRanges, Imports: GDSArray (>= 1.11.1), DelayedDataFrame (>= 1.6.0), tools, utils, stats, methods, gdsfmt, SNPRelate, SeqArray, DelayedArray, Biostrings, IRanges Suggests: testthat, knitr, rmarkdown, markdown, BiocStyle License: GPL-3 MD5sum: 3a594bdb35c6af774f0617be2218f4d5 NeedsCompilation: no Title: A RangedSummarizedExperiment Container for VCF/GDS Data with GDS Backend Description: VariantExperiment is a Bioconductor package for saving data in VCF/GDS format into RangedSummarizedExperiment object. The high-throughput genetic/genomic data are saved in GDSArray objects. The annotation data for features/samples are saved in DelayedDataFrame format with mono-dimensional GDSArray in each column. The on-disk representation of both assay data and annotation data achieves on-disk reading and processing and saves memory space significantly. The interface of RangedSummarizedExperiment data format enables easy and common manipulations for high-throughput genetic/genomic data with common SummarizedExperiment metaphor in R and Bioconductor. biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, GenotypingArray Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/VariantExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/VariantExperiment/issues git_url: https://git.bioconductor.org/packages/VariantExperiment git_branch: RELEASE_3_22 git_last_commit: 4846558 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VariantExperiment_1.24.0.tar.gz vignettes: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.html vignetteTitles: VariantExperiment-class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.R dependencyCount: 35 Package: VariantFiltering Version: 1.46.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.25.1), VariantAnnotation (>= 1.13.29) Imports: utils, stats, Biobase, S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Seqinfo (>= 0.99.2), GenomeInfoDb (>= 1.45.7), Biostrings (>= 2.77.2), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), GenomicFeatures (>= 1.61.4), Rsamtools (>= 2.25.1), BSgenome (>= 1.77.1), GenomicScores (>= 2.21.4), Gviz (>= 1.53.1), shiny, shinythemes, shinyjs, DT, shinyTree LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit, BiocStyle, org.Hs.eg.db, BSgenome.Hsapiens.1000genomes.hs37d5, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, MafDb.1Kgenomes.phase1.hs37d5, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 MD5sum: 23908c9f8682f5b7c3261dbcd2152909 NeedsCompilation: yes Title: Filtering of coding and non-coding genetic variants Description: Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc. biocViews: Genetics, Homo_sapiens, Annotation, SNP, Sequencing, HighThroughputSequencing Author: Robert Castelo [aut, cre], Dei Martinez Elurbe [ctb], Pau Puigdevall [ctb], Joan Fernandez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering BugReports: https://github.com/rcastelo/VariantFiltering/issues git_url: https://git.bioconductor.org/packages/VariantFiltering git_branch: RELEASE_3_22 git_last_commit: 958023b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VariantFiltering_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VariantFiltering_1.45.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VariantFiltering_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VariantFiltering_1.46.0.tgz vignettes: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.pdf vignetteTitles: VariantFiltering: filter coding and non-coding genetic variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.R dependencyCount: 177 Package: VariantTools Version: 1.51.0 Depends: R (>= 3.5.0), S4Vectors (>= 0.17.33), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.31.2), BiocGenerics, Biostrings, parallel, GenomicFeatures (>= 1.31.3), Matrix, rtracklayer (>= 1.39.7), BiocParallel, GenomeInfoDb, BSgenome, Biobase Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph, gmapR (>= 1.21.3), TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: Artistic-2.0 MD5sum: 6d1a588ccb10d7f44b411af668fe1efb NeedsCompilation: no Title: Tools for Exploratory Analysis of Variant Calls Description: Explore, diagnose, and compare variant calls using filters. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/VariantTools git_branch: devel git_last_commit: 1b0edab git_last_commit_date: 2025-07-15 Date/Publication: 2025-10-07 source.ver: src/contrib/VariantTools_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VariantTools_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VariantTools_1.51.0.tgz vignettes: vignettes/VariantTools/inst/doc/tutorial.pdf, vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: tutorial.pdf, Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R suggestsMe: VariantToolsData dependencyCount: 80 Package: VaSP Version: 1.22.0 Depends: R (>= 4.0), ballgown Imports: IRanges, GenomicRanges, S4Vectors, parallel, matrixStats, GenomicAlignments, GenomeInfoDb, Rsamtools, cluster, stats, graphics, methods Suggests: knitr, rmarkdown License: GPL (>= 2.0) Archs: x64 MD5sum: dc32f890c628b9000a45fe1746ee42bd NeedsCompilation: no Title: Quantification and Visualization of Variations of Splicing in Population Description: Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures in a population. (Warning: The visualizing function is removed due to the dependent package Sushi deprecated. If you want to use it, please change back to an older version.) biocViews: RNASeq, AlternativeSplicing, DifferentialSplicing, StatisticalMethod, Visualization, Preprocessing, Clustering, DifferentialExpression, KEGG, ImmunoOncology Author: Huihui Yu [aut, cre] (ORCID: ), Qian Du [aut] (ORCID: ), Chi Zhang [aut] (ORCID: ) Maintainer: Huihui Yu URL: https://github.com/yuhuihui2011/VaSP VignetteBuilder: knitr BugReports: https://github.com/yuhuihui2011/VaSP/issues git_url: https://git.bioconductor.org/packages/VaSP git_branch: RELEASE_3_22 git_last_commit: 905a5ca git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VaSP_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VaSP_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VaSP_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VaSP_1.22.0.tgz vignettes: vignettes/VaSP/inst/doc/VaSP.html vignetteTitles: user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VaSP/inst/doc/VaSP.R dependencyCount: 92 Package: vbmp Version: 1.78.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) Archs: x64 MD5sum: 6c515e2307bec899e07ba2a4262386a7 NeedsCompilation: no Title: Variational Bayesian Multinomial Probit Regression Description: Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior probability employing variational and sparse approximation to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. biocViews: Classification Author: Nicola Lama , Mark Girolami Maintainer: Nicola Lama URL: http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1 git_url: https://git.bioconductor.org/packages/vbmp git_branch: RELEASE_3_22 git_last_commit: f68de66 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/vbmp_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vbmp_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vbmp_1.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vbmp_1.78.0.tgz vignettes: vignettes/vbmp/inst/doc/vbmp.pdf vignetteTitles: vbmp Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vbmp/inst/doc/vbmp.R dependencyCount: 0 Package: VCFArray Version: 1.26.0 Depends: R (>= 3.6), methods, BiocGenerics, DelayedArray (>= 0.7.28) Imports: tools, GenomicRanges, VariantAnnotation (>= 1.29.3), GenomicFiles (>= 1.17.3), S4Vectors (>= 0.19.19), Rsamtools Suggests: SeqArray, BiocStyle, BiocManager, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 9f4e79fbf7c34e51977366d509390836 NeedsCompilation: no Title: Representing on-disk / remote VCF files as array-like objects Description: VCFArray extends the DelayedArray to represent VCF data entries as array-like objects with on-disk / remote VCF file as backend. Data entries from VCF files, including info fields, FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER) could be converted into VCFArray instances with different dimensions. biocViews: Infrastructure, DataRepresentation, Sequencing, VariantAnnotation Author: Qian Liu [aut, cre], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Liubuntu/VCFArray VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/VCFArray/issues git_url: https://git.bioconductor.org/packages/VCFArray git_branch: RELEASE_3_22 git_last_commit: 830be92 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VCFArray_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VCFArray_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VCFArray_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VCFArray_1.26.0.tgz vignettes: vignettes/VCFArray/inst/doc/VCFArray.html vignetteTitles: VCFArray: DelayedArray objects with on-disk/remote VCF backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VCFArray/inst/doc/VCFArray.R dependencyCount: 82 Package: VDJdive Version: 1.12.0 Depends: R (>= 4.2) Imports: BiocParallel, cowplot, ggplot2, gridExtra, IRanges, Matrix, methods, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, utils LinkingTo: Rcpp Suggests: breakaway, covr, knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 6548ab7ba63c5d1177340579cef401ed NeedsCompilation: yes Title: Analysis Tools for 10X V(D)J Data Description: This package provides functions for handling and analyzing immune receptor repertoire data, such as produced by the CellRanger V(D)J pipeline. This includes reading the data into R, merging it with paired single-cell data, quantifying clonotype abundances, calculating diversity metrics, and producing common plots. It implements the E-M Algorithm for clonotype assignment, along with other methods, which makes use of ambiguous cells for improved quantification. biocViews: Software, ImmunoOncology, SingleCell, Annotation, RNASeq, TargetedResequencing Author: Kelly Street [aut, cre] (ORCID: ), Mercedeh Movassagh [aut] (ORCID: ), Jill Lundell [aut] (ORCID: ), Jared Brown [ctb], Linglin Huang [ctb], Mingzhi Ye [ctb] Maintainer: Kelly Street URL: https://github.com/kstreet13/VDJdive VignetteBuilder: knitr BugReports: https://github.com/kstreet13/VDJdive/issues git_url: https://git.bioconductor.org/packages/VDJdive git_branch: RELEASE_3_22 git_last_commit: a0d5c4f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VDJdive_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VDJdive_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VDJdive_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VDJdive_1.12.0.tgz vignettes: vignettes/VDJdive/inst/doc/workflow.html vignetteTitles: VDJdive Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VDJdive/inst/doc/workflow.R dependencyCount: 55 Package: VegaMC Version: 3.48.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods License: GPL-2 MD5sum: 4d887e8dc8d23fb889260c044f2798bc NeedsCompilation: yes Title: VegaMC: A Package Implementing a Variational Piecewise Smooth Model for Identification of Driver Chromosomal Imbalances in Cancer Description: This package enables the detection of driver chromosomal imbalances including loss of heterozygosity (LOH) from array comparative genomic hybridization (aCGH) data. VegaMC performs a joint segmentation of a dataset and uses a statistical framework to distinguish between driver and passenger mutation. VegaMC has been implemented so that it can be immediately integrated with the output produced by PennCNV tool. In addition, VegaMC produces in output two web pages that allows a rapid navigation between both the detected regions and the altered genes. In the web page that summarizes the altered genes, the link to the respective Ensembl gene web page is reported. biocViews: aCGH, CopyNumberVariation Author: S. Morganella and M. Ceccarelli Maintainer: Sandro Morganella git_url: https://git.bioconductor.org/packages/VegaMC git_branch: RELEASE_3_22 git_last_commit: 57a07b6 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VegaMC_3.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VegaMC_3.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VegaMC_3.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VegaMC_3.48.0.tgz vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf vignetteTitles: VegaMC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VegaMC/inst/doc/VegaMC.R dependencyCount: 64 Package: velociraptor Version: 1.20.0 Depends: SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, reticulate, S4Vectors, DelayedArray, basilisk, zellkonverter, scuttle, SingleCellExperiment, BiocParallel, BiocSingular Suggests: BiocStyle, testthat, knitr, rmarkdown, pkgdown, scran, scater, scRNAseq, Rtsne, graphics, grDevices, ggplot2, cowplot, GGally, patchwork, metR License: MIT + file LICENSE MD5sum: 2392bb90a3387e381a6c34c452098186 NeedsCompilation: no Title: Toolkit for Single-Cell Velocity Description: This package provides Bioconductor-friendly wrappers for RNA velocity calculations in single-cell RNA-seq data. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment (R) and AnnData (Python). The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class. biocViews: SingleCell, GeneExpression, Sequencing, Coverage Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Aaron Lun [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Michael Stadler [aut] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/velociraptor VignetteBuilder: knitr BugReports: https://github.com/kevinrue/velociraptor/issues git_url: https://git.bioconductor.org/packages/velociraptor git_branch: RELEASE_3_22 git_last_commit: 67b4a9e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/velociraptor_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/velociraptor_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/velociraptor_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/velociraptor_1.20.0.tgz vignettes: vignettes/velociraptor/inst/doc/velociraptor.html vignetteTitles: User's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/velociraptor/inst/doc/velociraptor.R dependsOnMe: OSCA.advanced dependencyCount: 58 Package: veloviz Version: 1.16.0 Depends: R (>= 4.1) Imports: Rcpp, Matrix, igraph, mgcv, RSpectra, grDevices, graphics, stats LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: ed79c5a4368e0f7d8334037d7b094b86 NeedsCompilation: yes Title: VeloViz: RNA-velocity informed 2D embeddings for visualizing cell state trajectories Description: VeloViz uses each cell’s current observed and predicted future transcriptional states inferred from RNA velocity analysis to build a nearest neighbor graph between cells in the population. Edges are then pruned based on a cosine correlation threshold and/or a distance threshold and the resulting graph is visualized using a force-directed graph layout algorithm. VeloViz can help ensure that relationships between cell states are reflected in the 2D embedding, allowing for more reliable representation of underlying cellular trajectories. biocViews: Transcriptomics, Visualization, GeneExpression, Sequencing, RNASeq, DimensionReduction Author: Lyla Atta [aut, cre] (ORCID: ), Jean Fan [aut] (ORCID: ), Arpan Sahoo [aut] (ORCID: ) Maintainer: Lyla Atta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/veloviz git_branch: RELEASE_3_22 git_last_commit: 97d9beb git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/veloviz_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/veloviz_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/veloviz_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/veloviz_1.16.0.tgz vignettes: vignettes/veloviz/inst/doc/vignette.html vignetteTitles: Visualizing cell cycle trajectory in MERFISH data using VeloViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/veloviz/inst/doc/vignette.R dependencyCount: 23 Package: VennDetail Version: 1.26.0 Depends: R (>= 4.0.0), Imports: dplyr, DT, methods, ggplot2, grDevices, magrittr, patchwork, plotly, purrr, rlang, shiny, stats, tibble, tidyr, htmlwidgets, utils Suggests: knitr, markdown, RColorBrewer, rmarkdown, rstudioapi, testthat (>= 3.0.0) License: GPL-2 MD5sum: 39b36390be01e22c73bbce0b1d2f171c NeedsCompilation: no Title: Comprehensive Visualization and Analysis of Multi-Set Intersections Description: A comprehensive package for visualizing multi-set intersections and extracting detailed subset information. VennDetail generates high-resolution visualizations including traditional Venn diagrams, Venn-pie plots, and UpSet-style plots. It provides functions to extract and combine subset details with user datasets in various formats. The package is particularly useful for bioinformatics applications but can be used for any multi-set analysis. biocViews: DataRepresentation, GraphAndNetwork, Visualization, Software Author: Kai Guo [aut, cre], Brett McGregor [aut] Maintainer: Kai Guo URL: https://github.com/guokai8/VennDetail VignetteBuilder: knitr BugReports: https://github.com/guokai8/VennDetail/issues git_url: https://git.bioconductor.org/packages/VennDetail git_branch: RELEASE_3_22 git_last_commit: c84826d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VennDetail_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VennDetail_1.25.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VennDetail_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VennDetail_1.26.0.tgz vignettes: vignettes/VennDetail/inst/doc/VennDetail.html vignetteTitles: VennDetail hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VennDetail/inst/doc/VennDetail.R dependencyCount: 78 Package: VERSO Version: 1.20.0 Depends: R (>= 4.1.0) Imports: utils, data.tree, ape, parallel, Rfast, stats Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: ab1e6c3bb50424a04c0cb73252c71be3 NeedsCompilation: no Title: Viral Evolution ReconStructiOn (VERSO) Description: Mutations that rapidly accumulate in viral genomes during a pandemic can be used to track the evolution of the virus and, accordingly, unravel the viral infection network. To this extent, sequencing samples of the virus can be employed to estimate models from genomic epidemiology and may serve, for instance, to estimate the proportion of undetected infected people by uncovering cryptic transmissions, as well as to predict likely trends in the number of infected, hospitalized, dead and recovered people. VERSO is an algorithmic framework that processes variants profiles from viral samples to produce phylogenetic models of viral evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a log-likelihood function. VERSO includes two separate and subsequent steps; in this package we provide an R implementation of VERSO STEP 1. biocViews: BiomedicalInformatics, Sequencing, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] (ORCID: ) Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/VERSO VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/VERSO git_url: https://git.bioconductor.org/packages/VERSO git_branch: RELEASE_3_22 git_last_commit: b93d51c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VERSO_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VERSO_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VERSO_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VERSO_1.20.0.tgz vignettes: vignettes/VERSO/inst/doc/v1_introduction.html, vignettes/VERSO/inst/doc/v2_running_VERSO.html vignetteTitles: Introduction, Running VERSO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VERSO/inst/doc/v1_introduction.R, vignettes/VERSO/inst/doc/v2_running_VERSO.R dependencyCount: 20 Package: vidger Version: 1.30.0 Depends: R (>= 3.5) Imports: Biobase, DESeq2, edgeR, GGally, ggplot2, ggrepel, knitr, RColorBrewer, rmarkdown, scales, stats, SummarizedExperiment, tidyr, utils Suggests: BiocStyle, testthat License: GPL-3 | file LICENSE MD5sum: 0d5ab32eeaf5fcd9c1b74201d66c5c3b NeedsCompilation: no Title: Create rapid visualizations of RNAseq data in R Description: The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR. biocViews: ImmunoOncology, Visualization, RNASeq, DifferentialExpression, GeneExpression, ImmunoOncology Author: Brandon Monier [aut, cre], Adam McDermaid [aut], Jing Zhao [aut], Qin Ma [aut, fnd] Maintainer: Brandon Monier URL: https://github.com/btmonier/vidger, https://bioconductor.org/packages/release/bioc/html/vidger.html VignetteBuilder: knitr BugReports: https://github.com/btmonier/vidger/issues git_url: https://git.bioconductor.org/packages/vidger git_branch: RELEASE_3_22 git_last_commit: 2dc9656 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/vidger_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vidger_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vidger_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vidger_1.30.0.tgz vignettes: vignettes/vidger/inst/doc/vidger.html vignetteTitles: Visualizing RNA-seq data with ViDGER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vidger/inst/doc/vidger.R dependencyCount: 99 Package: viper Version: 1.44.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE MD5sum: 90ef058a94dbd110587fb70ac8f56f28 NeedsCompilation: no Title: Virtual Inference of Protein-activity by Enriched Regulon analysis Description: Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/viper git_branch: RELEASE_3_22 git_last_commit: c5fb2c9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/viper_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/viper_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/viper_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/viper_1.44.0.tgz vignettes: vignettes/viper/inst/doc/viper.pdf vignetteTitles: Using VIPER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/viper/inst/doc/viper.R dependsOnMe: vulcan, aracne.networks importsMe: diggit, RTN, diggitdata suggestsMe: decoupleR, easier, MethReg, MOMA, dorothea, vulcandata dependencyCount: 87 Package: ViSEAGO Version: 1.24.0 Depends: R (>= 3.6) Imports: data.table, AnnotationDbi, dendextend, dynamicTreeCut, GOSemSim, GO.db, heatmaply, topGO, AnnotationForge, DT, DiagrammeR, R.utils, RColorBrewer, UpSetR, biomaRt, fgsea, ggplot2, htmlwidgets, igraph, methods, plotly, scales, ComplexHeatmap, circlize Suggests: htmltools, org.Mm.eg.db, limma, Rgraphviz, BiocStyle, knitr, rmarkdown, corrplot, remotes, BiocManager, stats, utils, grDevices, processx License: GPL-3 bioconductor.org MD5sum: 5f0bbb1c2c41d1f5a446a130893954bc NeedsCompilation: no Title: ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity Description: The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. biocViews: Software, Annotation, GO, GeneSetEnrichment, MultipleComparison, Clustering, Visualization Author: Aurelien Brionne [aut, cre], Amelie Juanchich [aut], Christelle hennequet-antier [aut] Maintainer: Aurelien Brionne URL: https://www.bioconductor.org/packages/release/bioc/html/ViSEAGO.html, https://forgemia.inra.fr/UMR-BOA/ViSEAGO VignetteBuilder: knitr BugReports: https://forgemia.inra.fr/UMR-BOA/ViSEAGO/issues git_url: https://git.bioconductor.org/packages/ViSEAGO git_branch: RELEASE_3_22 git_last_commit: 9020dee git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ViSEAGO_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ViSEAGO_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ViSEAGO_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ViSEAGO_1.24.0.tgz vignettes: vignettes/ViSEAGO/inst/doc/fgsea_alternative.html, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.html, vignettes/ViSEAGO/inst/doc/SS_choice.html, vignettes/ViSEAGO/inst/doc/ViSEAGO.html vignetteTitles: 3: fgsea_alternative, 2: mouse_bionconductor, 4: SS_choice, 1: ViSEAGO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ViSEAGO/inst/doc/fgsea_alternative.R, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.R, vignettes/ViSEAGO/inst/doc/SS_choice.R, vignettes/ViSEAGO/inst/doc/ViSEAGO.R dependencyCount: 174 Package: VisiumIO Version: 1.6.0 Depends: R (>= 4.5.0), TENxIO Imports: BiocBaseUtils, BiocGenerics, BiocIO (>= 1.15.1), jsonlite, methods, S4Vectors, SingleCellExperiment, SpatialExperiment, SummarizedExperiment Suggests: arrow, BiocStyle, data.table, knitr, readr, rmarkdown, sf, tinytest License: Artistic-2.0 MD5sum: f1b3882905e2c7e8012714c2b75ddd6d NeedsCompilation: no Title: Import Visium data from the 10X Space Ranger pipeline Description: The package allows users to readily import spatial data obtained from either the 10X website or from the Space Ranger pipeline. Supported formats include tar.gz, h5, and mtx files. Multiple files can be imported at once with *List type of functions. The package represents data mainly as SpatialExperiment objects. biocViews: Software, Infrastructure, DataImport, SingleCell, Spatial Author: Marcel Ramos [aut, cre] (ORCID: ), Estella YiXing Dong [aut, ctb], Dario Righelli [aut, ctb], Helena Crowell [aut, ctb] Maintainer: Marcel Ramos URL: https://github.com/waldronlab/VisiumIO VignetteBuilder: knitr BugReports: https://github.com/waldronlab/VisiumIO/issues git_url: https://git.bioconductor.org/packages/VisiumIO git_branch: RELEASE_3_22 git_last_commit: 0a06453 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VisiumIO_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VisiumIO_1.5.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VisiumIO_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VisiumIO_1.6.0.tgz vignettes: vignettes/VisiumIO/inst/doc/VisiumIO.html vignetteTitles: VisiumIO Quick Start Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VisiumIO/inst/doc/VisiumIO.R importsMe: XeniumIO, OSTA suggestsMe: ggspavis, OSTA.data, SpatialExperiment, SpatialFeatureExperiment dependencyCount: 89 Package: visiumStitched Version: 1.2.0 Depends: R (>= 4.4), SpatialExperiment Imports: BiocBaseUtils, BiocGenerics, clue, dplyr, DropletUtils, grDevices, imager, Matrix, methods, pkgcond, readr, rjson, S4Vectors, SingleCellExperiment, spatialLIBD (>= 1.17.8), stringr, SummarizedExperiment, tibble, tidyr, xml2 Suggests: BiocFileCache, BiocStyle, ggplot2, knitr, RefManageR, rmarkdown, sessioninfo, Seurat, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 0b98ebbc94306a20add0906f89796ae7 NeedsCompilation: no Title: Enable downstream analysis of Visium capture areas stitched together with Fiji Description: This package provides helper functions for working with multiple Visium capture areas that overlap each other. This package was developed along with the companion example use case data available from https://github.com/LieberInstitute/visiumStitched_brain. visiumStitched prepares SpaceRanger (10x Genomics) output files so you can stitch the images from groups of capture areas together with Fiji. Then visiumStitched builds a SpatialExperiment object with the stitched data and makes an artificial hexagonal grid enabling the seamless use of spatial clustering methods that rely on such grid to identify neighboring spots, such as PRECAST and BayesSpace. The SpatialExperiment objects created by visiumStitched are compatible with spatialLIBD, which can be used to build interactive websites for stitched SpatialExperiment objects. visiumStitched also enables casting SpatialExperiment objects as Seurat objects. biocViews: Software, Spatial, Transcriptomics, Transcription, GeneExpression, Visualization, DataImport Author: Nicholas J. Eagles [aut, cre] (ORCID: ), Leonardo Collado-Torres [ctb] (ORCID: ) Maintainer: Nicholas J. Eagles URL: https://github.com/LieberInstitute/visiumStitched VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/visiumStitched git_url: https://git.bioconductor.org/packages/visiumStitched git_branch: RELEASE_3_22 git_last_commit: a3f730b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/visiumStitched_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/visiumStitched_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/visiumStitched_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/visiumStitched_1.2.0.tgz vignettes: vignettes/visiumStitched/inst/doc/misc.html, vignettes/visiumStitched/inst/doc/visiumStitched.html vignetteTitles: Miscellaneous notes, Introduction to visiumStitched hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/visiumStitched/inst/doc/misc.R, vignettes/visiumStitched/inst/doc/visiumStitched.R dependencyCount: 227 Package: vissE Version: 1.18.0 Depends: R (>= 4.1) Imports: igraph, methods, plyr, ggplot2, scico, RColorBrewer, tm, ggwordcloud, GSEABase, reshape2, grDevices, ggforce, msigdb, ggrepel, textstem, tidygraph, stats, scales, ggraph Suggests: testthat, org.Hs.eg.db, org.Mm.eg.db, patchwork, singscore, knitr, rmarkdown, prettydoc, BiocStyle, pkgdown, covr License: GPL-3 MD5sum: 24d20f3796734390a650dfe30350631c NeedsCompilation: no Title: Visualising Set Enrichment Analysis Results Description: This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches. biocViews: Software, GeneExpression, GeneSetEnrichment, NetworkEnrichment, Network Author: Dharmesh D. Bhuva [aut, cre] (ORCID: ), Ahmed Mohamed [ctb] Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/vissE VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/vissE/issues git_url: https://git.bioconductor.org/packages/vissE git_branch: RELEASE_3_22 git_last_commit: 0391f1b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/vissE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vissE_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vissE_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vissE_1.18.0.tgz vignettes: vignettes/vissE/inst/doc/vissE.html vignetteTitles: vissE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vissE/inst/doc/vissE.R suggestsMe: msigdb dependencyCount: 139 Package: vmrseq Version: 1.2.0 Depends: R (>= 4.5.0) Imports: bumphunter, dplyr, BiocParallel, DelayedArray, GenomicRanges, ggplot2, methods, tidyr, locfit, gamlss.dist, recommenderlab, HDF5Array, data.table, SummarizedExperiment, IRanges, S4Vectors, devtools Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: c42e67dc06069da7ef429a3e9c85cad9 NeedsCompilation: no Title: Probabilistic Modeling of Single-cell Methylation Heterogeneity Description: High-throughput single-cell measurements of DNA methylation allows studying inter-cellular epigenetic heterogeneity, but this task faces the challenges of sparsity and noise. We present vmrseq, a statistical method that overcomes these challenges and identifies variably methylated regions accurately and robustly. biocViews: Software, ImmunoOncology, DNAMethylation, Epigenetics, SingleCell, Sequencing, WholeGenome Author: Ning Shen [aut, cre] Maintainer: Ning Shen URL: https://github.com/nshen7/vmrseq VignetteBuilder: knitr BugReports: https://github.com/nshen7/vmrseq/issues git_url: https://git.bioconductor.org/packages/vmrseq git_branch: RELEASE_3_22 git_last_commit: 3ca9867 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/vmrseq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vmrseq_1.1.