Package: a4 Version: 1.16.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo License: GPL-3 MD5sum: 1bd22944d9e6d7bb2f6c742dcf7e340f NeedsCompilation: no Title: Automated Affymetrix Array Analysis Umbrella Package Description: Automated Affymetrix Array Analysis Umbrella Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4_1.16.0.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 Package: a4Base Version: 1.16.0 Depends: methods, graphics, grid, Biobase, AnnotationDbi, annaffy, mpm, genefilter, limma, multtest, glmnet, a4Preproc, a4Core, gplots Suggests: Cairo, ALL Enhances: gridSVG, JavaGD License: GPL-3 MD5sum: 0be52bafda21bc84cb17c27376a0b61a NeedsCompilation: no Title: Automated Affymetrix Array Analysis Base Package Description: Automated Affymetrix Array Analysis biocViews: Microarray Author: Willem Talloen, Tobias Verbeke, Tine Casneuf, An De Bondt, Steven Osselaer and Hinrich Goehlmann, Willem Ligtenberg Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Base_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4Base_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4Base_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4Base_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4Base_1.16.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Classif Version: 1.16.0 Depends: methods, a4Core, a4Preproc, MLInterfaces, ROCR, pamr, glmnet, varSelRF Imports: a4Core Suggests: ALL License: GPL-3 MD5sum: f3edcbe5ebf21eb2f8d55cef76dceda2 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Classification Package Description: Automated Affymetrix Array Analysis Classification Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Classif_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4Classif_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4Classif_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4Classif_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4Classif_1.16.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Core Version: 1.16.0 Depends: methods, Biobase, glmnet License: GPL-3 MD5sum: 7b82d788c25366eb67a2ea62751aee80 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Core Package Description: Automated Affymetrix Array Analysis Core Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Core_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4Core_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4Core_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4Core_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4Core_1.16.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif importsMe: a4Classif Package: a4Preproc Version: 1.16.0 Depends: methods, AnnotationDbi Suggests: ALL, hgu95av2.db License: GPL-3 MD5sum: 97a150b95e571c81bafe27c31d68e1eb NeedsCompilation: no Title: Automated Affymetrix Array Analysis Preprocessing Package Description: Automated Affymetrix Array Analysis Preprocessing Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Preproc_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4Preproc_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4Preproc_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4Preproc_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4Preproc_1.16.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif Package: a4Reporting Version: 1.16.0 Depends: methods, annaffy Imports: xtable, utils License: GPL-3 MD5sum: accd7a9d7ea3d718476b4d28f621e8b3 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Reporting Package Description: Automated Affymetrix Array Analysis Reporting Package biocViews: Microarray Author: Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Reporting_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4Reporting_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4Reporting_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4Reporting_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4Reporting_1.16.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: ABarray Version: 1.36.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: dfaa0b75cc72d65dd8d614acb8f7b608 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 source.ver: src/contrib/ABarray_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ABarray_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ABarray_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ABarray_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ABarray_1.36.0.tgz vignettes: vignettes/ABarray/inst/doc/ABarrayGUI.pdf, vignettes/ABarray/inst/doc/ABarray.pdf vignetteTitles: ABarray gene expression GUI interface, ABarray gene expression hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABarray/inst/doc/ABarrayGUI.R, vignettes/ABarray/inst/doc/ABarray.R Package: ABSSeq Version: 1.5.1 Depends: R (>= 2.10), methods Imports: locfit, limma License: GPL (>= 3) MD5sum: 90d841ec8472f0da9c7d67691d540d6a 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 source.ver: src/contrib/ABSSeq_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ABSSeq_1.5.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ABSSeq_1.5.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ABSSeq_1.5.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ABSSeq_1.5.1.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 Package: aCGH Version: 1.46.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, cluster, grDevices, graphics, methods, multtest, stats, survival, splines, utils License: GPL-2 Archs: i386, x64 MD5sum: 90add5e956186bcaf5f30e10f7d6acd5 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 source.ver: src/contrib/aCGH_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/aCGH_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/aCGH_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/aCGH_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/aCGH_1.46.0.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, snapCGH suggestsMe: beadarraySNP Package: ACME Version: 2.24.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: f05fcc4f0a106d184dc62bb99afe106a 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 source.ver: src/contrib/ACME_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ACME_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ACME_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ACME_2.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ACME_2.24.0.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 Package: ADaCGH2 Version: 2.8.1 Depends: R (>= 2.15.0), parallel, ff Imports: bit, ffbase, DNAcopy, tilingArray, GLAD, waveslim, cluster, aCGH, snapCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: i386, x64 MD5sum: 1c783a658e8f4090d3929073fd51c8f0 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, BioHMM, 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 . Maintainer: Ramon Diaz-Uriarte source.ver: src/contrib/ADaCGH2_2.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ADaCGH2_2.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ADaCGH2_2.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ADaCGH2_2.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ADaCGH2_2.8.1.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 Package: adSplit Version: 1.38.0 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, KEGG.db (>= 1.8.1), methods, 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: i386, x64 MD5sum: 2885542c12221570e9d0887877f646d6 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/index.shtml source.ver: src/contrib/adSplit_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/adSplit_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/adSplit_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/adSplit_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/adSplit_1.38.0.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 Package: affxparser Version: 1.40.0 Depends: R (>= 2.6.0) Suggests: R.oo (>= 1.19.0), R.utils (>= 2.0.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: i386, x64 MD5sum: 2c815120c393fa4e665f0e58b0e6af37 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 Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman [ctb], Kasper Daniel Hansen [aut, cre], Martin Morgan [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/affxparser SystemRequirements: GNU make BugReports: https://github.com/HenrikBengtsson/affxparser/issues source.ver: src/contrib/affxparser_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affxparser_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affxparser_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affxparser_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affxparser_1.40.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, rMAT, Starr importsMe: affyILM, AffyTiling, cn.farms, GeneRegionScan, ITALICS, oligo, rMAT suggestsMe: TIN Package: affy Version: 1.46.1 Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5) Imports: affyio (>= 1.13.3), BiocInstaller, graphics, grDevices, methods, preprocessCore, stats, utils, zlibbioc LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 2615da0d39535d3353f7f900be839a45 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: Rafael A. Irizarry source.ver: src/contrib/affy_1.46.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/affy_1.46.1.zip win64.binary.ver: bin/windows64/contrib/3.2/affy_1.46.1.zip mac.binary.ver: bin/macosx/contrib/3.2/affy_1.46.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affy_1.46.1.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, affycoretools, AffyExpress, affyPara, affypdnn, affyPLM, affyQCReport, AffyRNADegradation, altcdfenvs, arrayMvout, ArrayTools, bgx, Cormotif, DrugVsDisease, dualKS, ExiMiR, farms, frmaTools, gcrma, LMGene, logitT, maskBAD, MLP, panp, plw, prebs, qpcrNorm, ReadqPCR, RefPlus, rHVDM, Risa, RPA, SCAN.UPC, simpleaffy, sscore, Starr, webbioc importsMe: affyILM, affylmGUI, affyQCReport, AffyTiling, ArrayExpress, arrayQualityMetrics, ArrayTools, CAFE, ChIPXpress, Cormotif, farms, ffpe, frma, gcrma, GEOsubmission, Harshlight, HTqPCR, lumi, LVSmiRNA, makecdfenv, MSnbase, PECA, plier, plw, puma, pvac, Rnits, simpleaffy, STATegRa, tilingArray, TurboNorm, vsn, waveTiling suggestsMe: AnnotationForge, beadarray, beadarraySNP, BiocCaseStudies, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, ExpressionView, factDesign, gCMAPWeb, GeneRegionScan, limma, made4, MLSeq, oneChannelGUI, paxtoolsr, piano, PREDA, qcmetrics, siggenes Package: affycomp Version: 1.44.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: 61865b10d99aec6500c65f1f19b6f17f 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: Rafael A. Irizarry source.ver: src/contrib/affycomp_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affycomp_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affycomp_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affycomp_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affycomp_1.44.0.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 Package: AffyCompatible Version: 1.28.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: 453dfa717a2d6f847bd950c61ebad9c5 NeedsCompilation: no Title: Affymetrix GeneChip software compatibility Description: This package provides an interface to Affymetrix chip annotation and sample attribute files. The package allows an easy way for users to download and manage local data bases of Affynmetrix NetAffx annotation files. The package also provides access to GeneChip Operating System (GCOS) and GeneChip Command Console (AGCC)-compatible sample annotation files. biocViews: Infrastructure, Microarray, OneChannel Author: Martin Morgan, Robert Gentleman Maintainer: Martin Morgan source.ver: src/contrib/AffyCompatible_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AffyCompatible_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AffyCompatible_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AffyCompatible_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AffyCompatible_1.28.0.tgz vignettes: vignettes/AffyCompatible/inst/doc/MAGEAndARR.pdf, vignettes/AffyCompatible/inst/doc/NetAffxResource.pdf vignetteTitles: Retrieving MAGE and ARR sample attributes, Annotation retrieval with NetAffxResource hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyCompatible/inst/doc/MAGEAndARR.R, vignettes/AffyCompatible/inst/doc/NetAffxResource.R importsMe: IdMappingRetrieval Package: affyContam Version: 1.26.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata License: Artistic-2.0 MD5sum: b97622e1447d5cc160b8071128840e13 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 source.ver: src/contrib/affyContam_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyContam_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyContam_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyContam_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyContam_1.26.0.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 Package: affycoretools Version: 1.40.5 Depends: affy, Biobase, Imports: limma, GOstats, gcrma, splines, xtable, AnnotationDbi, lattice, gplots, oligoClasses, ReportingTools, hwriter Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl License: Artistic-2.0 MD5sum: bf7dee486f9815e552ad5558e3ca969e 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 source.ver: src/contrib/affycoretools_1.40.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/affycoretools_1.40.5.zip win64.binary.ver: bin/windows64/contrib/3.2/affycoretools_1.40.5.zip mac.binary.ver: bin/macosx/contrib/3.2/affycoretools_1.40.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affycoretools_1.40.5.tgz vignettes: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.pdf vignetteTitles: affycoretools,, refactored hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycoretools/inst/doc/affycoretools_biomaRt.R, vignettes/affycoretools/inst/doc/affycoretools.R, vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R Package: AffyExpress Version: 1.34.0 Depends: R (>= 2.10), affy (>= 1.23.4), limma Suggests: simpleaffy, R2HTML, affyPLM, hgu95av2cdf, hgu95av2, test3cdf, genefilter, estrogen, annaffy, gcrma License: LGPL MD5sum: 732728a78fc67442ef744f68d5d9067a NeedsCompilation: no Title: Affymetrix Quality Assessment and Analysis Tool Description: The purpose of this package is to provide a comprehensive and easy-to-use tool for quality assessment and to identify differentially expressed genes in the Affymetrix gene expression data. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu , Xuejun Arthur Li Maintainer: Xuejun Arthur Li source.ver: src/contrib/AffyExpress_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AffyExpress_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AffyExpress_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AffyExpress_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AffyExpress_1.34.0.tgz vignettes: vignettes/AffyExpress/inst/doc/AffyExpress.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyExpress/inst/doc/AffyExpress.R Package: affyILM Version: 1.20.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, methods, Biobase Suggests: AffymetrixDataTestFiles License: GPL version 3 MD5sum: bcd46be3921fd28e71419c9834d10e81 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 source.ver: src/contrib/affyILM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyILM_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyILM_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyILM_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyILM_1.20.0.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 Package: affyio Version: 1.36.0 Depends: R (>= 2.6.0) Imports: zlibbioc License: LGPL (>= 2) Archs: i386, x64 MD5sum: 1cb97b04c9703f010ffe1bddce87a0d2 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: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad source.ver: src/contrib/affyio_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyio_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyio_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyio_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyio_1.36.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPara, makecdfenv, SCAN.UPC, sscore importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods Package: affylmGUI Version: 1.42.0 Imports: limma, tcltk, affy, BiocInstaller, affyio, tkrplot, affyPLM, R2HTML, xtable, gcrma, AnnotationDbi License: LGPL MD5sum: a0b834838fde039d7b8e718c395b7d96 NeedsCompilation: no Title: GUI for affy analysis using limma package Description: A Graphical User Interface for affy analysis using the limma Microarray package biocViews: Microarray, OneChannel, DataImport, QualityControl, Preprocessing, DifferentialExpression, MultipleComparison, GUI Author: James Wettenhall and Ken Simpson Division of Genetics and Bioinformatics, WEHI. Maintainer: Keith Satterley URL: http://bioinf.wehi.edu.au/affylmGUI/ source.ver: src/contrib/affylmGUI_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affylmGUI_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affylmGUI_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affylmGUI_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affylmGUI_1.42.0.tgz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R, vignettes/affylmGUI/inst/doc/extract.R dependsOnMe: oneChannelGUI Package: affyPara Version: 1.28.0 Depends: R (>= 2.5.0), methods, affy (>= 1.20.0), snow (>= 0.2-3), vsn (>= 3.6.0), aplpack (>= 1.1.1), affyio Suggests: affydata Enhances: affy License: GPL-3 MD5sum: 7aea59bcadfb07f992b17e2370ffd713 NeedsCompilation: no Title: Parallelized preprocessing methods for Affymetrix Oligonucleotide Arrays Description: The package contains parallelized functions for exploratory oligonucleotide array analysis. The package is designed for large numbers of microarray data. biocViews: Microarray, Preprocessing Author: Markus Schmidberger , Esmeralda Vicedo , Ulrich Mansmann Maintainer: Markus Schmidberger URL: http://www.ibe.med.uni-muenchen.de source.ver: src/contrib/affyPara_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyPara_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyPara_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyPara_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyPara_1.28.0.tgz vignettes: vignettes/affyPara/inst/doc/affyPara.pdf, vignettes/affyPara/inst/doc/vsnStudy.pdf vignetteTitles: Parallelized affy functions for preprocessing, Simulation Study for VSN Add-On Normalization and Subsample Size hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPara/inst/doc/affyPara.R, vignettes/affyPara/inst/doc/vsnStudy.R Package: affypdnn Version: 1.42.0 Depends: R (>= 2.13.0), affy (>= 1.5) Suggests: affydata, hgu95av2probe License: LGPL MD5sum: 853eee82541acbad0f7c05b2c145fc69 NeedsCompilation: no Title: Probe Dependent Nearest Neighbours (PDNN) for the affy package Description: The package contains functions to perform the PDNN method described by Li Zhang et al. biocViews: OneChannel, Microarray, Preprocessing Author: H. Bjorn Nielsen and Laurent Gautier (Many thanks to Li Zhang early communications about the existence of the PDNN program and related publications). Maintainer: Laurent Gautier source.ver: src/contrib/affypdnn_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affypdnn_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affypdnn_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affypdnn_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affypdnn_1.42.0.tgz vignettes: vignettes/affypdnn/inst/doc/affypdnn.pdf vignetteTitles: affypdnn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affypdnn/inst/doc/affypdnn.R Package: affyPLM Version: 1.44.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: BiocGenerics, zlibbioc, graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS License: GPL (>= 2) Archs: i386, x64 MD5sum: 7652d0e58b2aafd1f6a60c6e183549c5 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: http://bmbolstad.com source.ver: src/contrib/affyPLM_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyPLM_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyPLM_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyPLM_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyPLM_1.44.0.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: RefPlus importsMe: affylmGUI, affyQCReport, arrayQualityMetrics suggestsMe: AffyExpress, arrayMvout, ArrayTools, BiocCaseStudies, BiocGenerics, ELBOW, frmaTools, metahdep, oneChannelGUI, piano Package: affyQCReport Version: 1.46.0 Depends: Biobase (>= 1.13.16), affy, lattice Imports: affy, affyPLM, Biobase, genefilter, graphics, grDevices, lattice, RColorBrewer, simpleaffy, stats, utils, xtable Suggests: tkWidgets (>= 1.5.23), affydata (>= 1.4.1) License: LGPL (>= 2) MD5sum: 8f18981081c96ec94dfffd66a3e7cf5a NeedsCompilation: no Title: QC Report Generation for affyBatch objects Description: This package creates a QC report for an AffyBatch object. The report is intended to allow the user to quickly assess the quality of a set of arrays in an AffyBatch object. biocViews: Microarray,OneChannel,QualityControl Author: Craig Parman , Conrad Halling , Robert Gentleman Maintainer: Craig Parman source.ver: src/contrib/affyQCReport_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyQCReport_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyQCReport_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyQCReport_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyQCReport_1.46.0.tgz vignettes: vignettes/affyQCReport/inst/doc/affyQCReport.pdf vignetteTitles: affyQCReport: Methods for Generating Affymetrix QC Reports hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyQCReport/inst/doc/affyQCReport.R suggestsMe: BiocCaseStudies Package: AffyRNADegradation Version: 1.14.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: 3828e6f2320def473285438953297841 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 source.ver: src/contrib/AffyRNADegradation_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AffyRNADegradation_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AffyRNADegradation_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AffyRNADegradation_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AffyRNADegradation_1.14.0.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 Package: AffyTiling Version: 1.26.0 Depends: R (>= 2.6) Imports: affxparser, affy (>= 1.16), stats, utils, preprocessCore License: GPL (>= 2) Archs: i386, x64 MD5sum: 8af7a6c0b8740c5c58dfbcf3866745af NeedsCompilation: yes Title: Easy extraction of individual probes in Affymetrix tiling arrays Description: This package provides easy, fast functions for the extraction and annotation of individual probes from Affymetrix tiling arrays. biocViews: Microarray, Preprocessing Author: Charles G. Danko Maintainer: Charles G. Danko source.ver: src/contrib/AffyTiling_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AffyTiling_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AffyTiling_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AffyTiling_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AffyTiling_1.26.0.tgz vignettes: vignettes/AffyTiling/inst/doc/AffyTiling.pdf vignetteTitles: AffyTiling hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyTiling/inst/doc/AffyTiling.R Package: AGDEX Version: 1.16.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: 460d23c19e0778f3c60be192236c6642 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 source.ver: src/contrib/AGDEX_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AGDEX_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AGDEX_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AGDEX_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AGDEX_1.16.0.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 Package: agilp Version: 3.0.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: 511d2b133cdf43a1f9a419a42715a5d7 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 source.ver: src/contrib/agilp_3.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/agilp_3.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/agilp_3.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/agilp_3.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/agilp_3.0.0.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 Package: AgiMicroRna Version: 2.18.0 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: 0a693b585703bfe20519c8f08804d35b 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 source.ver: src/contrib/AgiMicroRna_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AgiMicroRna_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AgiMicroRna_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AgiMicroRna_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AgiMicroRna_2.18.0.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 Package: AIMS Version: 1.0.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 2c5e61a07806f57a84535a8eb3f82e01 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: Classification, RNASeq, Microarray, Software, GeneExpression Author: Eric R. Paquet, Michael T. Hallett Maintainer: Eric R Paquet URL: http://www.bci.mcgill.ca/AIMS source.ver: src/contrib/AIMS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AIMS_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AIMS_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AIMS_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AIMS_1.0.0.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 Package: ALDEx2 Version: 1.0.0 Depends: methods, GenomicRanges Suggests: parallel, BiocParallel License: file LICENSE MD5sum: fbac7e111bec4f345c5491d845f4aa77 NeedsCompilation: no Title: Analysis of differential abundance taking sample variation into account Description: A differential abundance analysis for the comparison of two or more conditions. For example, single-organism and meta-RNA-seq high-throughput sequencing assays, or of selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, that has been optimized for three or more experimental replicates. Infers sampling variation and calculates the expected false discovery rate given the biological and sampling variation using the Wilcox rank test or Welches t-test (aldex.ttest) or the glm and Kruskal Wallis tests (aldex.glm). Reports both P and fdr values calculated by the Benjamini Hochberg correction. biocViews: DifferentialExpression, RNASeq, DNASeq, ChIPSeq, GeneExpression, Bayesian, Sequencing, Software Author: Greg Gloor, Ruth Grace Wong, Andrew Fernandes, Arianne Albert, Matt Links Maintainer: Greg Gloor source.ver: src/contrib/ALDEx2_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ALDEx2_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ALDEx2_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ALDEx2_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ALDEx2_1.0.0.tgz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.pdf vignetteTitles: An R Package for determining differential abundance in high throughput sequencing experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R Package: AllelicImbalance Version: 1.6.2 Depends: R (>= 3.1.0), grid, GenomicRanges, GenomicAlignments, VariantAnnotation, Gviz Imports: methods, BiocGenerics, AnnotationDbi, Biostrings, S4Vectors, IRanges, Rsamtools, GenomicFeatures, Gviz, lattice, seqinr, GenomeInfoDb, VariantAnnotation Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20120608, knitr License: GPL-3 MD5sum: 0ba194276a50d1458c09ad1cf54311cf 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 source.ver: src/contrib/AllelicImbalance_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/AllelicImbalance_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.2/AllelicImbalance_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.2/AllelicImbalance_1.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AllelicImbalance_1.6.2.tgz vignettes: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf vignetteTitles: AllelicImbalance-vignette.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance.R Package: alsace Version: 1.4.0 Depends: R (>= 2.10), ALS, ptw (>= 1.0.6) Suggests: lattice License: GPL (>= 2) MD5sum: d2686f738e99e00d63d2a0530b69b788 NeedsCompilation: no Title: ALS for the Automatic Chemical Exploration of mixtures Description: Alternating Least Squares (or Multivariate Curve Resolution) for analytical chemical data, in particular hyphenated data where the first direction is a retention time axis, and the second a spectral axis. Package builds on the basic als function from the ALS package and adds functionality for high-throughput analysis, including definition of time windows, clustering of profiles, retention time correction, etcetera. Author: Ron Wehrens Maintainer: Ron Wehrens source.ver: src/contrib/alsace_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/alsace_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/alsace_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/alsace_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/alsace_1.4.0.tgz vignettes: vignettes/alsace/inst/doc/alsace.pdf vignetteTitles: alsace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/alsace/inst/doc/alsace.R Package: altcdfenvs Version: 2.30.0 Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings, hypergraph Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db, hgu133acdf, Rgraphviz, RColorBrewer License: GPL (>= 2) MD5sum: e34877b73717a414cf1c7a9b34203090 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 source.ver: src/contrib/altcdfenvs_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/altcdfenvs_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/altcdfenvs_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/altcdfenvs_2.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/altcdfenvs_2.30.0.tgz vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf, vignettes/altcdfenvs/inst/doc/modify.pdf, vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf vignetteTitles: altcdfenvs, affy primer, affy primer 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 importsMe: Harshlight Package: ampliQueso Version: 1.6.0 Depends: R (>= 2.15.0), rnaSeqMap (>= 2.17.1), knitr, rgl, ggplot2, gplots, parallel, doParallel, foreach, VariantAnnotation,genefilter,statmod,xtable Imports: edgeR, DESeq, samr License: GPL-2 MD5sum: 7866a1bbaf573369cd4d53d419d28431 NeedsCompilation: no Title: Analysis of amplicon enrichment panels Description: The package provides tools and reports for the analysis of amplicon sequencing panels, such as AmpliSeq biocViews: ReportWriting, Transcription, GeneExpression, DifferentialExpression, Sequencing, RNASeq, Visualization Author: Alicja Szabelska ; Marek Wiewiorka ; Michal Okoniewski Maintainer: Michal Okoniewski source.ver: src/contrib/ampliQueso_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ampliQueso_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ampliQueso_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ampliQueso_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ampliQueso_1.6.0.tgz vignettes: vignettes/ampliQueso/inst/doc/ampliQueso.pdf vignetteTitles: ampliQueso primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ampliQueso/inst/doc/ampliQueso.R Package: AnalysisPageServer Version: 1.0.0 Imports: log4r, tools, rjson, Biobase, graph Suggests: RUnit, XML, SVGAnnotation, knitr Enhances: Rook (>= 1.1), fork, FastRWeb, ggplot2 License: Artistic-2.0 Archs: i386, x64 MD5sum: f989ac46afcff99689f2516b32488443 NeedsCompilation: yes Title: A framework for sharing interactive data and plots from R through the web. Description: AnalysisPageServer is a modular system that enables sharing of customizable R analyses via the web. biocViews: GUI, Visualization, DataRepresentation Author: Brad Friedman , Adrian Nowicki, Hunter Whitney , Matthew Brauer Maintainer: Brad Friedman VignetteBuilder: knitr source.ver: src/contrib/AnalysisPageServer_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AnalysisPageServer_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AnalysisPageServer_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AnalysisPageServer_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AnalysisPageServer_1.0.0.tgz vignettes: vignettes/AnalysisPageServer/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/AnalysisPageServer/inst/doc/AnalysisPageServer.R, vignettes/AnalysisPageServer/inst/doc/ApacheDeployment.R, vignettes/AnalysisPageServer/inst/doc/embedding.R, vignettes/AnalysisPageServer/inst/doc/ExampleServers.R, vignettes/AnalysisPageServer/inst/doc/FastRWebDeployment.R, vignettes/AnalysisPageServer/inst/doc/InteractiveApps.R, vignettes/AnalysisPageServer/inst/doc/Interactivity.R, vignettes/AnalysisPageServer/inst/doc/StaticContent.R htmlDocs: vignettes/AnalysisPageServer/inst/doc/AnalysisPageServer.html, vignettes/AnalysisPageServer/inst/doc/ApacheDeployment.html, vignettes/AnalysisPageServer/inst/doc/embedding.html, vignettes/AnalysisPageServer/inst/doc/ExampleServers.html, vignettes/AnalysisPageServer/inst/doc/FastRWebDeployment.html, vignettes/AnalysisPageServer/inst/doc/InteractiveApps.html, vignettes/AnalysisPageServer/inst/doc/Interactivity.html, vignettes/AnalysisPageServer/inst/doc/Licenses.html, vignettes/AnalysisPageServer/inst/doc/StaticContent.html htmlTitles: "0. AnalysisPageServer", "6. Apache Deployment", "2. Embedding APS datasets in other documents", "4. Non-interactive servers and Rook Deployment", "7. FastRWeb Deployment", "5. Interactive Apps AnalysisPageServer", "3. AnalysisPageServer Interactivity", "8. Licenses", "1. Making Static Content Interactive with AnalysisPageServer" Package: annaffy Version: 1.40.0 Depends: R (>= 2.5.0), methods, Biobase, GO.db, KEGG.db Imports: AnnotationDbi (>= 0.1.15) Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: a669be84320f6aef11aca8b2fc5a26fb 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 source.ver: src/contrib/annaffy_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/annaffy_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/annaffy_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/annaffy_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/annaffy_1.40.0.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: a4Base, a4Reporting, PGSEA, webbioc suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies Package: annmap Version: 1.10.0 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: c4797b502588a16efb2a920338d562ba 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. biocViews: Annotation, Microarray, OneChannel, ReportWriting, Transcription, Visualization Author: Tim Yates Maintainer: Chris Wirth URL: http://annmap.cruk.manchester.ac.uk source.ver: src/contrib/annmap_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/annmap_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/annmap_1.10.0.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: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annmap/inst/doc/annmap.R, vignettes/annmap/inst/doc/cookbook.R, vignettes/annmap/inst/doc/INSTALL.R Package: annotate Version: 1.46.1 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8) Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db, org.Mm.eg.db, hom.Hs.inp.db, humanCHRLOC, Rgraphviz, RUnit, License: Artistic-2.0 MD5sum: 9f87de70e77c7e35d8fd7bf5d96ae194 NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/annotate_1.46.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/annotate_1.46.1.zip win64.binary.ver: bin/windows64/contrib/3.2/annotate_1.46.1.zip mac.binary.ver: bin/macosx/contrib/3.2/annotate_1.46.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/annotate_1.46.1.tgz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/chromLoc.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useDataPkgs.pdf, vignettes/annotate/inst/doc/useHomology.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf vignetteTitles: Annotation Overview, HowTo: use chromosomal information, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Data Packages, Using the homology package, Using Affymetrix Probe Level Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotate/inst/doc/annotate.R, vignettes/annotate/inst/doc/chromLoc.R, vignettes/annotate/inst/doc/GOusage.R, vignettes/annotate/inst/doc/prettyOutput.R, vignettes/annotate/inst/doc/query.R, vignettes/annotate/inst/doc/useDataPkgs.R, vignettes/annotate/inst/doc/useHomology.R, vignettes/annotate/inst/doc/useProbeInfo.R dependsOnMe: ChromHeatMap, GeneAnswers, geneplotter, GOSim, GSEABase, idiogram, macat, MineICA, MLInterfaces, PCpheno, phenoTest, PREDA, RpsiXML, ScISI, SemDist importsMe: CAFE, Category, categoryCompare, codelink, DOQTL, DrugVsDisease, facopy, gCMAP, gCMAPWeb, GeneAnswers, genefilter, GlobalAncova, globaltest, GOstats, lumi, methyAnalysis, methylumi, mvGST, phenoTest, qpgraph, ScISI, splicegear, systemPipeR, tigre suggestsMe: BiocCaseStudies, BiocGenerics, biomaRt, cogena, GenomicRanges, GlobalAncova, globaltest, GOstats, GSAR, GSEAlm, maigesPack, metagenomeSeq, MLP, oneChannelGUI, RnBeads, siggenes Package: AnnotationDbi Version: 1.30.1 Depends: R (>= 2.7.0), methods, utils, stats4, BiocGenerics (>= 0.13.8), Biobase (>= 1.17.0), GenomeInfoDb(>= 0.99.17) Imports: methods, utils, DBI, RSQLite, stats4, BiocGenerics, Biobase, S4Vectors Suggests: DBI (>= 0.2-4), RSQLite (>= 0.6-4), hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, KEGG.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, hom.Hs.inp.db, org.Hs.eg.db, reactome.db, AnnotationForge, graph, org.TguttataTestingSubset.eg.db, BiocStyle, knitr License: Artistic-2.0 MD5sum: 88481759bd3a6e45f04eca326738983b NeedsCompilation: no Title: Annotation Database Interface Description: Provides user interface and database connection code for annotation data packages using SQLite data storage. biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation Author: Herve Pages, Marc Carlson, Seth Falcon, Nianhua Li Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik source.ver: src/contrib/AnnotationDbi_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/AnnotationDbi_1.30.1.zip win64.binary.ver: bin/windows64/contrib/3.2/AnnotationDbi_1.30.1.zip mac.binary.ver: bin/macosx/contrib/3.2/AnnotationDbi_1.30.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AnnotationDbi_1.30.1.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: How to use bimaps from the ".db" annotation packages, AnnotationDbi: 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: a4Base, a4Preproc, annotate, AnnotationForge, AnnotationFuncs, attract, Category, chimera, ChromHeatMap, customProDB, eisa, ExpressionView, GenomicFeatures, GOFunction, goProfiles, miRNAtap, MLP, OrganismDbi, PAnnBuilder, pathRender, PGSEA, proBAMr, RpsiXML, safe, SemDist, topGO importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, AnnotationHub, attract, beadarray, biomaRt, BioNet, biovizBase, bumphunter, CancerMutationAnalysis, Category, categoryCompare, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, compEpiTools, csaw, customProDB, derfinder, domainsignatures, DOSE, EnrichmentBrowser, ensembldb, ExpressionView, FunciSNP, gage, gCMAP, gCMAPWeb, genefilter, geneplotter, GGBase, GGtools, GlobalAncova, globaltest, GOFunction, GOSemSim, goseq, GOSim, GOstats, goTools, gQTLstats, graphite, GSEABase, Gviz, HTSanalyzeR, InPAS, interactiveDisplay, IVAS, limmaGUI, lumi, mAPKL, mdgsa, MeSHDbi, methyAnalysis, methylumi, MineICA, MiRaGE, mvGST, NanoStringQCPro, OrganismDbi, PADOG, PAnnBuilder, pathview, pcaGoPromoter, PCpheno, phenoTest, pwOmics, qpgraph, ReactomePA, REDseq, rgsepd, rTRM, ScISI, SGSeq, SLGI, SVM2CRM, tigre, topGO, UniProt.ws, VariantAnnotation, VariantFiltering suggestsMe: BiocCaseStudies, BiocGenerics, FGNet, geecc, GeneAnswers, GeneRegionScan, GenomicRanges, GenoView, limma, MmPalateMiRNA, neaGUI, oligo, oneChannelGUI, piano, qcmetrics, R3CPET, sigPathway Package: AnnotationForge Version: 1.10.1 Depends: R (>= 2.7.0), methods, utils, BiocGenerics (>= 0.1.13), Biobase (>= 1.17.0), AnnotationDbi (>= 1.19.15), org.Hs.eg.db Imports: methods, utils, DBI, RSQLite, BiocGenerics, S4Vectors, Biobase Suggests: DBI (>= 0.2-4), RSQLite (>= 0.6-4), XML, RCurl, hgu95av2.db, human.db0, affy, Homo.sapiens, hom.Hs.inp.db, GO.db, BiocStyle, knitr License: Artistic-2.0 MD5sum: be020a893bbb932ecf062147a4f3cac2 NeedsCompilation: no Title: Code for Building Annotation Database 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, Herve Pages Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/AnnotationForge_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/AnnotationForge_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.2/AnnotationForge_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.2/AnnotationForge_1.10.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AnnotationForge_1.10.1.tgz vignettes: vignettes/AnnotationForge/inst/doc/makeProbePackage.pdf, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf vignetteTitles: Creating probe packages, AnnotationForge: Creating select Interfaces for custom Annotation resources, SQLForge: An easy way to create a new annotation package with a standard database schema. 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 htmlDocs: vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html htmlTitles: "Making New Organism Packages" importsMe: GOstats suggestsMe: AnnotationDbi, AnnotationHub Package: AnnotationFuncs Version: 1.18.0 Depends: R (>= 2.7.0), AnnotationDbi Suggests: org.Bt.eg.db, GO.db, org.Hs.eg.db, hom.Hs.inp.db License: GPL-2 MD5sum: b77da58d0c668740a3362dd357e1ec7e NeedsCompilation: no Title: Annotation translation functions Description: Functions for handling translating between different identifieres using the Biocore Data Team data-packages (e.g. org.Bt.eg.db). biocViews: AnnotationData, Software Author: Stefan McKinnon Edwards Maintainer: Stefan McKinnon Edwards URL: http://www.iysik.com/index.php?page=annotation-functions source.ver: src/contrib/AnnotationFuncs_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AnnotationFuncs_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AnnotationFuncs_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AnnotationFuncs_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AnnotationFuncs_1.18.0.tgz vignettes: vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.pdf vignetteTitles: Annotation mapping functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: AnnotationHub Version: 2.0.4 Imports: utils, methods, RSQLite, BiocInstaller, BiocGenerics, S4Vectors, interactiveDisplayBase, httr, AnnotationDbi Suggests: GenomicRanges, VariantAnnotation, Rsamtools, rtracklayer, GenomeInfoDb, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit License: Artistic-2.0 MD5sum: 02680fac1c2cdc519fd1659181fecc0c NeedsCompilation: no 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. Author: Martin Morgan [cre], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/AnnotationHub_2.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/AnnotationHub_2.0.4.zip win64.binary.ver: bin/windows64/contrib/3.2/AnnotationHub_2.0.4.zip mac.binary.ver: bin/macosx/contrib/3.2/AnnotationHub_2.0.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AnnotationHub_2.0.4.tgz vignettes: vignettes/AnnotationHub/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub.R, vignettes/AnnotationHub/inst/doc/AnnotationHubRecipes.R htmlDocs: vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/AnnotationHubRecipes.html htmlTitles: "AnnotationHub: Access the AnnotationHub Web Service", "AnnotationHub: How to write recipes for new resources for the AnnotationHub" dependsOnMe: RefNet importsMe: pwOmics suggestsMe: GenomicRanges, Pbase Package: annotationTools Version: 1.42.0 Imports: Biobase, stats License: GPL MD5sum: 2d8ec251ffe92760201535297680dcbf 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 source.ver: src/contrib/annotationTools_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/annotationTools_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/annotationTools_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/annotationTools_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/annotationTools_1.42.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R importsMe: DOQTL Package: anota Version: 1.16.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: 8e073b93def633e675b94fd3abd9ecfb 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 source.ver: src/contrib/anota_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/anota_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/anota_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/anota_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/anota_1.16.0.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 Package: antiProfiles Version: 1.8.0 Depends: R (>= 3.0), matrixStats (>= 0.5), methods (>= 2.14), Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: e144ce241a3addccee44e18fcefd5448 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 source.ver: src/contrib/antiProfiles_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/antiProfiles_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/antiProfiles_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/antiProfiles_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/antiProfiles_1.8.0.tgz vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf vignetteTitles: Introduction to antiProfiles hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R Package: apComplex Version: 2.34.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: dd74d1b27079456791b4d4b8eaf9dbfe 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: NetworkInference, MassSpectrometry, GraphAndNetwork Author: Denise Scholtens Maintainer: Denise Scholtens source.ver: src/contrib/apComplex_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/apComplex_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/apComplex_2.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/apComplex_2.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/apComplex_2.34.0.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 dependsOnMe: ScISI suggestsMe: BiocCaseStudies Package: aroma.light Version: 2.4.0 Depends: R (>= 2.14.0) Imports: R.methodsS3 (>= 1.6.1), R.oo (>= 1.18.0), R.utils (>= 1.34.0), matrixStats (>= 0.14.0) Suggests: princurve (>= 1.1-12) License: GPL (>= 2) MD5sum: 06817d11841fb020a27bb500a86f5b11 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] Maintainer: Henrik Bengtsson URL: http://www.aroma-project.org/, https://github.com/HenrikBengtsson/aroma.light/ BugReports: https://github.com/HenrikBengtsson/aroma.light/issues source.ver: src/contrib/aroma.light_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/aroma.light_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/aroma.light_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/aroma.light_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/aroma.light_2.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq suggestsMe: TIN Package: ArrayExpress Version: 1.28.1 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, affy, limma License: Artistic-2.0 MD5sum: eb9f55db507e506b0747311619d14149 NeedsCompilation: no Title: Access the ArrayExpress Microarray Database at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet Description: Access the ArrayExpress Repository at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet biocViews: Microarray, DataImport, OneChannel, TwoChannel Author: Audrey Kauffmann, Ibrahim Emam Maintainer: Ugis Sarkans source.ver: src/contrib/ArrayExpress_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ArrayExpress_1.28.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ArrayExpress_1.28.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ArrayExpress_1.28.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ArrayExpress_1.28.1.tgz vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets into R object hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R dependsOnMe: DrugVsDisease suggestsMe: gCMAPWeb Package: ArrayExpressHTS Version: 1.18.0 Depends: sampling, Rsamtools (>= 1.19.36), snow Imports: Biobase, BiocGenerics, Biostrings, DESeq, GenomicRanges, Hmisc, IRanges, R2HTML, RColorBrewer, Rsamtools, ShortRead, XML, biomaRt, edgeR, grDevices, graphics, methods, rJava, stats, svMisc, utils, sendmailR, bitops LinkingTo: Rsamtools License: Artistic License 2.0 MD5sum: cbea0a54f2a41c0cf4a373383fce67af NeedsCompilation: yes Title: ArrayExpress High Throughput Sequencing Processing Pipeline Description: RNA-Seq processing pipeline for public ArrayExpress experiments or local datasets biocViews: RNASeq, Sequencing Author: Angela Goncalves, Andrew Tikhonov Maintainer: Angela Goncalves , Andrew Tikhonov source.ver: src/contrib/ArrayExpressHTS_1.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/ArrayExpressHTS_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ArrayExpressHTS_1.18.0.tgz vignettes: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.pdf vignetteTitles: ArrayExpressHTS: RNA-Seq Pipeline for transcription profiling experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.R Package: arrayMvout Version: 1.26.0 Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy, lumi Imports: simpleaffy, mdqc, affyContam, Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata, hgu133atagcdf License: Artistic-2.0 MD5sum: 02872e8c025edf7b24eb07f056eafd5e 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 source.ver: src/contrib/arrayMvout_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/arrayMvout_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/arrayMvout_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/arrayMvout_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/arrayMvout_1.26.0.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 Package: arrayQuality Version: 1.46.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: c03391d2b58573eda51067c5fd2f2149 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/ source.ver: src/contrib/arrayQuality_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/arrayQuality_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/arrayQuality_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/arrayQuality_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/arrayQuality_1.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: arrayQualityMetrics Version: 3.24.0 Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, Cairo (>= 1.4-6), genefilter, graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter, lattice, latticeExtra, limma, methods, RColorBrewer, setRNG, stats, SVGAnnotation (>= 0.9-0), utils, vsn (>= 3.23.3), XML Suggests: ALLMLL, CCl4, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 9669922d619be01ef99171b564a8a7b1 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: Audrey Kauffmann VignetteBuilder: knitr source.ver: src/contrib/arrayQualityMetrics_3.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/arrayQualityMetrics_3.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/arrayQualityMetrics_3.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/arrayQualityMetrics_3.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/arrayQualityMetrics_3.24.0.tgz vignettes: vignettes/arrayQualityMetrics/inst/doc/aqm.pdf, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.pdf vignetteTitles: Advanced topics: Customizing arrayQualityMetrics reports and programmatic processing of the output, Introduction: microarray quality assessment with arrayQualityMetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayQualityMetrics/inst/doc/aqm.R, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.R Package: ArrayTools Version: 1.28.0 Depends: R (>= 2.7.0), affy (>= 1.23.4), Biobase (>= 2.5.5), methods Imports: affy, Biobase, graphics, grDevices, limma, methods, stats, utils, xtable Suggests: simpleaffy, R2HTML, affydata, affyPLM, genefilter, annaffy, gcrma, hugene10sttranscriptcluster.db License: LGPL (>= 2.0) MD5sum: 6b797902482840a934fa3cd19fa0454f NeedsCompilation: no Title: geneChip Analysis Package Description: This package is designed to provide solutions for quality assessment and to detect differentially expressed genes for the Affymetrix GeneChips, including both 3' -arrays and gene 1.0-ST arrays. The package generates comprehensive analysis reports in HTML format. Hyperlinks on the report page will lead to a series of QC plots, processed data, and differentially expressed gene lists. Differentially expressed genes are reported in tabular format with annotations hyperlinked to online biological databases. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, StatisticalMethod, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu, Arthur Li Maintainer: Arthur Li source.ver: src/contrib/ArrayTools_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ArrayTools_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ArrayTools_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ArrayTools_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ArrayTools_1.28.0.tgz vignettes: vignettes/ArrayTools/inst/doc/ArrayTools.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayTools/inst/doc/ArrayTools.R Package: ArrayTV Version: 1.6.0 Depends: R (>= 2.14) Imports: foreach, DNAcopy, methods, oligoClasses (>= 1.21.3) Suggests: RColorBrewer, crlmm, ff, BSgenome.Hsapiens.UCSC.hg18,BSgenome.Hsapiens.UCSC.hg19, lattice, latticeExtra, RUnit, BiocGenerics Enhances: doMC, doSNOW, doParallel License: GPL (>= 2) MD5sum: 81161733c7cf823833941ad3545ec2ee NeedsCompilation: no Title: Implementation of wave correction for arrays Description: Wave correction for genotyping and copy number arrays biocViews: CopyNumberVariation Author: Eitan Halper-Stromberg Maintainer: Eitan Halper-Stromberg source.ver: src/contrib/ArrayTV_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ArrayTV_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ArrayTV_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ArrayTV_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ArrayTV_1.6.0.tgz vignettes: vignettes/ArrayTV/inst/doc/ArrayTV.pdf vignetteTitles: ArrayTV Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayTV/inst/doc/ArrayTV.R suggestsMe: VanillaICE Package: ARRmNormalization Version: 1.8.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: a2ad20a3aa9c4eaf292f0fd4006354aa 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 source.ver: src/contrib/ARRmNormalization_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ARRmNormalization_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ARRmNormalization_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ARRmNormalization_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ARRmNormalization_1.8.0.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 Package: ASEB Version: 1.12.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: de91fdea306a4306fa4fb53a5f23802b 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 source.ver: src/contrib/ASEB_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ASEB_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ASEB_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ASEB_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ASEB_1.12.0.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 Package: ASGSCA Version: 1.2.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: ce6f8ce941e46db4e2a14d25b7076589 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 source.ver: src/contrib/ASGSCA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ASGSCA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ASGSCA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ASGSCA_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ASGSCA_1.2.0.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 Package: ASSET Version: 1.6.1 Depends: MASS, msm, rmeta Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 65587bed2f3ea4783e805a58ba8b7d29 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 subtypes biocViews: Software Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee and William Wheeler Maintainer: William Wheeler source.ver: src/contrib/ASSET_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ASSET_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ASSET_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ASSET_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ASSET_1.6.1.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 Package: ASSIGN Version: 1.4.0 Depends: Rlab, msm, gplots Imports: graphics, grDevices, stats, utils License: MIT MD5sum: e336f3a9cd9b7e98e57e6eb9cad6db79 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 literatures 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, and W. Evan Johnson Maintainer: Ying Shen source.ver: src/contrib/ASSIGN_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ASSIGN_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ASSIGN_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ASSIGN_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ASSIGN_1.4.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R Package: AtlasRDF Version: 1.4.0 Depends: R (>= 2.10), hash, SPARQL, methods License: Apache License 2.0 MD5sum: 147753a78c2adb57589ada12398ab15e NeedsCompilation: no Title: Gene Expression Atlas query and gene set enrichment package. Description: Query the Gene Expression Atlas RDF data at the European Bioinformatics Institute using genes, experimental factors (such as disease, cell type, compound treatments), pathways and proteins. Also contains a function to perform an enrichment of your gene list across Experimental Factor Ontology (EFO) using the Atlas background set. biocViews: Microarray, DataImport, GeneSetEnrichment, GeneExpression, DifferentialExpression, DataRepresentation Author: James Malone, Simon Jupp, Maryam Soleimani Maintainer: James Malone source.ver: src/contrib/AtlasRDF_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AtlasRDF_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AtlasRDF_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AtlasRDF_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AtlasRDF_1.4.0.tgz vignettes: vignettes/AtlasRDF/inst/doc/AtlasRDF_vignette.pdf vignetteTitles: An introduction to the AtlasRDF-R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AtlasRDF/inst/doc/AtlasRDF_vignette.R Package: attract Version: 1.20.0 Depends: R (>= 2.10.0), AnnotationDbi, KEGG.db, limma, cluster, GOstats, graphics, methods, stats Imports: Biobase, AnnotationDbi, KEGG.db, limma, cluster, GOstats, graphics, methods, stats Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) MD5sum: f0446c8491a785c5677bcc53b815327d 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: StatisticalMethod, GeneExpression, KEGG Author: Jessica Mar Maintainer: Jessica Mar source.ver: src/contrib/attract_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/attract_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/attract_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/attract_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/attract_1.20.0.tgz vignettes: vignettes/attract/inst/doc/attract.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/attract/inst/doc/attract.R Package: BAC Version: 1.28.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: i386, x64 MD5sum: 1f31a8caf77fac035f84b7a18fb8ddd7 NeedsCompilation: yes Title: Bayesian Analysis of Chip-chip experiment Description: This package uses a Bayesian hierarchical model to detect enriched regions from ChIP-chip experiments biocViews: Microarray, Transcription Author: Raphael Gottardo Maintainer: Raphael Gottardo source.ver: src/contrib/BAC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BAC_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BAC_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BAC_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BAC_1.28.0.tgz vignettes: vignettes/BAC/inst/doc/BAC.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAC/inst/doc/BAC.R Package: BADER Version: 1.6.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: i386, x64 MD5sum: c60b8a21540e96ebd769114d910cf0a2 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: Sequencing, RNASeq, DifferentialExpression, Software, SAGE Author: Andreas Neudecker, Matthias Katzfuss Maintainer: Andreas Neudecker source.ver: src/contrib/BADER_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BADER_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BADER_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BADER_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BADER_1.6.0.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 Package: BAGS Version: 2.8.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 8896a295979a3a1d811a21f3f5d10a89 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 source.ver: src/contrib/BAGS_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BAGS_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BAGS_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BAGS_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BAGS_2.8.0.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 Package: ballgown Version: 2.0.0 Depends: R (>= 3.1.1), methods Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors (>= 0.1.2), RColorBrewer, splines, sva, limma, rtracklayer (>= 1.25.13), Biobase (>= 2.25.0), GenomeInfoDb Suggests: testthat, knitr License: Artistic-2.0 MD5sum: 925a1eb1311a7ed331134f059fa6088c 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: RNASeq, StatisticalMethod, Preprocessing, DifferentialExpression Author: Alyssa C. Frazee [aut, cre], Leonardo Collado-Torres [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Alyssa Frazee VignetteBuilder: knitr BugReports: https://github.com/alyssafrazee/ballgown/issues source.ver: src/contrib/ballgown_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ballgown_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ballgown_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ballgown_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ballgown_2.0.0.tgz vignettes: vignettes/ballgown/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ballgown/inst/doc/ballgown.R htmlDocs: vignettes/ballgown/inst/doc/ballgown.html htmlTitles: "Flexible isoform-level differential expression analysis with Ballgown" suggestsMe: polyester Package: bamsignals Version: 1.0.0 Depends: R (>= 3.2.0) Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges, GenomicRanges, zlibbioc LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 165f6420cbaf4eed735ac02069bc6e33 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: Alessandro Mammana VignetteBuilder: knitr source.ver: src/contrib/bamsignals_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bamsignals_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bamsignals_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bamsignals_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bamsignals_1.0.0.tgz vignettes: vignettes/bamsignals/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R htmlDocs: vignettes/bamsignals/inst/doc/bamsignals.html htmlTitles: "Introduction to the bamsignals package" Package: BaseSpaceR Version: 1.12.1 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: 7da7dca311ef60217930719dc17063ac 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 source.ver: src/contrib/BaseSpaceR_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/BaseSpaceR_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/BaseSpaceR_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/BaseSpaceR_1.12.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BaseSpaceR_1.12.1.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 Package: Basic4Cseq Version: 1.4.0 Depends: R (>= 3.0.0), Biostrings, ShortRead, caTools, GenomicRanges Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: f9e040d8cf8e7b2bc1f7e55eeb21436d 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: Visualization, QualityControl Author: Carolin Walter Maintainer: Carolin Walter source.ver: src/contrib/Basic4Cseq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Basic4Cseq_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Basic4Cseq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Basic4Cseq_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Basic4Cseq_1.4.0.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 Package: BayesPeak Version: 1.20.0 Depends: R (>= 2.14), IRanges Imports: IRanges, graphics Suggests: BiocStyle, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 89f6f2cb3ca70b76edd835871d371a64 NeedsCompilation: yes Title: Bayesian Analysis of ChIP-seq Data Description: This package is an implementation of the BayesPeak algorithm for peak-calling in ChIP-seq data. biocViews: ChIPSeq Author: Christiana Spyrou, Jonathan Cairns, Rory Stark, Andy Lynch, Simon Tavar\\'{e}, Maintainer: Jonathan Cairns source.ver: src/contrib/BayesPeak_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BayesPeak_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BayesPeak_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BayesPeak_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BayesPeak_1.20.0.tgz vignettes: vignettes/BayesPeak/inst/doc/BayesPeak.pdf vignetteTitles: BayesPeak Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesPeak/inst/doc/BayesPeak.R Package: baySeq Version: 2.2.0 Depends: R (>= 2.3.0), methods, GenomicRanges, abind, perm Suggests: edgeR, BiocStyle, BiocGenerics License: GPL-3 MD5sum: 8704b4420019f708849967585489abd2 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 Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle source.ver: src/contrib/baySeq_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/baySeq_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/baySeq_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/baySeq_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/baySeq_2.2.0.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: Rcade, segmentSeq, TCC importsMe: EDDA, metaseqR suggestsMe: compcodeR, oneChannelGUI, riboSeqR Package: BCRANK Version: 1.30.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: i386, x64 MD5sum: 26e6a3bfde1a9584b28f21f0e6d5fc36 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 source.ver: src/contrib/BCRANK_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BCRANK_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BCRANK_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BCRANK_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BCRANK_1.30.0.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 Package: beadarray Version: 2.18.0 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), methods, ggplot2 Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, illuminaio Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr License: GPL-2 Archs: i386, x64 MD5sum: d2bbe18b54555ccdb44f1b406ebadce5 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 source.ver: src/contrib/beadarray_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/beadarray_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/beadarray_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/beadarray_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/beadarray_2.18.0.tgz vignettes: vignettes/beadarray/inst/doc/beadarray.pdf, vignettes/beadarray/inst/doc/beadlevel.pdf, vignettes/beadarray/inst/doc/beadsummary.pdf, vignettes/beadarray/inst/doc/ImageProcessing.pdf vignetteTitles: beadarray.pdf, beadlevel.pdf, beadsummary.pdf, ImageProcessing.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beadarray/inst/doc/beadarray.R, vignettes/beadarray/inst/doc/beadlevel.R, vignettes/beadarray/inst/doc/beadsummary.R, vignettes/beadarray/inst/doc/ImageProcessing.R importsMe: arrayQualityMetrics, blima, epigenomix suggestsMe: beadarraySNP, lumi Package: beadarraySNP Version: 1.34.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: 3081a9152042a614bbb6dd9a8802917e NeedsCompilation: no Title: Normalization and reporting of Illumina SNP bead arrays Description: Importing data from Illumina SNP experiments and performing copy number calculations and reports. biocViews: CopyNumberVariation, SNP, GeneticVariability, TwoChannel, Preprocessing, DataImport Author: Jan Oosting Maintainer: Jan Oosting source.ver: src/contrib/beadarraySNP_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/beadarraySNP_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/beadarraySNP_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/beadarraySNP_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/beadarraySNP_1.34.0.tgz vignettes: vignettes/beadarraySNP/inst/doc/beadarraySNP.pdf vignetteTitles: beadarraySNP.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beadarraySNP/inst/doc/beadarraySNP.R Package: BeadDataPackR Version: 1.20.0 Suggests: BiocStyle, knitr License: GPL-2 Archs: i386, x64 MD5sum: 1bf20436d2fc02d6bbedcc1be54c4353 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 source.ver: src/contrib/BeadDataPackR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BeadDataPackR_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BeadDataPackR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BeadDataPackR_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BeadDataPackR_1.20.0.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 importsMe: beadarray Package: BEAT Version: 1.6.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: 0e3fdd334c1df8a39af7e1ef24be44b3 NeedsCompilation: no Title: BEAT - BS-Seq Epimutation Analysis Toolkit Description: Model-based analysis of single-cell methylation data biocViews: Genetics, MethylSeq, Software, DNAMethylation, Epigenetics Author: Kemal Akman Maintainer: Kemal Akman source.ver: src/contrib/BEAT_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BEAT_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BEAT_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BEAT_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BEAT_1.6.0.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 Package: BEclear Version: 1.0.0 Depends: snowfall, Matrix License: GPL-2 MD5sum: 44dea904aa17dbb4c101c0bb0fd9a38f NeedsCompilation: no Title: Correct for batch effects in DNA methylation data Description: Provides some functions to detect and correct for batch effects in DNA methylation data. The core function "BEclear" 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 Author: Markus Merl, Ruslan Akulenko Maintainer: Markus Merl source.ver: src/contrib/BEclear_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BEclear_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BEclear_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BEclear_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BEclear_1.0.0.tgz vignettes: vignettes/BEclear/inst/doc/BEclear.pdf vignetteTitles: BEclear tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEclear/inst/doc/BEclear.R Package: betr Version: 1.24.0 Depends: R(>= 2.6.0) Imports: Biobase (>= 2.5.5), limma, mvtnorm, methods, stats Suggests: Biobase License: LGPL MD5sum: 14be947c6acb8496691c2a02a31e3890 NeedsCompilation: no Title: Identify differentially expressed genes in microarray time-course data Description: The betr package implements the BETR (Bayesian Estimation of Temporal Regulation) algorithm to identify differentially expressed genes in microarray time-course data. biocViews: Microarray, DifferentialExpression, TimeCourse Author: Martin Aryee Maintainer: Martin Aryee source.ver: src/contrib/betr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/betr_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/betr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/betr_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/betr_1.24.0.tgz vignettes: vignettes/betr/inst/doc/betr.pdf vignetteTitles: BETR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/betr/inst/doc/betr.R Package: bgafun Version: 1.30.0 Depends: made4, seqinr,ade4 License: Artistic-2.0 MD5sum: 44b1a2b2b0ba80d25d3f6f9aaec6555d NeedsCompilation: no Title: BGAfun A method to identify specifity determining residues in protein families Description: A method to identify specifity determining residues in protein families using Between Group Analysis biocViews: Classification Author: Iain Wallace Maintainer: Iain Wallace source.ver: src/contrib/bgafun_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bgafun_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bgafun_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bgafun_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bgafun_1.30.0.tgz vignettes: vignettes/bgafun/inst/doc/bgafun.pdf vignetteTitles: bgafun.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgafun/inst/doc/bgafun.R Package: BGmix Version: 1.28.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: 795c34fd1f83de1f4a72a8efcdbe9a0b NeedsCompilation: yes Title: Bayesian models for differential gene expression Description: Fully Bayesian mixture models for differential gene expression biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Alex Lewin, Natalia Bochkina Maintainer: Alex Lewin source.ver: src/contrib/BGmix_1.28.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/BGmix_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BGmix_1.28.0.tgz vignettes: vignettes/BGmix/inst/doc/BGmix.pdf vignetteTitles: BGmix Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BGmix/inst/doc/BGmix.R Package: bgx Version: 1.34.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Suggests: affydata, hgu95av2cdf License: GPL-2 Archs: i386, x64 MD5sum: 4e1ef9860cc0f27045d767b2bdff9813 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 source.ver: src/contrib/bgx_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bgx_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bgx_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bgx_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bgx_1.34.0.tgz vignettes: vignettes/bgx/inst/doc/bgx.pdf vignetteTitles: HowTo BGX hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgx/inst/doc/bgx.R Package: BHC Version: 1.20.0 License: GPL-3 Archs: i386, x64 MD5sum: 7b914f8503b8ea376bb6eea2b3591c5c NeedsCompilation: yes Title: Bayesian Hierarchical Clustering Description: The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets. biocViews: Microarray, Clustering Author: Rich Savage, Emma Cooke, Robert Darkins, Yang Xu Maintainer: Rich Savage source.ver: src/contrib/BHC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BHC_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BHC_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BHC_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BHC_1.20.0.tgz vignettes: vignettes/BHC/inst/doc/bhc.pdf vignetteTitles: Bayesian Hierarchical Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BHC/inst/doc/bhc.R Package: BicARE Version: 1.26.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: i386, x64 MD5sum: 388212c926eda0e912775fa316fd2b1b 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 source.ver: src/contrib/BicARE_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BicARE_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BicARE_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BicARE_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BicARE_1.26.0.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 Package: BiGGR Version: 1.4.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph License: file LICENSE MD5sum: 0c655adfc299bd20acdc6bd55f168253 NeedsCompilation: no 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/ source.ver: src/contrib/BiGGR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiGGR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BiGGR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BiGGR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiGGR_1.4.0.tgz vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf vignetteTitles: BiGGR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiGGR/inst/doc/BiGGR.R Package: bigmemoryExtras Version: 1.12.0 Depends: R (>= 2.12), bigmemory (>= 4.3) Imports: methods, Biobase Suggests: biganalytics, BiocGenerics, RUnit License: Artistic-2.0 OS_type: unix MD5sum: 81cd97ff9cc8b96482df18d09e5c2b09 NeedsCompilation: no Title: An extension of the bigmemory package with added safety, convenience, and a factor class. Description: This package defines a "BigMatrix" ReferenceClass which adds safety and convenience features to the filebacked.big.matrix class from the bigmemory package. BigMatrix protects against segfaults by monitoring and gracefully restoring the connection to on-disk data and it also protects against accidental data modification with a filesystem-based permissions system. We provide utilities for using BigMatrix-derived classes as assayData matrices within the Biobase package's eSet family of classes. BigMatrix provides some optimizations related to attaching to, and indexing into, file-backed matrices with dimnames. Additionally, the package provides a "BigMatrixFactor" class, a file-backed matrix with factor properties. biocViews: Infrastructure, DataRepresentation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/bigmemoryExtras source.ver: src/contrib/bigmemoryExtras_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/bigmemoryExtras_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bigmemoryExtras_1.12.0.tgz vignettes: vignettes/bigmemoryExtras/inst/doc/bigmemoryExtras.pdf vignetteTitles: bigmemoryExtras hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigmemoryExtras/inst/doc/bigmemoryExtras.R Package: bioassayR Version: 1.6.1 Depends: R (>= 3.1.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods, Matrix, rjson, BiocGenerics (>= 0.13.8) Imports: XML Suggests: BiocStyle, RCurl, ape, ChemmineR, cellHTS2 License: Artistic-2.0 MD5sum: c50d6de348ced670615521a6d418d7dd NeedsCompilation: no Title: R library for Bioactivity analysis Description: bioassayR provides tools for statistical analysis of small molecule bioactivity data biocViews: MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics Author: Tyler Backman, Ronly Schlenk, Thomas Girke Maintainer: Tyler Backman source.ver: src/contrib/bioassayR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/bioassayR_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/bioassayR_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/bioassayR_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bioassayR_1.6.1.tgz vignettes: vignettes/bioassayR/inst/doc/bioassayR.pdf vignetteTitles: bioassayR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R Package: Biobase Version: 2.28.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets License: Artistic-2.0 Archs: i386, x64 MD5sum: 8294404fb4dbef63723cd5b4c614fee8 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, V. Carey, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/Biobase_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Biobase_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Biobase_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Biobase_2.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Biobase_2.28.0.tgz vignettes: vignettes/Biobase/inst/doc/BiobaseDevelopment.pdf, vignettes/Biobase/inst/doc/esApply.pdf, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf vignetteTitles: Notes for eSet developers, esApply Introduction, An introduction to Biobase and ExpressionSets 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: a4Base, a4Core, ACME, affy, affycomp, affyContam, affyPLM, affyQCReport, AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi, AnnotationForge, ArrayExpress, arrayMvout, ArrayTools, BAGS, beadarray, beadarraySNP, bgx, BicARE, BiocCaseStudies, BioMVCClass, birta, BrainStars, CAMERA, cancerclass, Cardinal, casper, Category, categoryCompare, cellHTS, cellHTS2, CGHbase, CGHcall, CGHregions, charm, chimera, chroGPS, ClassifyR, clippda, clusterStab, CMA, cn.farms, cn.mops, codelink, convert, copa, CopyNumber450k, ddCt, DESeq, DEXSeq, DFP, diggit, dualKS, dyebias, EBarrays, EDASeq, edge, eisa, EnrichmentBrowser, epigenomix, epivizr, ExiMiR, fabia, factDesign, fastseg, flowBeads, flowClust, frma, gaga, gCMAPWeb, GeneAnswers, GeneExpressionSignature, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, GeneSelector, geNetClassifier, genoset, GEOquery, GOexpress, GOFunction, goProfiles, GOstats, GSEABase, GSEAlm, GWASTools, hapFabia, HCsnip, HELP, hopach, HTqPCR, htSeqTools, HTSFilter, HybridMTest, IdeoViz, idiogram, inSilicoDb, inSilicoMerging, isobar, iterativeBMA, LMGene, lumi, macat, mAPKL, maSigPro, massiR, MergeMaid, metagenomeSeq, methyAnalysis, methylumi, Mfuzz, MiChip, MIMOSA, MineICA, minfi, MiRaGE, MLInterfaces, MLSeq, MmPalateMiRNA, monocle, MSnbase, Mulcom, multtest, NOISeq, nondetects, NormqPCR, oligo, oneChannelGUI, OrderedList, OTUbase, OutlierD, PAnnBuilder, panp, pcaMethods, pcot2, pdInfoBuilder, pdmclass, pepStat, PGSEA, phenoTest, PLPE, plrs, prada, PREDA, PROMISE, qpcrNorm, R453Plus1Toolbox, RbcBook1, rbsurv, rcellminer, ReadqPCR, reb, RefPlus, rHVDM, Ringo, Risa, Rmagpie, rMAT, RNAinteract, rnaSeqMap, Rnits, Roleswitch, RpsiXML, RTopper, safe, SCAN.UPC, SeqGSEA, sigaR, SigCheck, siggenes, simpleaffy, simulatorZ, SpeCond, SPEM, spkTools, splicegear, stepwiseCM, TDARACNE, tigre, tilingArray, topGO, TPP, tRanslatome, tspair, twilight, UNDO, VegaMC, viper, vsn, waveTiling, webbioc, xcms, XDE importsMe: ABarray, aCGH, adSplit, affyILM, affyQCReport, AgiMicroRna, AnalysisPageServer, annmap, annotate, AnnotationDbi, AnnotationForge, annotationTools, ArrayExpressHTS, 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vbmp, widgetTools Package: BiocCaseStudies Version: 1.30.0 Depends: tools, methods, utils, Biobase Suggests: affy (>= 1.17.3), affyPLM (>= 1.15.1), affyQCReport (>= 1.17.0), ALL (>= 1.4.3), annaffy (>= 1.11.1), annotate (>= 1.17.3), AnnotationDbi (>= 1.1.6), apComplex (>= 2.5.0), Biobase (>= 1.17.5), bioDist (>= 1.11.3), biocGraph (>= 1.1.1), biomaRt (>= 1.13.5), CCl4 (>= 1.0.6), CLL (>= 1.2.4), Category (>= 2.5.0), class (>= 7.2-38), cluster (>= 1.11.9), convert (>= 1.15.0), gcrma (>= 2.11.1), genefilter (>= 1.17.6), geneplotter (>= 1.17.2), GO.db (>= 2.0.2), GOstats (>= 2.5.0), graph (>= 1.17.4), GSEABase (>= 1.1.13), hgu133a.db (>= 2.0.2), hgu95av2.db, hgu95av2cdf (>= 2.0.0), hgu95av2probe (>= 2.0.0), hopach (>= 1.13.0), KEGG.db (>= 2.0.2), kohonen (>= 2.0.2), lattice (>= 0.17.2), latticeExtra (>= 0.3-1), limma (>= 2.13.1), MASS (>= 7.2-38), MLInterfaces (>= 1.13.17), multtest (>= 1.19.0), org.Hs.eg.db (>= 2.0.2), ppiStats (>= 1.5.4), randomForest (>= 4.5-20), RBGL (>= 1.15.6), RColorBrewer (>= 1.0-2), Rgraphviz (>= 1.17.11), vsn (>= 3.4.0), weaver (>= 1.5.0), xtable (>= 1.5-2), yeastExpData (>= 0.9.11) License: Artistic-2.0 MD5sum: d2f26a8c1eac070b7b32540688672378 NeedsCompilation: no Title: BiocCaseStudies: Support for the Case Studies Monograph Description: Software and data to support the case studies. biocViews: Infrastructure Author: R. Gentleman, W. Huber, F. Hahne, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/BiocCaseStudies_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiocCaseStudies_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BiocCaseStudies_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BiocCaseStudies_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiocCaseStudies_1.30.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BiocCheck Version: 1.4.0 Depends: R (>= 3.1.0) Imports: biocViews (>= 1.33.7), BiocInstaller, graph, devtools (>= 1.4.1), httr, knitr, tools, optparse, codetools, methods Suggests: RUnit, BiocGenerics, Biobase, RJSONIO, knitrBootstrap Enhances: codetoolsBioC License: Artistic-2.0 MD5sum: 031680bc9c346c6cd700f05e48e64382 NeedsCompilation: no Title: Bioconductor-specific package checks Description: Bioconductor-specific package checks biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut, cre] Maintainer: 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cbd7e99237e39e225aea3b66ee0cfa15 NeedsCompilation: no Title: S4 generic functions for Bioconductor Description: S4 generic functions needed by many Bioconductor packages. biocViews: Infrastructure Author: The Bioconductor Dev Team Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/BiocGenerics_0.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiocGenerics_0.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BiocGenerics_0.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BiocGenerics_0.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiocGenerics_0.14.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, affyPLM, altcdfenvs, AnnotationDbi, AnnotationForge, beadarray, bioassayR, Biobase, Biostrings, BSgenome, bsseq, Cardinal, Category, categoryCompare, chipseq, ChIPseqR, ChromHeatMap, cleanUpdTSeq, cn.mops, codelink, copynumber, CopyNumber450k, CRISPRseek, cummeRbund, DESeq, dexus, ensembldb, 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sangerseqR, SANTA, sapFinder, segmentSeq, SeqArray, seqPattern, seqTools, SeqVarTools, sigsquared, SIMAT, similaRpeak, simulatorZ, SNPRelate, SpacePAC, specL, STATegRa, STRINGdb, TCC, TIN, ToPASeq, trackViewer, TRONCO Package: biocGraph Version: 1.30.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: 7267f92f567535ecd3a9bce07f7e4252 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 source.ver: src/contrib/biocGraph_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/biocGraph_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/biocGraph_1.30.0.zip mac.binary.ver: 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src/contrib/BiocInstaller_1.18.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiocInstaller_1.18.5.zip win64.binary.ver: bin/windows64/contrib/3.2/BiocInstaller_1.18.5.zip mac.binary.ver: bin/macosx/contrib/3.2/BiocInstaller_1.18.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiocInstaller_1.18.5.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: affy, affylmGUI, AnnotationHub, BiocCheck, ChIPpeakAnno, gcrma, limmaGUI, oligoClasses, QuasR, webbioc suggestsMe: BSgenome, GOSemSim, metaseqR, pkgDepTools Package: BiocParallel Version: 1.2.22 Depends: methods Imports: futile.logger, parallel, snow Suggests: BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, Rsamtools, GenomicAlignments, ShortRead, codetools, RUnit, BiocStyle, knitr License: GPL-2 | GPL-3 MD5sum: a20d88093f85e8be5626a6381f456d51 NeedsCompilation: no 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: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/BiocParallel_1.2.22.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiocParallel_1.2.22.zip win64.binary.ver: bin/windows64/contrib/3.2/BiocParallel_1.2.22.zip mac.binary.ver: bin/macosx/contrib/3.2/BiocParallel_1.2.22.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiocParallel_1.2.22.tgz vignettes: vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.pdf, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.pdf vignetteTitles: Errors,, Logs and Debugging, Introduction to BiocParallel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.R dependsOnMe: ClassifyR, CopywriteR, DEXSeq, GenomicFiles, hiReadsProcessor, InPAS, MBASED, metagene, MSnbase, pRoloc, Rqc, ShortRead, SigCheck importsMe: ChIPQC, cpvSNP, derfinder, DESeq2, easyRNASeq, EMDomics, flowcatchR, GenomicAlignments, gmapR, h5vc, HTSeqGenie, MethylAid, parglms, qpgraph, QuasR, RUVcorr, soGGi, synapter, TFBSTools, VariantFiltering, VariantTools suggestsMe: ALDEx2, chimera, DEGreport, oneChannelGUI, specL, systemPipeR Package: BiocStyle Version: 1.6.0 Suggests: knitr (>= 1.7), rmarkdown, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 856e961b5266583b0791ca15f7816245 NeedsCompilation: no Title: Standard styles for vignettes and other Bioconductor documents Description: Provides standard formatting styles for Bioconductor PDF and HTML documents. 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Ding, R. Gentleman and Vincent Carey Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/bioDist_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bioDist_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bioDist_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bioDist_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bioDist_1.40.0.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 dependsOnMe: flowQ suggestsMe: BiocCaseStudies Package: biomaRt Version: 2.24.1 Depends: methods Imports: utils, XML, RCurl, AnnotationDbi Suggests: annotate License: Artistic-2.0 MD5sum: 7b6840e26e8778bd596b12b8bf71a157 NeedsCompilation: no Title: Interface to BioMart databases (e.g. Ensembl, COSMIC ,Wormbase and Gramene) 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 (http://www.biomart.org). 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. Examples of BioMart databases are Ensembl, COSMIC, Uniprot, HGNC, Gramene, Wormbase and dbSNP mapped to Ensembl. These major databases give biomaRt users direct access to a diverse set of data and enable a wide range of powerful online queries from gene annotation to database mining. biocViews: Annotation Author: Steffen Durinck , Wolfgang Huber Maintainer: Steffen Durinck source.ver: src/contrib/biomaRt_2.24.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/biomaRt_2.24.1.zip win64.binary.ver: bin/windows64/contrib/3.2/biomaRt_2.24.1.zip mac.binary.ver: bin/macosx/contrib/3.2/biomaRt_2.24.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/biomaRt_2.24.1.tgz vignettes: vignettes/biomaRt/inst/doc/biomaRt.pdf vignetteTitles: The biomaRt users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/biomaRt.R dependsOnMe: ChIPpeakAnno, coMET, customProDB, dagLogo, domainsignatures, DrugVsDisease, genefu, GenomeGraphs, MineICA, PSICQUIC, Roleswitch, VegaMC importsMe: ArrayExpressHTS, cobindR, customProDB, DEXSeq, DOQTL, easyRNASeq, EnrichmentBrowser, GenomicFeatures, gespeR, GOexpress, Gviz, HTSanalyzeR, IdMappingRetrieval, MEDIPS, metagene, metaseqR, methyAnalysis, oposSOM, Pbase, phenoTest, pRoloc, pwOmics, R453Plus1Toolbox, rgsepd, RNAither, seq2pathway, SeqGSEA suggestsMe: BiocCaseStudies, DEGreport, GeneAnswers, Genominator, h5vc, isobar, massiR, MineICA, MiRaGE, oligo, oneChannelGUI, paxtoolsr, piano, R3CPET, Rcade, RIPSeeker, RnBeads, rTANDEM, rTRM, ShortRead, SIM, sincell, systemPipeR, trackViewer Package: BioMVCClass Version: 1.36.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 23ae60de6ad0559c4ae06a79f059e7ad 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 source.ver: src/contrib/BioMVCClass_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BioMVCClass_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BioMVCClass_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BioMVCClass_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BioMVCClass_1.36.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioMVCClass/inst/doc/BioMVCClass.R Package: biomvRCNS Version: 1.8.1 Depends: IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: i386, x64 MD5sum: d62cf1a00c89b7a6eb0b2caebd5e850e 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, Sequencing, Visualization, Genetics Author: Yang Du Maintainer: Yang Du source.ver: src/contrib/biomvRCNS_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/biomvRCNS_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/biomvRCNS_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/biomvRCNS_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/biomvRCNS_1.8.1.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 Package: BioNet Version: 1.29.1 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) MD5sum: aa9d400a86f4bbbd0e7be79fa82a8121 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/ source.ver: src/contrib/BioNet_1.29.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/BioNet_1.29.1.zip win64.binary.ver: bin/windows64/contrib/3.2/BioNet_1.29.1.zip mac.binary.ver: bin/macosx/contrib/3.2/BioNet_1.29.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BioNet_1.29.1.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: HTSanalyzeR suggestsMe: SANTA Package: BioSeqClass Version: 1.26.0 Depends: R (>= 2.10), scatterplot3d Imports: Biostrings, ipred, e1071, klaR, randomForest, class, tree, nnet, rpart, party, foreign, Biobase, utils, stats, grDevices Suggests: scatterplot3d License: LGPL (>= 2.0) MD5sum: fcf4d2e24544df4c2078799f6a051a44 NeedsCompilation: no Title: Classification for Biological Sequences Description: Extracting Features from Biological Sequences and Building Classification Model biocViews: Classification Author: Li Hong sysptm@gmail.com Maintainer: Li Hong source.ver: src/contrib/BioSeqClass_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BioSeqClass_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BioSeqClass_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BioSeqClass_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BioSeqClass_1.26.0.tgz vignettes: vignettes/BioSeqClass/inst/doc/BioSeqClass.pdf vignetteTitles: Using the BioSeqClass Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioSeqClass/inst/doc/BioSeqClass.R Package: Biostrings Version: 2.36.4 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.5.21), IRanges (>= 2.1.2), XVector (>= 0.7.3) Imports: graphics, methods, stats, utils, BiocGenerics, IRanges, XVector, zlibbioc LinkingTo: S4Vectors (>= 0.5.21), IRanges, XVector Suggests: 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 Enhances: Rmpi License: Artistic-2.0 Archs: i386, x64 MD5sum: d26a06687262874b711893088aaa3ca0 NeedsCompilation: yes Title: String objects representing biological sequences, and matching algorithms 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: H. Pages, P. Aboyoun, R. Gentleman, and S. DebRoy Maintainer: H. Pages source.ver: src/contrib/Biostrings_2.36.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/Biostrings_2.36.4.zip win64.binary.ver: bin/windows64/contrib/3.2/Biostrings_2.36.4.zip mac.binary.ver: bin/macosx/contrib/3.2/Biostrings_2.36.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Biostrings_2.36.4.tgz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/matchprobes.pdf, vignettes/Biostrings/inst/doc/MultipleAlignments.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Handling probe sequence information, Multiple Alignments, Pairwise Sequence Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.R, vignettes/Biostrings/inst/doc/matchprobes.R, vignettes/Biostrings/inst/doc/MultipleAlignments.R, vignettes/Biostrings/inst/doc/PairwiseAlignments.R dependsOnMe: altcdfenvs, Basic4Cseq, BRAIN, BSgenome, ChIPpeakAnno, ChIPsim, cleaver, CRISPRseek, DASiR, DECIPHER, deepSNV, GeneRegionScan, genomes, GenomicAlignments, GOTHiC, hiReadsProcessor, iPAC, kebabs, LowMACA, MethTargetedNGS, methVisual, minfi, MotifDb, motifRG, motifStack, msa, muscle, oligo, oneChannelGUI, qrqc, R453Plus1Toolbox, REDseq, rGADEM, Roleswitch, rRDP, Rsamtools, RSVSim, sangerseqR, SCAN.UPC, scsR, SELEX, seqbias, ShortRead, systemPipeR, triplex, waveTiling importsMe: AffyCompatible, AllelicImbalance, ArrayExpressHTS, BCRANK, BEAT, BioSeqClass, biovizBase, BSgenome, charm, ChIPseqR, ChIPsim, CNEr, cobindR, compEpiTools, customProDB, dagLogo, diffHic, easyRNASeq, ensemblVEP, FourCSeq, gcrma, GeneRegionScan, GenomicAlignments, GenomicFeatures, ggbio, GGtools, ggtree, girafe, gmapR, GoogleGenomics, Gviz, gwascat, h5vc, HiTC, HTSeqGenie, KEGGREST, MatrixRider, MEDIPS, MEDME, methVisual, methylPipe, microRNA, MotIV, oligoClasses, OTUbase, Pbase, pdInfoBuilder, phyloseq, podkat, polyester, proBAMr, Pviz, qrqc, QuasR, r3Cseq, Rcpi, REDseq, Repitools, rGADEM, RNAprobR, Rolexa, Rqc, rSFFreader, rtracklayer, SeqArray, seqPattern, seqplots, SGSeq, soGGi, SomaticSignatures, synapter, TFBSTools, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: annotate, CSAR, exomeCopy, GenomicFiles, GenomicRanges, genoset, methylumi, microRNA, MiRaGE, pcaGoPromoter, procoil, rpx, rTRM, XVector Package: biosvd Version: 2.4.0 Depends: R (>= 3.1.0) Imports: BiocGenerics, Biobase, methods, grid, graphics, NMF License: Artistic-2.0 MD5sum: bee2fc90176f76e14942415550e1a455 NeedsCompilation: no Title: Package for high-throughput data processing, outlier detection, noise removal and dynamic modeling Description: The biosvd package contains functions to reduce the input data set from the feature x assay space to the reduced diagonalized eigenfeature x eigenassay space, with the eigenfeatures and eigenassays unique orthonormal superpositions of the features and assays, respectively. Results of SVD applied to the data can subsequently be inspected based on generated graphs, such as a heatmap of the eigenfeature x assay matrix and a bar plot with the eigenexpression fractions of all eigenfeatures. These graphs aid in deciding which eigenfeatures and eigenassays to filter out (i.e., eigenfeatures representing steady state, noise, or experimental artifacts; or when applied to the variance in the data, eigenfeatures representing steady-scale variance). After possible removal of steady state expression, steady-scale variance, noise and experimental artifacts, and after re-applying SVD to the normalized data, a summary html report of the eigensystem is generated, containing among others polar plots of the assays and features, a table with the list of features sortable according to their coordinates, radius and phase in the polar plot, and a visualization of the data sorted according to the two selected eigenfeatures and eigenassays with colored feature/assay annotation information when provided. This gives a global picture of the dynamics of expression/intensity levels, in which individual features and assays are classified in groups of similar regulation and function or similar cellular state and biological phenotype. biocViews: TimeCourse, Visualization Author: Anneleen Daemen , Matthew Brauer Maintainer: Anneleen Daemen , Matthew Brauer source.ver: src/contrib/biosvd_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/biosvd_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/biosvd_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/biosvd_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/biosvd_2.4.0.tgz vignettes: vignettes/biosvd/inst/doc/biosvd.pdf vignetteTitles: biosvd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosvd/inst/doc/biosvd.R Package: biovizBase Version: 1.16.0 Depends: R (>= 2.10), methods Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.2.4), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.19), Biostrings (>= 2.33.11), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), GenomicFeatures (>= 1.17.13), AnnotationDbi, VariantAnnotation (>= 1.11.4) Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer License: Artistic-2.0 Archs: i386, x64 MD5sum: 3748f50054180b2fe5b9fa67c3132483 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, Michael Lawrence, Dianne Cook Maintainer: Tengfei Yin source.ver: src/contrib/biovizBase_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/biovizBase_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/biovizBase_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/biovizBase_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/biovizBase_1.16.0.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, qrqc importsMe: GenoView, ggbio, Gviz, Pviz, qrqc suggestsMe: derfinder, derfinderPlot, R3CPET, regionReport Package: BiRewire Version: 2.2.3 Depends: igraph, slam, tsne Suggests: RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 85185575e82610bce59deb43788fa98e NeedsCompilation: yes Title: High-performing routines for the randomization of a bipartite graph (or a binary event matrix) 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 function for the analysis of the introduced randomness across the switching and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks (not bipartite) 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 useful and a visual montioring of the underlying Markov Chain has been implemented. biocViews: Network Author: Andrea Gobbi [aut], Davide Albanese [cbt], Francesco Iorio [cbt], Giuseppe Jurman [cbt], Julio Saez-Rodriguez [cbt] . Maintainer: Andrea Gobbi URL: http://www.ebi.ac.uk/~iorio/BiRewire source.ver: src/contrib/BiRewire_2.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiRewire_2.2.3.zip win64.binary.ver: bin/windows64/contrib/3.2/BiRewire_2.2.3.zip mac.binary.ver: bin/macosx/contrib/3.2/BiRewire_2.2.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiRewire_2.2.3.tgz vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf vignetteTitles: BiRewire hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiRewire/inst/doc/BiRewire.R Package: birta Version: 1.12.0 Depends: limma, MASS, R(>= 2.10), Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 10d124e253842a86d423e8d78acb3a8b NeedsCompilation: yes Title: Bayesian Inference of Regulation of Transcriptional Activity Description: Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. birta (Bayesian Inference of Regulation of Transcriptional Activity) uses the regulatory networks of TFs and miRNAs together with mRNA and miRNA expression data to predict switches in regulatory activity between two conditions. A Bayesian network is used to model the regulatory structure and Markov-Chain-Monte-Carlo is applied to sample the activity states. biocViews: Microarray, Sequencing, GeneExpression, Transcription, GraphAndNetwork Author: Benedikt Zacher, Khalid Abnaof, Stephan Gade, Erfan Younesi, Achim Tresch, Holger Froehlich Maintainer: Benedikt Zacher , Holger Froehlich source.ver: src/contrib/birta_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/birta_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/birta_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/birta_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/birta_1.12.0.tgz vignettes: vignettes/birta/inst/doc/birta.pdf vignetteTitles: Bayesian Inference of Regulation of Transcriptional Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/birta/inst/doc/birta.R Package: birte Version: 1.2.0 Depends: R(>= 3.0.0), RcppArmadillo (>= 0.3.6.1), Rcpp Imports: MASS, limma(>= 3.22.0), ridge, Biobase, nem LinkingTo: RcppArmadillo, Rcpp Suggests: knitr Enhances: Rgraphviz License: GPL (>= 2) Archs: i386, x64 MD5sum: 13fe3dc5dbc110dcbecc056a103cfc01 NeedsCompilation: yes Title: Bayesian Inference of Regulatory Influence on Expression (biRte) Description: Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. biRte uses regulatory networks of TFs, miRNAs and possibly other factors, together with mRNA, miRNA and other available expression data to predict the relative influence of a regulator on the expression of its target genes. Inference is done in a Bayesian modeling framework using Markov-Chain-Monte-Carlo. A special feature is the possibility for follow-up network reverse engineering between active regulators. biocViews: Microarray, Sequencing, GeneExpression, Transcription, Network, Bayesian, Regression, NetworkInference Author: Holger Froehlich, contributions by Benedikt Zacher Maintainer: Holger Froehlich SystemRequirements: BLAS, LAPACK VignetteBuilder: knitr source.ver: src/contrib/birte_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/birte_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/birte_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/birte_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/birte_1.2.0.tgz vignettes: vignettes/birte/inst/doc/birte.pdf vignetteTitles: Bayesian Inference of Regulation of Transcriptional Activity hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/birte/inst/doc/birte.R Package: BiSeq Version: 1.8.0 Depends: R (>= 2.15.2), methods, S4Vectors, IRanges (>= 1.17.24), GenomicRanges, Formula Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, rtracklayer, parallel, betareg, lokern, Formula, globaltest License: LGPL-3 MD5sum: 111bdce675b8392125beccba8514dd46 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 source.ver: src/contrib/BiSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiSeq_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BiSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BiSeq_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiSeq_1.8.0.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 importsMe: M3D Package: BitSeq Version: 1.12.0 Depends: Rsamtools, zlibbioc Imports: S4Vectors, IRanges LinkingTo: Rsamtools (>= 1.19.38), zlibbioc Suggests: edgeR, DESeq, BiocStyle License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: 75803b4a8dc000702ccb68c9f24bd398 NeedsCompilation: yes Title: Transcript expression inference and differential expression analysis for RNA-seq data Description: The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process. In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments. The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression. biocViews: GeneExpression, DifferentialExpression, Sequencing, RNASeq, Bayesian, AlternativeSplicing, DifferentialSplicing, Transcription Author: Peter Glaus, Antti Honkela and Magnus Rattray Maintainer: Antti Honkela , Panagiotis Papastamoulis source.ver: src/contrib/BitSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BitSeq_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BitSeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BitSeq_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BitSeq_1.12.0.tgz vignettes: vignettes/BitSeq/inst/doc/BitSeq.pdf vignetteTitles: BitSeq User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BitSeq/inst/doc/BitSeq.R Package: blima Version: 1.2.0 Depends: R(>= 3.0.0) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), BiocGenerics, grDevices, stats, graphics Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi License: GPL-3 MD5sum: a11e68e677f12d239724b7e04252643c NeedsCompilation: no Title: Package 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 Author: Vojtech Kulvait Maintainer: Vojtech Kulvait URL: https://bitbucket.org/kulvait/blima source.ver: src/contrib/blima_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/blima_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/blima_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/blima_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/blima_1.2.0.tgz vignettes: vignettes/blima/inst/doc/blima.pdf vignetteTitles: blima.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/blima/inst/doc/blima.R Package: BRAIN Version: 1.14.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: f4a52ac89e47aa687b525486b32c56ad 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: MassSpectrometry, Proteomics Author: Piotr Dittwald, with contributions of Dirk Valkenborg and Jurgen Claesen Maintainer: Piotr Dittwald source.ver: src/contrib/BRAIN_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BRAIN_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BRAIN_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BRAIN_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BRAIN_1.14.0.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 Package: BrainStars Version: 1.12.0 Depends: RCurl, Biobase, methods Imports: RJSONIO, Biobase License: Artistic-2.0 MD5sum: 78b75600a74c25cc3f0f4f56186516f1 NeedsCompilation: no Title: query gene expression data and plots from BrainStars (B*) Description: This package can search and get gene expression data and plots from BrainStars (B*). BrainStars is a quantitative expression database of the adult mouse brain. The database has genome-wide expression profile at 51 adult mouse CNS regions. biocViews: Microarray, OneChannel, DataImport Author: Itoshi NIKAIDO Maintainer: Itoshi NIKAIDO source.ver: src/contrib/BrainStars_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BrainStars_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BrainStars_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BrainStars_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BrainStars_1.12.0.tgz vignettes: vignettes/BrainStars/inst/doc/BrainStars.pdf vignetteTitles: BrainStars hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrainStars/inst/doc/BrainStars.R Package: bridge Version: 1.32.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) Archs: i386, x64 MD5sum: 4bda70c6e07d9df4ad01a3c7bdfc7460 NeedsCompilation: yes Title: Bayesian Robust Inference for Differential Gene Expression Description: Test for differentially expressed genes with microarray data. This package can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. biocViews: Microarray,OneChannel,TwoChannel,DifferentialExpression Author: Raphael Gottardo Maintainer: Raphael Gottardo source.ver: src/contrib/bridge_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bridge_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bridge_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bridge_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bridge_1.32.0.tgz vignettes: vignettes/bridge/inst/doc/bridge.pdf vignetteTitles: bridge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bridge/inst/doc/bridge.R Package: BridgeDbR Version: 1.2.0 Depends: R (>= 2.0.0), rJava Imports: RCurl License: AGPL-3 MD5sum: 1288a782f041d111bf8ba74f85bccd14 NeedsCompilation: no Title: Code for using BridgeDb identifier mapping framework from within R Description: Use BridgeDb functions and load identifier mapping databases in R biocViews: Software, Annotation Author: Christ Leemans , Egon Willighagen , Anwesha Bohler Maintainer: Anwesha Bohler URL: https://github.com/bridgedb/BridgeDb, https://github.com/BiGCAT-UM/bridgedb-r BugReports: https://github.com/BiGCAT-UM/bridgedb-r/issues source.ver: src/contrib/BridgeDbR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BridgeDbR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BridgeDbR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BridgeDbR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BridgeDbR_1.2.0.tgz vignettes: vignettes/BridgeDbR/inst/doc/tutorial.pdf vignetteTitles: tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R Package: BrowserViz Version: 1.0.1 Depends: R (>= 3.1.2), Rcpp (>= 0.11.5), jsonlite (>= 0.9.15), httpuv(>= 1.3.2) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle License: GPL-2 MD5sum: 50416efc0695fddf5ebe1b57b99c6aa7 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: Paul Shannon source.ver: src/contrib/BrowserViz_1.0.1.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/BrowserViz_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BrowserViz_1.0.0.tgz vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.pdf vignetteTitles: BrowserViz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R dependsOnMe: BrowserVizDemo, RCyjs Package: BrowserVizDemo Version: 1.0.0 Depends: R (>= 3.1.2), BrowserViz, Rcpp (>= 0.11.5), jsonlite (>= 0.9.15), httpuv(>= 1.3.2) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle License: GPL-2 MD5sum: f354022f1c425e3f4931729c1a37e596 NeedsCompilation: no Title: BrowserVizDemo: How to subclass BrowserViz Description: A BrowserViz subclassing example, xy plotting in the browser using d3 biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/BrowserVizDemo_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/BrowserVizDemo_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BrowserVizDemo_1.0.0.tgz vignettes: vignettes/BrowserVizDemo/inst/doc/BrowserVizDemo.pdf vignetteTitles: BrowserVizDemo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserVizDemo/inst/doc/BrowserVizDemo.R Package: BSgenome Version: 1.36.3 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.5.10), IRanges (>= 2.1.33), GenomeInfoDb (>= 1.3.19), GenomicRanges (>= 1.19.23), Biostrings (>= 2.35.3), rtracklayer (>= 1.25.8) Imports: methods, stats, BiocGenerics, S4Vectors, IRanges, XVector, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, rtracklayer Suggests: BiocInstaller, Biobase, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, SNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, hgu95av2probe, RUnit License: Artistic-2.0 MD5sum: e4c860bfa444c76ede09e8097833f9f7 NeedsCompilation: no Title: Infrastructure for Biostrings-based genome data packages Description: Infrastructure shared by all the Biostrings-based genome data packages biocViews: Genetics, Infrastructure, DataRepresentation, SequenceMatching, Annotation, SNP Author: Herve Pages Maintainer: H. Pages source.ver: src/contrib/BSgenome_1.36.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/BSgenome_1.36.3.zip win64.binary.ver: bin/windows64/contrib/3.2/BSgenome_1.36.3.zip mac.binary.ver: bin/macosx/contrib/3.2/BSgenome_1.36.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BSgenome_1.36.3.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/BSgenomeForge.R, vignettes/BSgenome/inst/doc/GenomeSearching.R dependsOnMe: CAGEr, chipseq, cleanUpdTSeq, CRISPRseek, GOTHiC, htSeqTools, MEDIPS, motifRG, REDseq, regioneR, rGADEM importsMe: BEAT, charm, ChIPpeakAnno, chipseq, cobindR, diffHic, ggbio, gmapR, GreyListChIP, Gviz, hiAnnotator, InPAS, MethylSeekR, PING, podkat, QuasR, R453Plus1Toolbox, regioneR, Repitools, seqplots, TFBSTools, VariantAnnotation, VariantFiltering, VariantTools suggestsMe: Biostrings, biovizBase, easyRNASeq, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, genoset, metaseqR, MiRaGE, oneChannelGUI, QDNAseq, rtracklayer, spliceR, waveTiling Package: bsseq Version: 1.4.0 Depends: R (>= 2.15), methods, BiocGenerics, S4Vectors, IRanges (>= 2.1.10), GenomicRanges (>= 1.19.6), parallel, matrixStats, GenomeInfoDb Imports: scales, stats, graphics, Biobase, locfit, gtools Suggests: RUnit, bsseqData License: Artistic-2.0 MD5sum: becf8de6b5e817a7664ed9146a9d17d9 NeedsCompilation: no Title: Analyze, manage and store bisulfite sequencing data. Description: Tools for analyzing and visualizing bisulfite sequencing data biocViews: DNAMethylation Author: Kasper Daniel Hansen [aut, cre] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq source.ver: src/contrib/bsseq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bsseq_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bsseq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bsseq_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bsseq_1.4.0.tgz vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.pdf, vignettes/bsseq/inst/doc/bsseq.pdf vignetteTitles: Analyzing WGBS with bsseq, The 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 Package: BubbleTree Version: 1.0.0 Depends: R (>= 3.0) Imports: GenomicRanges, IRanges, plyr, geosphere, mixdist, dplyr, shape Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=3.1) MD5sum: 4eef08797fcaf705ec2449b5b38727f5 NeedsCompilation: no Title: A method to elucidate purity and clonality in tumors using copy number ratio and allele frequency Description: BubbleTree utilizes homogenous pertinent somatic copy number alterations (SCNAs) as markers of tumor clones to extract estimates of tumor ploidy, purity and clonality. biocViews: CopyNumberVariation Author: Wei Zhu Michael Kuziora Brandon Higgs Maintainer: Wei Zhu VignetteBuilder: knitr source.ver: src/contrib/BubbleTree_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BubbleTree_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BubbleTree_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BubbleTree_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BubbleTree_1.0.0.tgz vignettes: vignettes/BubbleTree/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.R htmlDocs: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html htmlTitles: "BubbleTree Package Vignette" Package: BufferedMatrix Version: 1.32.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: e00b7d3ed6b71ccebd58e20b1c533547 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: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad source.ver: src/contrib/BufferedMatrix_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BufferedMatrix_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BufferedMatrix_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BufferedMatrix_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BufferedMatrix_1.32.0.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 Package: BufferedMatrixMethods Version: 1.32.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: f6da2413026bd75e8a62d9446505e1ac NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: B. M. Bolstad Maintainer: B. M. Bolstad URL: http://www.bmbolstad.com source.ver: src/contrib/BufferedMatrixMethods_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BufferedMatrixMethods_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BufferedMatrixMethods_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BufferedMatrixMethods_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BufferedMatrixMethods_1.32.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: bumphunter Version: 1.8.0 Depends: R (>= 2.10), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils, GenomicFeatures, AnnotationDbi Suggests: testthat, RUnit, doParallel, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: c132286a6a6e2a5f6d6c765c9902ac26 NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison Author: Rafael A. Irizarry [cre, 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] Maintainer: Rafael A. Irizarry URL: https://github.com/ririzarr/bumphunter source.ver: src/contrib/bumphunter_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bumphunter_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bumphunter_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bumphunter_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bumphunter_1.8.0.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: derfinder, regionReport suggestsMe: derfinderPlot Package: BUS Version: 1.24.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: i386, x64 MD5sum: 838082cc3f6aeca244831a72d8431793 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 source.ver: src/contrib/BUS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BUS_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BUS_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BUS_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BUS_1.24.0.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 Package: CAFE Version: 1.4.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 8ecee6f135defdff427f358d2a54bfe4 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 source.ver: src/contrib/CAFE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CAFE_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CAFE_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CAFE_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CAFE_1.4.0.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 Package: CAGEr Version: 1.10.1 Depends: methods, R (>= 2.15.0), BSgenome Imports: utils, Rsamtools, GenomicRanges, IRanges, data.table, beanplot, rtracklayer, som, VGAM Suggests: BSgenome.Drerio.UCSC.danRer7, FANTOM3and4CAGE Enhances: parallel License: GPL-3 MD5sum: af81bf85acd909b3a850842f960e8769 NeedsCompilation: no Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining Description: Preprocessing of CAGE sequencing data, identification and normalization of transcription start sites and downstream analysis of transcription start sites clusters (promoters). biocViews: Preprocessing, Sequencing, HighThroughputSequencing, Transcription, Clustering, Visualization Author: Vanja Haberle, Department of Biology, University of Bergen, Norway Maintainer: Vanja Haberle source.ver: src/contrib/CAGEr_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/CAGEr_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.2/CAGEr_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.2/CAGEr_1.10.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CAGEr_1.10.1.tgz vignettes: vignettes/CAGEr/inst/doc/CAGEr.pdf vignetteTitles: CAGEr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGEr.R suggestsMe: seqPattern Package: CALIB Version: 1.34.0 Depends: R (>= 2.10), limma, methods Imports: limma, methods, graphics, stats, utils License: LGPL Archs: i386, x64 MD5sum: c131b15c6b42c7166ee99a73290ba2a2 NeedsCompilation: yes Title: Calibration model for estimating absolute expression levels from microarray data Description: This package contains functions for normalizing spotted microarray data, based on a physically motivated calibration model. The model parameters and error distributions are estimated from external control spikes. biocViews: Microarray,TwoChannel,Preprocessing Author: Hui Zhao, Kristof Engelen, Bart De Moor and Kathleen Marchal Maintainer: Hui Zhao source.ver: src/contrib/CALIB_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CALIB_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CALIB_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CALIB_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CALIB_1.34.0.tgz vignettes: vignettes/CALIB/inst/doc/quickstart.pdf vignetteTitles: CALIB Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CALIB/inst/doc/quickstart.R Package: CAMERA Version: 1.24.1 Depends: R (>= 2.1.0), methods, Biobase, xcms (>= 1.13.5), igraph Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils, Hmisc, igraph Suggests: faahKO, RUnit, BiocGenerics Enhances: Rmpi, snow License: GPL (>= 2) Archs: i386, x64 MD5sum: 09e2c51a1f0d258d838ecbc0d41613f4 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, EIC correlation based tagging of unknown adducts and fragments biocViews: MassSpectrometry, Metabolomics Author: Carsten Kuhl, Ralf Tautenhahn, Steffen Neumann {ckuhl|sneumann}@ipb-halle.de, rtautenh@scripps.edu Maintainer: Carsten Kuhl URL: http://msbi.ipb-halle.de/msbi/CAMERA/ BugReports: https://github.com/sneumann/CAMERA/issues/new source.ver: src/contrib/CAMERA_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/CAMERA_1.24.1.zip win64.binary.ver: bin/windows64/contrib/3.2/CAMERA_1.24.1.zip mac.binary.ver: bin/macosx/contrib/3.2/CAMERA_1.24.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CAMERA_1.24.1.tgz vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf vignetteTitles: Molecule Identification with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R dependsOnMe: flagme, MAIT, metaMS suggestsMe: RMassBank Package: canceR Version: 1.0.0 Depends: R (>= 3.0.0), tcltk, tcltk2, cgdsr Imports: GSEABase,GSEAlm,tkrplot, geNetClassifier,RUnit, Formula, rpart, Biobase, phenoTest License: GPL-2 MD5sum: fa0fd60e0c3781a914af64e8353ee288 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, Software Author: Karim Mezhoud. Nuclear Safety Department. Nuclear Science Center of Tunisia. Maintainer: Karim Mezhoud source.ver: src/contrib/canceR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/canceR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/canceR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/canceR_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/canceR_1.0.0.tgz vignettes: vignettes/canceR/inst/doc/canceR.pdf vignetteTitles: canceR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/canceR/inst/doc/canceR.R Package: cancerclass Version: 1.12.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 Archs: i386, x64 MD5sum: 2679d7cb5b6c35ad2d64fda035ab61aa 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 source.ver: src/contrib/cancerclass_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cancerclass_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cancerclass_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cancerclass_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cancerclass_1.12.0.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 Package: CancerMutationAnalysis Version: 1.12.0 Depends: R (>= 2.10.0), qvalue Imports: AnnotationDbi, limma, methods, stats Suggests: KEGG.db License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 302782236cc1cabab0e42486286c102f NeedsCompilation: yes Title: Cancer mutation analysis Description: This package implements gene and gene-set level analysis methods for somatic mutation studies of cancer. The gene-level methods distinguish between driver genes (which play an active role in tumorigenesis) and passenger genes (which are mutated in tumor samples, but have no role in tumorigenesis) and incorporate a two-stage study design. The gene-set methods implement a patient-oriented approach, which calculates gene-set scores for each sample, then combines them across samples; a gene-oriented approach which uses the Wilcoxon test is also provided for comparison. biocViews: Genetics, Software Author: Giovanni Parmigiani, Simina M. Boca Maintainer: Simina M. Boca source.ver: src/contrib/CancerMutationAnalysis_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CancerMutationAnalysis_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CancerMutationAnalysis_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CancerMutationAnalysis_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CancerMutationAnalysis_1.12.0.tgz vignettes: vignettes/CancerMutationAnalysis/inst/doc/CancerMutationAnalysis.pdf vignetteTitles: CancerMutationAnalysisTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: CAnD Version: 1.0.0 Imports: methods, ggplot2, reshape Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: 39d427d5eab05484376ff4170f21d1e6 NeedsCompilation: no Title: Perform Chromosomal Ancestry Differences (CAnD) Analyses Description: Functions to perform the non-parametric and parametric CAnD tests on a set of ancestry proportions. For a particular ancestral subpopulation, a user will supply the estimated ancestry proportion for each sample, and each chromosome or chromosomal segment of interest. A p-value for each chromosome as well as an overall CAnD p-value will be returned for each test. Plotting functions are also available. biocViews: Genetics, StatisticalMethod, GeneticVariability, SNP Author: Caitlin McHugh Maintainer: Caitlin McHugh source.ver: src/contrib/CAnD_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CAnD_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CAnD_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CAnD_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CAnD_1.0.0.tgz vignettes: vignettes/CAnD/inst/doc/CAnD.pdf vignetteTitles: Detecting heterogenity in population structure across chromosomes with the "CAnD" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAnD/inst/doc/CAnD.R Package: Cardinal Version: 1.0.0 Depends: BiocGenerics, Biobase, graphics, methods, stats, ProtGenerics Imports: fields, grid, irlba, lattice, signal, sp, stats4, utils Suggests: BiocStyle, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 8a6714367cda9f3873a71d5f3fc0ae86 NeedsCompilation: yes 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, Normalization, MassSpectrometry, ImagingMassSpectrometry, Clustering, Classification Author: Kyle D. Bemis Maintainer: Kyle D. Bemis URL: http://www.cardinalmsi.org source.ver: src/contrib/Cardinal_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Cardinal_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Cardinal_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Cardinal_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Cardinal_1.0.0.tgz vignettes: vignettes/Cardinal/inst/doc/Cardinal-development.pdf, vignettes/Cardinal/inst/doc/Cardinal-walkthrough.pdf vignetteTitles: Cardinal design and development, Cardinal: Analytic tools for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cardinal/inst/doc/Cardinal-development.R, vignettes/Cardinal/inst/doc/Cardinal-walkthrough.R Package: casper Version: 2.2.0 Depends: R (>= 2.14.1), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics, EBarrays, gaga, gtools, GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors, sqldf, survival, VGAM Enhances: parallel License: GPL (>=2) Archs: i386, x64 MD5sum: 8e60435f0b37de788ff3c9f8e90826f7 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: GeneExpression, DifferentialExpression, Transcription, RNASeq, Sequencing Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda Stobbe, Victor Pena Maintainer: David Rossell source.ver: src/contrib/casper_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/casper_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/casper_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/casper_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/casper_2.2.0.tgz 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 Package: Category Version: 2.34.2 Depends: methods, stats4, Matrix, BiocGenerics (>= 0.13.8), AnnotationDbi, Biobase, GO.db Imports: methods, utils, stats, stats4, BiocGenerics, graph, Biobase, AnnotationDbi, RBGL, GSEABase (>= 1.19.3), genefilter, annotate (>= 1.15.6) Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGG.db, SNPchip, geneplotter, limma, lattice, graph, Biobase, genefilter, methods, RUnit, org.Sc.sgd.db, GOstats License: Artistic-2.0 MD5sum: d7e03822525dc654b1dc83f55b6de6f5 NeedsCompilation: no Title: Category Analysis Description: A collection of tools for performing category analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: R. Gentleman with contributions from S. Falcon and D.Sarkar Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/Category_2.34.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/Category_2.34.2.zip win64.binary.ver: bin/windows64/contrib/3.2/Category_2.34.2.zip mac.binary.ver: bin/macosx/contrib/3.2/Category_2.34.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Category_2.34.2.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, meshr, PCpheno importsMe: categoryCompare, cellHTS2, eisa, gCMAP, GOstats, interactiveDisplay, PCpheno, phenoTest, ppiStats, RDAVIDWebService suggestsMe: BiocCaseStudies, cellHTS, MmPalateMiRNA, qpgraph, RnBeads Package: categoryCompare Version: 1.12.0 Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8), Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1), GOstats, annotate, colorspace, graph, RCytoscape (>= 1.5.11) Suggests: knitr, methods, GO.db, KEGG.db, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter License: GPL-2 MD5sum: 56d8a7b6f77c35154b060ce1f73692b6 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 (>= 2.8.0) (if used for visualization of results, heavily suggested), CytoscapeRPC plugin (>= 1.8) VignetteBuilder: knitr BugReports: https://github.com/rmflight/categoryCompare/issues source.ver: src/contrib/categoryCompare_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/categoryCompare_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/categoryCompare_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/categoryCompare_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/categoryCompare_1.12.0.tgz vignettes: vignettes/categoryCompare/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R htmlDocs: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html htmlTitles: "categoryCompare: High-throughput data meta-analysis using feature annotations" Package: ccrepe Version: 1.4.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: 834e28e391b556fb33907608ea34f3b2 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: Statistics, Metagenomics, Bioinformatics, Software Author: Emma Schwager ,Craig Bielski, George Weingart Maintainer: Emma Schwager ,Craig Bielski, George Weingart VignetteBuilder: knitr source.ver: src/contrib/ccrepe_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ccrepe_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ccrepe_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ccrepe_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ccrepe_1.4.0.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 Package: cellGrowth Version: 1.12.0 Depends: R (>= 2.12.0), locfit (>= 1.5-4) Imports: lattice License: Artistic-2.0 MD5sum: 8cef721fb3f6e70d4aa6df95dfb57317 NeedsCompilation: no Title: Fitting cell population growth models Description: This package provides functionalities for the fitting of cell population growth models on experimental OD curves. biocViews: CellBasedAssays, MicrotitrePlateAssay, DataImport, Visualization, TimeCourse Author: Julien Gagneur , Andreas Neudecker Maintainer: Julien Gagneur source.ver: src/contrib/cellGrowth_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cellGrowth_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cellGrowth_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cellGrowth_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cellGrowth_1.12.0.tgz vignettes: vignettes/cellGrowth/inst/doc/cellGrowth.pdf vignetteTitles: Overview of the cellGrowth package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellGrowth/inst/doc/cellGrowth.R Package: cellHTS Version: 1.38.0 Depends: R (>= 2.10), prada (>= 1.9.4), RColorBrewer, Biobase (>= 1.11.12), genefilter (>= 1.11.2) Suggests: Category, GO.db, vsn (>= 2.0.35) License: Artistic-2.0 MD5sum: d3d6e35cc8b705550332c7ee36e42847 NeedsCompilation: no Title: Analysis of cell-based screens Description: Analysis of cell-based RNA interference screens biocViews: CellBasedAssays, Visualization Author: Wolfgang Huber , Ligia Bras , Michael Boutros Maintainer: Ligia Bras URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber source.ver: src/contrib/cellHTS_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cellHTS_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cellHTS_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cellHTS_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cellHTS_1.38.0.tgz vignettes: vignettes/cellHTS/inst/doc/cellhts.pdf, vignettes/cellHTS/inst/doc/twoChannels.pdf, vignettes/cellHTS/inst/doc/twoWay.pdf vignetteTitles: Main vignette: End-to-end analysis of cell-based screens, Supplement: multi-channel assays, Supplement: two-way assays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellHTS/inst/doc/cellhts.R, vignettes/cellHTS/inst/doc/twoChannels.R, vignettes/cellHTS/inst/doc/twoWay.R suggestsMe: prada Package: cellHTS2 Version: 2.32.0 Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter, splots, vsn, hwriter, locfit, grid Imports: prada, GSEABase, Category, stats4 License: Artistic-2.0 MD5sum: a94204f39e7fd44f1fd99759bcc38ae7 NeedsCompilation: no Title: Analysis of cell-based screens - revised version of cellHTS Description: This package provides tools for the analysis of high-throughput assays that were performed in microtitre plate formats (including but not limited to 384-well plates). The functionality includes data import and management, normalisation, quality assessment, replicate summarisation and statistical scoring. A webpage that provides a detailed graphical overview over the data and analysis results is produced. In our work, we have applied the package to RNAi screens on fly and human cells, and for screens of yeast libraries. See ?cellHTS2 for a brief introduction. biocViews: CellBasedAssays, Preprocessing, Visualization Author: Ligia Bras, Wolfgang Huber , Michael Boutros , Gregoire Pau , Florian Hahne Maintainer: Joseph Barry URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber source.ver: src/contrib/cellHTS2_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cellHTS2_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cellHTS2_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cellHTS2_2.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cellHTS2_2.32.0.tgz vignettes: vignettes/cellHTS2/inst/doc/cellhts2Complete.pdf, vignettes/cellHTS2/inst/doc/cellhts2.pdf, vignettes/cellHTS2/inst/doc/twoChannels.pdf, vignettes/cellHTS2/inst/doc/twoWay.pdf vignetteTitles: Main vignette (complete version): End-to-end analysis of cell-based screens, Main vignette: End-to-end analysis of cell-based screens, Supplement: multi-channel assays, Supplement: enhancer-suppressor screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellHTS2/inst/doc/cellhts2Complete.R, vignettes/cellHTS2/inst/doc/cellhts2.R, vignettes/cellHTS2/inst/doc/twoChannels.R, vignettes/cellHTS2/inst/doc/twoWay.R dependsOnMe: coRNAi, imageHTS, staRank importsMe: gespeR, HTSanalyzeR, RNAinteract suggestsMe: bioassayR Package: CellNOptR Version: 1.14.0 Depends: R (>= 2.15.0), RBGL, graph, methods, hash, ggplot2, RCurl, Rgraphviz, XML Suggests: RUnit, BiocGenerics, igraph License: GPL-3 Archs: i386, x64 MD5sum: 3b616e510c84fb905e2b39de31d7b3ef 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, TimeCourse Author: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve Maintainer: T.Cokelaer SystemRequirements: Graphviz version >= 2.2 source.ver: src/contrib/CellNOptR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CellNOptR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CellNOptR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CellNOptR_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CellNOptR_1.14.0.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CellNOptR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R dependsOnMe: CNORdt, CNORfeeder, CNORfuzzy, CNORode suggestsMe: MEIGOR Package: CexoR Version: 1.6.0 Depends: R (>= 2.10.0), S4Vectors, IRanges Imports: Rsamtools, GenomeInfoDb, GenomicRanges, rtracklayer, idr Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 | GPL-2 + file LICENSE MD5sum: 1198c4671da60c273c85607b7cbd05ff 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 (package 'skellam') is used to detect significant normalized count differences of opposed sign at each DNA strand (peak-pairs). Irreproducible discovery rate for overlapping peak-pairs across biological replicates is estimated using the package 'idr'. biocViews: Transcription, Genetics, Sequencing Author: Pedro Madrigal Maintainer: Pedro Madrigal source.ver: src/contrib/CexoR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CexoR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CexoR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CexoR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CexoR_1.6.0.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 Package: CFAssay Version: 1.2.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: de58eedcb034192f08f4d4fe24d1b6fe 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, Regression, Survival Author: Herbert Braselmann Maintainer: Herbert Braselmann source.ver: src/contrib/CFAssay_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CFAssay_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CFAssay_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CFAssay_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CFAssay_1.2.0.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 Package: CGEN Version: 3.2.0 Depends: R (>= 2.10.1), survival, mvtnorm Suggests: cluster License: GPL-2 + file LICENSE Archs: i386, x64 MD5sum: 613eb05882b29fdd397c23a397154851 NeedsCompilation: yes Title: An R package for analysis of case-control studies in genetic epidemiology Description: An R package for analysis of case-control studies in genetic epidemiology biocViews: SNP, MultipleComparisons, Clustering Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee, Summer Han and William Wheeler Maintainer: William Wheeler source.ver: src/contrib/CGEN_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CGEN_3.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CGEN_3.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CGEN_3.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CGEN_3.2.0.tgz vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf, vignettes/CGEN/inst/doc/vignette.pdf vignetteTitles: CGEN 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 Package: CGHbase Version: 1.28.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: 87dfb459dd6c2575b1718f8a5f4d11c2 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 source.ver: src/contrib/CGHbase_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CGHbase_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CGHbase_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CGHbase_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CGHbase_1.28.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, sigaR importsMe: CGHnormaliter, plrs, QDNAseq Package: CGHcall Version: 2.30.0 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) MD5sum: 695a7ef0f341029f7d137752cba008cd 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 source.ver: src/contrib/CGHcall_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CGHcall_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CGHcall_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CGHcall_2.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CGHcall_2.30.0.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, focalCall importsMe: CGHnormaliter, QDNAseq Package: cghMCR Version: 1.26.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 39e439018277774db4c1e0ae2c75292c 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 source.ver: src/contrib/cghMCR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cghMCR_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cghMCR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cghMCR_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cghMCR_1.26.0.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 Package: CGHnormaliter Version: 1.22.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: 5c7bfd76cb34b37bf11dbe782418a37b 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 source.ver: src/contrib/CGHnormaliter_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CGHnormaliter_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CGHnormaliter_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CGHnormaliter_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CGHnormaliter_1.22.0.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 Package: CGHregions Version: 1.26.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: d71803f3f3b6779183be937c0835a8b1 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 source.ver: src/contrib/CGHregions_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CGHregions_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CGHregions_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CGHregions_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CGHregions_1.26.0.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 Package: ChAMP Version: 1.6.2 Depends: R (>= 3.0.1), minfi, ChAMPdata, Illumina450ProbeVariants.db Imports: sva, IlluminaHumanMethylation450kmanifest, limma, RPMM, DNAcopy, preprocessCore, impute, marray, wateRmelon, plyr, IRanges, GenomicRanges License: GPL-3 MD5sum: 54858c86364c8da4ed1226562a44a9c9 NeedsCompilation: no Title: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 Description: The package includes quality control metrics, a selection of normalisation methods and novel methods to identify differentially methylated regions and to highlight copy number alterations. In addition there is a method to help calculate hmC using BS and oxBS samples. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber Author: Tiffany Morris [cre, aut], Lee Butcher [ctb], Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur Chakravarthy [ctb] Maintainer: Tiffany Morris source.ver: src/contrib/ChAMP_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChAMP_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.2/ChAMP_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.2/ChAMP_1.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChAMP_1.6.2.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R Package: charm Version: 2.14.0 Depends: R (>= 2.14.0), Biobase, SQN, fields, RColorBrewer, genefilter Imports: BSgenome, Biobase, oligo (>= 1.11.31), oligoClasses(>= 1.17.39), ff, preprocessCore, methods, stats, Biostrings, IRanges, siggenes, nor1mix, gtools, grDevices, graphics, utils, limma, parallel, sva(>= 3.1.2) Suggests: charmData, BSgenome.Hsapiens.UCSC.hg18, corpcor License: LGPL (>= 2) MD5sum: a170d5c13ecebc8fda94ae265cea8f5f NeedsCompilation: no Title: Analysis of DNA methylation data from CHARM microarrays Description: This package implements analysis tools for DNA methylation data generated using Nimblegen microarrays and the McrBC protocol. It finds differentially methylated regions between samples, calculates percentage methylation estimates and includes array quality assessment tools. biocViews: Microarray, DNAMethylation Author: Martin Aryee, Peter Murakami, Harris Jaffee, Rafael Irizarry Maintainer: Peter Murakami source.ver: src/contrib/charm_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/charm_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/charm_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/charm_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/charm_2.14.0.tgz vignettes: vignettes/charm/inst/doc/charm.pdf vignetteTitles: charm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/charm/inst/doc/charm.R Package: ChemmineOB Version: 1.6.1 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp Suggests: ChemmineR, BiocStyle, knitr, knitcitations, knitrBootstrap Enhances: ChemmineR (>= 2.13.0) License: file LICENSE Archs: i386, x64 MD5sum: 188ceb796d0b595d3cda46424177c57d 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 Author: Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineOB SystemRequirements: OpenBabel (>= 2.3.1) with headers. http://openbabel.org VignetteBuilder: knitr source.ver: src/contrib/ChemmineOB_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChemmineOB_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ChemmineOB_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ChemmineOB_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChemmineOB_1.6.1.tgz vignettes: vignettes/ChemmineOB/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R htmlDocs: vignettes/ChemmineOB/inst/doc/ChemmineOB.html htmlTitles: "ChemmineOB" suggestsMe: ChemmineR Package: ChemmineR Version: 2.20.4 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0), ggplot2 LinkingTo: Rcpp, BH Suggests: RSQLite, scatterplot3d, gplots, fmcsR,snow, RPostgreSQL, BiocStyle,knitr,knitcitations, knitrBootstrap, ChemmineOB (>= 1.3.8), ChemmineDrugs, grid,gridExtra,png Enhances: ChemmineOB License: Artistic-2.0 Archs: i386, x64 MD5sum: 56b0c16434b774f2d6e04cb9927f622b 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 Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineR-release VignetteBuilder: knitr source.ver: src/contrib/ChemmineR_2.20.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChemmineR_2.20.4.zip win64.binary.ver: bin/windows64/contrib/3.2/ChemmineR_2.20.4.zip mac.binary.ver: bin/macosx/contrib/3.2/ChemmineR_2.20.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChemmineR_2.20.4.tgz vignettes: vignettes/ChemmineR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R htmlDocs: vignettes/ChemmineR/inst/doc/ChemmineR.html htmlTitles: "ChemmineR" dependsOnMe: eiR, fmcsR importsMe: Rchemcpp, Rcpi suggestsMe: bioassayR, ChemmineOB Package: chimera Version: 1.10.0 Depends: Biobase, GenomicRanges (>= 1.13.3), Rsamtools (>= 1.13.1), GenomicAlignments, methods, AnnotationDbi, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, Homo.sapiens Suggests: BiocParallel, geneplotter Enhances: Rsubread, BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, Mus.musculus, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 Archs: i386, x64 MD5sum: 2a362a7cb9cfd8a54f88b9271cf113e7 NeedsCompilation: yes Title: A package for secondary analysis of fusion products Description: This package facilitates the characterisation of fusion products events. It allows to import fusion data results from the following fusion finders: chimeraScan, bellerophontes, deFuse, FusionFinder, FusionHunter, mapSplice, tophat-fusion, FusionMap, STAR, Rsubread, fusionCatcher. biocViews: Infrastructure Author: Raffaele A Calogero, Matteo Carrara, Marco Beccuti, Francesca Cordero Maintainer: Raffaele A Calogero SystemRequirements: STAR, TopHat, bowtie and samtools are required for some functionalities source.ver: src/contrib/chimera_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/chimera_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/chimera_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/chimera_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chimera_1.10.0.tgz vignettes: vignettes/chimera/inst/doc/chimera.pdf vignetteTitles: chimera hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimera/inst/doc/chimera.R dependsOnMe: oneChannelGUI Package: chipenrich Version: 1.6.1 Depends: R (>= 2.15.1) Imports: chipenrich.data, methods, GenomicRanges (>= 1.10.0), IRanges (>= 1.16.0), mgcv, plyr (>= 1.7.0), lattice, latticeExtra, grid, stringr (>= 0.6), rms Enhances: parallel License: GPL-3 MD5sum: 7c122393c420fec213d9e8b6815da4aa NeedsCompilation: no Title: Gene set enrichment for ChIP-seq peak data Description: ChIP-Enrich performs 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: Software, Bioinformatics, Enrichment, GeneSetEnrichment Author: Ryan P. Welch [aut, cre, cph], Chee Lee [aut, cre], Raymond G. Cavalcante [aut, cre], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Ryan P. Welch , Chee Lee , Raymond G. Cavalcante source.ver: src/contrib/chipenrich_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/chipenrich_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/chipenrich_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/chipenrich_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chipenrich_1.6.1.tgz vignettes: vignettes/chipenrich/inst/doc/chipenrich.pdf vignetteTitles: ChIP-Enrich Vignette/Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich.R Package: ChIPpeakAnno Version: 3.2.2 Depends: R (>= 2.10), grid, VennDiagram, biomaRt, IRanges, Biostrings, GenomicRanges Imports: BiocGenerics (>= 0.1.0), GO.db, BSgenome, GenomicFeatures, AnnotationDbi, limma, multtest, RBGL, graph, BiocInstaller, stats Suggests: reactome.db, BSgenome.Ecoli.NCBI.20080805, org.Ce.eg.db, org.Hs.eg.db, BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, gplots, RUnit, BiocStyle, rtracklayer License: GPL (>= 2) MD5sum: e8fa04c1d4ac03908b4dbb3683bc6150 NeedsCompilation: no Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments or any experiments resulted in large number of chromosome ranges. Description: The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs). This package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages biocViews: Annotation, ChIPSeq, ChIPchip Author: Lihua Julie Zhu, Jianhong Ou, Herve Pages, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe and Michael Green Maintainer: Lihua Julie Zhu , Jianhong Ou source.ver: src/contrib/ChIPpeakAnno_3.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChIPpeakAnno_3.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/ChIPpeakAnno_3.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/ChIPpeakAnno_3.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPpeakAnno_3.2.2.tgz vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.pdf vignetteTitles: ChIPpeakAnno Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R dependsOnMe: REDseq importsMe: FunciSNP, REDseq suggestsMe: oneChannelGUI, R3CPET, RIPSeeker Package: ChIPQC Version: 1.4.4 Depends: R (>= 3.0.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19) 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, BiocParallel, methods, reshape2, Nozzle.R1, Biobase, grDevices, stats, utils Suggests: BiocStyle, 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, TxDb.Hsapiens.UCSC.hg38.knownGene License: GPL (>= 3) MD5sum: 41d4884796ede91abe948b15b441eb8c 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 source.ver: src/contrib/ChIPQC_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChIPQC_1.4.4.zip win64.binary.ver: bin/windows64/contrib/3.2/ChIPQC_1.4.4.zip mac.binary.ver: bin/macosx/contrib/3.2/ChIPQC_1.4.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPQC_1.4.4.tgz vignettes: vignettes/ChIPQC/inst/doc/ChIPQC.pdf, vignettes/ChIPQC/inst/doc/ChIPQCSampleReport.pdf vignetteTitles: Assessing ChIP-seq sample quality with ChIPQC, ChIPQCSampleReport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPQC/inst/doc/ChIPQC.R Package: ChIPseeker Version: 1.4.7 Depends: R (>= 3.1.0) Imports: BiocGenerics, boot, AnnotationDbi, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, grDevices, gtools, methods, plotrix, dplyr, parallel, plyr, magrittr, RColorBrewer, rtracklayer, S4Vectors, TxDb.Hsapiens.UCSC.hg19.knownGene Suggests: clusterProfiler, DOSE, ReactomePA, org.Hs.eg.db, knitr, BiocStyle License: Artistic-2.0 MD5sum: fb98a51c3b2c69b35317d1174a82eb4e 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 with contributions from Yun Yan. Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/ChIPseeker VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ChIPseeker/issues source.ver: src/contrib/ChIPseeker_1.4.