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vmrseq_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vmrseq_1.2.0.tgz vignettes: vignettes/vmrseq/inst/doc/vmrseq-vignette.html vignetteTitles: Analyzing single-cell bisulfite sequencing data with vmrseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vmrseq/inst/doc/vmrseq-vignette.R dependencyCount: 188 Package: Voyager Version: 1.12.0 Depends: R (>= 4.2.0), SpatialFeatureExperiment (>= 1.7.3) Imports: BiocParallel, bluster, DelayedArray, ggnewscale, ggplot2 (>= 3.4.0), grDevices, grid, lifecycle, Matrix, MatrixGenerics, memuse, methods, patchwork, rlang, RSpectra, S4Vectors, scales, scico, sf, SingleCellExperiment, SpatialExperiment, spdep, stats, SummarizedExperiment, terra, utils, zeallot Suggests: arrow, automap, BiocSingular, BiocStyle, cowplot, data.table, DelayedMatrixStats, EBImage, ExperimentHub, ggh4x, gstat, hexbin, knitr, matrixStats, pheatmap, RBioFormats, rhdf5, rmarkdown, scater, scattermore, scran, sfarrow, SFEData, testthat (>= 3.0.0), vdiffr, xml2 License: Artistic-2.0 MD5sum: 87f219b5f21ce09b25b11533d21e2eb3 NeedsCompilation: no Title: From geospatial to spatial omics Description: SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results. biocViews: GeneExpression, Spatial, Transcriptomics, Visualization Author: Lambda Moses [aut, cre] (ORCID: ), Alik Huseynov [aut] (ORCID: ), Kayla Jackson [aut] (ORCID: ), Laura Luebbert [aut] (ORCID: ), Lior Pachter [aut, rev] (ORCID: ) Maintainer: Lambda Moses URL: https://github.com/pachterlab/voyager VignetteBuilder: knitr BugReports: https://github.com/pachterlab/voyager/issues git_url: https://git.bioconductor.org/packages/Voyager git_branch: RELEASE_3_22 git_last_commit: d689ab8 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Voyager_1.12.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Voyager_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Voyager_1.12.0.tgz vignettes: vignettes/Voyager/inst/doc/overview.html vignetteTitles: Functionality overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Voyager/inst/doc/overview.R importsMe: OSTA suggestsMe: alabaster.sfe, SpatialFeatureExperiment dependencyCount: 172 Package: VplotR Version: 1.20.0 Depends: R (>= 4.0), GenomicRanges, IRanges, ggplot2 Imports: cowplot, magrittr, Seqinfo, GenomeInfoDb, GenomicAlignments, RColorBrewer, zoo, Rsamtools, S4Vectors, parallel, reshape2, methods, graphics, stats Suggests: GenomicFeatures, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, testthat, covr, knitr, rmarkdown, pkgdown License: GPL (>= 3) MD5sum: cd208edc42fef65d30146d8b9df5e5fd NeedsCompilation: no Title: Set of tools to make V-plots and compute footprint profiles Description: The pattern of digestion and protection from DNA nucleases such as DNAse I, micrococcal nuclease, and Tn5 transposase can be used to infer the location of associated proteins. This package contains useful functions to analyze patterns of paired-end sequencing fragment density. VplotR facilitates the generation of V-plots and footprint profiles over single or aggregated genomic loci of interest. biocViews: NucleosomePositioning, Coverage, Sequencing, BiologicalQuestion, ATACSeq, Alignment Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/VplotR VignetteBuilder: knitr BugReports: https://github.com/js2264/VplotR/issues git_url: https://git.bioconductor.org/packages/VplotR git_branch: RELEASE_3_22 git_last_commit: 33ae55c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/VplotR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VplotR_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VplotR_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VplotR_1.20.0.tgz vignettes: vignettes/VplotR/inst/doc/VplotR.html vignetteTitles: VplotR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VplotR/inst/doc/VplotR.R dependencyCount: 75 Package: vsclust Version: 1.12.0 Depends: R (>= 4.2.0) Imports: matrixStats, limma, parallel, shiny, qvalue, grDevices, stats, MultiAssayExperiment, clusterProfiler, DOSE, httr, graphics LinkingTo: Rcpp Suggests: knitr, yaml, testthat (>= 3.0.0), rmarkdown, BiocStyle, httr License: GPL-2 MD5sum: 197b818ceec1541d45404e2415e01db6 NeedsCompilation: yes Title: Feature-based variance-sensitive quantitative clustering Description: Feature-based variance-sensitive clustering of omics data. Optimizes cluster assignment by taking into account individual feature variance. Includes several modules for statistical testing, clustering and enrichment analysis. biocViews: Clustering, Annotation, PrincipalComponent, DifferentialExpression, Visualization, Proteomics, Metabolomics Author: Veit Schwammle [aut, cre] Maintainer: Veit Schwammle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/vsclust git_branch: RELEASE_3_22 git_last_commit: 5f4dab1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/vsclust_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vsclust_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vsclust_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vsclust_1.12.0.tgz vignettes: vignettes/vsclust/inst/doc/Integrate_With_Bioconductor_Objects.html, vignettes/vsclust/inst/doc/Run_VSClust_Workflow.html vignetteTitles: VSClust on Bioconductor object, VSClust workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vsclust/inst/doc/Integrate_With_Bioconductor_Objects.R, vignettes/vsclust/inst/doc/Run_VSClust_Workflow.R dependencyCount: 157 Package: vsn Version: 3.78.0 Depends: R (>= 4.0.0), methods, Biobase Imports: affy, limma, lattice, ggplot2 Suggests: affydata, hgu95av2cdf, BiocStyle, knitr, rmarkdown, dplyr, testthat License: Artistic-2.0 MD5sum: 827ef5df707cdf60b5216aa237536d0e NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities from single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing Author: Wolfgang Huber, with contributions from Anja von Heydebreck. Many comments and suggestions by users are acknowledged, among them Dennis Kostka, David Kreil, Hans-Ulrich Klein, Robert Gentleman, Deepayan Sarkar and Gordon Smyth Maintainer: Wolfgang Huber URL: http://www.r-project.org, http://www.ebi.ac.uk/huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/vsn git_branch: RELEASE_3_22 git_last_commit: 909160b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/vsn_3.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vsn_3.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vsn_3.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vsn_3.78.0.tgz vignettes: vignettes/vsn/inst/doc/C-likelihoodcomputations.pdf, vignettes/vsn/inst/doc/D-convergence.pdf, vignettes/vsn/inst/doc/A-vsn.html vignetteTitles: Likelihood Calculations for vsn, Verifying and assessing the performance with simulated data, Introduction to vsn (HTML version) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/A-vsn.R, vignettes/vsn/inst/doc/C-likelihoodcomputations.R dependsOnMe: webbioc, rnaseqGene importsMe: autonomics, bnem, DEP, Doscheda, MatrixQCvis, metaseqR2, MSnbase, NormalyzerDE, PRONE, pvca, SmartPhos, tilingArray, ExpressionNormalizationWorkflow, lfproQC suggestsMe: adSplit, DAPAR, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, MsCoreUtils, PAA, QFeatures, qmtools, ribosomeProfilingQC, scp, twilight, estrogen, wrMisc dependencyCount: 32 Package: vtpnet Version: 0.50.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 0359184b09f09daddece919ba2bb227c NeedsCompilation: no Title: variant-transcription factor-phenotype networks Description: variant-transcription factor-phenotype networks, inspired by Maurano et al., Science (2012), PMID 22955828 biocViews: Network Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/vtpnet git_branch: RELEASE_3_22 git_last_commit: 78afef5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/vtpnet_0.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vtpnet_0.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vtpnet_0.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vtpnet_0.50.0.tgz vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf vignetteTitles: vtpnet: variant-transcription factor-network tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vtpnet/inst/doc/vtpnet.R dependencyCount: 116 Package: vulcan Version: 1.32.0 Depends: R (>= 4.0), ChIPpeakAnno,TxDb.Hsapiens.UCSC.hg19.knownGene, zoo, GenomicRanges, S4Vectors, viper, DiffBind, locfit Imports: wordcloud, csaw, gplots, stats, utils, caTools, graphics, DESeq2, Biobase Suggests: vulcandata License: LGPL-3 MD5sum: bbe4771ee252f93ea9082d93da294285 NeedsCompilation: no Title: VirtUaL ChIP-Seq data Analysis using Networks Description: Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a package that interrogates gene regulatory networks to infer cofactors significantly enriched in a differential binding signature coming from ChIP-Seq data. In order to do so, our package combines strategies from different BioConductor packages: DESeq for data normalization, ChIPpeakAnno and DiffBind for annotation and definition of ChIP-Seq genomic peaks, csaw to define optimal peak width and viper for applying a regulatory network over a differential binding signature. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, ChIPSeq Author: Federico M. Giorgi, Andrew N. Holding, Florian Markowetz Maintainer: Federico M. Giorgi git_url: https://git.bioconductor.org/packages/vulcan git_branch: RELEASE_3_22 git_last_commit: 55315e7 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/vulcan_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vulcan_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vulcan_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vulcan_1.32.0.tgz vignettes: vignettes/vulcan/inst/doc/vulcan.pdf vignetteTitles: Vulcan: VirtUaL ChIP-Seq Analysis through Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vulcan/inst/doc/vulcan.R dependencyCount: 199 Package: waddR Version: 1.24.0 Depends: R (>= 3.6.0) Imports: Rcpp (>= 1.0.1), arm (>= 1.10-1), eva, BiocFileCache (>= 2.6.0), BiocParallel, SingleCellExperiment, parallel, methods, stats LinkingTo: Rcpp, RcppArmadillo, Suggests: knitr, devtools, testthat, roxygen2, rprojroot, rmarkdown, scater License: MIT + file LICENSE MD5sum: 3a2ee526f040e50cb3ebee8924b0dc22 NeedsCompilation: yes Title: Statistical tests for detecting differential distributions based on the 2-Wasserstein distance Description: The package offers statistical tests based on the 2-Wasserstein distance for detecting and characterizing differences between two distributions given in the form of samples. Functions for calculating the 2-Wasserstein distance and testing for differential distributions are provided, as well as a specifically tailored test for differential expression in single-cell RNA sequencing data. biocViews: Software, StatisticalMethod, SingleCell, DifferentialExpression Author: Roman Schefzik [aut], Julian Flesch [cre] Maintainer: Julian Flesch URL: https://github.com/goncalves-lab/waddR.git VignetteBuilder: knitr BugReports: https://github.com/goncalves-lab/waddR/issues git_url: https://git.bioconductor.org/packages/waddR git_branch: RELEASE_3_22 git_last_commit: 6d40862 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/waddR_1.24.0.tar.gz vignettes: vignettes/waddR/inst/doc/waddR.html, vignettes/waddR/inst/doc/wasserstein_metric.html, vignettes/waddR/inst/doc/wasserstein_singlecell.html, vignettes/waddR/inst/doc/wasserstein_test.html vignetteTitles: waddR, wasserstein_metric, wasserstein_singlecell, wasserstein_test hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/waddR/inst/doc/waddR.R, vignettes/waddR/inst/doc/wasserstein_metric.R, vignettes/waddR/inst/doc/wasserstein_singlecell.R, vignettes/waddR/inst/doc/wasserstein_test.R dependencyCount: 99 Package: wateRmelon Version: 2.16.0 Depends: R (>= 3.5.0), Biobase, limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19, illuminaio Imports: Biobase Suggests: RPMM, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BiocStyle, knitr, rmarkdown, IlluminaHumanMethylationEPICmanifest, irlba, FlowSorted.Blood.EPIC, FlowSorted.Blood.450k, preprocessCore Enhances: minfi License: GPL-3 MD5sum: 042b107d9cbb9d98b13c932caf2e4443 NeedsCompilation: no Title: Illumina DNA methylation array normalization and metrics Description: 15 flavours of betas and three performance metrics, with methods for objects produced by methylumi and minfi packages. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Leo C Schalkwyk [cre, aut], Tyler J Gorrie-Stone [aut], Ruth Pidsley [aut], Chloe CY Wong [aut], Nizar Touleimat [ctb], Matthieu Defrance [ctb], Andrew Teschendorff [ctb], Jovana Maksimovic [ctb], Louis Y El Khoury [ctb], Yucheng Wang [ctb], Alexandria Andrayas [ctb] Maintainer: Leo C Schalkwyk VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wateRmelon git_branch: RELEASE_3_22 git_last_commit: 59f6d9f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/wateRmelon_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/wateRmelon_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/wateRmelon_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/wateRmelon_2.16.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.html vignetteTitles: wateRmelon User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R dependsOnMe: bigmelon, skewr importsMe: ChAMP, MEAT suggestsMe: RnBeads dependencyCount: 171 Package: wavClusteR Version: 2.44.0 Depends: R (>= 3.2), GenomicRanges (>= 1.31.8), Rsamtools Imports: methods, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Biostrings (>= 2.47.6), foreach, GenomicFeatures (>= 1.31.3), ggplot2, Hmisc, mclust, rtracklayer (>= 1.39.7), seqinr, stringr Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: 1f80336c436f6e6dcdf37dbee1dfcc47 NeedsCompilation: no Title: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data Description: The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq). biocViews: ImmunoOncology, Sequencing, Technology, RIPSeq, RNASeq, Bayesian Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wavClusteR git_branch: RELEASE_3_22 git_last_commit: 767728a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/wavClusteR_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/wavClusteR_2.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/wavClusteR_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/wavClusteR_2.44.0.tgz vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.html vignetteTitles: wavClusteR: a workflow for PAR-CLIP data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R dependencyCount: 130 Package: weaver Version: 1.76.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: 8de517cdd55b0531cf305f758754f0de NeedsCompilation: no Title: Tools and extensions for processing Sweave documents Description: This package provides enhancements on the Sweave() function in the base package. In particular a facility for caching code chunk results is included. biocViews: Infrastructure Author: Seth Falcon Maintainer: Seth Falcon git_url: https://git.bioconductor.org/packages/weaver git_branch: RELEASE_3_22 git_last_commit: 02351f5 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/weaver_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/weaver_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/weaver_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/weaver_1.76.0.tgz vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf vignetteTitles: Using weaver to process Sweave documents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/weaver/inst/doc/weaver_howTo.R dependencyCount: 4 Package: webbioc Version: 1.82.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocManager License: GPL (>= 2) MD5sum: 0b0cd1da2e59a8730d6ebecaafde55ef NeedsCompilation: no Title: Bioconductor Web Interface Description: An integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. (Currently only Affymetrix oligonucleotide analysis is supported.) biocViews: Infrastructure, Microarray, OneChannel, DifferentialExpression Author: Colin A. Smith Maintainer: Colin A. Smith URL: http://www.bioconductor.org/ SystemRequirements: Unix, Perl (>= 5.6.0), Netpbm git_url: https://git.bioconductor.org/packages/webbioc git_branch: RELEASE_3_22 git_last_commit: 6f493bf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/webbioc_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/webbioc_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/webbioc_1.82.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/webbioc_1.82.0.tgz vignettes: vignettes/webbioc/inst/doc/demoscript.pdf, vignettes/webbioc/inst/doc/webbioc.pdf vignetteTitles: webbioc Demo Script, webbioc Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 77 Package: weitrix Version: 1.22.0 Depends: R (>= 3.6), SummarizedExperiment Imports: methods, utils, stats, grDevices, assertthat, S4Vectors, DelayedArray, DelayedMatrixStats, BiocParallel, BiocGenerics, limma, topconfects, dplyr, purrr, ggplot2, rlang, scales, reshape2, splines, Ckmeans.1d.dp, glm2, RhpcBLASctl Suggests: knitr, rmarkdown, BiocStyle, tidyverse, airway, edgeR, EnsDb.Hsapiens.v86, org.Sc.sgd.db, AnnotationDbi, ComplexHeatmap, patchwork, testthat (>= 2.1.0) License: LGPL-2.1 | file LICENSE MD5sum: a5b8f8d80a2a1ae43f85f06d18edbf49 NeedsCompilation: no Title: Tools for matrices with precision weights, test and explore weighted or sparse data Description: Data type and tools for working with matrices having precision weights and missing data. This package provides a common representation and tools that can be used with many types of high-throughput data. The meaning of the weights is compatible with usage in the base R function "lm" and the package "limma". Calibrate weights to account for known predictors of precision. Find rows with excess variability. Perform differential testing and find rows with the largest confident differences. Find PCA-like components of variation even with many missing values, rotated so that individual components may be meaningfully interpreted. DelayedArray matrices and BiocParallel are supported. biocViews: Software, DataRepresentation, DimensionReduction, GeneExpression, Transcriptomics, RNASeq, SingleCell, Regression Author: Paul Harrison [aut, cre] (ORCID: ) Maintainer: Paul Harrison VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/weitrix git_branch: RELEASE_3_22 git_last_commit: 46b4f6c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/weitrix_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/weitrix_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/weitrix_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/weitrix_1.22.0.tgz vignettes: vignettes/weitrix/inst/doc/V1_overview.html, vignettes/weitrix/inst/doc/V2_tail_length.html, vignettes/weitrix/inst/doc/V3_shift.html, vignettes/weitrix/inst/doc/V4_airway.html, vignettes/weitrix/inst/doc/V5_slam_seq.html vignetteTitles: 1. Concepts and practical details, 2. poly(A) tail length example, 3. Alternative polyadenylation, 4. RNA-Seq expression example, 5. Proportions data example with SLAM-Seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/weitrix/inst/doc/V2_tail_length.R, vignettes/weitrix/inst/doc/V3_shift.R, vignettes/weitrix/inst/doc/V4_airway.R, vignettes/weitrix/inst/doc/V5_slam_seq.R dependencyCount: 76 Package: widgetTools Version: 1.88.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: 93b5df792dd32f0f138024e839773543 NeedsCompilation: no Title: Creates an interactive tcltk widget Description: This packages contains tools to support the construction of tcltk widgets biocViews: Infrastructure Author: Jianhua Zhang Maintainer: Jianhua Zhang git_url: https://git.bioconductor.org/packages/widgetTools git_branch: RELEASE_3_22 git_last_commit: 8bc181e git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/widgetTools_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/widgetTools_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/widgetTools_1.88.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/widgetTools_1.88.0.tgz vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf vignetteTitles: widgetTools Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/widgetTools/inst/doc/widgetTools.R dependsOnMe: tkWidgets importsMe: OLINgui, SeqFeatR suggestsMe: affy dependencyCount: 3 Package: wiggleplotr Version: 1.34.0 Depends: R (>= 3.6) Imports: dplyr, ggplot2 (>= 2.2.0), GenomicRanges, rtracklayer, cowplot, assertthat, purrr, S4Vectors, IRanges, GenomeInfoDb Suggests: knitr, rmarkdown, biomaRt, GenomicFeatures, testthat, ensembldb, EnsDb.Hsapiens.v86, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi, AnnotationFilter License: Apache License 2.0 MD5sum: 3ef5f75e12f003fb7b7815e70a00967b NeedsCompilation: no Title: Make read coverage plots from BigWig files Description: Tools to visualise read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualisation of exonic read coverage. biocViews: ImmunoOncology, Coverage, RNASeq, ChIPSeq, Sequencing, Visualization, GeneExpression, Transcription, AlternativeSplicing Author: Kaur Alasoo [aut, cre] Maintainer: Kaur Alasoo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wiggleplotr git_branch: RELEASE_3_22 git_last_commit: 67a899c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/wiggleplotr_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/wiggleplotr_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/wiggleplotr_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/wiggleplotr_1.34.0.tgz vignettes: vignettes/wiggleplotr/inst/doc/wiggleplotr.html vignetteTitles: Introduction to wiggleplotr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wiggleplotr/inst/doc/wiggleplotr.R importsMe: chevreulPlot dependencyCount: 84 Package: wpm Version: 1.20.0 Depends: R (>= 4.1.0) Imports: utils, methods, cli, Biobase, SummarizedExperiment, config, golem, shiny, DT, ggplot2, dplyr, rlang, stringr, shinydashboard, shinyWidgets, shinycustomloader, RColorBrewer, logging Suggests: MSnbase, testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: e7d206877a207c1c9b4543c8ccffc382 NeedsCompilation: no Title: Well Plate Maker Description: The Well-Plate Maker (WPM) is a shiny application deployed as an R package. Functions for a command-line/script use are also available. The WPM allows users to generate well plate maps to carry out their experiments while improving the handling of batch effects. In particular, it helps controlling the "plate effect" thanks to its ability to randomize samples over multiple well plates. The algorithm for placing the samples is inspired by the backtracking algorithm: the samples are placed at random while respecting specific spatial constraints. biocViews: GUI, Proteomics, MassSpectrometry, BatchEffect, ExperimentalDesign Author: Helene Borges [aut, cre], Thomas Burger [aut] Maintainer: Helene Borges URL: https://github.com/HelBor/wpm, https://bioconductor.org/packages/release/bioc/html/wpm.html VignetteBuilder: knitr BugReports: https://github.com/HelBor/wpm/issues git_url: https://git.bioconductor.org/packages/wpm git_branch: RELEASE_3_22 git_last_commit: 497196c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/wpm_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/wpm_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/wpm_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/wpm_1.20.0.tgz vignettes: vignettes/wpm/inst/doc/wpm_vignette.html vignetteTitles: How to use Well Plate Maker hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wpm/inst/doc/wpm_vignette.R dependencyCount: 93 Package: Wrench Version: 1.28.0 Depends: R (>= 3.5.0) Imports: limma, matrixStats, locfit, stats, graphics Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR License: Artistic-2.0 MD5sum: 433827e76e92b09d9ab8abf198a2317c NeedsCompilation: no Title: Wrench normalization for sparse count data Description: Wrench is a package for normalization sparse genomic count data, like that arising from 16s metagenomic surveys. biocViews: Normalization, Sequencing, Software Author: Senthil Kumar Muthiah [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/Wrench VignetteBuilder: knitr BugReports: https://github.com/HCBravoLab/Wrench/issues git_url: https://git.bioconductor.org/packages/Wrench git_branch: RELEASE_3_22 git_last_commit: 9dea78f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Wrench_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Wrench_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Wrench_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Wrench_1.28.0.tgz vignettes: vignettes/Wrench/inst/doc/vignette.html vignetteTitles: Wrench hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Wrench/inst/doc/vignette.R importsMe: metagenomeSeq dependencyCount: 11 Package: XAItest Version: 1.2.0 Depends: R (>= 3.5.0) Imports: limma, randomForest, kernelshap, caret, lime, DT, methods, SummarizedExperiment, ggplot2 Suggests: knitr, ggforce, shapr (>= 1.0.1), airway, xgboost, BiocGenerics, RUnit, S4Vectors License: MIT + file LICENSE MD5sum: b0f41db8d45f8e2409691d305c3d0e81 NeedsCompilation: no Title: XAItest: Enhancing Feature Discovery with eXplainable AI Description: XAItest is an R Package that identifies features using eXplainable AI (XAI) methods such as SHAP or LIME. This package allows users to compare these methods with traditional statistical tests like t-tests, empirical Bayes, and Fisher's test. Additionally, it includes simThresh, a system that enables the comparison of feature importance with p-values by incorporating calibrated simulated data. biocViews: Software, StatisticalMethod, FeatureExtraction, Classification, Regression Author: Ghislain FIEVET [aut, cre] (ORCID: ), Sébastien HERGALANT [aut] (ORCID: ) Maintainer: Ghislain FIEVET URL: https://github.com/GhislainFievet/XAItest VignetteBuilder: knitr BugReports: https://github.com/GhislainFievet/XAItest/issues git_url: https://git.bioconductor.org/packages/XAItest git_branch: RELEASE_3_22 git_last_commit: 520604b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/XAItest_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/XAItest_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/XAItest_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/XAItest_1.2.0.tgz vignettes: vignettes/XAItest/inst/doc/XAItest.html vignetteTitles: 01_XAItest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/XAItest/inst/doc/XAItest.R dependencyCount: 133 Package: xCell2 Version: 1.2.0 Depends: R (>= 4.0.0) Imports: SummarizedExperiment, SingleCellExperiment, Rfast, singscore, AnnotationHub, ontologyIndex, tibble, dplyr, BiocParallel, Matrix, minpack.lm, pracma, methods, readr, magrittr, progress, quadprog Suggests: testthat, knitr, rmarkdown, ggplot2, randomForest, tidyr, EnhancedVolcano, BiocStyle License: GPL (>= 3) Archs: x64 MD5sum: ff28478b742907149c30c290c1bc29ab NeedsCompilation: no Title: A Tool for Generic Cell Type Enrichment Analysis Description: xCell2 provides methods for cell type enrichment analysis using cell type signatures. It includes three main functions - 1. xCell2Train for training custom references objects from bulk or single-cell RNA-seq datasets. 