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChIPseeker_1.4.7.zip win64.binary.ver: bin/windows64/contrib/3.2/ChIPseeker_1.4.7.zip mac.binary.ver: bin/macosx/contrib/3.2/ChIPseeker_1.4.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPseeker_1.4.7.tgz vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.pdf 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 Package: chipseq Version: 1.18.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.0.1), IRanges (>= 1.99.1), GenomicRanges (>= 1.17.7), BSgenome, ShortRead Imports: methods, BiocGenerics, IRanges, BSgenome, GenomicRanges, lattice, ShortRead, stats Suggests: GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene License: Artistic-2.0 Archs: i386, x64 MD5sum: 528d08d3023c0441ea9219f8de4fde7b 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, Robert Gentleman, Michael Lawrence, Zizhen Yao Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/chipseq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/chipseq_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/chipseq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/chipseq_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chipseq_1.18.0.tgz vignettes: vignettes/chipseq/inst/doc/Workflow.pdf vignetteTitles: A Sample ChIP-Seq analysis workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseq/inst/doc/Workflow.R dependsOnMe: PING importsMe: ChIPQC, CopywriteR, HTSeqGenie, soGGi suggestsMe: ggbio, oneChannelGUI Package: ChIPseqR Version: 1.22.1 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors Imports: Biostrings, fBasics, GenomicRanges, IRanges, graphics, grDevices, HilbertVis, ShortRead, stats, timsac, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 5dccb4724809633eb560823e08adfd6e 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 URL: https://github.com/humburg/ChIPseqR BugReports: https://github.com/humburg/ChIPseqR/issue source.ver: src/contrib/ChIPseqR_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChIPseqR_1.22.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ChIPseqR_1.22.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ChIPseqR_1.22.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPseqR_1.22.1.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 Package: ChIPsim Version: 1.22.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: 156e5041bc849e517000e794d74d4816 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 source.ver: src/contrib/ChIPsim_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChIPsim_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ChIPsim_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ChIPsim_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPsim_1.22.0.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 Package: ChIPXpress Version: 1.10.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 31bda48d152fb616b4d23453d0fb0886 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 source.ver: src/contrib/ChIPXpress_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/ChIPXpress_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPXpress_1.10.0.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 Package: chopsticks Version: 1.32.0 Depends: R(>= 2.10.0), survival, methods Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: 667fd857244b3688880081bd538b645a 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/ source.ver: src/contrib/chopsticks_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/chopsticks_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/chopsticks_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/chopsticks_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chopsticks_1.32.0.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 Package: chroGPS Version: 1.12.0 Depends: R (>= 2.13.0), IRanges, methods, Biobase, MASS, graphics, stats, changepoint Imports: cluster, DPpackage, ICSNP Enhances: parallel, XML, rgl License: GPL (>=2.14) MD5sum: 9fc165b5d304e13dcec1d9d78338c864 NeedsCompilation: no Title: chroGPS: visualizing the epigenome Description: We provide intuitive maps to visualize the association between genetic elements, with emphasis on epigenetics. The approach is based on Multi-Dimensional Scaling. We provide several sensible distance metrics, and adjustment procedures to remove systematic biases typically observed when merging data obtained under different technologies or genetic backgrounds. biocViews: Visualization, Clustering Author: Oscar Reina, David Rossell Maintainer: Oscar Reina source.ver: src/contrib/chroGPS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/chroGPS_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/chroGPS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/chroGPS_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chroGPS_1.12.0.tgz vignettes: vignettes/chroGPS/inst/doc/chroGPS.pdf vignetteTitles: Manual for the chroGPS library hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chroGPS/inst/doc/chroGPS.R Package: chromDraw Version: 1.0.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 Archs: i386, x64 MD5sum: 556692a71c9d7b25ba2bc5068cb1cb97 NeedsCompilation: yes Title: chromDraw an R package for visualization of linear and circular karyotypes. Description: Package chromDraw is a simple package for linear and circular type of karyotype visualization. The linear type of visualization is usually used for demonstrating chromosomes structures in karyotype and the circular type of visualization is used for comparing of karyotypes between each other. This tool has own input data format or genomicRanges structure can be used as input. Each chromosome containing definition of blocks and centromere position. Output file formats 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) source.ver: src/contrib/chromDraw_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/chromDraw_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/chromDraw_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/chromDraw_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chromDraw_1.0.0.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 Package: ChromHeatMap Version: 1.22.0 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 Suggests: ALL, hgu95av2.db License: Artistic-2.0 MD5sum: c0300fb3b13756f0a25ce47cfe5abc00 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 source.ver: src/contrib/ChromHeatMap_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChromHeatMap_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ChromHeatMap_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ChromHeatMap_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChromHeatMap_1.22.0.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 Package: cisPath Version: 1.8.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: 6d5680db652f1fdd854fc5f73d8d969b 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 source.ver: src/contrib/cisPath_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cisPath_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cisPath_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cisPath_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cisPath_1.8.0.tgz vignettes: vignettes/cisPath/inst/doc/cisPath.pdf vignetteTitles: cisPath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cisPath/inst/doc/cisPath.R Package: ClassifyR Version: 1.2.4 Depends: R (>= 3.0.3), methods, Biobase, BiocParallel Imports: locfit, ROCR, grid Suggests: limma, edgeR, car, Rmixmod, ggplot2, gridExtra, BiocStyle, pamr, sparsediscrim, PoiClaClu, curatedOvarianData, parathyroidSE, knitr, klaR, gtable, scales, e1071, rmarkdown License: GPL-3 MD5sum: 1bacc4dcecb9cc15e9f1e0b09d615325 NeedsCompilation: no Title: A framework for two-class classification problems, with applications to differential variability and differential distribution testing. Description: The software formalises a framework for classification in R. There are four stages. Data transformation, feature selection, and prediction. The requirements of variable types and names are fixed, but specialised variables for functions can also be provided. The classification framework is wrapped in a driver loop, that reproducibly does a couple of cross-validation schemes. Functions for differential expression, differential variability, and differential distribution are included. Additional functions may be developed by the user, if they have better performing methods. biocViews: Classification, Survival Author: Dario Strbenac, John Ormerod, Graham Mann, Jean Yang Maintainer: Dario Strbenac VignetteBuilder: knitr source.ver: src/contrib/ClassifyR_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/ClassifyR_1.2.4.zip win64.binary.ver: bin/windows64/contrib/3.2/ClassifyR_1.2.4.zip mac.binary.ver: bin/macosx/contrib/3.2/ClassifyR_1.2.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ClassifyR_1.2.4.tgz vignettes: vignettes/ClassifyR/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R htmlDocs: vignettes/ClassifyR/inst/doc/ClassifyR.html htmlTitles: "An Introduction to ClassifyR" Package: cleanUpdTSeq Version: 1.6.1 Depends: R (>= 2.15), BiocGenerics (>= 0.1.0), methods, BSgenome, BSgenome.Drerio.UCSC.danRer7, GenomicRanges, seqinr, e1071 License: GPL-2 MD5sum: e2813454645dcef244e11252958ef593 NeedsCompilation: no Title: This package classifies putative polyadenylation sites as true or false/internally oligodT primed. Description: This package uses the Naive Bayes classifier (from e1071) to assign probability values to putative polyadenylation sites (pA sites) based on training data from zebrafish. This will allow the user to separate true, biologically relevant pA sites from false, oligodT primed pA sites. biocViews: Sequencing, SequenceMatching, Genetics, GeneRegulation Author: Sarah Sheppard, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Sarah Sheppard ; Jianhong Ou ; Lihua Julie Zhu source.ver: src/contrib/cleanUpdTSeq_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/cleanUpdTSeq_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/cleanUpdTSeq_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/cleanUpdTSeq_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cleanUpdTSeq_1.6.1.tgz vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.pdf vignetteTitles: cleanUpdTSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R importsMe: InPAS Package: cleaver Version: 1.6.0 Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8) Imports: IRanges Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), BRAIN, UniProt.ws (>= 2.1.4) License: GPL (>= 3) MD5sum: 7cfe614c9f448458acaa7544ba40f6b2 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] Maintainer: Sebastian Gibb URL: https://github.com/sgibb/cleaver/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/cleaver/issues/ source.ver: src/contrib/cleaver_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cleaver_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cleaver_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cleaver_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cleaver_1.6.0.tgz vignettes: vignettes/cleaver/inst/doc/cleaver.pdf vignetteTitles: in-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleaver/inst/doc/cleaver.R importsMe: Pbase, synapter Package: clippda Version: 1.18.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: f6d5e29f08f133036aaae877900445fe 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 source.ver: src/contrib/clippda_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/clippda_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/clippda_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/clippda_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clippda_1.18.0.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 Package: clipper Version: 1.8.2 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor, RBGL Suggests: RUnit, BiocGenerics, RCytoscape (>= 1.6.3), graphite, ALL, hgu95av2.db, MASS, BiocStyle License: AGPL-3 MD5sum: 02dbe6025da3926c7c03ccfca9c86ae7 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 source.ver: src/contrib/clipper_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/clipper_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.2/clipper_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.2/clipper_1.8.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clipper_1.8.2.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 importsMe: ToPASeq suggestsMe: graphite Package: Clomial Version: 1.4.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: 027ea9b4cc417026051c3572b2e8a8c8 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 Author: Habil Zare and Alex Hu Maintainer: Habil Zare source.ver: src/contrib/Clomial_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Clomial_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Clomial_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Clomial_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Clomial_1.4.0.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 Package: Clonality Version: 1.16.0 Depends: R (>= 2.12.2), DNAcopy Imports: DNAcopy, grDevices, graphics, stats, utils Suggests: gdata, DNAcopy License: GPL-3 MD5sum: f92e4882cd7aaf9b11da138266730a23 NeedsCompilation: no Title: Clonality testing Description: Statistical tests for clonality versus independence of tumors from the same patient based on their LOH or genomewide copy number profiles biocViews: Microarray, CopyNumberVariation, Classification, aCGH Author: Irina Ostrovnaya Maintainer: Irina Ostrovnaya source.ver: src/contrib/Clonality_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Clonality_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Clonality_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Clonality_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Clonality_1.16.0.tgz vignettes: vignettes/Clonality/inst/doc/Clonality.pdf vignetteTitles: Clonality hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clonality/inst/doc/Clonality.R Package: clonotypeR Version: 1.6.0 Imports: methods Suggests: BiocGenerics, edgeR, knitr, pvclust, RUnit, vegan License: file LICENSE MD5sum: 17b5a825f6f63d2da8a96d659a3316ab NeedsCompilation: no Title: High throughput analysis of T cell antigen receptor sequences Description: High throughput analysis of T cell antigen receptor sequences The genes encoding T cell receptors are created by somatic recombination, generating an immense combination of V, (D) and J segments. Additional processes during the recombination create extra sequence diversity between the V an J segments. Collectively, this hyper-variable region is called the CDR3 loop. . The purpose of this package is to process and quantitatively analyse millions of V-CDR3-J combination, called clonotypes, from multiple sequence libraries. biocViews: Sequencing Author: Charles Plessy Maintainer: Charles Plessy VignetteBuilder: knitr source.ver: src/contrib/clonotypeR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/clonotypeR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/clonotypeR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/clonotypeR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clonotypeR_1.6.0.tgz vignettes: vignettes/clonotypeR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clonotypeR/inst/doc/clonotypeR.R htmlDocs: vignettes/clonotypeR/inst/doc/clonotypeR.html htmlTitles: "clonotypeR User's Guide" Package: clst Version: 1.16.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: 7273d1dec4698dc5a77a03b425e9f646 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 source.ver: src/contrib/clst_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/clst_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/clst_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/clst_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clst_1.16.0.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 Package: clstutils Version: 1.16.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit, RSVGTipsDevice License: GPL-3 MD5sum: 417147754b69b1112dba8fd8f0693cd3 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 source.ver: src/contrib/clstutils_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/clstutils_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/clstutils_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/clstutils_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clstutils_1.16.0.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 Package: clusterProfiler Version: 2.2.7 Depends: R (>= 3.1.0) Imports: DOSE, GOSemSim, AnnotationDbi, methods, stats4, plyr, ggplot2, GO.db, KEGGREST, magrittr, qvalue Suggests: org.Hs.eg.db, ReactomePA, pathview, KEGG.db, RDAVIDWebService, knitr, BiocStyle License: Artistic-2.0 MD5sum: 379e8c611a5a5b4dd04e66bb9525f954 NeedsCompilation: no Title: statistical analysis and visulization of functional profiles for genes and gene clusters Description: This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters. biocViews: Clustering, GO, Pathways, Visualization, MultipleComparison, GeneSetEnrichment Author: Guangchuang Yu with contributions from Li-Gen Wang and Giovanni Dall'Olio. Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/clusterProfiler VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues source.ver: src/contrib/clusterProfiler_2.2.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/clusterProfiler_2.2.7.zip win64.binary.ver: bin/windows64/contrib/3.2/clusterProfiler_2.2.7.zip mac.binary.ver: bin/macosx/contrib/3.2/clusterProfiler_2.2.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clusterProfiler_2.2.7.tgz vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.pdf vignetteTitles: An introduction to clusterProfiler hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R suggestsMe: ChIPseeker, DOSE, GOSemSim, ReactomePA Package: clusterStab Version: 1.40.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: 53d829d6172bac8416956668f7a68ad1 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 source.ver: src/contrib/clusterStab_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/clusterStab_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/clusterStab_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/clusterStab_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clusterStab_1.40.0.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 Package: CMA Version: 1.26.0 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: 0ae1e34b3fd2375753616228386a2c20 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: Christoph Bernau source.ver: src/contrib/CMA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CMA_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CMA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CMA_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CMA_1.26.0.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 Package: CNAnorm Version: 1.14.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: i386, x64 MD5sum: d8d36837ef638676be84d1361a230b4d 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, source.ver: src/contrib/CNAnorm_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNAnorm_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNAnorm_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNAnorm_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNAnorm_1.14.0.tgz vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf vignetteTitles: CNAnorm.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CNEr Version: 1.4.0 Depends: R (>= 3.0.2) Imports: Biostrings(>= 2.33.4), RSQLite(>= 0.11.4), GenomeInfoDb(>= 1.1.3), GenomicRanges(>= 1.17.11), rtracklayer(>= 1.25.5), XVector(>= 0.5.4), DBI(>= 0.2-7), GenomicAlignments(>= 1.1.9), methods, S4Vectors(>= 0.0.4), IRanges(>= 1.99.6) LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz(>= 1.7.4), RUnit, BiocGenerics License: GPL-2 | file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: fd008ffd76410a801580e19cf9769a85 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: Ge Tan URL: http://ancora.genereg.net/ SystemRequirements: blat source.ver: src/contrib/CNEr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNEr_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNEr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNEr_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNEr_1.4.0.tgz vignettes: vignettes/CNEr/inst/doc/CNEr.pdf vignetteTitles: CNEr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R importsMe: TFBSTools Package: cn.farms Version: 1.16.0 Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice Suggests: pd.mapping250k.sty, pd.mapping250k.nsp, pd.genomewidesnp.5, pd.genomewidesnp.6 License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: c53fe4ba731478a0d23d092e48e298b5 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 source.ver: src/contrib/cn.farms_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cn.farms_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cn.farms_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cn.farms_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cn.farms_1.16.0.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 Package: cn.mops Version: 1.14.1 Depends: R (>= 2.12), BiocGenerics, Biobase, IRanges, GenomicRanges Imports: methods, graphics, Rsamtools, parallel Suggests: DNAcopy License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 70520df08f7f5c4185795b22894cc8bc 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 Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html source.ver: src/contrib/cn.mops_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/cn.mops_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.2/cn.mops_1.14.1.zip mac.binary.ver: bin/macosx/contrib/3.2/cn.mops_1.14.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cn.mops_1.14.1.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 Package: CNORdt Version: 1.10.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: i386, x64 MD5sum: 6dba87350715bbb5f2d7fc7ec18056ab 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: CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: A. MacNamara Maintainer: A. MacNamara source.ver: src/contrib/CNORdt_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNORdt_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNORdt_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNORdt_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNORdt_1.10.0.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.R Package: CNORfeeder Version: 1.8.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, igraph, Rgraphviz, RUnit, BiocGenerics License: GPL-3 MD5sum: 9e4b2f2a82cff839823444d7acb5e2c4 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: F.Eduati Maintainer: F.Eduati source.ver: src/contrib/CNORfeeder_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNORfeeder_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNORfeeder_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNORfeeder_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNORfeeder_1.8.0.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 Package: CNORfuzzy Version: 1.10.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: 6303f1bd05b6c8f2b409f61a275d21e3 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 source.ver: src/contrib/CNORfuzzy_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNORfuzzy_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNORfuzzy_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNORfuzzy_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNORfuzzy_1.10.0.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 Package: CNORode Version: 1.10.0 Depends: CellNOptR (>= 1.5.14), genalg Enhances: MEIGOR License: GPL-2 Archs: i386, x64 MD5sum: 2f797321d988cf8562878650e0b8f56c NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: ODE add-on to CellNOptR biocViews: CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer Maintainer: David Henriques source.ver: src/contrib/CNORode_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNORode_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNORode_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNORode_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNORode_1.10.0.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR Package: CNTools Version: 1.24.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: i386, x64 MD5sum: 5edc5184d87b829e3cfae6f416100b3d 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 source.ver: src/contrib/CNTools_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNTools_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNTools_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNTools_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNTools_1.24.0.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 Package: cnvGSA Version: 1.12.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: 51a2226fe113fbbc6fc42da5d1d38173 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 source.ver: src/contrib/cnvGSA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cnvGSA_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cnvGSA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cnvGSA_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cnvGSA_1.12.0.tgz vignettes: vignettes/cnvGSA/inst/doc/cnvGSAUsersGuide.pdf, vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf vignetteTitles: cnvGSAUsersGuide.pdf, cnvGSA - Gene-Set Analysis of Rare Copy Number Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.R Package: CNVrd2 Version: 1.6.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: 11398475d2b22e2c245fa1a1461ce03d 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 source.ver: src/contrib/CNVrd2_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNVrd2_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNVrd2_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNVrd2_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNVrd2_1.6.0.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 Package: CNVtools Version: 1.62.0 Depends: R (>= 2.10), survival License: GPL-3 Archs: i386, x64 MD5sum: f9a3ad5ac14cd6cac204be229f273642 NeedsCompilation: yes Title: A package to test genetic association with CNV data Description: This package is meant to facilitate the testing of Copy Number Variant data for genetic association, typically in case-control studies. biocViews: GeneticVariability Author: Chris Barnes and Vincent Plagnol Maintainer: Chris Barnes source.ver: src/contrib/CNVtools_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNVtools_1.62.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNVtools_1.62.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNVtools_1.62.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNVtools_1.62.0.tgz vignettes: vignettes/CNVtools/inst/doc/CNVtools-vignette.pdf vignetteTitles: Copy Number Variation Tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVtools/inst/doc/CNVtools-vignette.R Package: cobindR Version: 1.6.0 Imports: methods, seqinr, yaml, rtfbs, gplots, mclust, gmp, BiocGenerics (>= 0.13.8), IRanges, Biostrings, BSgenome, biomaRt Suggests: RUnit Enhances: rGADEM, seqLogo, genoPlotR, parallel, VennDiagram, RColorBrewer, vcd, MotifDb, snowfall License: Artistic-2.0 MD5sum: 89d682707648a7bc186533f12f9cc29f NeedsCompilation: no Title: Finding Co-occuring motifs of transcription factor binding sites Description: Finding and analysing co-occuring motifs of transcription factor binding sites in groups of genes biocViews: ChIPSeq, CellBiology, MultipleComparison, SequenceMatching Author: Manuela Benary, Stefan Kroeger, Yuehien Lee, Robert Lehmann Maintainer: Manuela Benary source.ver: src/contrib/cobindR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cobindR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cobindR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cobindR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cobindR_1.6.0.tgz vignettes: vignettes/cobindR/inst/doc/cobindR.pdf vignetteTitles: Using cobindR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cobindR/inst/doc/cobindR.R Package: CoCiteStats Version: 1.40.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 64d7b1fc1fa8e99b97417a415cc45b3d 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 source.ver: src/contrib/CoCiteStats_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CoCiteStats_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CoCiteStats_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CoCiteStats_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CoCiteStats_1.40.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: codelink Version: 1.36.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: d7fbd80266b5b7b63c976128fd27514a 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 VignetteBuilder: knitr source.ver: src/contrib/codelink_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/codelink_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/codelink_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/codelink_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/codelink_1.36.0.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 Package: CODEX Version: 1.0.0 Depends: R (>= 3.1.2), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19 Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: b506598cca66a0acfd8bb5c6f8ec9a86 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: ExomeSeq, Normalization, QualityControl, CopyNumberVariation Author: Yuchao Jiang, Nancy R. Zhang Maintainer: Yuchao Jiang source.ver: src/contrib/CODEX_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CODEX_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CODEX_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CODEX_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CODEX_1.0.0.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 Package: CoGAPS Version: 2.2.0 Depends: R (>= 3.0.1), Rcpp (>= 0.11.2), RColorBrewer (>= 1.0.5), gplots (>= 2.8.0) Imports: graphics, grDevices, methods, stats, utils LinkingTo: Rcpp, BH License: GPL (==2) Archs: i386, x64 MD5sum: 502098c4b2e8520055f04399e3e5354f 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, Microarray Author: Elana J. Fertig, Michael F. Ochs Maintainer: Elana J. Fertig , Michael F. Ochs source.ver: src/contrib/CoGAPS_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CoGAPS_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CoGAPS_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CoGAPS_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CoGAPS_2.2.0.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPSUsersManual.pdf vignetteTitles: GAPS/CoGAPS Users Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPSUsersManual.R Package: cogena Version: 1.0.0 Depends: R (>= 3.2), cluster, ggplot2, gplots, amap Imports: methods, class, foreach, parallel, doParallel, fastcluster, corrplot, reshape2, devtools Suggests: Biobase, annotate, kohonen, mclust, biwt, knitr License: LGPL-3 MD5sum: bd407eda38d832c5596e992bb773430d NeedsCompilation: no Title: co-expressed gene-set enrichment analysis Description: Description: Gene set enrichment analysis is a valuable tool for the study of molecular mechanisms that underpin complex biological traits. As the method is conventionally used on entire omic datasets, such as transcriptomes, it may be dominated by pathways and processes that are substantially represented in a dataset, however the approach may overlook smaller scale, but highly correlated cellular events that may be of great biological relevance. In order to detect these discrete molecular triggers, we developed a tool, co-expressed gene-set enrichment analysis (cogena), for clustering differentially expressed genes and identification of highly correlated molecular expression clusters. Cogena offers the user a range of clustering methods, including hierarchical clustering, model based clustering and self-organised mapping, based on different distance metrics like correlation and mutual information. After obtaining and visualising clusters, cogena performs gene set enrichment. These gene sets can be sourced from the Molecular Signatures Database (MSigDB) or user-defined gene sets. By performing gene set enrichment across expression clusters, we find considerable enhancement in the resolution of molecular signatures in omic data at the cluster level compared to the whole. biocViews: Clustering, GeneSetEnrichment, GeneExpression, Visualization, Pathways, 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 source.ver: src/contrib/cogena_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cogena_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cogena_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cogena_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cogena_1.0.0.tgz vignettes: vignettes/cogena/inst/doc/cogena-vignette.pdf vignetteTitles: cogena-vignette.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: coGPS Version: 1.12.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: 58d80a94c468540fe84de885af556107 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 source.ver: src/contrib/coGPS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/coGPS_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/coGPS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/coGPS_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/coGPS_1.12.0.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 Package: COHCAP Version: 1.6.0 Depends: WriteXLS, COHCAPanno License: GPL-3 MD5sum: c0b9d0a2f15e0ac264b9fc581411477a NeedsCompilation: no Title: CpG Island Analysis Pipeline for Illumina Methylation Array and Targeted BS-Seq Data Description: This package provides a pipeline to analyze single-nucleotide resolution methylation data (Illumina 450k methylation array, targeted BS-Seq, etc.). It provides QC metrics, differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, and visualization of methylation values. biocViews: DNAMethylation, Microarray, MethylSeq, Epigenetics, DifferentialMethylation Author: Charles Warden Maintainer: Charles Warden source.ver: src/contrib/COHCAP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/COHCAP_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/COHCAP_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/COHCAP_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/COHCAP_1.6.0.tgz vignettes: vignettes/COHCAP/inst/doc/COHCAP.pdf vignetteTitles: COHCAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COHCAP/inst/doc/COHCAP.R Package: coMET Version: 1.0.0 Depends: R (>= 3.1.0), grid, biomaRt, Gviz (>= 1.10.9), psych Imports: colortools, hash, grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, ggbio, ggplot2, trackViewer Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 38516aa9e4aa4e87a097d633e8f8e534 NeedsCompilation: no Title: coMET: visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns. Description: Visualisation of EWAS results in a genomic region. In addition to phenotype-association P-values, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. It can be used to other omic-wide association scans as long as the data can be translated to genomic level and for any species. biocViews: Software, DifferentialMethylation, Visualization, Sequencing, Genetics, FunctionalGenomics, Microarray, MethylationArray, MethylSeq, ChIPSeq, DNASeq, RIPSeq, RNASeq, ExomeSeq, DNAMethylation, GenomeWideAssociation Author: Tiphaine C. Martin, Idil Yet, Pei-Chien Tsai, Jordana T. Bell Maintainer: Tiphaine Martin URL: http://epigen.kcl.ac.uk/comet VignetteBuilder: knitr source.ver: src/contrib/coMET_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/coMET_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/coMET_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/coMET_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/coMET_1.0.0.tgz vignettes: vignettes/coMET/inst/doc/coMET_manual.pdf, vignettes/coMET/inst/doc/coMET.pdf vignetteTitles: coMET_manual.pdf, coMET users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/coMET/inst/doc/coMET.R Package: COMPASS Version: 1.6.0 Depends: R (>= 3.0.2) Imports: Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.9.66), shiny, testthat, devtools, Kmisc License: Artistic-2.0 Archs: i386, x64 MD5sum: 9bf3652b6f1cdcaaaabb33e82fd27ed4 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: FlowCytometry Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap) Maintainer: Greg Finak VignetteBuilder: knitr BugReports: https://github.com/RGLab/COMPASS/issues source.ver: src/contrib/COMPASS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/COMPASS_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/COMPASS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/COMPASS_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/COMPASS_1.6.0.tgz vignettes: vignettes/COMPASS/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R htmlDocs: vignettes/COMPASS/inst/doc/COMPASS.html htmlTitles: "COMPASS" Package: compcodeR Version: 1.4.0 Depends: R (>= 3.0.2), sm Imports: tcltk, knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, gdata, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods Suggests: BiocStyle, EBSeq, DESeq, DESeq2 (>= 1.1.31), baySeq (>= 1.16.0), genefilter, NOISeq, TCC, samr, NBPSeq Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: 7cf5b0602ed2fb54df1600340343e703 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 and interfaces to several packages for performing the differential expression analysis. biocViews: RNASeq, DifferentialExpression Author: Charlotte Soneson Maintainer: Charlotte Soneson VignetteBuilder: knitr source.ver: src/contrib/compcodeR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/compcodeR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/compcodeR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/compcodeR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/compcodeR_1.4.0.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.pdf vignetteTitles: compcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R Package: compEpiTools Version: 1.2.6 Depends: R (>= 3.1.1), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, GenomeInfoDb, ggplot2 Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: 91311698c4d02c538789ef86b19f3083 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: http://genomics.iit.it/groups/computational-epigenomics.html Maintainer: Kamal Kishore VignetteBuilder: knitr source.ver: src/contrib/compEpiTools_1.2.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/compEpiTools_1.2.6.zip win64.binary.ver: bin/windows64/contrib/3.2/compEpiTools_1.2.6.zip mac.binary.ver: bin/macosx/contrib/3.2/compEpiTools_1.2.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/compEpiTools_1.2.6.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 Package: CompGO Version: 1.4.0 Depends: RDAVIDWebService Imports: rtracklayer, Rgraphviz, ggplot2, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, pcaMethods, reshape2, pathview License: GPL-2 MD5sum: d9811bb0762b6a5f38c26e7c8a824214 NeedsCompilation: no Title: An R pipeline for .bed file annotation, comparing GO term enrichment between gene sets and data visualisation Description: This package contains functions to accomplish several tasks. It is able to download full genome databases from UCSC, import .bed files easily, annotate these .bed file regions with genes (plus distance) from aforementioned database dumps, interface with DAVID to create functional annotation and gene ontology enrichment charts based on gene lists (such as those generated from input .bed files) and finally visualise and compare these enrichments using either directed acyclic graphs or scatterplots. biocViews: GeneSetEnrichment, MultipleComparison, GO, Visualization Author: Sam D. Bassett [aut], Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg source.ver: src/contrib/CompGO_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CompGO_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CompGO_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CompGO_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CompGO_1.4.0.tgz vignettes: vignettes/CompGO/inst/doc/CompGO-Intro.pdf, vignettes/CompGO/inst/doc/CompGO-vignette.pdf vignetteTitles: Introduction, CompGO-vignette.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompGO/inst/doc/CompGO-Intro.R Package: ComplexHeatmap Version: 1.0.0 Depends: R (>= 3.1.0), grid Imports: methods, circlize (>= 0.2.3), GetoptLong, colorspace, RColorBrewer Suggests: testthat (>= 0.3), knitr, markdown, cluster, dendextend License: GPL (>= 2) MD5sum: 40333a476af29e6b719c59338c2141a4 NeedsCompilation: no Title: Making Complex Heatmaps Description: Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential features. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports self-defined annotation graphics. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/ComplexHeatmap VignetteBuilder: knitr source.ver: src/contrib/ComplexHeatmap_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ComplexHeatmap_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ComplexHeatmap_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ComplexHeatmap_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ComplexHeatmap_1.0.0.tgz vignettes: vignettes/ComplexHeatmap/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ComplexHeatmap/inst/doc/ComplexHeatmap.R htmlDocs: vignettes/ComplexHeatmap/inst/doc/ComplexHeatmap.html htmlTitles: "Making Complex Heatmaps" importsMe: EnrichmentBrowser Package: ConsensusClusterPlus Version: 1.22.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: 6ea4353334683a250c17027b89d8508f 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 source.ver: src/contrib/ConsensusClusterPlus_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ConsensusClusterPlus_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ConsensusClusterPlus_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ConsensusClusterPlus_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ConsensusClusterPlus_1.22.0.tgz vignettes: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf vignetteTitles: ConsensusClusterPlus Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: FlowSOM Package: conumee Version: 1.0.0 Depends: R (>= 3.0), minfi, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19 Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges, GenomeInfoDb Suggests: BiocStyle, knitr, rmarkdown, minfiData, CopyNumber450kData, RCurl License: GPL (>= 2) MD5sum: c44abcf4ff30dd8a6ccfba2c9b4c91e6 NeedsCompilation: no Title: Enhanced copy-number variation analysis using Illumina 450k methylation arrays Description: This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k methylation arrays. biocViews: CopyNumberVariation, DNAMethylation, MethylationArray, Microarray, Normalization, Preprocessing, QualityControl, Software Author: Volker Hovestadt, Marc Zapatka Maintainer: Volker Hovestadt VignetteBuilder: knitr source.ver: src/contrib/conumee_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/conumee_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/conumee_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/conumee_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/conumee_1.0.0.tgz vignettes: vignettes/conumee/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conumee/inst/doc/conumee.R htmlDocs: vignettes/conumee/inst/doc/conumee.html htmlTitles: "The conumee vignette" Package: convert Version: 1.44.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: 8763b95cb6430518375a54950620a942 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 Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html source.ver: src/contrib/convert_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/convert_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/convert_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/convert_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/convert_1.44.0.tgz vignettes: vignettes/convert/inst/doc/convert.pdf vignetteTitles: Converting Between Microarray Data Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/convert/inst/doc/convert.R dependsOnMe: maigesPack, TurboNorm suggestsMe: BiocCaseStudies, dyebias, OLIN Package: copa Version: 1.36.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: i386, x64 MD5sum: bb20c0e6d420123815db02d9bee649bd 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 source.ver: src/contrib/copa_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/copa_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/copa_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/copa_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/copa_1.36.0.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 Package: copynumber Version: 1.8.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 MD5sum: f3c08a834fbbfbccd9fa891db36892fa NeedsCompilation: no Title: Segmentation of single- and multi-track copy number data by penalized least squares regression. Description: Penalized least squares regression is applied to fit piecewise constant curves to copy number data to locate genomic regions of constant copy number. Procedures are available for individual segmentation of each sample, joint segmentation of several samples and joint segmentation of the two data tracks from SNP-arrays. Several plotting functions are available for visualization of the data and the segmentation results. biocViews: aCGH, SNP, CopyNumberVariation, Genetics, Visualization Author: Gro Nilsen, Knut Liestoel and Ole Christian Lingjaerde. Maintainer: Gro Nilsen source.ver: src/contrib/copynumber_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/copynumber_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/copynumber_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/copynumber_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/copynumber_1.8.0.tgz vignettes: vignettes/copynumber/inst/doc/copynumber.pdf vignetteTitles: copynumber.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copynumber/inst/doc/copynumber.R Package: CopyNumber450k Version: 1.4.0 Depends: Biobase, minfi, DNAcopy, preprocessCore, BiocGenerics Imports: methods Suggests: CopyNumber450kData, minfiData License: Artistic-2.0 MD5sum: 3b805d0c19585461e74eba06932b9660 NeedsCompilation: no Title: R package for calling CNV from Illumina 450k methylation microarrays Description: This package contains a set of functions that allow CNV calling from Illumina 450k methylation microarrays. biocViews: DNAMethylation, Microarray, Preprocessing, QualityControl, CopyNumberVariation Author: Simon Papillon-Cavanagh, Jean-Philippe Fortin, Nicolas De Jay Maintainer: Simon Papillon-Cavanagh source.ver: src/contrib/CopyNumber450k_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CopyNumber450k_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CopyNumber450k_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CopyNumber450k_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CopyNumber450k_1.4.0.tgz vignettes: vignettes/CopyNumber450k/inst/doc/CopyNumber450k.pdf vignetteTitles: CopyNumber450k User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopyNumber450k/inst/doc/CopyNumber450k.R Package: CopywriteR Version: 2.0.6 Depends: R(>= 3.2), BiocParallel Imports: matrixStats, gtools, data.table, S4Vectors, chipseq, IRanges, Rsamtools, DNAcopy, GenomicAlignments, GenomicRanges, CopyhelpeR, GenomeInfoDb, futile.logger Suggests: BiocStyle, SCLCBam, snow License: GPL-2 MD5sum: 1dab8a478781952a2a196b27d5a1301b NeedsCompilation: no Title: Copy number information from targeted sequencing using off-target reads Description: CopywriteR extracts DNA copy number information from targeted sequencing by utiizing off-target reads. It allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. Thereby, CopywriteR constitutes a widely applicable alternative to available copy number detection tools. biocViews: TargetedResequencing, ExomeSeq, CopyNumberVariation, Preprocessing, Visualization, Coverage Author: Thomas Kuilman Maintainer: Thomas Kuilman URL: https://github.com/PeeperLab/CopywriteR source.ver: src/contrib/CopywriteR_2.0.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/CopywriteR_2.0.6.zip win64.binary.ver: bin/windows64/contrib/3.2/CopywriteR_2.0.6.zip mac.binary.ver: bin/macosx/contrib/3.2/CopywriteR_2.0.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CopywriteR_2.0.6.tgz vignettes: vignettes/CopywriteR/inst/doc/CopywriteR.pdf vignetteTitles: CopywriteR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopywriteR/inst/doc/CopywriteR.R Package: CoRegNet Version: 1.4.0 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr License: GPL-3 Archs: i386, x64 MD5sum: a5622de6fbd363f97f9d3631207d24fa NeedsCompilation: yes Title: CoRegNet : reconstruction and integrated analysis of co-regulatory networks Description: This package provides methods to identify active transcriptional programs. Methods and classes are provided to import or infer large scale co-regulatory network from transcriptomic data. The specificity of the encoded networks is to model Transcription Factor cooperation. External regulation evidences (TFBS, ChIP,...) can be integrated to assess the inferred network and refine it if necessary. Transcriptional activity of the regulators in the network can be estimated using an measure of their influence in a given sample. Finally, an interactive UI can be used to navigate through the network of cooperative regulators and to visualize their activity in a specific sample or subgroup sample. The proposed visualization tool can be used to integrate gene expression, transcriptional activity, copy number status, sample classification and a transcriptional network including co-regulation information. biocViews: NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork,SystemsBiology, Network, Visualization, Transcription Author: Remy Nicolle, Thibault Venzac and Mohamed Elati Maintainer: Remy Nicolle VignetteBuilder: knitr source.ver: src/contrib/CoRegNet_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CoRegNet_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CoRegNet_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CoRegNet_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CoRegNet_1.4.0.tgz vignettes: vignettes/CoRegNet/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegNet/inst/doc/CoRegNet.R htmlDocs: vignettes/CoRegNet/inst/doc/CoRegNet.html htmlTitles: "CoRegNet: Reconstruction and integrated analysis of Co-Regulatory Networks" Package: Cormotif Version: 1.14.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: 984d92cd1ba5fc33ed981fcb72e511a5 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 source.ver: src/contrib/Cormotif_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Cormotif_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Cormotif_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Cormotif_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Cormotif_1.14.0.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 Package: CorMut Version: 1.10.0 Depends: seqinr,igraph License: GPL-2 MD5sum: ab9ecaf557491cb185e494af76e5ac0f NeedsCompilation: no Title: Detect the correlated mutations based on selection pressure Description: CorMut provides functions for computing kaks for individual sites or specific amino acids and detecting correlated mutations among them. Three methods are provided for detecting correlated mutations ,including conditional selection pressure, mutual information and Jaccard index. The computation consists of two steps: First, the positive selection sites are detected; Second, the mutation correlations are computed among the positive selection sites. Note that the first step is optional. Meanwhile, CorMut facilitates the comparison of the correlated mutations between two conditions by the means of correlated mutation network. biocViews: Sequencing Author: Zhenpeng Li, Yang Huang, Yabo Ouyang, Yiming Shao, Liying Ma Maintainer: Zhenpeng Li source.ver: src/contrib/CorMut_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CorMut_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CorMut_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CorMut_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CorMut_1.10.0.tgz vignettes: vignettes/CorMut/inst/doc/CorMut.pdf vignetteTitles: CorMut hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CorMut/inst/doc/CorMut.R Package: coRNAi Version: 1.18.0 Depends: R (>= 2.10), cellHTS2, limma, locfit Imports: MASS, gplots, lattice, grDevices, graphics, stats License: Artistic-2.0 MD5sum: 0b9cc83e05cee8cf02b6be98275fb4aa NeedsCompilation: no Title: Analysis of co-knock-down RNAi data Description: Analysis of combinatorial cell-based RNAi screens biocViews: CellBasedAssays Author: Elin Axelsson Maintainer: Elin Axelsson SystemRequirements: Graphviz source.ver: src/contrib/coRNAi_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/coRNAi_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/coRNAi_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/coRNAi_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/coRNAi_1.18.0.tgz vignettes: vignettes/coRNAi/inst/doc/coRNAi.pdf vignetteTitles: coRNAi hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coRNAi/inst/doc/coRNAi.R Package: CORREP Version: 1.34.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: 99b9807da0c8f785ea319dc51e625925 NeedsCompilation: no Title: Multivariate Correlation Estimator and Statistical Inference Procedures. Description: Multivariate correlation estimation and statistical inference. See package vignette. biocViews: Microarray, Clustering, GraphAndNetwork Author: Dongxiao Zhu and Youjuan Li Maintainer: Dongxiao Zhu source.ver: src/contrib/CORREP_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CORREP_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CORREP_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CORREP_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CORREP_1.34.0.tgz vignettes: vignettes/CORREP/inst/doc/CORREP.pdf vignetteTitles: Multivariate Correlation Estimator hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CORREP/inst/doc/CORREP.R Package: cosmiq Version: 1.2.0 Depends: R (>= 3.0.2), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 08c70adf5a8ac3cb27b1395c50b7f151 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: MassSpectrometry, Metabolomics Author: David Fischer , Christian Panse , Endre Laczko Maintainer: David Fischer , Christian Panse URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html source.ver: src/contrib/cosmiq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cosmiq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cosmiq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cosmiq_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cosmiq_1.2.0.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 Package: COSNet Version: 1.3.3 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 1714c4ee750d68dd1288864804b1f829 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 source.ver: src/contrib/COSNet_1.3.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/COSNet_1.3.3.zip win64.binary.ver: bin/windows64/contrib/3.2/COSNet_1.3.3.zip mac.binary.ver: bin/macosx/contrib/3.2/COSNet_1.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/COSNet_1.3.3.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 Package: CoverageView Version: 1.5.2 Depends: R (>= 2.10), methods,Rsamtools (>= 1.19.17),rtracklayer Imports: S4Vectors,IRanges,GenomicRanges,GenomicAlignments,parallel,tools License: Artistic-2.0 MD5sum: c42a9a54cfb50957545842c7eb6b5a33 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: Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software Author: Ernesto Lowy Maintainer: Ernesto Lowy source.ver: src/contrib/CoverageView_1.5.2.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/CoverageView_1.5.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CoverageView_1.5.2.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 Package: cpvSNP Version: 1.0.0 Depends: R (>= 2.10), 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: a8113caabd0171574e8d6d385c0bec01 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 source.ver: src/contrib/cpvSNP_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cpvSNP_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cpvSNP_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cpvSNP_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cpvSNP_1.0.0.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 Package: cqn Version: 1.14.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: 61bc3cb3303f886f5081efb0b973c948 NeedsCompilation: no Title: Conditional quantile normalization Description: A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. biocViews: RNASeq, Preprocessing, DifferentialExpression Author: Jean (Zhijin) Wu, Kasper Daniel Hansen Maintainer: Kasper Daniel Hansen source.ver: src/contrib/cqn_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cqn_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cqn_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cqn_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cqn_1.14.0.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 importsMe: tweeDEseq Package: CRImage Version: 1.16.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 7a5a2cf3bbeac72f10f4bc230ad7585c 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 source.ver: src/contrib/CRImage_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CRImage_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CRImage_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CRImage_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CRImage_1.16.0.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 Package: CRISPRseek Version: 1.8.1 Depends: R (>= 3.0.1), BiocGenerics, Biostrings, BSgenome, seqinr Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: e09e79b6d378c7ae071b75a50fc31289 NeedsCompilation: no Title: Design of target-specific guide RNAs in CRISPR-Cas9, genome-editing systems Description: The package includes functions to find potential guide RNAs for input target sequences, optionally filter guide RNAs without restriction enzyme cut site, or without paired guide RNAs, genome-wide search for off-targets, score, rank, fetch flank sequence and indicate whether the target and off-targets are located in exon region or not. Potential guide RNAs are annotated with gRNA efficacy, total score of the top5 and topN off-targets, detailed topN mismatch sites, restriction enzyme cut sites, and paired guide RNAs. If GeneRfold is installed, then the minimum free energy and bracket notation of secondary structure of gRNA and gRNA backbone constant region will be included in the summary file. This package leverages Biostrings and BSgenome packages. biocViews: GeneRegulation, SequenceMatching Author: Lihua Julie Zhu, Benjamin R. Holmes, Herve Pages, Isana Veksler-Lublinsky, Victor Ambros, Neil Aronin and Michael Brodsky Maintainer: Lihua Julie Zhu source.ver: src/contrib/CRISPRseek_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/CRISPRseek_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/CRISPRseek_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/CRISPRseek_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CRISPRseek_1.8.1.tgz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.pdf vignetteTitles: CRISPRseek Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R Package: crlmm Version: 1.26.0 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, SNPchip, utils, lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats, VGAM, parallel LinkingTo: preprocessCore (>= 1.17.7) Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), GGdata, snpStats, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 18a9d771220e9cac10c3fa44f5fd3dc4 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 source.ver: src/contrib/crlmm_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/crlmm_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/crlmm_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/crlmm_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/crlmm_1.26.0.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/AffyGW.R, vignettes/crlmm/inst/doc/CopyNumberOverview.R, vignettes/crlmm/inst/doc/genotyping.R, vignettes/crlmm/inst/doc/gtypeDownstream.R, vignettes/crlmm/inst/doc/IlluminaPreprocessCN.R, vignettes/crlmm/inst/doc/Infrastructure.R importsMe: VanillaICE suggestsMe: ArrayTV, oligoClasses, SNPchip Package: CSAR Version: 1.20.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 Archs: i386, x64 MD5sum: c6d596e23979cc5ff3b0bf442768b859 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 source.ver: src/contrib/CSAR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CSAR_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CSAR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CSAR_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CSAR_1.20.0.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 importsMe: NarrowPeaks suggestsMe: NarrowPeaks Package: csaw Version: 1.2.1 Depends: R (>= 3.2.0), GenomicRanges Imports: Rsamtools, edgeR, limma, GenomicFeatures, AnnotationDbi, methods, GenomicAlignments, S4Vectors, IRanges, GenomeInfoDb Suggests: org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-3 Archs: i386, x64 MD5sum: efeb91e351dcda5e8897fdd2741690a0 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 Author: Aaron Lun , Gordon Smyth Maintainer: Aaron Lun source.ver: src/contrib/csaw_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/csaw_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/csaw_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/csaw_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/csaw_1.2.1.tgz vignettes: vignettes/csaw/inst/doc/csaw.pdf, vignettes/csaw/inst/doc/csawUserGuide.pdf vignetteTitles: csaw Vignette, csawUserGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csaw/inst/doc/csaw.R importsMe: diffHic Package: CSSP Version: 1.6.0 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: i386, x64 MD5sum: 14bd54bf20a7070ed3527f809d61d373 NeedsCompilation: yes Title: ChIP-Seq Statistical Power Description: Power computation for ChIP-Seq data based on Bayesian estimation for local poisson counting process. biocViews: ChIPSeq, Sequencing, QualityControl, Bayesian Author: Chandler Zuo, Sunduz Keles Maintainer: Chandler Zuo source.ver: src/contrib/CSSP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CSSP_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CSSP_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CSSP_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CSSP_1.6.0.tgz vignettes: vignettes/CSSP/inst/doc/cssp.pdf vignetteTitles: cssp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSP/inst/doc/cssp.R Package: ctc Version: 1.42.0 Depends: amap License: GPL-2 MD5sum: 4c0f7feeb25a800b463f04e85529f559 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 source.ver: src/contrib/ctc_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ctc_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ctc_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ctc_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ctc_1.42.0.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 Package: cummeRbund Version: 2.10.0 Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite, ggplot2, reshape2, fastcluster, rtracklayer, Gviz Imports: methods, plyr, BiocGenerics, Biobase Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges, rjson License: Artistic-2.0 MD5sum: 2b03fefa85fb68510162d1e43b6dd8c6 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 source.ver: src/contrib/cummeRbund_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cummeRbund_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cummeRbund_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cummeRbund_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cummeRbund_2.10.0.tgz vignettes: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf, vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf vignetteTitles: Sample cummeRbund workflow, CummeRbund User Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cummeRbund/inst/doc/cummeRbund-manual.R dependsOnMe: meshr, spliceR suggestsMe: oneChannelGUI Package: customProDB Version: 1.8.2 Depends: R (>= 3.0.1), IRanges, AnnotationDbi, biomaRt Imports: IRanges, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.17.13), biomaRt (>= 2.17.1), stringr, RCurl, plyr, VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite, AnnotationDbi Suggests: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 9e0e4f5873eda7ef87739c3dc1f3eef8 NeedsCompilation: no Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search. Description: Generate customized protein sequence database from RNA-Seq data for proteomics search biocViews: MassSpectrometry, Proteomics, SNP, RNASeq, Software, Transcription, AlternativeSplicing Author: xiaojing wang Maintainer: xiaojing wang source.ver: src/contrib/customProDB_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/customProDB_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.2/customProDB_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.2/customProDB_1.8.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/customProDB_1.8.2.tgz vignettes: vignettes/customProDB/inst/doc/customProDB.pdf vignetteTitles: Introduction to customProDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customProDB/inst/doc/customProDB.R Package: cycle Version: 1.22.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: 2745596aec743800a8152fa6d008c65a 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://itb.biologie.hu-berlin.de/~futschik/software/R/cycle/index.html source.ver: src/contrib/cycle_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cycle_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cycle_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cycle_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cycle_1.22.0.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 Package: cytofkit Version: 1.1.0 Depends: R (>= 2.10.0) Imports: tcltk, stats, Rtsne, e1071, flowCore, ggplot2, gplots, vegan, reshape, Biobase Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 685dd78779f3141d42982bffdf5c62ee NeedsCompilation: no Title: cytofkit: an integrated analysis pipeline for mass cytometry data Description: An integrated mass cytometry data analysis pipeline that enables simultaneous illustration of cellular diversity and progression. biocViews: BiomedicalInformatics, GUI, CellBiology, Clustering, DimensionReduction, MassSpectrometry Author: Jinmiao Chen, Hao Chen Maintainer: Jinmiao Chen VignetteBuilder: knitr source.ver: src/contrib/cytofkit_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cytofkit_1.1.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cytofkit_1.1.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cytofkit_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cytofkit_1.1.0.tgz vignettes: vignettes/cytofkit/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytofkit/inst/doc/cytofkit_example.R, vignettes/cytofkit/inst/doc/cytofkit_workflow.R htmlDocs: vignettes/cytofkit/inst/doc/cytofkit_example.html, vignettes/cytofkit/inst/doc/cytofkit_workflow.html htmlTitles: "cytofkit: run with an example", "cytofkit: workflow of mass cytometry data analysis" Package: dagLogo Version: 1.6.0 Depends: R (>= 3.0.1), methods, biomaRt, grImport, grid, motifStack Imports: pheatmap, Biostrings Suggests: XML, UniProt.ws, RUnit, BiocGenerics, BiocStyle License: GPL (>=2) MD5sum: a00becf1b70d4160523dd99dc253cba0 NeedsCompilation: no Title: dagLogo Description: Visualize significant conserved amino acid sequence pattern in groups based on probability theory biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Alexey Stukalov, Niraj Nirala, Usha Acharya, Lihua Julie Zhu Maintainer: Jianhong Ou source.ver: src/contrib/dagLogo_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/dagLogo_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/dagLogo_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/dagLogo_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/dagLogo_1.6.0.tgz vignettes: vignettes/dagLogo/inst/doc/dagLogo.pdf vignetteTitles: dagLogo Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R Package: daMA Version: 1.40.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: 52850640a0ae941fcfce4ba487ded306 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 source.ver: src/contrib/daMA_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/daMA_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/daMA_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/daMA_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/daMA_1.40.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DART Version: 1.16.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: 3322bec7aa0fe7c138ae62ba8a083e91 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 source.ver: src/contrib/DART_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DART_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DART_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DART_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DART_1.16.0.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 Package: DASiR Version: 1.8.0 Depends: IRanges, GenomicRanges, XML, Biostrings License: LGPL (>= 3) MD5sum: de115661a2c7748dcde34ed57e7e17dd NeedsCompilation: no Title: Distributed Annotation System in R Description: R package for programmatic retrieval of information from DAS servers biocViews: Annotation Author: Oscar Flores, Anna Mantsoki Maintainer: Oscar Flores , Anna Mantsoki source.ver: src/contrib/DASiR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DASiR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DASiR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DASiR_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DASiR_1.8.0.tgz vignettes: vignettes/DASiR/inst/doc/DASiR.pdf vignetteTitles: Programmatic retrieval of information from DAS servers hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DASiR/inst/doc/DASiR.R Package: DAVIDQuery Version: 1.28.0 Depends: RCurl (>= 1.4.0), utils License: GPL-2 MD5sum: 8d1d70c49a3c5f2af4d7570b89f06f12 NeedsCompilation: no Title: Retrieval from the DAVID bioinformatics data resource into R Description: Tools to retrieve data from DAVID, the Database for Annotation, Visualization and Integrated Discovery biocViews: Annotation Author: Roger Day, Alex Lisovich Maintainer: Roger Day source.ver: src/contrib/DAVIDQuery_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DAVIDQuery_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DAVIDQuery_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DAVIDQuery_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DAVIDQuery_1.30.4.tgz vignettes: vignettes/DAVIDQuery/inst/doc/DAVIDQuery.pdf vignetteTitles: An R Package for retrieving data from DAVID into R objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DAVIDQuery/inst/doc/DAVIDQuery.R importsMe: IdMappingRetrieval, R3CPET Package: DBChIP Version: 1.12.0 Depends: R (>= 2.15.0), edgeR, DESeq Suggests: ShortRead, BiocGenerics License: GPL (>= 2) MD5sum: 081e9e4be5970242c557f5e06af6080d NeedsCompilation: no Title: Differential Binding of Transcription Factor with ChIP-seq Description: DBChIP detects differentially bound sharp binding sites across multiple conditions, with or without matching control samples. biocViews: ChIPSeq, Sequencing, Transcription, Genetics Author: Kun Liang Maintainer: Kun Liang source.ver: src/contrib/DBChIP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DBChIP_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DBChIP_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DBChIP_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DBChIP_1.12.0.tgz vignettes: vignettes/DBChIP/inst/doc/DBChIP.pdf vignetteTitles: DBChIP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DBChIP/inst/doc/DBChIP.R Package: ddCt Version: 1.22.0 Depends: R (>= 2.3.0), Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, methods Suggests: RUnit License: LGPL-3 MD5sum: c728ea3d8ea33e8e072594ec832691fe 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 source.ver: src/contrib/ddCt_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ddCt_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ddCt_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ddCt_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ddCt_1.22.0.tgz vignettes: vignettes/ddCt/inst/doc/rtPCR.pdf, vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf, vignettes/ddCt/inst/doc/rtPCR-usage.pdf vignetteTitles: Introduction to the ddCt method for qRT-PCR data analysis: background,, algorithm and example, How to apply the ddCt method, Analyse RT-PCR data with the end-to-end script in ddCt package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddCt/inst/doc/rtPCR.R, vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R, vignettes/ddCt/inst/doc/rtPCR-usage.R Package: ddgraph Version: 1.12.0 Depends: graph, methods, Rcpp Imports: bnlearn (>= 2.8), gtools, pcalg, RColorBrewer, plotrix, MASS LinkingTo: Rcpp Suggests: Rgraphviz, e1071, ROCR, testthat License: GPL-3 Archs: i386, x64 MD5sum: bb47aa11654dcb47ab301b091d2ebc0e NeedsCompilation: yes Title: Distinguish direct and indirect interactions with Graphical Modelling Description: Distinguish direct from indirect interactions in gene regulation and infer combinatorial code from highly correlated variables such as transcription factor binding profiles. The package implements the Neighbourhood Consistent PC algorithm (NCPC) and draws Direct Dependence Graphs to represent dependence structure around a target variable. The package also provides a unified interface to other Graphical Modelling (Bayesian Network) packages for distinguishing direct and indirect interactions. biocViews: GraphAndNetwork Author: Robert Stojnic Maintainer: Robert Stojnic source.ver: src/contrib/ddgraph_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ddgraph_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ddgraph_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ddgraph_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ddgraph_1.12.0.tgz vignettes: vignettes/ddgraph/inst/doc/ddgraph.pdf vignetteTitles: Overview of the 'ddgraph' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddgraph/inst/doc/ddgraph.R Package: DECIPHER Version: 1.14.5 Depends: R (>= 2.13.0), Biostrings (>= 2.35.12), RSQLite (>= 1.0.0), stats, parallel Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, RSQLite, S4Vectors, IRanges, XVector License: GPL-3 Archs: i386, x64 MD5sum: 3aa0270970ccc170fa5dfd478512e279 NeedsCompilation: yes Title: Database Enabled Code for Ideal Probe Hybridization Employing R Description: A toolset for deciphering and managing biological sequences. biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization, Microarray, QualityControl, qPCR, Alignment Author: Erik Wright Maintainer: Erik Wright source.ver: src/contrib/DECIPHER_1.14.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/DECIPHER_1.14.5.zip win64.binary.ver: bin/windows64/contrib/3.2/DECIPHER_1.14.5.zip mac.binary.ver: bin/macosx/contrib/3.2/DECIPHER_1.14.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DECIPHER_1.14.5.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.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 vignetteTitles: The Art of Multiple Sequence Alignment in R, Getting Started DECIPHERing, Design Microarray Probes, Design Group-Specific Primers, Design Group-Specific FISH Probes, Design Primers That Yield Group-Specific Signatures, Finding Chimeric Sequences hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.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 Package: DeconRNASeq Version: 1.10.