2. xCell2Analysis for conducting the cell type enrichment analysis using the custom reference. 3. xCell2GetLineage for identifying dependencies between different cell types using ontology. biocViews: GeneExpression, Transcriptomics, Microarray, RNASeq, SingleCell, DifferentialExpression, ImmunoOncology, GeneSetEnrichment Author: Almog Angel [aut, cre] (ORCID: ), Dvir Aran [aut] (ORCID: ) Maintainer: Almog Angel URL: https://github.com/AlmogAngel/xCell2 VignetteBuilder: knitr BugReports: https://github.com/AlmogAngel/xCell2/issues git_url: https://git.bioconductor.org/packages/xCell2 git_branch: RELEASE_3_22 git_last_commit: d42142a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/xCell2_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/xCell2_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/xCell2_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/xCell2_1.2.0.tgz vignettes: vignettes/xCell2/inst/doc/xCell2-vignette.html vignetteTitles: Introduction to xCell2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xCell2/inst/doc/xCell2-vignette.R dependencyCount: 146 Package: xcms Version: 4.8.0 Depends: R (>= 4.1.0), BiocParallel (>= 1.8.0) Imports: MSnbase (>= 2.33.3), mzR (>= 2.25.3), methods, Biobase, BiocGenerics, ProtGenerics (>= 1.37.1), lattice, MassSpecWavelet (>= 1.66.0), S4Vectors, IRanges, SummarizedExperiment, MsCoreUtils (>= 1.19.2), MsFeatures, MsExperiment (>= 1.5.4), Spectra (>= 1.16.1), progress, RColorBrewer, MetaboCoreUtils (>= 1.11.2), data.table Suggests: BiocStyle, caTools, knitr (>= 1.1.0), faahKO, msdata (>= 0.25.1), ncdf4, testthat (>= 3.1.9), pander, rmarkdown, MALDIquant, pheatmap, RANN, multtest, MsBackendMgf, signal, mgcv, rhdf5 Enhances: Rgraphviz, rgl License: GPL (>= 2) + file LICENSE Archs: x64 MD5sum: 02689a7c89e8ce2cc838435eb9737d9c NeedsCompilation: yes Title: LC-MS and GC-MS Data Analysis Description: Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Colin A. Smith [aut], Ralf Tautenhahn [aut], Steffen Neumann [aut, cre] (ORCID: ), Paul Benton [aut], Christopher Conley [aut], Johannes Rainer [aut] (ORCID: ), Michael Witting [ctb], William Kumler [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Pablo Vangeenderhuysen [ctb] (ORCID: ), Carl Brunius [ctb] (ORCID: ) Maintainer: Steffen Neumann URL: https://github.com/sneumann/xcms VignetteBuilder: knitr BugReports: https://github.com/sneumann/xcms/issues/new git_url: https://git.bioconductor.org/packages/xcms git_branch: RELEASE_3_22 git_last_commit: 8c7e9cf git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/xcms_4.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/xcms_4.7.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/xcms_4.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/xcms_4.8.0.tgz vignettes: vignettes/xcms/inst/doc/LC-MS-feature-grouping.html, vignettes/xcms/inst/doc/xcms-direct-injection.html, vignettes/xcms/inst/doc/xcms-lcms-ms.html, vignettes/xcms/inst/doc/xcms.html vignetteTitles: LC-MS feature grouping, Grouping FTICR-MS data with xcms, LC-MS/MS data analysis with xcms, LC-MS data preprocessing and analysis with xcms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/xcms/inst/doc/LC-MS-feature-grouping.R, vignettes/xcms/inst/doc/xcms-direct-injection.R, vignettes/xcms/inst/doc/xcms-lcms-ms.R, vignettes/xcms/inst/doc/xcms.R dependsOnMe: CAMERA, flagme, IPO, LOBSTAHS, metaMS, ncGTW, PtH2O2lipids importsMe: CAMERA, cliqueMS, cosmiq, squallms, faahKO suggestsMe: CluMSID, msPurity, RMassBank, msdata, mtbls2, RforProteomics, CorrectOverloadedPeaks, isatabr, LCMSQA, MetabolomicsBasics dependencyCount: 139 Package: xcore Version: 1.14.0 Depends: R (>= 4.2) Imports: DelayedArray (>= 0.18.0), edgeR (>= 3.34.1), foreach (>= 1.5.1), GenomicRanges (>= 1.44.0), glmnet (>= 4.1.2), IRanges (>= 2.26.0), iterators (>= 1.0.13), magrittr (>= 2.0.1), Matrix (>= 1.3.4), methods (>= 4.1.1), MultiAssayExperiment (>= 1.18.0), stats, S4Vectors (>= 0.30.0), utils Suggests: AnnotationHub (>= 3.0.2), BiocGenerics (>= 0.38.0), BiocParallel (>= 1.28), BiocStyle (>= 2.20.2), data.table (>= 1.14.0), devtools (>= 2.4.2), doParallel (>= 1.0.16), ExperimentHub (>= 2.2.0), knitr (>= 1.37), pheatmap (>= 1.0.12), proxy (>= 0.4.26), ridge (>= 3.0), rmarkdown (>= 2.11), rtracklayer (>= 1.52.0), testthat (>= 3.0.0), usethis (>= 2.0.1), xcoredata License: GPL-2 MD5sum: 49b98e421712b64a1969ca2f4343ed85 NeedsCompilation: no Title: xcore expression regulators inference Description: xcore is an R package for transcription factor activity modeling based on known molecular signatures and user's gene expression data. Accompanying xcoredata package provides a collection of molecular signatures, constructed from publicly available ChiP-seq experiments. xcore use ridge regression to model changes in expression as a linear combination of molecular signatures and find their unknown activities. Obtained, estimates can be further tested for significance to select molecular signatures with the highest predicted effect on the observed expression changes. biocViews: GeneExpression, GeneRegulation, Epigenetics, Regression, Sequencing Author: Maciej Migdał [aut, cre] (ORCID: ), Bogumił Kaczkowski [aut] (ORCID: ) Maintainer: Maciej Migdał VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/xcore git_branch: RELEASE_3_22 git_last_commit: b03d46b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/xcore_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/xcore_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/xcore_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/xcore_1.14.0.tgz vignettes: vignettes/xcore/inst/doc/xcore_vignette.html vignetteTitles: xcore vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xcore/inst/doc/xcore_vignette.R suggestsMe: xcoredata dependencyCount: 59 Package: XDE Version: 2.56.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5) Imports: BiocGenerics, genefilter, graphics, grDevices, gtools, methods, stats, utils, mvtnorm, RColorBrewer, GeneMeta, siggenes Suggests: MASS, RUnit Enhances: coda License: LGPL-2 Archs: x64 MD5sum: 7e5b305690090f64f2903f3b73f1964a NeedsCompilation: yes Title: XDE: a Bayesian hierarchical model for cross-study analysis of differential gene expression Description: Multi-level model for cross-study detection of differential gene expression. biocViews: Microarray, DifferentialExpression Author: R.B. Scharpf, G. Parmigiani, A.B. Nobel, and H. Tjelmeland Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/XDE git_branch: RELEASE_3_22 git_last_commit: 76ff15d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/XDE_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/XDE_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/XDE_2.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/XDE_2.56.0.tgz vignettes: vignettes/XDE/inst/doc/XDE.pdf, vignettes/XDE/inst/doc/XdeParameterClass.pdf vignetteTitles: XDE Vignette, XdeParameterClass Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XDE/inst/doc/XDE.R, vignettes/XDE/inst/doc/XdeParameterClass.R dependencyCount: 62 Package: XeniumIO Version: 1.2.0 Depends: TENxIO, R (>= 4.5.0) Imports: BiocBaseUtils, BiocGenerics, BiocIO, jsonlite, methods, S4Vectors, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, VisiumIO Suggests: arrow, BiocFileCache, BiocStyle, knitr, rmarkdown, tinytest License: Artistic-2.0 MD5sum: 10c5f3dd20e1aed9de6dfa388959746d NeedsCompilation: no Title: Import and represent Xenium data from the 10X Xenium Analyzer Description: The package allows users to readily import spatial data obtained from the 10X Xenium Analyzer pipeline. Supported formats include 'parquet', 'h5', and 'mtx' files. The package mainly represents data as SpatialExperiment objects. biocViews: Software, Infrastructure, DataImport, SingleCell, Spatial Author: Marcel Ramos [aut, cre] (ORCID: ), Dario Righelli [ctb], Estella Dong [ctb] Maintainer: Marcel Ramos URL: https://github.com/waldronlab/XeniumIO VignetteBuilder: knitr BugReports: https://github.com/waldronlab/XeniumIO/issues git_url: https://git.bioconductor.org/packages/XeniumIO git_branch: RELEASE_3_22 git_last_commit: 727bab9 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/XeniumIO_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/XeniumIO_1.1.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/XeniumIO_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/XeniumIO_1.2.0.tgz vignettes: vignettes/XeniumIO/inst/doc/XeniumIO.html vignetteTitles: VisiumIO Quick Start Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XeniumIO/inst/doc/XeniumIO.R dependencyCount: 90 Package: xenLite Version: 1.4.0 Depends: R (>= 4.1) Imports: SpatialExperiment, BiocFileCache, Matrix, S4Vectors, SummarizedExperiment, methods, utils, EBImage, shiny, HDF5Array, arrow, ggplot2, SingleCellExperiment, TENxIO, dplyr, graphics, stats Suggests: knitr, testthat, BiocStyle, yesno, terra, SpatialFeatureExperiment, SFEData, tiff License: Artistic-2.0 Archs: x64 MD5sum: 511f10caa51f35df9d4ffb8fcf0cc31a NeedsCompilation: no Title: Simple classes and methods for managing Xenium datasets Description: Define a relatively light class for managing Xenium data using Bioconductor. Address use of parquet for coordinates, SpatialExperiment for assay and sample data. Address serialization and use of cloud storage. biocViews: Infrastructure Author: Vincent Carey [aut, cre] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/xenLite VignetteBuilder: knitr BugReports: https://github.com/vjcitn/xenLite/issues git_url: https://git.bioconductor.org/packages/xenLite git_branch: RELEASE_3_22 git_last_commit: cc38f6b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/xenLite_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/xenLite_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/xenLite_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/xenLite_1.4.0.tgz vignettes: vignettes/xenLite/inst/doc/xenLite.html vignetteTitles: xenLite: exploration of a class for Xenium demonstration data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xenLite/inst/doc/xenLite.R dependencyCount: 131 Package: Xeva Version: 1.26.0 Depends: R (>= 3.6) Imports: methods, stats, utils, BBmisc, Biobase, grDevices, ggplot2, scales, ComplexHeatmap, parallel, doParallel, Rmisc, grid, nlme, PharmacoGx, downloader Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 8f834975b44fd2eb7512d33c261a6df7 NeedsCompilation: no Title: Analysis of patient-derived xenograft (PDX) data Description: The Xeva package provides efficient and powerful functions for patient-drived xenograft (PDX) based pharmacogenomic data analysis. This package contains a set of functions to perform analysis of patient-derived xenograft data. This package was developed by the BHKLab, for further information please see our documentation. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Arvind Mer [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/Xeva/issues git_url: https://git.bioconductor.org/packages/Xeva git_branch: RELEASE_3_22 git_last_commit: 1097e32 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/Xeva_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Xeva_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Xeva_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Xeva_1.26.0.tgz vignettes: vignettes/Xeva/inst/doc/Xeva.pdf vignetteTitles: The Xeva User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Xeva/inst/doc/Xeva.R dependencyCount: 159 Package: XINA Version: 1.28.0 Depends: R (>= 3.5) Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools, grDevices, graphics, utils, STRINGdb Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 53b934a4e9ae4e5eb93373ed3bac11dc NeedsCompilation: no Title: Multiplexes Isobaric Mass Tagged-based Kinetics Data for Network Analysis Description: The aim of XINA is to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions. XINA imports multiple datasets, tags dataset in silico, and combines the data for subsequent subgrouping into multiple clusters. The result is a single output depicting the variation across all conditions. XINA, not only extracts coabundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs. biocViews: SystemsBiology, Proteomics, RNASeq, Network Author: Lang Ho Lee and Sasha A. Singh Maintainer: Lang Ho Lee and Sasha A. Singh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XINA git_branch: RELEASE_3_22 git_last_commit: a4cb97b git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/XINA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/XINA_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/XINA_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/XINA_1.28.0.tgz vignettes: vignettes/XINA/inst/doc/xina_user_code.html vignetteTitles: xina_user_code hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XINA/inst/doc/xina_user_code.R dependencyCount: 63 Package: xmapbridge Version: 1.68.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: 18843b2f9aaad8373bb230e3e0a66a57 NeedsCompilation: no Title: Export plotting files to the xmapBridge for visualisation in X:Map Description: xmapBridge can plot graphs in the X:Map genome browser. This package exports plotting files in a suitable format. biocViews: Annotation, ReportWriting, Visualization Author: Tim Yates and Crispin J Miller Maintainer: Chris Wirth URL: http://xmap.picr.man.ac.uk, http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/xmapbridge git_branch: RELEASE_3_22 git_last_commit: 373ed23 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/xmapbridge_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/xmapbridge_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/xmapbridge_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/xmapbridge_1.68.0.tgz vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf vignetteTitles: xmapbridge primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xmapbridge/inst/doc/xmapbridge.R dependencyCount: 1 Package: XVector Version: 0.50.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.43.8) Imports: methods, utils, stats, tools, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 MD5sum: 929b5a997ee6325853057c28ca3df77d NeedsCompilation: yes Title: Foundation of external vector representation and manipulation in Bioconductor Description: Provides memory efficient S4 classes for storing sequences "externally" (e.g. behind an R external pointer, or on disk). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès and Patrick Aboyoun Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/XVector BugReports: https://github.com/Bioconductor/XVector/issues git_url: https://git.bioconductor.org/packages/XVector git_branch: RELEASE_3_22 git_last_commit: 6b7e2a1 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/XVector_0.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/XVector_0.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/XVector_0.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/XVector_0.50.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, triplex importsMe: Bioc.gff, BSgenome, ChIPsim, CNEr, compEpiTools, crisprScore, dada2, DECIPHER, gcrma, GenomAutomorphism, GenomicFeatures, Gviz, HiLDA, IONiseR, IsoformSwitchAnalyzeR, kebabs, MatrixRider, Modstrings, monaLisa, ProteoDisco, ribosomeProfilingQC, Rsamtools, rtracklayer, SparseArray, Structstrings, TFBSTools, tracktables, tRNA, tRNAscanImport, VariantAnnotation suggestsMe: IRanges, musicatk, inDAGO linksToMe: Bioc.gff, Biostrings, CNEr, DECIPHER, kebabs, MatrixRider, pwalign, Rsamtools, rtracklayer, ShortRead, SparseArray, triplex, VariantAnnotation, VariantFiltering dependencyCount: 10 Package: yamss Version: 1.36.0 Depends: R (>= 4.3.0), methods, BiocGenerics (>= 0.15.3), SummarizedExperiment Imports: IRanges, stats, S4Vectors, EBImage, Matrix, mzR, data.table, grDevices, limma Suggests: BiocStyle, knitr, rmarkdown, digest, mtbls2, testthat License: Artistic-2.0 MD5sum: 888cbf2e5885005c8a3fc779c6388164 NeedsCompilation: no Title: Tools for high-throughput metabolomics Description: Tools to analyze and visualize high-throughput metabolomics data aquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis. At the core of these methods is a peak detection phase that pools information across all samples simultaneously. This is in contrast to other methods that detect peaks in a sample-by-sample basis. biocViews: MassSpectrometry, Metabolomics, PeakDetection, Software Author: Leslie Myint [cre, aut] (ORCID: ), Kasper Daniel Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/yamss VignetteBuilder: knitr BugReports: https://github.com/hansenlab/yamss/issues git_url: https://git.bioconductor.org/packages/yamss git_branch: RELEASE_3_22 git_last_commit: 268fc64 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/yamss_1.36.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/yamss_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/yamss_1.36.0.tgz vignettes: vignettes/yamss/inst/doc/yamss.html vignetteTitles: yamss User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yamss/inst/doc/yamss.R dependencyCount: 68 Package: YAPSA Version: 1.36.0 Depends: R (>= 4.0.0), GenomicRanges, ggplot2, grid Imports: limSolve, SomaticSignatures, VariantAnnotation, Seqinfo, reshape2, gridExtra, corrplot, dendextend, GetoptLong, circlize, gtrellis, doParallel, parallel, PMCMRplus, ggbeeswarm, ComplexHeatmap, KEGGREST, grDevices, Biostrings, BSgenome.Hsapiens.UCSC.hg19, magrittr, pracma, dplyr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: d73f7da119c01951845338021666f72b NeedsCompilation: no Title: Yet Another Package for Signature Analysis Description: This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata. biocViews: Sequencing, DNASeq, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod, BiologicalQuestion Author: Daniel Huebschmann [aut], Lea Jopp-Saile [aut], Carolin Andresen [aut], Zuguang Gu [aut, cre], Matthias Schlesner [aut] Maintainer: Zuguang Gu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/YAPSA git_branch: RELEASE_3_22 git_last_commit: 568357f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/YAPSA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/YAPSA_1.35.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/YAPSA_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/YAPSA_1.36.0.tgz vignettes: vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.html, vignettes/YAPSA/inst/doc/vignette_exomes.html, vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.html, vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.html, vignettes/YAPSA/inst/doc/vignettes_Indel.html, vignettes/YAPSA/inst/doc/YAPSA.html vignetteTitles: 3. Confidence Intervals, 6. Usage of YAPSA for WES data, 2. Signature-specific cutoffs, 4. Stratified Analysis of Mutational Signatures, 5. Indel signature analysis, 1. Usage of YAPSA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.R, vignettes/YAPSA/inst/doc/vignette_exomes.R, vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.R, vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.R, vignettes/YAPSA/inst/doc/vignettes_Indel.R, vignettes/YAPSA/inst/doc/YAPSA.R dependencyCount: 179 Package: yarn Version: 1.36.0 Depends: Biobase Imports: biomaRt, downloader, edgeR, gplots, graphics, limma, matrixStats, preprocessCore, readr, RColorBrewer, stats, quantro Suggests: knitr, rmarkdown, testthat (>= 0.8) License: Artistic-2.0 MD5sum: c4a174d56a35b16f82e3b9c3b6896412 NeedsCompilation: no Title: YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization Description: Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments. biocViews: Software, QualityControl, GeneExpression, Sequencing, Preprocessing, Normalization, Annotation, Visualization, Clustering Author: Joseph N Paulson [aut, cre], Cho-Yi Chen [aut], Camila Lopes-Ramos [aut], Marieke Kuijjer [aut], John Platig [aut], Abhijeet Sonawane [aut], Maud Fagny [aut], Kimberly Glass [aut], John Quackenbush [aut] Maintainer: Joseph N Paulson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/yarn git_branch: RELEASE_3_22 git_last_commit: ade31dd git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/yarn_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/yarn_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/yarn_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/yarn_1.36.0.tgz vignettes: vignettes/yarn/inst/doc/yarn.pdf vignetteTitles: YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yarn/inst/doc/yarn.R dependencyCount: 163 Package: zellkonverter Version: 1.20.0 Imports: basilisk, cli, DelayedArray, Matrix, methods, reticulate, S4Vectors, SingleCellExperiment (>= 1.11.6), SparseArray, SummarizedExperiment, utils Suggests: anndata, BiocFileCache, BiocStyle, covr, HDF5Array, knitr, pkgload, rhdf5 (>= 2.45.1), rmarkdown, scRNAseq, SpatialExperiment, spelling, testthat, withr License: MIT + file LICENSE MD5sum: ff4317219bb0aa04de15ce6a31a5e75f NeedsCompilation: no Title: Conversion Between scRNA-seq Objects Description: Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk. biocViews: SingleCell, DataImport, DataRepresentation Author: Luke Zappia [aut, cre] (ORCID: , github: lazappi), Aaron Lun [aut] (ORCID: , github: LTLA), Jack Kamm [ctb] (ORCID: , github: jackkamm), Robrecht Cannoodt [ctb] (ORCID: , github: rcannood), Gabriel Hoffman [ctb] (ORCID: , github: GabrielHoffman), Marek Cmero [ctb] (ORCID: , github: mcmero) Maintainer: Luke Zappia URL: https://github.com/theislab/zellkonverter VignetteBuilder: knitr BugReports: https://github.com/theislab/zellkonverter/issues git_url: https://git.bioconductor.org/packages/zellkonverter git_branch: RELEASE_3_22 git_last_commit: cf55b0f git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/zellkonverter_1.20.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/zellkonverter_1.19.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/zellkonverter_1.19.2.tgz vignettes: vignettes/zellkonverter/inst/doc/zellkonverter.html vignetteTitles: Converting to/from AnnData to SingleCellExperiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zellkonverter/inst/doc/zellkonverter.R dependsOnMe: scATAC.Explorer, OSCA.intro importsMe: BgeeDB, DOtools, singleCellTK, velociraptor, OSTA suggestsMe: cellxgenedp, CuratedAtlasQueryR, GloScope, HDF5Array, HCATonsilData dependencyCount: 41 Package: zenith Version: 1.12.0 Depends: R (>= 4.2.0), limma, methods Imports: variancePartition (>= 1.26.0), EnrichmentBrowser (>= 2.22.0), GSEABase (>= 1.54.0), msigdbr, Rfast, ggplot2, tidyr, dplyr, reshape2, progress, utils, Rdpack, stats Suggests: BiocStyle, BiocGenerics, knitr, pander, rmarkdown, tweeDEseqCountData, edgeR, kableExtra, RUnit License: Artistic-2.0 MD5sum: 4a444c2b68c9b11082dea1ceaee09ee7 NeedsCompilation: no Title: Gene set analysis following differential expression using linear (mixed) modeling with dream Description: Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream(). biocViews: RNASeq, GeneExpression, GeneSetEnrichment, DifferentialExpression, BatchEffect, QualityControl, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Preprocessing, Microarray, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] (ORCID: ) Maintainer: Gabriel Hoffman URL: https://DiseaseNeuroGenomics.github.io/zenith VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeuroGenomics/zenith/issues git_url: https://git.bioconductor.org/packages/zenith git_branch: RELEASE_3_22 git_last_commit: 5d3238d git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/zenith_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/zenith_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/zenith_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/zenith_1.12.0.tgz vignettes: vignettes/zenith/inst/doc/loading_genesets.html, vignettes/zenith/inst/doc/zenith.html vignetteTitles: Example usage of zenith on GEUVAIDIS RNA-seq, Example usage of zenith on RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/zenith/inst/doc/loading_genesets.R, vignettes/zenith/inst/doc/zenith.R importsMe: dreamlet suggestsMe: variancePartition dependencyCount: 161 Package: zFPKM Version: 1.32.0 Depends: R (>= 3.4.0) Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment Suggests: knitr, limma, edgeR, GEOquery, stringr, printr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 76ea02fc47fe715109c89972d5db266f NeedsCompilation: no Title: A suite of functions to facilitate zFPKM transformations Description: Perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113). Reference recommends using zFPKM > -3 to select expressed genes. Validated with encode open/closed chromosome data. Works well for gene level data using FPKM or TPM. Does not appear to calibrate well for transcript level data. biocViews: ImmunoOncology, RNASeq, FeatureExtraction, Software, GeneExpression Author: Ron Ammar [aut, cre], John Thompson [aut] Maintainer: Ron Ammar URL: https://github.com/ronammar/zFPKM/ VignetteBuilder: knitr BugReports: https://github.com/ronammar/zFPKM/issues git_url: https://git.bioconductor.org/packages/zFPKM git_branch: RELEASE_3_22 git_last_commit: 1b16803 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/zFPKM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/zFPKM_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/zFPKM_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/zFPKM_1.32.0.tgz vignettes: vignettes/zFPKM/inst/doc/zFPKM.html vignetteTitles: Introduction to zFPKM Transformation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zFPKM/inst/doc/zFPKM.R suggestsMe: DGEobj.utils dependencyCount: 55 Package: zinbwave Version: 1.32.0 Depends: R (>= 3.4), methods, SummarizedExperiment, SingleCellExperiment Imports: BiocParallel, softImpute, stats, genefilter, edgeR, Matrix Suggests: knitr, rmarkdown, testthat, matrixStats, magrittr, scRNAseq, ggplot2, biomaRt, BiocStyle, Rtsne, DESeq2, sparseMatrixStats License: Artistic-2.0 MD5sum: f24774599de155586fb955eaff6f6173 NeedsCompilation: no Title: Zero-Inflated Negative Binomial Model for RNA-Seq Data Description: Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data. biocViews: ImmunoOncology, DimensionReduction, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny Perraudeau [aut], Jean-Philippe Vert [aut], Clara Bagatin [aut] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/drisso/zinbwave/issues git_url: https://git.bioconductor.org/packages/zinbwave git_branch: RELEASE_3_22 git_last_commit: cfc6ec2 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/zinbwave_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/zinbwave_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/zinbwave_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/zinbwave_1.32.0.tgz vignettes: vignettes/zinbwave/inst/doc/intro.html vignetteTitles: zinbwave Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/zinbwave/inst/doc/intro.R importsMe: clusterExperiment, scBFA, singleCellTK, SpatialDDLS suggestsMe: MAST, splatter dependencyCount: 75 Package: zitools Version: 1.4.0 Depends: R (>= 4.4.0), methods Imports: phyloseq, pscl, ggplot2, MatrixGenerics, SummarizedExperiment, stats, VGAM, matrixStats, tidyr, tibble, dplyr, DESeq2, reshape2, RColorBrewer, magrittr, BiocGenerics, graphics, utils Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), tidyverse, microbiome License: BSD_3_clause + file LICENSE MD5sum: dedf881c5788976864f35a903fe39068 NeedsCompilation: no Title: Analysis of zero-inflated count data Description: zitools allows for zero inflated count data analysis by either using down-weighting of excess zeros or by replacing an appropriate proportion of excess zeros with NA. Through overloading frequently used statistical functions (such as mean, median, standard deviation), plotting functions (such as boxplots or heatmap) or differential abundance