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: b76f84f5f85f1a1e6b34f9080af53c29 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 source.ver: src/contrib/DeconRNASeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DeconRNASeq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DeconRNASeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DeconRNASeq_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DeconRNASeq_1.10.0.tgz vignettes: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf vignetteTitles: DeconRNASeq Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.R Package: DEDS Version: 1.42.0 Depends: R (>= 1.7.0) License: LGPL Archs: i386, x64 MD5sum: 795e7848f6196c77bc7254c13f9d5527 NeedsCompilation: yes Title: Differential Expression via Distance Summary for Microarray Data Description: This library contains functions that calculate various statistics of differential expression for microarray data, including t statistics, fold change, F statistics, SAM, moderated t and F statistics and B statistics. It also implements a new methodology called DEDS (Differential Expression via Distance Summary), which selects differentially expressed genes by integrating and summarizing a set of statistics using a weighted distance approach. biocViews: Microarray, DifferentialExpression Author: Yuanyuan Xiao , Jean Yee Hwa Yang . Maintainer: Yuanyuan Xiao source.ver: src/contrib/DEDS_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DEDS_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DEDS_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DEDS_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DEDS_1.42.0.tgz vignettes: vignettes/DEDS/inst/doc/DEDS.pdf vignetteTitles: DEDS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEDS/inst/doc/DEDS.R Package: deepSNV Version: 1.14.2 Depends: R (>= 2.13.0), methods, graphics, parallel, Rhtslib, IRanges, GenomicRanges, Biostrings, VGAM, VariantAnnotation (>= 1.13.44), Imports: Rhtslib LinkingTo: Rhtslib Suggests: RColorBrewer, knitr License: GPL-3 Archs: i386, x64 MD5sum: 900f651b03b531484b4ffd5edc97e13f 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], David Jones [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre] Maintainer: Moritz Gerstung URL: http://github.com/mg14/deepSNV VignetteBuilder: knitr source.ver: src/contrib/deepSNV_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/deepSNV_1.14.2.zip win64.binary.ver: bin/windows64/contrib/3.2/deepSNV_1.14.2.zip mac.binary.ver: bin/macosx/contrib/3.2/deepSNV_1.14.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/deepSNV_1.14.2.tgz vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R, vignettes/deepSNV/inst/doc/shearwaterML.R, vignettes/deepSNV/inst/doc/shearwater.R htmlDocs: vignettes/deepSNV/inst/doc/shearwaterML.html htmlTitles: "Shearwater ML" suggestsMe: GenomicFiles Package: DEGraph Version: 1.20.0 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: bf40d13a822f5ac37b15e6e942a0562e 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 source.ver: src/contrib/DEGraph_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DEGraph_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DEGraph_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DEGraph_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DEGraph_1.20.0.tgz 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 suggestsMe: graphite, ToPASeq Package: DEGreport Version: 1.2.0 Depends: R (>= 3.0.0), rjags, quantreg Imports: plyr, utils, ggplot2, Nozzle.R1, coda, edgeR Suggests: knitr, biomaRt, RUnit, BiocStyle, BiocGenerics, BiocParallel License: GPL (>=2) MD5sum: 839b26a2fc2eeb9517f209a36782137c NeedsCompilation: no Title: Report of DEG analysis Description: Creation of a HTML report 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 Author: Lorena Pantano Maintainer: Lorena Pantano SystemRequirements: jags (>= 3.0.0) VignetteBuilder: knitr source.ver: src/contrib/DEGreport_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DEGreport_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DEGreport_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DEGreport_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DEGreport_1.2.0.tgz vignettes: vignettes/DEGreport/inst/doc/DEGreport.pdf vignetteTitles: DEGreport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R Package: DEGseq Version: 1.22.0 Depends: R (>= 2.8.0), qvalue, samr, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) Archs: i386, x64 MD5sum: 7f3fe0cb4fcc547f4cf55df505c63dbb 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 Author: Likun Wang and Xi Wang . Maintainer: Likun Wang source.ver: src/contrib/DEGseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DEGseq_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DEGseq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DEGseq_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DEGseq_1.22.0.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 Package: deltaGseg Version: 1.8.1 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: 4f347fb62ebc2b887a38968cb5126cfd 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 source.ver: src/contrib/deltaGseg_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/deltaGseg_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/deltaGseg_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/deltaGseg_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/deltaGseg_1.8.1.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 Package: derfinder Version: 1.2.1 Depends: R(>= 3.2) Imports: AnnotationDbi (>= 1.27.9), BiocParallel, bumphunter (>= 1.7.6), derfinderHelper (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges (>= 1.17.40), Hmisc, IRanges (>= 1.99.28), qvalue (>= 1.99.0), Rsamtools, rtracklayer, S4Vectors (>= 0.2.3) Suggests: biovizBase, devtools (>= 1.6), derfinderData (>= 0.99.0), ggplot2, knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 270bda4d704d9b672fabdc9e203cdcee NeedsCompilation: no Title: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution Description: This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. biocViews: DifferentialExpression, Sequencing, RNASeq, Software Author: Leonardo Collado-Torres [aut, cre], Alyssa C. Frazee [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinder VignetteBuilder: knitr BugReports: https://github.com/lcolladotor/derfinder/issues source.ver: src/contrib/derfinder_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/derfinder_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/derfinder_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/derfinder_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/derfinder_1.2.1.tgz vignettes: vignettes/derfinder/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinderAdvanced.R, vignettes/derfinder/inst/doc/derfinder.R htmlDocs: vignettes/derfinder/inst/doc/derfinderAdvanced.html, vignettes/derfinder/inst/doc/derfinder.html htmlTitles: "derfinder advanced details and usage", "Introduction to derfinder" importsMe: derfinderPlot, regionReport Package: derfinderHelper Version: 1.2.0 Depends: R(>= 3.2) Imports: IRanges (>= 1.99.27), Matrix, S4Vectors (>= 0.2.2) Suggests: devtools (>= 1.6), knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), rmarkdown (>= 0.3.3), testthat License: Artistic-2.0 MD5sum: 5b2da6b3be1fe65ce338cd0956b3bbac NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. biocViews: DifferentialExpression, Sequencing, RNASeq, Software Author: Leonardo Collado-Torres [aut, cre], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinderHelper VignetteBuilder: knitr BugReports: https://github.com/lcolladotor/derfinderHelper/issues source.ver: src/contrib/derfinderHelper_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/derfinderHelper_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/derfinderHelper_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/derfinderHelper_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/derfinderHelper_1.2.0.tgz vignettes: vignettes/derfinderHelper/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R htmlDocs: vignettes/derfinderHelper/inst/doc/derfinderHelper.html htmlTitles: "Introduction to derfinderHelper" importsMe: derfinder Package: derfinderPlot Version: 1.2.0 Depends: R(>= 3.2) Imports: derfinder (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>= 1.13.13), ggplot2, IRanges (>= 1.99.28), plyr, RColorBrewer, reshape2, scales Suggests: biovizBase, bumphunter (>= 1.7.6), derfinderData (>= 0.99.0), devtools (>= 1.6), knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), org.Hs.eg.db, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 84ce4fe450bcef75f105d8a0bf54af89 NeedsCompilation: no Title: Plotting functions for derfinder Description: Plotting functions for derfinder biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization Author: Leonardo Collado-Torres [aut, cre], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinderPlot VignetteBuilder: knitr BugReports: https://github.com/lcolladotor/derfinderPlot/issues source.ver: src/contrib/derfinderPlot_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/derfinderPlot_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/derfinderPlot_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/derfinderPlot_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/derfinderPlot_1.2.0.tgz vignettes: vignettes/derfinderPlot/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R htmlDocs: vignettes/derfinderPlot/inst/doc/derfinderPlot.html htmlTitles: "Introduction to derfinderPlot" importsMe: regionReport Package: DESeq Version: 1.20.0 Depends: BiocGenerics (>= 0.7.5), Biobase (>= 2.21.7), locfit, lattice Imports: genefilter, geneplotter, methods, MASS, RColorBrewer Suggests: pasilla (>= 0.2.10), vsn, gplots License: GPL (>= 3) Archs: i386, x64 MD5sum: f765accf73baab8802e31a6a81745889 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, ChIPSeq, RNASeq, SAGE, DifferentialExpression Author: Simon Anders, EMBL Heidelberg Maintainer: Simon Anders URL: http://www-huber.embl.de/users/anders/DESeq source.ver: src/contrib/DESeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DESeq_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DESeq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DESeq_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DESeq_1.20.0.tgz vignettes: vignettes/DESeq/inst/doc/DESeq.pdf vignetteTitles: Analysing RNA-Seq data with the "DESeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq/inst/doc/DESeq.R dependsOnMe: DBChIP, metaseqR, Polyfit, SeqGSEA, TCC, tRanslatome importsMe: ampliQueso, ArrayExpressHTS, easyRNASeq, EDASeq, EDDA, gCMAP, HTSFilter, rnaSeqMap, ToPASeq suggestsMe: BitSeq, compcodeR, DESeq2, dexus, DiffBind, ELBOW, gage, genefilter, GenomicAlignments, GenomicRanges, oneChannelGUI, RUVSeq, SSPA Package: DESeq2 Version: 1.8.2 Depends: S4Vectors, IRanges, GenomicRanges, Rcpp (>= 0.10.1), RcppArmadillo (>= 0.3.4.4) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, genefilter, methods, locfit, geneplotter, ggplot2, Hmisc LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, gplots, knitr, RColorBrewer, BiocStyle, airway, pasilla (>= 0.2.10), DESeq, vsn License: LGPL (>= 3) Archs: i386, x64 MD5sum: 4c4de29066e55cb9648f05a56a6ff844 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, ChIPSeq, RNASeq, SAGE, DifferentialExpression, GeneExpression Author: Michael Love (HSPH Boston), Simon Anders, Wolfgang Huber (EMBL Heidelberg) Maintainer: Michael Love VignetteBuilder: knitr source.ver: src/contrib/DESeq2_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/DESeq2_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.2/DESeq2_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.2/DESeq2_1.8.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DESeq2_1.8.2.tgz vignettes: vignettes/DESeq2/inst/doc/DESeq2.pdf vignetteTitles: Analyzing RNA-Seq data with the "DESeq2" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: DEXSeq, FourCSeq, MLSeq, rgsepd, TCC importsMe: EnrichmentBrowser, FourCSeq, HTSFilter, phyloseq, ReportingTools, systemPipeR, ToPASeq suggestsMe: BiocGenerics, compcodeR, DiffBind, gage, oneChannelGUI Package: DEXSeq Version: 1.14.2 Depends: BiocParallel, Biobase, IRanges (>= 2.1.10), GenomicRanges (>= 1.19.6), DESeq2 (>= 1.5.63) Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter, RColorBrewer Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr Enhances: parallel License: GPL (>= 3) MD5sum: ac3f6b36c3710cba99c4a0551fa5a642 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: Sequencing, RNASeq, DifferentialExpression Author: Simon Anders and Alejandro Reyes , both at EMBL Heidelberg Maintainer: Alejandro Reyes VignetteBuilder: knitr source.ver: src/contrib/DEXSeq_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/DEXSeq_1.14.2.zip win64.binary.ver: bin/windows64/contrib/3.2/DEXSeq_1.14.2.zip mac.binary.ver: bin/macosx/contrib/3.2/DEXSeq_1.14.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DEXSeq_1.14.2.tgz vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.pdf vignetteTitles: Analyzing RNA-seq data for differential exon usage with the "DEXSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R suggestsMe: GenomicRanges, oneChannelGUI Package: dexus Version: 1.8.0 Depends: R (>= 2.15), methods, BiocGenerics Suggests: parallel, statmod, stats, DESeq, RColorBrewer License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 94ea926a7f06711d6089bcb8f1f7551f NeedsCompilation: yes Title: DEXUS - Identifying Differential Expression in RNA-Seq Studies with Unknown Conditions or without Replicates Description: DEXUS identifies differentially expressed genes in RNA-Seq data under all possible study designs such as studies without replicates, without sample groups, and with unknown conditions. DEXUS works also for known conditions, for example for RNA-Seq data with two or multiple conditions. RNA-Seq read count data can be provided both by the S4 class Count Data Set and by read count matrices. Differentially expressed transcripts can be visualized by heatmaps, in which unknown conditions, replicates, and samples groups are also indicated. This software is fast since the core algorithm is written in C. For very large data sets, a parallel version of DEXUS is provided in this package. DEXUS is a statistical model that is selected in a Bayesian framework by an EM algorithm. DEXUS does not need replicates to detect differentially expressed transcripts, since the replicates (or conditions) are estimated by the EM method for each transcript. The method provides an informative/non-informative value to extract differentially expressed transcripts at a desired significance level or power. biocViews: Sequencing, RNASeq, GeneExpression, DifferentialExpression, CellBiology, Classification, QualityControl Author: Guenter Klambauer Maintainer: Guenter Klambauer source.ver: src/contrib/dexus_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/dexus_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/dexus_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/dexus_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/dexus_1.8.0.tgz vignettes: vignettes/dexus/inst/doc/dexus.pdf vignetteTitles: dexus: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dexus/inst/doc/dexus.R Package: DFP Version: 1.26.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: 133ed6f50cd51e71434af30f29312a0b 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 source.ver: src/contrib/DFP_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DFP_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DFP_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DFP_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DFP_1.26.0.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 Package: DiffBind Version: 1.14.6 Depends: R (>= 3.1.0), GenomicRanges, limma, GenomicAlignments, locfit Imports: RColorBrewer, amap, edgeR (>= 2.3.58), gplots, grDevices, stats, utils, IRanges, zlibbioc, lattice, systemPipeR, tools LinkingTo: Rsamtools (>= 1.19.38) Suggests: DESeq, Rsamtools, DESeq2, BiocStyle Enhances: rgl, parallel, BiocParallel, XLConnect License: Artistic-2.0 Archs: i386, x64 MD5sum: 3e465390a5989fb13599f08616cd351f 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, DifferentialPeakCalling Author: Rory Stark, Gord Brown Maintainer: Rory Stark source.ver: src/contrib/DiffBind_1.14.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/DiffBind_1.14.6.zip win64.binary.ver: bin/windows64/contrib/3.2/DiffBind_1.14.6.zip mac.binary.ver: bin/macosx/contrib/3.2/DiffBind_1.14.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DiffBind_1.14.6.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: ChIPQC Package: diffGeneAnalysis Version: 1.50.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: 1adaf34f62dd6005a3feaed221596cf2 NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi source.ver: src/contrib/diffGeneAnalysis_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/diffGeneAnalysis_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.2/diffGeneAnalysis_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.2/diffGeneAnalysis_1.50.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/diffGeneAnalysis_1.50.0.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 Package: diffHic Version: 1.0.1 Depends: R (>= 3.2.0), GenomicRanges Imports: Rsamtools, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics Suggests: BSgenome.Ecoli.NCBI.20080805 License: GPL-3 Archs: i386, x64 MD5sum: abc07971e7644ec704108c5e15407743 NeedsCompilation: yes Title: Differential analyis 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 Maintainer: Aaron Lun source.ver: src/contrib/diffHic_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/diffHic_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/diffHic_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/diffHic_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/diffHic_1.0.1.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 Rfiles: vignettes/diffHic/inst/doc/diffHic.R Package: diggit Version: 1.0.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: GPL (>=2) MD5sum: 5a7682cacbc8e6913df82965e97a9153 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 source.ver: src/contrib/diggit_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/diggit_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/diggit_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/diggit_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/diggit_1.0.0.tgz vignettes: vignettes/diggit/inst/doc/diggit.pdf vignetteTitles: Using DIGGIT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diggit/inst/doc/diggit.R Package: DirichletMultinomial Version: 1.10.0 Depends: S4Vectors, IRanges Imports: stats4, methods Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 Archs: i386, x64 MD5sum: e6fb73a698df4a4034120aeb3ea6e595 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: Microbiome, Sequencing, Clustering, Classification Author: Martin Morgan Maintainer: Martin Morgan SystemRequirements: gsl source.ver: src/contrib/DirichletMultinomial_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DirichletMultinomial_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DirichletMultinomial_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DirichletMultinomial_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DirichletMultinomial_1.10.0.tgz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf vignetteTitles: An introduction to DirichletMultinomial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: TFBSTools Package: dks Version: 1.14.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: 6cd8280f3f674a19ad55905e246171e9 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 source.ver: src/contrib/dks_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/dks_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/dks_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/dks_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/dks_1.14.0.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 Package: DMRcaller Version: 1.0.0 Depends: R (>= 3.2), GenomicRanges, IRanges, S4Vectors Imports: parallel, Rcpp, RcppRoll Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 50036e3405031aec49ed1f27b303165f 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 and Jonathan Michael Foonlan Tsang Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr source.ver: src/contrib/DMRcaller_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DMRcaller_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DMRcaller_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DMRcaller_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DMRcaller_1.0.0.tgz vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.pdf vignetteTitles: DMRcaller hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRcaller/inst/doc/DMRcaller.R Package: DMRcate Version: 1.4.2 Depends: R (>= 3.1.3), limma, minfi, DMRcatedata Imports: methods, graphics Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19 License: file LICENSE MD5sum: f50ce7f24200b7d470dadc24a158594b NeedsCompilation: no Title: Illumina 450K methylation array spatial analysis methods Description: De novo identification and extraction of differentially methylated regions (DMRs) in the human genome using Illumina Infinium HumanMethylation450 BeadChip array data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes bedGraph generation, GRanges generation and plotting functions. biocViews: DifferentialMethylation, GeneExpression, Microarray, MethylationArray, Genetics, DifferentialExpression, GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel, MultipleComparison, QualityControl, TimeCourse Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr source.ver: src/contrib/DMRcate_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/DMRcate_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.2/DMRcate_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.2/DMRcate_1.4.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DMRcate_1.4.2.tgz vignettes: vignettes/DMRcate/inst/doc/DMRcate.pdf vignetteTitles: The DMRcate package user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DMRcate/inst/doc/DMRcate.R Package: DMRforPairs Version: 1.4.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: 409c7061820c89faede48df7799263a8 NeedsCompilation: no Title: DMRforPairs: identifying Differentially Methylated Regions between unique samples using array based methylation profiles Description: DMRforPairs (formerly DMR2+) allows researchers to compare n>=2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishes DMRforPairs from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance. biocViews: Microarray, DNAMethylation, DifferentialMethylation, ReportWriting, Visualization, Annotation Author: Martin Rijlaarsdam [aut, cre], Yvonne vd Zwan [aut], Lambert Dorssers [aut], Leendert Looijenga [aut] Maintainer: Martin Rijlaarsdam URL: http://www.martinrijlaarsdam.nl, http://www.erasmusmc.nl/pathologie/research/lepo/3898639/ source.ver: src/contrib/DMRforPairs_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DMRforPairs_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DMRforPairs_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DMRforPairs_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DMRforPairs_1.4.0.tgz vignettes: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.pdf vignetteTitles: DMRforPairs_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.R Package: DNAcopy Version: 1.42.0 License: GPL (>= 2) Archs: i386, x64 MD5sum: 1767a4f9824d568a3a6401b0720f81f4 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 source.ver: src/contrib/DNAcopy_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DNAcopy_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DNAcopy_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DNAcopy_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DNAcopy_1.42.0.tgz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R dependsOnMe: CGHcall, cghMCR, Clonality, CopyNumber450k, CRImage, snapCGH, SomatiCA importsMe: ADaCGH2, ArrayTV, ChAMP, Clonality, CNAnorm, cn.farms, CNVrd2, conumee, CopywriteR, GWASTools, MEDIPS, MinimumDistance, QDNAseq, Repitools, snapCGH, SomatiCA suggestsMe: beadarraySNP, Clonality, cn.mops, fastseg, genoset Package: domainsignatures Version: 1.28.0 Depends: R (>= 2.4.0), KEGG.db, prada, biomaRt, methods Imports: AnnotationDbi License: Artistic-2.0 MD5sum: de672a73b2e4fca634d982bd68f98e12 NeedsCompilation: no Title: Geneset enrichment based on InterPro domain signatures Description: Find significantly enriched gene classifications in a list of functionally undescribed genes based on their InterPro domain structure. biocViews: Annotation, Pathways, GeneSetEnrichment Author: Florian Hahne, Tim Beissbarth Maintainer: Florian Hahne source.ver: src/contrib/domainsignatures_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/domainsignatures_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/domainsignatures_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/domainsignatures_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/domainsignatures_1.28.0.tgz vignettes: vignettes/domainsignatures/inst/doc/domainenrichment.pdf vignetteTitles: Gene set enrichment using InterPro domain signatures hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/domainsignatures/inst/doc/domainenrichment.R Package: DOQTL Version: 1.2.0 Depends: R (>= 2.10.0) Imports: annotate, annotationTools, biomaRt, Biobase, BiocGenerics, corpcor, GenomicRanges, hwriter, IRanges, mclust, MUGAExampleData, org.Hs.eg.db, org.Mm.eg.db, QTLRel, Rsamtools, RUnit, XML, S4Vectors License: GPL-3 Archs: i386, x64 MD5sum: e3f61a5b214b147f95ca5eaf67356633 NeedsCompilation: yes Title: Genotyping and QTL Mapping in DO Mice Description: DOQTL is a quantitative trait locus (QTL) mapping pipeline designed for Diversity Outbred mice and other multi-parent outbred populations. The package reads in data from genotyping arrays and perform haplotype reconstruction using a hidden Markov model (HMM). The haplotype probabilities from the HMM are then used to perform linkage mapping. When founder sequences are available, DOQTL can use the haplotype reconstructions to impute the founder sequences onto DO genomes and perform association mapping. biocViews: GeneticVariability, SNP, Genetics, HiddenMarkovModel Author: Daniel Gatti, Karl Broman, Andrey Shabalin Maintainer: Daniel Gatti URL: http://do.jax.org source.ver: src/contrib/DOQTL_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DOQTL_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DOQTL_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DOQTL_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DOQTL_1.2.0.tgz vignettes: vignettes/DOQTL/inst/doc/QTL_Mapping_DO_Mice.pdf vignetteTitles: QTL Mapping using Diversity Outbred Mice hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOQTL/inst/doc/QTL_Mapping_DO_Mice.R Package: DOSE Version: 2.6.6 Depends: R (>= 3.1.0) Imports: methods, plyr, qvalue, stats4, AnnotationDbi, DO.db, igraph, scales, reshape2, graphics, GOSemSim, grid, ggplot2 Suggests: org.Hs.eg.db, clusterProfiler, knitr, BiocStyle License: Artistic-2.0 MD5sum: d5d5282695863a95daef1bea5ad7bc14 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, Li-Gen Wang Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/DOSE VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues source.ver: src/contrib/DOSE_2.6.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/DOSE_2.6.6.zip win64.binary.ver: bin/windows64/contrib/3.2/DOSE_2.6.6.zip mac.binary.ver: bin/macosx/contrib/3.2/DOSE_2.6.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DOSE_2.6.6.tgz vignettes: vignettes/DOSE/inst/doc/DOSE.pdf vignetteTitles: Disease Ontology Semantic and Enrichment analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOSE/inst/doc/DOSE.R importsMe: clusterProfiler, facopy, ReactomePA suggestsMe: ChIPseeker, GOSemSim Package: DriverNet Version: 1.8.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: a1eb3b6cce3c6c6b07f3f5c5c25c7dcd 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 source.ver: src/contrib/DriverNet_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DriverNet_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DriverNet_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DriverNet_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DriverNet_1.8.0.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 Package: DrugVsDisease Version: 2.8.0 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: dd3f6d1e32eaeab83a55f9fc70955453 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 source.ver: src/contrib/DrugVsDisease_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DrugVsDisease_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DrugVsDisease_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DrugVsDisease_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DrugVsDisease_2.8.0.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 Package: DSS Version: 2.6.0 Depends: Biobase,bsseq,splines,methods Suggests: BiocStyle License: GPL Archs: i386, x64 MD5sum: ff1cce83c366cb6df08d7988d299bfba NeedsCompilation: yes Title: Dispersion shrinakge 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, ChIPSeq, DNAMethylation, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu source.ver: src/contrib/DSS_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DSS_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DSS_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DSS_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DSS_2.6.0.tgz vignettes: vignettes/DSS/inst/doc/DSS.pdf vignetteTitles: Differential expression for RNA-seq data with dispersion shrinkage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DSS/inst/doc/DSS.R Package: DTA Version: 2.14.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: 10d1e4646be33639797389eb8392b637 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 source.ver: src/contrib/DTA_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DTA_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DTA_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DTA_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DTA_2.14.0.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 Package: dualKS Version: 1.28.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.0), affy, methods Imports: graphics License: LGPL (>= 2.0) MD5sum: 5609bb41eceaf31b889f037bbfa5e03b NeedsCompilation: no Title: Dual KS Discriminant Analysis and Classification Description: This package implements a Kolmogorov Smirnov rank-sum based algorithm for training (i.e. discriminant analysis--identification of genes that discriminate between classes) and classification of gene expression data sets. One of the chief strengths of this approach is that it is amenable to the "multiclass" problem. That is, it can discriminate between more than 2 classes. biocViews: Microarray, Classification Author: Eric J. Kort, Yarong Yang Maintainer: Eric J. Kort , Yarong Yang source.ver: src/contrib/dualKS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/dualKS_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/dualKS_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/dualKS_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/dualKS_1.28.0.tgz vignettes: vignettes/dualKS/inst/doc/dualKS.pdf vignetteTitles: dualKS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dualKS/inst/doc/dualKS.R Package: DupChecker Version: 1.6.0 Imports: tools, R.utils, RCurl Suggests: knitr License: GPL (>= 2) MD5sum: f6819ece3851e25685a5e7847737e865 NeedsCompilation: no Title: a package for checking high-throughput genomic data redundancy in meta-analysis Description: Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates would make study results questionable. We developed a Bioconductor package DupChecker that efficiently identifies duplicated samples by generating MD5 fingerprints for raw data. biocViews: Preprocessing Author: Quanhu Sheng, Yu Shyr, Xi Chen Maintainer: "Quanhu SHENG" VignetteBuilder: knitr source.ver: src/contrib/DupChecker_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DupChecker_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DupChecker_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DupChecker_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DupChecker_1.6.0.tgz vignettes: vignettes/DupChecker/inst/doc/DupChecker.pdf vignetteTitles: Validate genomic data with "DupChecker" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DupChecker/inst/doc/DupChecker.R Package: dyebias Version: 1.26.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: bd5e4d838b82bdd379b93ac927f820c1 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 source.ver: src/contrib/dyebias_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/dyebias_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/dyebias_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/dyebias_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/dyebias_1.26.0.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 Package: DynDoc Version: 1.46.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: c950636eb6e0ba15433134b095a956ba 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 source.ver: src/contrib/DynDoc_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DynDoc_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DynDoc_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DynDoc_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DynDoc_1.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets Package: EasyqpcR Version: 1.10.2 Imports: plyr, matrixStats, plotrix, gWidgetsRGtk2 Suggests: SLqPCR, qpcrNorm, qpcR, knitr License: GPL (>=2) MD5sum: 6b13312ba2f00ee98c02a4f4b35b689e NeedsCompilation: no Title: EasyqpcR for low-throughput real-time quantitative PCR data analysis Description: This package is based on the qBase algorithms published by Hellemans et al. in 2007. The EasyqpcR package allows you to import easily qPCR data files as described in the vignette. Thereafter, you can calculate amplification efficiencies, relative quantities and their standard errors, normalization factors based on the best reference genes choosen (using the SLqPCR package), and then the normalized relative quantities, the NRQs scaled to your control and their standard errors. It proposes also a function to aggregate the qPCR biological replicates followed by the application of the algorithm published by Willems et al. (2008) which is a standardization procedure that can be applied to data sets that display high variations between biological replicates. This enables proper statistical analysis to draw relevant conclusions. This package has been created for low-throughput qPCR data analysis. biocViews: qPCR, GeneExpression Author: Le Pape Sylvain Maintainer: Le Pape Sylvain source.ver: src/contrib/EasyqpcR_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/EasyqpcR_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.2/EasyqpcR_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.2/EasyqpcR_1.10.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EasyqpcR_1.10.2.tgz vignettes: vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.pdf vignetteTitles: EasyqpcR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.R Package: easyRNASeq Version: 2.4.7 Imports: Biobase (>= 2.28.0), BiocGenerics (>= 0.14.0), BiocParallel (>= 1.2.7), biomaRt (>= 2.24.0), Biostrings (>= 2.36.1), DESeq (>= 1.20.0), edgeR (>= 3.10.2), genomeIntervals (>= 1.24.1), GenomicAlignments (>= 1.4.1), GenomeInfoDb (>= 1.4.1), GenomicRanges (>= 1.20.5), graphics, IRanges (>= 2.2.5), LSD (>= 3.0), methods, parallel, Rsamtools (>= 1.20.4), S4Vectors (>= 0.6.1), ShortRead (>= 1.26.0), locfit, utils Suggests: BiocStyle (>= 1.6.0), BSgenome (>= 1.36.2), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), GenomicFeatures (>= 1.20.1), RnaSeqTutorial (>= 0.6.0), RUnit (>= 0.4.28) License: Artistic-2.0 MD5sum: 6f1fb995fc4e675bcb5b306624700847 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 Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler Maintainer: Nicolas Delhomme source.ver: src/contrib/easyRNASeq_2.4.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/easyRNASeq_2.4.7.zip win64.binary.ver: bin/windows64/contrib/3.2/easyRNASeq_2.4.7.zip mac.binary.ver: bin/macosx/contrib/3.2/easyRNASeq_2.4.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/easyRNASeq_2.4.7.tgz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf vignetteTitles: easyRNASeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R suggestsMe: SeqGSEA Package: EBarrays Version: 2.32.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: a8767595497664bccdac57ecb8d85794 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 source.ver: src/contrib/EBarrays_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EBarrays_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EBarrays_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EBarrays_2.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EBarrays_2.32.0.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 Package: EBcoexpress Version: 1.12.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: i386, x64 MD5sum: ef0a54341117a3b82da5c03090a9dd07 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 source.ver: src/contrib/EBcoexpress_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EBcoexpress_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EBcoexpress_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EBcoexpress_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EBcoexpress_1.12.0.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 Package: EBImage Version: 4.10.1 Imports: BiocGenerics (>= 0.7.1), methods, graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7) Suggests: BiocStyle License: LGPL Archs: i386, x64 MD5sum: 11a1411b0c3d8054f030e3b8097a9d95 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 data visualization. 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 source.ver: src/contrib/EBImage_4.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/EBImage_4.10.1.zip win64.binary.ver: bin/windows64/contrib/3.2/EBImage_4.10.1.zip mac.binary.ver: bin/macosx/contrib/3.2/EBImage_4.10.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EBImage_4.10.1.tgz vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.pdf vignetteTitles: Introduction to EBImage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R dependsOnMe: CRImage, flowcatchR, imageHTS importsMe: flowCHIC, ggtree suggestsMe: HilbertVis Package: EBSeq Version: 1.8.0 Depends: blockmodeling, gplots, R (>= 3.0.0) License: Artistic-2.0 MD5sum: 7e579b371f895d69c12bcb851319fdc0 NeedsCompilation: no 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: StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng source.ver: src/contrib/EBSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EBSeq_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EBSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EBSeq_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EBSeq_1.8.0.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: EBSeqHMM suggestsMe: compcodeR Package: EBSeqHMM Version: 1.2.0 Depends: EBSeq License: Artistic-2.0 MD5sum: 4888acf04ce1dc606290e339d85fcafc NeedsCompilation: no Title: Bayesian analysis for identifying gene or isoform expression changes in ordered RNA-seq experiments Description: The EBSeqHMM package implements an auto-regressive hidden Markov model for statistical analysis in ordered RNA-seq experiments (e.g. time course or spatial course data). The EBSeqHMM package provides functions to identify genes and isoforms that have non-constant expression profile over the time points/positions, and cluster them into expression paths. biocViews: StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing, GeneExpression, Bayesian, HiddenMarkovModel, TimeCourse Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng source.ver: src/contrib/EBSeqHMM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EBSeqHMM_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EBSeqHMM_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EBSeqHMM_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EBSeqHMM_1.2.0.tgz vignettes: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.pdf vignetteTitles: HMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.R Package: ecolitk Version: 1.40.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: f1ff226d59643b39390d4189b99ca266 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 source.ver: src/contrib/ecolitk_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ecolitk_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ecolitk_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ecolitk_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ecolitk_1.40.0.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 Package: EDASeq Version: 2.2.0 Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42) Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9), DESeq, aroma.light, Rsamtools (>= 1.5.75) Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR, KernSmooth License: Artistic-2.0 MD5sum: b023717658e6d65ef355c8d0a56fef85 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: Sequencing, RNASeq, Preprocessing, QualityControl, DifferentialExpression Author: Davide Risso and Sandrine Dudoit Maintainer: Davide Risso VignetteBuilder: knitr source.ver: src/contrib/EDASeq_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EDASeq_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EDASeq_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EDASeq_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EDASeq_2.2.0.tgz vignettes: vignettes/EDASeq/inst/doc/EDASeq.pdf vignetteTitles: EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R dependsOnMe: metaseqR, RUVSeq importsMe: EnrichmentBrowser suggestsMe: HTSFilter, oneChannelGUI Package: EDDA Version: 1.6.0 Depends: Rcpp (>= 0.10.4),parallel,methods,ROCR,DESeq,baySeq,snow,edgeR Imports: graphics, stats, utils, parallel, methods, ROCR, DESeq, baySeq, snow, edgeR LinkingTo: Rcpp License: GPL (>= 2) Archs: i386, x64 MD5sum: d4c3e220abeb1e712c0f191f18251410 NeedsCompilation: yes Title: Experimental Design in Differential Abundance analysis Description: EDDA can aid in the design of a range of common experiments such as RNA-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance. This work was published on 3 December 2014 at Genome Biology under the title "The importance of study design for detecting differentially abundant features in high-throughput experiments" (http://genomebiology.com/2014/15/12/527). biocViews: Sequencing, ExperimentalDesign, Normalization, RNASeq, ChIPSeq Author: Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan Maintainer: Chia Kuan Hui Burton , Niranjan Nagarajan URL: http://edda.gis.a-star.edu.sg/, http://genomebiology.com/2014/15/12/527 source.ver: src/contrib/EDDA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EDDA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EDDA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EDDA_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EDDA_1.6.0.tgz vignettes: vignettes/EDDA/inst/doc/EDDA.pdf vignetteTitles: EDDA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDDA/inst/doc/EDDA.R Package: edge Version: 2.0.0 Depends: R(>= 3.2.0), Biobase Imports: methods, splines, sva, snm, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE Archs: i386, x64 MD5sum: a6d8b558f43969c0acfd5218c28a095b 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 snm, 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 [aut, cre, cph], Jeffrey T. Leek [aut], Andrew J. Bass [aut] Maintainer: John D. Storey URL: https://github.com/jdstorey/edge VignetteBuilder: knitr BugReports: https://github.com/jdstorey/edge/issues source.ver: src/contrib/edge_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/edge_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/edge_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/edge_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/edge_2.0.0.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 Package: edgeR Version: 3.10.5 Depends: R (>= 2.15.0), limma Imports: methods Suggests: MASS, statmod, splines, locfit, KernSmooth License: GPL (>=2) Archs: i386, x64 MD5sum: 1233edbcc0ce65e410031f47fa2ee018 NeedsCompilation: yes Title: Empirical analysis of digital gene expression data in R Description: Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, SAGE and CAGE. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, Genetics, Bayesian, Clustering, Regression, TimeCourse, SAGE, Sequencing, ChIPSeq, RNASeq, BatchEffect, MultipleComparison, Normalization, QualityControl Author: Yunshun Chen , Aaron Lun , Davis McCarthy , Xiaobei Zhou , Mark Robinson , Gordon Smyth Maintainer: Yunshun Chen , Aaron Lun , Mark Robinson , Davis McCarthy , Gordon Smyth URL: http://bioinf.wehi.edu.au/edgeR source.ver: src/contrib/edgeR_3.10.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/edgeR_3.10.5.zip win64.binary.ver: bin/windows64/contrib/3.2/edgeR_3.10.5.zip mac.binary.ver: bin/macosx/contrib/3.2/edgeR_3.10.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/edgeR_3.10.5.tgz vignettes: vignettes/edgeR/inst/doc/edgeR.pdf, vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf vignetteTitles: edgeR Vignette, edgeRUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/edgeR/inst/doc/edgeR.R dependsOnMe: DBChIP, manta, methylMnM, MLSeq, RUVSeq, TCC, tRanslatome importsMe: ampliQueso, ArrayExpressHTS, compcodeR, csaw, DEGreport, DiffBind, diffHic, easyRNASeq, EDDA, EnrichmentBrowser, erccdashboard, HTSFilter, MEDIPS, metaseqR, msmsTests, PROPER, Repitools, rnaSeqMap, STATegRa, systemPipeR, ToPASeq, tweeDEseq suggestsMe: baySeq, BitSeq, ClassifyR, clonotypeR, cqn, EDASeq, gage, GenomicAlignments, GenomicRanges, goseq, groHMM, GSAR, GSVA, missMethyl, oneChannelGUI, SSPA Package: eiR Version: 1.8.3 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods,ChemmineR,RCurl,digest, BiocGenerics LinkingTo: BH Suggests: RCurl,snow,BiocStyle,knitcitations,knitr,knitrBootstrap License: Artistic-2.0 MD5sum: e3bec31a3fb8cac8b84ad871a36ab540 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 Author: Kevin Horan, Yiqun Cao and Tyler Backman Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineR SystemRequirements: GSL (>=1.14) http://www.gnu.org/software/gsl/ VignetteBuilder: knitr source.ver: src/contrib/eiR_1.8.3.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/eiR_1.8.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/eiR_1.8.3.tgz vignettes: vignettes/eiR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R htmlDocs: vignettes/eiR/inst/doc/eiR.html htmlTitles: "eiR" Package: eisa Version: 1.20.0 Depends: isa2, Biobase (>= 2.17.8), AnnotationDbi, methods Imports: BiocGenerics, Category, genefilter, DBI Suggests: igraph (>= 0.6), Matrix, GOstats, GO.db, KEGG.db, biclust, MASS, xtable, ALL, hgu95av2.db, targetscan.Hs.eg.db, org.Hs.eg.db License: GPL (>= 2) MD5sum: 0a7e4ad2b31b6777929aa5c83789912e NeedsCompilation: no Title: Expression data analysis via the Iterative Signature Algorithm Description: The Iterative Signature Algorithm (ISA) is a biclustering method; it finds correlated blocks (transcription modules) in gene expression (or other tabular) data. The ISA is capable of finding overlapping modules and it is resilient to noise. This package provides a convenient interface to the ISA, using standard BioConductor data structures; and also contains various visualization tools that can be used with other biclustering algorithms. biocViews: Classification, Visualization, Microarray, GeneExpression Author: Gabor Csardi Maintainer: Gabor Csardi source.ver: src/contrib/eisa_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/eisa_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/eisa_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/eisa_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/eisa_1.20.0.tgz vignettes: vignettes/eisa/inst/doc/EISA_biclust.pdf, vignettes/eisa/inst/doc/EISA_tutorial.pdf vignetteTitles: The eisa and the biclust packages, The Iterative Signature Algorithm for Gene Expression Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eisa/inst/doc/EISA_biclust.R, vignettes/eisa/inst/doc/EISA_tutorial.R dependsOnMe: ExpressionView importsMe: ExpressionView Package: ELBOW Version: 1.4.0 Depends: R (>= 2.15.0) Suggests: DESeq, GEOquery, limma, simpleaffy, affyPLM, RColorBrewer License: file LICENSE License_is_FOSS: yes License_restricts_use: no MD5sum: 6d8e7f298dfc95c39b0a6dac82ffecb0 NeedsCompilation: no Title: ELBOW - Evaluating foLd change By the lOgit Way Description: Elbow an improved fold change test that uses cluster analysis and pattern recognition to set cut off limits that are derived directly from intrareplicate variance without assuming a normal distribution for as few as 2 biological replicates. Elbow also provides the same consistency as fold testing in cross platform analysis. Elbow has lower false positive and false negative rates than standard fold testing when both are evaluated using T testing and Statistical Analysis of Microarray using 12 replicates (six replicates each for initial and final conditions). Elbow provides a null value based on initial condition replicates and gives error bounds for results to allow better evaluation of significance. biocViews: Technology, Microarray, RNASeq, Sequencing, Sequencing, Software, MultiChannel, OneChannel, TwoChannel, GeneExpression Author: Xiangli Zhang, Natalie Bjorklund, Graham Alvare, Tom Ryzdak, Richard Sparling, Brian Fristensky Maintainer: Graham Alvare , Xiangli Zhang source.ver: src/contrib/ELBOW_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ELBOW_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ELBOW_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ELBOW_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ELBOW_1.4.0.tgz vignettes: vignettes/ELBOW/inst/doc/Elbow_tutorial_vignette.pdf vignetteTitles: Using ELBOW --- the definitive ELBOW tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ELBOW/inst/doc/Elbow_tutorial_vignette.R Package: EMDomics Version: 1.0.0 Depends: R (>= 3.2.0) Imports: emdist, BiocParallel, matrixStats, ggplot2 Suggests: knitr License: MIT + file LICENSE MD5sum: 40de782a31404ec70fc891b6405fa55c NeedsCompilation: no Title: Earth Mover's Distance for Differential Analysis of Genomics Data Description: The EMDomics algorithm is used to perform a supervised two-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between two 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 two 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 the other, 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. biocViews: Software, DifferentialExpression, GeneExpression, Microarray Author: Daniel Schmolze [aut, cre], Andrew Beck [aut], Sheida Nabavi [aut] Maintainer: Daniel Schmolze VignetteBuilder: knitr source.ver: src/contrib/EMDomics_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EMDomics_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EMDomics_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EMDomics_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EMDomics_1.0.0.tgz vignettes: vignettes/EMDomics/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R htmlDocs: vignettes/EMDomics/inst/doc/EMDomics.html htmlTitles: "EMDomics Vignette" Package: ENCODExplorer Version: 1.0.6 Depends: R (>= 3.2) Imports: tools, jsonlite, RSQLite Suggests: RUnit,BiocGenerics,knitr, curl, httr License: Artistic-2.0 MD5sum: 848c87a6603422fe433cc2784c19fdcd NeedsCompilation: no Title: A compilation of ENCODE metadata Description: This package allows user to quickly access ENCODE project files metadata and give access to helper functions to query the ENCODE rest api, download ENCODE datasets and save the database in SQLite format. biocViews: Infrastructure, DataImport Author: Charles Joly Beauparlant , Audrey Lemacon and Arnaud Droit Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/ENCODExplorer/issues source.ver: src/contrib/ENCODExplorer_1.0.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/ENCODExplorer_1.0.6.zip win64.binary.ver: bin/windows64/contrib/3.2/ENCODExplorer_1.0.6.zip mac.binary.ver: bin/macosx/contrib/3.2/ENCODExplorer_1.0.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ENCODExplorer_1.0.6.tgz vignettes: vignettes/ENCODExplorer/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENCODExplorer/inst/doc/DataUpdate.R, vignettes/ENCODExplorer/inst/doc/DBmodel.R, vignettes/ENCODExplorer/inst/doc/ENCODExplorer.R htmlDocs: vignettes/ENCODExplorer/inst/doc/DataUpdate.html, vignettes/ENCODExplorer/inst/doc/DBmodel.html, vignettes/ENCODExplorer/inst/doc/ENCODExplorer.html htmlTitles: "Data update", "Database model", "Introduction to ENCODExplorer" Package: ENmix Version: 1.0.0 Depends: minfi,parallel,doParallel,Biobase (>= 2.17.8),foreach Imports: MASS,preprocessCore,wateRmelon,sva,geneplotter Suggests: minfiData (>= 0.4.1), RPMM, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: b71dafe1b11c0c4bfeb96f3793fb12ed NeedsCompilation: no Title: Data preprocessing and quality control for Illumina HumanMethylation450 BeadChip Description: Illumina HumanMethylation450 BeadChip has a complex array design, and the measurement is subject to experimental variations. The ENmix R package provides tools for low level data preprocessing to improve data quality. It incorporates a model based background correction method ENmix, and provides functions for inter-array quantile normalization, data quality checking, exploration of multimodally distributed CpGs and source of data variation. To support large scale data analysis, the package also provides multi-processor parallel computing wrappers for some commonly used data preprocessing methods, such as BMIQ probe design type bias correction and ComBat batch effect correction. biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel, Microarray, OneChannel, MethylationArray, BatchEffect, Normalization, DataImport Author: Zongli Xu [cre, aut], Liang Niu [aut], Leping Li [ctb], Jack Taylor [ctb] Maintainer: Zongli Xu source.ver: src/contrib/ENmix_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ENmix_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ENmix_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ENmix_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ENmix_1.0.0.tgz vignettes: vignettes/ENmix/inst/doc/ENmix.pdf vignetteTitles: ENmix User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENmix/inst/doc/ENmix.R Package: EnrichmentBrowser Version: 1.2.2 Depends: R(>= 3.0.0), Biobase, GSEABase, pathview Imports: AnnotationDbi, ComplexHeatmap, DESeq2, EDASeq, GenomicRanges, GO.db, KEGGREST, KEGGgraph, MASS, PathNet, ReportingTools, Rgraphviz, S4Vectors, SparseM, SPIA, biocGraph, biomaRt, edgeR, geneplotter, graph, hwriter, limma, mixtools, neaGUI, npGSEA, safe, stringr, topGO Suggests: ALL, BiocStyle, airway, hgu95av2.db License: Artistic-2.0 MD5sum: d3305b63541aaff41c25c4edeb4d1d04 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: Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger Maintainer: Ludwig Geistlinger source.ver: src/contrib/EnrichmentBrowser_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/EnrichmentBrowser_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/EnrichmentBrowser_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/EnrichmentBrowser_1.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EnrichmentBrowser_1.2.2.tgz vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.pdf vignetteTitles: EnrichmentBrowser Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R Package: ensembldb Version: 1.0.1 Depends: BiocGenerics, GenomicRanges, GenomicFeatures Imports: methods, RSQLite, DBI, Biobase, GenomeInfoDb, AnnotationDbi, rtracklayer, S4Vectors Suggests: knitr, BiocStyle, EnsDb.Hsapiens.v75 (>= 0.99.7), RUnit License: LGPL MD5sum: 6fefe24a7e4a12b4b3495e9aaad54f5b NeedsCompilation: no Title: Utilities to create and use an Ensembl based annotation database 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, the ensembldb package provides also a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. biocViews: Genetics, AnnotationData, Sequencing, Coverage Author: J. Rainer Maintainer: Johannes Rainer VignetteBuilder: knitr source.ver: src/contrib/ensembldb_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ensembldb_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ensembldb_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ensembldb_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ensembldb_1.0.1.tgz vignettes: vignettes/ensembldb/inst/doc/ensembldb.pdf vignetteTitles: Generating an using Ensembl based annotation packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensembldb/inst/doc/ensembldb.R Package: ensemblVEP Version: 1.8.1 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: Biostrings Suggests: RUnit License: Artistic-2.0 MD5sum: 5e64a8da025c772e33177f73a3f4fcf3 NeedsCompilation: no Title: R Interface to Ensembl Variant Effect Predictor Description: Query the Ensembl Variant Effect Predictor via the perl API biocViews: Annotation, VariantAnnotation, SNP Author: Valerie Obenchain , Maintainer: Valerie Obenchain SystemRequirements: Ensembl VEP (API version 80) and the Perl package DBD::mysql must be installed. See the package README and Ensembl web site, http://www.ensembl.org/info/docs/tools/vep/index.html for installation instructions. source.ver: src/contrib/ensemblVEP_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ensemblVEP_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ensemblVEP_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ensemblVEP_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ensemblVEP_1.8.1.tgz vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf vignetteTitles: ensemblVEP hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensemblVEP/inst/doc/ensemblVEP.R Package: ENVISIONQuery Version: 1.16.0 Depends: rJava, XML, utils License: GPL-2 MD5sum: ad9517f76b56d5d9a13e3310263b5472 NeedsCompilation: no Title: Retrieval from the ENVISION bioinformatics data portal into R Description: Tools to retrieve data from ENVISION, the Database for Annotation, Visualization and Integrated Discovery portal biocViews: Annotation Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day source.ver: src/contrib/ENVISIONQuery_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ENVISIONQuery_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ENVISIONQuery_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ENVISIONQuery_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ENVISIONQuery_1.16.0.tgz vignettes: vignettes/ENVISIONQuery/inst/doc/ENVISIONQuery.pdf vignetteTitles: An R Package for retrieving data from EnVision into R objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENVISIONQuery/inst/doc/ENVISIONQuery.R importsMe: IdMappingRetrieval Package: epigenomix Version: 1.8.0 Depends: R (>= 2.12.0), methods, Biobase, IRanges, GenomicRanges Imports: BiocGenerics, Rsamtools, beadarray License: LGPL-3 MD5sum: b5bf05327ff12a6bd52d5a2b35c6c14d 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 source.ver: src/contrib/epigenomix_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/epigenomix_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/epigenomix_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/epigenomix_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/epigenomix_1.8.0.tgz vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf vignetteTitles: epigenomix package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epigenomix/inst/doc/epigenomix.R Package: epivizr Version: 1.6.7 Depends: R (>= 3.0.1), methods, Biobase, GenomicRanges (>= 1.13.47), rtracklayer Imports: S4Vectors, httpuv (>= 1.3.0), rjson, OrganismDbi, R6 (>= 2.0.0), mime (>= 0.2), GenomeInfoDb, GenomicFeatures Suggests: testthat, roxygen2, knitr, antiProfilesData, hgu133plus2.db, knitrBootstrap, Mus.musculus License: Artistic-2.0 MD5sum: 02b389b7d17eb969e0bea6cf10056a6c NeedsCompilation: no Title: R Interface to epiviz web app Description: This package provides Websocket communication 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 SummarizedExperiment objects), while providing an easy mechanism to support other data structures. 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 Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA source.ver: src/contrib/epivizr_1.6.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/epivizr_1.6.7.zip win64.binary.ver: bin/windows64/contrib/3.2/epivizr_1.6.7.zip mac.binary.ver: bin/macosx/contrib/3.2/epivizr_1.6.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/epivizr_1.6.7.tgz vignettes: vignettes/epivizr/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R htmlDocs: vignettes/epivizr/inst/doc/IntroToEpivizr.html htmlTitles: "Introduction to epivizr" Package: erccdashboard Version: 1.2.1 Depends: R (>= 3.1), ggplot2, gridExtra Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, QuasiSeq, qvalue, reshape2, ROCR, scales, stringr License: GPL (>=2) MD5sum: ca81f5fb769bf6ba36a73976a9901595 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: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Genetics, Microarray, mRNAMicroarray, RNASeq, BatchEffect, MultipleComparison, QualityControl Author: Sarah Munro, Steve Lund Maintainer: Sarah Munro URL: http://www.nist.gov/mml/bbd/erccdashboard.cfm, https://github.com/usnistgov/erccdashboard, http://tinyurl.com/erccsrm source.ver: src/contrib/erccdashboard_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/erccdashboard_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/erccdashboard_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/erccdashboard_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/erccdashboard_1.2.1.tgz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.pdf vignetteTitles: erccdashboard examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R Package: ExiMiR Version: 2.10.0 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) License: GPL-2 MD5sum: a1325e3c42d8181a64688486700faddb 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 source.ver: src/contrib/ExiMiR_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ExiMiR_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ExiMiR_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ExiMiR_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ExiMiR_2.10.0.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 Package: exomeCopy Version: 1.14.0 Depends: IRanges, GenomicRanges, Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) Archs: i386, x64 MD5sum: a8de038921aae338b40677c3c9e04cd8 NeedsCompilation: yes Title: Copy number variant detection from exome sequencing read depth Description: Detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. The package implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count. biocViews: CopyNumberVariation, Sequencing, Genetics Author: Michael Love Maintainer: Michael Love source.ver: src/contrib/exomeCopy_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/exomeCopy_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/exomeCopy_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/exomeCopy_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/exomeCopy_1.14.0.tgz vignettes: vignettes/exomeCopy/inst/doc/exomeCopy.pdf vignetteTitles: Copy number variant detection in exome sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomeCopy/inst/doc/exomeCopy.R importsMe: Rariant Package: exomePeak Version: 1.8.0 Depends: Rsamtools, GenomicFeatures (>= 1.14.5), rtracklayer License: GPL-2 MD5sum: 8bfefa35bc158d381ba726335e5ff801 NeedsCompilation: no Title: exome-based anlaysis of MeRIP-Seq data: peak calling and differential analysis Description: The package is developed for the analysis of affinity-based epitranscriptome shortgun sequencing data from MeRIP-seq (maA-seq). It was built on the basis of the exomePeak MATLAB package (Meng, Jia, et al. "Exome-based analysis for RNA epigenome sequencing data." Bioinformatics 29.12 (2013): 1565-1567.) with new functions for differential analysis of two experimental conditions to unveil the dynamics in post-transcriptional regulation of the RNA methylome. The exomePeak R-package accepts and statistically supports multiple biological replicates, internally removes PCR artifacts and multi-mapping reads, outputs exome-based binding sites (RNA methylation sites) and detects differential post-transcriptional RNA modification sites between two experimental conditions in term of percentage rather the absolute amount. The package is still under active development, and we welcome all biology and computation scientist for all kinds of collaborations and communications. Please feel free to contact Dr. Jia Meng if you have any questions. biocViews: Sequencing, HighThroughputSequencing, Methylseq, RNAseq Author: Jia Meng Maintainer: Jia Meng source.ver: src/contrib/exomePeak_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/exomePeak_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/exomePeak_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/exomePeak_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/exomePeak_1.8.0.tgz vignettes: vignettes/exomePeak/inst/doc/exomePeak-Overview.pdf vignetteTitles: An introduction to exomePeak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomePeak/inst/doc/exomePeak-Overview.R Package: explorase Version: 1.32.0 Depends: R (>= 2.6.2) Imports: limma, rggobi, RGtk2 Suggests: cairoDevice License: GPL-2 MD5sum: 34f733c8bb76fd2d7908ffbacd0b566f NeedsCompilation: no Title: GUI for exploratory data analysis of systems biology data Description: explore and analyze *omics data with R and GGobi biocViews: Visualization,Microarray,GUI Author: Michael Lawrence, Eun-kyung Lee, Dianne Cook, Jihong Kim, Hogeun An, and Dongshin Kim Maintainer: Michael Lawrence URL: http://www.metnetdb.org/MetNet_exploRase.htm source.ver: src/contrib/explorase_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/explorase_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/explorase_1.32.0.zip vignettes: vignettes/explorase/inst/doc/explorase.pdf vignetteTitles: Introduction to exploRase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/explorase/inst/doc/explorase.R Package: ExpressionView Version: 1.20.0 Depends: caTools, bitops, methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Imports: methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Suggests: ALL, hgu95av2.db, biclust, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: a6c005c1f11b4d3c5c4a40ee3050e437 NeedsCompilation: yes Title: Visualize biclusters identified in gene expression data Description: ExpressionView visualizes possibly overlapping biclusters in a gene expression matrix. It can use the result of the ISA method (eisa package) or the algorithms in the biclust package or others. The viewer itself was developed using Adobe Flex and runs in a flash-enabled web browser. biocViews: Classification, Visualization, Microarray, GeneExpression, GO, KEGG Author: Andreas Luscher Maintainer: Gabor Csardi source.ver: src/contrib/ExpressionView_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ExpressionView_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ExpressionView_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ExpressionView_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ExpressionView_1.20.0.tgz vignettes: vignettes/ExpressionView/inst/doc/ExpressionView.format.pdf, vignettes/ExpressionView/inst/doc/ExpressionView.ordering.pdf, vignettes/ExpressionView/inst/doc/ExpressionView.tutorial.pdf vignetteTitles: ExpressionView file format, How the ordering algorithm works, ExpressionView hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionView/inst/doc/ExpressionView.format.R, vignettes/ExpressionView/inst/doc/ExpressionView.ordering.R, vignettes/ExpressionView/inst/doc/ExpressionView.tutorial.R Package: fabia Version: 2.14.0 Depends: R (>= 2.8.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 4f9dc063193c55220fe9a9ef594bd25c 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: Sepp Hochreiter URL: http://www.bioinf.jku.at/software/fabia/fabia.html source.ver: src/contrib/fabia_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/fabia_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/fabia_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/fabia_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/fabia_2.14.0.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 Package: facopy Version: 1.3.1 Depends: R (>= 3.0), methods, cgdsr (>= 1.1.30), coin (>= 1.0), ggplot2, gridExtra, facopy.annot, grid Imports: annotate, data.table, DOSE, FactoMineR, GO.db, GOstats, graphite, igraph, IRanges, MASS, nnet, reshape2, Rgraphviz, scales License: CC BY-NC 4.0 MD5sum: f97fe661042560a89e1b7cf1c5c69fe3 NeedsCompilation: no Title: Feature-based association and gene-set enrichment for copy number alteration analysis in cancer Description: facopy is an R package for fine-tuned cancer CNA association modeling. Association is measured directly at the genomic features of interest and, in the case of genes, downstream gene-set enrichment analysis can be performed thanks to novel internal processing of the data. The software opens a way to systematically scrutinize the differences in CNA distribution across tumoral phenotypes, such as those that relate to tumor type, location and progression. Currently, the output format from 11 different methods that analyze data from whole-genome/exome sequencing and SNP microarrays, is supported. Multiple genomes, alteration types and variable types are also supported. biocViews: Software, CopyNumberVariation, GeneSetEnrichment, GenomicVariation, Genetics, Microarray, Sequencing, Visualization Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena source.ver: src/contrib/facopy_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/facopy_1.3.1.zip win64.binary.ver: bin/windows64/contrib/3.2/facopy_1.3.1.zip mac.binary.ver: bin/macosx/contrib/3.2/facopy_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/facopy_1.3.1.tgz vignettes: vignettes/facopy/inst/doc/facopy.pdf vignetteTitles: facopy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/facopy/inst/doc/facopy.R Package: factDesign Version: 1.44.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: 0b9c27e9f5b387ab64f5729fc9ebc8de 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 source.ver: src/contrib/factDesign_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/factDesign_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/factDesign_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/factDesign_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/factDesign_1.44.0.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 Package: farms Version: 1.20.0 Depends: R (>= 2.8), affy (>= 1.20.0), MASS, methods Imports: affy, MASS, Biobase (>= 1.13.41), methods, graphics Suggests: affydata, Biobase, utils License: LGPL (>= 2.1) MD5sum: b8e5f47853d8c81a85abe8e89dea6e78 NeedsCompilation: no Title: FARMS - Factor Analysis for Robust Microarray Summarization Description: The package provides the summarization algorithm called Factor Analysis for Robust Microarray Summarization (FARMS) and a novel unsupervised feature selection criterion called "I/NI-calls" biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Djork-Arne Clevert Maintainer: Djork-Arne Clevert URL: http://www.bioinf.jku.at/software/farms/farms.html source.ver: src/contrib/farms_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/farms_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/farms_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/farms_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/farms_1.20.0.tgz vignettes: vignettes/farms/inst/doc/farms.pdf vignetteTitles: Using farms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/farms/inst/doc/farms.R Package: fastLiquidAssociation Version: 1.4.0 Depends: methods, LiquidAssociation, parallel, stats, Hmisc Imports: WGCNA Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: cde3471c2f5e1fdafaf566f5c9cbfa64 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 source.ver: src/contrib/fastLiquidAssociation_1.4.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/fastLiquidAssociation_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/fastLiquidAssociation_1.4.0.tgz vignettes: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf vignetteTitles: fastLiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: fastseg Version: 1.14.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: graphics, stats, IRanges, BiocGenerics Suggests: DNAcopy, oligo License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 37c5e84b1f2a29f383f77fc61713cead 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 Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/fastseg/fastseg.html source.ver: src/contrib/fastseg_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/fastseg_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/fastseg_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/fastseg_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/fastseg_1.14.0.tgz vignettes: vignettes/fastseg/inst/doc/fastseg.pdf vignetteTitles: fastseg: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastseg/inst/doc/fastseg.R Package: fdrame Version: 1.40.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: c56d857101798305ae6526844eb0eedb 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 source.ver: src/contrib/fdrame_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/fdrame_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/fdrame_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/fdrame_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/fdrame_1.40.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fdrame/inst/doc/fdrame.R Package: FEM Version: 2.2.1 Depends: AnnotationDbi,Matrix,marray,corrplot,igraph,impute,limma,org.Hs.eg.db Imports: graph License: GPL (>=2) MD5sum: a512e8e50ba2a95b2c0283d31f17cadf NeedsCompilation: no Title: Identification of Functional Epigenetic Modules Description: The FEM package performs a systems-level integrative analysis of DNA methylation and gene expression data. It seeks modules of functionally related genes which exhibit differential promoter DNA methylation and differential expression, where an inverse association between promoter DNA methylation and gene expression is assumed. For full details, see Jiao et al Bioinformatics 2014. biocViews: SystemsBiology,NetworkEnrichment,DifferentialMethylation,DifferentialExpression Author: Andrew E. Teschendorff and Yinming Jiao Maintainer: Andrew E. Teschendorff source.ver: src/contrib/FEM_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/FEM_2.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/FEM_2.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/FEM_2.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FEM_2.2.1.tgz vignettes: vignettes/FEM/inst/doc/IntroDoFEM.pdf vignetteTitles: The FEM package performs a systems-level integrative analysis of DNA methylationa and gene expression. It seeks modules of functionally related genes which exhibit differential promoter DNA methylation and differential expression,, where an inverse association between promoter DNA methylation and gene expression is assumed. For full details,, see Jiao et al Bioinformatics 2014. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FEM/inst/doc/IntroDoFEM.R Package: ffpe Version: 1.12.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: 4a58bdab47233fbb58063d04d2d345d3 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 source.ver: src/contrib/ffpe_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ffpe_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ffpe_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ffpe_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ffpe_1.12.0.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 Package: FGNet Version: 3.2.3 Depends: R (>= 2.15) Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png Suggests: RGtk2, RCurl, RDAVIDWebService, gage, topGO, KEGGprofile, GO.db, KEGG.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi License: GPL (>= 2) MD5sum: a1708be8cb24b0224d3321b9c5e382c2 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 source.ver: src/contrib/FGNet_3.