tests, it allows a wide range of downstream analyses for zero-inflated data in a less biased manner. This becomes applicable in the context of microbiome analyses, where the data is often overdispersed and zero-inflated, therefore making data analysis extremly challenging. biocViews: Software, StatisticalMethod, Microbiome Author: Carlotta Meyring [aut, cre] (ORCID: ) Maintainer: Carlotta Meyring URL: https://github.com/kreutz-lab/zitools VignetteBuilder: knitr BugReports: https://github.com/kreutz-lab/zitools/issues git_url: https://git.bioconductor.org/packages/zitools git_branch: RELEASE_3_22 git_last_commit: c4def98 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/zitools_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/zitools_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/zitools_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/zitools_1.4.0.tgz vignettes: vignettes/zitools/inst/doc/zitools_tutorial.pdf vignetteTitles: An Introduction to zitools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zitools/inst/doc/zitools_tutorial.R dependencyCount: 97 Package: ZygosityPredictor Version: 1.10.0 Depends: R (>= 4.3.0) Imports: GenomicAlignments, GenomicRanges, Rsamtools, IRanges, VariantAnnotation, DelayedArray, dplyr, stringr, purrr, tibble, methods, knitr, igraph, readr, stats, magrittr, rlang Suggests: rmarkdown, testthat, BiocStyle License: GPL-2 MD5sum: a2158392ead403a22e525e27c85199fa NeedsCompilation: no Title: Package for prediction of zygosity for variants/genes in NGS data Description: The ZygosityPredictor allows to predict how many copies of a gene are affected by small variants. In addition to the basic calculations of the affected copy number of a variant, the Zygosity-Predictor can integrate the influence of several variants on a gene and ultimately make a statement if and how many wild-type copies of the gene are left. This information proves to be of particular use in the context of translational medicine. For example, in cancer genomes, the Zygosity-Predictor can address whether unmutated copies of tumor-suppressor genes are present. Beyond this, it is possible to make this statement for all genes of an organism. The Zygosity-Predictor was primarily developed to handle SNVs and INDELs (later addressed as small-variants) of somatic and germline origin. In order not to overlook severe effects outside of the small-variant context, it has been extended with the assessment of large scale deletions, which cause losses of whole genes or parts of them. biocViews: BiomedicalInformatics, FunctionalPrediction, SomaticMutation, GenePrediction Author: Marco Rheinnecker [aut, cre] (ORCID: ), Marc Ruebsam [aut], Daniel Huebschmann [aut], Martina Froehlich [aut], Barbara Hutter [aut] Maintainer: Marco Rheinnecker VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ZygosityPredictor git_branch: RELEASE_3_22 git_last_commit: 52dc6d0 git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 source.ver: src/contrib/ZygosityPredictor_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ZygosityPredictor_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ZygosityPredictor_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ZygosityPredictor_1.10.0.tgz vignettes: vignettes/ZygosityPredictor/inst/doc/Usage.html vignetteTitles: Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ZygosityPredictor/inst/doc/Usage.R dependencyCount: 100 Package: amplican Version: 1.31.2 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.22.0), Biostrings (>= 2.44.2), pwalign Imports: Rcpp, utils (>= 3.4.1), S4Vectors (>= 0.14.3), ShortRead (>= 1.34.0), IRanges (>= 2.10.2), GenomicRanges (>= 1.61.1), Seqinfo, BiocParallel (>= 1.10.1), gtable (>= 0.2.0), gridExtra (>= 2.2.1), ggplot2 (>= 3.3.4), ggthemes (>= 3.4.0), waffle (>= 0.7.0), stringr (>= 1.2.0), stats (>= 3.4.1), matrixStats (>= 0.52.2), Matrix (>= 1.2-10), dplyr (>= 0.7.2), data.table (>= 1.10.4-3), rmarkdown (>= 1.6), knitr (>= 1.16), cluster (>= 2.1.4) LinkingTo: Rcpp Suggests: testthat, BiocStyle, GenomicAlignments License: GPL-3 NeedsCompilation: yes Title: Automated analysis of CRISPR experiments Description: `amplican` performs alignment of the amplicon reads, normalizes gathered data, calculates multiple statistics (e.g. cut rates, frameshifts) and presents results in form of aggregated reports. Data and statistics can be broken down by experiments, barcodes, user defined groups, guides and amplicons allowing for quick identification of potential problems. biocViews: ImmunoOncology, Technology, Alignment, qPCR, CRISPR Author: Kornel Labun [aut], Eivind Valen [cph, cre] Maintainer: Eivind Valen URL: https://github.com/valenlab/amplican VignetteBuilder: knitr BugReports: https://github.com/valenlab/amplican/issues git_url: https://git.bioconductor.org/packages/amplican git_branch: devel git_last_commit: 48e5f7d git_last_commit_date: 2025-07-11 Date/Publication: 2025-07-11 win.binary.ver: bin/windows/contrib/4.5/amplican_1.31.2.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: APAlyzer Version: 1.23.0 Depends: R (>= 3.5.0) Imports: GenomicRanges, GenomicFeatures, GenomicAlignments, DESeq2, ggrepel, SummarizedExperiment, Rsubread, stats, ggplot2, methods, rtracklayer, VariantAnnotation, dplyr, tidyr, repmis, Rsamtools, HybridMTest Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, AnnotationDbi, TBX20BamSubset, testthat, pasillaBamSubset License: LGPL-3 NeedsCompilation: no Title: A toolkit for APA analysis using RNA-seq data Description: Perform 3'UTR APA, Intronic APA and gene expression analysis using RNA-seq data. biocViews: Sequencing, RNASeq, DifferentialExpression, GeneExpression, GeneRegulation, Annotation, DataImport, Software Author: Ruijia Wang [cre, aut] (ORCID: ), Bin Tian [aut], Wei-Chun Chen [aut] Maintainer: Ruijia Wang URL: https://github.com/RJWANGbioinfo/APAlyzer/ VignetteBuilder: knitr BugReports: https://github.com/RJWANGbioinfo/APAlyzer/issues git_url: https://git.bioconductor.org/packages/APAlyzer git_branch: devel git_last_commit: 1bff29d git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/APAlyzer_1.23.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: arrayQualityMetrics Version: 3.65.0 Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, genefilter, graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter, lattice, latticeExtra, limma, methods, RColorBrewer, setRNG, stats, utils, vsn (>= 3.23.3), XML, svglite Suggests: ALLMLL, CCl4, BiocStyle, knitr License: LGPL (>= 2) Archs: x64 NeedsCompilation: no Title: Quality metrics report for microarray data sets Description: This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported. biocViews: Microarray, QualityControl, OneChannel, TwoChannel, ReportWriting Author: Audrey Kauffmann, Wolfgang Huber Maintainer: Mike Smith VignetteBuilder: knitr BugReports: https://github.com/grimbough/arrayQualityMetrics/issues git_url: https://git.bioconductor.org/packages/arrayQualityMetrics git_branch: devel git_last_commit: 4bc554f git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/arrayQualityMetrics_3.65.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: barcodetrackR Version: 1.17.0 Depends: R (>= 4.1) Imports: cowplot, circlize, dplyr, ggplot2, ggdendro, ggridges, graphics, grDevices, magrittr, plyr, proxy, RColorBrewer, rlang, scales, shiny, stats, SummarizedExperiment, S4Vectors, tibble, tidyr, vegan, viridis, utils Suggests: BiocStyle, knitr, magick, rmarkdown, testthat License: file LICENSE NeedsCompilation: no Title: Functions for Analyzing Cellular Barcoding Data Description: barcodetrackR is an R package developed for the analysis and visualization of clonal tracking data. Data required is samples and tag abundances in matrix form. Usually from cellular barcoding experiments, integration site retrieval analyses, or similar technologies. biocViews: Software, Visualization, Sequencing Author: Diego Alexander Espinoza [aut, cre], Ryland Mortlock [aut] Maintainer: Diego Alexander Espinoza URL: https://github.com/dunbarlabNIH/barcodetrackR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/barcodetrackR git_branch: devel git_last_commit: 5840ca2 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/barcodetrackR_1.17.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: beadarray Version: 2.59.0 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), hexbin Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, illuminaio, methods, ggplot2 Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr License: MIT + file LICENSE NeedsCompilation: yes Title: Quality assessment and low-level analysis for Illumina BeadArray data Description: The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided. biocViews: Microarray, OneChannel, QualityControl, Preprocessing Author: Mark Dunning, Mike Smith, Jonathan Cairns, Andy Lynch, Matt Ritchie Maintainer: Mark Dunning VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beadarray git_branch: devel git_last_commit: a89dbc2 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/beadarray_2.59.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: bgx Version: 1.75.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Imports: Rcpp (>= 0.11.0) LinkingTo: Rcpp Suggests: affydata, hgu95av2cdf License: GPL-2 NeedsCompilation: yes Title: Bayesian Gene eXpression Description: Bayesian integrated analysis of Affymetrix GeneChips biocViews: Microarray, DifferentialExpression Author: Ernest Turro, Graeme Ambler, Anne-Mette K Hein Maintainer: Ernest Turro git_url: https://git.bioconductor.org/packages/bgx git_branch: devel git_last_commit: c9f70c0 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/bgx_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bgx_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bgx_1.76.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: biodbHmdb Version: 1.15.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.3.2), Rcpp, zip LinkingTo: Rcpp, testthat Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr, lgr License: AGPL-3 NeedsCompilation: yes Title: biodbHmdb, a library for connecting to the HMDB Database Description: The biodbHmdb library is an extension of the biodb framework package that provides access to the HMDB Metabolites database. It allows to download the whole HMDB Metabolites database locally, access entries and search for entries by name or description. A future version of this package will also include a search by mass and mass spectra annotation. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] (ORCID: ) Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbHmdb VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbHmdb/issues git_url: https://git.bioconductor.org/packages/biodbHmdb git_branch: devel git_last_commit: 9920d92 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/biodbHmdb_1.15.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: biodbNcbi Version: 1.13.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.2), R6, XML, chk Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 NeedsCompilation: no Title: biodbNcbi, a library for connecting to NCBI Databases. Description: The biodbNcbi library provides access to the NCBI databases CCDS, Gene, Pubchem Comp and Pubchem Subst, using biodb package framework. It allows to retrieve entries by their accession number. Web services can be accessed for searching the database by name or mass. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] (ORCID: ) Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbNcbi VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbNCbi/issues git_url: https://git.bioconductor.org/packages/biodbNcbi git_branch: devel git_last_commit: 79256f8 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/biodbNcbi_1.13.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: biodbNci Version: 1.13.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.1), R6, Rcpp, chk LinkingTo: Rcpp, testthat Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 NeedsCompilation: yes Title: biodbNci, a library for connecting to biodbNci, a library for connecting to the National Cancer Institute (USA) CACTUS Database Description: The biodbNci library is an extension of the biodb framework package. It provides access to biodbNci, a library for connecting to the National Cancer Institute (USA) CACTUS Database. It allows to retrieve entries by their accession number, and run specific web services. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] (ORCID: ) Maintainer: Pierrick Roger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biodbNci git_branch: devel git_last_commit: 536802f git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/biodbNci_1.13.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: biodbUniprot Version: 1.15.0 Depends: R (>= 4.1.0) Imports: R6, biodb (>= 1.4.2) Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, lgr, covr License: AGPL-3 NeedsCompilation: no Title: biodbUniprot, a library for connecting to the Uniprot Database Description: The biodbUniprot library is an extension of the biodb framework package. It provides access to the UniProt database. It allows to retrieve entries by their accession number, and run web service queries for searching for entries. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] (ORCID: ) Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbUniprot VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbUniprot/issues git_url: https://git.bioconductor.org/packages/biodbUniprot git_branch: devel git_last_commit: 4ba9515 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/biodbUniprot_1.15.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: blima Version: 1.43.0 Depends: R(>= 3.3) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), Rcpp (>= 0.12.8), BiocGenerics, grDevices, stats, graphics LinkingTo: Rcpp Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi, knitr License: GPL-3 NeedsCompilation: yes Title: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level Description: Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing. biocViews: Microarray, Preprocessing, Normalization, DifferentialExpression, GeneRegulation, GeneExpression Author: Vojtěch Kulvait Maintainer: Vojtěch Kulvait URL: https://bitbucket.org/kulvait/blima VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/blima git_branch: devel git_last_commit: da83163 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/blima_1.43.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BPRMeth Version: 1.35.0 Depends: R (>= 3.5.0), GenomicRanges Imports: assertthat, methods, MASS, doParallel, parallel, e1071, earth, foreach, randomForest, stats, IRanges, S4Vectors, data.table, graphics, truncnorm, mvtnorm, Rcpp (>= 0.12.14), matrixcalc, magrittr, kernlab, ggplot2, cowplot, BiocStyle LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE NeedsCompilation: yes Title: Model higher-order methylation profiles Description: The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: Chantriolnt-Andreas Kapourani [aut, cre] Maintainer: Chantriolnt-Andreas Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BPRMeth git_branch: devel git_last_commit: 148d790 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/BPRMeth_1.35.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: branchpointer Version: 1.35.1 Depends: caret, R(>= 3.4) Imports: plyr, kernlab, gbm, stringr, cowplot, ggplot2, biomaRt, Biostrings, parallel, utils, stats, BSgenome.Hsapiens.UCSC.hg38, rtracklayer, GenomicRanges, Seqinfo, IRanges, S4Vectors, data.table Suggests: knitr, BiocStyle License: BSD_3_clause + file LICENSE Archs: x64 NeedsCompilation: no Title: Prediction of intronic splicing branchpoints Description: Predicts branchpoint probability for sites in intronic branchpoint windows. Queries can be supplied as intronic regions; or to evaluate the effects of mutations, SNPs. biocViews: Software, GenomeAnnotation, GenomicVariation, MotifAnnotation Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/branchpointer git_branch: devel git_last_commit: 2b05809 git_last_commit_date: 2025-07-22 Date/Publication: 2025-07-23 win.binary.ver: bin/windows/contrib/4.5/branchpointer_1.35.1.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: broadSeq Version: 1.3.3 Depends: dplyr, ggpubr, SummarizedExperiment Imports: BiocStyle, DELocal, EBSeq (>= 1.38.0), DESeq2 (>= 1.38.2), NOISeq, forcats (>= 1.0.0), genefilter, ggplot2, ggplotify, plyr, clusterProfiler (>= 4.8.2), pheatmap, sechm (>= 1.6.0), stringr, purrr (>= 0.3.5), edgeR (>= 3.40.1) Suggests: knitr, limma (>= 3.54.0), rmarkdown, stats (>= 4.2.2), samr License: MIT + file LICENSE Archs: x64 NeedsCompilation: no Title: broadSeq : for streamlined exploration of RNA-seq data Description: This package helps user to do easily RNA-seq data analysis with multiple methods (usually which needs many different input formats). Here the user will provid the expression data as a SummarizedExperiment object and will get results from different methods. It will help user to quickly evaluate different methods. biocViews: GeneExpression, DifferentialExpression, RNASeq, Transcriptomics, Sequencing, Coverage, GeneSetEnrichment, GO Author: Rishi Das Roy [aut, cre] (ORCID: ) Maintainer: Rishi Das Roy URL: https://github.com/dasroy/broadSeq VignetteBuilder: knitr BugReports: https://github.com/dasroy/broadSeq/issues git_url: https://git.bioconductor.org/packages/broadSeq git_branch: devel git_last_commit: 0ee75b2 git_last_commit_date: 2025-07-11 Date/Publication: 2025-07-11 win.binary.ver: bin/windows/contrib/4.5/broadSeq_1.3.3.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BubbleTree Version: 2.39.0 Depends: R (>= 3.5), IRanges, GenomicRanges, plyr, dplyr, magrittr Imports: BiocGenerics (>= 0.31.6), BiocStyle, Biobase, ggplot2, WriteXLS, gtools, RColorBrewer, limma, grid, gtable, gridExtra, biovizBase, e1071, methods, grDevices, stats, utils Suggests: knitr, rmarkdown License: LGPL (>= 3) Archs: x64 NeedsCompilation: no Title: BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality in somatic mosaicism using next generation sequencing data Description: CNV analysis in groups of tumor samples. biocViews: CopyNumberVariation, Software, Sequencing, Coverage Author: Wei Zhu , Michael Kuziora , Todd Creasy , Brandon Higgs Maintainer: Todd Creasy , Wei Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BubbleTree git_branch: devel git_last_commit: c8fc528 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/BubbleTree_2.39.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: chevreulShiny Version: 1.1.2 Depends: R (>= 4.5.0), SingleCellExperiment, shiny (>= 1.6.0), shinydashboard, chevreulProcess, chevreulPlot Imports: alabaster.base, clustree, ComplexHeatmap, DataEditR (>= 0.0.9), DBI, dplyr, DT, EnhancedVolcano, fs, future, ggplot2, ggplotify, grDevices, methods, patchwork, plotly, purrr, rappdirs, readr, RSQLite, S4Vectors, scales, shinyFiles, shinyhelper, shinyjs, shinyWidgets, stats, stringr, tibble, tidyr, tidyselect, utils, waiter, wiggleplotr Suggests: BiocStyle, knitr, RefManageR, rmarkdown, testthat (>= 3.0.0), EnsDb.Mmusculus.v79, EnsDb.Hsapiens.v86 License: MIT + file LICENSE NeedsCompilation: no Title: Tools for managing SingleCellExperiment objects as projects Description: Tools for managing SingleCellExperiment objects as projects. Includes functions for analysis and visualization of single-cell data. Also included is a shiny app for visualization of pre-processed scRNA data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik. biocViews: Coverage, RNASeq, Sequencing, Visualization, GeneExpression, Transcription, SingleCell, Transcriptomics, Normalization, Preprocessing, QualityControl, DimensionReduction, DataImport Author: Kevin Stachelek [aut, cre] (ORCID: ), Bhavana Bhat [aut] Maintainer: Kevin Stachelek URL: https://github.com/whtns/chevreulShiny, https://whtns.github.io/chevreulShiny/ VignetteBuilder: knitr BugReports: https://github.com/cobriniklab/chevreulShiny/issues git_url: https://git.bioconductor.org/packages/chevreulShiny git_branch: devel git_last_commit: c77a2a9 git_last_commit_date: 2025-06-04 Date/Publication: 2025-06-05 win.binary.ver: bin/windows/contrib/4.5/chevreulShiny_1.1.2.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ChIPQC Version: 1.45.0 Depends: R (>= 3.5.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19), BiocParallel Imports: BiocGenerics (>= 0.11.3), S4Vectors (>= 0.1.0), IRanges (>= 1.99.17), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), chipseq (>= 1.12.0), gtools, methods, reshape2, Nozzle.R1, Biobase, grDevices, stats, utils, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene Suggests: BiocStyle License: GPL (>= 3) NeedsCompilation: no Title: Quality metrics for ChIPseq data Description: Quality metrics for ChIPseq data. biocViews: Sequencing, ChIPSeq, QualityControl, ReportWriting Author: Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark Maintainer: Tom Carroll , Rory Stark git_url: https://git.bioconductor.org/packages/ChIPQC git_branch: devel git_last_commit: e901de2 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/ChIPQC_1.45.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CINdex Version: 1.37.2 Depends: R (>= 3.3), GenomicRanges Imports: bitops,gplots,grDevices,som, dplyr,gridExtra,png,stringr,S4Vectors, IRanges, Seqinfo,graphics, stats, utils Suggests: knitr, testthat, ReactomePA, RUnit, BiocGenerics, AnnotationHub, rtracklayer, pd.genomewidesnp.6, org.Hs.eg.db, biovizBase, TxDb.Hsapiens.UCSC.hg18.knownGene, methods, Biostrings,Homo.sapiens, R.utils License: GPL (>= 2) NeedsCompilation: no Title: Chromosome Instability Index Description: The CINdex package addresses important area of high-throughput genomic analysis. It allows the automated processing and analysis of the experimental DNA copy number data generated by Affymetrix SNP 6.0 arrays or similar high throughput technologies. It calculates the chromosome instability (CIN) index that allows to quantitatively characterize genome-wide DNA copy number alterations as a measure of chromosomal instability. This package calculates not only overall genomic instability, but also instability in terms of copy number gains and losses separately at the chromosome and cytoband level. biocViews: Software, CopyNumberVariation, GenomicVariation, aCGH, Microarray, Genetics, Sequencing Author: Lei Song [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Krithika Bhuvaneshwar [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Yue Wang [aut, ths] (Virginia Polytechnic Institute and State University), Yuanjian Feng [aut] (Virginia Polytechnic Institute and State University), Ie-Ming Shih [aut] (Johns Hopkins University School of Medicine), Subha Madhavan [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Yuriy Gusev [aut, cre] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center) Maintainer: Yuriy Gusev VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/CINdex git_branch: devel git_last_commit: c04e651 git_last_commit_date: 2025-07-24 Date/Publication: 2025-07-25 win.binary.ver: bin/windows/contrib/4.5/CINdex_1.37.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CINdex_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CINdex_1.38.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: cisPath Version: 1.49.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) NeedsCompilation: yes Title: Visualization and management of the protein-protein interaction networks. Description: cisPath is an R package that uses web browsers to visualize and manage protein-protein interaction networks. biocViews: Proteomics Author: Likun Wang Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/cisPath git_branch: devel git_last_commit: fcb30b8 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/cisPath_1.49.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: comapr Version: 1.13.0 Depends: R (>= 4.1.0) Imports: methods, ggplot2, reshape2, dplyr, gridExtra, plotly, circlize, rlang, GenomicRanges, IRanges, foreach, BiocParallel, GenomeInfoDb, scales, RColorBrewer, tidyr, S4Vectors, utils, Matrix, grid, stats, SummarizedExperiment, plyr, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), statmod License: MIT + file LICENSE NeedsCompilation: no Title: Crossover analysis and genetic map construction Description: comapr detects crossover intervals for single gametes from their haplotype states sequences and stores the crossovers in GRanges object. The genetic distances can then be calculated via the mapping functions using estimated crossover rates for maker intervals. Visualisation functions for plotting interval-based genetic map or cumulative genetic distances are implemented, which help reveal the variation of crossovers landscapes across the genome and across individuals. biocViews: Software, SingleCell, Visualization, Genetics Author: Ruqian Lyu [aut, cre] (ORCID: ) Maintainer: Ruqian Lyu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/comapr git_branch: devel git_last_commit: 30e00f7 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/comapr_1.13.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CoSIA Version: 1.9.0 Depends: R (>= 4.3.0), methods (>= 4.3.0), ExperimentHub (>= 2.7.0) Imports: dplyr (>= 1.0.7), magrittr (>= 2.0.1), RColorBrewer (>= 1.1-2), tidyr (>= 1.2.0), plotly (>= 4.10.0), stringr (>= 1.4.0), ggplot2 (>= 3.3.5), tibble (>= 3.1.7), org.Hs.eg.db (>= 3.12.0), org.Mm.eg.db (>= 3.12.0), org.Dr.eg.db (>= 3.12.0), org.Ce.eg.db (>= 3.12.0), org.Dm.eg.db (>= 3.12.0), org.Rn.eg.db (>= 3.12.0), AnnotationDbi (>= 1.52.0), biomaRt (>= 2.46.3), homologene (>= 1.4.68.19), annotationTools (>= 1.64.0), readr (>= 2.1.1), tidyselect (>= 1.1.2), stats (>= 4.1.2) Suggests: BiocStyle (>= 2.22.0), tidyverse (>= 1.3.1), knitr (>= 1.42), rmarkdown (>= 2.20), testthat (>= 3.1.6), qpdf (>= 1.3.0) License: MIT + file LICENSE NeedsCompilation: no Title: An Investigation Across Different Species and Tissues Description: Cross-Species Investigation and Analysis (CoSIA) is a package that provides researchers with an alternative methodology for comparing across species and tissues using normal wild-type RNA-Seq Gene Expression data from Bgee. Using RNA-Seq Gene Expression data, CoSIA provides multiple visualization tools to explore the transcriptome diversity and variation across genes, tissues, and species. CoSIA uses the Coefficient of Variation and Shannon Entropy and Specificity to calculate transcriptome diversity and variation. CoSIA also provides additional conversion tools and utilities to provide a streamlined methodology for cross-species comparison. biocViews: Software, BiologicalQuestion, GeneExpression, MultipleComparison, ThirdPartyClient, DataImport, GUI Author: Anisha Haldar [aut] (ORCID: ), Vishal H. Oza [aut] (ORCID: ), Amanda D. Clark [cre, aut] (ORCID: ), Nathaniel S. DeVoss [aut] (ORCID: ), Brittany N. Lasseigne [aut] (ORCID: ) Maintainer: Amanda D. Clark URL: https://www.lasseigne.org/ VignetteBuilder: knitr BugReports: https://github.com/lasseignelab/CoSIA/issues git_url: https://git.bioconductor.org/packages/CoSIA git_branch: devel git_last_commit: d3df2fb git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/CoSIA_1.9.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CoSIA_1.10.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CRISPRball Version: 1.5.0 Depends: R (>= 4.4.0), shinyBS Imports: DT, shiny, grid, ComplexHeatmap, InteractiveComplexHeatmap, graphics, stats, ggplot2, plotly, shinyWidgets, shinycssloaders, shinyjqui, dittoSeq, matrixStats, colourpicker, shinyjs, circlize, PCAtools, utils, grDevices, htmlwidgets, methods Suggests: BiocStyle, msigdbr, depmap, pool, RSQLite, mygene, testthat (>= 3.0.0), knitr, rmarkdown License: MIT + file LICENSE NeedsCompilation: no Title: Shiny Application for Interactive CRISPR Screen Visualization, Exploration, Comparison, and Filtering Description: A Shiny application for visualization, exploration, comparison, and filtering of CRISPR screens analyzed with MAGeCK RRA or MLE. Features include interactive plots with on-click labeling, full customization of plot aesthetics, data upload and/or download, and much more. Quickly and easily explore your CRISPR screen results and generate publication-quality figures in seconds. biocViews: Software, ShinyApps, CRISPR, QualityControl, Visualization, GUI Author: Jared Andrews [aut, cre] (ORCID: ), Jacob Steele [ctb] (ORCID: ) Maintainer: Jared Andrews URL: https://github.com/j-andrews7/CRISPRball VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/CRISPRball git_branch: devel git_last_commit: 2d08022 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/CRISPRball_1.5.