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/FGNet_3.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/FGNet_3.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/FGNet_3.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FGNet_3.2.3.tgz vignettes: vignettes/FGNet/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet.R, vignettes/FGNet/inst/doc/FGNet-vignette.R htmlDocs: vignettes/FGNet/inst/doc/FGNet.html htmlTitles: "FGNet" Package: FISHalyseR Version: 1.2.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: 9c2f7db7daba5e81232f58f933ac0351 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 source.ver: src/contrib/FISHalyseR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FISHalyseR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FISHalyseR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FISHalyseR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FISHalyseR_1.2.0.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 Package: flagme Version: 1.24.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: i386, x64 MD5sum: 74de1fe0095e7e945954fed993fa66ab NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography - mass spectrometry metabolomics data biocViews: DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli source.ver: src/contrib/flagme_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flagme_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flagme_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flagme_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flagme_1.24.0.tgz vignettes: vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GCMS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flagme/inst/doc/flagme.R Package: flipflop Version: 1.6.1 Depends: R (>= 2.10.0) Imports: methods, Matrix, IRanges, GenomicRanges, parallel Suggests: GenomicFeatures License: GPL-3 Archs: i386, x64 MD5sum: c014abd104da7eb5e4f27971cf04a7f6 NeedsCompilation: yes Title: Fast lasso-based isoform prediction as a flow problem Description: Flipflop discovers which isoforms of a gene are expressed in a given sample together with their abundances, based on RNA-Seq read data. It takes an alignment file in SAM format as input. It can also discover transcripts from several samples simultaneously, increasing statistical power. biocViews: RNASeq, RNASeqData, AlternativeSplicing, Regression Author: Elsa Bernard, Laurent Jacob, Julien Mairal and Jean-Philippe Vert Maintainer: Elsa Bernard URL: http://cbio.ensmp.fr/flipflop SystemRequirements: GNU make source.ver: src/contrib/flipflop_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/flipflop_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/flipflop_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/flipflop_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flipflop_1.6.1.tgz vignettes: vignettes/flipflop/inst/doc/flipflop.pdf vignetteTitles: FlipFlop: Fast Lasso-based Isoform Prediction as a Flow Problem hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flipflop/inst/doc/flipflop.R Package: flowBeads Version: 1.6.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: 91a42f4193148f00aff00e592698d9c2 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: Infrastructure, FlowCytometry, CellBasedAssays Author: Nikolas Pontikos Maintainer: Nikolas Pontikos source.ver: src/contrib/flowBeads_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowBeads_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowBeads_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowBeads_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowBeads_1.6.0.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 Package: flowBin Version: 1.4.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: 63a612a20263cb961cc369f503cc4176 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: CellBasedAssays, FlowCytometry Author: Kieran O'Neill Maintainer: Kieran O'Neill source.ver: src/contrib/flowBin_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowBin_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowBin_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowBin_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowBin_1.4.0.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 Package: flowcatchR Version: 1.2.2 Depends: R (>= 2.10), methods, EBImage Imports: rgl, colorRamps, abind, BiocParallel Suggests: BiocStyle, knitr, shiny License: BSD_3_clause + file LICENSE MD5sum: 2d6a54e60c0812bf6bcc31141837c39b 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 Author: Federico Marini [aut, cre] Maintainer: Federico Marini URL: https://github.com/federicomarini/flowcatchR SystemRequirements: ImageMagick VignetteBuilder: knitr BugReports: https://github.com/federicomarini/flowcatchR/issues source.ver: src/contrib/flowcatchR_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowcatchR_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/flowcatchR_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/flowcatchR_1.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowcatchR_1.2.2.tgz vignettes: vignettes/flowcatchR/inst/doc/flowcatchR-vignette.pdf 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 Package: flowCHIC Version: 1.2.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: 75d9ae0a8eacf2cc463162ebfbeff1c9 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: 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 source.ver: src/contrib/flowCHIC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowCHIC_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowCHIC_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowCHIC_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowCHIC_1.2.0.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 Package: flowCL Version: 1.6.0 Depends: R (>= 3.0.2), Rgraphviz, SPARQL Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 9771d12145b36e5d8fd141056a2000fc NeedsCompilation: no Title: Semantic labelling of flow cytometric cell populations Description: Semantic labelling of flow cytometric cell populations. biocViews: FlowCytometry Author: Justin Meskas, Radina Droumeva Maintainer: Justin Meskas source.ver: src/contrib/flowCL_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowCL_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowCL_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowCL_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowCL_1.6.0.tgz vignettes: vignettes/flowCL/inst/doc/flowCL.pdf vignetteTitles: flowCL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCL/inst/doc/flowCL.R Package: flowClean Version: 1.4.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: efdf4dcc14c2e30a5982d833f816874b NeedsCompilation: no Title: flowClean Description: A quality control tool for flow cytometry data based on compositional data analysis. biocViews: FlowCytometry, QualityControl Author: Kipper Fletez-Brant Maintainer: Kipper Fletez-Brant source.ver: src/contrib/flowClean_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowClean_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowClean_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowClean_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowClean_1.4.0.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 Package: flowClust Version: 3.6.0 Depends: R(>= 2.5.0),methods, Biobase, graph, RBGL,ellipse, flowViz, mnormt, corpcor, flowCore, clue Imports: BiocGenerics, MCMCpack Suggests: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 0ade323bac392b53cbff1e9fc4811a03 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: Clustering, Visualization, FlowCytometry Author: Raphael Gottardo , Kenneth Lo , Greg Finak Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make source.ver: src/contrib/flowClust_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowClust_3.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowClust_3.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowClust_3.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowClust_3.6.0.tgz vignettes: vignettes/flowClust/inst/doc/flowClust.pdf vignetteTitles: flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClust/inst/doc/flowClust.R importsMe: flowTrans, flowType suggestsMe: BiocGenerics Package: flowCore Version: 1.34.11 Depends: R (>= 2.10.0) Imports: Biobase, BiocGenerics (>= 0.1.14), graph, graphics, methods, rrcov, stats, utils, stats4, corpcor, Rcpp LinkingTo: Rcpp, BH Suggests: Rgraphviz, flowViz, flowStats, testthat, flowWorkspace,flowWorkspaceData,openCyto License: Artistic-2.0 Archs: i386, x64 MD5sum: 6dcb815bc2c180882b964e3da2261be8 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: Infrastructure, FlowCytometry, CellBasedAssays Author: B. Ellis, P. Haaland, F. Hahne, N. Le Meur, N. Gopalakrishnan, J. Spidlen, M. Jiang Maintainer: M.Jiang SystemRequirements: GNU make source.ver: src/contrib/flowCore_1.34.11.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowCore_1.34.11.zip win64.binary.ver: bin/windows64/contrib/3.2/flowCore_1.34.11.zip mac.binary.ver: bin/macosx/contrib/3.2/flowCore_1.34.11.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowCore_1.34.11.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowClust, flowFP, flowMatch, FlowSOM, flowStats, flowTrans, flowUtils, flowViz, flowVS, immunoClust, ncdfFlow, plateCore importsMe: cytofkit, flowBeads, flowCHIC, flowDensity, flowFit, flowMeans, flowQ, flowStats, flowTrans, flowType, flowViz, plateCore, spade suggestsMe: flowQB, FlowRepositoryR, RchyOptimyx Package: flowCyBar Version: 1.4.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 4897015348095dfa03d2c95661aaf940 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: 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 source.ver: src/contrib/flowCyBar_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowCyBar_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowCyBar_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowCyBar_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowCyBar_1.4.0.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 Package: flowDensity Version: 1.2.0 Depends: R (>= 2.10.0), methods Imports: flowCore, graphics, car, gplots, RFOC, GEOmap, methods, grDevices License: Artistic-2.0 MD5sum: dcc3eb1c4b8a1ff82d8c348f55f55006 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, StemCells, DensityGating Author: M. Jafar Taghiyar, Mehrnoush Malek Maintainer: Mehrnoush Malek source.ver: src/contrib/flowDensity_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowDensity_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowDensity_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowDensity_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowDensity_1.2.0.tgz vignettes: vignettes/flowDensity/inst/doc/flowDensityVignette.pdf vignetteTitles: Automated alternative to the current manual gating practice hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensityVignette.R Package: flowFit Version: 1.6.0 Depends: R (>= 2.12.2) Imports: flowCore, flowViz, graphics, kza, methods, minpack.lm, gplots Suggests: flowFitExampleData License: Artistic-2.0 MD5sum: 297cdc77246a15004ce6f291dd47e64e NeedsCompilation: no Title: Estimate proliferation in cell-tracking dye studies Description: This package estimate the proliferation of a cell population in cell-tracking dye studies. The package uses an R implementation of the Levenberg-Marquardt algorithm (minpack.lm) to fit a set of peaks (corresponding to different generations of cells) over the proliferation-tracking dye distribution in a FACS experiment. biocViews: FlowCytometry, CellBasedAssays Author: Davide Rambaldi Maintainer: Davide Rambaldi BugReports: Davide Rambaldi source.ver: src/contrib/flowFit_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowFit_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowFit_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowFit_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowFit_1.6.0.tgz vignettes: vignettes/flowFit/inst/doc/HowTo-flowFit.pdf vignetteTitles: Fitting Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFit/inst/doc/HowTo-flowFit.R Package: flowFP Version: 1.26.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: fa8b0db462cb22e330b6cb5965ae3fdd 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 source.ver: src/contrib/flowFP_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowFP_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowFP_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowFP_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowFP_1.26.0.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 Package: flowMap Version: 1.6.0 Depends: R (>= 3.0.1), ade4(>= 1.5-2), doParallel(>= 1.0.3), abind(>= 1.4.0), reshape2(>= 1.2.2), scales(>= 0.2.3), Matrix(>= 1.1-4), methods (>= 2.14) Suggests: BiocStyle, knitr License: GPL (>=2) MD5sum: 5c2c041d2a11f8bd54b1d9074dc3ef89 NeedsCompilation: no Title: Mapping cell populations in flow cytometry data for cross-sample comparisons using the Friedman-Rafsky Test Description: flowMap quantifies the similarity of cell populations across multiple flow cytometry samples using a nonparametric multivariate statistical test. The method is able to map cell populations of different size, shape, and proportion across multiple flow cytometry samples. The algorithm can be incorporate in any flow cytometry work flow that requires accurat quantification of similarity between cell populations. biocViews: MultipleComparison, FlowCytometry Author: Chiaowen Joyce Hsiao, Yu Qian, and Richard H. Scheuermann Maintainer: Chiaowen Joyce Hsiao VignetteBuilder: knitr source.ver: src/contrib/flowMap_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowMap_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowMap_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowMap_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowMap_1.6.0.tgz vignettes: vignettes/flowMap/inst/doc/flowMap.pdf vignetteTitles: Mapping cell populations in flow cytometry data flowMap-FR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMap/inst/doc/flowMap.R Package: flowMatch Version: 1.4.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 Archs: i386, x64 MD5sum: 5a9a56afa4de3f4fce8aa16f90f36bd5 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: Clustering, FlowCytometry Author: Ariful Azad Maintainer: Ariful Azad source.ver: src/contrib/flowMatch_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowMatch_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowMatch_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowMatch_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowMatch_1.4.0.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 Package: flowMeans Version: 1.22.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 MD5sum: e7442379d43c2a45501ecabd15085a61 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: FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour Maintainer: Nima Aghaeepour source.ver: src/contrib/flowMeans_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowMeans_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowMeans_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowMeans_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowMeans_1.22.0.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: flowType Package: flowMerge Version: 2.16.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 424d5a0820017eeea8445287dd2c0146 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: Clustering, FlowCytometry Author: Greg Finak , Raphael Gottardo Maintainer: Greg Finak source.ver: src/contrib/flowMerge_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowMerge_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowMerge_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowMerge_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowMerge_2.16.0.tgz vignettes: vignettes/flowMerge/inst/doc/flowMerge.pdf vignetteTitles: flowMerge package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMerge/inst/doc/flowMerge.R importsMe: flowType Package: flowPeaks Version: 1.10.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 Archs: i386, x64 MD5sum: 2e424ad4733bf937a88fa811e05b7591 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: FlowCytometry, Clustering, Gating Author: Yongchao Ge Maintainer: Yongchao Ge source.ver: src/contrib/flowPeaks_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowPeaks_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowPeaks_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowPeaks_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowPeaks_1.10.0.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 Package: flowPlots Version: 1.16.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: 6641ef502feeaf925711c78a165354e9 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: FlowCytometry, CellBasedAssays, Visualization, DataRepresentation Author: N. Hawkins, S. Self Maintainer: N. Hawkins source.ver: src/contrib/flowPlots_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowPlots_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowPlots_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowPlots_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowPlots_1.16.0.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 Package: flowQ Version: 1.28.1 Depends: R (>= 2.10.0), methods, BiocGenerics, outliers, lattice, flowViz, mvoutlier, bioDist, parody, RColorBrewer, latticeExtra Imports: methods, BiocGenerics, geneplotter, flowCore, flowViz, IRanges Suggests: flowStats License: Artistic-2.0 MD5sum: c1bfc6b989623b65cd18936441ca7ab4 NeedsCompilation: no Title: Quality control for flow cytometry Description: Provides quality control and quality assessment tools for flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: R. Gentleman, F. Hahne, J. Kettman, N. Le Meur, N. Gopalakrishnan Maintainer: Mike Jiang SystemRequirements: ImageMagick source.ver: src/contrib/flowQ_1.28.1.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/flowQ_1.28.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowQ_1.28.1.tgz vignettes: vignettes/flowQ/inst/doc/DataQualityAssessment.pdf, vignettes/flowQ/inst/doc/Extending-flowQ.pdf vignetteTitles: Data Quality Assesment for Ungated Flow Cytometry Data, Basic Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowQ/inst/doc/DataQualityAssessment.R, vignettes/flowQ/inst/doc/Extending-flowQ.R Package: flowQB Version: 1.12.0 Imports: Biobase, graphics,methods, flowCore,stats,MASS Suggests: MASS, flowCore License: Artistic-2.0 MD5sum: 4aafd00008073a857fb542aa4c57a3f1 NeedsCompilation: no Title: Automated Quadratic Characterization of Flow Cytometer Instrument Sensitivity: Q, B and CVinstrinsic calculations. Description: flowQB is a fully automated R Bioconductor package to calculate automatically the detector efficiency (Q), optical background (B) and intrinsic CV of the beads. biocViews: FlowCytometry Author: Faysal El Khettabi Maintainer: Faysal El Khettabi source.ver: src/contrib/flowQB_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowQB_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowQB_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowQB_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowQB_1.12.0.tgz vignettes: vignettes/flowQB/inst/doc/flowQBVignettes.pdf vignetteTitles: flowQB package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowQB/inst/doc/flowQBVignettes.R Package: FlowRepositoryR Version: 1.0.0 Depends: R (>= 3.2) Imports: XML, RCurl, tools Suggests: RUnit, BiocGenerics, flowCore, methods License: Artistic-2.0 MD5sum: 6bd8ff801915f93f6db29192a750bca2 NeedsCompilation: no Title: FlowRepository R Interface Description: This package provides an interface to search and download data and annotations from FlowRepository (flowrepository.org). It uses the FlowRepository programming interface to communicate with a FlowRepository server. biocViews: Infrastructure, FlowCytometry Author: Josef Spidlen [aut, cre] Maintainer: Josef Spidlen source.ver: src/contrib/FlowRepositoryR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FlowRepositoryR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FlowRepositoryR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FlowRepositoryR_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FlowRepositoryR_1.0.0.tgz vignettes: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.pdf vignetteTitles: FlowRepository R Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.R Package: FlowSOM Version: 1.0.0 Depends: R (>= 2.11), flowCore, igraph, ConsensusClusterPlus, BiocGenerics, tsne Suggests: flowUtils, BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: ed337b242a8b190c32038421ad2b087a 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, Britt Callebaut and Yvan Saeys Maintainer: Sofie Van Gassen URL: http://www.r-project.org, http://dambi.ugent.be source.ver: src/contrib/FlowSOM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FlowSOM_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FlowSOM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FlowSOM_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FlowSOM_1.0.0.tgz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: Using SOMs for visualization of cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R Package: flowStats Version: 3.26.0 Depends: R (>= 2.10), flowCore, fda (>= 2.2.6), mvoutlier, cluster, flowWorkspace Imports: BiocGenerics, MASS, flowViz, flowCore, fda (>= 2.2.6), Biobase, methods, grDevices, graphics, stats, utils, KernSmooth, lattice,ks Suggests: xtable Enhances: RBGL,ncdfFlow,graph License: Artistic-2.0 MD5sum: 250cc1eeb4ae6a7f64173ac379900341 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: FlowCytometry, CellBasedAssays Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi, Chao-Jen Wong, Kyongryun Lee Maintainer: Greg Finak and Mike Jiang source.ver: src/contrib/flowStats_3.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowStats_3.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowStats_3.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowStats_3.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowStats_3.26.0.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 importsMe: plateCore suggestsMe: flowCore, flowQ Package: flowTrans Version: 1.20.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: 4791e855c9d7b1edb6653781167e0f46 NeedsCompilation: no Title: Parameter Optimization for Flow Cytometry Data Transformation Description: Profile maximum likelihood estimation of parameters for flow cytometry data transformations. biocViews: FlowCytometry Author: Greg Finak , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak source.ver: src/contrib/flowTrans_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowTrans_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowTrans_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowTrans_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowTrans_1.20.0.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 Package: flowType Version: 2.6.0 Depends: R (>= 2.10), Rcpp (>= 0.10.4), BH (>= 1.51.0-3) Imports: Biobase, graphics, grDevices, methods, flowCore, flowMeans, sfsmisc, rrcov, flowClust, flowMerge, stats LinkingTo: Rcpp, BH Suggests: xtable License: Artistic-2.0 Archs: i386, x64 MD5sum: 7e6115017d2a1c081e93eded635fc9b3 NeedsCompilation: yes Title: Phenotyping Flow Cytometry Assays Description: Phenotyping Flow Cytometry Assays using multidimentional expansion of single dimentional partitions. biocViews: FlowCytometry Author: Nima Aghaeepour, Kieran O'Neill, Adrin Jalali Maintainer: Nima Aghaeepour source.ver: src/contrib/flowType_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowType_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowType_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowType_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowType_2.6.0.tgz vignettes: vignettes/flowType/inst/doc/flowType.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowType/inst/doc/flowType.R importsMe: RchyOptimyx Package: flowUtils Version: 1.32.0 Depends: R (>= 2.2.0), flowCore (>= 1.32.0) Imports: Biobase, graph, methods, stats, utils, flowViz, corpcor, RUnit, XML Suggests: gatingMLData License: Artistic-2.0 MD5sum: 3a92a673e5354bacaab8583e902add01 NeedsCompilation: no Title: Utilities for flow cytometry Description: Provides utilities for flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays, DecisionTree Author: J. Spidlen., N. Gopalakrishnan, F. Hahne, B. Ellis, R. Gentleman, M. Dalphin, N. Le Meur, B. Purcell Maintainer: Josef Spidlen source.ver: src/contrib/flowUtils_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowUtils_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowUtils_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowUtils_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowUtils_1.32.0.tgz vignettes: vignettes/flowUtils/inst/doc/HowTo-flowUtils.pdf vignetteTitles: Gating-ML support in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowUtils/inst/doc/HowTo-flowUtils.R suggestsMe: FlowSOM Package: flowViz Version: 1.32.0 Depends: R (>= 2.7.0), flowCore, lattice Imports: stats4, Biobase, flowCore, graphics, grDevices, grid, KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer, stats, utils, hexbin,IDPmisc Suggests: colorspace, flowStats,knitr License: Artistic-2.0 MD5sum: 1ecec47364b19a923b7bf546c44ed78e NeedsCompilation: no Title: Visualization for flow cytometry Description: Provides visualization tools for flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays, Visualization Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar Maintainer: Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/flowViz_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowViz_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowViz_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowViz_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowViz_1.32.0.tgz vignettes: vignettes/flowViz/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R htmlDocs: vignettes/flowViz/inst/doc/filters.html htmlTitles: "Visualizing Gates with Flow Cytometry Data" dependsOnMe: flowClust, flowFP, flowQ, flowVS, ncdfFlow, plateCore importsMe: flowFit, flowQ, flowStats, flowTrans, flowUtils suggestsMe: flowBeads, flowClean, flowCore, spade Package: flowVS Version: 1.0.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: f5f6d45a63ea154c9fcc982de4f6efa2 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: FlowCytometry, CellBasedAssays, Microarray Author: Ariful Azad Maintainer: Ariful Azad VignetteBuilder: knitr source.ver: src/contrib/flowVS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowVS_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowVS_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowVS_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowVS_1.0.0.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 Package: flowWorkspace Version: 3.14.9 Depends: R (>= 2.16.0),flowCore(>= 1.33.13),flowViz(>= 1.29.27),ncdfFlow(>= 2.13.04),gridExtra Imports: Biobase, BiocGenerics, graph, graphics, lattice, methods, stats, stats4, utils, RBGL, XML, tools,gridExtra,Rgraphviz ,data.table ,plyr ,latticeExtra ,Rcpp ,RColorBrewer ,stringr LinkingTo: Rcpp, BH(>= 1.58.0-1) Suggests: testthat ,flowWorkspaceData ,RSVGTipsDevice ,knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 871421b9dab776ff39c60b89b4b52325 NeedsCompilation: yes Title: Import flowJo Workspaces into BioConductor and replicate flowJo gating with flowCore 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: FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Greg Finak, Mike Jiang Maintainer: Greg Finak ,Mike Jiang SystemRequirements: xml2, GNU make VignetteBuilder: knitr source.ver: src/contrib/flowWorkspace_3.14.9.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowWorkspace_3.14.9.zip win64.binary.ver: bin/windows64/contrib/3.2/flowWorkspace_3.14.9.zip mac.binary.ver: bin/macosx/contrib/3.2/flowWorkspace_3.14.9.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowWorkspace_3.14.9.tgz vignettes: vignettes/flowWorkspace/inst/doc/flowWorkspace.pdf vignetteTitles: Importing flowJo Workspaces into R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace.R, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R, vignettes/flowWorkspace/inst/doc/plotGate.R htmlDocs: vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html, vignettes/flowWorkspace/inst/doc/plotGate.html htmlTitles: "How to merge GatingSets", "How to plot gated data" dependsOnMe: flowStats, openCyto suggestsMe: COMPASS Package: fmcsR Version: 1.10.3 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods,ChemmineR, BiocGenerics,parallel Suggests: BiocStyle,knitr,knitcitations,knitrBootstrap License: Artistic-2.0 Archs: i386, x64 MD5sum: 17ef26314073df90738b4cf25aeb735a 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 Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/fmcsR VignetteBuilder: knitr source.ver: src/contrib/fmcsR_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/fmcsR_1.10.3.zip win64.binary.ver: bin/windows64/contrib/3.2/fmcsR_1.10.3.zip mac.binary.ver: bin/macosx/contrib/3.2/fmcsR_1.10.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/fmcsR_1.10.3.tgz vignettes: vignettes/fmcsR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R htmlDocs: vignettes/fmcsR/inst/doc/fmcsR.html htmlTitles: "fmcsR" importsMe: Rcpi Package: focalCall Version: 1.2.0 Depends: R(>= 2.10.0), CGHcall Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: a54a521b227ee5f0344fcf41542f7322 NeedsCompilation: no Title: Detection of focal aberrations in DNA copy number data Description: Detection of genomic focal aberrations in high-resolution DNA copy number data biocViews: Microarray,Preprocessing,Visualization,Sequencing Author: Oscar Krijgsman Maintainer: Oscar Krijgsman URL: https://github.com/OscarKrijgsman/focalCall source.ver: src/contrib/focalCall_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/focalCall_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/focalCall_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/focalCall_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/focalCall_1.2.0.tgz vignettes: vignettes/focalCall/inst/doc/focalCall.pdf vignetteTitles: focalCall hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/focalCall/inst/doc/focalCall.R Package: FourCSeq Version: 1.2.0 Depends: R (>= 3.0), GenomicRanges, ggplot2, DESeq2, splines, methods, LSD Imports: DESeq2, Biobase, Biostrings, GenomicRanges, Rsamtools, ggbio, reshape2, rtracklayer, fda, GenomicAlignments, gtools, Matrix Suggests: BiocStyle, knitr, TxDb.Dmelanogaster.UCSC.dm3.ensGene License: GPL (>= 3) MD5sum: a5aa99d38114579178593246407f999f NeedsCompilation: no Title: Package analyse 4C sequencing data Description: FourCSeq is an R package dedicated to the analysis of (multiplexed) 4C sequencing data. The package provides a pipeline to detect specific interactions between DNA elements and identify differential interactions between conditions. The statistical analysis in R starts with individual bam files for each sample as inputs. To obtain these files, the package contains a python script (extdata/python/demultiplex.py) to demultiplex libraries and trim off primer sequences. With a standard alignment software the required bam files can be then be generated. biocViews: Software, Preprocessing, Sequencing Author: Felix A. Klein, EMBL Heidelberg Maintainer: Felix A. Klein VignetteBuilder: knitr source.ver: src/contrib/FourCSeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FourCSeq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FourCSeq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FourCSeq_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FourCSeq_1.2.0.tgz vignettes: vignettes/FourCSeq/inst/doc/FourCSeq.pdf vignetteTitles: FourCSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FourCSeq/inst/doc/FourCSeq.R Package: FRGEpistasis Version: 1.4.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: 1da2c237c5ce767c01cd09ae4c6c8305 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 source.ver: src/contrib/FRGEpistasis_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FRGEpistasis_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FRGEpistasis_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FRGEpistasis_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FRGEpistasis_1.4.0.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 Package: frma Version: 1.20.0 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: 84998d97ff79e943ecbeeaca1152294b 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 source.ver: src/contrib/frma_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/frma_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/frma_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/frma_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/frma_1.20.0.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 suggestsMe: frmaTools Package: frmaTools Version: 1.20.0 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) MD5sum: d04e30d14278dfc438726fb1e633e1af 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 source.ver: src/contrib/frmaTools_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/frmaTools_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/frmaTools_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/frmaTools_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/frmaTools_1.20.0.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 Package: FunciSNP Version: 1.10.0 Depends: R (>= 2.14.0), ggplot2, TxDb.Hsapiens.UCSC.hg19.knownGene, FunciSNP.data Imports: AnnotationDbi, IRanges, Rsamtools (>= 1.6.1), rtracklayer(>= 1.14.1), methods, ChIPpeakAnno (>= 2.2.0), GenomicRanges, VariantAnnotation, plyr, org.Hs.eg.db, snpStats, ggplot2 (>= 0.9.0), reshape (>= 0.8.4), scales Enhances: parallel License: GPL-3 MD5sum: 889463cca84c06cffb235d304c6725e2 NeedsCompilation: no Title: Integrating Functional Non-coding Datasets with Genetic Association Studies to Identify Candidate Regulatory SNPs Description: FunciSNP integrates information from GWAS, 1000genomes and chromatin feature to identify functional SNP in coding or non-coding regions. biocViews: Infrastructure, DataRepresentation, DataImport, SequenceMatching, Annotation Author: Simon G. Coetzee and Houtan Noushmehr, PhD Maintainer: Simon G. Coetzee URL: http://coetzeeseq.usc.edu/publication/Coetzee_SG_et_al_2012/ source.ver: src/contrib/FunciSNP_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FunciSNP_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FunciSNP_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FunciSNP_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FunciSNP_1.10.0.tgz vignettes: vignettes/FunciSNP/inst/doc/FunciSNP_vignette.pdf vignetteTitles: FunciSNP Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FunciSNP/inst/doc/FunciSNP_vignette.R Package: gaga Version: 2.14.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: b04ec2c974cda1447db7d719114c27c4 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: OneChannel, MassSpectrometry, MultipleComparison, DifferentialExpression, Classification Author: David Rossell . Maintainer: David Rossell source.ver: src/contrib/gaga_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gaga_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gaga_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gaga_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gaga_2.14.0.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 Package: gage Version: 2.18.0 Depends: R (>= 2.10) Imports: graph, KEGGREST, AnnotationDbi Suggests: pathview, gageData, GO.db, org.Hs.eg.db, hgu133a.db, GSEABase, Rsamtools, GenomicAlignments, TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq, DESeq2, edgeR, limma License: GPL (>=2.0) MD5sum: 330da32ddb04dffcab74796f1bbaffa3 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: http://www.biomedcentral.com/1471-2105/10/161 source.ver: src/contrib/gage_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gage_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gage_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gage_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gage_2.18.0.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 suggestsMe: FGNet, pathview Package: gaggle Version: 1.36.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: 21de47b28105bcaf24641a9d9d9cf824 NeedsCompilation: no Title: Broadcast data between R and Gaggle Description: This package contains functions enabling data exchange between R and Gaggle enabled bioinformatics software, including Cytoscape, Firegoose and Gaggle Genome Browser. biocViews: ThirdPartyClient, Visualization, Annotation, GraphAndNetwork, DataImport Author: Paul Shannon Maintainer: Christopher Bare URL: http://gaggle.systemsbiology.net/docs/geese/r/ source.ver: src/contrib/gaggle_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gaggle_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gaggle_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gaggle_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gaggle_1.36.0.tgz vignettes: vignettes/gaggle/inst/doc/gaggle.pdf vignetteTitles: Gaggle Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaggle/inst/doc/gaggle.R Package: gaia Version: 2.12.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: 641f549d2b70a554e61457b657c990ff NeedsCompilation: no Title: GAIA: An R package for genomic analysis of significant chromosomal aberrations. Description: This package allows to assess the statistical significance of chromosomal aberrations. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella et al. Maintainer: S. Morganella source.ver: src/contrib/gaia_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gaia_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gaia_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gaia_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gaia_2.12.0.tgz vignettes: vignettes/gaia/inst/doc/gaia.pdf vignetteTitles: gaia hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaia/inst/doc/gaia.R Package: gaucho Version: 1.4.0 Depends: R (>= 3.0.0), compiler, GA, graph, heatmap.plus, png, Rgraphviz Suggests: knitr License: GPL-3 MD5sum: b64d1ab7fb898bf43a4aebd1eb4742be NeedsCompilation: no Title: Genetic Algorithms for Understanding Clonal Heterogeneity and Ordering Description: Use genetic algorithms to determine the relationship between clones in heterogenous populations such as cancer sequencing samples biocViews: Software,Genetics,SNP,Sequencing,SomaticMutation Author: Alex Murison [aut, cre], Christopher Wardell [aut, cre] Maintainer: Alex Murison , Christopher Wardell VignetteBuilder: knitr source.ver: src/contrib/gaucho_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gaucho_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gaucho_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gaucho_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gaucho_1.4.0.tgz vignettes: vignettes/gaucho/inst/doc/gaucho_vignette.pdf vignetteTitles: An introduction to gaucho hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaucho/inst/doc/gaucho_vignette.R Package: gCMAP Version: 1.12.0 Depends: GSEABase, limma (>= 3.20.0) Imports: Biobase, methods, GSEAlm, Category, Matrix (>= 1.0.9), parallel, annotate, genefilter, AnnotationDbi, DESeq Suggests: BiocGenerics, KEGG.db, reactome.db, RUnit, GO.db, mgsa Enhances: bigmemory, bigmemoryExtras (>= 1.1.2) License: Artistic-2.0 MD5sum: 3d02e140dd67e0ad63dff5287318d7c6 NeedsCompilation: no Title: Tools for Connectivity Map-like analyses Description: The gCMAP package provides a toolkit for comparing differential gene expression profiles through gene set enrichment analysis. Starting from normalized microarray or RNA-seq gene expression values (stored in lists of ExpressionSet and CountDataSet objects) the package performs differential expression analysis using the limma or DESeq packages. Supplying a simple list of gene identifiers, global differential expression profiles or data from complete experiments as input, users can use a unified set of several well-known gene set enrichment analysis methods to retrieve experiments with similar changes in gene expression. To take into account the directionality of gene expression changes, gCMAPQuery introduces the SignedGeneSet class, directly extending GeneSet from the GSEABase package. To increase performance of large queries, multiple gene sets are stored as sparse incidence matrices within CMAPCollection eSets. gCMAP offers implementations of 1. Fisher's exact test (Fisher, J R Stat Soc, 1922) 2. The "connectivity map" method (Lamb et al, Science, 2006) 3. Parametric and non-parametric t-statistic summaries (Jiang & Gentleman, Bioinformatics, 2007) and 4. Wilcoxon / Mann-Whitney rank sum statistics (Wilcoxon, Biometrics Bulletin, 1945) as well as wrappers for the 5. camera (Wu & Smyth, Nucleic Acid Res, 2012) 6. mroast and romer (Wu et al, Bioinformatics, 2010) functions from the limma package and 7. wraps the gsea method from the mgsa package (Bauer et al, NAR, 2010). All methods return CMAPResult objects, an S4 class inheriting from AnnotatedDataFrame, containing enrichment statistics as well as annotation data and providing simple high-level summary plots. biocViews: Microarray, Software, Pathways, Annotation Author: Thomas Sandmann , Richard Bourgon and Sarah Kummerfeld Maintainer: Thomas Sandmann source.ver: src/contrib/gCMAP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gCMAP_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gCMAP_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gCMAP_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gCMAP_1.12.0.tgz vignettes: vignettes/gCMAP/inst/doc/diffExprAnalysis.pdf, vignettes/gCMAP/inst/doc/gCMAP.pdf vignetteTitles: Creating reference datasets, gCMAP classes and methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCMAP/inst/doc/diffExprAnalysis.R, vignettes/gCMAP/inst/doc/gCMAP.R dependsOnMe: gCMAPWeb Package: gCMAPWeb Version: 1.8.0 Depends: Biobase, gCMAP (>= 1.3.0), methods, R (>= 2.15.0), Rook Imports: brew, BiocGenerics, annotate, AnnotationDbi, grDevices, GSEABase, hwriter, parallel, yaml Suggests: affy, ArrayExpress, hgfocus.db, hgu133a.db, mgug4104a.db, org.Hs.eg.db, org.Mm.eg.db, RUnit Enhances: bigmemory, bigmemoryExtras License: Artistic-2.0 MD5sum: 050d1614dfe8af49bdfe18e83f749958 NeedsCompilation: no Title: A web interface for gene-set enrichment analyses Description: The gCMAPWeb R package provides a graphical user interface for the gCMAP package. gCMAPWeb uses the Rook package and can be used either on a local machine, leveraging R's internal web server, or run on a dedicated rApache web server installation. gCMAPWeb allows users to search their own data sources and instructions to generate reference datasets from public repositories are included with the package. The package supports three common types of analyses, specifically queries with 1. one or two sets of query gene identifiers, whose members are expected to show changes in gene expression in a consistent direction. For example, an up-regulated gene set might contain genes activated by a transcription factor, a down-regulated geneset targets repressed by the same factor. 2. a single set of query gene identifiers, whose members are expected to show divergent differential expression (non-directional query). For example, members of a particular signaling pathway, some of which may be up- some down-regulated in response to a stimulus. 3. a query with the complete results of a differential expression profiling experiment. For example, gene identifiers and z-scores from a previous perturbation experiment. gCMAPWeb accepts three types of identifiers: EntreIds, gene Symbols and microarray probe ids and can be configured to work with any species supported by Bioconductor. For each query submission, significantly similar reference datasets will be identified and reported in graphical and tabular form. biocViews: GUI, GeneSetEnrichment, Visualization Author: Thomas Sandmann Maintainer: Thomas Sandmann source.ver: src/contrib/gCMAPWeb_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gCMAPWeb_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gCMAPWeb_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gCMAPWeb_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gCMAPWeb_1.8.0.tgz vignettes: vignettes/gCMAPWeb/inst/doc/gCMAPWeb.pdf, vignettes/gCMAPWeb/inst/doc/referenceDatasets.pdf vignetteTitles: gCMAPWeb configuration, Recreating the Broad Connectivity Map v1 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCMAPWeb/inst/doc/gCMAPWeb.R, vignettes/gCMAPWeb/inst/doc/referenceDatasets.R Package: gcrma Version: 2.40.0 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, BiocInstaller Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL Archs: i386, x64 MD5sum: 95e679cf25dd0eb52496af5219e5d2df 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 source.ver: src/contrib/gcrma_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gcrma_2.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gcrma_2.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gcrma_2.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gcrma_2.40.0.tgz vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf vignetteTitles: gcrma1.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcrma/inst/doc/gcrma2.0.R dependsOnMe: affyILM, affyPLM, bgx, maskBAD, simpleaffy, webbioc importsMe: affycoretools, affylmGUI, limmaGUI, simpleaffy suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies, panp Package: gdsfmt Version: 1.4.0 Depends: R (>= 2.14.0) Imports: methods Suggests: parallel, RUnit, knitr, BiocGenerics License: LGPL-3 Archs: i386, x64 MD5sum: 6af31435bec312d837cbeef66d766424 NeedsCompilation: yes Title: R Interface to CoreArray Genomic Data Structure (GDS) Files Description: This package provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are portable across platforms and include 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 with less than 8 bits, since a single genetic/genomic variant, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are also supported with relatively efficient random access. It is allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. biocViews: Software, 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) Maintainer: Xiuwen Zheng URL: http://corearray.sourceforge.net/, http://github.com/zhengxwen/gdsfmt VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/gdsfmt/issues source.ver: src/contrib/gdsfmt_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gdsfmt_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gdsfmt_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gdsfmt_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gdsfmt_1.4.0.tgz vignettes: vignettes/gdsfmt/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt_vignette.R htmlDocs: vignettes/gdsfmt/inst/doc/gdsfmt_vignette.html htmlTitles: "R Interface to CoreArray Genomic Data Structure (GDS) Files" dependsOnMe: SeqArray, SNPRelate importsMe: GWASTools, SeqVarTools suggestsMe: GENESIS, HIBAG Package: geecc Version: 1.2.0 Depends: R (>= 3.0.0), methods Imports: MASS, hypergea (>= 1.2.3), gplots Suggests: hgu133plus2.db, GO.db, AnnotationDbi License: GPL (>= 2) MD5sum: 16eccd3d5cbb9dd14d7084635310be31 NeedsCompilation: no Title: Gene set Enrichment analysis Extended to Contingency Cubes Description: Use log-linear models to perform hypergeometric and chi-squared tests for gene set enrichments for two (based on contingency tables) or three categories (contingency cubes). Categories can be differentially expressed genes, GO terms, sequence length, GC content, chromosmal position, phylostrata, .... biocViews: BiologicalQuestion, GeneSetEnrichment, WorkflowStep, GO, StatisticalMethod, GeneExpression, Transcription, RNASeq, Microarray Author: Markus Boenn Maintainer: Markus Boenn source.ver: src/contrib/geecc_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/geecc_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/geecc_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/geecc_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/geecc_1.2.0.tgz vignettes: vignettes/geecc/inst/doc/geecc.pdf vignetteTitles: geecc User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geecc/inst/doc/geecc.R Package: genArise Version: 1.44.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: f1c8db0159d80cd41f281580db1861ea 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 source.ver: src/contrib/genArise_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genArise_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genArise_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genArise_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genArise_1.44.0.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 Package: GeneAnswers Version: 2.10.0 Depends: R (>= 3.0.0), igraph, RCurl, annotate, Biobase (>= 1.12.0), methods, XML, RSQLite, MASS, Heatplus, RColorBrewer Imports: RBGL, annotate, downloader Suggests: GO.db, KEGG.db, reactome.db, biomaRt, AnnotationDbi, org.Hs.eg.db, org.Rn.eg.db, org.Mm.eg.db, org.Dm.eg.db, graph License: LGPL (>= 2) MD5sum: 3c4a7a8e83594e54a54e567bf561cb61 NeedsCompilation: no Title: Integrated Interpretation of Genes Description: GeneAnswers provides an integrated tool for biological or medical interpretation of the given one or more groups of genes by means of statistical test. biocViews: Infrastructure, DataRepresentation, Visualization, GraphsAndNetworks Author: Lei Huang, Gang Feng, Pan Du, Tian Xia, Xishu Wang, Jing, Wen, Warren Kibbe and Simon Lin Maintainer: Lei Huang and Gang Feng source.ver: src/contrib/GeneAnswers_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneAnswers_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneAnswers_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneAnswers_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneAnswers_2.10.0.tgz vignettes: vignettes/GeneAnswers/inst/doc/geneAnswers.pdf, vignettes/GeneAnswers/inst/doc/getListGIF.pdf vignetteTitles: GeneAnswers, GeneAnswers web-based visualization module hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneAnswers/inst/doc/geneAnswers.R, vignettes/GeneAnswers/inst/doc/getListGIF.R Package: GENE.E Version: 1.8.0 Depends: R (>= 2.7.0), rhdf5 (>= 2.8.0), RCurl (>= 1.6-6) Imports: rhdf5, RCurl Suggests: RUnit, BiocGenerics, knitr, golubEsets (>= 1.0) License: GPL-2 MD5sum: 0ccac80cca09a7116c87a0c54fe97921 NeedsCompilation: no Title: Interact with GENE-E from R Description: Interactive exploration of matrices in GENE-E. biocViews: ThirdPartyClient Author: Joshua Gould Maintainer: Joshua Gould URL: http://www.broadinstitute.org/cancer/software/GENE-E SystemRequirements: GENE-E software. VignetteBuilder: knitr source.ver: src/contrib/GENE.E_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GENE.E_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GENE.E_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GENE.E_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GENE.E_1.8.0.tgz vignettes: vignettes/GENE.E/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENE.E/inst/doc/GENE.E-vignette.R htmlDocs: vignettes/GENE.E/inst/doc/GENE.E-vignette.html htmlTitles: "GENE.E Overview" Package: GeneExpressionSignature Version: 1.14.0 Depends: R (>= 2.13), Biobase, PGSEA Suggests: apcluster,GEOquery License: GPL-2 MD5sum: 0484afb057051bcf172031762f63a9f4 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 Maintainer: Yang Cao , Fei Li ,Lu Han source.ver: src/contrib/GeneExpressionSignature_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneExpressionSignature_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneExpressionSignature_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneExpressionSignature_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneExpressionSignature_1.14.0.tgz vignettes: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.pdf vignetteTitles: GeneExpressionSignature hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: genefilter Version: 1.50.0 Imports: AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, DESeq, pasilla, BiocStyle, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 5acccf062fcace407ef6d671850c0629 NeedsCompilation: yes Title: genefilter: methods for filtering genes from high-throughput experiments Description: Some basic functions for filtering genes biocViews: Microarray Author: R. Gentleman, V. Carey, W. Huber, F. Hahne Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/genefilter_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genefilter_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genefilter_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genefilter_1.50.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genefilter_1.50.0.tgz vignettes: vignettes/genefilter/inst/doc/howtogenefilter.pdf, vignettes/genefilter/inst/doc/howtogenefinder.pdf, vignettes/genefilter/inst/doc/independent_filtering.pdf, vignettes/genefilter/inst/doc/independent_filtering_plots.pdf vignetteTitles: Using the genefilter function to filter genes from a microarray dataset, How to find genes whose expression profile is similar to that of specified genes, Diagnostics for independent filtering, Additional plots for: Independent filtering increases power for detecting differentially expressed genes,, Bourgon et al.,, PNAS (2010) hasREADME: FALSE hasNEWS: FALSE 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, vignettes/genefilter/inst/doc/independent_filtering.R dependsOnMe: a4Base, cellHTS, cellHTS2, charm, CNTools, GeneMeta, simpleaffy, sva importsMe: affyQCReport, annmap, arrayQualityMetrics, Category, DESeq, DESeq2, DEXSeq, eisa, gCMAP, GGBase, GSRI, methyAnalysis, methylumi, minfi, MLInterfaces, mogsa, PECA, phenoTest, Ringo, simpleaffy, tilingArray, XDE suggestsMe: AffyExpress, annotate, ArrayTools, BiocCaseStudies, BioNet, Category, categoryCompare, clusterStab, codelink, compcodeR, factDesign, ffpe, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, logicFS, lumi, MCRestimate, npGSEA, oligo, oneChannelGUI, phyloseq, pvac, qpgraph, rtracklayer, siggenes, SSPA, topGO, XDE Package: genefu Version: 1.18.0 Depends: survcomp, mclust, biomaRt, R (>= 2.10) Imports: amap Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable License: Artistic-2.0 MD5sum: 76e3b23b8003f96b0feb723fee115944 NeedsCompilation: no Title: Relevant Functions for Gene Expression Analysis, Especially in Breast Cancer. Description: 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, survival analysis, ... biocViews: DifferentialExpression, GeneExpression, Visualization, Clustering, Classification Author: Benjamin Haibe-Kains, Markus Schroeder, Gianluca Bontempi, Christos Sotiriou, John Quackenbush Maintainer: Benjamin Haibe-Kains , Markus Schroeder URL: http://www.pmgenomics.ca/bhklab/ source.ver: src/contrib/genefu_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genefu_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genefu_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genefu_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genefu_1.18.0.tgz vignettes: vignettes/genefu/inst/doc/genefu.pdf vignetteTitles: genefu An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefu/inst/doc/genefu.R Package: GeneGA Version: 1.18.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: 8edb02d4f58602e116de7954bf49e2b8 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/ source.ver: src/contrib/GeneGA_1.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/GeneGA_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneGA_1.18.0.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 Package: GeneMeta Version: 1.40.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: 161bddd4f73d37c046b7b5298e3a056d 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 source.ver: src/contrib/GeneMeta_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneMeta_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneMeta_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneMeta_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneMeta_1.40.0.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 suggestsMe: genefu, XDE Package: GeneNetworkBuilder Version: 1.10.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13), graph Imports: plyr, graph LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, Rgraphviz, XML, RCytoscape, RBGL License: GPL (>= 2) Archs: i386, x64 MD5sum: 28efccc9cc9f62c895911c4d015bf514 NeedsCompilation: yes Title: Build Regulatory Network from ChIP-chip/ChIP-seq and 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 source.ver: src/contrib/GeneNetworkBuilder_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneNetworkBuilder_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneNetworkBuilder_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneNetworkBuilder_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneNetworkBuilder_1.10.0.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder.pdf vignetteTitles: GeneNetworkBuilder Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder.R Package: GeneOverlap Version: 1.4.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 61dab28e47f2187bce7d2d3554946499 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, Mount Sinai Maintainer: Li Shen, Mount Sinai URL: http://shenlab-sinai.github.io/shenlab-sinai/ source.ver: src/contrib/GeneOverlap_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneOverlap_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneOverlap_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneOverlap_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneOverlap_1.4.0.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 Package: geneplotter Version: 1.46.0 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 License: Artistic-2.0 MD5sum: 7ca9c1a9be4902fb453ea4b482c4424b NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: R. Gentleman, Biocore Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/geneplotter_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/geneplotter_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/geneplotter_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/geneplotter_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/geneplotter_1.46.0.tgz vignettes: vignettes/geneplotter/inst/doc/byChroms.pdf, vignettes/geneplotter/inst/doc/visualize.pdf vignetteTitles: How to assemble a chromLocation object, Visualization of Microarray Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplotter/inst/doc/byChroms.R, vignettes/geneplotter/inst/doc/visualize.R dependsOnMe: HMMcopy importsMe: biocGraph, DESeq, DESeq2, DEXSeq, EnrichmentBrowser, flowQ, IsoGeneGUI, MethylSeekR, RNAinteract, RNAither suggestsMe: BiocCaseStudies, biocGraph, Category, chimera, GOstats Package: geneRecommender Version: 1.40.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: 04aa67f9343d6402548ab6dcfcde38c1 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 source.ver: src/contrib/geneRecommender_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/geneRecommender_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/geneRecommender_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/geneRecommender_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/geneRecommender_1.40.0.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 Package: GeneRegionScan Version: 1.24.0 Depends: methods, Biobase (>= 2.5.5), Biostrings Imports: Biobase (>= 2.5.5), affxparser, RColorBrewer, Biostrings Suggests: BSgenome, affy, AnnotationDbi License: GPL (>= 2) MD5sum: e38a35134d9a02f34a89dc1bc08bff54 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 source.ver: src/contrib/GeneRegionScan_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneRegionScan_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneRegionScan_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneRegionScan_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneRegionScan_1.24.0.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 Package: geneRxCluster Version: 1.4.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 378c9e6c7a9c4a0d3f2521aa83befb30 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 source.ver: src/contrib/geneRxCluster_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/geneRxCluster_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/geneRxCluster_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/geneRxCluster_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/geneRxCluster_1.4.0.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 Package: GeneSelectMMD Version: 2.12.0 Depends: R (>= 2.13.2), Biobase Imports: Biobase, MASS, graphics, stats, survival, limma Suggests: ALL License: GPL (>= 2) Archs: i386, x64 MD5sum: d786ab8b87bef6647054c7077962f4d8 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 source.ver: src/contrib/GeneSelectMMD_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneSelectMMD_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneSelectMMD_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneSelectMMD_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneSelectMMD_2.12.0.tgz vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf vignetteTitles: Gene Selection based on a mixture of marginal distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R Package: GeneSelector Version: 2.18.0 Depends: R (>= 2.5.1), methods, stats, Biobase Imports: multtest, siggenes, samr, limma Suggests: multtest, siggenes, samr, limma License: GPL (>= 2) Archs: i386, x64 MD5sum: 4038af7e63e3436db3dc8e97f18d6b10 NeedsCompilation: yes Title: Stability and Aggregation of ranked gene lists Description: The term 'GeneSelector' refers to a filter selecting those genes which are consistently identified as differentially expressed using various statistical procedures. 'Selected' genes are those present at the top of the list in various ranking methods (currently 14). In addition, the stability of the findings can be taken into account in the final ranking by examining perturbed versions of the original data set, e.g. by leaving samples, swapping class labels, generating bootstrap replicates or adding noise. Given multiple ranked lists, one can use aggregation methods in order to find a synthesis. biocViews: StatisticalMethod, DifferentialExpression Author: Martin Slawski , Anne-Laure Boulesteix . Maintainer: Martin Slawski source.ver: src/contrib/GeneSelector_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneSelector_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneSelector_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneSelector_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneSelector_2.18.0.tgz vignettes: vignettes/GeneSelector/inst/doc/GeneSelector.pdf vignetteTitles: GeneSelector.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelector/inst/doc/GeneSelector.R Package: GENESIS Version: 1.0.0 Depends: GWASTools Suggests: gdsfmt, SNPRelate, RUnit, BiocGenerics, knitr License: GPL-3 MD5sum: 961bbeb536dcfcc747b5294e6950da0f NeedsCompilation: no 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): a Principal Components Analysis with genome-wide SNP genotype data for robust population structure inference in samples with related individuals (known or cryptic). biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod, DimensionReduction, PrincipalComponent, GenomeWideAssociation, QualityControl, BiocViews Author: Matthew P. Conomos and Timothy Thornton Maintainer: Matthew P. Conomos VignetteBuilder: knitr source.ver: src/contrib/GENESIS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GENESIS_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GENESIS_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GENESIS_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GENESIS_1.0.0.tgz vignettes: vignettes/GENESIS/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENESIS/inst/doc/pcair.R htmlDocs: vignettes/GENESIS/inst/doc/pcair.html htmlTitles: "Principal Components Analysis in Related Samples (PC-AiR) using the GENESIS Package" Package: geNetClassifier Version: 1.8.2 Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods Imports: e1071, graphics Suggests: leukemiasEset, RUnit, BiocGenerics Enhances: RColorBrewer, igraph, infotheo License: GPL (>= 2) MD5sum: 5f96dd9b8f4b3a1d364416f9789c7d7a 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 source.ver: src/contrib/geNetClassifier_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/geNetClassifier_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.2/geNetClassifier_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.2/geNetClassifier_1.8.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/geNetClassifier_1.8.2.tgz vignettes: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf vignetteTitles: geNetClassifier-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneticsDesign Version: 1.36.0 Imports: gmodels, graphics, gtools (>= 2.4.0), mvtnorm, stats License: GPL-2 MD5sum: 2b05a3510ed2c01e88d492d2e735d42f NeedsCompilation: no Title: Functions for designing genetics studies Description: This package contains functions useful for designing genetics studies, including power and sample-size calculations. biocViews: Genetics Author: Gregory Warnes David Duffy , Michael Man Weiliang Qiu Ross Lazarus Maintainer: The R Genetics Project source.ver: src/contrib/GeneticsDesign_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneticsDesign_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneticsDesign_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneticsDesign_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneticsDesign_1.36.0.tgz vignettes: vignettes/GeneticsDesign/inst/doc/GPC.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneticsDesign/inst/doc/GPC.R Package: GeneticsPed Version: 1.30.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE Archs: i386, x64 MD5sum: ab37fdd24dc1bf5982c517e243d29bd3 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 source.ver: src/contrib/GeneticsPed_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneticsPed_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneticsPed_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneticsPed_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneticsPed_1.30.0.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 Package: genoCN Version: 1.20.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: i386, x64 MD5sum: a4bfea36fa3e5bdd4a2329c28fd8a70d NeedsCompilation: yes Title: genotyping and copy number study tools Description: Simultaneous identification of copy number states and genotype calls for regions of either copy number variations or copy number aberrations biocViews: Microarray, Genetics Author: Wei Sun and ZhengZheng Tang Maintainer: Wei Sun source.ver: src/contrib/genoCN_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genoCN_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genoCN_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genoCN_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genoCN_1.20.0.tgz vignettes: vignettes/genoCN/inst/doc/genoCN.pdf vignetteTitles: add stuff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoCN/inst/doc/genoCN.R Package: genomation Version: 1.0.0 Depends: R (>= 3.0.0),grid Imports: GenomicRanges, GenomicAlignments, IRanges, Rsamtools, data.table, plyr, reshape2, ggplot2, methods, rtracklayer, gridBase, impute Suggests: RUnit, knitr, RColorBrewer, genomationData, BiocGenerics, rmarkdown, knitrBootstrap License: Artistic-2.0 MD5sum: 6ff61a224c81565f04566fb9551f2cee NeedsCompilation: no 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, Vedran Franke Maintainer: Altuna Akalin , Vedran Franke VignetteBuilder: knitr source.ver: src/contrib/genomation_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genomation_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genomation_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genomation_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genomation_1.0.0.tgz vignettes: vignettes/genomation/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomation/inst/doc/GenomationManual-knitr.R htmlDocs: vignettes/genomation/inst/doc/GenomationManual-knitr.html htmlTitles: "genomation" Package: GenomeGraphs Version: 1.28.0 Depends: R (>= 2.10), methods, biomaRt, grid License: Artistic-2.0 MD5sum: ce171e5d6c2a29de12c2d0db6516f1cf NeedsCompilation: no Title: Plotting genomic information from Ensembl Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. GenomeGraphs uses the biomaRt package to perform live annotation queries to Ensembl 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. Another strength of GenomeGraphs is to plot different data types such as array CGH, gene expression, sequencing and other data, together in one plot using the same genome coordinate system. biocViews: Visualization, Microarray Author: Steffen Durinck , James Bullard Maintainer: Steffen Durinck source.ver: src/contrib/GenomeGraphs_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomeGraphs_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomeGraphs_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomeGraphs_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomeGraphs_1.28.0.tgz vignettes: vignettes/GenomeGraphs/inst/doc/GenomeGraphs.pdf vignetteTitles: The GenomeGraphs users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeGraphs/inst/doc/GenomeGraphs.R dependsOnMe: Genominator, waveTiling suggestsMe: oligo, rMAT, triplex Package: GenomeInfoDb Version: 1.4.3 Depends: R (>= 3.1), methods, stats4, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.2.0), IRanges (>= 1.99.26) Imports: methods, BiocGenerics, S4Vectors Suggests: GenomicRanges, Rsamtools, GenomicAlignments, BSgenome, GenomicFeatures, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Dmelanogaster.UCSC.dm3.ensGene, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: b1f1331db6b25b15cf4ed92b6979b678 NeedsCompilation: no Title: Utilities for manipulating chromosome and other 'seqname' identifiers 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, Martin Morgan, Marc Carlson, H. Pages Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w source.ver: src/contrib/GenomeInfoDb_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomeInfoDb_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomeInfoDb_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomeInfoDb_1.4.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomeInfoDb_1.4.3.tgz vignettes: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.pdf, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf vignetteTitles: GenomeInfoDb: Submitting your organism to GenomeInfoDb, GenomeInfoDb :Introduction to GenomeInfoDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R dependsOnMe: AnnotationDbi, BSgenome, bsseq, bumphunter, CODEX, CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, htSeqTools, methyAnalysis, Rsamtools, TitanCNA, VariantAnnotation importsMe: AllelicImbalance, ballgown, biovizBase, BiSeq, BSgenome, casper, CexoR, ChIPseeker, CNEr, compEpiTools, conumee, CopywriteR, csaw, derfinder, derfinderPlot, diffHic, easyRNASeq, ensembldb, epivizr, exomeCopy, GenomicInteractions, genoset, ggbio, GGtools, GoogleGenomics, gQTLstats, GreyListChIP, Gviz, gwascat, h5vc, InPAS, IVAS, methylPipe, methylumi, minfi, MinimumDistance, NarrowPeaks, podkat, prebs, qpgraph, QuasR, r3Cseq, Rariant, regionReport, Repitools, rtracklayer, seqplots, SGSeq, ShortRead, SNPchip, soGGi, SomaticSignatures, SplicingGraphs, VanillaICE, VariantFiltering, VariantTools suggestsMe: AnnotationHub, QDNAseq Package: genomeIntervals Version: 1.24.1 Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics (>= 0.10.0) License: Artistic-2.0 MD5sum: 96f14c66776b7f4eadd7450242f65eaa 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 source.ver: src/contrib/genomeIntervals_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/genomeIntervals_1.24.1.zip win64.binary.ver: bin/windows64/contrib/3.2/genomeIntervals_1.24.1.zip mac.binary.ver: bin/macosx/contrib/3.2/genomeIntervals_1.24.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genomeIntervals_1.24.1.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 dependsOnMe: girafe importsMe: easyRNASeq Package: genomes Version: 2.14.0 Depends: R (>= 2.11), XML, RCurl, GenomicRanges, IRanges, Biostrings License: Artistic-2.0 MD5sum: 558401cd64c05d84c4c6a2b322b0792a NeedsCompilation: no Title: Genome sequencing project metadata Description: Collects genome sequencing project data from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben source.ver: src/contrib/genomes_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genomes_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genomes_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genomes_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genomes_2.14.0.tgz vignettes: vignettes/genomes/inst/doc/genome-tables.pdf vignetteTitles: Introduction to genome projects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomes/inst/doc/genome-tables.R Package: GenomicAlignments Version: 1.4.2 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.5.14), IRanges (>= 2.1.26), GenomeInfoDb (>= 1.1.20), GenomicRanges (>= 1.19.23), Biostrings (>= 2.35.1), Rsamtools (>= 1.19.39) Imports: methods, stats, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel 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, DESeq, edgeR, RUnit, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: fb58bb6ff5ba14464853698d1ec6e3be 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: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, SNP, Coverage, Alignment Author: Herv\'e Pag\`es, Valerie Obenchain, Martin Morgan Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs source.ver: src/contrib/GenomicAlignments_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicAlignments_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicAlignments_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicAlignments_1.4.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicAlignments_1.4.2.tgz vignettes: vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.pdf vignetteTitles: Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R dependsOnMe: AllelicImbalance, chimera, DiffBind, GoogleGenomics, groHMM, hiReadsProcessor, prebs, RIPSeeker, rnaSeqMap, ShortRead, SplicingGraphs importsMe: biovizBase, ChIPQC, CNEr, CopywriteR, csaw, customProDB, derfinder, easyRNASeq, FourCSeq, genomation, GenomicFiles, ggbio, gmapR, GreyListChIP, Gviz, HTSeqGenie, metagene, methylPipe, PICS, QuasR, Repitools, roar, rtracklayer, SGSeq, similaRpeak, soGGi, SplicingGraphs, systemPipeR, trackViewer suggestsMe: BiocParallel, gage, GenomeInfoDb, GenomicRanges, oneChannelGUI, Rsamtools, Streamer Package: GenomicFeatures Version: 1.20.6 Depends: BiocGenerics (>= 0.1.0), S4Vectors (>= 0.1.5), IRanges (>= 2.1.36), GenomeInfoDb (>= 1.4.3), GenomicRanges (>= 1.17.12), AnnotationDbi (>= 1.27.9) Imports: methods, utils, tools, DBI (>= 0.2-5), RSQLite (>= 0.8-1), RCurl, XVector, Biostrings (>= 2.23.3), rtracklayer (>= 1.25.2), biomaRt (>= 2.17.1), Biobase (>= 2.15.1) Suggests: org.Mm.eg.db, org.Hs.eg.db, BSgenome, BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17), BSgenome.Celegans.UCSC.ce2, BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.17), mirbase.db, FDb.UCSC.tRNAs, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1), TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, SNPlocs.Hsapiens.dbSNP141.GRCh38, Rsamtools, pasillaBamSubset (>= 0.0.5), RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: d77d7903b7522d078fe9368b9c27a388 NeedsCompilation: no Title: Tools for making and manipulating transcript centric annotations Description: A set of tools and methods for making and manipulating transcript centric annotations. With these tools the user can easily download the genomic locations of the transcripts, exons and cds of a given organism, from either the UCSC Genome Browser or a BioMart database (more sources will be supported in the future). This information is then stored in a local database that keeps track of the relationship between transcripts, exons, cds and genes. Flexible methods are provided for extracting the desired features in a convenient format. biocViews: Genetics, Infrastructure, Annotation, Sequencing, GenomeAnnotation Author: M. Carlson, H. Pages, P. Aboyoun, S. Falcon, M. Morgan, D. Sarkar, M. Lawrence Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/GenomicFeatures_1.20.