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: cummeRbund Version: 2.51.0 Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite, ggplot2, reshape2, fastcluster, rtracklayer, Gviz Imports: methods, plyr, BiocGenerics, S4Vectors (>= 0.9.25), Biobase Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges, rjson License: Artistic-2.0 NeedsCompilation: no Title: Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data. Description: Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations. biocViews: HighThroughputSequencing, HighThroughputSequencingData, RNAseq, RNAseqData, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Bioinformatics, Clustering, MultipleComparisons, QualityControl Author: L. Goff, C. Trapnell, D. Kelley Maintainer: Loyal A. Goff git_url: https://git.bioconductor.org/packages/cummeRbund git_branch: devel git_last_commit: b8ee61a git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/cummeRbund_2.51.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: customProDB Version: 1.49.0 Depends: R (>= 3.5.0), IRanges, AnnotationDbi, biomaRt (>= 2.17.1) Imports: S4Vectors (>= 0.9.25), DBI, GenomeInfoDb, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.32.0), stringr, RCurl, plyr, VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite, txdbmaker, AhoCorasickTrie, methods Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 NeedsCompilation: no Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search Description: Database search is the most widely used approach for peptide and protein identification in mass spectrometry-based proteomics studies. Our previous study showed that sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in the samples and thus improve protein identification. More importantly, single nucleotide variations, short insertion and deletions and novel junctions identified from RNA-Seq data make protein database more complete and sample-specific. Here, we report an R package customProDB that enables the easy generation of customized databases from RNA-Seq data for proteomics search. This work bridges genomics and proteomics studies and facilitates cross-omics data integration. biocViews: ImmunoOncology, Sequencing, MassSpectrometry, Proteomics, SNP, RNASeq, Software, Transcription, AlternativeSplicing, FunctionalGenomics Author: Xiaojing Wang Maintainer: Xiaojing Wang Bo Wen git_url: https://git.bioconductor.org/packages/customProDB git_branch: devel git_last_commit: 2d2db50 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/customProDB_1.49.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: cytofQC Version: 1.9.0 Imports: CATALYST, flowCore, e1071, EZtune, gbm, ggplot2, hrbrthemes, matrixStats, randomForest, rmarkdown, SingleCellExperiment, stats, SummarizedExperiment, ssc, S4Vectors, graphics, methods Suggests: gridExtra, knitr, RColorBrewer, testthat, uwot License: Artistic-2.0 NeedsCompilation: no Title: Labels normalized cells for CyTOF data and assigns probabilities for each label Description: cytofQC is a package for initial cleaning of CyTOF data. It uses a semi-supervised approach for labeling cells with their most likely data type (bead, doublet, debris, dead) and the probability that they belong to each label type. This package does not remove data from the dataset, but provides labels and information to aid the data user in cleaning their data. Our algorithm is able to distinguish between doublets and large cells. biocViews: Software, SingleCell, Annotation Author: Jill Lundell [aut, cre] (ORCID: ), Kelly Street [aut] (ORCID: ) Maintainer: Jill Lundell URL: https://github.com/jillbo1000/cytofQC VignetteBuilder: knitr BugReports: https://github.com/jillbo1000/cytofQC/issues git_url: https://git.bioconductor.org/packages/cytofQC git_branch: devel git_last_commit: 82c3fdc git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/cytofQC_1.9.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: debCAM Version: 1.27.0 Depends: R (>= 3.5) Imports: methods, rJava, BiocParallel, stats, Biobase, SummarizedExperiment, corpcor, geometry, NMF, nnls, DMwR2, pcaPP, apcluster, graphics Suggests: knitr, rmarkdown, BiocStyle, testthat, GEOquery, rgl License: GPL-2 NeedsCompilation: no Title: Deconvolution by Convex Analysis of Mixtures Description: An R package for fully unsupervised deconvolution of complex tissues. It provides basic functions to perform unsupervised deconvolution on mixture expression profiles by Convex Analysis of Mixtures (CAM) and some auxiliary functions to help understand the subpopulation-specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures. biocViews: Software, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr BugReports: https://github.com/Lululuella/debCAM/issues git_url: https://git.bioconductor.org/packages/debCAM git_branch: devel git_last_commit: 6b7802d git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/debCAM_1.27.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DeconRNASeq Version: 1.51.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 NeedsCompilation: no Title: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data Description: DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It modeled expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles. biocViews: DifferentialExpression Author: Ting Gong Joseph D. Szustakowski Maintainer: Ting Gong git_url: https://git.bioconductor.org/packages/DeconRNASeq git_branch: devel git_last_commit: b62408c git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-11 win.binary.ver: bin/windows/contrib/4.5/DeconRNASeq_1.51.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DELocal Version: 1.9.0 Imports: DESeq2, dplyr, reshape2, limma, SummarizedExperiment, ggplot2, matrixStats, stats Suggests: biomaRt, knitr, rmarkdown, stringr, BiocStyle License: MIT + file LICENSE NeedsCompilation: no Title: Identifies differentially expressed genes with respect to other local genes Description: The goal of DELocal is to identify DE genes compared to their neighboring genes from the same chromosomal location. It has been shown that genes of related functions are generally very far from each other in the chromosome. DELocal utilzes this information to identify DE genes comparing with their neighbouring genes. biocViews: GeneExpression, DifferentialExpression, RNASeq, Transcriptomics Author: Rishi Das Roy [aut, cre] (ORCID: ) Maintainer: Rishi Das Roy URL: https://github.com/dasroy/DELocal VignetteBuilder: knitr BugReports: https://github.com/dasroy/DELocal/issues git_url: https://git.bioconductor.org/packages/DELocal git_branch: devel git_last_commit: 61fdf94 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/DELocal_1.9.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: destiny Version: 3.23.0 Depends: R (>= 3.4.0) Imports: methods, graphics, grDevices, grid, utils, stats, Matrix, Rcpp (>= 0.10.3), RcppEigen, RSpectra (>= 0.14-0), irlba, pcaMethods, Biobase, BiocGenerics, SummarizedExperiment, SingleCellExperiment, ggplot2, ggplot.multistats, rlang, tidyr, tidyselect, ggthemes, VIM, knn.covertree, proxy, RcppHNSW, smoother, scales, scatterplot3d LinkingTo: Rcpp, RcppEigen, grDevices Suggests: knitr, rmarkdown, igraph, testthat, FNN, tidyverse, gridExtra, cowplot, conflicted, viridis, rgl, scRNAseq, org.Mm.eg.db, scran, repr Enhances: rgl, SingleCellExperiment License: GPL-3 Archs: x64 NeedsCompilation: yes Title: Creates diffusion maps Description: Create and plot diffusion maps. biocViews: CellBiology, CellBasedAssays, Clustering, Software, Visualization Author: Philipp Angerer [cre, aut] (ORCID: ), Laleh Haghverdi [ctb], Maren Büttner [ctb] (ORCID: ), Fabian Theis [ctb] (ORCID: ), Carsten Marr [ctb] (ORCID: ), Florian Büttner [ctb] (ORCID: ) Maintainer: Philipp Angerer URL: https://theislab.github.io/destiny/, https://github.com/theislab/destiny/, https://www.helmholtz-muenchen.de/icb/destiny, https://bioconductor.org/packages/destiny, https://doi.org/10.1093/bioinformatics/btv715 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/theislab/destiny/issues git_url: https://git.bioconductor.org/packages/destiny git_branch: devel git_last_commit: edce2d7 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/destiny_3.23.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: drawProteins Version: 1.29.0 Depends: R (>= 4.0) Imports: ggplot2, httr, dplyr, readr, tidyr Suggests: covr, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE NeedsCompilation: no Title: Package to Draw Protein Schematics from Uniprot API output Description: This package draws protein schematics from Uniprot API output. From the JSON returned by the GET command, it creates a dataframe from the Uniprot Features API. This dataframe can then be used by geoms based on ggplot2 and base R to draw protein schematics. biocViews: Visualization, FunctionalPrediction, Proteomics Author: Paul Brennan [aut, cre] Maintainer: Paul Brennan URL: https://github.com/brennanpincardiff/drawProteins VignetteBuilder: knitr BugReports: https://github.com/brennanpincardiff/drawProteins/issues/new git_url: https://git.bioconductor.org/packages/drawProteins git_branch: devel git_last_commit: 3f29211 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/drawProteins_1.29.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: EnhancedVolcano Version: 1.27.0 Depends: ggplot2, ggrepel Imports: methods Suggests: ggalt, ggrastr, RUnit, BiocGenerics, knitr, DESeq2, pasilla, airway, org.Hs.eg.db, gridExtra, magrittr, rmarkdown License: GPL-3 NeedsCompilation: no Title: Publication-ready volcano plots with enhanced colouring and labeling Description: Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via colour, shape, size, and shade parameter configurations. biocViews: RNASeq, GeneExpression, Transcription, DifferentialExpression, ImmunoOncology Author: Kevin Blighe [aut, cre], Sharmila Rana [aut], Emir Turkes [ctb], Benjamin Ostendorf [ctb], Andrea Grioni [ctb], Myles Lewis [aut] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/EnhancedVolcano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnhancedVolcano git_branch: devel git_last_commit: d462e8c git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/EnhancedVolcano_1.27.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: epigenomix Version: 1.49.0 Depends: R (>= 3.5.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 NeedsCompilation: no Title: Epigenetic and gene transcription data normalization and integration with mixture models Description: A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types. biocViews: ChIPSeq, GeneExpression, DifferentialExpression, Classification Author: Hans-Ulrich Klein, Martin Schaefer Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/epigenomix git_branch: devel git_last_commit: 0ea237a git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/epigenomix_1.49.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: factR Version: 1.11.0 Depends: R (>= 4.2) Imports: BiocGenerics (>= 0.46), Biostrings (>= 2.68), GenomeInfoDb (>= 1.36), dplyr (>= 1.1), GenomicFeatures (>= 1.52), GenomicRanges (>= 1.52), IRanges (>= 2.34), purrr (>= 1.0), rtracklayer (>= 1.60), tidyr (>= 1.3), methods (>= 4.3), BiocParallel (>= 1.34), S4Vectors (>= 0.38), data.table (>= 1.14), rlang (>= 1.1), tibble (>= 3.2), wiggleplotr (>= 1.24), RCurl (>= 1.98), XML (>= 3.99), drawProteins (>= 1.20), ggplot2 (>= 3.4), stringr (>= 1.5), pbapply (>= 1.7), stats (>= 4.3), utils (>= 4.3), graphics (>= 4.3), crayon (>= 1.5) Suggests: AnnotationHub (>= 2.22), BSgenome (>= 1.58), BSgenome.Mmusculus.UCSC.mm10, testthat, knitr, rmarkdown, markdown, zeallot, rmdformats, bio3d (>= 2.4), signalHsmm (>= 1.5), tidyverse (>= 1.3), covr, patchwork License: file LICENSE NeedsCompilation: no Title: Functional Annotation of Custom Transcriptomes Description: factR contain tools to process and interact with custom-assembled transcriptomes (GTF). At its core, factR constructs CDS information on custom transcripts and subsequently predicts its functional output. In addition, factR has tools capable of plotting transcripts, correcting chromosome and gene information and shortlisting new transcripts. biocViews: AlternativeSplicing, FunctionalPrediction, GenePrediction Author: Fursham Hamid [aut, cre] Maintainer: Fursham Hamid URL: https://fursham-h.github.io/factR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/factR git_branch: devel git_last_commit: f039609 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/factR_1.11.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: famat Version: 1.19.0 Depends: R (>= 4.0) Imports: KEGGREST, mgcv, stats, BiasedUrn, dplyr, gprofiler2, rWikiPathways, reactome.db, stringr, GO.db, ontologyIndex, tidyr, shiny, shinydashboard, shinyBS, plotly, magrittr, DT, clusterProfiler, org.Hs.eg.db Suggests: BiocStyle, knitr, rmarkdown, testthat, BiocManager License: GPL-3 Archs: x64 NeedsCompilation: no Title: Functional analysis of metabolic and transcriptomic data Description: Famat is made to collect data about lists of genes and metabolites provided by user, and to visualize it through a Shiny app. Information collected is: - Pathways containing some of the user's genes and metabolites (obtained using a pathway enrichment analysis). - Direct interactions between user's elements inside pathways. - Information about elements (their identifiers and descriptions). - Go terms enrichment analysis performed on user's genes. The Shiny app is composed of: - information about genes, metabolites, and direct interactions between them inside pathways. - an heatmap showing which elements from the list are in pathways (pathways are structured in hierarchies). - hierarchies of enriched go terms using Molecular Function and Biological Process. biocViews: FunctionalPrediction, GeneSetEnrichment, Pathways, GO, Reactome, KEGG Author: Mathieu Charles [aut, cre] (ORCID: ) Maintainer: Mathieu Charles URL: https://github.com/emiliesecherre/famat VignetteBuilder: knitr BugReports: https://github.com/emiliesecherre/famat/issues git_url: https://git.bioconductor.org/packages/famat git_branch: devel git_last_commit: 3d31790 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/famat_1.19.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeDi Version: 1.5.0 Depends: R (>= 4.4.0) Imports: GOSemSim, Matrix, shiny, shinyWidgets, bs4Dash, rintrojs, utils, DT, dplyr, shinyBS, STRINGdb, igraph, visNetwork, shinycssloaders, fontawesome, grDevices, parallel, stats, ggplot2, plotly, GeneTonic, RColorBrewer, scales, readxl, ggdendro, ComplexHeatmap, BiocNeighbors, tm, wordcloud2, tools, BiocParallel, BiocFileCache, cluster, circlize Suggests: knitr, rmarkdown, testthat (>= 3.0.0), DESeq2, htmltools, pcaExplorer, AnnotationDbi, macrophage, topGO, biomaRt, ReactomePA, clusterProfiler, BiocStyle, org.Hs.eg.db License: MIT + file LICENSE NeedsCompilation: no Title: Defining and visualizing the distances between different genesets Description: The package provides different distances measurements to calculate the difference between genesets. Based on these scores the genesets are clustered and visualized as graph. This is all presented in an interactive Shiny application for easy usage. biocViews: GUI, GeneSetEnrichment, Software, Transcription, RNASeq, Visualization, Clustering, Pathways, ReportWriting, GO, KEGG, Reactome, ShinyApps Author: Annekathrin Nedwed [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Annekathrin Nedwed URL: https://github.com/AnnekathrinSilvia/GeDi VignetteBuilder: knitr BugReports: https://github.com/AnnekathrinSilvia/GeDi/issues git_url: https://git.bioconductor.org/packages/GeDi git_branch: devel git_last_commit: 54df8c8 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-24 win.binary.ver: bin/windows/contrib/4.5/GeDi_1.5.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneTonic Version: 3.3.2 Depends: R (>= 4.0.0) Imports: AnnotationDbi, backbone, bs4Dash (>= 2.0.0), circlize, colorspace, colourpicker, ComplexHeatmap, ComplexUpset, dendextend, DESeq2, dplyr, DT, dynamicTreeCut, expm, ggforce, ggplot2 (>= 3.5.0), ggrepel, ggridges, GO.db, graphics, grDevices, grid, igraph, matrixStats, methods, mosdef (>= 1.1.0), plotly, RColorBrewer, rintrojs, rlang, rmarkdown, S4Vectors, scales, shiny, shinyAce, shinycssloaders, shinyWidgets, stats, SummarizedExperiment, tidyr, tippy, tools, utils, viridis, visNetwork Suggests: knitr, BiocStyle, htmltools, clusterProfiler, macrophage, org.Hs.eg.db, magrittr, testthat (>= 2.1.0) License: MIT + file LICENSE NeedsCompilation: no Title: Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis Description: This package provides functionality to combine the existing pieces of the transcriptome data and results, making it easier to generate insightful observations and hypothesis. Its usage is made easy with a Shiny application, combining the benefits of interactivity and reproducibility e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. Using the GeneTonicList as a standardized container for all the required components, it is possible to simplify the generation of multiple visualizations and summaries. biocViews: GUI, GeneExpression, Software, Transcription, Transcriptomics, Visualization, DifferentialExpression, Pathways, ReportWriting, GeneSetEnrichment, Annotation, GO, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ), Annekathrin Nedwed [aut] (ORCID: ), Edoardo Filippi [ctb] Maintainer: Federico Marini URL: https://github.com/federicomarini/GeneTonic VignetteBuilder: knitr BugReports: https://github.com/federicomarini/GeneTonic/issues git_url: https://git.bioconductor.org/packages/GeneTonic git_branch: devel git_last_commit: 666ed57 git_last_commit_date: 2025-05-09 Date/Publication: 2025-06-24 win.binary.ver: bin/windows/contrib/4.5/GeneTonic_3.3.2.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GenVisR Version: 1.41.2 Depends: R (>= 3.3.0), methods Imports: AnnotationDbi, biomaRt (>= 2.45.8), BiocGenerics, Biostrings, DBI, GenomicFeatures, GenomicRanges (>= 1.25.4), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0), gtable, gtools, IRanges (>= 2.7.5), plyr (>= 1.8.3), reshape2, Rsamtools, scales, viridis, data.table, BSgenome, Seqinfo, VariantAnnotation Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL, roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, rmarkdown, vdiffr, formatR, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38 License: GPL-3 + file LICENSE Archs: x64 NeedsCompilation: no Title: Genomic Visualizations in R Description: Produce highly customizable publication quality graphics for genomic data primarily at the cohort level. biocViews: Infrastructure, DataRepresentation, Classification, DNASeq Author: Zachary Skidmore [aut, cre], Alex Wagner [aut], Robert Lesurf [aut], Katie Campbell [aut], Jason Kunisaki [aut], Obi Griffith [aut], Malachi Griffith [aut] Maintainer: Zachary Skidmore VignetteBuilder: knitr BugReports: https://github.com/griffithlab/GenVisR/issues git_url: https://git.bioconductor.org/packages/GenVisR git_branch: devel git_last_commit: 2383d6f git_last_commit_date: 2025-07-22 Date/Publication: 2025-07-23 win.binary.ver: bin/windows/contrib/4.5/GenVisR_1.41.2.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GEOexplorer Version: 1.15.0 Depends: shiny, limma, Biobase, plotly, enrichR, R (>= 4.1.0) Imports: DT, XML, httr, sva, xfun, edgeR, htmltools, factoextra, heatmaply, pheatmap, scales, shinyHeatmaply, shinybusy, ggplot2, stringr, umap, GEOquery, impute, grDevices, stats, graphics, markdown, knitr, utils, xml2, R.utils, readxl, shinycssloaders, car Suggests: rmarkdown, usethis, testthat (>= 3.0.0) License: GPL-3 Archs: x64 NeedsCompilation: no Title: GEOexplorer: a webserver for gene expression analysis and visualisation Description: GEOexplorer is a webserver and R/Bioconductor package and web application that enables users to perform gene expression analysis. The development of GEOexplorer was made possible because of the excellent code provided by GEO2R (https: //www.ncbi.nlm.nih.gov/geo/geo2r/). biocViews: Software, GeneExpression, mRNAMicroarray, DifferentialExpression, Microarray, MicroRNAArray, Transcriptomics, RNASeq Author: Guy Hunt [aut, cre] (ORCID: ), Rafael Henkin [ctb, ths] (ORCID: ), Alfredo Iacoangeli [ctb, ths] (ORCID: ), Fabrizio Smeraldi [ctb, ths] (ORCID: ), Michael Barnes [ctb, ths] (ORCID: ) Maintainer: Guy Hunt URL: https://github.com/guypwhunt/GEOexplorer/ VignetteBuilder: knitr BugReports: https://github.com/guypwhunt/GEOexplorer/issues git_url: https://git.bioconductor.org/packages/GEOexplorer git_branch: devel git_last_commit: 5350b90 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/GEOexplorer_1.15.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: gINTomics Version: 1.5.0 Depends: R (>= 4.4.0) Imports: BiocParallel, biomaRt, OmnipathR, edgeR, ggplot2, ggridges, gtools, MultiAssayExperiment, plyr, stringi, stringr, SummarizedExperiment, methods, stats, reshape2, randomForest, limma, org.Hs.eg.db, org.Mm.eg.db, BiocGenerics, GenomicFeatures, ReactomePA, clusterProfiler, dplyr, AnnotationDbi, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, shiny, GenomicRanges, ggtree, shinydashboard, plotly, DT, MASS, InteractiveComplexHeatmap, ComplexHeatmap, visNetwork, shiny.gosling, ggvenn, RColorBrewer, utils, grDevices, callr, circlize, MethylMix, shinyjs Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 NeedsCompilation: no Title: Multi-Omics data integration Description: gINTomics is an R package for Multi-Omics data integration and visualization. gINTomics is designed to detect the association between the expression of a target and of its regulators, taking into account also their genomics modifications such as Copy Number Variations (CNV) and methylation. What is more, gINTomics allows integration results visualization via a Shiny-based interactive app. biocViews: GeneExpression, RNASeq, Microarray, Visualization, CopyNumberVariation, GeneTarget Author: Angelo Velle [cre, aut] (ORCID: ), Francesco Patane' [aut] (ORCID: ), Chiara Romualdi [aut] (ORCID: ) Maintainer: Angelo Velle URL: https://github.com/angelovelle96/gINTomics VignetteBuilder: knitr BugReports: https://github.com/angelovelle96/gINTomics/issues git_url: https://git.bioconductor.org/packages/gINTomics git_branch: devel git_last_commit: f809e93 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/gINTomics_1.5.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: gpuMagic Version: 1.25.1 Depends: R (>= 3.6.0), methods, utils Imports: Deriv, DescTools, digest, pryr, stringr, BiocGenerics LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 NeedsCompilation: yes Title: An openCL compiler with the capacity to compile R functions and run the code on GPU Description: The package aims to help users write openCL code with little or no effort. It is able to compile an user-defined R function and run it on a device such as a CPU or a GPU. The user can also write and run their openCL code directly by calling .kernel function. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang SystemRequirements: 1. C++11, 2. a graphic driver or a CPU SDK. 3. ICD loader For Windows user, an ICD loader is required at C:/windows/system32/OpenCL.dll (Usually it is installed by the graphic driver). For Linux user (Except mac): ocl-icd-opencl-dev package is required. For Mac user, no action is needed for the system has installed the dependency. 4. GNU make VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/gpuMagic/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/gpuMagic git_branch: devel git_last_commit: 7dae404 git_last_commit_date: 2025-07-24 Date/Publication: 2025-07-24 win.binary.ver: bin/windows/contrib/4.5/gpuMagic_1.25.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gpuMagic_1.26.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: granulator Version: 1.17.0 Depends: R (>= 4.1) Imports: cowplot, e1071, epiR, dplyr, dtangle, ggplot2, ggplotify, grDevices, limSolve, magrittr, MASS, nnls, parallel, pheatmap, purrr, rlang, stats, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 NeedsCompilation: no Title: Rapid benchmarking of methods for *in silico* deconvolution of bulk RNA-seq data Description: granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. Data for the deconvolution of peripheral blood mononuclear cells (PBMCs) into individual immune cell types is provided as well. biocViews: RNASeq, GeneExpression, DifferentialExpression, Transcriptomics, SingleCell, StatisticalMethod, Regression Author: Sabina Pfister [aut, cre], Vincent Kuettel [aut], Enrico Ferrero [aut] Maintainer: Sabina Pfister URL: https://github.com/xanibas/granulator VignetteBuilder: knitr BugReports: https://github.com/xanibas/granulator/issues git_url: https://git.bioconductor.org/packages/granulator git_branch: devel git_last_commit: 0a7bef1 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-11 win.binary.ver: bin/windows/contrib/4.5/granulator_1.17.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: hca Version: 1.17.0 Depends: R (>= 4.1) Imports: httr, jsonlite, dplyr, tibble, tidyr, readr, BiocFileCache, tools, utils, digest, shiny, miniUI, DT Suggests: LoomExperiment, SummarizedExperiment, SingleCellExperiment, S4Vectors, methods, testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: MIT + file LICENSE NeedsCompilation: no Title: Exploring the Human Cell Atlas Data Coordinating Platform Description: This package provides users with the ability to query the Human Cell Atlas data repository for single-cell experiment data. The `projects()`, `files()`, `samples()` and `bundles()` functions retrieve summary information on each of these indexes; corresponding `*_details()` are available for individual entries of each index. File-based resources can be downloaded using `files_download()`. Advanced use of the package allows the user to page through large result sets, and to flexibly query the 'list-of-lists' structure representing query responses. biocViews: Software, SingleCell Author: Maya McDaniel [aut], Martin Morgan [aut, cre] (ORCID: ), Kayla Interdonato [ctb] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hca git_branch: devel git_last_commit: 0e37a6e git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/hca_1.17.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: hiAnnotator Version: 1.43.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales, methods Suggests: knitr, doParallel, testthat, BiocGenerics, markdown License: GPL (>= 2) Archs: x64 NeedsCompilation: no Title: Functions for annotating GRanges objects Description: hiAnnotator contains set of functions which allow users to annotate a GRanges object with custom set of annotations. The basic philosophy of this package is to take two GRanges objects (query & subject) with common set of seqnames (i.e. chromosomes) and return associated annotation per seqnames and rows from the query matching seqnames and rows from the subject (i.e. genes or cpg islands). The package comes with three types of annotation functions which calculates if a position from query is: within a feature, near a feature, or count features in defined window sizes. Moreover, each function is equipped with parallel backend to utilize the foreach package. In addition, the package is equipped with wrapper functions, which finds appropriate columns needed to make a GRanges object from a common data frame. biocViews: Software, Annotation Author: Nirav V Malani Maintainer: Nirav V Malani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiAnnotator git_branch: devel git_last_commit: f03d061 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/hiAnnotator_1.43.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: hipathia Version: 3.9.0 Depends: R (>= 4.1), igraph (>= 1.0.1), AnnotationHub(>= 2.6.5), MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1) Imports: coin, stats, limma, grDevices, utils, graphics, preprocessCore, servr, DelayedArray, matrixStats, methods, S4Vectors, ggplot2, ggpubr, dplyr, tibble, visNetwork, reshape2, MetBrewer Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 Archs: x64 NeedsCompilation: no Title: HiPathia: High-throughput Pathway Analysis Description: Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway. biocViews: Pathways, GraphAndNetwork, GeneExpression, GeneSignaling, GO Author: Marta R. Hidalgo [aut, cre], José Carbonell-Caballero [ctb], Francisco Salavert [ctb], Alicia Amadoz [ctb], Çankut Cubuk [ctb], Joaquin Dopazo [ctb] Maintainer: Marta R. Hidalgo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hipathia git_branch: devel git_last_commit: 29effee git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/hipathia_3.9.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: HPiP Version: 1.15.0 Depends: R (>= 4.1) Imports: dplyr (>= 1.0.6), httr (>= 1.4.2), readr, tidyr, tibble, utils, stringr, magrittr, caret, corrplot, ggplot2, pROC, PRROC, igraph, graphics, stats, purrr, grDevices, protr, MCL Suggests: rmarkdown, colorspace, e1071, kernlab, ranger, SummarizedExperiment, Biostrings, randomForest, gprofiler2, gridExtra, ggthemes, BiocStyle, BiocGenerics, RUnit, tools, knitr License: MIT + file LICENSE NeedsCompilation: no Title: Host-Pathogen Interaction Prediction Description: HPiP (Host-Pathogen Interaction Prediction) uses an ensemble learning algorithm for prediction of host-pathogen protein-protein interactions (HP-PPIs) using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins.The proposed package can effectively address data shortages and data unavailability for HP-PPI network reconstructions. Moreover, establishing computational frameworks in that regard will reveal mechanistic insights into infectious diseases and suggest potential HP-PPI targets, thus narrowing down the range of possible candidates for subsequent wet-lab experimental validations. biocViews: Proteomics, SystemsBiology, NetworkInference, StructuralPrediction, GenePrediction, Network Author: Matineh Rahmatbakhsh [aut, trl, cre], Mohan Babu [led] Maintainer: Matineh Rahmatbakhsh URL: https://github.com/mrbakhsh/HPiP VignetteBuilder: knitr BugReports: https://github.com/mrbakhsh/HPiP/issues git_url: https://git.bioconductor.org/packages/HPiP git_branch: devel git_last_commit: b0c624c git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/HPiP_1.15.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: IMAS Version: 1.33.1 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, IVAS Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, Seqinfo, stats, ggfortify, grDevices, methods, Matrix, utils, graphics, gridExtra, grid, lattice, Rsamtools, survival, BiocParallel, GenomicAlignments, parallel Suggests: BiocStyle, RUnit License: GPL-2 Archs: x64 NeedsCompilation: no Title: Integrative analysis of Multi-omics data for Alternative Splicing Description: Integrative analysis of Multi-omics data for Alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Younghee Lee Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IMAS git_branch: devel git_last_commit: c62d1a5 git_last_commit_date: 2025-07-22 Date/Publication: 2025-07-23 win.binary.ver: bin/windows/contrib/4.5/IMAS_1.33.1.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: iNETgrate Version: 1.7.0 Depends: R (>= 4.3.0), BiocStyle (>= 2.18.1) Imports: SummarizedExperiment, GenomicRanges (>= 1.24.1), stats, WGCNA, grDevices, graphics, survival, igraph, Pigengene (>= 1.19.26), Homo.sapiens, glmnet, caret, gplots, minfi, matrixStats, Rfast, tidyr, tidyselect, utils Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, IlluminaHumanMethylation450kanno.ilmn12.hg19, AnnotationDbi, sesameData, TCGAbiolinks (>= 2.29.4) License: GPL-3 NeedsCompilation: no Title: Integrates DNA methylation data with gene expression in a single gene network Description: The iNETgrate package provides functions to build a correlation network in which nodes are genes. DNA methylation and gene expression data are integrated to define the connections between genes. This network is used to identify modules (clusters) of genes. The biological information in each of the resulting modules is represented by an eigengene. These biological signatures can be used as features e.g., for classification of patients into risk categories. The resulting biological signatures are very robust and give a holistic view of the underlying molecular changes. biocViews: GeneExpression, RNASeq, DNAMethylation, NetworkInference, Network, GraphAndNetwork, BiomedicalInformatics, SystemsBiology, Transcriptomics, Classification, Clustering, DimensionReduction, PrincipalComponent, mRNAMicroarray, Normalization, GenePrediction, KEGG, Survival Author: Isha Mehta [aut] (), Ghazal Ebrahimi [aut], Hanie Samimi [aut], Habil Zare [aut, cre] () Maintainer: Habil Zare VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocManager/issues git_url: https://git.bioconductor.org/packages/iNETgrate git_branch: devel git_last_commit: 9b0b9a7 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/iNETgrate_1.7.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: isomiRs Version: 1.37.1 Depends: R (>= 4.4), SummarizedExperiment Imports: AnnotationDbi, BiocGenerics, Biobase, broom, cluster, cowplot, DEGreport, DESeq2, IRanges, dplyr, GenomicRanges, gplots, ggplot2, gtools, gridExtra, grid, grDevices, graphics, GGally, limma, methods, RColorBrewer, readr, reshape, rlang, stats, stringr, S4Vectors, tidyr, tibble Suggests: knitr, rmarkdown, org.Mm.eg.db, pheatmap, BiocStyle, testthat License: MIT + file LICENSE NeedsCompilation: no Title: Analyze isomiRs and miRNAs from small RNA-seq Description: Characterization of miRNAs and isomiRs, clustering and differential expression. biocViews: miRNA, RNASeq, DifferentialExpression, Clustering, ImmunoOncology Author: Lorena Pantano [aut, cre], Georgia Escaramis [aut] (CIBERESP - CIBER Epidemiologia y Salud Publica) Maintainer: Lorena Pantano VignetteBuilder: knitr BugReports: https://github.com/lpantano/isomiRs/issues git_url: https://git.bioconductor.org/packages/isomiRs git_branch: devel git_last_commit: ffb8b4b git_last_commit_date: 2025-08-01 Date/Publication: 2025-08-03 win.binary.ver: bin/windows/contrib/4.5/isomiRs_1.37.1.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Maaslin2 Version: 1.23.0 Depends: R (>= 3.6) Imports: robustbase, biglm, pcaPP, edgeR, metagenomeSeq, pbapply, car, dplyr, vegan, chemometrics, ggplot2, pheatmap, logging, data.table, lmerTest, hash, optparse, grDevices, stats, utils, glmmTMB, MASS, cplm, pscl, lme4, tibble Suggests: knitr, testthat (>= 2.1.0), rmarkdown, markdown License: MIT + file LICENSE NeedsCompilation: no Title: "Multivariable Association Discovery in Population-scale Meta-omics Studies" Description: MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin. biocViews: Metagenomics, Software, Microbiome, Normalization Author: Himel Mallick [aut], Ali Rahnavard [aut], Lauren McIver [aut, cre] Maintainer: Lauren McIver URL: http://huttenhower.sph.harvard.edu/maaslin2 VignetteBuilder: knitr BugReports: https://github.com/biobakery/maaslin2/issues git_url: https://git.bioconductor.org/packages/Maaslin2 git_branch: devel git_last_commit: 43d70cc git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/Maaslin2_1.23.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Macarron Version: 1.13.2 Depends: R (>= 4.2.0), SummarizedExperiment Imports: BiocParallel, DelayedArray, WGCNA, ff, data.table, dynamicTreeCut, Maaslin2, plyr, stats, psych, xml2, httr, RJSONIO, logging, methods, utils Suggests: knitr, BiocStyle, optparse, testthat (>= 2.1.0), rmarkdown, markdown License: MIT + file LICENSE NeedsCompilation: no Title: Prioritization of potentially bioactive metabolic features from epidemiological and environmental metabolomics datasets Description: Macarron is a workflow for the prioritization of potentially bioactive metabolites from metabolomics experiments. Prioritization integrates strengths of evidences of bioactivity such as covariation with a known metabolite, abundance relative to a known metabolite and association with an environmental or phenotypic indicator of bioactivity. Broadly, the workflow consists of stratified clustering of metabolic spectral features which co-vary in abundance in a condition, transfer of functional annotations, estimation of relative abundance and differential abundance analysis to identify associations between features and phenotype/condition. biocViews: Sequencing, Metabolomics, Coverage, FunctionalPrediction, Clustering Author: Amrisha Bhosle [aut], Ludwig Geistlinger [aut], Sagun Maharjan [aut, cre] Maintainer: Sagun Maharjan URL: http://huttenhower.sph.harvard.edu/macarron VignetteBuilder: knitr BugReports: https://forum.biobakery.org/c/microbial-community-profiling/macarron git_url: https://git.bioconductor.org/packages/Macarron git_branch: devel git_last_commit: 143bd9d git_last_commit_date: 2025-06-20 Date/Publication: 2025-06-22 win.binary.ver: bin/windows/contrib/4.5/Macarron_1.13.2.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: mCSEA Version: 1.29.0 Depends: R (>= 3.5), mCSEAdata, Homo.sapiens Imports: biomaRt, fgsea, GenomicFeatures, GenomicRanges, ggplot2, graphics, grDevices, Gviz, IRanges, limma, methods, parallel, S4Vectors, stats, SummarizedExperiment, utils Suggests: Biobase, BiocGenerics, BiocStyle, FlowSorted.Blood.450k, knitr, leukemiasEset, minfi, minfiData, rmarkdown, RUnit License: GPL-2 NeedsCompilation: no Title: Methylated CpGs Set Enrichment Analysis Description: Identification of diferentially methylated regions (DMRs) in predefined regions (promoters, CpG islands...) from the human genome using Illumina's 450K or EPIC microarray data. Provides methods to rank CpG probes based on linear models and includes plotting functions. biocViews: ImmunoOncology, DifferentialMethylation, DNAMethylation, Epigenetics, Genetics, GenomeAnnotation, MethylationArray, Microarray, MultipleComparison, TwoChannel Author: Jordi Martorell-Marugán and Pedro Carmona-Sáez Maintainer: Jordi Martorell-Marugán VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mCSEA git_branch: devel git_last_commit: 7f1573f git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/mCSEA_1.29.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Melissa Version: 1.25.0 Depends: R (>= 3.5.0), BPRMeth, GenomicRanges Imports: data.table, parallel, ROCR, matrixcalc, mclust, ggplot2, doParallel, foreach, MCMCpack, cowplot, magrittr, mvtnorm, truncnorm, assertthat, BiocStyle, stats, utils Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE NeedsCompilation: no Title: Bayesian clustering and imputationa of single cell methylomes Description: Melissa is a Baysian probabilistic model for jointly clustering and imputing single cell methylomes. This is done by taking into account local correlations via a Generalised Linear Model approach and global similarities using a mixture modelling approach. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: C. A. Kapourani [aut, cre] Maintainer: C. A. Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Melissa git_branch: devel git_last_commit: c76418b git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/Melissa_1.25.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: metabolomicsWorkbenchR Version: 1.19.0 Depends: R (>= 4.0) Imports: data.table, httr, jsonlite, methods, MultiAssayExperiment, struct, SummarizedExperiment, utils Suggests: BiocStyle, covr, knitr, HDF5Array, httptest, rmarkdown, structToolbox, testthat, pmp, grid, png License: GPL-3 NeedsCompilation: no Title: Metabolomics Workbench in R Description: This package provides functions for interfacing with the Metabolomics Workbench RESTful API. Study, compound, protein and gene information can be searched for using the API. Methods to obtain study data in common Bioconductor formats such as SummarizedExperiment and MultiAssayExperiment are also included. biocViews: Software, Metabolomics Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metabolomicsWorkbenchR git_branch: devel git_last_commit: 8863450 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/metabolomicsWorkbenchR_1.19.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: mfa Version: 1.31.0 Depends: R (>= 3.4.0) Imports: methods, stats, ggplot2, Rcpp, dplyr, ggmcmc, MCMCpack, MCMCglmm, coda, magrittr, tibble, Biobase LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) NeedsCompilation: yes Title: Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations Description: MFA models genomic bifurcations using a Bayesian hierarchical mixture of factor analysers. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mfa git_branch: devel git_last_commit: 4382e89 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/mfa_1.31.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MGFR Version: 1.35.0 Depends: R (>= 3.5) Imports: biomaRt, annotate License: GPL-3 NeedsCompilation: no Title: Marker Gene Finder in RNA-seq data Description: The package is designed to detect marker genes from RNA-seq data. biocViews: ImmunoOncology, Genetics, GeneExpression, RNASeq Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFR git_branch: devel git_last_commit: 6b691b5 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/MGFR_1.35.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MMUPHin Version: 1.23.0 Depends: R (>= 3.6) Imports: Maaslin2, metafor, fpc, igraph, ggplot2, dplyr, tidyr, stringr, cowplot, utils, stats, grDevices Suggests: testthat, BiocStyle, knitr, rmarkdown, magrittr, vegan, phyloseq, curatedMetagenomicData, genefilter License: MIT + file LICENSE NeedsCompilation: no Title: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies Description: MMUPHin is an R package for meta-analysis tasks of microbiome cohorts. It has function interfaces for: a) covariate-controlled batch- and cohort effect adjustment, b) meta-analysis differential abundance testing, c) meta-analysis unsupervised discrete structure (clustering) discovery, and d) meta-analysis unsupervised continuous structure discovery. biocViews: Metagenomics, Microbiome, BatchEffect Author: Siyuan Ma Maintainer: Siyuan MA SystemRequirements: glpk (>= 4.57) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMUPHin git_branch: devel git_last_commit: 5f25fe9 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/MMUPHin_1.23.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: OmicsMLRepoR Version: 1.3.1 Depends: R (>= 4.4.0) Imports: dplyr, stringr, rols, tidyr, methods, stats, tibble, data.tree, jsonlite, plyr, BiocFileCache, readr, DiagrammeR, rlang, lubridate Suggests: arrow, knitr, BiocStyle, curatedMetagenomicData, testthat (>= 3.0.0), cBioPortalData License: Artistic-2.0 Archs: x64 NeedsCompilation: no Title: Search harmonized metadata created under the OmicsMLRepo project Description: This package provides functions to browse the harmonized metadata for large omics databases. This package also supports data navigation if the metadata incorporates ontology. biocViews: Software, Infrastructure, DataRepresentation Author: Sehyun Oh [aut, cre] (ORCID: ), Kaelyn Long [aut] Maintainer: Sehyun Oh URL: https://github.com/shbrief/OmicsMLRepoR VignetteBuilder: knitr BugReports: https://github.com/shbrief/OmicsMLRepoR/issues git_url: https://git.bioconductor.org/packages/OmicsMLRepoR git_branch: devel git_last_commit: ae71217 git_last_commit_date: 2025-06-06 Date/Publication: 2025-06-08 win.binary.ver: bin/windows/contrib/4.5/OmicsMLRepoR_1.3.1.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: OmnipathR Version: 3.17.4 Depends: R(>= 4.0) Imports: checkmate, crayon, curl, digest, dplyr(>= 1.1.0), fs, httr2, igraph, jsonlite, later, logger, lubridate, magrittr, progress, purrr, rappdirs, readr(>= 2.0.0), readxl, rlang, rmarkdown, RSQLite, R.utils, rvest, sessioninfo, stats, stringi, stringr, tibble, tidyr, tidyselect, tools, utils, vctrs, withr, XML, xml2, yaml, zip Suggests: BiocStyle, bookdown, ggplot2, ggraph, gprofiler2, knitr, mlrMBO, parallelMap, ParamHelpers, R.matlab, sigmajs, smoof, testthat License: MIT + file LICENSE Archs: x64 NeedsCompilation: no Title: OmniPath web service client and more Description: A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github). biocViews: GraphAndNetwork, Network, Pathways, Software, ThirdPartyClient, DataImport, DataRepresentation, GeneSignaling, GeneRegulation, SystemsBiology, Transcriptomics, SingleCell, Annotation, KEGG Author: Alberto Valdeolivas [aut] (ORCID: ), Denes Turei [cre, aut] (ORCID: ), Attila Gabor [aut] (ORCID: ), Diego Mananes [aut] (ORCID: ), Aurelien Dugourd [aut] (ORCID: ) Maintainer: Denes Turei URL: https://r.omnipathdb.org/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/OmnipathR/issues git_url: https://git.bioconductor.org/packages/OmnipathR git_branch: devel git_last_commit: a7f3b9d git_last_commit_date: 2025-07-16 Date/Publication: 2025-07-16 win.binary.ver: bin/windows/contrib/4.5/OmnipathR_3.17.4.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: PCAtools Version: 2.21.0 Depends: ggplot2, ggrepel Imports: lattice, grDevices, cowplot, methods, reshape2, stats, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng LinkingTo: Rcpp, beachmat, BH, dqrng Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2, airway, org.Hs.eg.db, magrittr, rmarkdown License: GPL-3 NeedsCompilation: yes Title: PCAtools: Everything Principal Components Analysis Description: Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data. biocViews: RNASeq, ATACSeq, GeneExpression, Transcription, SingleCell, PrincipalComponent Author: Kevin Blighe [aut, cre], Anna-Leigh Brown [ctb], Vincent Carey [ctb], Guido Hooiveld [ctb], Aaron Lun [aut, ctb] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/PCAtools SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAtools git_branch: devel git_last_commit: 1c1d8a9 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/PCAtools_2.21.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Pigengene Version: 1.35.0 Depends: R (>= 4.0.3), graph, BiocStyle (>= 2.28.0) Imports: bnlearn (>= 4.7), C50 (>= 0.1.2), MASS, matrixStats, partykit, Rgraphviz, WGCNA, GO.db, impute, preprocessCore, grDevices, graphics, stats, utils, parallel, pheatmap (>= 1.0.8), dplyr, gdata, clusterProfiler, ReactomePA, ggplot2, openxlsx, DBI, DOSE Suggests: org.Hs.eg.db (>= 3.7.0), org.Mm.eg.db (>= 3.7.0), biomaRt (>= 2.30.0), knitr, AnnotationDbi, energy License: GPL (>=2) NeedsCompilation: no Title: Infers biological signatures from gene expression data Description: Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes. biocViews: GeneExpression, RNASeq, NetworkInference, Network, GraphAndNetwork, BiomedicalInformatics, SystemsBiology, Transcriptomics, Classification, Clustering, DecisionTree, DimensionReduction, PrincipalComponent, Microarray, Normalization, ImmunoOncology Author: Habil Zare, Amir Foroushani, Rupesh Agrahari, Meghan Short, Isha Mehta, Neda Emami, and Sogand Sajedi Maintainer: Habil Zare VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pigengene git_branch: devel git_last_commit: ab1017c git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/Pigengene_1.35.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: profileplyr Version: 1.25.1 Depends: R (>= 3.6), BiocGenerics, SummarizedExperiment Imports: GenomicRanges, stats, soGGi, methods, utils, S4Vectors, R.utils, dplyr, magrittr, tidyr, IRanges, rjson, ChIPseeker,GenomicFeatures,TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene,org.Hs.eg.db,org.Mm.eg.db,rGREAT, pheatmap, ggplot2, EnrichedHeatmap, ComplexHeatmap, grid, circlize, BiocParallel, rtracklayer, Seqinfo, grDevices, rlang, tiff, Rsamtools Suggests: GenomeInfoDb, BiocStyle, testthat, knitr, rmarkdown, png, Cairo License: GPL (>= 3) NeedsCompilation: no Title: Visualization and annotation of read signal over genomic ranges with profileplyr Description: Quick and straightforward visualization of read signal over genomic intervals is key for generating hypotheses from sequencing data sets (e.g. ChIP-seq, ATAC-seq, bisulfite/methyl-seq). Many tools both inside and outside of R and Bioconductor are available to explore these types of data, and they typically start with a bigWig or BAM file and end with some representation of the signal (e.g. heatmap). profileplyr leverages many Bioconductor tools to allow for both flexibility and additional functionality in workflows that end with visualization of the read signal. biocViews: ChIPSeq, DataImport, Sequencing, ChipOnChip, Coverage Author: Tom Carroll and Doug Barrows Maintainer: Tom Carroll , Doug Barrows VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileplyr git_branch: devel git_last_commit: adab25c git_last_commit_date: 2025-07-23 Date/Publication: 2025-07-24 win.binary.ver: bin/windows/contrib/4.5/profileplyr_1.25.1.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: R453Plus1Toolbox Version: 1.59.0 Depends: R (>= 2.12.0), methods, VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), pwalign, Biobase Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML, TeachingDemos, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector, GenomicRanges (>= 1.31.8), SummarizedExperiment, biomaRt, BSgenome (>= 1.47.3), Rsamtools, ShortRead (>= 1.37.1) Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 NeedsCompilation: yes Title: A package for importing and analyzing data from Roche's Genome Sequencer System Description: The R453Plus1 Toolbox comprises useful functions for the analysis of data generated by Roche's 454 sequencing platform. It adds functions for quality assurance as well as for annotation and visualization of detected variants, complementing the software tools shipped by Roche with their product. Further, a pipeline for the detection of structural variants is provided. biocViews: Sequencing, Infrastructure, DataImport, DataRepresentation, Visualization, QualityControl, ReportWriting Author: Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/R453Plus1Toolbox git_branch: devel git_last_commit: edf5064 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/R453Plus1Toolbox_1.59.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: receptLoss Version: 1.21.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, magrittr, tidyr, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 2.1.0), here License: GPL-3 + file LICENSE NeedsCompilation: no Title: Unsupervised Identification of Genes with Expression Loss in Subsets of Tumors Description: receptLoss identifies genes whose expression is lost in subsets of tumors relative to normal tissue. It is particularly well-suited in cases where the number of normal tissue samples is small, as the distribution of gene expression in normal tissue samples is approximated by a Gaussian. Originally designed for identifying nuclear hormone receptor expression loss but can be applied transcriptome wide as well. biocViews: GeneExpression, StatisticalMethod Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/receptLoss git_branch: devel git_last_commit: 4e6b0ef git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/receptLoss_1.21.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: regionalpcs Version: 1.7.0 Depends: R (>= 4.3.0) Imports: dplyr, PCAtools, tibble, GenomicRanges Suggests: knitr, rmarkdown, RMTstat, testthat (>= 3.0.0), BiocStyle, tidyr, minfiData, TxDb.Hsapiens.UCSC.hg19.knownGene, IRanges License: MIT + file LICENSE NeedsCompilation: no Title: Summarizing Regional Methylation with Regional Principal Components Analysis Description: Functions to summarize DNA methylation data using regional principal components. Regional principal components are computed using principal components analysis within genomic regions to summarize the variability in methylation levels across CpGs. The number of principal components is chosen using either the Marcenko-Pasteur or Gavish-Donoho method to identify relevant signal in the data. biocViews: DNAMethylation, DifferentialMethylation, StatisticalMethod, Software, MethylationArray Author: Tiffany Eulalio [aut, cre] (ORCID: ) Maintainer: Tiffany Eulalio URL: https://github.com/tyeulalio/regionalpcs VignetteBuilder: knitr BugReports: https://github.com/tyeulalio/regionalpcs/issues git_url: https://git.bioconductor.org/packages/regionalpcs git_branch: devel git_last_commit: 2561ac0 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/regionalpcs_1.7.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RepViz Version: 1.25.0 Depends: R (>= 3.5.1), GenomicRanges (>= 1.30.0), Rsamtools (>= 1.34.1), IRanges (>= 2.14.0), biomaRt (>= 2.36.0), S4Vectors (>= 0.18.0), graphics, grDevices, utils Suggests: rmarkdown, knitr, testthat License: GPL-3 NeedsCompilation: no Title: Replicate oriented Visualization of a genomic region Description: RepViz enables the view of a genomic region in a simple and efficient way. RepViz allows simultaneous viewing of both intra- and intergroup variation in sequencing counts of the studied conditions, as well as their comparison to the output features (e.g. identified peaks) from user selected data analysis methods.The RepViz tool is primarily designed for chromatin data such as ChIP-seq and ATAC-seq, but can also be used with other sequencing data such as RNA-seq, or combinations of different types of genomic data. biocViews: WorkflowStep, Visualization, Sequencing, ChIPSeq, ATACSeq, Software, Coverage, GenomicVariation Author: Thomas Faux, Kalle Rytkönen, Asta Laiho, Laura L. Elo Maintainer: Thomas Faux, Asta Laiho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RepViz git_branch: devel git_last_commit: 5586708 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/RepViz_1.25.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: rGADEM Version: 2.57.1 Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo Imports: Biostrings, GenomicRanges, methods, graphics, seqLogo Suggests: BSgenome.Hsapiens.UCSC.hg19, rtracklayer License: Artistic-2.0 NeedsCompilation: yes Title: de novo motif discovery Description: rGADEM is an efficient de novo motif discovery tool for large-scale genomic sequence data. It is an open-source R package, which is based on the GADEM software. biocViews: Microarray, ChIPchip, Sequencing, ChIPSeq, MotifDiscovery Author: Arnaud Droit, Raphael Gottardo, Gordon Robertson and Leiping Li Maintainer: Arnaud Droit PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/rGADEM git_branch: devel git_last_commit: c61c6c1 git_last_commit_date: 2025-07-24 Date/Publication: 2025-07-24 win.binary.ver: bin/windows/contrib/4.5/rGADEM_2.57.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rGADEM_2.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rGADEM_2.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RNAdecay Version: 1.29.0 Depends: R (>= 3.5) Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr, scales Suggests: parallel, knitr, reshape2, rmarkdown License: GPL-2 NeedsCompilation: yes Title: Maximum Likelihood Decay Modeling of RNA Degradation Data Description: RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions. biocViews: ImmunoOncology, Software, GeneExpression, GeneRegulation, DifferentialExpression, Transcription, Transcriptomics, TimeCourse, Regression, RNASeq, Normalization, WorkflowStep Author: Reed Sorenson [aut, cre], Katrina Johnson [aut], Frederick Adler [aut], Leslie Sieburth [aut] Maintainer: Reed Sorenson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAdecay git_branch: devel git_last_commit: 117b083 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/RNAdecay_1.29.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: rols Version: 3.5.0 Depends: R (>= 4.1.0), methods Imports: httr2, jsonlite, utils, Biobase, BiocGenerics (>= 0.23.1) Suggests: GO.db, knitr (>= 1.1.0), BiocStyle (>= 2.5.19), testthat, lubridate, DT, rmarkdown, License: GPL-2 NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: The rols package is an interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R. biocViews: ImmunoOncology, Software, Annotation, MassSpectrometry, GO Author: Laurent Gatto [aut, cre] (ORCID: ), Tiage Chedraoui Silva [ctb], Andrew Clugston [ctb] Maintainer: Laurent Gatto URL: http://lgatto.github.io/rols/ VignetteBuilder: knitr BugReports: https://github.com/lgatto/rols/issues git_url: https://git.bioconductor.org/packages/rols git_branch: devel git_last_commit: 960374a git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/rols_3.5.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: sampleClassifier Version: 1.33.0 Depends: R (>= 4.0), MGFM, MGFR, annotate Imports: e1071, ggplot2, stats, utils Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db License: Artistic-2.0 NeedsCompilation: no Title: Sample Classifier Description: The package is designed to classify microarray RNA-seq gene expression profiles. biocViews: ImmunoOncology, Classification, Microarray, RNASeq, GeneExpression Author: Khadija El Amrani [aut, cre] Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/sampleClassifier git_branch: devel git_last_commit: 993e1fd git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/sampleClassifier_1.33.