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicFeatures_1.20.6.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicFeatures_1.20.6.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicFeatures_1.20.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicFeatures_1.20.6.tgz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.pdf vignetteTitles: Making and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R dependsOnMe: cpvSNP, ensembldb, exomePeak, InPAS, OrganismDbi, RNAprobR, SplicingGraphs importsMe: AllelicImbalance, biovizBase, bumphunter, casper, ChIPpeakAnno, ChIPseeker, compEpiTools, CompGO, csaw, customProDB, derfinder, derfinderPlot, epivizr, ggbio, gmapR, gQTLstats, Gviz, HTSeqGenie, lumi, methyAnalysis, OrganismDbi, proBAMr, qpgraph, QuasR, SGSeq, SplicingGraphs, trackViewer, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: biomvRCNS, Biostrings, chipseq, cummeRbund, DEXSeq, easyRNASeq, flipflop, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, MiRaGE, RIPSeeker, Rsamtools, ShortRead, systemPipeR Package: GenomicFiles Version: 1.4.0 Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2), S4Vectors (>= 0.4.0), IRanges(>= 2.0.0), Rsamtools (>= 1.17.29), GenomicRanges (>= 1.19.43), rtracklayer (>= 1.25.3), BiocParallel (>= 1.1.0) Imports: GenomicAlignments Suggests: BiocStyle, RUnit, genefilter, deepSNV, RNAseqData.HNRNPC.bam.chr14, Biostrings License: Artistic-2.0 MD5sum: 221c7c189cd16a80cdff37fc26b18ddb 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: Valerie Obenchain, Michael Love, Martin Morgan Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=3PK_jx44QTs source.ver: src/contrib/GenomicFiles_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicFiles_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicFiles_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicFiles_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicFiles_1.4.0.tgz vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.pdf vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R importsMe: derfinder Package: GenomicInteractions Version: 1.2.3 Depends: R (>= 2.10) Imports: Rsamtools, GenomicRanges, IRanges, BiocGenerics, data.table, stringr, rtracklayer, GenomeInfoDb, ggplot2, gridExtra, methods, igraph, plotrix, S4Vectors, dplyr Suggests: knitr, BiocStyle, testthat License: GPL-3 MD5sum: d88740b95be8cb7c6a68a655925ebc5b NeedsCompilation: no Title: R package for handling genomic interaction data Description: R package for handling Genomic interaction data, such as ChIA-PET/Hi-C, annotating genomic features with interaction information and producing various plots / statistics biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard, B. Maintainer: Malcolm Perry , Liz Ing-Simmons URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr source.ver: src/contrib/GenomicInteractions_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicInteractions_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicInteractions_1.2.3.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicInteractions_1.2.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicInteractions_1.2.3.tgz vignettes: vignettes/GenomicInteractions/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R htmlDocs: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html htmlTitles: "GenomicInteractions-ChIAPET", "GenomicInteractions-HiC" Package: GenomicRanges Version: 1.20.8 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.6.6), IRanges (>= 2.2.4), GenomeInfoDb (>= 1.1.20) Imports: utils, stats, XVector LinkingTo: S4Vectors, IRanges Suggests: AnnotationDbi (>= 1.21.1), AnnotationHub, BSgenome, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2, Biobase, BiocStyle, Biostrings (>= 2.25.3), DESeq, DEXSeq, GenomicAlignments, GenomicFeatures, KEGG.db, KEGGgraph, RUnit, Rsamtools (>= 1.13.53), TxDb.Athaliana.BioMart.plantsmart22, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, annotate, digest, edgeR, hgu95av2.db, org.Hs.eg.db, org.Mm.eg.db, org.Sc.sgd.db, pasilla, pasillaBamSubset, rtracklayer License: Artistic-2.0 Archs: i386, x64 MD5sum: 58e92ba688065705260a8ddff18a524e 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 analyze high-throughput sequencing data (a.k.a. NGS data). The package defines general purpose containers for storing genomic intervals. Specialized containers for representing and manipulating short alignments against a reference genome are defined in the GenomicAlignments package. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: P. Aboyoun, H. Pages and M. Lawrence Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GenomicRanges_1.20.8.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicRanges_1.20.8.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicRanges_1.20.8.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicRanges_1.20.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicRanges_1.20.8.tgz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.pdf, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf vignetteTitles: Extending GenomicRanges, GenomicRanges HOWTOs, An Introduction to GenomicRanges, A quick introduction to GRanges and GRangesList objects 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 dependsOnMe: ALDEx2, AllelicImbalance, annmap, Basic4Cseq, baySeq, biomvRCNS, BiSeq, BSgenome, bsseq, bumphunter, CAFE, casper, chimera, ChIPpeakAnno, ChIPQC, chipseq, cleanUpdTSeq, cn.mops, cnvGSA, compEpiTools, CSAR, csaw, DASiR, deepSNV, DESeq2, DEXSeq, DiffBind, diffHic, DMRcaller, DMRforPairs, ensembldb, ensemblVEP, epigenomix, epivizr, exomeCopy, fastseg, FourCSeq, genomes, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicTuples, genoset, GenoView, gmapR, GOTHiC, GreyListChIP, groHMM, Gviz, hiAnnotator, HiTC, htSeqTools, IdeoViz, InPAS, intansv, MBASED, metagene, methyAnalysis, methylPipe, minfi, PING, podkat, QuasR, r3Cseq, R453Plus1Toolbox, Rariant, Rcade, regioneR, rfPred, rGREAT, riboSeqR, RIPSeeker, RnBeads, Rsamtools, RSVSim, rtracklayer, segmentSeq, seqbias, SGSeq, SigFuge, SomatiCA, SomaticSignatures, trackViewer, VanillaICE, VariantAnnotation, VariantTools, vtpnet, wavClusteR importsMe: ArrayExpressHTS, ballgown, bamsignals, beadarray, BEAT, biovizBase, BiSeq, BSgenome, BubbleTree, CAGEr, CexoR, ChAMP, chipenrich, ChIPseeker, chipseq, ChIPseqR, chromDraw, CNEr, coMET, conumee, copynumber, CopywriteR, customProDB, derfinder, derfinderPlot, DOQTL, easyRNASeq, EnrichmentBrowser, flipflop, FourCSeq, FunciSNP, genomation, GenomicAlignments, GenomicInteractions, GGBase, ggbio, GGtools, GoogleGenomics, gQTLBase, gQTLstats, gwascat, h5vc, hiReadsProcessor, HTSeqGenie, IVAS, lumi, M3D, MEDIPS, methyAnalysis, MethylSeekR, methylumi, MinimumDistance, NarrowPeaks, nucleR, oligoClasses, Pbase, pepStat, PICS, prebs, proBAMr, Pviz, pwOmics, qpgraph, regioneR, regionReport, Repitools, rgsepd, RNAprobR, rnaSeqMap, roar, seq2pathway, SeqArray, seqPattern, seqplots, SeqVarTools, ShortRead, simulatorZ, SNPchip, soGGi, SomatiCA, spliceR, SplicingGraphs, SVM2CRM, systemPipeR, TFBSTools, ToPASeq, tracktables, triplex, VariantFiltering, waveTiling suggestsMe: AnnotationHub, BiocGenerics, BiocParallel, cummeRbund, GenomeInfoDb, gtrellis, IRanges, metaseqR, MiRaGE, NarrowPeaks, NGScopy, SeqGSEA, STAN Package: GenomicTuples Version: 1.2.1 Depends: R (>= 3.2.0), GenomicRanges (>= 1.19.47), GenomeInfoDb, BiocGenerics, methods Imports: Rcpp (>= 0.11.2), S4Vectors, Biobase, IRanges LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: d628daa1867bb4afbe1d96b9bc56804b 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 , with contributions from Marcin Cieslik and Herve Pages. Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/GenomicTuples/issues source.ver: src/contrib/GenomicTuples_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicTuples_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicTuples_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicTuples_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicTuples_1.2.1.tgz vignettes: vignettes/GenomicTuples/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R htmlDocs: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html htmlTitles: "GenomicTuples: Classes and Methods" Package: Genominator Version: 1.22.0 Depends: R (>= 2.10), methods, RSQLite, DBI (>= 0.2-5), BiocGenerics (>= 0.1.0), IRanges, GenomeGraphs Imports: graphics, stats, utils Suggests: biomaRt, ShortRead, yeastRNASeq License: Artistic-2.0 MD5sum: cbb6291a4129cdf4fd7477e5eee10a55 NeedsCompilation: no Title: Analyze, manage and store genomic data Description: Tools for storing, accessing, analyzing and visualizing genomic data. biocViews: Infrastructure Author: James Bullard, Kasper Daniel Hansen Maintainer: James Bullard source.ver: src/contrib/Genominator_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Genominator_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Genominator_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Genominator_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Genominator_1.22.0.tgz vignettes: vignettes/Genominator/inst/doc/Genominator.pdf, vignettes/Genominator/inst/doc/plotting.pdf, vignettes/Genominator/inst/doc/withShortRead.pdf vignetteTitles: The Genominator User Guide, Plotting with Genominator, Working with the ShortRead Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Genominator/inst/doc/Genominator.R, vignettes/Genominator/inst/doc/plotting.R, vignettes/Genominator/inst/doc/withShortRead.R suggestsMe: oneChannelGUI Package: genoset Version: 1.22.0 Depends: R (>= 2.10), BiocGenerics (>= 0.11.3), Biobase (>= 2.15.1), GenomicRanges (>= 1.17.19) Imports: S4Vectors (>= 0.2.3), GenomeInfoDb (>= 1.1.3), IRanges, methods, graphics Suggests: RUnit, DNAcopy, stats, BSgenome, Biostrings Enhances: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 6ac4ae220a84f245c4e0ae2cd4b84293 NeedsCompilation: yes Title: Provides classes similar to ExpressionSet for copy number analysis Description: Load, manipulate, and plot copynumber and BAF data. GenoSet class extends eSet by adding a "locData" slot for a GRanges object. This object contains feature genome location data and provides for efficient subsetting on genome location. Provides convenience functions for processing of copy number and B-Allele Frequency data. Provides the class RleDataFrame to store runs of data along the genome for multiple samples. biocViews: Infrastructure, DataRepresentation, Microarray, SNP, CopyNumberVariation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/genoset source.ver: src/contrib/genoset_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genoset_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genoset_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genoset_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genoset_1.22.0.tgz vignettes: vignettes/genoset/inst/doc/genoset.pdf vignetteTitles: genoset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoset/inst/doc/genoset.R dependsOnMe: VegaMC importsMe: methyAnalysis Package: GenoView Version: 1.2.0 Depends: R (>= 2.10), gridExtra, GenomicRanges Imports: ggbio, ggplot2, grid, biovizBase Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, PFAM.db, AnnotationDbi, gtable, gWidgets, gWidgetsRGtk2, RGtk2, RColorBrewer License: GPL-3 MD5sum: b6ce4a0438053060c6ba7a2d20f8a12d NeedsCompilation: no Title: Condensed, overlapped plotting of genomic data tracks Description: Superimposing input data over existing genomic references allows for fast, accurate visual comparisons. The GenoView package is a novel bioinformatics package which condenses genomic data tracks to offer a comprehensive view of genetic variants. Its main function is to display mutation data over exons and protein domains, which easily identifies potential genomic locations of interest. biocViews: Visualization Author: Sharon Lee, Dennis Wang Maintainer: Sharon Lee source.ver: src/contrib/GenoView_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenoView_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GenoView_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GenoView_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenoView_1.2.0.tgz vignettes: vignettes/GenoView/inst/doc/GenoView.pdf vignetteTitles: GenoView hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenoView/inst/doc/GenoView.R Package: GEOmetadb Version: 1.28.0 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 MD5sum: d7fce749d3449d1e5b96231a58c167a5 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 URL: http://gbnci.abcc.ncifcrf.gov/geo/ VignetteBuilder: knitr source.ver: src/contrib/GEOmetadb_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GEOmetadb_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GEOmetadb_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GEOmetadb_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GEOmetadb_1.28.0.tgz vignettes: vignettes/GEOmetadb/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R htmlDocs: vignettes/GEOmetadb/inst/doc/GEOmetadb.html htmlTitles: "GEOmetadb" Package: GEOquery Version: 2.34.0 Depends: methods, Biobase Imports: XML, RCurl Suggests: limma, knitr, rmarkdown License: GPL-2 MD5sum: 4f1a48ce93ce1275de5172fb8181c1c4 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 Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery VignetteBuilder: knitr BugReports: https://github.com/seandavi/GEOquery/issues/new source.ver: src/contrib/GEOquery_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GEOquery_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GEOquery_2.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GEOquery_2.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GEOquery_2.34.0.tgz vignettes: vignettes/GEOquery/inst/doc/GEOquery.pdf vignetteTitles: GEOquery.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R htmlDocs: vignettes/GEOquery/inst/doc/GEOquery.html htmlTitles: "Using GEOquery" dependsOnMe: DrugVsDisease, SCAN.UPC importsMe: ChIPXpress, minfi, SRAdb suggestsMe: dyebias, ELBOW, PGSEA, RGSEA, RnBeads, Rnits, skewr, TargetScore Package: GEOsubmission Version: 1.20.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: 1919b5c1e1ca6720600a82a95fcd4de7 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 source.ver: src/contrib/GEOsubmission_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GEOsubmission_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GEOsubmission_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GEOsubmission_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GEOsubmission_1.20.0.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 Package: gespeR Version: 1.0.0 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2 Suggests: knitr License: GPL-3 MD5sum: 51787f9297b60746ea56e997f284c240 NeedsCompilation: no Title: Gene-Specific Phenotype EstimatoR Description: Estimates gene-specific phenotypes from off-target confounded RNAi screens. The phenotype of each siRNA is modeled based on on-targeted and off-targeted genes, using a regularized linear regression model. biocViews: CellBasedAssays, Preprocessing, GeneTarget, Regression, Visualization Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://www.cbg.ethz.ch/software/gespeR VignetteBuilder: knitr source.ver: src/contrib/gespeR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gespeR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gespeR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gespeR_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gespeR_1.0.0.tgz vignettes: vignettes/gespeR/inst/doc/gespeR.pdf vignetteTitles: An R package for deconvoluting off-target confounded RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gespeR/inst/doc/gespeR.R Package: GEWIST Version: 1.12.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 6bdca393602a2b9a0d5c8befca906de4 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 source.ver: src/contrib/GEWIST_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GEWIST_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GEWIST_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GEWIST_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GEWIST_1.12.0.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 Package: GGBase Version: 3.30.1 Depends: R (>= 2.14), methods, snpStats Imports: limma, genefilter, Biobase, BiocGenerics, Matrix, AnnotationDbi, digest, GenomicRanges Suggests: GGtools, illuminaHumanv1.db License: Artistic-2.0 MD5sum: 2d9b9636916a9b45c0d4ab7966c1ea7d NeedsCompilation: no Title: GGBase infrastructure for genetics of gene expression package GGtools Description: infrastructure biocViews: Genetics, Infrastructure Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/GGBase_3.30.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/GGBase_3.30.1.zip win64.binary.ver: bin/windows64/contrib/3.2/GGBase_3.30.1.zip mac.binary.ver: bin/macosx/contrib/3.2/GGBase_3.30.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GGBase_3.30.1.tgz vignettes: vignettes/GGBase/inst/doc/ggbase.pdf vignetteTitles: GGBase -- infrastructure for GGtools,, genetics of gene expression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGBase/inst/doc/ggbase.R dependsOnMe: GGtools Package: ggbio Version: 1.16.1 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, biovizBase (>= 1.13.8), reshape2, gtable, Biobase, S4Vectors (>= 0.2.3), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.40), Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), BSgenome, gridExtra, scales, VariantAnnotation (>= 1.11.4), Hmisc, rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.17.13), OrganismDbi, GGally Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, chipseq, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat License: Artistic-2.0 MD5sum: 4c96c01cec0a1ba5e8f218fe1f666638 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, Dianne Cook, Michael Lawrence Maintainer: Tengfei Yin URL: http://tengfei.github.com/ggbio/ VignetteBuilder: knitr BugReports: https://github.com/tengfei/ggbio/issues source.ver: src/contrib/ggbio_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ggbio_1.16.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ggbio_1.16.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ggbio_1.16.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ggbio_1.16.1.tgz vignettes: vignettes/ggbio/inst/doc/ggbio.pdf vignetteTitles: Part 0: Introduction and quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggbio/inst/doc/ggbio.R dependsOnMe: CAFE, intansv importsMe: coMET, derfinderPlot, FourCSeq, GenoView, Rariant, regionReport, ReportingTools, SomaticSignatures suggestsMe: beadarray, GoogleGenomics, gQTLstats, gwascat, interactiveDisplay, RnBeads Package: GGtools Version: 5.4.0 Depends: R (>= 2.14), GGBase (>= 3.19.7), data.table, parallel Imports: methods, utils, stats, BiocGenerics, snpStats, ff, Rsamtools, AnnotationDbi, Biobase, bit, VariantAnnotation, hexbin, rtracklayer, Gviz, stats4, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, iterators, Biostrings, ROCR, biglm, ggplot2, reshape2 Suggests: GGdata, illuminaHumanv1.db, SNPlocs.Hsapiens.dbSNP.20120608, multtest, aod, rmeta Enhances: MatrixEQTL, Homo.sapiens, foreach, doParallel, gwascat License: Artistic-2.0 MD5sum: 0ce09ba62f5d1b9dc19b9f222d73f09c NeedsCompilation: no Title: software and data for analyses in genetics of gene expression Description: software and data for analyses in genetics of gene expression and/or DNA methylation biocViews: Genetics, GeneExpression, GeneticVariability, SNP Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/GGtools_5.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GGtools_5.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GGtools_5.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GGtools_5.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GGtools_5.4.0.tgz vignettes: vignettes/GGtools/inst/doc/GGtools.pdf vignetteTitles: GGtools: software for eQTL identification hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGtools/inst/doc/GGtools.R suggestsMe: GGBase, gQTLBase Package: ggtree Version: 1.0.21 Depends: R (>= 3.1.0) Imports: ape, Biostrings, colorspace, EBImage, ggplot2, grid, gridExtra, jsonlite, magrittr, methods, reshape2, stats4 Suggests: phylobase, phytools, BiocStyle, knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: 0d32b06203171c5ebd707e9e7ca17f80 NeedsCompilation: no Title: a phylogenetic tree viewer for different types of tree annotations Description: ggtree extends the ggplot2 plotting system which implemented the grammar of graphics. ggtree is designed for visualizing phylogenetic tree and different types of associated annotation data. biocViews: Software, Annotation, Clustering, DataImport, Visualization Author: Guangchuang Yu and Tommy Tsan-Yuk Lam Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/ggtree VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ggtree/issues source.ver: src/contrib/ggtree_1.0.21.tar.gz win.binary.ver: bin/windows/contrib/3.2/ggtree_1.0.21.zip win64.binary.ver: bin/windows64/contrib/3.2/ggtree_1.0.21.zip mac.binary.ver: bin/macosx/contrib/3.2/ggtree_1.0.21.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ggtree_1.0.21.tgz vignettes: vignettes/ggtree/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtree/inst/doc/ggtree.R htmlDocs: vignettes/ggtree/inst/doc/ggtree.html htmlTitles: "ggtree: a phylogenetic tree viewer for different types of tree annotations" Package: girafe Version: 1.20.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.8), S4Vectors, Rsamtools, intervals (>= 0.13.1), ShortRead (>= 1.3.21), genomeIntervals (>= 1.7.3), grid Imports: methods, Biobase, Biostrings, graphics, grDevices, stats, utils, IRanges (>= 1.3.53) Suggests: MASS, org.Mm.eg.db, RColorBrewer Enhances: genomeIntervals License: Artistic-2.0 Archs: i386, x64 MD5sum: f2dd1320789d585feb1a648f29947ece NeedsCompilation: yes Title: Genome Intervals and Read Alignments for Functional Exploration Description: The package 'girafe' deals with the genome-level representation of aligned reads from next-generation sequencing data. It contains an object class for enabling a detailed description of genome intervals with aligned reads and functions for comparing, visualising, exporting and working with such intervals and the aligned reads. As such, the package interacts with and provides a link between the packages ShortRead, IRanges and genomeIntervals. biocViews: Sequencing Author: Joern Toedling, with contributions from Constance Ciaudo, Olivier Voinnet, Edith Heard, Emmanuel Barillot, and Wolfgang Huber Maintainer: J. Toedling source.ver: src/contrib/girafe_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/girafe_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/girafe_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/girafe_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/girafe_1.20.0.tgz vignettes: vignettes/girafe/inst/doc/girafe.pdf vignetteTitles: Genome intervals and read alignments for functional exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/girafe/inst/doc/girafe.R Package: GLAD Version: 2.32.0 Depends: R (>= 2.10) Suggests: aws, tcltk License: GPL-2 Archs: i386, x64 MD5sum: a9c10667ad5e717dfc66966207e7a5f3 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'. source.ver: src/contrib/GLAD_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GLAD_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GLAD_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GLAD_2.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GLAD_2.32.0.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, MANOR, seqCNA importsMe: ADaCGH2, ITALICS, MANOR, snapCGH Package: GlobalAncova Version: 3.36.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi Suggests: Biobase, annotate, GO.db, KEGG.db, golubEsets, hu6800.db, vsn, GSEABase, Rgraphviz License: GPL (>= 2) Archs: i386, x64 MD5sum: 0f4257e80f96a213858e0a8709af318c NeedsCompilation: yes Title: Calculates a global test for differential gene expression between groups Description: 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. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany. biocViews: Microarray, OneChannel, DifferentialExpression, Pathways Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel Maintainer: Manuela Hummel source.ver: src/contrib/GlobalAncova_3.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GlobalAncova_3.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GlobalAncova_3.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GlobalAncova_3.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GlobalAncova_3.36.0.tgz vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf, vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf vignetteTitles: GlobalAncovaDecomp.pdf, GlobalAncova.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R, vignettes/GlobalAncova/inst/doc/GlobalAncova.R Package: globaltest Version: 5.22.0 Depends: methods Imports: Biobase, survival, AnnotationDbi, annotate, multtest, graphics Suggests: vsn, golubEsets, KEGG.db, hu6800.db, Rgraphviz, GO.db, lungExpression, org.Hs.eg.db, annotate, Biobase, survival, GSEABase, penalized, gss, MASS, boot, rpart License: GPL (>= 2) MD5sum: 4b208c14b9a952e10f841475e8e4d3f1 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, DifferentialExpression, GO, Pathways, KEGG Author: Jelle Goeman and Jan Oosting, with contributions by Livio Finos and Aldo Solari Maintainer: Jelle Goeman URL: http://www.msbi.nl/goeman source.ver: src/contrib/globaltest_5.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/globaltest_5.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/globaltest_5.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/globaltest_5.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/globaltest_5.22.0.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, SIM suggestsMe: topGO Package: gmapR Version: 1.10.0 Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.12) Imports: S4Vectors, IRanges, Rsamtools (>= 1.17.8), rtracklayer (>= 1.25.5), GenomicFeatures (>= 1.17.13), Biostrings, VariantAnnotation (>= 1.11.4), tools, Biobase, BSgenome, GenomicAlignments (>= 1.1.9), BiocParallel Suggests: 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: 93447930fdbbd8e605a280ed36ecd919 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 source.ver: src/contrib/gmapR_1.10.0.tar.gz vignettes: vignettes/gmapR/inst/doc/gmapR.pdf vignetteTitles: gmapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmapR/inst/doc/gmapR.R dependsOnMe: HTSeqGenie importsMe: VariantTools Package: GOexpress Version: 1.2.2 Depends: R (>= 3.0.2), grid, Biobase (>= 2.22.0), VennDiagram (>= 1.6.5) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6) Suggests: RCurl (>= 1.95), BiocStyle License: GPL (>= 3) MD5sum: a63ed323d395cae889e73b01f2e4fd94 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 enriched in genes with expression levels best clustering 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 ability 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 Author: Kevin Rue-Albrecht [aut, cre], 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] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/GOexpress source.ver: src/contrib/GOexpress_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOexpress_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/GOexpress_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/GOexpress_1.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOexpress_1.2.2.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 Package: GOFunction Version: 1.16.0 Depends: R (>= 2.11.0), methods, Biobase (>= 2.8.0), graph (>= 1.26.0), Rgraphviz (>= 1.26.0), GO.db (>= 2.4.1), AnnotationDbi (>= 1.10.2), SparseM (>= 0.85) Imports: methods, Biobase, graph, Rgraphviz, GO.db, AnnotationDbi, SparseM License: GPL (>= 2) MD5sum: 6b6a625f058db3b4c55abe6a0518c655 NeedsCompilation: no Title: GO-function: deriving biologcially relevant functions from statistically significant functions Description: The GO-function package provides a tool to address the redundancy that result from the GO structure or multiple annotation genes and derive biologically relevant functions from the statistically significant functions based on some intuitive assumption and statistical testing. biocViews: GO, Pathways, Microarray, GeneSetEnrichment Author: Jing Wang Maintainer: Jing Wang source.ver: src/contrib/GOFunction_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOFunction_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GOFunction_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GOFunction_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOFunction_1.16.0.tgz vignettes: vignettes/GOFunction/inst/doc/GOFunction.pdf vignetteTitles: GO-function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOFunction/inst/doc/GOFunction.R Package: GoogleGenomics Version: 1.0.0 Depends: R (>= 3.1.0), GenomicAlignments (>= 1.0.1), VariantAnnotation Imports: Biostrings, GenomeInfoDb, GenomicRanges, IRanges, httr, rjson, Rsamtools, S4Vectors Suggests: BiocStyle, httpuv, knitr, rmarkdown, testthat, ggbio, ggplot2, BSgenome.Hsapiens.UCSC.hg19, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Apache License (== 2.0) | file LICENSE MD5sum: 3efef2cdea8978ff691f9e626d7cdf2b NeedsCompilation: no Title: R Client for Google Genomics API Description: Provides an R package to interact with the Google Genomics API. biocViews: DataImport, ThirdPartyClient Author: Cassie Doll [aut], Nicole Deflaux [aut], Siddhartha Bagaria [aut, cre] Maintainer: Siddhartha Bagaria URL: https://cloud.google.com/genomics/ VignetteBuilder: knitr BugReports: https://github.com/googlegenomics/api-client-r/issues source.ver: src/contrib/GoogleGenomics_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GoogleGenomics_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GoogleGenomics_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GoogleGenomics_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GoogleGenomics_1.0.0.tgz vignettes: vignettes/GoogleGenomics/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GoogleGenomics/inst/doc/PlottingAlignments.R, vignettes/GoogleGenomics/inst/doc/VariantAnnotation-comparison-test.R htmlDocs: vignettes/GoogleGenomics/inst/doc/PlottingAlignments.html, vignettes/GoogleGenomics/inst/doc/VariantAnnotation-comparison-test.html htmlTitles: "Plotting Alignments", "Reproducing Variant Annotation Results" Package: goProfiles Version: 1.30.0 Depends: Biobase, AnnotationDbi, GO.db Suggests: org.Hs.eg.db License: GPL-2 MD5sum: bba8025b25fc8c3b690622190bec8a8c 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: Microarray, GO Author: Alex Sanchez, Jordi Ocana and Miquel Salicru Maintainer: Alex Sanchez source.ver: src/contrib/goProfiles_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/goProfiles_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/goProfiles_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/goProfiles_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/goProfiles_1.30.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R Package: GOSemSim Version: 1.26.0 Depends: R (>= 3.1.0) Imports: Rcpp, AnnotationDbi, GO.db LinkingTo: Rcpp Suggests: DOSE, clusterProfiler, org.Hs.eg.db, knitr, BiocStyle, BiocInstaller License: Artistic-2.0 Archs: i386, x64 MD5sum: d92db2633c635110860275670b96ef75 NeedsCompilation: yes Title: GO-terms Semantic Similarity Measures Description: Implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for estimating GO semantic similarities. Support many species, including Anopheles, Arabidopsis, Bovine, Canine, Chicken, Chimp, Coelicolor, E coli strain K12 and Sakai, Fly, Gondii, Human, Malaria, Mouse, Pig, Rhesus, Rat, Worm, Xenopus, Yeast, and Zebrafish. biocViews: Annotation, GO, Clustering, Pathways, Network, Software Author: Guangchuang Yu with contributions from Alexey Stukalov. Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/GOSemSim VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/GOSemSim/issues source.ver: src/contrib/GOSemSim_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOSemSim_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GOSemSim_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GOSemSim_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOSemSim_1.26.0.tgz vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.pdf vignetteTitles: An introduction to GOSemSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R dependsOnMe: tRanslatome importsMe: clusterProfiler, DOSE, Rcpi suggestsMe: SemDist Package: goseq Version: 1.20.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db,BiocGenerics Suggests: edgeR, org.Hs.eg.db, rtracklayer License: LGPL (>= 2) MD5sum: cf04fa6cecc9ebb4de1c40c2cfa16617 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: Sequencing, GO, GeneExpression, Transcription, RNASeq Author: Matthew Young Maintainer: Nadia Davidson source.ver: src/contrib/goseq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/goseq_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/goseq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/goseq_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/goseq_1.20.0.tgz vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goseq/inst/doc/goseq.R dependsOnMe: rgsepd suggestsMe: oneChannelGUI Package: GOSim Version: 1.6.0 Depends: GO.db, annotate Imports: org.Hs.eg.db, AnnotationDbi, topGO, cluster, flexmix, RBGL, graph, Matrix, corpcor, Rcpp LinkingTo: Rcpp Enhances: igraph License: GPL (>= 2) Archs: i386, x64 MD5sum: 962a0c0e02a7d63a67e473072d94754b NeedsCompilation: yes Title: Computation of functional similarities between GO terms and gene products; GO enrichment analysis Description: This package implements several functions useful for computing similarities between GO terms and gene products based on their GO annotation. Moreover it allows for computing a GO enrichment analysis biocViews: GO, Clustering, Software, Pathways Author: Holger Froehlich Maintainer: Holger Froehlich source.ver: src/contrib/GOSim_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOSim_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GOSim_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GOSim_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOSim_1.6.0.tgz vignettes: vignettes/GOSim/inst/doc/GOSim.pdf vignetteTitles: GOsim hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSim/inst/doc/GOSim.R Package: GOstats Version: 2.34.0 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.3.26), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), Biobase (>= 1.15.29), Category (>= 2.3.26), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), graph (>= 1.15.15), AnnotationForge Suggests: hgu95av2.db (>= 1.13.0), ALL, GO.db (>= 1.13.0), annotate, multtest, genefilter, RColorBrewer, Rgraphviz, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: ef68357c5e65c41daa0551b74b61e616 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: R. Gentleman and S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GOstats_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOstats_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GOstats_2.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GOstats_2.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOstats_2.34.0.tgz vignettes: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.pdf, vignettes/GOstats/inst/doc/GOstatsHyperG.pdf, vignettes/GOstats/inst/doc/GOvis.pdf vignetteTitles: Hypergeometric tests for less common model organisms, Hypergeometric Tests Using GOstats, Visualizing Data Using GOstats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOstats/inst/doc/GOstatsHyperG.R, vignettes/GOstats/inst/doc/GOvis.R dependsOnMe: attract, MineICA, RDAVIDWebService importsMe: affycoretools, attract, categoryCompare, facopy, mvGST, ProCoNA, systemPipeR suggestsMe: BiocCaseStudies, Category, eisa, fastLiquidAssociation, GSEAlm, HTSanalyzeR, interactiveDisplay, MineICA, MLP, MmPalateMiRNA, oneChannelGUI, phenoDist, qpgraph, RnBeads, safe Package: GOsummaries Version: 2.2.0 Depends: R (>= 2.15), ggplot2, Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) Archs: i386, x64 MD5sum: ec6fbcdd4399e2ef6005dae4ca41e095 NeedsCompilation: yes Title: Word cloud summaries of GO enrichment analysis Description: A package to visualise Gene Ontology (GO) enrichment analysis results on gene lists arising from different analyses such clustering or PCA. The significant GO categories are visualised as word clouds that can be combined with different plots summarising the underlying data. biocViews: GeneExpression, Clustering, GO, Visualization Author: Raivo Kolde Maintainer: Raivo Kolde URL: https://github.com/raivokolde/GOsummaries source.ver: src/contrib/GOsummaries_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOsummaries_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GOsummaries_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GOsummaries_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOsummaries_2.2.0.tgz vignettes: vignettes/GOsummaries/inst/doc/GOsummaries-basics.pdf vignetteTitles: GOsummaries basics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOsummaries/inst/doc/GOsummaries-basics.R Package: GOTHiC Version: 1.4.2 Depends: R (>= 2.15.1), methods, utils, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors, IRanges, ShortRead, rtracklayer, ggplot2 Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 MD5sum: ed4edede0034e238d4a2edbd8359b9e5 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: Sequencing, Preprocessing, Epigenetics, HiC Author: Borbala Mifsud and Robert Sugar Maintainer: Borbala Mifsud source.ver: src/contrib/GOTHiC_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOTHiC_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.2/GOTHiC_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.2/GOTHiC_1.4.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOTHiC_1.4.2.tgz vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf vignetteTitles: package_vignettes.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: goTools Version: 1.42.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: 750be0df8cc2a9823eafa80d171969d8 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 source.ver: src/contrib/goTools_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/goTools_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/goTools_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/goTools_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/goTools_1.42.0.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 Package: gpls Version: 1.40.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: b55ca706a4c4a7d663e05d42401351d7 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 source.ver: src/contrib/gpls_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gpls_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gpls_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gpls_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gpls_1.40.0.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: MCRestimate, MLInterfaces Package: gprege Version: 1.12.0 Depends: R (>= 2.8.0), gptk Suggests: spam License: AGPL-3 MD5sum: f1d42b41e3ef5961995fe60af8f79a34 NeedsCompilation: no Title: Gaussian Process Ranking and Estimation of Gene Expression time-series Description: The gprege package implements the methodology described in Kalaitzis & Lawrence (2011) "A simple approach to ranking differentially expressed gene expression time-courses through Gaussian process regression". The software fits two GPs with the an RBF (+ noise diagonal) kernel on each profile. One GP kernel is initialised wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimised via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via ROC curves is performed against BATS (Angelini et.al, 2007). A detailed discussion of the ranking approach and dataset used can be found in the paper (http://www.biomedcentral.com/1471-2105/12/180). biocViews: Microarray, Preprocessing, DifferentialExpression, TimeCourse Author: Alfredo Kalaitzis Maintainer: Alfredo Kalaitzis BugReports: alkalait@gmail.com source.ver: src/contrib/gprege_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gprege_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gprege_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gprege_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gprege_1.12.0.tgz vignettes: vignettes/gprege/inst/doc/gprege_quick.pdf vignetteTitles: gprege Quick Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gprege/inst/doc/gprege_quick.R Package: gQTLBase Version: 1.0.0 Imports: GenomicRanges, methods, BatchJobs, BBmisc, S4Vectors, ff, ffbase, BiocGenerics, foreach, doParallel Suggests: geuvStore, knitr, rmarkdown, BiocStyle, RUnit, GGtools, Homo.sapiens, IRanges License: Artistic-2.0 MD5sum: 009e75e6f1e8b62df51ae6d0117d7de2 NeedsCompilation: no Title: gQTLBase: infrastructure for eQTL, mQTL and similar studies Description: Infrastructure for eQTL, mQTL and similar studies. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr source.ver: src/contrib/gQTLBase_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gQTLBase_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gQTLBase_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gQTLBase_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gQTLBase_1.0.0.tgz vignettes: vignettes/gQTLBase/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLBase/inst/doc/gQTLBase.R htmlDocs: vignettes/gQTLBase/inst/doc/gQTLBase.html htmlTitles: "gQTLBase infrastructure for eQTL archives" importsMe: gQTLstats Package: gQTLstats Version: 1.0.0 Depends: R (>= 3.1.0) Imports: snpStats, GenomicRanges, VariantAnnotation, methods, Biobase, BatchJobs, gQTLBase, GenomeInfoDb, S4Vectors, limma, BiocGenerics, gam, dplyr, AnnotationDbi, IRanges, GenomicFeatures, ggplot2, reshape2, doParallel, foreach Suggests: geuvPack, geuvStore, Rsamtools, knitr, rmarkdown, ggbio, BiocStyle, Homo.sapiens License: Artistic-2.0 MD5sum: c714736696b30a6c05a3830dfe675ea3 NeedsCompilation: no Title: gQTLstats: computationally efficient analysis for eQTL and allied studies Description: computationally efficient analysis of eQTL, mQTL, dsQTL, etc. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr source.ver: src/contrib/gQTLstats_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gQTLstats_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gQTLstats_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gQTLstats_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gQTLstats_1.0.0.tgz vignettes: vignettes/gQTLstats/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLstats/inst/doc/gQTLstats.R htmlDocs: vignettes/gQTLstats/inst/doc/gQTLstats.html htmlTitles: "gQTLstats: statistics for genetics of genomic features" importsMe: gwascat Package: graph Version: 1.46.0 Depends: R (>= 2.10), methods Imports: stats, stats4, tools, utils, BiocGenerics (>= 0.13.11) Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster Enhances: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: 6233bcd193473c04cd454346da2113bd 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, Elizabeth Whalen, W. Huber, S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/graph_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/graph_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/graph_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/graph_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/graph_1.46.0.tgz vignettes: vignettes/graph/inst/doc/clusterGraph.pdf, vignettes/graph/inst/doc/graphAttributes.pdf, vignettes/graph/inst/doc/GraphClass.pdf, vignettes/graph/inst/doc/graph.pdf, vignettes/graph/inst/doc/MultiGraphClass.pdf vignetteTitles: clusterGraph and distGraph, Attributes for Graph Objects, Graph Design, Graph, graphBAM and MultiGraph classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graph/inst/doc/clusterGraph.R, vignettes/graph/inst/doc/graphAttributes.R, vignettes/graph/inst/doc/GraphClass.R, vignettes/graph/inst/doc/graph.R, vignettes/graph/inst/doc/MultiGraphClass.R dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, CellNOptR, clipper, CNORfeeder, ddgraph, flowClust, gaggle, gaucho, GeneNetworkBuilder, GOFunction, GOstats, GraphAT, GSEABase, hypergraph, KEGGgraph, maigesPack, MineICA, NCIgraph, NetSAM, pathRender, pkgDepTools, RbcBook1, RBGL, RBioinf, RCyjs, RCytoscape, RDAVIDWebService, Rgraphviz, ROntoTools, RpsiXML, SRAdb, ToPASeq, topGO, TRONCO, vtpnet importsMe: AnalysisPageServer, BiocCheck, biocGraph, biocViews, CAMERA, Category, categoryCompare, ChIPpeakAnno, DEGraph, EnrichmentBrowser, FEM, flowCore, flowUtils, flowWorkspace, gage, GeneNetworkBuilder, GOFunction, GOSim, GOstats, GraphAT, graphite, HTSanalyzeR, hyperdraw, KEGGgraph, keggorthology, mvGST, NCIgraph, nem, netresponse, OncoSimulR, OrganismDbi, pathview, PCpheno, pkgDepTools, ppiStats, qpgraph, RchyOptimyx, rsbml, Rtreemix, SplicingGraphs, Streamer, topGO, VariantFiltering suggestsMe: AnnotationDbi, BiocCaseStudies, Category, DEGraph, EBcoexpress, ecolitk, GeneAnswers, gwascat, mmnet, MmPalateMiRNA, NetPathMiner, rBiopaxParser, rTRM, SPIA, VariantTools Package: GraphAlignment Version: 1.32.0 License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: a1a051970acf9022b3ca2ec1a0d1f9ff 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/ source.ver: src/contrib/GraphAlignment_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GraphAlignment_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GraphAlignment_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GraphAlignment_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GraphAlignment_1.32.0.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 Package: GraphAT Version: 1.40.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL MD5sum: 2cfb531a169a5bd18c201aa2a3a07c9e 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 source.ver: src/contrib/GraphAT_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GraphAT_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GraphAT_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GraphAT_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GraphAT_1.40.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: graphite Version: 1.14.1 Depends: R (>= 2.10), BiocGenerics, methods Imports: AnnotationDbi, graph, stats, utils Suggests: BiocStyle, DEGraph (>= 1.4), hgu133plus2.db, RCytoscape (>= 1.6), SPIA (>= 2.2), topologyGSA (>= 1.4.0), clipper, ALL License: AGPL-3 MD5sum: f3ecdd9194e887103574cfd2be4e3cc6 NeedsCompilation: no Title: GRAPH Interaction from pathway Topological Environment Description: Graph objects from pathway topology derived from Biocarta, HumanCyc, KEGG, NCI, Panther, Reactome and SPIKE databases. biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network, Reactome, KEGG, BioCarta Author: Gabriele Sales , Enrica Calura , Chiara Romualdi Maintainer: Gabriele Sales source.ver: src/contrib/graphite_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/graphite_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.2/graphite_1.14.1.zip mac.binary.ver: bin/macosx/contrib/3.2/graphite_1.14.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/graphite_1.14.1.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/graphite/inst/doc/graphite.R dependsOnMe: ToPASeq importsMe: facopy, mogsa, ReactomePA suggestsMe: clipper Package: GraphPAC Version: 1.10.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: b6a74e41b940e744316da3549e088577 NeedsCompilation: no Title: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach. Description: Identifies mutational clusters of amino acids in a protein while utilizing the proteins tertiary structure via a graph theoretical model. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik source.ver: src/contrib/GraphPAC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GraphPAC_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GraphPAC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GraphPAC_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GraphPAC_1.10.0.tgz vignettes: vignettes/GraphPAC/inst/doc/GraphPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GraphPAC/inst/doc/GraphPAC.R dependsOnMe: QuartPAC Package: GRENITS Version: 1.20.0 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) Archs: i386, x64 MD5sum: 86f0e7cfa9e9e39f27cb63fdf0442df0 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 source.ver: src/contrib/GRENITS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GRENITS_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GRENITS_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GRENITS_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GRENITS_1.20.0.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 Package: GreyListChIP Version: 1.0.1 Depends: R (>= 3.1), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, GenomeInfoDb Suggests: BiocStyle, BiocGenerics, RUnit Enhances: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 0940e2cd978cc38021b049f37fe294ae 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: Gord Brown Maintainer: Gordon Brown source.ver: src/contrib/GreyListChIP_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/GreyListChIP_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/GreyListChIP_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/GreyListChIP_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GreyListChIP_1.0.1.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 Package: groHMM Version: 1.3.1 Depends: R (>= 3.0.2), MASS, S4Vectors, IRanges, GenomicRanges, GenomicAlignments, rtracklayer, parallel Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: i386, x64 MD5sum: d9c66766dcfdfb9ccc930e55db0f02d8 NeedsCompilation: yes Title: GRO-seq Analysis Pipeline. Description: A pipeline for the analysis of GRO-seq data. biocViews: Sequencing, Software Author: Charles G. Danko, Minho Chae, Andre Martins, W. Lee Kraus Maintainer: Anusha Nagari, W. Lee Kraus source.ver: src/contrib/groHMM_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/groHMM_1.3.1.zip win64.binary.ver: bin/windows64/contrib/3.2/groHMM_1.3.1.zip mac.binary.ver: bin/macosx/contrib/3.2/groHMM_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/groHMM_1.3.1.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 Package: GSAR Version: 1.2.2 Depends: R (>= 3.0.1), igraph (>= 0.7.0) Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate, org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR, BiocStyle License: GPL (>=2) MD5sum: 4c688706cc81f809fb1e5a824c64320f 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 source.ver: src/contrib/GSAR_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSAR_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/GSAR_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/GSAR_1.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSAR_1.2.2.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 Package: GSCA Version: 1.6.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 2dab72c160bc50502b717fa499504dab 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 source.ver: src/contrib/GSCA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSCA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GSCA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GSCA_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSCA_1.6.0.tgz vignettes: vignettes/GSCA/inst/doc/GSCA.pdf vignetteTitles: GSCA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSCA/inst/doc/GSCA.R Package: GSEABase Version: 1.30.2 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 License: Artistic-2.0 MD5sum: 8f71cabde958881ec543a31a9948e093 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, Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GSEABase_1.30.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSEABase_1.30.2.zip win64.binary.ver: bin/windows64/contrib/3.2/GSEABase_1.30.2.zip mac.binary.ver: bin/macosx/contrib/3.2/GSEABase_1.30.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSEABase_1.30.2.tgz vignettes: vignettes/GSEABase/inst/doc/GSEABase.pdf vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R dependsOnMe: AGDEX, BicARE, cpvSNP, EnrichmentBrowser, gCMAP, npGSEA, PROMISE importsMe: Category, categoryCompare, cellHTS2, gCMAPWeb, GSRI, GSVA, HTSanalyzeR, mogsa, PCpheno, phenoTest, PROMISE, ReportingTools suggestsMe: BiocCaseStudies, gage, GlobalAncova, globaltest, GOstats, GSAR, PGSEA, phenoTest Package: GSEAlm Version: 1.28.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: a3b12f969956d1f6d7701ea6d54eaed1 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 source.ver: src/contrib/GSEAlm_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSEAlm_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GSEAlm_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GSEAlm_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSEAlm_1.28.0.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 importsMe: gCMAP Package: GSReg Version: 1.2.0 Depends: R (>= 2.13.1) Suggests: GSBenchMark License: GPL-2 Archs: i386, x64 MD5sum: 927343473c3c34aa84ed5781d2693c8e NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions. It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari source.ver: src/contrib/GSReg_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSReg_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GSReg_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GSReg_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSReg_1.2.0.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 Package: GSRI Version: 2.16.0 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: 0e6b3e8a0b342cbe1493b004c01f50a7 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 URL: http://julian-gehring.github.com/GSRI/ source.ver: src/contrib/GSRI_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSRI_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GSRI_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GSRI_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSRI_2.16.0.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 Package: GSVA Version: 1.16.0 Depends: R (>= 2.13.0) Imports: methods, BiocGenerics, Biobase, GSEABase (>= 1.17.4) Suggests: limma, RColorBrewer, genefilter, mclust, edgeR, snow, parallel, GSVAdata License: GPL (>= 2) Archs: i386, x64 MD5sum: 5b178364abe6b825d664758c60ed82a1 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: Microarray, Pathways, GeneSetEnrichment Author: Justin Guinney with contributions from Robert Castelo Maintainer: Justin Guinney URL: http://www.sagebase.org source.ver: src/contrib/GSVA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSVA_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GSVA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GSVA_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSVA_1.16.0.tgz vignettes: vignettes/GSVA/inst/doc/GSVA.pdf vignetteTitles: Gene Set Variation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSVA/inst/doc/GSVA.R Package: gtrellis Version: 1.0.0 Depends: R (>= 3.1.0), grid Imports: circlize (>= 0.2.3), GetoptLong Suggests: testthat (>= 0.3), knitr, GenomicRanges, RColorBrewer, markdown License: GPL (>= 2) MD5sum: 6fd568c326762ee1828cdd873cd6e1ea 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 add self-defined graphics in the plot. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/gtrellis VignetteBuilder: knitr source.ver: src/contrib/gtrellis_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gtrellis_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gtrellis_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gtrellis_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gtrellis_1.0.0.tgz vignettes: vignettes/gtrellis/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R htmlDocs: vignettes/gtrellis/inst/doc/gtrellis.html htmlTitles: "Make Genome Level Trellis Graph" Package: Gviz Version: 1.12.1 Depends: R (>= 2.10.0), methods, S4Vectors (>= 0.1.0), IRanges (>= 1.99.18), GenomicRanges (>= 1.17.20), grid Imports: XVector (>= 0.5.7), rtracklayer (>= 1.25.13), lattice, RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5), Biobase (>= 2.15.3), GenomicFeatures (>= 1.17.22), BSgenome (>= 1.33.1), Biostrings (>= 2.33.11), biovizBase (>= 1.13.8), Rsamtools (>= 1.17.28), latticeExtra (>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>= 1.1.16), GenomeInfoDb (>= 1.1.3), BiocGenerics (>= 0.11.3), digest(>= 0.6.8) Suggests: xtable, BSgenome.Hsapiens.UCSC.hg19, BiocStyle License: Artistic-2.0 MD5sum: ed852fd4e5a9e78a1bd354d64d7e53d9 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 Author: Florian Hahne, Steffen Durinck, Robert Ivanek, Arne Mueller, Steve Lianoglou, Ge Tan , Lance Parsons Maintainer: Florian Hahne source.ver: src/contrib/Gviz_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/Gviz_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/Gviz_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/Gviz_1.12.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Gviz_1.12.1.tgz vignettes: vignettes/Gviz/inst/doc/Gviz.pdf vignetteTitles: Gviz users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Gviz/inst/doc/Gviz.R dependsOnMe: AllelicImbalance, biomvRCNS, coMET, cummeRbund, DMRforPairs, Pbase, Pviz importsMe: AllelicImbalance, GGtools, gwascat, InPAS, methyAnalysis, methylPipe, PING, trackViewer, VariantFiltering suggestsMe: annmap, CNEr, interactiveDisplay, QuasR, RnBeads, SplicingGraphs, STAN Package: gwascat Version: 1.12.0 Depends: R (>= 3.0.0) Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, snpStats, Biostrings, Rsamtools, rtracklayer, gQTLstats, Gviz, VariantAnnotation Suggests: DO.db, ggbio, graph Enhances: SNPlocs.Hsapiens.dbSNP.20120608 License: Artistic-2.0 MD5sum: be09cd48e83a35ac147a8bb0068ae8b6 NeedsCompilation: no Title: representing and modeling data in the NHGRI GWAS catalog Description: representing and modeling data in the NHGRI GWAS catalog biocViews: Genetics Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/gwascat_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gwascat_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gwascat_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gwascat_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gwascat_1.12.0.tgz vignettes: vignettes/gwascat/inst/doc/gwascat.pdf vignetteTitles: gwascat -- exploring NHGRI GWAS catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R dependsOnMe: vtpnet Package: GWASTools Version: 1.14.2 Depends: Biobase Imports: methods, ncdf, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth Suggests: GWASdata, BiocGenerics, RUnit, SNPRelate, snpStats, VariantAnnotation License: Artistic-2.0 MD5sum: dcf0cfeea1589cd48bb08e1c9127378d 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, Cathy Laurie, Tushar Bhangale, Matthew P. Conomos, Cecelia Laurie, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar, Adrienne Stilp, Sarah Nelson Maintainer: Stephanie M. Gogarten , Adrienne Stilp source.ver: src/contrib/GWASTools_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/GWASTools_1.14.2.zip win64.binary.ver: bin/windows64/contrib/3.2/GWASTools_1.14.2.zip mac.binary.ver: bin/macosx/contrib/3.2/GWASTools_1.14.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GWASTools_1.14.2.tgz vignettes: vignettes/GWASTools/inst/doc/Affymetrix.pdf, vignettes/GWASTools/inst/doc/DataCleaning.pdf, vignettes/GWASTools/inst/doc/Formats.pdf vignetteTitles: Preparing Affymetrix Data, GWAS Data Cleaning, Data formats in GWASTools 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: GENESIS suggestsMe: podkat Package: h5vc Version: 2.2.0 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>= 1.19.38), methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb LinkingTo: Rsamtools Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, bit64, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics License: GPL (>= 3) Archs: i386, x64 MD5sum: c951c677607d477821488e838b37e3d9 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. For detail see the webpage at http://www.ebi.ac.uk/~pyl/h5vc. Author: Paul Theodor Pyl Maintainer: Paul Theodor Pyl VignetteBuilder: knitr source.ver: src/contrib/h5vc_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/h5vc_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/h5vc_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/h5vc_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/h5vc_2.2.0.tgz vignettes: vignettes/h5vc/inst/doc/ 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 htmlDocs: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html htmlTitles: "Building a minimal genome browser with h5vc and shiny", "h5vc -- Tour" Package: hapFabia Version: 1.10.0 Depends: R (>= 2.12.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 37212770636812617bc00b7ddffebdba 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: Sepp Hochreiter URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html source.ver: src/contrib/hapFabia_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hapFabia_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hapFabia_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hapFabia_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hapFabia_1.10.0.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 Package: Harshlight Version: 1.40.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 35d65240ab7f97aaea22b787f4fad621 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/ source.ver: src/contrib/Harshlight_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Harshlight_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Harshlight_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Harshlight_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Harshlight_1.40.0.tgz vignettes: vignettes/Harshlight/inst/doc/Harshlight.pdf vignetteTitles: Harshlight hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harshlight/inst/doc/Harshlight.R Package: HCsnip Version: 1.8.0 Depends: R(>= 2.10.0), survival, coin, fpc, clusterRepro, impute, randomForestSRC, sm, sigaR, Biobase License: GPL (>= 2) MD5sum: de85be2196a5297064eb23e332075d25 NeedsCompilation: no Title: Semi-supervised adaptive-height snipping of the Hierarchical Clustering tree Description: Decompose given hierarchical clustering tree into non-overlapping clusters in a semi-supervised way by using available patients follow-up information as guidance. Contains functions for snipping HC tree, various cluster quality evaluation criteria, assigning new patients to one of the two given HC trees, testing the significance of clusters with permutation argument and clusters visualization using sample's molecular entropy. biocViews: Microarray, aCGH, GeneExpression, Clustering Author: Askar Obulkasim Maintainer: Askar Obulkasim source.ver: src/contrib/HCsnip_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HCsnip_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HCsnip_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HCsnip_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HCsnip_1.8.0.tgz vignettes: vignettes/HCsnip/inst/doc/HCsnip.pdf vignetteTitles: HCsnip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HCsnip/inst/doc/HCsnip.R Package: HDTD Version: 1.2.0 License: GPL-3 MD5sum: 70842f823a7da46eceecae8dc40cb493 NeedsCompilation: no 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, John C. Marioni and Simon Tavare Maintainer: Anestis Touloumis source.ver: src/contrib/HDTD_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HDTD_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HDTD_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HDTD_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HDTD_1.2.0.tgz vignettes: vignettes/HDTD/inst/doc/Manual.pdf vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/Manual.R Package: Heatplus Version: 2.14.0 Imports: graphics, grDevices, stats Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: 0c03236ff337c51c294999eac7a7da8b 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 source.ver: src/contrib/Heatplus_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Heatplus_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Heatplus_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Heatplus_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Heatplus_2.14.0.tgz vignettes: vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf, vignettes/Heatplus/inst/doc/annHeatmap.pdf, vignettes/Heatplus/inst/doc/oldHeatplus.pdf vignetteTitles: Commented package source, Annotated and regular heatmaps, Old functions (deprecated) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R, vignettes/Heatplus/inst/doc/annHeatmap.R, vignettes/Heatplus/inst/doc/oldHeatplus.R dependsOnMe: GeneAnswers, phenoTest, tRanslatome Package: HELP Version: 1.26.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: e1d0fcaa538e7a56f5474dde3943a0d1 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 source.ver: src/contrib/HELP_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HELP_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HELP_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HELP_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HELP_1.26.0.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 Package: HEM Version: 1.40.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 4b4741a58540be9ab8b394de78028a26 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/ source.ver: src/contrib/HEM_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HEM_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HEM_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HEM_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HEM_1.40.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HEM/inst/doc/HEM.R Package: hiAnnotator Version: 1.2.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, plyr, BSgenome, ggplot2, scales Suggests: knitr, doParallel, testthat, BiocGenerics License: GPL (>= 2) MD5sum: c98323d133aa7a64dbea75b9f58cda76 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 source.ver: src/contrib/hiAnnotator_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hiAnnotator_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hiAnnotator_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hiAnnotator_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hiAnnotator_1.2.0.tgz vignettes: vignettes/hiAnnotator/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R htmlDocs: vignettes/hiAnnotator/inst/doc/Intro.html htmlTitles: "Introduction" dependsOnMe: hiReadsProcessor Package: HIBAG Version: 1.4.0 Depends: R (>= 2.14.0) Imports: methods Suggests: parallel, BiocStyle, knitr, gdsfmt (>= 1.2.2), SNPRelate (>= 1.1.6) License: GPL-3 Archs: i386, x64 MD5sum: bdfe30abe6be8f6c7e4a4b3cb8426b6f NeedsCompilation: yes Title: HLA Genotype Imputation with Attribute Bagging Description: It is a software package for imputing HLA types using SNP data, and 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], Bruce Weir [ctb, ths] Maintainer: Xiuwen Zheng URL: http://www.biostat.washington.edu/~bsweir/HIBAG/, http://github.com/zhengxwen/HIBAG VignetteBuilder: knitr source.ver: src/contrib/HIBAG_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HIBAG_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HIBAG_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HIBAG_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HIBAG_1.4.0.tgz vignettes: vignettes/HIBAG/inst/doc/HIBAG_Tutorial.pdf vignetteTitles: HIBAG vignette pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIBAG/inst/doc/HIBAG_Tutorial_html.R, vignettes/HIBAG/inst/doc/HIBAG_Tutorial.R htmlDocs: vignettes/HIBAG/inst/doc/HIBAG_Tutorial_html.html htmlTitles: "HIBAG – an R Package for HLA Genotype Imputation with Attribute Bagging" Package: HilbertVis Version: 1.26.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) Archs: i386, x64 MD5sum: 583e15c72d6e05f2fdc61a717791db79 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 source.ver: src/contrib/HilbertVis_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HilbertVis_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HilbertVis_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HilbertVis_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HilbertVis_1.26.0.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 Package: HilbertVisGUI Version: 1.26.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) Archs: i386, x64 MD5sum: 6a830cd013f34262edc5f26e9153f2c5 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 source.ver: src/contrib/HilbertVisGUI_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HilbertVisGUI_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HilbertVisGUI_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HilbertVisGUI_1.26.0.tgz vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.R Package: hiReadsProcessor Version: 1.2.0 Depends: Biostrings, GenomicAlignments, xlsx, BiocParallel, hiAnnotator, R (>= 3.0) Imports: sonicLength, plyr, GenomicRanges, BiocGenerics, rSFFreader Suggests: knitr, testthat License: GPL-3 MD5sum: c2f6c768b087a8cc222e756cc1989539 NeedsCompilation: no 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, JRE, UCSC hg18 in 2bit format for BLAT VignetteBuilder: knitr source.ver: src/contrib/hiReadsProcessor_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hiReadsProcessor_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hiReadsProcessor_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hiReadsProcessor_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hiReadsProcessor_1.2.0.tgz vignettes: vignettes/hiReadsProcessor/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R htmlDocs: vignettes/hiReadsProcessor/inst/doc/Tutorial.html htmlTitles: "Introduction" Package: HiTC Version: 1.12.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix Suggests: BiocStyle, HiCDataHumanIMR90 License: Artistic-2.0 MD5sum: 1661415f31cb168956737cd31095c284 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 source.ver: src/contrib/HiTC_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HiTC_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HiTC_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HiTC_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HiTC_1.12.0.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 Package: HMMcopy Version: 1.10.0 Depends: R (>= 2.10.0), IRanges (>= 1.4.16), geneplotter (>= 1.24.0) License: GPL-3 Archs: i386, x64 MD5sum: 4dd76148f7ea95cf485e138e0573f1c0 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 , Gavin Ha , Sohrab Shah source.ver: src/contrib/HMMcopy_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HMMcopy_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HMMcopy_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HMMcopy_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HMMcopy_1.10.0.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 Package: hopach Version: 2.28.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: d8d64ab5ea58a0de3f2c8a45e24bfcf9 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/ source.ver: src/contrib/hopach_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hopach_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hopach_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hopach_2.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hopach_2.28.0.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 suggestsMe: BiocCaseStudies Package: hpar Version: 1.10.0 Depends: R (>= 2.15) Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle License: Artistic-2.0 MD5sum: 7cba87f68e187a61c1210dba6c61df1f NeedsCompilation: no Title: Human Protein Atlas in R Description: A simple interface to and data from the Human Protein Atlas project. biocViews: Proteomics, Homo_sapiens, CellBiology Author: Laurent Gatto Maintainer: Laurent Gatto VignetteBuilder: knitr source.ver: src/contrib/hpar_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hpar_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hpar_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hpar_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hpar_1.10.0.tgz vignettes: vignettes/hpar/inst/doc/hpar.pdf vignetteTitles: Human Protein Atlas in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hpar/inst/doc/hpar.R suggestsMe: pRoloc Package: HTqPCR Version: 1.22.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 MD5sum: 6f858b51830cbd5df8d52dfe81412ea5 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: Heidi Dvinge URL: http://www.ebi.ac.uk/bertone/software source.ver: src/contrib/HTqPCR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HTqPCR_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HTqPCR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HTqPCR_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HTqPCR_1.22.0.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, unifiedWMWqPCR Package: HTSanalyzeR Version: 2.20.0 Depends: R (>= 2.15), igraph, methods Imports: graph, igraph, GSEABase, BioNet, cellHTS2, AnnotationDbi, biomaRt, RankProd Suggests: KEGG.db, GO.db, org.Dm.eg.db, GOstats, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, snow License: Artistic-2.0 MD5sum: 250a189fca2c3d0e7259e5e35ed68798 NeedsCompilation: no Title: Gene set over-representation, enrichment and network analyses for high-throughput screens Description: This package provides classes and methods for gene set over-representation, enrichment and network analyses on high-throughput screens. The over-representation analysis is performed based on hypergeometric tests. The enrichment analysis is based on the GSEA algorithm (Subramanian et al. PNAS 2005). The network analysis identifies enriched subnetworks based on algorithms from the BioNet package (Beisser et al., Bioinformatics 2010). A pipeline is also specifically designed for cellHTS2 object to perform integrative network analyses of high-throughput RNA interference screens. The users can build their own analysis pipeline for their own data set based on this package. biocViews: CellBasedAssays, MultipleComparison Author: Xin Wang , Camille Terfve , John C. Rose , Florian Markowetz Maintainer: Xin Wang source.ver: src/contrib/HTSanalyzeR_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HTSanalyzeR_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HTSanalyzeR_2.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HTSanalyzeR_2.