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SARC Version: 1.7.1 Depends: R (>= 4.3), RaggedExperiment, GenomicRanges Imports: tidyverse, utils, reshape2, DescTools, metap, multtest, plyranges, data.table, scales, RColorBrewer, grid, gtable, gridExtra, GenomicFeatures, stats, ggplot2, plotly, IRanges Suggests: knitr, kableExtra, testthat, TxDb.Hsapiens.UCSC.hg38.knownGene, Homo.sapiens, TxDb.Mmusculus.UCSC.mm10.knownGene, Mus.musculus, GenomicAlignments License: GPL-3 NeedsCompilation: no Title: Statistical Analysis of Regions with CNVs Description: Imports a cov/coverage file (normalised read coverages from BAM files) and a cnv file (list of CNVs - similiar to a BED file) from WES/ WGS CNV (copy number variation) detection pipelines and utilises several metrics to weigh the likelihood of a sample containing a detected CNV being a true CNV or a false positive. Highly useful for diagnostic testing to filter out false positives to provide clinicians with fewer variants to interpret. SARC uniquely only used cov and csv (similiar to BED file) files which are the common CNV pipeline calling filetypes, and can be used as to supplement the Interactive Genome Browser (IGV) to generate many figures automatedly, which can be especially helpful in large cohorts with 100s-1000s of patients. biocViews: Software, CopyNumberVariation, Visualization, DNASeq, Sequencing Author: Krutik Patel [aut, cre] (ORCID: ) Maintainer: Krutik Patel URL: https://github.com/Krutik6/SARC/ VignetteBuilder: knitr BugReports: https://github.com/Krutik6/SARC/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/SARC git_branch: devel git_last_commit: 1115634 git_last_commit_date: 2025-07-24 Date/Publication: 2025-07-24 win.binary.ver: bin/windows/contrib/4.5/SARC_1.7.1.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: scFeatures Version: 1.9.0 Depends: R (>= 4.2.0) Imports: DelayedArray, DelayedMatrixStats, EnsDb.Hsapiens.v79, EnsDb.Mmusculus.v79, GSVA, ape, glue, dplyr, ensembldb, gtools, msigdbr, proxyC, reshape2, spatstat.explore, spatstat.geom, tidyr, AUCell, BiocParallel, rmarkdown, methods, stats, cli, SingleCellSignalR, MatrixGenerics, Seurat, DT Suggests: knitr, S4Vectors, survival, survminer, BiocStyle, ClassifyR, org.Hs.eg.db, clusterProfiler License: GPL-3 NeedsCompilation: no Title: scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction Description: scFeatures constructs multi-view representations of single-cell and spatial data. scFeatures is a tool that generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types. These features can then be used for a variety of analyses using other software in Biocondutor. biocViews: CellBasedAssays, SingleCell, Spatial, Software, Transcriptomics Author: Yue Cao [aut, cre], Yingxin Lin [aut], Ellis Patrick [aut], Pengyi Yang [aut], Jean Yee Hwa Yang [aut] Maintainer: Yue Cao URL: https://sydneybiox.github.io/scFeatures/ https://github.com/SydneyBioX/scFeatures/ VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scFeatures/issues git_url: https://git.bioconductor.org/packages/scFeatures git_branch: devel git_last_commit: 0d8daf3 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/scFeatures_1.9.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: scQTLtools Version: 1.1.7 Depends: R (>= 4.4.1.0) Imports: ggplot2(>= 3.5.1), Matrix (>= 1.7-0), stats (>= 4.4.1), progress(>= 1.2.3), stringr(>= 1.5.1), dplyr(>= 1.1.4), SeuratObject(>= 5.0.2), methods(>= 4.4.1), magrittr(>= 2.0.3), patchwork(>= 1.2.0), DESeq2 (>= 1.45.3), VGAM (>= 1.1-11), limma (>= 3.61.9), biomaRt(>= 2.61.3), gamlss (>= 5.4-22), SingleCellExperiment(>= 1.27.2), SummarizedExperiment(>= 1.32.0), GOSemSim(>= 2.31.2) Suggests: BiocStyle, knitr, rmarkdown, org.Hs.eg.db, org.Mm.eg.db, org.Ce.eg.db, org.At.tair.db, testthat (>= 3.2.1.1) License: MIT + file LICENSE NeedsCompilation: no Title: scQTLtools: an R/Bioconductor package for comprehensive identification and visualization of single-cell eQTLs Description: scQTLtools is a comprehensive R/Bioconductor package that facilitates end-to-end single-cell eQTL analysis, from preprocessing to visualization biocViews: Software, GeneExpression, GeneticVariability, SNP, DifferentialExpression, GenomicVariation, VariantDetection, Genetics, FunctionalGenomics, SystemsBiology, Regression, SingleCell, Normalization, Visualization, Preprocessing Author: Xiaofeng Wu [aut, cre, cph] (ORCID: ), Xin Huang [aut, cph] (ORCID: ), Jingtong Kang [com] (ORCID: ), Siwen Xu [aut, cph] (ORCID: ) Maintainer: Xiaofeng Wu <1427972815@qq.com> URL: https://github.com/XFWuCN/scQTLtools VignetteBuilder: knitr BugReports: https://github.com/XFWuCN/scQTLtools/issues git_url: https://git.bioconductor.org/packages/scQTLtools git_branch: devel git_last_commit: 6c77f29 git_last_commit_date: 2025-06-15 Date/Publication: 2025-06-16 win.binary.ver: bin/windows/contrib/4.5/scQTLtools_1.1.7.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ScreenR Version: 1.11.0 Depends: R (>= 4.2) Imports: methods (>= 4.0), rlang (>= 0.4), stringr (>= 1.4), limma (>= 3.46), patchwork (>= 1.1), tibble (>= 3.1.6), scales (>= 1.1.1), ggvenn (>= 0.1.9), purrr (>= 0.3.4), ggplot2 (>= 3.3), stats, tidyr (>= 1.2), magrittr (>= 1.0), dplyr (>= 1.0), edgeR (>= 3.32), tidyselect (>= 1.1.2) Suggests: rmarkdown (>= 2.11), knitr (>= 1.37), testthat (>= 3.0.0), BiocStyle (>= 2.22.0), covr (>= 3.5) License: MIT + file LICENSE Archs: x64 NeedsCompilation: no Title: Package to Perform High Throughput Biological Screening Description: ScreenR is a package suitable to perform hit identification in loss of function High Throughput Biological Screenings performed using barcoded shRNA-based libraries. ScreenR combines the computing power of software such as edgeR with the simplicity of use of the Tidyverse metapackage. ScreenR executes a pipeline able to find candidate hits from barcode counts, and integrates a wide range of visualization modes for each step of the analysis. biocViews: Software, AssayDomain, GeneExpression Author: Emanuel Michele Soda [aut, cre] (ORICD: 0000-0002-2301-6465), Elena Ceccacci [aut] (ORICD: 0000-0002-2285-8994), Saverio Minucci [fnd, ths] (ORICD: 0000-0001-5678-536X) Maintainer: Emanuel Michele Soda URL: https://emanuelsoda.github.io/ScreenR/ VignetteBuilder: knitr BugReports: https://github.com/EmanuelSoda/ScreenR/issues git_url: https://git.bioconductor.org/packages/ScreenR git_branch: devel git_last_commit: 8f08b42 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/ScreenR_1.11.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: seq2pathway Version: 1.41.0 Depends: R (>= 3.6.2) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 NeedsCompilation: no Title: a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data Description: Seq2pathway is a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data, consisting of "seq2gene" and "gene2path" components. The seq2gene links sequence-level measurements of genomic regions (including SNPs or point mutation coordinates) to gene-level scores, and the gene2pathway summarizes gene scores to pathway-scores for each sample. The seq2gene has the feasibility to assign both coding and non-exon regions to a broader range of neighboring genes than only the nearest one, thus facilitating the study of functional non-coding regions. The gene2pathway takes into account the quantity of significance for gene members within a pathway compared those outside a pathway. The output of seq2pathway is a general structure of quantitative pathway-level scores, thus allowing one to functional interpret such datasets as RNA-seq, ChIP-seq, GWAS, and derived from other next generational sequencing experiments. biocViews: Software Author: Xinan Yang ; Bin Wang Maintainer: Arjun Kinstlick git_url: https://git.bioconductor.org/packages/seq2pathway git_branch: devel git_last_commit: c419f1b git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/seq2pathway_1.41.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: seqArchRplus Version: 1.9.0 Depends: R (>= 4.2), GenomicRanges, IRanges, S4Vectors Imports: BiocParallel, Biostrings, BSgenome, ChIPseeker, cli, clusterProfiler, cowplot, factoextra, GenomeInfoDb, ggplot2, ggimage, graphics, grDevices, gridExtra, heatmaps, magick, methods, RColorBrewer, scales, seqArchR, seqPattern, stats, utils Suggests: BSgenome.Dmelanogaster.UCSC.dm6, BiocStyle, CAGEr (>= 2.0.2), covr, knitr (>= 1.22), org.Dm.eg.db, pdftools, rmarkdown (>= 1.12), slickR, TxDb.Dmelanogaster.UCSC.dm6.ensGene, xfun License: GPL-3 Archs: x64 NeedsCompilation: no Title: Downstream analyses of promoter sequence architectures and HTML report generation Description: seqArchRplus facilitates downstream analyses of promoter sequence architectures/clusters identified by seqArchR (or any other tool/method). With additional available information such as the TPM values and interquantile widths (IQWs) of the CAGE tag clusters, seqArchRplus can order the input promoter clusters by their shape (IQWs), and write the cluster information as browser/IGV track files. Provided visualizations are of two kind: per sample/stage and per cluster visualizations. Those of the first kind include: plot panels for each sample showing per cluster shape, TPM and other score distributions, sequence logos, and peak annotations. The second include per cluster chromosome-wise and strand distributions, motif occurrence heatmaps and GO term enrichments. Additionally, seqArchRplus can also generate HTML reports for easy viewing and comparison of promoter architectures between samples/stages. biocViews: Annotation, Visualization, ReportWriting, GO, MotifAnnotation, Clustering Author: Sarvesh Nikumbh [aut, cre, cph] (ORCID: ) Maintainer: Sarvesh Nikumbh URL: https://github.com/snikumbh/seqArchRplus VignetteBuilder: knitr BugReports: https://github.com/snikumbh/seqArchRplus/issues git_url: https://git.bioconductor.org/packages/seqArchRplus git_branch: devel git_last_commit: 81ae5d1 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/seqArchRplus_1.9.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SGCP Version: 1.9.0 Depends: R (>= 4.2.0) Imports: ggplot2, expm, caret, plyr, dplyr, GO.db, annotate, SummarizedExperiment, genefilter, GOstats, RColorBrewer, xtable, Rgraphviz, reshape2, openxlsx, ggridges, DescTools, org.Hs.eg.db, methods, grDevices, stats, RSpectra, graph Suggests: knitr, rmarkdown, BiocManager, devtools, BiocStyle License: GPL-3 NeedsCompilation: no Title: SGCP: A semi-supervised pipeline for gene clustering using self-training approach in gene co-expression networks Description: SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules. biocViews: GeneExpression, GeneSetEnrichment, NetworkEnrichment, SystemsBiology, Classification, Clustering, DimensionReduction, GraphAndNetwork, NeuralNetwork, Network, mRNAMicroarray, RNASeq, Visualization Author: Niloofar AghaieAbiane [aut, cre] (ORCID: ), Ioannis Koutis [aut] Maintainer: Niloofar AghaieAbiane URL: https://github.com/na396/SGCP VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SGCP git_branch: devel git_last_commit: fc6b613 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/SGCP_1.9.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: shiny.gosling Version: 1.5.0 Depends: R (>= 4.1.0) Imports: htmltools, jsonlite, rlang, shiny, shiny.react, fs, digest, rjson Suggests: config, covr, knitr, lintr, mockery (>= 0.4.3), rcmdcheck, rmarkdown, sessioninfo, spelling, testthat (>= 3.0.0), GenomicRanges, VariantAnnotation, StructuralVariantAnnotation, biovizBase, ggbio License: LGPL-3 NeedsCompilation: no Title: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization for R and Shiny Description: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization. http://gosling-lang.org/. This R package is based on gosling.js. It uses R functions to create gosling plots that could be embedded onto R Shiny apps. biocViews: ShinyApps, Genetics, Visualization Author: Appsilon [aut, cre], Anirban Shaw [aut] (ORCID: ), Federico Rivadeneira [aut] (ORCID: ), Vedha Viyash [aut] Maintainer: Appsilon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/shiny.gosling git_branch: devel git_last_commit: 5339d8b git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/shiny.gosling_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/shiny.gosling_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/shiny.gosling_1.5.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SigFuge Version: 1.47.0 Depends: R (>= 3.5.0), GenomicRanges Imports: ggplot2, matlab, reshape, sigclust Suggests: org.Hs.eg.db, prebsdata, Rsamtools (>= 1.17.0), TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle License: GPL-3 NeedsCompilation: no Title: SigFuge Description: Algorithm for testing significance of clustering in RNA-seq data. biocViews: Clustering, Visualization, RNASeq, ImmunoOncology Author: Patrick Kimes, Christopher Cabanski Maintainer: Patrick Kimes git_url: https://git.bioconductor.org/packages/SigFuge git_branch: devel git_last_commit: 4cc5913 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/SigFuge_1.47.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: soGGi Version: 1.41.1 Depends: R (>= 3.5.0), BiocGenerics, SummarizedExperiment Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, Seqinfo, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, preprocessCore, chipseq, BiocParallel Suggests: testthat, BiocStyle, knitr License: GPL (>= 3) NeedsCompilation: no Title: Visualise ChIP-seq, MNase-seq and motif occurrence as aggregate plots Summarised Over Grouped Genomic Intervals Description: The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events. biocViews: Sequencing, ChIPSeq, Coverage Author: Gopuraja Dharmalingam, Doug Barrows, Tom Carroll Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/soGGi git_branch: devel git_last_commit: 827dbf0 git_last_commit_date: 2025-07-24 Date/Publication: 2025-07-25 win.binary.ver: bin/windows/contrib/4.5/soGGi_1.41.1.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: spatialHeatmap Version: 2.15.1 Depends: R (>= 3.5.0) Imports: data.table, dplyr, edgeR, genefilter, ggplot2, grImport, grid, gridExtra, igraph, methods, Matrix, rsvg, shiny, grDevices, graphics, ggplotify, parallel, reshape2, stats, SummarizedExperiment, SingleCellExperiment, shinydashboard, S4Vectors, spsComps (>= 0.3.3.0), tibble, utils, xml2 Suggests: AnnotationDbi, av, BiocParallel, BiocFileCache, BiocGenerics, BiocStyle, BiocSingular, Biobase, cachem, DESeq2, dendextend, DT, dynamicTreeCut, flashClust, gplots, ggdendro, HDF5Array, htmltools, htmlwidgets, kableExtra, knitr, limma, magick, memoise, ExpressionAtlas, GEOquery, org.Hs.eg.db, org.Mm.eg.db, org.At.tair.db, org.Dr.eg.db, org.Dm.eg.db, pROC, plotly, rmarkdown, rols, rappdirs, RUnit, Rtsne, rhdf5, scater, scuttle, scran, shinyWidgets, shinyjs, shinyBS, sortable, Seurat, sparkline, spsUtil, uwot, UpSetR, visNetwork, WGCNA, xlsx, yaml License: Artistic-2.0 NeedsCompilation: no Title: spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions Description: The spatialHeatmap package offers the primary functionality for visualizing cell-, tissue- and organ-specific assay data in spatial anatomical images. Additionally, it provides extended functionalities for large-scale data mining routines and co-visualizing bulk and single-cell data. A description of the project is available here: https://spatialheatmap.org. biocViews: Spatial, Visualization, Microarray, Sequencing, GeneExpression, DataRepresentation, Network, Clustering, GraphAndNetwork, CellBasedAssays, ATACSeq, DNASeq, TissueMicroarray, SingleCell, CellBiology, GeneTarget Author: Jianhai Zhang [aut, trl, cre], Le Zhang [aut], Jordan Hayes [aut], Brendan Gongol [aut], Alexander Borowsky [aut], Julia Bailey-Serres [aut], Thomas Girke [aut] Maintainer: Jianhai Zhang URL: https://spatialheatmap.org, https://github.com/jianhaizhang/spatialHeatmap VignetteBuilder: knitr BugReports: https://github.com/jianhaizhang/spatialHeatmap/issues git_url: https://git.bioconductor.org/packages/spatialHeatmap git_branch: devel git_last_commit: 5c1ec61 git_last_commit_date: 2025-06-19 Date/Publication: 2025-06-22 win.binary.ver: bin/windows/contrib/4.5/spatialHeatmap_2.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spatialHeatmap_2.15.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spatialHeatmap_2.15.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: spatzie Version: 1.15.1 Depends: R (>= 4.3) Imports: BiocGenerics, BSgenome, Seqinfo, GenomicFeatures, GenomicInteractions, GenomicRanges, ggplot2, IRanges, MatrixGenerics, matrixStats, motifmatchr, S4Vectors, stats, SummarizedExperiment, TFBSTools, utils Suggests: BiocManager, Biostrings, knitr, pheatmap, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 Archs: x64 NeedsCompilation: no Title: Identification of enriched motif pairs from chromatin interaction data Description: Identifies motifs that are significantly co-enriched from enhancer-promoter interaction data. While enhancer-promoter annotation is commonly used to define groups of interaction anchors, spatzie also supports co-enrichment analysis between preprocessed interaction anchors. Supports BEDPE interaction data derived from genome-wide assays such as HiC, ChIA-PET, and HiChIP. Can also be used to look for differentially enriched motif pairs between two interaction experiments. biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics, FunctionalGenomics, Classification, HiC, Transcription Author: Jennifer Hammelman [aut, cre, cph] (ORCID: ), Konstantin Krismer [aut] (ORCID: ), David Gifford [ths, cph] (ORCID: ) Maintainer: Jennifer Hammelman URL: https://spatzie.mit.edu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/spatzie git_branch: devel git_last_commit: 319405d git_last_commit_date: 2025-07-23 Date/Publication: 2025-07-24 win.binary.ver: bin/windows/contrib/4.5/spatzie_1.15.1.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SQLDataFrame Version: 1.23.0 Depends: DelayedArray, S4Vectors Imports: stats, utils, methods, BiocGenerics, RSQLite, duckdb, DBI Suggests: knitr, rmarkdown, BiocStyle, testthat License: LGPL (>= 3); File LICENSE NeedsCompilation: no Title: Representation of SQL tables in DataFrame metaphor Description: Implements bindings for SQL tables that are compatible with Bioconductor S4 data structures, namely the DataFrame and DelayedArray. This allows SQL-derived data to be easily used inside other Bioconductor objects (e.g., SummarizedExperiments) while keeping everything on disk. biocViews: DataRepresentation, Infrastructure, Software Author: Qian Liu [aut, cre] (ORCID: ), Aaron Lun [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/SQLDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SQLDataFrame/issues git_url: https://git.bioconductor.org/packages/SQLDataFrame git_branch: devel git_last_commit: f23bd64 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/SQLDataFrame_1.23.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: struct Version: 1.21.1 Depends: R (>= 4.0) Imports: methods,ontologyIndex, datasets, graphics, stats, utils, knitr, SummarizedExperiment, S4Vectors, rols Suggests: testthat, rstudioapi, rmarkdown, covr, BiocStyle, openxlsx, ggplot2, magick License: GPL-3 NeedsCompilation: no Title: Statistics in R Using Class-based Templates Description: Defines and includes a set of class-based templates for developing and implementing data processing and analysis workflows, with a strong emphasis on statistics and machine learning. The templates can be used and where needed extended to 'wrap' tools and methods from other packages into a common standardised structure to allow for effective and fast integration. Model objects can be combined into sequences, and sequences nested in iterators using overloaded operators to simplify and improve readability of the code. Ontology lookup has been integrated and implemented to provide standardised definitions for methods, inputs and outputs wrapped using the class-based templates. biocViews: WorkflowStep Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/struct git_branch: devel git_last_commit: 9083c8d git_last_commit_date: 2025-04-24 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/struct_1.21.1.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: structToolbox Version: 1.21.0 Depends: R (>= 4.0), struct (>= 1.5.1) Imports: ggplot2, ggthemes, grid, gridExtra, methods, scales, sp, stats Suggests: agricolae, BiocFileCache, BiocStyle, car, covr, cowplot, e1071, emmeans, ggdendro, knitr, magick, nlme, openxlsx, pls, pmp, reshape2, ropls, rmarkdown, Rtsne, testthat, rappdirs License: GPL-3 NeedsCompilation: no Title: Data processing & analysis tools for Metabolomics and other omics Description: An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs. biocViews: WorkflowStep, Metabolomics Author: Gavin Rhys Lloyd [aut, cre] (ORCID: ), Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd URL: https://github.com/computational-metabolomics/structToolbox, https://computational-metabolomics.github.io/structToolbox/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/structToolbox git_branch: devel git_last_commit: 36d5ff1 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/structToolbox_1.21.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: tidytof Version: 1.3.0 Depends: R (>= 4.3) Imports: doParallel, dplyr, flowCore, foreach, ggplot2, ggraph, glmnet, methods, parallel, purrr, readr, recipes, rlang, stringr, survival, tidygraph, tidyr, tidyselect, yardstick, Rcpp, tibble, stats, utils, RcppHNSW LinkingTo: Rcpp Suggests: ConsensusClusterPlus, Biobase, broom, covr, diffcyt, emdist, FlowSOM, forcats, ggrepel, HDCytoData, knitr, markdown, philentropy, rmarkdown, Rtsne, statmod, SummarizedExperiment, testthat (>= 3.0.0), lmerTest, lme4, ggridges, spelling, scattermore, preprocessCore, SingleCellExperiment, Seurat, SeuratObject, embed, rsample, BiocGenerics License: MIT + file LICENSE NeedsCompilation: yes Title: Analyze High-dimensional Cytometry Data Using Tidy Data Principles Description: This package implements an interactive, scientific analysis pipeline for high-dimensional cytometry data built using tidy data principles. It is specifically designed to play well with both the tidyverse and Bioconductor software ecosystems, with functionality for reading/writing data files, data cleaning, preprocessing, clustering, visualization, modeling, and other quality-of-life functions. tidytof implements a "grammar" of high-dimensional cytometry data analysis. biocViews: SingleCell, FlowCytometry Author: Timothy Keyes [cre] (ORCID: ), Kara Davis [rth, own], Garry Nolan [rth, own] Maintainer: Timothy Keyes URL: https://keyes-timothy.github.io/tidytof, https://keyes-timothy.github.io/tidytof/ VignetteBuilder: knitr BugReports: https://github.com/keyes-timothy/tidytof/issues git_url: https://git.bioconductor.org/packages/tidytof git_branch: devel git_last_commit: 7757e98 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/tidytof_1.3.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: TransView Version: 1.53.1 Depends: methods, GenomicRanges Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, gplots LinkingTo: Rhtslib (>= 1.99.1) Suggests: RUnit, pasillaBamSubset, BiocManager License: GPL-3 NeedsCompilation: yes Title: Read density map construction and accession. Visualization of ChIPSeq and RNASeq data sets Description: This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, Transcription, Microarray, Sequencing, Sequencing, ChIPSeq, RNASeq, MethylSeq, DataImport, Visualization, Clustering, MultipleComparison Author: Julius Muller Maintainer: Julius Muller URL: http://bioconductor.org/packages/release/bioc/html/TransView.html SystemRequirements: GNU make PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/TransView git_branch: devel git_last_commit: b526566 git_last_commit_date: 2025-07-24 Date/Publication: 2025-07-24 win.binary.ver: bin/windows/contrib/4.5/TransView_1.53.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TransView_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TransView_1.54.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: traviz Version: 1.15.0 Depends: R (>= 4.0) Imports: ggplot2, viridis, mgcv, SingleCellExperiment, slingshot, princurve, Biobase, methods, RColorBrewer, SummarizedExperiment, grDevices, graphics, rgl Suggests: scater, dplyr, testthat (>= 3.0.0), covr, S4Vectors, rmarkdown, knitr License: MIT + file LICENSE NeedsCompilation: no Title: Trajectory functions for visualization and interpretation. Description: traviz provides a suite of functions to plot trajectory related objects from Bioconductor packages. It allows plotting trajectories in reduced dimension, as well as averge gene expression smoothers as a function of pseudotime. Asides from general utility functions, traviz also allows plotting trajectories estimated by Slingshot, as well as smoothers estimated by tradeSeq. Furthermore, it allows for visualization of Slingshot trajectories using ggplot2. biocViews: GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, Visualization Author: Hector Roux de Bezieux [aut, ctb], Kelly Street [aut, ctb], Koen Van den Berge [aut, cre] Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/traviz git_branch: devel git_last_commit: 7c9f77b git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/traviz_1.15.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: wppi Version: 1.17.0 Depends: R(>= 4.1) Imports: dplyr, igraph, logger, methods, magrittr, Matrix, OmnipathR(>= 2.99.8), progress, purrr, rlang, RCurl, stats, tibble, tidyr Suggests: knitr, testthat, rmarkdown License: MIT + file LICENSE NeedsCompilation: no Title: Weighting protein-protein interactions Description: Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm. biocViews: GraphAndNetwork, Network, Pathways, Software, GeneSignaling, GeneTarget, SystemsBiology, Transcriptomics, Annotation Author: Ana Galhoz [cre, aut] (ORCID: ), Denes Turei [aut] (ORCID: ), Michael P. Menden [aut] (ORCID: ), Albert Krewinkel [ctb, cph] (pagebreak Lua filter) Maintainer: Ana Galhoz URL: https://github.com/AnaGalhoz37/wppi VignetteBuilder: knitr BugReports: https://github.com/AnaGalhoz37/wppi/issues git_url: https://git.bioconductor.org/packages/wppi git_branch: devel git_last_commit: 718f698 git_last_commit_date: 2025-04-15 Date/Publication: 2025-06-04 win.binary.ver: bin/windows/contrib/4.5/wppi_1.17.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Harshlight Version: 1.82.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) MD5sum: 66cbd8a977c17c0d3eab0cc2055de84e NeedsCompilation: yes Title: A "corrective make-up" program for microarray chips Description: The package is used to detect extended, diffuse and compact blemishes on microarray chips. Harshlight automatically marks the areas in a collection of chips (affybatch objects) and a corrected AffyBatch object is returned, in which the defected areas are substituted with NAs or the median of the values of the same probe in the other chips in the collection. The new version handle the substitute value as whole matrix to solve the memory problem. biocViews: Microarray, QualityControl, Preprocessing, OneChannel, ReportWriting Author: Mayte Suarez-Farinas, Maurizio Pellegrino, Knut M. Wittkowski, Marcelo O. Magnasco Maintainer: Maurizio Pellegrino URL: http://asterion.rockefeller.edu/Harshlight/ PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/Harshlight git_branch: RELEASE_3_22 git_last_commit: 8597b4a git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Harshlight_1.82.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: HiCool Version: 1.10.0 Depends: R (>= 4.2), HiCExperiment Imports: BiocIO, S4Vectors, GenomicRanges, IRanges, InteractionSet, vroom, basilisk.utils, basilisk, reticulate, rmarkdown, rmdformats, plotly, dplyr, stringr, sessioninfo, utils Suggests: HiContacts, HiContactsData, AnnotationHub, BiocFileCache, BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: d9b3e57efa99d570b32ebd45293ecdab NeedsCompilation: no Title: HiCool Description: HiCool provides an R interface to process and normalize Hi-C paired-end fastq reads into .(m)cool files. .