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HTSanalyzeR_2.20.0.tgz vignettes: vignettes/HTSanalyzeR/inst/doc/HTSanalyzeR-Vignette.pdf vignetteTitles: Main vignette:Gene set enrichment and network analysis of high-throughput RNAi screen data using HTSanalyzeR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSanalyzeR/inst/doc/HTSanalyzeR-Vignette.R importsMe: phenoTest suggestsMe: RTN Package: HTSeqGenie Version: 3.18.0 Depends: R (>= 3.0.0), gmapR (>= 1.6.4), ShortRead (>= 1.19.13), VariantAnnotation (>= 1.8.3) Imports: BiocGenerics (>= 0.2.0), IRanges (>= 1.21.39), GenomicRanges (>= 1.7.12), Rsamtools (>= 1.8.5), Biostrings (>= 2.24.1), chipseq (>= 1.6.1), hwriter (>= 1.3.0), Cairo (>= 1.5.5), GenomicFeatures (>= 1.9.31), BiocParallel, parallel, tools, rtracklayer (>= 1.17.19), GenomicAlignments, VariantTools (>= 1.6.1) Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, LungCancerLines, org.Hs.eg.db, RUnit License: Artistic-2.0 MD5sum: 8f8fd7885af502feab252081579221e6 NeedsCompilation: no Title: A NGS analysis pipeline. Description: Libraries to perform NGS analysis. Author: Gregoire Pau, Jens Reeder Maintainer: Jens Reeder source.ver: src/contrib/HTSeqGenie_3.18.0.tar.gz vignettes: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.pdf vignetteTitles: HTSeqGenie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.R Package: htSeqTools Version: 1.14.0 Depends: R (>= 2.12.2), methods, BiocGenerics (>= 0.1.0), Biobase, IRanges, methods, MASS, BSgenome, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.11) Enhances: parallel,multicore License: GPL (>=2) MD5sum: 339f414d9a2527ce6a8131c826875d87 NeedsCompilation: no Title: Quality Control, Visualization and Processing for High-Throughput Sequencing data Description: We provide efficient, easy-to-use tools for High-Throughput Sequencing (ChIP-seq, RNAseq etc.). These include MDS plots (analogues to PCA), detecting inefficient immuno-precipitation or over-amplification artifacts, tools to identify and test for genomic regions with large accumulation of reads, and visualization of coverage profiles. biocViews: Sequencing, QualityControl Author: Evarist Planet, Camille Stephan-Otto, Oscar Reina, Oscar Flores, David Rossell Maintainer: Oscar Reina source.ver: src/contrib/htSeqTools_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/htSeqTools_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/htSeqTools_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/htSeqTools_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/htSeqTools_1.14.0.tgz vignettes: vignettes/htSeqTools/inst/doc/htSeqTools.pdf vignetteTitles: Manual for the htSeqTools library hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/htSeqTools/inst/doc/htSeqTools.R Package: HTSFilter Version: 1.8.0 Depends: methods, Biobase (>= 2.27.3), R (>= 3.2) Imports: DESeq (>= 1.19.0), edgeR (>= 3.9.14), DESeq2 (>= 1.6.3) Suggests: EDASeq (>= 2.1.4), BiocStyle License: Artistic-2.0 MD5sum: 02a5525edadf693ca5dfe1cb219a44b4 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 Author: Andrea Rau, Melina Gallopin, Gilles Celeux, and Florence Jaffrezic Maintainer: Andrea Rau source.ver: src/contrib/HTSFilter_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HTSFilter_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HTSFilter_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HTSFilter_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HTSFilter_1.8.0.tgz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.pdf vignetteTitles: HTSFilter Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R Package: HybridMTest Version: 1.12.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: b2f7267433888e7a155d1a17d8887ce8 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 source.ver: src/contrib/HybridMTest_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HybridMTest_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HybridMTest_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HybridMTest_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HybridMTest_1.12.0.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 Package: hyperdraw Version: 1.20.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: 2115909d9782160b3fff0866033f33e9 NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz source.ver: src/contrib/hyperdraw_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hyperdraw_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hyperdraw_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hyperdraw_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hyperdraw_1.20.0.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 dependsOnMe: BiGGR Package: hypergraph Version: 1.40.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: e6a6974bb5395f694c9db0f5cc0b3dc7 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 source.ver: src/contrib/hypergraph_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hypergraph_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hypergraph_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hypergraph_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hypergraph_1.40.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs, RpsiXML importsMe: BiGGR, hyperdraw Package: iASeq Version: 1.12.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 164e012b52fffbcff47c3ce1703dc7cc 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: SNP, RNASeq, ChIPSeq Author: Yingying Wei, Hongkai Ji Maintainer: Yingying Wei source.ver: src/contrib/iASeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iASeq_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iASeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iASeq_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iASeq_1.12.0.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 Package: iBBiG Version: 1.12.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 Archs: i386, x64 MD5sum: 4723688f8407b2177a55c76dd56175f9 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/ source.ver: src/contrib/iBBiG_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iBBiG_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iBBiG_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iBBiG_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iBBiG_1.12.0.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 Package: ibh Version: 1.16.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: 738ef39f8f94d36f7a0a4962eb811fc1 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 source.ver: src/contrib/ibh_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ibh_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ibh_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ibh_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ibh_1.16.0.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 Package: iBMQ Version: 1.8.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 Archs: i386, x64 MD5sum: 37daa1a7344fd8e95d0afff604d6e48d 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 source.ver: src/contrib/iBMQ_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iBMQ_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iBMQ_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iBMQ_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iBMQ_1.8.0.tgz 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 Package: Icens Version: 1.40.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: dc26d703037042d8b9396c4ae855f8f4 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 source.ver: src/contrib/Icens_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Icens_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Icens_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Icens_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Icens_1.40.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess importsMe: PROcess Package: iChip Version: 1.22.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) Archs: i386, x64 MD5sum: 398364c10a29392b44cf4dcefc53b0b6 NeedsCompilation: yes Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models Description: This package uses hidden Ising models to identify enriched genomic regions in ChIP-chip data. It 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 source.ver: src/contrib/iChip_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iChip_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iChip_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iChip_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iChip_1.22.0.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 Package: iClusterPlus Version: 1.4.0 Depends: R (>= 2.15.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 6b27b7c5782731bee1ca51965467661e NeedsCompilation: yes Title: Integrative clustering of multi-type genomic data Description: Integrative clustering of multiple genomic data using a joint latent variable model biocViews: Microarray, Clustering Author: Qianxing Mo, Ronglai Shen Maintainer: Qianxing Mo , Ronglai Shen source.ver: src/contrib/iClusterPlus_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iClusterPlus_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iClusterPlus_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iClusterPlus_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iClusterPlus_1.4.0.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 Rfiles: vignettes/iClusterPlus/inst/doc/iClusterPlus.R Package: IdeoViz Version: 1.2.0 Depends: Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer,graphics,GenomeInfoDb License: GPL-2 MD5sum: 26b207354d74ae5da4fc68e02e758700 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 source.ver: src/contrib/IdeoViz_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IdeoViz_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IdeoViz_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IdeoViz_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IdeoViz_1.2.0.tgz vignettes: vignettes/IdeoViz/inst/doc/Vignette.pdf vignetteTitles: IdeoViz: a package for plotting simple data along ideograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdeoViz/inst/doc/Vignette.R Package: idiogram Version: 1.44.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: e0fdc7e8704fc8537cd6527ee0443795 NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema source.ver: src/contrib/idiogram_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/idiogram_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/idiogram_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/idiogram_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/idiogram_1.44.0.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 dependsOnMe: reb Package: IdMappingAnalysis Version: 1.12.0 Depends: R (>= 2.14), R.oo (>= 1.13.0), rChoiceDialogs Imports: boot, mclust, RColorBrewer, Biobase License: GPL-2 MD5sum: 1e4d5044fd41215a49ad1bd8c1ee76bf NeedsCompilation: no Title: ID Mapping Analysis Description: Identifier mapping performance analysis biocViews: Annotation, MultipleComparison Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day source.ver: src/contrib/IdMappingAnalysis_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IdMappingAnalysis_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IdMappingAnalysis_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IdMappingAnalysis_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IdMappingAnalysis_1.12.0.tgz vignettes: vignettes/IdMappingAnalysis/inst/doc/IdMappingAnalysis.pdf vignetteTitles: Critically comparing identifier maps retrieved from bioinformatics annotation resources. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdMappingAnalysis/inst/doc/IdMappingAnalysis.R Package: IdMappingRetrieval Version: 1.16.0 Depends: R.oo, XML, RCurl, rChoiceDialogs Imports: biomaRt, ENVISIONQuery, DAVIDQuery, AffyCompatible, R.methodsS3, utils License: GPL-2 MD5sum: a3a9e049c0db3e837d07534032496bdc NeedsCompilation: no Title: ID Mapping Data Retrieval Description: Data retrieval for identifier mapping performance analysis biocViews: Annotation, MultipleComparison Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day source.ver: src/contrib/IdMappingRetrieval_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IdMappingRetrieval_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IdMappingRetrieval_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IdMappingRetrieval_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IdMappingRetrieval_1.16.0.tgz vignettes: vignettes/IdMappingRetrieval/inst/doc/IdMappingRetrieval.pdf vignetteTitles: Collection and subsequent fast retrieval of identifier mapping related information from various online sources. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdMappingRetrieval/inst/doc/IdMappingRetrieval.R Package: illuminaio Version: 0.10.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: eacd3dbead7d0fdb063974b267b3a721 NeedsCompilation: yes Title: Parsing Illumina microarray output files Description: Tools for parsing Illuminas microarray output files, including IDAT. biocViews: Infrastructure, DataImport 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 source.ver: src/contrib/illuminaio_0.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/illuminaio_0.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/illuminaio_0.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/illuminaio_0.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/illuminaio_0.10.0.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/EncryptedFormat.R, vignettes/illuminaio/inst/doc/illuminaio.R dependsOnMe: RnBeads importsMe: beadarray, crlmm, methylumi, minfi suggestsMe: limma Package: imageHTS Version: 1.18.2 Depends: R (>= 2.9.0), EBImage (>= 4.3.12), cellHTS2 (>= 2.10.0) Imports: tools, Biobase, hwriter, methods, vsn, stats, utils, e1071 Suggests: BiocStyle, MASS License: LGPL-2.1 MD5sum: f6e039cf9048f7335a024ce5f1822cea NeedsCompilation: no Title: Analysis of high-throughput microscopy-based screens Description: imageHTS is an R package dedicated to the analysis of high-throughput microscopy-based screens. The package provides a modular and extensible framework to segment cells, extract quantitative cell features, predict cell types and browse screen data through web interfaces. Designed to operate in distributed environments, imageHTS provides a standardized access to remote data and facilitates the dissemination of high-throughput microscopy-based datasets. biocViews: Software, CellBasedAssays, Preprocessing, Visualization Author: Gregoire Pau, Xian Zhang, Michael Boutros, Wolfgang Huber Maintainer: Joseph Barry source.ver: src/contrib/imageHTS_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/imageHTS_1.18.2.zip win64.binary.ver: bin/windows64/contrib/3.2/imageHTS_1.18.2.zip mac.binary.ver: bin/macosx/contrib/3.2/imageHTS_1.18.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/imageHTS_1.18.2.tgz vignettes: vignettes/imageHTS/inst/doc/imageHTS-introduction.pdf vignetteTitles: Analysis of high-throughput microscopy-based screens with imageHTS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imageHTS/inst/doc/imageHTS-introduction.R dependsOnMe: phenoDist Package: immunoClust Version: 1.0.0 Depends: R(>= 2.13.0), methods, stats, graphics, grid, lattice, flowCore Suggests: BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 7193f75d0e1bcc0cd4a71610d0795c53 NeedsCompilation: yes Title: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry Description: Model based clustering and meta-clustering of Flow Cytometry Data biocViews: Clustering, FlowCytometry Author: Till Soerensen Maintainer: Till Soerensen source.ver: src/contrib/immunoClust_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/immunoClust_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/immunoClust_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/immunoClust_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/immunoClust_1.0.0.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 Package: IMPCdata Version: 1.2.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: ec8cdbe02b84952c4c658413d84d15a9 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 source.ver: src/contrib/IMPCdata_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IMPCdata_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IMPCdata_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IMPCdata_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IMPCdata_1.2.0.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 Package: impute Version: 1.42.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: b644e60949c71a70ce96101d7518b94a 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 source.ver: src/contrib/impute_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/impute_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/impute_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/impute_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/impute_1.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, HCsnip, TIN importsMe: ChAMP, genomation, MSnbase, Rnits suggestsMe: BioNet Package: InPAS Version: 1.0.6 Depends: R (>= 3.1), GenomicRanges, GenomicFeatures, BiocParallel, S4Vectors Imports: AnnotationDbi, BSgenome, cleanUpdTSeq, Gviz, seqinr, limma, IRanges, GenomeInfoDb Suggests: RUnit, BiocGenerics, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, org.Hs.eg.db, org.Mm.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, rtracklayer License: GPL (>= 2) MD5sum: b8dc17c1730843075d4da178029e3c3d NeedsCompilation: no Title: Identification of Novel alternative PolyAdenylation Sites (PAS) 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 from RNAseq data. It leverages cleanUpdTSeq to fine tune identified APA sites. biocViews: RNASeq, Sequencing, AlternativeSplicing, Coverage, DifferentialSplicing, GeneRegulation, Transcription Author: Jianhong Ou, Sung Mi Park, Michael R. Green and Lihua Julie Zhu Maintainer: Jianhong Ou source.ver: src/contrib/InPAS_1.0.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/InPAS_1.0.6.zip win64.binary.ver: bin/windows64/contrib/3.2/InPAS_1.0.6.zip mac.binary.ver: bin/macosx/contrib/3.2/InPAS_1.0.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/InPAS_1.0.6.tgz vignettes: vignettes/InPAS/inst/doc/InPAS.pdf vignetteTitles: InPAS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InPAS/inst/doc/InPAS.R Package: INPower Version: 1.4.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: d701647a656b958048c473317cb242d8 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 source.ver: src/contrib/INPower_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/INPower_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/INPower_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/INPower_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/INPower_1.4.0.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 Package: inSilicoDb Version: 2.4.1 Depends: R (>= 3.0.0), rjson, Biobase, RCurl Suggests: limma License: GPL-2 MD5sum: 1faaabd6ea76f6ccc5e2d6288b26dbc0 NeedsCompilation: no Title: Access to the InSilico Database Description: Access expert curated and normalized microarray eSet datasets from the InSilico Database. biocViews: Microarray, DataImport Author: Jaro Vanderheijden [ctb], Quentin De Clerck [ctb], Jonatan Taminau [cre] Maintainer: InSilico DB URL: https://insilicodb.com source.ver: src/contrib/inSilicoDb_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/inSilicoDb_2.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/inSilicoDb_2.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/inSilicoDb_2.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/inSilicoDb_2.4.1.tgz vignettes: vignettes/inSilicoDb/inst/doc/inSilicoDb2.pdf, vignettes/inSilicoDb/inst/doc/inSilicoDb.pdf vignetteTitles: Using the inSilicoDb v2 package, Using the inSilicoDb package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inSilicoDb/inst/doc/inSilicoDb2.R, vignettes/inSilicoDb/inst/doc/inSilicoDb.R suggestsMe: inSilicoMerging Package: inSilicoMerging Version: 1.12.0 Depends: R (>= 2.11.1), Biobase Suggests: BiocGenerics, inSilicoDb License: GPL-2 MD5sum: 5a3f00ca0ebc7614dce97bf70ae13a4d NeedsCompilation: no Title: Collection of Merging Techniques for Gene Expression Data Description: Collection of techniques to remove inter-study bias when combining gene expression data originating from different studies. biocViews: Microarray Author: Jaro Vanderheijden [ctb], Quentin De Clerck [ctb], Jonatan Taminau [cre] Maintainer: InSilico DB URL: http://insilicodb.com/ source.ver: src/contrib/inSilicoMerging_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/inSilicoMerging_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/inSilicoMerging_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/inSilicoMerging_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/inSilicoMerging_1.12.0.tgz vignettes: vignettes/inSilicoMerging/inst/doc/inSilicoMerging.pdf vignetteTitles: Using the inSilicoMerging package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inSilicoMerging/inst/doc/inSilicoMerging.R Package: intansv Version: 1.8.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: Artistic-2.0 MD5sum: 5ff41b005f06ad6771a0ed5a55d3bceb 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 source.ver: src/contrib/intansv_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/intansv_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/intansv_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/intansv_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/intansv_1.8.0.tgz vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf vignetteTitles: An Introduction to intansv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/intansv/inst/doc/intansvOverview.R Package: interactiveDisplay Version: 1.6.0 Depends: R (>= 2.10), methods, BiocGenerics, grid Imports: interactiveDisplayBase, shiny, RColorBrewer, ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi Suggests: RUnit, hgu95av2.db, knitr,GenomicRanges, GOstats, ggbio, GO.db, Gviz, rtracklayer, metagenomeSeq, gplots, vegan, Biobase Enhances: rstudio License: Artistic-2.0 MD5sum: 4544c2d0ef44d9525a529a7e799cbbc5 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 Author: Shawn Balcome, Marc Carlson Maintainer: Shawn Balcome VignetteBuilder: knitr source.ver: src/contrib/interactiveDisplay_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/interactiveDisplay_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/interactiveDisplay_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/interactiveDisplay_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/interactiveDisplay_1.6.0.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 Package: interactiveDisplayBase Version: 1.6.1 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny Suggests: knitr License: Artistic-2.0 MD5sum: e6eda02fbb962eaeecf4a177b6ca0b4b 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 Author: Shawn Balcome, Marc Carlson Maintainer: Shawn Balcome VignetteBuilder: knitr source.ver: src/contrib/interactiveDisplayBase_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/interactiveDisplayBase_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/interactiveDisplayBase_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/interactiveDisplayBase_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/interactiveDisplayBase_1.6.1.tgz vignettes: vignettes/interactiveDisplayBase/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R htmlDocs: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html htmlTitles: "Using interactiveDisplayBase for Bioconductor object visualization and modification" importsMe: AnnotationHub, interactiveDisplay Package: inveRsion Version: 1.16.0 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 516b485e65d069d4d296476493338337 NeedsCompilation: yes Title: Inversions in genotype data Description: Package to find genetic inversions in genotype (SNP array) data. biocViews: Microarray, SNP Author: Alejandro Caceres Maintainer: Alejandro Caceres source.ver: src/contrib/inveRsion_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/inveRsion_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/inveRsion_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/inveRsion_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/inveRsion_1.16.0.tgz vignettes: vignettes/inveRsion/inst/doc/inveRsion.pdf vignetteTitles: Quick start guide for inveRsion package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inveRsion/inst/doc/inveRsion.R Package: iontree Version: 1.14.0 Depends: methods, rJava, RSQLite, XML Suggests: iontreeData License: GPL-2 MD5sum: ea3aaf616332f0d81dce2210d05293ea NeedsCompilation: no Title: Data management and analysis of ion trees from ion-trap mass spectrometry Description: Ion fragmentation provides structural information for metabolite identification. This package provides utility functions to manage and analyse MS2/MS3 fragmentation data from ion trap mass spectrometry. It was designed for high throughput metabolomics data with many biological samples and a large numer of ion trees collected. Tests have been done with data from low-resolution mass spectrometry but could be readily extended to precursor ion based fragmentation data from high resoultion mass spectrometry. biocViews: Metabolomics, MassSpectrometry Author: Mingshu Cao Maintainer: Mingshu Cao source.ver: src/contrib/iontree_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iontree_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iontree_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iontree_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iontree_1.14.0.tgz vignettes: vignettes/iontree/inst/doc/iontree_doc.pdf vignetteTitles: MSn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iontree/inst/doc/iontree_doc.R Package: iPAC Version: 1.12.0 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 MD5sum: 1de0fe1a4aeb26d715f1a5abdffdc055 NeedsCompilation: no Title: Identification of Protein Amino acid Clustering Description: iPAC is a novel tool to identify somatic amino acid mutation clustering within proteins while taking into account protein structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik source.ver: src/contrib/iPAC_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iPAC_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iPAC_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iPAC_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iPAC_1.12.0.tgz vignettes: vignettes/iPAC/inst/doc/iPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPAC/inst/doc/iPAC.R dependsOnMe: QuartPAC Package: IPPD Version: 1.16.0 Depends: R (>= 2.12.0), MASS, Matrix, XML, digest, bitops Imports: methods, stats, graphics License: GPL (version 2 or later) Archs: i386, x64 MD5sum: 6342c75c137d41ea18bfd6b6d0e958e9 NeedsCompilation: yes Title: Isotopic peak pattern deconvolution for Protein Mass Spectrometry by template matching Description: The package provides functionality to extract isotopic peak patterns from raw mass spectra. This is done by fitting a large set of template basis functions to the raw spectrum using either nonnegative least squares or least absolute deviation fittting. The package offers a flexible function which tries to estimate model parameters in a way tailored to the peak shapes in the data. The package also provides functionality to process LCMS runs. biocViews: Proteomics Author: Martin Slawski , Rene Hussong , Andreas Hildebrandt , Matthias Hein Maintainer: Martin Slawski source.ver: src/contrib/IPPD_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IPPD_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IPPD_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IPPD_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IPPD_1.16.0.tgz vignettes: vignettes/IPPD/inst/doc/IPPD.pdf vignetteTitles: IPPD Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IPPD/inst/doc/IPPD.R Package: IRanges Version: 2.2.9 Depends: R (>= 3.1.0), methods, utils, stats, BiocGenerics (>= 0.13.6), S4Vectors (>= 0.6.1) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, BSgenome.Celegans.UCSC.ce2, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 3311f73a969d42830a7defadccbbfb61 NeedsCompilation: yes Title: Infrastructure for manipulating intervals on sequences Description: The package provides efficient low-level and highly reusable S4 classes for storing ranges of integers, RLE vectors (Run-Length Encoding), and, more generally, data that can be organized sequentially (formally defined as Vector objects), as well as views on these Vector objects. Efficient list-like classes are also provided for storing big collections of instances of the basic classes. All classes in the package use consistent naming and share the same rich and consistent "Vector API" as much as possible. biocViews: Infrastructure, DataRepresentation Author: H. Pages, P. Aboyoun and M. Lawrence Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/IRanges_2.2.9.tar.gz win.binary.ver: bin/windows/contrib/3.2/IRanges_2.2.9.zip win64.binary.ver: bin/windows64/contrib/3.2/IRanges_2.2.9.zip mac.binary.ver: bin/macosx/contrib/3.2/IRanges_2.2.9.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IRanges_2.2.9.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Introduction to IRanges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R, vignettes/IRanges/inst/doc/RleTricks.R dependsOnMe: BayesPeak, biomvRCNS, Biostrings, BiSeq, BSgenome, bsseq, bumphunter, CAFE, casper, CexoR, ChIPpeakAnno, chipseq, chroGPS, cn.mops, CSAR, customProDB, DASiR, deepSNV, DESeq2, DEXSeq, DirichletMultinomial, DMRcaller, epigenomix, exomeCopy, GenomeInfoDb, genomes, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges, Genominator, groHMM, Gviz, HiTC, HMMcopy, htSeqTools, IdeoViz, methyAnalysis, MotifDb, motifRG, oneChannelGUI, OTUbase, pepStat, PING, proBAMr, PSICQUIC, R453Plus1Toolbox, RefNet, rfPred, rGADEM, rGREAT, RIPSeeker, rMAT, Rsamtools, scsR, segmentSeq, SGSeq, SomatiCA, TEQC, TitanCNA, triform, triplex, VariantTools, XVector importsMe: AllelicImbalance, annmap, ArrayExpressHTS, ballgown, bamsignals, BayesPeak, beadarray, Biostrings, biovizBase, BiSeq, BitSeq, BSgenome, BubbleTree, CAGEr, ChAMP, charm, chipenrich, ChIPQC, ChIPseeker, chipseq, ChIPseqR, ChIPsim, ChromHeatMap, cleaver, CNEr, CNVrd2, cobindR, coMET, compEpiTools, conumee, copynumber, CopywriteR, csaw, customProDB, DECIPHER, derfinder, derfinderHelper, derfinderPlot, DiffBind, diffHic, DOQTL, easyRNASeq, EDASeq, facopy, fastseg, flipflop, flowQ, FunciSNP, genomation, GenomicAlignments, GenomicInteractions, GenomicTuples, genoset, ggbio, GGtools, girafe, gmapR, GoogleGenomics, GOTHiC, gQTLstats, gwascat, h5vc, HTSeqGenie, InPAS, intansv, IVAS, M3D, MatrixRider, MEDIPS, methVisual, methyAnalysis, methylPipe, MethylSeekR, methylumi, minfi, MinimumDistance, mosaics, MotIV, msa, MSnbase, NarrowPeaks, nucleR, oligoClasses, Pbase, pdInfoBuilder, PICS, PING, plethy, podkat, polyester, prebs, Pviz, qpgraph, QuasR, R3CPET, r3Cseq, Rariant, REDseq, regionReport, Repitools, ReportingTools, rGADEM, rMAT, rnaSeqMap, RnBeads, Rolexa, Rqc, rSFFreader, RSVSim, RTN, rtracklayer, SCAN.UPC, SeqArray, seqPattern, seqplots, SeqVarTools, ShortRead, skewr, SNPchip, soGGi, SomatiCA, SomaticSignatures, spliceR, SplicingGraphs, SVM2CRM, TFBSTools, tracktables, TransView, triform, TSSi, VanillaICE, VariantAnnotation, VariantFiltering, wavClusteR, waveTiling, XVector suggestsMe: BaseSpaceR, BiocGenerics, gQTLBase, HilbertVis, HilbertVisGUI, MiRaGE, S4Vectors, STAN Package: iSeq Version: 1.20.0 Depends: R (>= 2.10.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: 28c56ea07fb0ed5f609986e9e0d66138 NeedsCompilation: yes Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models Description: This package uses Bayesian hidden Ising models to identify IP-enriched genomic regions from ChIP-seq data. It can be used to analyze ChIP-seq data with and without controls and replicates. biocViews: ChIPSeq, Sequencing Author: Qianxing Mo Maintainer: Qianxing Mo source.ver: src/contrib/iSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iSeq_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iSeq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iSeq_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iSeq_1.20.0.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 Package: isobar Version: 1.14.0 Depends: R (>= 2.10.0), Biobase, stats, methods Imports: distr, plyr Suggests: MSnbase, OrgMassSpecR, XML, biomaRt, ggplot2, RJSONIO, Hmisc, gplots, RColorBrewer, gridExtra, limma, boot, distr, DBI, MASS License: LGPL-2 MD5sum: 3636b989d49339d20210f2780c86ac01 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: Proteomics, MassSpectrometry, Bioinformatics, MultipleComparisons, QualityControl Author: Florian P Breitwieser and Jacques Colinge , with contributions from Xavier Robin and Florent Gluck Maintainer: Florian P Breitwieser URL: http://www.ms-isobar.org source.ver: src/contrib/isobar_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/isobar_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/isobar_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/isobar_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/isobar_1.14.0.tgz vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf, vignettes/isobar/inst/doc/isobar.pdf, vignettes/isobar/inst/doc/isobar-ptm.pdf, vignettes/isobar/inst/doc/isobar-usecases.pdf vignetteTitles: isobar for developers, isobar package for iTRAQ and TMT protein quantification, isobar for quantification of PTM datasets, Usecases for isobar package hasREADME: TRUE 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.R, vignettes/isobar/inst/doc/isobar-usecases.R Package: IsoGeneGUI Version: 2.4.0 Depends: tcltk, xlsx Imports: Rcpp, tkrplot, multtest, relimp, geneplotter, RColorBrewer, Iso, IsoGene, ORCME, ORIClust, orQA, goric, ff, Biobase, jpeg Suggests: RUnit License: GPL-2 MD5sum: f1d2b92462130bca680f12e0d59e6572 NeedsCompilation: no Title: A graphical user interface to conduct a dose-response analysis of microarray data Description: The IsoGene Graphical User Interface (IsoGene-GUI) is a user friendly interface of the IsoGene package which is aimed to identify for genes with a monotonic trend in the expression levels with respect to the increasing doses. Additionally, GUI extension of original package contains various tools to perform clustering of dose-response profiles. Testing is addressed through several test statistics: global likelihood ratio test (E2), Bartholomew 1961, Barlow et al. 1972 and Robertson et al. 1988), Williams (1971, 1972), Marcus (1976), the M (Hu et al. 2005) and the modified M (Lin et al. 2007). The p-values of the global likelihood ratio test (E2) are obtained using the exact distribution and permutations. The other four test statistics are obtained using permutations. Several p-values adjustment are provided: Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for controlling the family-wise Type I error rate (FWER), and BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2001) procedures are used for controlling the FDR. The inference is based on resampling methods, which control the False Discovery Rate (FDR), for both permutations (Ge et al., 2003) and the Significance Analysis of Microarrays (SAM, Tusher et al., 2001). Clustering methods are outsourced from CRAN packages ORCME, ORIClust. The package ORCME is based on delta-clustering method (Cheng and Church, 2000) and ORIClust on Order Restricted Information Criterion (Liu et al., 2009), both perform same task but from different perspective and their outputs are clusters of genes. Additionally, profile selection for given gene based on Generalized ORIC (Kuiper et al., 2014) from package goric and permutation test for E2 based on package orQA are included in IsoGene-GUI. None of these four packages has GUI. biocViews: Microarray, DifferentialExpression, GUI Author: Setia Pramana, Dan Lin, Philippe Haldermans, Tobias Verbeke, Martin Otava Maintainer: Setia Pramana URL: http://ibiostat.be/online-resources/online-resources/isogenegui/isogenegui-package source.ver: src/contrib/IsoGeneGUI_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IsoGeneGUI_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IsoGeneGUI_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IsoGeneGUI_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IsoGeneGUI_2.4.0.tgz vignettes: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.pdf vignetteTitles: IsoGeneGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.R Package: ITALICS Version: 2.28.0 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: 5ba59ea2a2bcee7ab3d4222237467f7a 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 source.ver: src/contrib/ITALICS_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ITALICS_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ITALICS_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ITALICS_2.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ITALICS_2.28.0.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 Package: iterativeBMA Version: 1.26.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: 6ef228794f5db8a0e68d6a882c650382 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 source.ver: src/contrib/iterativeBMA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iterativeBMA_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iterativeBMA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iterativeBMA_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iterativeBMA_1.26.0.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 Package: iterativeBMAsurv Version: 1.26.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: a0c09f82635f82927fb55142a21868c6 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 source.ver: src/contrib/iterativeBMAsurv_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iterativeBMAsurv_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iterativeBMAsurv_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iterativeBMAsurv_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iterativeBMAsurv_1.26.0.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 Package: IVAS Version: 1.0.0 Depends: R (> 3.0.0),GenomicFeatures Imports: doParallel, lme4, Matrix, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb Suggests: BiocStyle License: GPL-2 MD5sum: 6b086a6db5c5b433fda6b3a45a36f920 NeedsCompilation: no Title: Identification of genetic Variants affecting Alternative Splicing Description: Identification of genetic variants affecting alternative splicing. biocViews: AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Sangsoo Kim Maintainer: Seonggyun Han source.ver: src/contrib/IVAS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IVAS_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IVAS_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IVAS_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IVAS_1.0.0.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 Package: jmosaics Version: 1.8.0 Depends: R (>= 2.15.2), mosaics License: GPL (>= 2) MD5sum: 80cc86fe05af49f67f526cd8beec6f58 NeedsCompilation: no Title: Joint analysis of multiple ChIP-Seq data sets Description: jmosaics detects enriched regions of ChIP-seq data sets jointly. biocViews: ChIPSeq, Sequencing, Transcription, Genetics Author: Xin Zeng Maintainer: Xin Zeng source.ver: src/contrib/jmosaics_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/jmosaics_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/jmosaics_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/jmosaics_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/jmosaics_1.8.0.tgz vignettes: vignettes/jmosaics/inst/doc/jmosaics.pdf vignetteTitles: jMOSAiCS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/jmosaics/inst/doc/jmosaics.R Package: joda Version: 1.16.0 Depends: R (>= 2.0), bgmm, RBGL License: GPL (>= 2) MD5sum: 8cd5642c5321a89f108121d1c537b8fa NeedsCompilation: no Title: JODA algorithm for quantifying gene deregulation using knowledge Description: Package 'joda' implements three steps of an algorithm called JODA. The algorithm computes gene deregulation scores. For each gene, its deregulation score reflects how strongly an effect of a certain regulator's perturbation on this gene differs between two different cell populations. The algorithm utilizes regulator knockdown expression data as well as knowledge about signaling pathways in which the regulators are involved (formalized in a simple matrix model). biocViews: Microarray, Pathways, GraphAndNetwork, StatisticalMethod, NetworkInference Author: Ewa Szczurek Maintainer: Ewa Szczurek URL: http://www.bioconductor.org source.ver: src/contrib/joda_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/joda_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/joda_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/joda_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/joda_1.16.0.tgz vignettes: vignettes/joda/inst/doc/JodaVignette.pdf vignetteTitles: Introduction to joda hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/joda/inst/doc/JodaVignette.R Package: KCsmart Version: 2.26.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: fe1c40bd02f3ee3e54cb1953d88c4e03 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 source.ver: src/contrib/KCsmart_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/KCsmart_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/KCsmart_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/KCsmart_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/KCsmart_2.26.0.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 Package: kebabs Version: 1.2.3 Depends: R (>= 3.2.0), Biostrings (>= 2.35.5), kernlab Imports: methods, stats, Rcpp (>= 0.11.2), Matrix, XVector (>= 0.7.3), S4Vectors (>= 0.5.11), e1071, LiblineaR LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, apcluster, Biobase, BiocGenerics License: GPL (>= 2.1) Archs: i386, x64 MD5sum: 9a256b354792e4406cce048307371e4b 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 Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/kebabs/ source.ver: src/contrib/kebabs_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/kebabs_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.2/kebabs_1.2.3.zip mac.binary.ver: bin/macosx/contrib/3.2/kebabs_1.2.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/kebabs_1.2.3.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, vignettes/kebabs/inst/doc/KeBABS.R Package: KEGGgraph Version: 1.26.0 Depends: R (>= 2.10), methods, XML (>= 2.3-0), graph Imports: methods, XML, graph Suggests: Rgraphviz, RBGL, RUnit, RColorBrewer, KEGG.db, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) MD5sum: f7cb796c201eec6665adfe9603f316d8 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 Maintainer: Jitao David Zhang URL: http://www.nextbiomotif.com source.ver: src/contrib/KEGGgraph_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/KEGGgraph_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/KEGGgraph_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/KEGGgraph_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/KEGGgraph_1.26.0.tgz vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf, vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf vignetteTitles: KEGGgraph: Application Examples, KEGGgraph: graph approach to KEGG PATHWAY hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R, vignettes/KEGGgraph/inst/doc/KEGGgraph.R dependsOnMe: pathview, ROntoTools, SPIA importsMe: clipper, DEGraph, EnrichmentBrowser, NCIgraph, ToPASeq suggestsMe: DEGraph, GenomicRanges Package: keggorthology Version: 2.20.0 Depends: R (>= 2.5.0),stats,graph,hgu95av2.db Imports: AnnotationDbi,graph,DBI, graph, grDevices, methods, stats, tools, utils Suggests: RBGL,ALL License: Artistic-2.0 MD5sum: 534604536ae3f0b06bcf91412288ab94 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 source.ver: src/contrib/keggorthology_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/keggorthology_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/keggorthology_2.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/keggorthology_2.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/keggorthology_2.20.0.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 Package: KEGGprofile Version: 1.10.0 Imports: AnnotationDbi,png,TeachingDemos,XML,KEGG.db,KEGGREST,biomaRt License: GPL (>= 2) MD5sum: cfcec3bd9e5b13dd1fda1d89e2c32752 NeedsCompilation: no Title: An annotation and visualization package for multi-types and multi-groups expression data in KEGG pathway Description: KEGGprofile is an annotation and visualization tool which integrated the expression profiles and the function annotation in KEGG pathway maps. The multi-types and multi-groups expression data can be visualized in one pathway map. KEGGprofile facilitated more detailed analysis about the specific function changes inner pathway or temporal correlations in different genes and samples. biocViews: Pathways, KEGG Author: Shilin Zhao, Yan Guo, Yu Shyr Maintainer: Shilin Zhao source.ver: src/contrib/KEGGprofile_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/KEGGprofile_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/KEGGprofile_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/KEGGprofile_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/KEGGprofile_1.10.0.tgz vignettes: vignettes/KEGGprofile/inst/doc/KEGGprofile.pdf vignetteTitles: KEGGprofile: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGprofile/inst/doc/KEGGprofile.R suggestsMe: FGNet Package: KEGGREST Version: 1.8.1 Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: 7b224fe5c40581b46c5b78a781c0a566 NeedsCompilation: no Title: Client-side REST access to KEGG Description: A package that provides a client interface to the KEGG REST server. 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 Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/KEGGREST_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/KEGGREST_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/KEGGREST_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/KEGGREST_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/KEGGREST_1.8.1.tgz vignettes: vignettes/KEGGREST/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R htmlDocs: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html htmlTitles: "Accessing the KEGG REST API" dependsOnMe: PAPi, ROntoTools importsMe: clusterProfiler, EnrichmentBrowser, gage, mmnet, pathview suggestsMe: limma Package: lapmix Version: 1.34.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: 2ec847cac088f3ad3f611c7b7b127232 NeedsCompilation: no 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 source.ver: src/contrib/lapmix_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/lapmix_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/lapmix_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/lapmix_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/lapmix_1.34.0.tgz vignettes: vignettes/lapmix/inst/doc/lapmix-example.pdf vignetteTitles: lapmix example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lapmix/inst/doc/lapmix-example.R Package: LBE Version: 1.36.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: 6255d7259c2eca620ef30cd22b35b252 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 source.ver: src/contrib/LBE_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LBE_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LBE_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LBE_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LBE_1.36.0.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 Package: LEA Version: 1.0.0 Depends: R (>= 3.1.0), methods License: GPL-3 Archs: i386, x64 MD5sum: 92826dcf76c02d86ba5d892886e37d3d NeedsCompilation: yes Title: LEA: an R package for Landscape and Ecological Association Studies Description: LEA is an R package dedicated to landscape genomics and ecological association tests. LEA can run analyses of population structure and genome scans for local adaptation. It includes statistical methods for estimating ancestry coefficients from large genotypic matrices and evaluating the number of ancestral populations (snmf, pca); and identifying genetic polymorphisms that exhibit high correlation with some environmental gradient or with the variables used as proxies for ecological pressures (lfmm), and controlling the false discovery rate. LEA is mainly based on optimized C programs that can scale with the dimension of very large data sets. biocViews: Software, StatisticalMethod, Clustering, Regression Author: Eric Frichot , Olivier Francois Maintainer: Eric Frichot URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html source.ver: src/contrib/LEA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LEA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LEA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LEA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LEA_1.0.0.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 Package: les Version: 1.18.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 9f9471d30f09edea0581c806c08c3bf2 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 URL: http://julian-gehring.github.com/les/ source.ver: src/contrib/les_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/les_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/les_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/les_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/les_1.18.0.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 Package: limma Version: 3.24.15 Depends: R (>= 2.3.0), methods Suggests: affy, AnnotationDbi, BiasedUrn, Biobase, ellipse, GO.db, illuminaio, KEGGREST, locfit, MASS, org.Hs.eg.db, splines, statmod (>= 1.2.2), vsn License: GPL (>=2) Archs: i386, x64 MD5sum: ca8c516a416718d512d442fbdedad4f5 NeedsCompilation: yes Title: Linear Models for Microarray Data Description: Data analysis, linear models and differential expression for microarray data. biocViews: ExonArray, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, DataImport, Genetics, Bayesian, Clustering, Regression, TimeCourse, Microarray, microRNAArray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: Gordon Smyth [cre,aut], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Natalie Thorne [ctb], Davis McCarthy [ctb], Di Wu [ctb], Yifang Hu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb], Aaron Lun [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limma source.ver: src/contrib/limma_3.24.15.tar.gz win.binary.ver: bin/windows/contrib/3.2/limma_3.24.15.zip win64.binary.ver: bin/windows64/contrib/3.2/limma_3.24.15.zip mac.binary.ver: bin/macosx/contrib/3.2/limma_3.24.15.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/limma_3.24.15.tgz vignettes: vignettes/limma/inst/doc/intro.pdf, vignettes/limma/inst/doc/usersguide.pdf vignetteTitles: Limma One Page Introduction, usersguide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limma/inst/doc/intro.R dependsOnMe: a4Base, AffyExpress, attract, birta, CALIB, cghMCR, codelink, convert, Cormotif, coRNAi, DiffBind, DMRcate, DrugVsDisease, edgeR, ExiMiR, gCMAP, HTqPCR, maigesPack, marray, metagenomeSeq, metaseqR, MLSeq, MmPalateMiRNA, qpcrNorm, qusage, RBM, Ringo, RnBeads, Rnits, snapCGH, SSPA, tRanslatome, TurboNorm, wateRmelon importsMe: ABSSeq, affycoretools, affylmGUI, ArrayExpress, arrayQuality, arrayQualityMetrics, ArrayTools, attract, ballgown, beadarray, betr, birte, bumphunter, CALIB, CancerMutationAnalysis, casper, ChAMP, charm, ChIPpeakAnno, compcodeR, csaw, diffHic, EnrichmentBrowser, erccdashboard, explorase, flowBin, GeneSelectMMD, GeneSelector, GGBase, GOsummaries, gQTLstats, HTqPCR, iChip, InPAS, limmaGUI, lmdme, LVSmiRNA, mAPKL, maSigPro, minfi, missMethyl, MmPalateMiRNA, monocle, nem, nethet, OLIN, PAA, PADOG, PECA, pepStat, phenoTest, polyester, Ringo, RNAinteract, RNAither, RTN, RTopper, SimBindProfiles, snapCGH, STATegRa, systemPipeR, timecourse, ToPASeq, tweeDEseq, vsn suggestsMe: ABarray, ADaCGH2, beadarraySNP, BiocCaseStudies, BioNet, Category, categoryCompare, ClassifyR, CMA, coGPS, dyebias, ELBOW, gage, GeneSelector, GEOquery, GSRI, GSVA, Heatplus, inSilicoDb, isobar, les, lumi, mdgsa, methylumi, MLP, npGSEA, oligo, oneChannelGUI, paxtoolsr, PGSEA, piano, plw, PREDA, puma, Rcade, RTopper, rtracklayer, sva Package: limmaGUI Version: 1.44.0 Imports: limma, tcltk, BiocInstaller, tkrplot, R2HTML, xtable, gcrma, AnnotationDbi License: LGPL MD5sum: 05746ed0dd58d56dc71cc7bff773d086 NeedsCompilation: no Title: GUI for limma package Description: A Graphical User Interface for the limma Microarray package. biocViews: Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, DifferentialExpression, MultipleComparison, GUI Author: James Wettenhall Division of Genetics and Bioinformatics, WEHI. Maintainer: Keith Satterley URL: http://bioinf.wehi.edu.au/limmaGUI/ source.ver: src/contrib/limmaGUI_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/limmaGUI_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/limmaGUI_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/limmaGUI_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/limmaGUI_1.44.0.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/extract.R, vignettes/limmaGUI/inst/doc/limmaGUI.R Package: LiquidAssociation Version: 1.22.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: cf995f72878808334708fe00982be36e 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 source.ver: src/contrib/LiquidAssociation_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LiquidAssociation_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LiquidAssociation_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LiquidAssociation_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LiquidAssociation_1.22.0.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 Package: lmdme Version: 1.10.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: b233dd225d865369c2c5438628cf9ca4 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 source.ver: src/contrib/lmdme_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/lmdme_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/lmdme_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/lmdme_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/lmdme_1.10.0.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 Package: LMGene Version: 2.24.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5), multtest, survival, affy Suggests: affydata License: LGPL MD5sum: 65209442800f8791b761ca382e606e36 NeedsCompilation: no Title: LMGene Software for Data Transformation and Identification of Differentially Expressed Genes in Gene Expression Arrays Description: LMGene package for analysis of microarray data using a linear model and glog data transformation biocViews: Microarray, DifferentialExpression, Preprocessing Author: David Rocke, Geun Cheol Lee, John Tillinghast, Blythe Durbin-Johnson, and Shiquan Wu Maintainer: Blythe Durbin-Johnson URL: http://dmrocke.ucdavis.edu/software.html source.ver: src/contrib/LMGene_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LMGene_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LMGene_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LMGene_2.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LMGene_2.24.0.tgz vignettes: vignettes/LMGene/inst/doc/LMGene.pdf vignetteTitles: LMGene User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LMGene/inst/doc/LMGene.R Package: logicFS Version: 1.38.0 Depends: LogicReg, mcbiopi Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 81560f05283612584e3ec9effc609e3d 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 Maintainer: Holger Schwender source.ver: src/contrib/logicFS_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/logicFS_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/logicFS_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/logicFS_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/logicFS_1.38.0.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 Package: logitT Version: 1.26.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) Archs: i386, x64 MD5sum: 6e1ed8a17497c8a2df39af2e3dce5cc1 NeedsCompilation: yes Title: logit-t Package Description: The logitT library implements the Logit-t algorithm introduced in --A high performance test of differential gene expression for oligonucleotide arrays-- by William J Lemon, Sandya Liyanarachchi and Ming You for use with Affymetrix data stored in an AffyBatch object in R. biocViews: Microarray, DifferentialExpression Author: Tobias Guennel Maintainer: Tobias Guennel URL: http://www.bioconductor.org source.ver: src/contrib/logitT_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/logitT_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/logitT_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/logitT_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/logitT_1.26.0.tgz vignettes: vignettes/logitT/inst/doc/logitT.pdf vignetteTitles: logitT primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logitT/inst/doc/logitT.R Package: lol Version: 1.16.0 Depends: penalized, Matrix Imports: Matrix, penalized, graphics, grDevices, stats License: GPL-2 MD5sum: 079fb2eef217e0dbc2765a8e11583707 NeedsCompilation: no Title: Lots Of Lasso Description: Various optimization methods for Lasso inference with matrix warpper biocViews: StatisticalMethod Author: Yinyin Yuan Maintainer: Yinyin Yuan source.ver: src/contrib/lol_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/lol_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/lol_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/lol_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/lol_1.16.0.tgz vignettes: vignettes/lol/inst/doc/lol.pdf vignetteTitles: An introduction to the lol package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lol/inst/doc/lol.R Package: LowMACA Version: 1.0.0 Depends: R (>= 2.10), Biostrings, motifStack Imports: cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, methods, LowMACAAnnotation Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 3c93a859e1be22e039886b8bc8f7ae56 NeedsCompilation: no Title: LowMACA - Low frequency Mutation Analysis via Consensus Alignment Description: The LowMACA package is a simple suite of tools to investigate and analyze the mutation profile of several proteins or pfam domains via consensus alignment. You can conduct an hypothesis driven exploratory analysis using our package simply providing a set of genes or pfam domains of your interest. biocViews: SomaticMutation, SequenceMatching, WholeGenome, Sequencing, Alignment, DataImport, MultipleSequenceAlignment Author: Stefano de Pretis , Giorgio Melloni Maintainer: Stefano de Pretis , Giorgio Melloni SystemRequirements: clustalo, gs, perl VignetteBuilder: knitr source.ver: src/contrib/LowMACA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LowMACA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LowMACA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LowMACA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LowMACA_1.0.0.tgz vignettes: vignettes/LowMACA/inst/doc/LowMACA.pdf vignetteTitles: LowMACA hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LowMACA/inst/doc/LowMACA.R Package: LPE Version: 1.42.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: d776715f6b611dc83383ab3aab655c6b 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/ source.ver: src/contrib/LPE_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LPE_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LPE_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LPE_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LPE_1.42.0.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: LPEadj, PLPE importsMe: LPEadj suggestsMe: ABarray Package: LPEadj Version: 1.28.0 Depends: LPE Imports: LPE, stats License: LGPL MD5sum: 6474633868edfec46c2942745bae6f8f NeedsCompilation: no Title: A correction of the local pooled error (LPE) method to replace the asymptotic variance adjustment with an unbiased adjustment based on sample size. Description: Two options are added to the LPE algorithm. The original LPE method sets all variances below the max variance in the ordered distribution of variances to the maximum variance. in LPEadj this option is turned off by default. The second option is to use a variance adjustment based on sample size rather than pi/2. By default the LPEadj uses the sample size based variance adjustment. biocViews: Microarray, Proteomics Author: Carl Murie , Robert Nadon Maintainer: Carl Murie source.ver: src/contrib/LPEadj_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LPEadj_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LPEadj_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LPEadj_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LPEadj_1.28.0.tgz vignettes: vignettes/LPEadj/inst/doc/LPEadj.pdf vignetteTitles: LPEadj test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPEadj/inst/doc/LPEadj.R Package: lpNet Version: 2.0.0 Depends: lpSolve, nem License: Artistic License 2.0 MD5sum: ea11247aa7fcf442fa889cfea73ad6bc 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: Bettina Knapp source.ver: src/contrib/lpNet_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/lpNet_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/lpNet_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/lpNet_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/lpNet_2.0.0.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 Package: lumi Version: 2.20.2 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures, GenomicRanges, annotate, Biobase (>= 2.5.5), 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: 2d89a2172e36f2269b043e8245801218 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: Pan Du source.ver: src/contrib/lumi_2.20.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/lumi_2.20.2.zip win64.binary.ver: bin/windows64/contrib/3.2/lumi_2.20.2.zip mac.binary.ver: bin/macosx/contrib/3.2/lumi_2.20.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/lumi_2.20.2.tgz vignettes: vignettes/lumi/inst/doc/IlluminaAnnotation.pdf, vignettes/lumi/inst/doc/lumi.pdf, vignettes/lumi/inst/doc/lumi_VST_evaluation.pdf, vignettes/lumi/inst/doc/methylationAnalysis.pdf vignetteTitles: Resolve the inconsistency of Illumina identifiers through nuID, Using lumi A package processing Illumina Microarray, Evaluation of VST algorithm in lumi package, Analyze Illumina Infinium methylation microarray data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lumi/inst/doc/IlluminaAnnotation.R, vignettes/lumi/inst/doc/lumi.R, vignettes/lumi/inst/doc/lumi_VST_evaluation.R, vignettes/lumi/inst/doc/methylationAnalysis.R dependsOnMe: arrayMvout, wateRmelon importsMe: ffpe, methyAnalysis, MineICA suggestsMe: beadarray, blima, methylumi, tigre Package: LVSmiRNA Version: 1.18.0 Depends: R (>= 3.1.0), methods, splines Imports: BiocGenerics, stats4, graphics, stats, utils, MASS, Biobase, quantreg, limma, affy, SparseM, vsn, zlibbioc Enhances: parallel,snow, Rmpi License: GPL-2 Archs: i386, x64 MD5sum: c2c8cccb6604c6562425008a62c35a19 NeedsCompilation: yes Title: LVS normalization for Agilent miRNA data Description: Normalization of Agilent miRNA arrays. biocViews: Microarray,AgilentChip,OneChannel,Preprocessing Author: Stefano Calza, Suo Chen, Yudi Pawitan Maintainer: Stefano Calza source.ver: src/contrib/LVSmiRNA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LVSmiRNA_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LVSmiRNA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LVSmiRNA_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LVSmiRNA_1.18.0.tgz vignettes: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.pdf vignetteTitles: LVSmiRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.R Package: M3D Version: 1.3.4 Depends: R (>= 3.0.0) Imports: GenomicRanges, IRanges, BiSeq, parallel Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: Artistic License 2.0 MD5sum: 2241c4bb7bcb3a5bbfaee45ac5d7c087 NeedsCompilation: no Title: Identifies differentially methylated regions across testing groups. Description: This package identifies statistically significantly differentially methylated regions of CpGs. It uses kernel methods (the Maxmimum Mean Discrepancy) to measure differences in methylation profiles, and relates these to inter-replicate changes, whilst accounting for variation in coverage profiles. biocViews: DNAMethylation, DifferentialMethylation, Coverage, CpGIsland Author: Tom Mayo Maintainer: Tom Mayo VignetteBuilder: knitr source.ver: src/contrib/M3D_1.3.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/M3D_1.3.4.zip win64.binary.ver: bin/windows64/contrib/3.2/M3D_1.3.4.zip mac.binary.ver: bin/macosx/contrib/3.2/M3D_1.3.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/M3D_1.3.4.tgz vignettes: vignettes/M3D/inst/doc/M3D_vignette.pdf vignetteTitles: An Introduction to the M$^3$D method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3D/inst/doc/M3D_vignette.R Package: maanova Version: 1.38.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) Archs: i386, x64 MD5sum: 90958fc83a85c83918da83e0867cc657 NeedsCompilation: yes Title: Tools for analyzing Micro Array experiments Description: Analysis of N-dye Micro Array experiment using mixed model effect. Containing analysis of variance, permutation and bootstrap, cluster and consensus tree. biocViews: Microarray, DifferentialExpression, Clustering Author: Hao Wu, modified by Hyuna Yang and Keith Sheppard with ideas from Gary Churchill, Katie Kerr and Xiangqin Cui. Maintainer: Keith Sheppard URL: http://research.jax.org/faculty/churchill source.ver: src/contrib/maanova_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maanova_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maanova_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maanova_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maanova_1.38.0.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maanova/inst/doc/maanova.R Package: macat Version: 1.42.0 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 MD5sum: dbb8daefa59df01ba1f1acd9b3ab8376 NeedsCompilation: no Title: MicroArray Chromosome Analysis Tool Description: This library contains functions to investigate links between differential gene expression and the chromosomal localization of the genes. MACAT is motivated by the common observation of phenomena involving large chromosomal regions in tumor cells. MACAT is the implementation of a statistical approach for identifying significantly differentially expressed chromosome regions. The functions have been tested on a publicly available data set about acute lymphoblastic leukemia (Yeoh et al.Cancer Cell 2002), which is provided in the library 'stjudem'. biocViews: Microarray, DifferentialExpression, Visualization Author: Benjamin Georgi, Matthias Heinig, Stefan Roepcke, Sebastian Schmeier, Joern Toedling Maintainer: Joern Toedling source.ver: src/contrib/macat_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/macat_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/macat_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/macat_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/macat_1.42.0.tgz vignettes: vignettes/macat/inst/doc/macat.pdf vignetteTitles: MicroArray Chromosome Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/macat/inst/doc/macat.R Package: maCorrPlot Version: 1.38.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: 2bcc257d0cb6a2492b1d2fb2517a59bf 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 source.ver: src/contrib/maCorrPlot_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maCorrPlot_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maCorrPlot_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maCorrPlot_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maCorrPlot_1.38.0.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 Package: made4 Version: 1.42.0 Depends: ade4, RColorBrewer,gplots,scatterplot3d Suggests: affy License: Artistic-2.0 MD5sum: 64a5d3d0a3b42b240198e499dfdb52bf NeedsCompilation: no Title: Multivariate analysis of microarray data using ADE4 Description: Multivariate data analysis and graphical display of microarray data. Functions include between group analysis and coinertia analysis. It contains functions that require ADE4. biocViews: Clustering, Classification, MultipleComparison Author: Aedin Culhane Maintainer: Aedin Culhane URL: http://www.hsph.harvard.edu/aedin-culhane/ source.ver: src/contrib/made4_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/made4_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/made4_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/made4_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/made4_1.42.0.tgz vignettes: vignettes/made4/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/made4/inst/doc/introduction.R dependsOnMe: bgafun importsMe: omicade4 Package: maigesPack Version: 1.32.0 Depends: R (>= 2.10), convert, graph, limma, marray, methods Suggests: amap, annotate, class, e1071, MASS, multtest, OLIN, R2HTML, rgl, som License: GPL (>= 2) Archs: i386, x64 MD5sum: 0ac7244b9f07e5e2e67d8c98ad53d258 NeedsCompilation: yes Title: Functions to handle cDNA microarray data, including several methods of data analysis Description: This package uses functions of various other packages together with other functions in a coordinated way to handle and analyse cDNA microarray data biocViews: Microarray, TwoChannel, Preprocessing, ThirdPartyClient, DifferentialExpression, Clustering, Classification, GraphAndNetwork Author: Gustavo H. Esteves , with contributions from Roberto Hirata Jr , E. Jordao Neves , Elier B. Cristo , Ana C. Simoes and Lucas Fahham Maintainer: Gustavo H. Esteves URL: http://www.maiges.org/en/software/ source.ver: src/contrib/maigesPack_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maigesPack_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maigesPack_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maigesPack_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maigesPack_1.32.0.tgz vignettes: vignettes/maigesPack/inst/doc/maigesPack_tutorial.pdf vignetteTitles: maigesPack Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maigesPack/inst/doc/maigesPack_tutorial.R Package: MAIT Version: 1.2.1 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Enhances: rgl License: GPL-2 MD5sum: 5d5eca583cbfc5a76019c505e153c274 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: MassSpectrometry, Metabolomics, Software Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina Andres-LaCueva, Alexandre Perera Maintainer: Francesc Fernandez-Albert source.ver: src/contrib/MAIT_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/MAIT_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/MAIT_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/MAIT_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MAIT_1.2.1.tgz vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf vignetteTitles: MAIT Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAIT/inst/doc/MAIT_Vignette.R Package: makecdfenv Version: 1.44.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) Archs: i386, x64 MD5sum: dead18c793349558c6f7fc8843da5834 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 source.ver: src/contrib/makecdfenv_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/makecdfenv_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/makecdfenv_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/makecdfenv_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/makecdfenv_1.44.0.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 Package: MANOR Version: 1.40.0 Depends: R (>= 2.10), GLAD Imports: GLAD, graphics, grDevices, stats, utils License: GPL-2 Archs: i386, x64 MD5sum: c50ea88b54daf7197129c0b84bd53726 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 Author: Pierre Neuvial , Philippe Hupe Maintainer: Pierre Neuvial URL: http://bioinfo.curie.fr/projects/manor/index.html source.ver: src/contrib/MANOR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MANOR_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MANOR_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MANOR_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MANOR_1.40.0.tgz vignettes: vignettes/MANOR/inst/doc/MANOR.pdf vignetteTitles: MANOR overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MANOR/inst/doc/MANOR.R Package: manta Version: 1.14.0 Depends: R (>= 1.8.0), methods, edgeR (>= 2.5.13) Imports: Hmisc, caroline(>= 0.6.6) Suggests: RSQLite, plotrix License: Artistic-2.0 MD5sum: 290d3bffd21f7989d5319f99c2a42efa NeedsCompilation: no Title: Microbial Assemblage Normalized Transcript Analysis Description: Tools for robust comparative metatranscriptomics. biocViews: DifferentialExpression, RNASeq, Genetics, GeneExpression, Sequencing, QualityControl, DataImport, Visualization Author: Ginger Armbrust, Adrian Marchetti Maintainer: Chris Berthiaume , Adrian Marchetti URL: http://manta.ocean.washington.edu/ source.ver: src/contrib/manta_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/manta_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/manta_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/manta_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/manta_1.14.0.tgz vignettes: vignettes/manta/inst/doc/manta.pdf vignetteTitles: manta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/manta/inst/doc/manta.R Package: MantelCorr Version: 1.38.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) MD5sum: 60136fb0371765184fe29d42a5c7b0f1 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 source.ver: src/contrib/MantelCorr_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MantelCorr_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MantelCorr_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MantelCorr_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MantelCorr_1.38.0.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 Package: mAPKL Version: 1.0.0 Depends: R (>= 3.2.0), Biobase Imports: multtest, clusterSim, apcluster, limma, e1071, AnnotationDbi, methods, parmigene,igraph,reactome.db Suggests: BiocStyle, knitr, mAPKLData, hgu133plus2.db, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 0fa5812596a73021e73efd63cec2480c NeedsCompilation: no Title: A Hybrid Feature Selection method for gene expression data Description: We propose a hybrid FS method (mAP-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes. biocViews: FeatureExtraction, DifferentialExpression, Microarray, GeneExpression Author: Argiris Sakellariou Maintainer: Argiris Sakellariou VignetteBuilder: knitr source.ver: src/contrib/mAPKL_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mAPKL_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mAPKL_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mAPKL_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mAPKL_1.0.0.tgz vignettes: vignettes/mAPKL/inst/doc/mAPKL.pdf vignetteTitles: mAPKL Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mAPKL/inst/doc/mAPKL.R Package: maPredictDSC Version: 1.6.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: bdaeffddddc512870ef458c51045215a 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 source.ver: src/contrib/maPredictDSC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maPredictDSC_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maPredictDSC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maPredictDSC_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maPredictDSC_1.6.0.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 Package: marray Version: 1.46.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: 53df28c3ef6b28e3d76e330d9259fd57 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/ source.ver: src/contrib/marray_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/marray_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/marray_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/marray_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/marray_1.46.0.tgz vignettes: 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/marray.pdf, vignettes/marray/inst/doc/marrayPlots.pdf vignetteTitles: marrayClasses Overview, marrayClasses Tutorial (short), marrayInput Introduction, marray Normalization, marray Overview, marrayPlots Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: 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, vignettes/marray/inst/doc/marray.R dependsOnMe: CGHbase, convert, dyebias, maigesPack, MineICA, nnNorm, OLIN, RBM, stepNorm, TurboNorm importsMe: arrayQuality, ChAMP, methylPipe, nnNorm, OLIN, OLINgui, piano, plrs, sigaR, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz Package: maSigPro Version: 1.40.0 Depends: R (>= 2.3.1), stats, Biobase, MASS Imports: Biobase, graphics, grDevices, limma, Mfuzz, stats, utils, MASS License: GPL (>= 2) MD5sum: d468e90cb8380b1b86bc4c13bde27795 NeedsCompilation: no Title: Significant Gene Expression Profile Differences in Time Course Microarray 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 experiments. biocViews: Microarray, DifferentialExpression, TimeCourse Author: Ana Conesa , Maria Jose Nueda Maintainer: Maria Jose Nueda URL: http://bioinfo.cipf.es/ source.ver: src/contrib/maSigPro_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maSigPro_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maSigPro_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maSigPro_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maSigPro_1.40.0.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 Rfiles: vignettes/maSigPro/inst/doc/maSigPro.R suggestsMe: oneChannelGUI Package: maskBAD Version: 1.12.