(m)cool is a compact, indexed HDF5 file format specifically tailored for efficiently storing HiC-based data. On top of processing fastq reads, HiCool provides a convenient reporting function to generate shareable reports summarizing Hi-C experiments and including quality controls. biocViews: HiC, DNA3DStructure, DataImport Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://github.com/js2264/HiCool VignetteBuilder: knitr BugReports: https://github.com/js2264/HiCool/issues git_url: https://git.bioconductor.org/packages/HiCool git_branch: RELEASE_3_22 git_last_commit: 901249c git_last_commit_date: 2025-10-29 Date/Publication: 2025-10-29 mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCool_1.10.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: lapmix Version: 1.76.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) Title: Laplace Mixture Model in Microarray Experiments Description: Laplace mixture modelling of microarray experiments. A hierarchical Bayesian approach is used, and the hyperparameters are estimated using empirical Bayes. The main purpose is to identify differentially expressed genes. biocViews: Microarray, OneChannel, DifferentialExpression Author: Yann Ruffieux, contributions from Debjani Bhowmick, Anthony C. Davison, and Darlene R. Goldstein Maintainer: Yann Ruffieux URL: http://www.r-project.org, http://www.bioconductor.org, http://stat.epfl.ch PackageStatus: Deprecated Package: Repitools Version: 1.56.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.8.0) Imports: parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Seqinfo, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, BSgenome (>= 1.47.3), gplots, grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Rsolnp, cluster Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) Title: Epigenomic tools Description: Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc. biocViews: DNAMethylation, GeneExpression, MethylSeq Author: Mark Robinson , Dario Strbenac , Aaron Statham , Andrea Riebler Maintainer: Mark Robinson PackageStatus: Deprecated Package: BiGGR Version: 1.46.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph, limSolve License: file LICENSE Title: Constraint based modeling in R using metabolic reconstruction databases Description: This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs. biocViews: Systems Biology,Pathway,Network,GraphAndNetwork, Visualization,Metabolomics Author: Anand K. Gavai, Hannes Hettling Maintainer: Anand K. Gavai , Hannes Hettling URL: http://www.bioconductor.org/ PackageStatus: Deprecated Package: PhenStat Version: 2.46.0 Depends: R (>= 3.5.0) Imports: SmoothWin, methods, car, nlme, nortest, MASS, msgps, logistf, knitr, tools, pingr, ggplot2, reshape, corrplot, graph, lme4, graphics, grDevices, utils, stats Suggests: RUnit, BiocGenerics License: file LICENSE NeedsCompilation: no Title: Statistical analysis of phenotypic data Description: Package contains methods for statistical analysis of phenotypic data. biocViews: StatisticalMethod Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason, Hamed Haselimashhadi Maintainer: Hamed Haselimashhadi PackageStatus: Deprecated Package: TitanCNA Version: 1.48.0 Depends: R (>= 3.5.1) Imports: BiocGenerics (>= 0.31.6), IRanges (>= 2.6.1), GenomicRanges (>= 1.24.3), VariantAnnotation (>= 1.18.7), foreach (>= 1.4.3), GenomeInfoDb (>= 1.8.7), data.table (>= 1.10.4), dplyr (>= 0.5.0), License: GPL-3 Title: Subclonal copy number and LOH prediction from whole genome sequencing of tumours Description: Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data. biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq, StatisticalMethod, CopyNumberVariation, HiddenMarkovModel, Genetics, GenomicVariation, ImmunoOncology Author: Gavin Ha Maintainer: Gavin Ha URL: https://github.com/gavinha/TitanCNA PackageStatus: Deprecated Package: hiReadsProcessor Version: 1.46.0 Depends: Biostrings, pwalign, GenomicAlignments, BiocParallel, hiAnnotator, R (>= 3.0) Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, readxl, methods Suggests: knitr, testthat, markdown License: GPL-3 Title: Functions to process LM-PCR reads from 454/Illumina data Description: hiReadsProcessor contains set of functions which allow users to process LM-PCR products sequenced using any platform. Given an excel/txt file containing parameters for demultiplexing and sample metadata, the functions automate trimming of adaptors and identification of the genomic product. Genomic products are further processed for QC and abundance quantification. biocViews: Sequencing, Preprocessing Author: Nirav V Malani Maintainer: Nirav V Malani SystemRequirements: BLAT, UCSC hg18 in 2bit format for BLAT VignetteBuilder: knitr PackageStatus: Deprecated Package: seqTools Version: 1.44.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 NeedsCompilation: yes Title: Analysis of nucleotide, sequence and quality content on fastq files Description: Analyze read length, phred scores and alphabet frequency and DNA k-mers on uncompressed and compressed fastq files. biocViews: QualityControl,Sequencing Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers PackageStatus: Deprecated Package: MADSEQ Version: 1.36.0 Depends: R(>= 3.4), rjags(>= 4-6), Imports: VGAM, coda, BSgenome, BSgenome.Hsapiens.UCSC.hg19, S4Vectors, methods, preprocessCore, GenomicAlignments, Rsamtools, Biostrings, GenomicRanges, IRanges, VariantAnnotation, SummarizedExperiment, GenomeInfoDb, rtracklayer, graphics, stats, grDevices, utils, zlibbioc, vcfR Suggests: knitr License: GPL(>=2) NeedsCompilation: no Title: Mosaic Aneuploidy Detection and Quantification using Massive Parallel Sequencing Data Description: The MADSEQ package provides a group of hierarchical Bayeisan models for the detection of mosaic aneuploidy, the inference of the type of aneuploidy and also for the quantification of the fraction of aneuploid cells in the sample. biocViews: GenomicVariation, SomaticMutation, VariantDetection, Bayesian, CopyNumberVariation, Sequencing, Coverage Author: Yu Kong, Adam Auton, John Murray Greally Maintainer: Yu Kong URL: https://github.com/ykong2/MADSEQ VignetteBuilder: knitr BugReports: https://github.com/ykong2/MADSEQ/issues PackageStatus: Deprecated Package: ccmap Version: 1.36.0 Imports: AnnotationDbi (>= 1.36.2), BiocManager (>= 1.30.4), ccdata (>= 1.1.2), doParallel (>= 1.0.10), data.table (>= 1.10.4), foreach (>= 1.4.3), parallel (>= 3.3.3), xgboost (>= 0.6.4), lsa (>= 0.73.1) Suggests: crossmeta, knitr, rmarkdown, testthat, lydata License: MIT + file LICENSE Title: Combination Connectivity Mapping Description: Finds drugs and drug combinations that are predicted to reverse or mimic gene expression signatures. These drugs might reverse diseases or mimic healthy lifestyles. biocViews: GeneExpression, Transcription, Microarray, DifferentialExpression Author: Alex Pickering Maintainer: Alex Pickering VignetteBuilder: knitr PackageStatus: Deprecated Package: CellScore Version: 1.30.0 Depends: R (>= 4.3.0) Imports: Biobase (>= 2.39.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), gplots (>= 3.0.1), lsa (>= 0.73.1), methods (>= 3.5.0), RColorBrewer(>= 1.1-2), squash (>= 1.0.8), stats (>= 3.5.0), utils(>= 3.5.0), SummarizedExperiment Suggests: hgu133plus2CellScore, knitr, testthat (>= 3.0.0) License: GPL-3 NeedsCompilation: no Title: Tool for Evaluation of Cell Identity from Transcription Profiles Description: The CellScore package contains functions to evaluate the cell identity of a test sample, given a cell transition defined with a starting (donor) cell type and a desired target cell type. The evaluation is based upon a scoring system, which uses a set of standard samples of known cell types, as the reference set. The functions have been carried out on a large set of microarray data from one platform (Affymetrix Human Genome U133 Plus 2.0). In principle, the method could be applied to any expression dataset, provided that there are a sufficient number of standard samples and that the data are normalized. biocViews: GeneExpression, Transcription, Microarray, MultipleComparison, ReportWriting, DataImport, Visualization Author: Nancy Mah [aut, cre], Katerina Taskova [aut], Justin Marsh [aut] Maintainer: Nancy Mah VignetteBuilder: knitr PackageStatus: Deprecated Package: hypeR Version: 2.8.0 Depends: R (>= 3.6.0) Imports: ggplot2, ggforce, R6, magrittr, dplyr, purrr, stats, stringr, scales, rlang, httr, openxlsx, htmltools, reshape2, reactable, msigdbr, kableExtra, rmarkdown, igraph, visNetwork, shiny, BiocStyle Suggests: tidyverse, devtools, testthat, knitr License: GPL-3 + file LICENSE Title: An R Package For Geneset Enrichment Workflows Description: An R Package for Geneset Enrichment Workflows. biocViews: GeneSetEnrichment, Annotation, Pathways Author: Anthony Federico [aut, cre], Andrew Chen [aut], Stefano Monti [aut] Maintainer: Anthony Federico URL: https://github.com/montilab/hypeR VignetteBuilder: knitr BugReports: https://github.com/montilab/hypeR/issues PackageStatus: Deprecated Package: qckitfastq Version: 1.26.0 Imports: magrittr, ggplot2, dplyr, seqTools, zlibbioc, data.table, reshape2, grDevices, graphics, stats, utils, Rcpp, rlang, RSeqAn LinkingTo: Rcpp, RSeqAn Suggests: knitr, rmarkdown, kableExtra, testthat License: Artistic-2.0 Title: FASTQ Quality Control Description: Assessment of FASTQ file format with multiple metrics including quality score, sequence content, overrepresented sequence and Kmers. biocViews: Software,QualityControl,Sequencing Author: Wenyue Xing [aut], August Guang [aut, cre] Maintainer: August Guang SystemRequirements: GNU make VignetteBuilder: knitr PackageStatus: Deprecated Package: oppti Version: 1.24.0 Depends: R (>= 3.5) Imports: limma, stats, reshape, ggplot2, grDevices, RColorBrewer, pheatmap, knitr, methods, devtools, parallelDist, Suggests: markdown License: MIT Title: Outlier Protein and Phosphosite Target Identifier Description: The aim of oppti is to analyze protein (and phosphosite) expressions to find outlying markers for each sample in the given cohort(s) for the discovery of personalized actionable targets. biocViews: Proteomics, Regression, DifferentialExpression, BiomedicalInformatics, GeneTarget, GeneExpression, Network Author: Abdulkadir Elmas Maintainer: Abdulkadir Elmas URL: https://github.com/Huang-lab/oppti VignetteBuilder: knitr BugReports: https://github.com/Huang-lab/oppti/issues PackageStatus: Deprecated Package: Rfastp Version: 1.20.0 Imports: Rcpp, rjson, ggplot2, reshape2 LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 + file LICENSE Title: An Ultra-Fast and All-in-One Fastq Preprocessor (Quality Control, Adapter, low quality and polyX trimming) and UMI Sequence Parsing). Description: Rfastp is an R wrapper of fastp developed in c++. fastp performs quality control for fastq files. including low quality bases trimming, polyX trimming, adapter auto-detection and trimming, paired-end reads merging, UMI sequence/id handling. Rfastp can concatenate multiple files into one file (like shell command cat) and accept multiple files as input. biocViews: QualityControl, Sequencing, Preprocessing, Software Author: Wei Wang [aut] (ORCID: ), Ji-Dung Luo [ctb] (ORCID: ), Thomas Carroll [cre, aut] (ORCID: ) Maintainer: Thomas Carroll SystemRequirements: GNU make VignetteBuilder: knitr PackageStatus: Deprecated Package: XNAString Version: 1.18.0 Depends: R (>= 4.1) Imports: utils, Biostrings, pwalign, BSgenome, data.table, GenomicRanges, IRanges, methods, Rcpp, stringi, S4Vectors, future.apply, stringr, formattable, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, BSgenome.Hsapiens.UCSC.hg38, pander License: GPL-2 Title: Efficient Manipulation of Modified Oligonucleotide Sequences Description: The XNAString package allows for description of base sequences and associated chemical modifications in a single object. XNAString is able to capture single stranded, as well as double stranded molecules. Chemical modifications are represented as independent strings associated with different features of the molecules (base sequence, sugar sequence, backbone sequence, modifications) and can be read or written to a HELM notation. It also enables secondary structure prediction using RNAfold from ViennaRNA. XNAString is designed to be efficient representation of nucleic-acid based therapeutics, therefore it stores information about target sequences and provides interface for matching and alignment functions from Biostrings and pwalign packages. biocViews: SequenceMatching, Alignment, Sequencing, Genetics Author: Anna Górska [aut], Marianna Plucinska [aut, cre], Lykke Pedersen [aut], Lukasz Kielpinski [aut], Disa Tehler [aut], Peter H. Hagedorn [aut] Maintainer: Marianna Plucinska VignetteBuilder: knitr PackageStatus: Deprecated Package: ReactomeGraph4R Version: 1.18.0 Depends: R (>= 4.1) Imports: neo4r, utils, getPass, jsonlite, purrr, magrittr, data.table, rlang, ReactomeContentService4R, doParallel, parallel, foreach Suggests: knitr, rmarkdown, testthat, stringr, networkD3, visNetwork, wesanderson License: Apache License (>= 2) Title: Interface for the Reactome Graph Database Description: Pathways, reactions, and biological entities in Reactome knowledge are systematically represented as an ordered network. Instances are represented as nodes and relationships between instances as edges; they are all stored in the Reactome Graph Database. This package serves as an interface to query the interconnected data from a local Neo4j database, with the aim of minimizing the usage of Neo4j Cypher queries. biocViews: DataImport, Pathways, Reactome, Network, GraphAndNetwork Author: Chi-Lam Poon [aut, cre] (ORCID: ), Reactome [cph] Maintainer: Chi-Lam Poon URL: https://github.com/reactome/ReactomeGraph4R VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGraph4R/issues PackageStatus: Deprecated Package: iPath Version: 1.16.0 Depends: R (>= 4.1), mclust, BiocParallel, survival Imports: Rcpp (>= 1.0.5), matrixStats, ggpubr, ggplot2, survminer, stats LinkingTo: Rcpp, RcppArmadillo Suggests: rmarkdown, BiocStyle, knitr License: GPL-2 NeedsCompilation: yes Title: iPath pipeline for detecting perturbed pathways at individual level Description: iPath is the Bioconductor package used for calculating personalized pathway score and test the association with survival outcomes. Abundant single-gene biomarkers have been identified and used in the clinics. However, hundreds of oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. We believe individual-level expression patterns of pre-defined pathways or gene sets are better biomarkers than single genes. In this study, we devised a computational method named iPath to identify prognostic biomarker pathways, one sample at a time. To test its utility, we conducted a pan-cancer analysis across 14 cancer types from The Cancer Genome Atlas and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor stage classifications. We found that pathway-based biomarkers are more robust and effective than single genes. biocViews: Pathways, Software, GeneExpression, Survival Author: Kenong Su [aut, cre], Zhaohui Qin [aut] Maintainer: Kenong Su SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/suke18/iPath/issues Package: benchdamic Version: 1.16.0 Depends: R (>= 4.3.0) Imports: stats, stats4, utils, methods, phyloseq, TreeSummarizedExperiment, BiocParallel, zinbwave, edgeR, DESeq2, limma, ALDEx2, corncob, SummarizedExperiment, MAST, Seurat, ANCOMBC, microbiome, mixOmics, lme4, NOISeq, dearseq, MicrobiomeStat, Maaslin2, maaslin3, GUniFrac, metagenomeSeq, MGLM, ggplot2, RColorBrewer, plyr, reshape2, ggdendro, ggridges, graphics, cowplot, grDevices, tidytext Suggests: knitr, rmarkdown, kableExtra, BiocStyle, magick, SPsimSeq, testthat License: Artistic-2.0 Title: Benchmark of differential abundance methods on microbiome data Description: Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization. biocViews: Metagenomics, Microbiome, DifferentialExpression, MultipleComparison, Normalization, Preprocessing, Software Author: Matteo Calgaro [aut, cre] (ORCID: ), Chiara Romualdi [aut] (ORCID: ), Davide Risso [aut] (ORCID: ), Nicola Vitulo [aut] (ORCID: ) Maintainer: Matteo Calgaro VignetteBuilder: knitr BugReports: https://github.com/mcalgaro93/benchdamic/issues Package: netZooR Version: 1.14.0 Depends: R (>= 4.2.0), igraph, reticulate, pandaR, Biobase, Imports: cmdstanr, AnnotationDbi, assertthat, biomaRt, cmdstanr, corpcor, data.table, doParallel, downloader, dplyr, edgeR, foreach, GeneNet, ggdendro, ggplot2, GO.db, GOstats, gplots, graphics, grid, limma, loo, MASS, Matrix, matrixcalc, matrixStats, matrixTests, methods, nnet, org.Hs.eg.db, parallel, penalized, preprocessCore, quantro, rARPACK, RColorBrewer, RCy3, readr, reshape, reshape2, stats, STRINGdb, tidyr, utils, vegan, viridisLite, Suggests: dorothea, knitr, pkgdown, rmarkdown, testthat (>= 2.1.0), License: GPL-3 Title: A menagerie of methods for the inference and analysis of gene regulatory networks Description: netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information. biocViews: GeneExpression, GeneRegulation, GraphAndNetwork, Microarray, Network, NetworkInference, Transcription Author: Tara Eicher [aut] (ORCID: ), Marouen Ben Guebila [aut, cre] (ORCID: ), Tian Wang [aut] (ORCID: ), John Platig [aut], Marieke Kuijjer [aut] (ORCID: ), Megha Padi [aut] (ORCID: ), Rebekka Burkholz [aut], Des Weighill [aut] (ORCID: ), Chen Chen [aut] (ORCID: ), Kate Shutta [aut] (ORCID: ) Maintainer: Marouen Ben Guebila URL: https://github.com/netZoo/netZooR, https://netzoo.github.io/ VignetteBuilder: knitr BugReports: https://github.com/netZoo/netZooR/issues PackageStatus: Deprecated Package: LinTInd Version: 1.14.0 Depends: R (>= 4.0), ggplot2, parallel, stats, S4Vectors Imports: data.tree, reshape2, networkD3, stringdist, purrr, ape, cowplot, ggnewscale, stringr, dplyr, rlist, pheatmap, Biostrings, pwalign, IRanges, BiocGenerics(>= 0.36.1), ggtree Suggests: knitr, rmarkdown License: MIT + file LICENSE Title: Lineage tracing by indels Description: When we combine gene-editing technology and sequencing technology, we need to reconstruct a lineage tree from alleles generated and calculate the similarity between each pair of groups. FindIndel() and IndelForm() function will help you align each read to reference sequence and generate scar form strings respectively. IndelIdents() function will help you to define a scar form for each cell or read. IndelPlot() function will help you to visualize the distribution of deletion and insertion. TagProcess() function will help you to extract indels for each cell or read. TagDist() function will help you to calculate the similarity between each pair of groups across the indwells they contain. BuildTree() function will help you to reconstruct a tree. PlotTree() function will help you to visualize the tree. biocViews: SingleCell, CRISPR, Alignment Author: Luyue Wang [aut, cre], Bin Xiang [ctb], Hengxin Liu [ctb], Wu Wei [ths] Maintainer: Luyue Wang VignetteBuilder: knitr PackageStatus: Deprecated Package: seqArchR Version: 1.14.0 Depends: R (>= 4.2.0) Imports: utils, graphics, cvTools (>= 0.3.2), MASS, Matrix, methods, stats, cluster, matrixStats, fpc, cli, prettyunits, reshape2 (>= 1.4.3), reticulate (>= 1.22), BiocParallel, Biostrings, grDevices, ggplot2 (>= 3.1.1), ggseqlogo (>= 0.1) Suggests: cowplot, hopach (>= 2.42.0), BiocStyle, knitr (>= 1.22), rmarkdown (>= 1.12), testthat (>= 3.0.2), covr, vdiffr (>= 0.3.0) License: GPL-3 | file LICENSE Title: Identify Different Architectures of Sequence Elements Description: seqArchR enables unsupervised discovery of _de novo_ clusters with characteristic sequence architectures characterized by position-specific motifs or composition of stretches of nucleotides, e.g., CG-richness. seqArchR does _not_ require any specifications w.r.t. the number of clusters, the length of any individual motifs, or the distance between motifs if and when they occur in pairs/groups; it directly detects them from the data. seqArchR uses non-negative matrix factorization (NMF) as its backbone, and employs a chunking-based iterative procedure that enables processing of large sequence collections efficiently. Wrapper functions are provided for visualizing cluster architectures as sequence logos. biocViews: MotifDiscovery, GeneRegulation, MathematicalBiology, SystemsBiology, Transcriptomics, Genetics, Clustering, DimensionReduction, FeatureExtraction, DNASeq Author: Sarvesh Nikumbh [aut, cre, cph] (ORCID: ) Maintainer: Sarvesh Nikumbh URL: https://snikumbh.github.io/seqArchR/, https://github.com/snikumbh/seqArchR SystemRequirements: Python (>= 3.5), scikit-learn (>= 0.21.2), packaging VignetteBuilder: knitr BugReports: https://github.com/snikumbh/seqArchR/issues PackageStatus: Deprecated Package: microSTASIS Version: 1.10.0 Depends: R (>= 4.2.0) Imports: BiocParallel, ggplot2, ggside, grid, rlang, stats, stringr, TreeSummarizedExperiment Suggests: BiocStyle, gghighlight, knitr, rmarkdown, methods, RefManageR, sessioninfo, SingleCellExperiment, SummarizedExperiment, testthat (>= 3.0.0) License: GPL-3 Title: Microbiota STability ASsessment via Iterative cluStering Description: The toolkit 'µSTASIS', or microSTASIS, has been developed for the stability analysis of microbiota in a temporal framework by leveraging on iterative clustering. Concretely, the core function uses Hartigan-Wong k-means algorithm as many times as possible for stressing out paired samples from the same individuals to test if they remain together for multiple numbers of clusters over a whole data set of individuals. Moreover, the package includes multiple functions to subset samples from paired times, validate the results or visualize the output. biocViews: GeneticVariability, BiomedicalInformatics, Clustering, MultipleComparison, Microbiome Author: Pedro Sánchez-Sánchez [aut, cre] (ORCID: ), Alfonso Benítez-Páez [aut] (ORCID: ) Maintainer: Pedro Sánchez-Sánchez URL: https://doi.org/10.1093/bib/bbac055 VignetteBuilder: knitr BugReports: https://github.com/BiotechPedro/microSTASIS Package: lute Version: 1.6.0 Depends: R (>= 4.3.0), stats, methods, utils, SummarizedExperiment, SingleCellExperiment, BiocGenerics Imports: S4Vectors, Biobase, scran, dplyr, ggplot2 Suggests: nnls, knitr, testthat, rmarkdown, BiocStyle, GenomicRanges, limma, ExperimentHub, AnnotationHub, DelayedMatrixStats, BisqueRNA, DelayedArray License: Artistic-2.0 Title: Framework for cell size scale factor normalized bulk transcriptomics deconvolution experiments Description: Provides a framework for adjustment on cell type size when performing bulk transcripomics deconvolution. The main framework function provides a means of reference normalization using cell size scale factors. It allows for marker selection and deconvolution using non-negative least squares (NNLS) by default. The framework is extensible for other marker selection and deconvolution algorithms, and users may reuse the generics, methods, and classes for these when developing new algorithms. biocViews: RNASeq, Sequencing, SingleCell, Coverage, Transcriptomics, Normalization Author: Sean K Maden [cre, aut] (ORCID: ), Stephanie Hicks [aut] (ORCID: ) Maintainer: Sean K Maden URL: https://github.com/metamaden/lute VignetteBuilder: knitr BugReports: https://github.com/metamaden/lute/issues PackageStatus: Deprecated Package: Pirat Version: 1.4.0 Depends: R (>= 4.5.0) Imports: basilisk, reticulate, progress, ggplot2, MASS, invgamma, grDevices, stats, graphics, SummarizedExperiment, S4Vectors Suggests: knitr, BiocStyle License: GPL-2 Title: Precursor or Peptide Imputation under Random Truncation Description: Pirat enables the imputation of missing values (either MNARs or MCARs) in bottom-up LC-MS/MS proteomics data using a penalized maximum likelihood strategy. It does not require any parameter tuning, it models the instrument censorship from the data available. It accounts for sibling peptides correlations and it can leverage complementary transcriptomics measurements. biocViews: Proteomics, MassSpectrometry, Preprocessing, Software Author: Lucas Etourneau [cre, aut] (ORCID: ), Laura Fancello [aut], Manon Gaudin [aut], Samuel Wieczorek [aut] (ORCID: ), Nelle Varoquaux [aut], Thomas Burger [aut] Maintainer: Lucas Etourneau URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/edyp-lab/Pirat/issues Package: MetMashR Version: 1.4.0 Depends: R (>= 4.3.0), struct Imports: dplyr, methods, httr, scales, ggthemes, ggplot2, utils, rlang, cowplot, stats Suggests: covr, httptest, knitr, rmarkdown, testthat (>= 3.0.0), rgoslin, DT, RSQLite, CompoundDb, BiocStyle, BiocFileCache, msPurity, rsvg, metabolomicsWorkbenchR, KEGGREST, plyr, magick, structToolbox, ggVennDiagram, patchwork, XML, GO.db, tidytext, tidyr, tidyselect, ComplexUpset, jsonlite, openxlsx, ggplotify License: GPL-3 Title: Metabolite Mashing with R Description: A package to merge, filter sort, organise and otherwise mash together metabolite annotation tables. Metabolite annotations can be imported from multiple sources (software) and combined using workflow steps based on S4 class templates derived from the `struct` package. Other modular workflow steps such as filtering, merging, splitting, normalisation and rest-api queries are included. biocViews: WorkflowStep, Metabolomics, KEGG Author: Gavin Rhys Lloyd [aut, cre] (ORCID: ), Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd URL: https://computational-metabolomics.github.io/MetMashR/ VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/MetMashR/issues Package: dandelionR Version: 1.2.0 Depends: R (>= 4.4.0) Imports: BiocGenerics, bluster, destiny, igraph, MASS, Matrix, methods, miloR, purrr, rlang, S4Vectors, SingleCellExperiment, spam, stats, SummarizedExperiment, uwot, RANN Suggests: BiocStyle, fields, knitr, rmarkdown, RColorBrewer, scater, scRepertoire, DelayedMatrixStats, slingshot, testthat License: MIT + file LICENSE Title: Single-cell Immune Repertoire Trajectory Analysis in R Description: dandelionR is an R package for performing single-cell immune repertoire trajectory analysis, based on the original python implementation. It provides the necessary functions to interface with scRepertoire and a custom implementation of an absorbing Markov chain for pseudotime inference, inspired by the Palantir Python package. biocViews: Software, ImmunoOncology, SingleCell Author: Jiawei Yu [aut] (ORCID: ), Nicholas Borcherding [aut] (ORCID: ), Kelvin Tuong [aut, cre] (ORCID: ) Maintainer: Kelvin Tuong URL: https://www.github.com/tuonglab/dandelionR/ VignetteBuilder: knitr BugReports: https://www.github.com/tuonglab/dandelionR/issues Package: goatea Version: 1.0.0 Depends: R (>= 4.5.0), dplyr (>= 1.1.4) Imports: goat (>= 1.0), shiny (>= 1.10.0), shinyjs (>= 2.1.0), shinyjqui (>= 0.4.1), shinydashboard (>= 0.7.2), openxlsx (>= 4.2.7.1), upsetjs (>= 1.11.1), ComplexHeatmap (>= 2.24.0), InteractiveComplexHeatmap (>= 1.12.0), EnhancedVolcano (>= 1.22.0), tidyr (>= 1.3.1), purrr (>= 1.0.2), ggplot2 (>= 3.5.1), plotly (>= 4.10.4), igraph (>= 2.1.4), visNetwork (>= 2.1.2), arrow (>= 18.1.0.1), htmltools (>= 0.5.8.1), methods (>= 4.5.0), AnnotationDbi (>= 1.69.1), DT (>= 0.33), enrichplot (>= 1.27.4), plyr (>= 1.8.9), tibble (>= 3.2.1) Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Dm.eg.db, org.Mmu.eg.db, org.Rn.eg.db, org.Ce.eg.db, org.Pt.eg.db, org.Dr.eg.db, knitr, rmarkdown, BiocStyle License: Apache License (>= 2) Title: Interactive Exploration of GSEA by the GOAT Method Description: Geneset Ordinal Association Test Enrichment Analysis (GOATEA) provides a 'Shiny' interface with interactive visualizations and utility functions for performing and exploring automated gene set enrichment analysis using the 'GOAT' package. 'GOATEA' is designed to support large-scale and user-friendly enrichment workflows across multiple gene lists and comparisons, with flexible plotting and output options. Visualizations pre-enrichment include interactive 'Volcano' and 'UpSet' (overlap) plots. Visualizations post-enrichment include interactive geneset dotplot, geneset treeplot, gene-effectsize heatmap, gene-geneset heatmap and 'STRING' database of protein-protein-interactions network graph. 'GOAT' reference: Frank Koopmans (2024) . biocViews: GeneSetEnrichment, NetworkEnrichment, Visualization, ShinyApps, GUI, Transcriptomics, Genetics, FunctionalGenomics, DifferentialExpression, Network Author: Maurits Unkel [aut, cre, fnd, cph] (ORCID: ) Maintainer: Maurits Unkel URL: https://github.com/mauritsunkel/goatea VignetteBuilder: knitr BugReports: https://github.com/mauritsunkel/goatea/issues