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe License: GPL version 2 or newer MD5sum: 1e3ca21c995252dcaf44e82182b92fdd 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 source.ver: src/contrib/maskBAD_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maskBAD_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maskBAD_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maskBAD_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maskBAD_1.12.0.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 Package: MassArray Version: 1.20.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, methods, stats, utils License: GPL (>=2) MD5sum: 2315c7c6b6ee6824580596ff88275608 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: DNAMethylation, SNP, MassSpectrometry, Genetics, DataImport, Visualization Author: Reid F. Thompson , John M. Greally Maintainer: Reid F. Thompson source.ver: src/contrib/MassArray_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MassArray_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MassArray_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MassArray_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MassArray_1.20.0.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 Package: massiR Version: 1.4.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: 4af0f7fa8bad67257ef100d60ddb8cf4 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 source.ver: src/contrib/massiR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/massiR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/massiR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/massiR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/massiR_1.4.0.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 Package: MassSpecWavelet Version: 1.34.0 Depends: waveslim Suggests: xcms, caTools License: LGPL (>= 2) Archs: i386, x64 MD5sum: d9c489dfe5701af38ebce5e2c4acec4c NeedsCompilation: yes Title: Mass spectrum processing by wavelet-based algorithms Description: Processing Mass Spectrometry spectrum by using wavelet based algorithm biocViews: MassSpectrometry, Proteomics Author: Pan Du, Warren Kibbe, Simon Lin Maintainer: Pan Du source.ver: src/contrib/MassSpecWavelet_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MassSpecWavelet_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MassSpecWavelet_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MassSpecWavelet_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MassSpecWavelet_1.34.0.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.pdf vignetteTitles: MassSpecWavelet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq suggestsMe: xcms Package: matchBox Version: 1.10.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: 8cbc51fd1fb2e1b1e0931266a515aa75 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 source.ver: src/contrib/matchBox_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/matchBox_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/matchBox_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/matchBox_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/matchBox_1.10.0.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 Package: MatrixRider Version: 1.0.0 Depends: R (>= 3.1.2) Imports: methods, TFBSTools, IRanges, XVector, Biostrings LinkingTo: IRanges, XVector, Biostrings, S4Vectors Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014 License: GPL-3 Archs: i386, x64 MD5sum: 94ed2e3141d5c05bd273fea6c1f00119 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 source.ver: src/contrib/MatrixRider_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MatrixRider_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MatrixRider_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MatrixRider_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MatrixRider_1.0.0.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 Package: MBAmethyl Version: 1.2.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: 538cf18f9b9a62f5ba9e0e02b2e6ae19 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 source.ver: src/contrib/MBAmethyl_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MBAmethyl_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MBAmethyl_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MBAmethyl_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MBAmethyl_1.2.0.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 Package: MBASED Version: 1.2.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges Suggests: BiocStyle License: Artistic-2.0 MD5sum: 7d6c9e7777b9ccb78077ef3197e6ab1a 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 source.ver: src/contrib/MBASED_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MBASED_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MBASED_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MBASED_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MBASED_1.2.0.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 Package: MBCB Version: 1.22.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: 2e5e4a4e875034ca6f8ccb0f8b97307d 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: Jeff Allen URL: http://www.utsouthwestern.edu source.ver: src/contrib/MBCB_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MBCB_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MBCB_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MBCB_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MBCB_1.22.0.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 Package: mBPCR Version: 1.22.0 Depends: oligoClasses, SNPchip Imports: Biobase Suggests: xtable License: GPL (>= 2) MD5sum: edfbd3afaae5434a1828c727f181442d NeedsCompilation: no Title: Bayesian Piecewise Constant Regression for DNA copy number estimation Description: Estimates the DNA copy number profile using mBPCR to detect 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 source.ver: src/contrib/mBPCR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mBPCR_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mBPCR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mBPCR_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mBPCR_1.22.0.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 Package: mcaGUI Version: 1.16.0 Depends: lattice, MASS, proto, foreign, gWidgets(>= 0.0-36), gWidgetsRGtk2(>= 0.0-53), OTUbase, vegan, bpca Enhances: iplots, reshape, ggplot2, cairoDevice, OTUbase License: GPL (>= 2) MD5sum: a7fbe99debee3848d4b5aa50abed20db NeedsCompilation: no Title: Microbial Community Analysis GUI Description: Microbial community analysis GUI for R using gWidgets. biocViews: GUI, Visualization, Clustering, Sequencing Author: Wade K. Copeland, Vandhana Krishnan, Daniel Beck, Matt Settles, James Foster, Kyu-Chul Cho, Mitch Day, Roxana Hickey, Ursel M.E. Schutte, Xia Zhou, Chris Williams, Larry J. Forney, Zaid Abdo, Poor Man's GUI (PMG) base code by John Verzani with contributions by Yvonnick Noel Maintainer: Wade K. Copeland URL: http://www.ibest.uidaho.edu/ibest/index.php source.ver: src/contrib/mcaGUI_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mcaGUI_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mcaGUI_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mcaGUI_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mcaGUI_1.16.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MCRestimate Version: 2.24.0 Depends: R (>= 2.7.2), golubEsets (>= 1.4.6) Imports: e1071 (>= 1.5-12), pamr (>= 1.22), randomForest (>= 3.9-6), RColorBrewer (>= 0.1-3), Biobase (>= 2.5.5), graphics, grDevices, stats, utils Suggests: xtable (>= 1.2-1), ROC (>= 1.8.0), genefilter (>= 1.12.0), gpls (>= 1.6.0) License: GPL (>= 2) MD5sum: fac5b72f05fbd52232c1ae1574228132 NeedsCompilation: no Title: Misclassification error estimation with cross-validation Description: This package includes a function for combining preprocessing and classification methods to calculate misclassification errors biocViews: Classification Author: Marc Johannes, Markus Ruschhaupt, Holger Froehlich, Ulrich Mansmann, Andreas Buness, Patrick Warnat, Wolfgang Huber, Axel Benner, Tim Beissbarth Maintainer: Marc Johannes source.ver: src/contrib/MCRestimate_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MCRestimate_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MCRestimate_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MCRestimate_2.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MCRestimate_2.24.0.tgz vignettes: vignettes/MCRestimate/inst/doc/UsingMCRestimate.pdf vignetteTitles: HOW TO use MCRestimate hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCRestimate/inst/doc/UsingMCRestimate.R Package: mdgsa Version: 1.0.0 Depends: R (>= 2.14) Imports: AnnotationDbi, DBI, GO.db, KEGG.db, cluster, Matrix Suggests: BiocStyle, knitr, rmarkdown, limma, ALL, hgu95av2.db, RUnit, BiocGenerics License: GPL MD5sum: e4739daf7c21e85545236fa6476d29ec NeedsCompilation: no Title: Multi Dimensional Gene Set Analysis. Description: Functions to preform a Gene Set Analysis in several genomic dimensions. Including methods for miRNAs. biocViews: GeneSetEnrichment, Annotation, Pathways, GO Author: David Montaner Maintainer: David Montaner URL: https://github.com/dmontaner/mdgsa, http://www.dmontaner.com VignetteBuilder: knitr source.ver: src/contrib/mdgsa_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mdgsa_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mdgsa_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mdgsa_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mdgsa_1.0.0.tgz vignettes: vignettes/mdgsa/inst/doc/mdgsa_vignette.pdf vignetteTitles: mdgsa_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdgsa/inst/doc/mdgsa_vignette.R htmlDocs: vignettes/mdgsa/inst/doc/mdgsa_vignette.html htmlTitles: "Introduction" Package: mdqc Version: 1.30.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 330b71871cbcb966809d34c67310b86c 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 source.ver: src/contrib/mdqc_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mdqc_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mdqc_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mdqc_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mdqc_1.30.0.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 Package: MeasurementError.cor Version: 1.40.0 License: LGPL MD5sum: 0bf1002b875a21442a2c232151ede63c 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 source.ver: src/contrib/MeasurementError.cor_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MeasurementError.cor_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MeasurementError.cor_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MeasurementError.cor_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MeasurementError.cor_1.40.0.tgz vignettes: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf vignetteTitles: MeasurementError.cor Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MEDIPS Version: 1.18.0 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: 7bd5f592e1010d364ddf684cea6d0d0e 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 source.ver: src/contrib/MEDIPS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MEDIPS_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MEDIPS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MEDIPS_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MEDIPS_1.18.0.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 Package: MEDME Version: 1.28.0 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) Archs: i386, x64 MD5sum: d38faafee590d0f6402b19e0265557b1 NeedsCompilation: yes Title: Modelling Experimental Data from MeDIP Enrichment Description: 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 source.ver: src/contrib/MEDME_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MEDME_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MEDME_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MEDME_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MEDME_1.28.0.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 Package: MEIGOR Version: 1.2.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR License: GPL-3 MD5sum: 495235aa92f30377c3ad15e4988f6870 NeedsCompilation: no Title: MEIGO - MEtaheuristics for bIoinformatics Global Optimization Description: Global Optimization biocViews: SystemsBiology Author: Jose Egea, David Henriques, Alexandre Fdez. Villaverde, Thomas Cokelaer Maintainer: Jose Egea source.ver: src/contrib/MEIGOR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MEIGOR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MEIGOR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MEIGOR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MEIGOR_1.2.0.tgz vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.pdf vignetteTitles: Main vignette:Global Optimization for Bioinformatics and Systems Biology hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R Package: MergeMaid Version: 2.40.0 Depends: R (>= 2.10.0), survival, Biobase, MASS, methods License: GPL (>= 2) MD5sum: 78bf0a3a1baadf916c3caa8f1e5d4038 NeedsCompilation: no Title: Merge Maid Description: The functions in this R extension are intended for cross-study comparison of gene expression array data. Required from the user is gene expression matrices, their corresponding gene-id vectors and other useful information, and they could be 'list','matrix', or 'ExpressionSet'. The main function is 'mergeExprs' which transforms the input objects into data in the merged format, such that common genes in different datasets can be easily found. And the function 'intcor' calculate the correlation coefficients. Other functions use the output from 'modelOutcome' to graphically display the results and cross-validate associations of gene expression data with survival. biocViews: Microarray, DifferentialExpression, Visualization Author: Xiaogang Zhong Leslie Cope Elizabeth Garrett Giovanni Parmigiani Maintainer: Xiaogang Zhong URL: http://astor.som.jhmi.edu/MergeMaid source.ver: src/contrib/MergeMaid_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MergeMaid_2.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MergeMaid_2.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MergeMaid_2.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MergeMaid_2.40.0.tgz vignettes: vignettes/MergeMaid/inst/doc/MergeMaid.pdf vignetteTitles: MergeMaid primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MergeMaid/inst/doc/MergeMaid.R importsMe: metaArray, XDE suggestsMe: oneChannelGUI Package: MeSHDbi Version: 1.4.0 Depends: R (>= 3.0.1) Imports: methods, AnnotationDbi (>= 1.16.10), RSQLite, Biobase Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 0d61be47771a0127b91e078e4dc9ced6 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 source.ver: src/contrib/MeSHDbi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MeSHDbi_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MeSHDbi_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MeSHDbi_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MeSHDbi_1.4.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeSHDbi/inst/doc/MeSHDbi.R dependsOnMe: meshr Package: meshr Version: 1.4.1 Depends: R (>= 3.0.1), fdrtool, Category, BiocGenerics, methods, cummeRbund, org.Hs.eg.db, MeSH.db, MeSH.AOR.db, MeSH.PCR.db, MeSHDbi, MeSH.Hsa.eg.db, MeSH.Aca.eg.db, MeSH.Bsu.168.eg.db, MeSH.Syn.eg.db, S4Vectors License: Artistic-2.0 MD5sum: d4eb9600db3fbf1e63b94f261003854b 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: Itoshi Nikaido, Koki Tsuyuzaki, Gota Morota Maintainer: Koki Tsuyuzaki source.ver: src/contrib/meshr_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/meshr_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/meshr_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/meshr_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/meshr_1.4.1.tgz vignettes: vignettes/meshr/inst/doc/MeSH.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshr/inst/doc/MeSH.R Package: MeSHSim Version: 1.0.0 Depends: R(>= 3.0.0) Imports: XML, RCurl License: GPL-2 MD5sum: 6ad5aeffcf83bcc447b8f2267f98f254 NeedsCompilation: no Title: MeSH(Medical Subject Headings) Semantic Similarity Measures Description: Provide for measuring semantic similarity over MeSH headings and MEDLINE documents biocViews: Clustering, Software Author: Jing Zhou, Yuxuan Shui Maintainer: Jing ZHou <12210240050@fudan.edu.cn> source.ver: src/contrib/MeSHSim_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MeSHSim_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MeSHSim_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MeSHSim_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MeSHSim_1.0.0.tgz vignettes: vignettes/MeSHSim/inst/doc/MeSHSim.pdf vignetteTitles: MeSHSim Quick Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeSHSim/inst/doc/MeSHSim.R Package: messina Version: 1.4.0 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) Archs: i386, x64 MD5sum: 534146c5e1cfe0f7918b8da094c45919 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 source.ver: src/contrib/messina_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/messina_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/messina_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/messina_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/messina_1.4.0.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 Package: metaArray Version: 1.46.0 Imports: Biobase, MergeMaid, graphics, stats License: LGPL-2 Archs: i386, x64 MD5sum: f5a2b0d3b1b42c9ea88cf7c87d6598f2 NeedsCompilation: yes Title: Integration of Microarray Data for Meta-analysis Description: 1) Data transformation for meta-analysis of microarray Data: Transformation of gene expression data to signed probability scale (MCMC/EM methods) 2) Combined differential expression on raw scale: Weighted Z-score after stabilizing mean-variance relation within platform biocViews: Microarray, DifferentialExpression Author: Debashis Ghosh Hyungwon Choi Maintainer: Hyungwon Choi source.ver: src/contrib/metaArray_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metaArray_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metaArray_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metaArray_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metaArray_1.46.0.tgz vignettes: vignettes/metaArray/inst/doc/metaArray.pdf vignetteTitles: metaArray Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaArray/inst/doc/metaArray.R suggestsMe: oneChannelGUI Package: Metab Version: 1.2.0 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: bdf9c09672d0dce79bbffa336a56dc47 NeedsCompilation: no Title: Metab: An R Package for a High-Throughput Analysis of Metabolomics Data Generated by GC-MS. Description: Metab is an R package for high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) (http://chemdata.nist.gov/mass-spc/amdis/downloads/). In addition, it performs statistical hypothesis test (t-test) and analysis of variance (ANOVA). Doing so, Metab considerably speed up the data mining process in metabolomics and produces better quality results. Metab was developed using interactive features, allowing users with lack of R knowledge to appreciate its functionalities. biocViews: Metabolomics, MassSpectrometry, AMDIS, GCMS Author: Raphael Aggio Maintainer: Raphael Aggio source.ver: src/contrib/Metab_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Metab_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Metab_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Metab_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Metab_1.2.0.tgz vignettes: vignettes/Metab/inst/doc/MetabPackage.pdf vignetteTitles: Applying Metab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Metab/inst/doc/MetabPackage.R Package: metabomxtr Version: 1.2.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest Suggests: xtable License: GPL-2 MD5sum: e3378d3dd58e53f70c877d9a0e6086dc 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: Metabolomics, MassSpectrometry Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens Maintainer: Michael Nodzenski source.ver: src/contrib/metabomxtr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metabomxtr_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metabomxtr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metabomxtr_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metabomxtr_1.2.0.tgz vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf vignetteTitles: metabomxtr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R Package: metagene Version: 2.0.0 Depends: R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, gplots, biomaRt, tools, GenomicAlignments, ggplot2, muStat, Rsamtools Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 | file LICENSE MD5sum: 0549831b67b8c77b58c8064e82c5eff5 NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare the behavior of DNA-interacting proteins at selected groups of genes/features. Bam files are used to increase the resolution. Multiple combination of group of bam files and/or group of genomic regions can be compared in a single analysis. Bootstraping analysis is used to compare the groups and locate regions with statistically different enrichment profiles. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment Author: Charles Joly Beauparlant , Fabien Claude Lamaze , Rawane Samb , Astrid Louise Deschenes and Arnaud Droit . Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/metagene/issues source.ver: src/contrib/metagene_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metagene_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metagene_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metagene_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metagene_2.0.0.tgz vignettes: vignettes/metagene/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metagene/inst/doc/metagene.R htmlDocs: vignettes/metagene/inst/doc/metagene.html htmlTitles: "A package to produce Metafeature plots" Package: metagenomeSeq Version: 1.10.0 Depends: R(>= 3.0), Biobase, limma, methods, RColorBrewer Imports: parallel, matrixStats, gplots Suggests: annotate, BiocGenerics, biom, knitr, gss, RUnit, vegan, interactiveDisplay License: Artistic-2.0 MD5sum: b3640eca6f4f3bed6c30706aec2bbb5b 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: Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software Author: Joseph Nathaniel Paulson, 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 source.ver: src/contrib/metagenomeSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metagenomeSeq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metagenomeSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metagenomeSeq_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metagenomeSeq_1.10.0.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 suggestsMe: interactiveDisplay Package: metahdep Version: 1.26.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 Archs: i386, x64 MD5sum: 44a7bc664df7f2524e229dd33afc8895 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 source.ver: src/contrib/metahdep_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metahdep_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metahdep_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metahdep_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metahdep_1.26.0.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 Package: metaMS Version: 1.4.0 Depends: R (>= 2.10), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: ad911e0fc407441fcc5e1cfbd3dffe87 NeedsCompilation: no Title: MS-based metabolomics annotation pipeline Description: MS-based metabolomics data processing and compound annotation pipeline. biocViews: MassSpectrometry, Metabolomics Author: Ron Wehrens [aut, cre] (author of GC-MS part), 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) Maintainer: Ron Wehrens source.ver: src/contrib/metaMS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metaMS_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metaMS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metaMS_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metaMS_1.4.0.tgz 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 Package: metaSeq Version: 1.8.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: b11e9562dc4c484ed880015df029fcd1 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 Author: Koki Tsuyuzaki, Itoshi Nikaido Maintainer: Koki Tsuyuzaki source.ver: src/contrib/metaSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metaSeq_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metaSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metaSeq_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metaSeq_1.8.0.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 Package: metaseqR Version: 1.8.1 Depends: R (>= 2.13.0), EDASeq, DESeq, limma, qvalue Imports: edgeR, NOISeq, baySeq, NBPSeq, biomaRt, utils, gplots, corrplot, vsn, brew, rjson, log4r Suggests: BiocGenerics, GenomicRanges, rtracklayer, Rsamtools, survcomp, VennDiagram, knitr, zoo, RUnit, BiocInstaller, BSgenome, RSQLite Enhances: parallel, TCC, RMySQL License: GPL (>= 3) MD5sum: fc2db56f0f8d6185accb7c6c9bc0a3c6 NeedsCompilation: no 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 Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr source.ver: src/contrib/metaseqR_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/metaseqR_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/metaseqR_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/metaseqR_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metaseqR_1.8.1.tgz vignettes: vignettes/metaseqR/inst/doc/metaseqr-pdf.pdf vignetteTitles: RNA-Seq data analysis using mulitple statistical algorithms with metaseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaseqR/inst/doc/metaseqr-pdf.R Package: MethTargetedNGS Version: 1.0.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings License: Artistic-2.0 MD5sum: 8c8aa6b02ea3ed89dd6b9a0b29ad281e 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 source.ver: src/contrib/MethTargetedNGS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MethTargetedNGS_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MethTargetedNGS_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MethTargetedNGS_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MethTargetedNGS_1.0.0.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 Package: methVisual Version: 1.20.0 Depends: R (>= 2.11.0), Biostrings(>= 2.4.8), plotrix,gsubfn, grid,sqldf Imports: Biostrings, ca, graphics, grDevices, grid, gridBase, IRanges, stats, utils License: GPL (>= 2) MD5sum: 3b287e878f86d08dff8ca1f1a8e036be NeedsCompilation: no Title: Methods for visualization and statistics on DNA methylation data Description: The package 'methVisual' allows the visualization of DNA methylation data after bisulfite sequencing. biocViews: DNAMethylation, Clustering, Classification Author: A. Zackay, C. Steinhoff Maintainer: Arie Zackay source.ver: src/contrib/methVisual_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/methVisual_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/methVisual_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/methVisual_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/methVisual_1.20.0.tgz vignettes: vignettes/methVisual/inst/doc/methVisual.pdf vignetteTitles: Introduction to methVisual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methVisual/inst/doc/methVisual.R Package: methyAnalysis Version: 1.10.0 Depends: R (>= 2.10), grid, BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biobase (>= 2.5.5), org.Hs.eg.db Imports: lumi, methylumi, Gviz, genoset, GenomicRanges, VariantAnnotation, IRanges, rtracklayer, GenomicFeatures, annotate, Biobase (>= 2.5.5), AnnotationDbi, genefilter, biomaRt, methods, parallel Suggests: FDb.InfiniumMethylation.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 42c72febd5c7a0ce248af58a72922951 NeedsCompilation: no Title: DNA methylation data analysis and visualization Description: The methyAnalysis package aims for the DNA methylation data analysis and visualization. A MethyGenoSet class is defined to keep the chromosome location information together with the data. The package also includes functions of estimating the methylation levels from Methy-Seq data. biocViews: Microarray, DNAMethylation, Visualization Author: Pan Du, Richard Bourgon Maintainer: Pan Du source.ver: src/contrib/methyAnalysis_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/methyAnalysis_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/methyAnalysis_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/methyAnalysis_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/methyAnalysis_1.10.0.tgz vignettes: vignettes/methyAnalysis/inst/doc/methyAnalysis.pdf vignetteTitles: An Introduction to the methyAnalysis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methyAnalysis/inst/doc/methyAnalysis.R suggestsMe: methylumi Package: MethylAid Version: 1.2.5 Depends: R (>= 3.0) Imports: Biobase, BiocParallel, BiocGenerics, FDb.InfiniumMethylation.hg19, ggplot2, grid, gridBase, hexbin, IlluminaHumanMethylation450kmanifest, matrixStats, minfi, methods, RColorBrewer, shiny Suggests: BiocStyle, knitr, MethylAidData, minfiData, RUnit License: GPL (>= 2) MD5sum: a6f829d344fcdd5620f7ad18117e9aaf NeedsCompilation: no Title: Visual and interactive quality control of large Illumina 450k 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, Elmar. Tobi, Roderick Slieker, Wouter den Hollander, Rene Luijk and Bas Heijmans Maintainer: M. van Iterson URL: http://shiny.bioexp.nl/MethylAid VignetteBuilder: knitr source.ver: src/contrib/MethylAid_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/MethylAid_1.2.5.zip win64.binary.ver: bin/windows64/contrib/3.2/MethylAid_1.2.5.zip mac.binary.ver: bin/macosx/contrib/3.2/MethylAid_1.2.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MethylAid_1.2.5.tgz vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf vignetteTitles: MethylAid: Visual and interactive quality control of large Illumina 450k data sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R Package: MethylMix Version: 1.2.0 Depends: R (>= 3.1.1) Imports: foreach,parallel,doParallel,RColorBrewer,optimx,RPMM Suggests: BiocStyle License: GPL-2 MD5sum: b5d6c6e5e23b489d5f6c537a4875b63f 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. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray, DifferentialExpression, Pathways, Network Author: Olivier Gevaert Maintainer: Olivier Gevaert source.ver: src/contrib/MethylMix_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MethylMix_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MethylMix_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MethylMix_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MethylMix_1.2.0.tgz vignettes: vignettes/MethylMix/inst/doc/MethylMix.pdf vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/MethylMix.R Package: methylMnM Version: 1.6.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 Archs: i386, x64 MD5sum: 70c3d0c058717f0e67ec8fd665e7906a 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 source.ver: src/contrib/methylMnM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/methylMnM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/methylMnM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/methylMnM_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/methylMnM_1.6.0.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 Package: methylPipe Version: 1.2.6 Depends: R (>= 3.2.0), methods, grDevices, graphics, stats, utils, GenomicRanges, Rsamtools Imports: marray, gplots, IRanges, BiocGenerics, Gviz, GenomicAlignments, Biostrings, parallel, data.table, GenomeInfoDb, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg18, TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR License: GPL(>=2) Archs: i386, x64 MD5sum: bfb70f44ff80058e6649f25079f9691c 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: Kamal Kishore Maintainer: Kamal Kishore VignetteBuilder: knitr source.ver: src/contrib/methylPipe_1.2.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/methylPipe_1.2.6.zip win64.binary.ver: bin/windows64/contrib/3.2/methylPipe_1.2.6.zip mac.binary.ver: bin/macosx/contrib/3.2/methylPipe_1.2.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/methylPipe_1.2.6.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 importsMe: compEpiTools Package: MethylSeekR Version: 1.8.0 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) Suggests: BSgenome.Hsapiens.UCSC.hg18 License: GPL (>=2) MD5sum: fd5a88c6e78f789dc96f1e64b91bdd78 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 source.ver: src/contrib/MethylSeekR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MethylSeekR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MethylSeekR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MethylSeekR_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MethylSeekR_1.8.0.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 Package: methylumi Version: 2.14.0 Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2, matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biobase, graphics, lattice, annotate, genefilter, AnnotationDbi, minfi, stats4, illuminaio Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, methyAnalysis, TCGAMethylation450k, IlluminaHumanMethylation450kanno.ilmn12.hg19, FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr License: GPL-2 MD5sum: 976aecc964510e0e9c32a3bc6500508a 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 source.ver: src/contrib/methylumi_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/methylumi_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/methylumi_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/methylumi_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/methylumi_2.14.0.tgz vignettes: vignettes/methylumi/inst/doc/methylumi450k.pdf, vignettes/methylumi/inst/doc/methylumi.pdf vignetteTitles: Working with Illumina 450k Arrays using methylumi, An Introduction to the methylumi package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylumi/inst/doc/methylumi450k.R, vignettes/methylumi/inst/doc/methylumi.R dependsOnMe: RnBeads, skewr, wateRmelon importsMe: ffpe, lumi, methyAnalysis, missMethyl Package: Mfuzz Version: 2.28.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: b69934aa8b2915447975bf5a769b3503 NeedsCompilation: no Title: Soft clustering of time series gene expression data Description: Package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface) biocViews: Microarray, Clustering, TimeCourse, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://w3.ualg.pt/~mfutschik/software/R/Mfuzz/index.html source.ver: src/contrib/Mfuzz_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Mfuzz_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Mfuzz_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Mfuzz_2.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Mfuzz_2.28.0.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 importsMe: maSigPro suggestsMe: pwOmics Package: MGFM Version: 1.2.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: febd375a3bbd4091c3639bdc7ac11bb4 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 source.ver: src/contrib/MGFM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MGFM_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MGFM_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MGFM_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MGFM_1.2.0.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 Package: mgsa Version: 1.16.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 0b60f813da03527ddcf91ac78774073a 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 source.ver: src/contrib/mgsa_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mgsa_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mgsa_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mgsa_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mgsa_1.16.0.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 suggestsMe: gCMAP Package: MiChip Version: 1.22.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: 50e0bade38fd43d8a2a3213f2b5cd3b2 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 source.ver: src/contrib/MiChip_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MiChip_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MiChip_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MiChip_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MiChip_1.22.0.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 Package: microRNA Version: 1.26.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) Suggests: Biostrings (>= 2.11.32) Enhances: Rlibstree License: Artistic-2.0 MD5sum: 344aa94c0ccccc7001755f04c05376c2 NeedsCompilation: no 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: "James F. Reid" source.ver: src/contrib/microRNA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/microRNA_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/microRNA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/microRNA_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/microRNA_1.26.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Roleswitch suggestsMe: MmPalateMiRNA, rtracklayer Package: MIMOSA Version: 1.6.0 Depends: R (>= 3.0.2), MASS, plyr, reshape, Biobase, ggplot2 Imports: methods, Formula, data.table, pracma, MCMCpack, coda, modeest, testthat, Rcpp, scales, Kmisc LinkingTo: Rcpp, RcppArmadillo Suggests: parallel, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 5442c14c0331340fc0fbb504dc991b3c NeedsCompilation: yes Title: Mixture Models for Single-Cell Assays Description: Modeling count data using Dirichlet-multinomial and beta-binomial mixtures with applications to single-cell assays. biocViews: FlowCytometry, CellBasedAssays Author: Greg Finak Maintainer: Greg Finak VignetteBuilder: knitr source.ver: src/contrib/MIMOSA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MIMOSA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MIMOSA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MIMOSA_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MIMOSA_1.6.0.tgz vignettes: vignettes/MIMOSA/inst/doc/MIMOSA.pdf vignetteTitles: MIMOSA: Mixture Models For Single Cell Assays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIMOSA/inst/doc/MIMOSA.R Package: MineICA Version: 1.8.0 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 Enhances: doMC License: GPL-2 MD5sum: 9adcaa5edd2ebad7763b78f75febccb3 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 source.ver: src/contrib/MineICA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MineICA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MineICA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MineICA_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MineICA_1.8.0.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 Package: minet Version: 3.26.0 Imports: infotheo License: file LICENSE Archs: i386, x64 MD5sum: 5f15796a569c69cae2444fd976810590 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 source.ver: src/contrib/minet_3.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/minet_3.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/minet_3.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/minet_3.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/minet_3.26.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: netbenchmark, RTN suggestsMe: CNORfeeder, predictionet Package: minfi Version: 1.14.0 Depends: methods, BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), lattice, GenomicRanges, Biostrings, utils, bumphunter (>= 1.1.9) Imports: S4Vectors, GenomeInfoDb, IRanges, beanplot, RColorBrewer, nor1mix, siggenes, limma, preprocessCore, illuminaio, matrixStats, mclust, genefilter, nlme, reshape, MASS, quadprog, GEOquery Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, minfiData (>= 0.4.1), FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest, data.table License: Artistic-2.0 MD5sum: 1c9c286de5e5f7ae9a78f4888c17a30a NeedsCompilation: no Title: Analyze Illumina's 450k methylation arrays Description: Tools for analyzing and visualizing Illumina's 450k array data. biocViews: DNAMethylation, Microarray, TwoChannel, DataImport, 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 Andrews [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/minfi source.ver: src/contrib/minfi_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/minfi_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/minfi_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/minfi_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/minfi_1.14.0.tgz vignettes: vignettes/minfi/inst/doc/minfi.pdf vignetteTitles: Minfi Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/minfi/inst/doc/minfi.R dependsOnMe: ChAMP, conumee, CopyNumber450k, DMRcate, methylumi, shinyMethyl importsMe: MethylAid, methylumi, missMethyl, quantro, skewr suggestsMe: RnBeads Package: MinimumDistance Version: 1.12.0 Depends: R (>= 3.01) Imports: methods, oligoClasses, S4Vectors, VanillaICE (>= 1.29.3), Biobase, DNAcopy, BiocGenerics, ff, foreach, matrixStats, IRanges, lattice, GenomicRanges (>= 1.17.16), GenomeInfoDb, data.table, grid, stats Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, SNPchip, RUnit Enhances: snow, doSNOW License: Artistic-2.0 MD5sum: 5698503705fdb6439ff242dbae8125ea 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 B Scharpf source.ver: src/contrib/MinimumDistance_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MinimumDistance_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MinimumDistance_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MinimumDistance_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MinimumDistance_1.12.0.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 Package: MiPP Version: 1.40.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: bd18e1bde0081eccd9a4b33f3983cd46 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/ source.ver: src/contrib/MiPP_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MiPP_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MiPP_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MiPP_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MiPP_1.40.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiPP/inst/doc/MiPP.R Package: MiRaGE Version: 1.10.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: AnnotationDbi, BiocGenerics 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: ce2371ba47bdbc563a4d2745e110e234 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: Microarray, GeneExpression, RNASeq, Sequencing, SAGE Author: Y-h. Taguchi Maintainer: Y-h. Taguchi source.ver: src/contrib/MiRaGE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MiRaGE_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MiRaGE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MiRaGE_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MiRaGE_1.10.0.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 Package: miRNApath Version: 1.28.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: bd930f935bb2add2a0401de1929db9e6 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 source.ver: src/contrib/miRNApath_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/miRNApath_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/miRNApath_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/miRNApath_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/miRNApath_1.28.0.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 suggestsMe: oneChannelGUI Package: miRNAtap Version: 1.2.1 Depends: R (>= 3.2.0), AnnotationDbi Imports: DBI, RSQLite, stringr, sqldf, plyr, methods Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat License: GPL-2 MD5sum: 920d5c3624f9440eaae0173fa7147521 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: Maciej Pajak source.ver: src/contrib/miRNAtap_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/miRNAtap_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/miRNAtap_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/miRNAtap_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/miRNAtap_1.2.1.tgz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R suggestsMe: oneChannelGUI Package: Mirsynergy Version: 1.4.0 Depends: R (>= 3.0.2), igraph, ggplot2 Imports: graphics, grDevices, gridExtra, Matrix, parallel, RColorBrewer, reshape, scales, utils Suggests: glmnet, RUnit, BiocGenerics, knitr License: GPL-2 MD5sum: 769da9b7e255fd63b3e21e5f77490ba6 NeedsCompilation: no Title: Mirsynergy Description: Detect synergistic miRNA regulatory modules by overlapping neighbourhood expansion. biocViews: Clustering Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/Mirsynergy.html VignetteBuilder: knitr source.ver: src/contrib/Mirsynergy_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Mirsynergy_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Mirsynergy_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Mirsynergy_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Mirsynergy_1.4.0.tgz vignettes: vignettes/Mirsynergy/inst/doc/Mirsynergy.pdf vignetteTitles: Mirsynergy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mirsynergy/inst/doc/Mirsynergy.R Package: missMethyl Version: 1.2.0 Depends: R (>= 2.3.0) Imports: limma, minfi, methylumi, IlluminaHumanMethylation450kmanifest, statmod, ruv, stringr, IlluminaHumanMethylation450kanno.ilmn12.hg19, org.Hs.eg.db Suggests: minfiData, BiocStyle, knitr, edgeR, tweeDEseqCountData License: GPL-2 MD5sum: 12854d3774039e025176705ec8881d3c NeedsCompilation: no Title: Analysis of methylation array data Description: Normalisation and testing for differential variability and differential methylation for data from Illumina's Infinium HumanMethylation450 array. 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. biocViews: Normalization, DNAMethylation, MethylationArray, GenomicVariation, GeneticVariability, DifferentialMethylation, GeneSetEnrichment Author: Belinda Phipson and Jovana Maksimovic Maintainer: Belinda Phipson , Jovana Maksimovic VignetteBuilder: knitr source.ver: src/contrib/missMethyl_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/missMethyl_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/missMethyl_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/missMethyl_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/missMethyl_1.2.0.tgz vignettes: vignettes/missMethyl/inst/doc/missMethyl.pdf vignetteTitles: missMethyl: analysing data from Illumina's HumanMethylation450 array hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R Package: mitoODE Version: 1.6.0 Depends: R (>= 2.14.0), minpack.lm, MASS, parallel, mitoODEdata, KernSmooth License: LGPL Archs: i386, x64 MD5sum: 6cd1b03d93f66eab4937525cd95db93c NeedsCompilation: yes Title: Implementation of the differential equation model described in "Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay" Description: The package contains the methods to fit a cell-cycle model on cell count data and the code to reproduce the results shown in our paper "Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay" by Pau, G., Walter, T., Neumann, B., Heriche, J.-K., Ellenberg, J., & Huber, W., BMC Bioinformatics (2013), 14(1), 308. doi:10.1186/1471-2105-14-308 biocViews: ExperimentData, TimeCourse, CellBasedAssays, Preprocessing Author: Gregoire Pau Maintainer: Gregoire Pau SystemRequirements: source.ver: src/contrib/mitoODE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mitoODE_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mitoODE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mitoODE_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mitoODE_1.6.0.tgz vignettes: vignettes/mitoODE/inst/doc/mitoODE-introduction.pdf vignetteTitles: mitoODE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitoODE/inst/doc/mitoODE-introduction.R Package: MLInterfaces Version: 1.48.0 Depends: R (>= 2.9), methods, BiocGenerics (>= 0.13.11), Biobase, annotate, cluster Imports: gdata, pls, sfsmisc, MASS, rpart, rda, genefilter Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, gbm, mlbench, hgu95av2.db, som, RColorBrewer, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party Enhances: parallel License: LGPL MD5sum: 17f88188e9ba5628efa62f8634d85f18 NeedsCompilation: no Title: Uniform interfaces to R machine learning procedures for data in Bioconductor containers Description: Uniform interfaces to machine learning code for data in Bioconductor containers biocViews: Classification, Clustering Author: Vince Carey , Robert Gentleman, Jess Mar, and contributions from Jason Vertrees and Laurent Gatto Maintainer: V. Carey source.ver: src/contrib/MLInterfaces_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MLInterfaces_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MLInterfaces_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MLInterfaces_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MLInterfaces_1.48.0.tgz vignettes: vignettes/MLInterfaces/inst/doc/MLint_devel.pdf, vignettes/MLInterfaces/inst/doc/MLInterfaces.pdf, vignettes/MLInterfaces/inst/doc/MLprac2_2.pdf, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf vignetteTitles: MLInterfaces devel for schema-based MLearn, MLInterfaces Primer, A machine learning tutorial: applications of the Bioconductor MLInterfaces package to expression and ChIP-Seq data, MLInterfaces Computer Cluster hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R, vignettes/MLInterfaces/inst/doc/MLInterfaces.R, vignettes/MLInterfaces/inst/doc/MLprac2_2.R, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R dependsOnMe: a4Classif, pRoloc, SigCheck suggestsMe: BiocCaseStudies Package: MLP Version: 1.16.0 Depends: AnnotationDbi, affy, plotrix, gplots, gmodels, gdata, gtools Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Cf.eg.db, KEGG.db, annotate, Rgraphviz, GOstats, limma, mouse4302.db, reactome.db License: GPL-3 MD5sum: 098b90a21f210c0ef29ab9063f7ef532 NeedsCompilation: no Title: MLP Description: Mean Log P Analysis biocViews: Genetics, Reactome, KEGG Author: Nandini Raghavan, Tobias Verbeke, An De Bondt with contributions by Javier Cabrera, Dhammika Amaratunga, Tine Casneuf and Willem Ligtenberg Maintainer: Tobias Verbeke source.ver: src/contrib/MLP_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MLP_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MLP_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MLP_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MLP_1.16.0.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 suggestsMe: a4 Package: MLSeq Version: 1.6.0 Depends: R (>= 3.0.0), caret, DESeq2, Biobase, limma, randomForest, edgeR Imports: methods Suggests: knitr, e1071, kernlab, earth, ellipse, fastICA, gam, ipred, klaR, MASS, mda, mgcv, mlbench, nnet, party, pls, pROC, proxy, RANN, spls, affy License: GPL(>=2) MD5sum: 5443e95e6f19166ca052effc8c0b9db6 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: Sequencing, RNASeq, Classification, Clustering Author: Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz, Vahap Eldem, Izzet Parug Duru, Turgay Unver, Ahmet Ozturk Maintainer: Gokmen Zararsiz VignetteBuilder: knitr source.ver: src/contrib/MLSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MLSeq_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MLSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MLSeq_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MLSeq_1.6.0.tgz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: MLSeq hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R Package: MMDiff Version: 1.8.0 Depends: R (>= 2.14.0),GenomicRanges,parallel,DiffBind,GMD,Rsamtools Imports: GenomicRanges,IRanges,Biobase Suggests: MMDiffBamSubset License: Artistic-2.0 MD5sum: 1060b566d65b83271d052756b3ad8d64 NeedsCompilation: no Title: Statistical Testing for ChIP-Seq data sets Description: This package detects statistically significant difference 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, MultipleComparison Author: Gabriele Schweikert Maintainer: Gabriele Schweikert source.ver: src/contrib/MMDiff_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MMDiff_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MMDiff_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MMDiff_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MMDiff_1.8.0.tgz vignettes: vignettes/MMDiff/inst/doc/MMDiff.pdf vignetteTitles: Analysing ChIP-Seq data with the "MMDiff" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMDiff/inst/doc/MMDiff.R Package: mmnet Version: 1.6.0 Depends: R (>= 2.14), igraph, biom Imports: Biobase, RJSONIO, stringr, reshape2, ggplot2, KEGGREST, plyr, XML, RCurl, flexmix, Matrix, methods, tools Suggests: RCytoscape, graph, knitr License: GPL (>= 2) MD5sum: 4858d30fc5456c9d4b9cbcefc3d74767 NeedsCompilation: no Title: A metagenomic pipeline for systems biology Description: This package gives the implementations microbiome metabolic network constructing and analyzing. It introduces a unique metagenomic systems biology approach, mapping metagenomic data to the KEGG global metabolic pathway and constructing a systems-level network. The system-level network and the next topological analysis will be of great help to analysis the various functional properties, including regulation and metabolic functionality of the metagenome. biocViews: GraphsAndNetwork, Sequencing, Pathways, Microbiome, SystemsBiology Author: Yang Cao, Fei Li Maintainer: Yang Cao , Fei Li VignetteBuilder: knitr source.ver: src/contrib/mmnet_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mmnet_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mmnet_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mmnet_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mmnet_1.6.0.tgz vignettes: vignettes/mmnet/inst/doc/mmnet.pdf vignetteTitles: mmnet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mmnet/inst/doc/mmnet.R Package: MmPalateMiRNA Version: 1.18.0 Depends: R (>= 2.13.0), methods, Biobase, xtable, limma, statmod, lattice, vsn Imports: limma, lattice, Biobase Suggests: GOstats, graph, Category, org.Mm.eg.db, microRNA, targetscan.Mm.eg.db, RSQLite, DBI, AnnotationDbi, clValid, class, cluster, multtest, RColorBrewer, latticeExtra License: GPL-3 MD5sum: 1b5d1993606a2d3a00b0b90c64037e72 NeedsCompilation: no Title: Murine Palate miRNA Expression Analysis Description: R package compendium for the analysis of murine palate miRNA two-color expression data. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, DifferentialExpression, MultipleComparison, Clustering, GO, Pathways, ReportWriting, SequenceMatching Author: Guy Brock , Partha Mukhopadhyay , Vasyl Pihur , Robert M. Greene , and M. Michele Pisano Maintainer: Guy Brock source.ver: src/contrib/MmPalateMiRNA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MmPalateMiRNA_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MmPalateMiRNA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MmPalateMiRNA_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MmPalateMiRNA_1.18.0.tgz vignettes: vignettes/MmPalateMiRNA/inst/doc/MmPalateMiRNA.pdf vignetteTitles: Palate miRNA Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MmPalateMiRNA/inst/doc/MmPalateMiRNA.R Package: mogsa Version: 1.0.3 Depends: R (>= 3.2.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase Suggests: BiocStyle, knitr License: GPL-2 MD5sum: 6bae468430a032c7a65e95625f89902f NeedsCompilation: no Title: Multiple omics data integration and gene set analysis Description: This package provide a method for doing gene set analysis based on multiple omics data. biocViews: GeneExpression, PrincipalComponent, StatisticalMethod, Software Author: Chen Meng Maintainer: Chen Meng VignetteBuilder: knitr source.ver: src/contrib/mogsa_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/mogsa_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.2/mogsa_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.2/mogsa_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mogsa_1.0.3.tgz vignettes: vignettes/mogsa/inst/doc/mogsa.pdf vignetteTitles: mogsa: gene set analysis on multiple omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mogsa/inst/doc/mogsa.R Package: monocle Version: 1.2.0 Depends: R (>= 2.7.0), HSMMSingleCell (>= 0.101.5), Biobase, ggplot2(>= 0.9.3.1), splines, VGAM (>= 0.9-5), igraph(>= 0.7.0), plyr Imports: BiocGenerics, cluster, combinat, fastICA, grid, irlba, matrixStats, methods, parallel, reshape2, stats, utils, limma Suggests: knitr, Hmisc License: Artistic-2.0 MD5sum: 5bec6b6c3048480f58d776556c45aee9 NeedsCompilation: no Title: Analysis tools for single-cell expression experiments. 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: Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: Cole Trapnell Maintainer: Cole Trapnell VignetteBuilder: knitr source.ver: src/contrib/monocle_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/monocle_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/monocle_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/monocle_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/monocle_1.2.0.tgz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Differential expression and time-series analysis for single-cell RNA-Seq and qPCR experiments. hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R suggestsMe: sincell Package: MoPS Version: 1.2.0 Imports: Biobase License: GPL-3 MD5sum: bb9c9327f6672be6052dac8615e079d5 NeedsCompilation: no Title: MoPS - Model-based Periodicity Screening Description: Identification and characterization of periodic fluctuations in time-series data. biocViews: GeneRegulation,Classification,TimeCourse,Regression Author: Philipp Eser, Achim Tresch Maintainer: Philipp Eser source.ver: src/contrib/MoPS_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MoPS_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MoPS_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MoPS_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MoPS_1.2.0.tgz vignettes: vignettes/MoPS/inst/doc/MoPS.pdf vignetteTitles: Model-based Periodicity Screening hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoPS/inst/doc/MoPS.R Package: mosaics Version: 2.2.0 Depends: R (>= 3.0.0), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges LinkingTo: Rcpp Suggests: mosaicsExample Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 06c10d8ac80b9efd5c450e6e247b91a0 NeedsCompilation: yes Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Description: This package provides functions for fitting MOSAiCS, a statistical framework to analyze one-sample or two-sample ChIP-seq data. biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics Author: Dongjun Chung, Pei Fen Kuan, Sunduz Keles Maintainer: Dongjun Chung URL: http://groups.google.com/group/mosaics_user_group SystemRequirements: Perl source.ver: src/contrib/mosaics_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mosaics_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mosaics_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mosaics_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mosaics_2.2.0.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 dependsOnMe: jmosaics Package: MotifDb Version: 1.10.0 Depends: R (>= 2.15.0), methods, BiocGenerics, S4Vectors, IRanges, Biostrings Imports: rtracklayer Suggests: RUnit, MotIV, seqLogo License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: 81e10ca93e8d3836fc8053ae32f570a3 NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 2000 annotated position frequency matrices from five public source, for multiple organisms biocViews: MotifAnnotation Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/MotifDb_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MotifDb_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MotifDb_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MotifDb_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MotifDb_1.10.0.tgz vignettes: vignettes/MotifDb/inst/doc/MotifDb.pdf vignetteTitles: MotifDb Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R importsMe: rTRMui suggestsMe: motifStack, PWMEnrich, rTRM, vtpnet Package: motifRG Version: 1.12.0 Depends: R (>= 2.15), Biostrings (>= 2.26), IRanges, seqLogo, parallel, methods, grid, graphics, BSgenome, XVector, BSgenome.Hsapiens.UCSC.hg19 Imports: Biostrings,IRanges,seqLogo,parallel,methods,grid,graphics,XVector License: Artistic-2.0 MD5sum: 6214206919bc591d3a976801ec640f38 NeedsCompilation: no Title: A package for discriminative motif discovery, designed for high throughput sequencing dataset Description: Tools for discriminative motif discovery using regression methods biocViews: Transcription,MotifDiscovery Author: Zizhen Yao Maintainer: Zizhen Yao source.ver: src/contrib/motifRG_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/motifRG_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/motifRG_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/motifRG_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/motifRG_1.12.0.tgz vignettes: vignettes/motifRG/inst/doc/motifRG.pdf vignetteTitles: motifRG: regression-based discriminative motif discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifRG/inst/doc/motifRG.R Package: motifStack Version: 1.12.0 Depends: R (>= 2.15.1), methods, grImport, grid, MotIV, ade4, Biostrings Imports: XML Suggests: RUnit, BiocGenerics, MotifDb, RColorBrewer, BiocStyle License: GPL (>= 2) MD5sum: f0b56efa5d8f2a6b17123d39e3856a59 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 source.ver: src/contrib/motifStack_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/motifStack_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/motifStack_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/motifStack_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/motifStack_1.12.0.tgz vignettes: vignettes/motifStack/inst/doc/motifStack.pdf vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack.R dependsOnMe: dagLogo, LowMACA Package: MotIV Version: 1.24.0 Depends: R (>= 2.10), BiocGenerics (>= 0.1.0) Imports: graphics, grid, methods, IRanges (>= 1.13.5), Biostrings (>= 1.24.0), lattice, rGADEM, stats, utils Suggests: rtracklayer License: GPL-2 Archs: i386, x64 MD5sum: 6c4cf3b564fa668ac3be7c10333b1b42 NeedsCompilation: yes Title: Motif Identification and Validation Description: This package makes use of STAMP for comparing a set of motifs to a given database (e.g. JASPAR). It can also be used to visualize motifs, motif distributions, modules and filter motifs. biocViews: Microarray, ChIPchip, ChIPSeq, GenomicSequence, MotifAnnotation Author: Eloi Mercier, Raphael Gottardo Maintainer: Eloi Mercier , Raphael Gottardo source.ver: src/contrib/MotIV_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MotIV_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MotIV_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MotIV_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MotIV_1.24.0.tgz vignettes: vignettes/MotIV/inst/doc/MotIV.pdf vignetteTitles: The MotIV users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MotIV/inst/doc/MotIV.R dependsOnMe: motifStack suggestsMe: MotifDb Package: MPFE Version: 1.2.0 License: GPL (>= 3) MD5sum: adb572419bfb3535a166cc6a4483ef7d 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 source.ver: src/contrib/MPFE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MPFE_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MPFE_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MPFE_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MPFE_1.2.0.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 Package: mQTL.NMR Version: 1.2.0 Depends: R (>= 2.15.0) Imports: qtl, GenABEL, MASS, outliers, graphics, stats, utils Suggests: BiocStyle License: Artistic-2.0 MD5sum: 7f6cded854dbfdd83913c4f073367244 NeedsCompilation: yes Title: Metabolomic Quantitative Trait Locus Mapping for 1H NMR data Description: mQTL.NMR provides a complete mQTL analysis pipeline for 1H NMR data. Distinctive features include normalisation using most-used approaches, peak alignment using RSPA approach, dimensionality reduction using SRV and binning approaches, and mQTL analysis for animal and human cohorts. biocViews: Cheminformatics, Metabolomics, Genetics, SNP Author: Lyamine Hedjazi and Jean-Baptiste Cazier Maintainer: Lyamine Hedjazi URL: http://www.ican-institute.org/tools/ source.ver: src/contrib/mQTL.NMR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mQTL.NMR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mQTL.NMR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mQTL.NMR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mQTL.NMR_1.2.0.tgz vignettes: vignettes/mQTL.NMR/inst/doc/FAQ.pdf, vignettes/mQTL.NMR/inst/doc/mQTLUse.pdf vignetteTitles: Frequently Asked Questions, How to use the mQTL.NMR package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mQTL.NMR/inst/doc/FAQ.R, vignettes/mQTL.NMR/inst/doc/mQTLUse.R Package: msa Version: 1.0.2 Depends: R (>= 3.1.0), methods, Biostrings (>= 2.30.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 13807292b1d3fed4dede4731951ebe36 NeedsCompilation: yes Title: Multiple Sequence Alignment Description: This 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, Christoph Horejs-Kainrath, Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/msa/ VignetteBuilder: knitr source.ver: src/contrib/msa_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/msa_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/msa_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/msa_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/msa_1.0.2.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 Package: MSGFgui Version: 1.2.0 Depends: mzR, xlsx Imports: shiny, mzID (>= 1.2), MSGFplus, shinyFiles (>= 0.4.0), tools Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 8fbbb8328e16c597709c162ddc31f4b3 NeedsCompilation: no Title: A shiny GUI for MSGFplus Description: This package makes it possible to perform analyses using the MSGFplus package in a GUI environment. Furthermore it enables the user to investigate the results using interactive plots, summary statistics and filtering. Lastly it exposes the current results to another R session so the user can seamlessly integrate the gui into other workflows. biocViews: MassSpectrometry, Proteomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr source.ver: src/contrib/MSGFgui_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MSGFgui_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MSGFgui_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MSGFgui_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MSGFgui_1.2.0.tgz vignettes: vignettes/MSGFgui/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFgui/inst/doc/Using_MSGFgui.R htmlDocs: vignettes/MSGFgui/inst/doc/Using_MSGFgui.html htmlTitles: "Using MSGFgui" Package: MSGFplus Version: 1.2.0 Depends: methods Imports: mzID Suggests: gWidgets, knitr, testthat License: GPL (>= 2) MD5sum: 74b520f0347400eed2599fcb51eb2a60 NeedsCompilation: no Title: An interface between R and MS-GF+ Description: This package contains function to perform peptide identification using MS-GF+ biocViews: MassSpectrometry, Proteomics Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr source.ver: src/contrib/MSGFplus_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MSGFplus_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MSGFplus_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MSGFplus_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MSGFplus_1.2.0.tgz vignettes: vignettes/MSGFplus/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFplus/inst/doc/Using_MSGFplus.R htmlDocs: vignettes/MSGFplus/inst/doc/Using_MSGFplus.html htmlTitles: "Using MSGFplus" importsMe: MSGFgui Package: msmsEDA Version: 1.6.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: a38aeddf86e3c32b63ce78460a4ee841 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: Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori source.ver: src/contrib/msmsEDA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/msmsEDA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/msmsEDA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/msmsEDA_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/msmsEDA_1.6.0.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 Package: msmsTests Version: 1.6.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: 8c6cad32ebfb463077da9515ffe327b0 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: Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori i Font source.ver: src/contrib/msmsTests_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/msmsTests_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/msmsTests_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/msmsTests_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/msmsTests_1.6.0.tgz vignettes: vignettes/msmsTests/inst/doc/msmsTests-Vignette2.pdf, vignettes/msmsTests/inst/doc/msmsTests-Vignette.pdf vignetteTitles: msmsTests: controlling batch effects by blocking, msmsTests: post test filters to improve reproducibility hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsTests/inst/doc/msmsTests-Vignette2.R, vignettes/msmsTests/inst/doc/msmsTests-Vignette.R suggestsMe: MSnID Package: MSnbase Version: 1.16.2 Depends: R (>= 3.1), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR, BiocParallel, ProtGenerics Imports: plyr, IRanges, preprocessCore, vsn, grid, reshape2, stats4, affy, impute, pcaMethods, mzID (>= 1.5.2), MALDIquant (>= 1.12.0), digest, lattice, ggplot2, S4Vectors, Rcpp LinkingTo: Rcpp Suggests: testthat, zoo, knitr (>= 1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.0.7), msdata, roxygen2, rgl, BiocStyle, imputeLCMD, norm, gplots License: Artistic-2.0 Archs: i386, x64 MD5sum: 7b60cee0e98e2bdd8b44f7edd2d830fd NeedsCompilation: yes Title: Base Functions and Classes for MS-based Proteomics Description: Basic plotting, data manipulation and processing of MS-based Proteomics data. biocViews: Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake and Sebastian Gibb. Maintainer: Laurent Gatto VignetteBuilder: knitr BugReports: https://github.com/lgatto/MSnbase/issues source.ver: src/contrib/MSnbase_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/MSnbase_1.16.2.zip win64.binary.ver: bin/windows64/contrib/3.2/MSnbase_1.16.2.zip mac.binary.ver: bin/macosx/contrib/3.2/MSnbase_1.16.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MSnbase_1.16.2.tgz vignettes: vignettes/MSnbase/inst/doc/MSnbase-demo.pdf, vignettes/MSnbase/inst/doc/MSnbase-development.pdf, vignettes/MSnbase/inst/doc/MSnbase-io.pdf vignetteTitles: Base Functions and Classes for MS-based Proteomics, MSnbase development, MSnbase IO capabilities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnbase/inst/doc/MSnbase-demo.R, vignettes/MSnbase/inst/doc/MSnbase-development.R, vignettes/MSnbase/inst/doc/MSnbase-io.R dependsOnMe: msmsEDA, msmsTests, MSstats, ProCoNA, pRoloc, pRolocGUI, proteoQC, synapter importsMe: MSnID, Pbase suggestsMe: BiocGenerics, isobar, qcmetrics, rpx Package: MSnID Version: 1.2.0 Depends: R (>= 2.10), Rcpp Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach, doParallel, parallel, reshape2, methods, iterators, data.table, Biobase, ProtGenerics Suggests: BiocStyle, msmsTests, ggplot2, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 099992c1a866e76700134ba2f5400240 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 Author: Vlad Petyuk with contributions from Laurent Gatto Maintainer: Vlad Petyuk source.ver: src/contrib/MSnID_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MSnID_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MSnID_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MSnID_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MSnID_1.2.0.tgz vignettes: vignettes/MSnID/inst/doc/msnid_vignette.pdf vignetteTitles: MSnID Package for Handling MS/MS Identifications hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnID/inst/doc/msnid_vignette.R Package: MSstats Version: 2.6.0 Depends: R (>= 3.0), Rcpp, MSnbase, reshape Imports: lme4,marray,limma,gplots,ggplot2, preprocessCore License: Artistic-2.0 MD5sum: 4ab5218b84280756c93b676c8d7e4e3c NeedsCompilation: no 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: MassSpectrometry, Proteomics, Software Author: Meena Choi , Ching-Yun Chang , Olga Vitek Maintainer: Meena Choi source.ver: src/contrib/MSstats_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MSstats_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MSstats_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MSstats_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MSstats_2.6.0.tgz vignettes: vignettes/MSstats/inst/doc/MSstats.pdf vignetteTitles: Protein quantification in LC-MS,, SRM,, DIA experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstats/inst/doc/MSstats.R Package: Mulcom Version: 1.18.0 Depends: R (>= 2.10), fields, Biobase Imports: graphics, grDevices, stats, methods License: GPL-2 Archs: i386, x64 MD5sum: a61c93a7152e446525d25c80dab36fd9 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 source.ver: src/contrib/Mulcom_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Mulcom_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Mulcom_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Mulcom_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Mulcom_1.18.0.tgz vignettes: vignettes/Mulcom/inst/doc/MulcomVignette.pdf vignetteTitles: Mulcom Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mulcom/inst/doc/MulcomVignette.R Package: MultiMed Version: 1.2.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: 8fa85c3e7e9a8f61d08fb618ad310659 NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements permutation method with joint correction for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Joshua N. Sampson Maintainer: Simina M. Boca source.ver: src/contrib/MultiMed_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MultiMed_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MultiMed_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MultiMed_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MultiMed_1.2.0.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 Package: multiscan Version: 1.28.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 319845e686477a39d3f49ca673a239a5 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 source.ver: src/contrib/multiscan_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/multiscan_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/multiscan_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/multiscan_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/multiscan_1.28.0.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 Package: multtest Version: 2.24.0 Depends: R (>= 2.10), methods, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL Archs: i386, x64 MD5sum: 53536afee30b2a5649722faedd95d5b1 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 source.ver: src/contrib/multtest_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/multtest_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/multtest_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/multtest_2.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/multtest_2.24.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4Base, aCGH, BicARE, iPAC, KCsmart, LMGene, PREDA, rain, REDseq, SAGx, siggenes, webbioc importsMe: ABarray, aCGH, adSplit, anota, ChIPpeakAnno, GeneSelector, globaltest, IsoGeneGUI, mAPKL, metabomxtr, nethet, OCplus, phyloseq, REDseq, RTopper, synapter, webbioc suggestsMe: annaffy, BiocCaseStudies, ecolitk, factDesign, GeneSelector, GGtools, GOstats, GSEAlm, maigesPack, MmPalateMiRNA, oneChannelGUI, pcot2, PECA, topGO, xcms Package: muscle Version: 3.10.0 Depends: Biostrings License: Unlimited Archs: i386, x64 MD5sum: 3358235c52bcbff973d5a890d9367aff 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/ source.ver: src/contrib/muscle_3.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/muscle_3.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/muscle_3.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/muscle_3.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/muscle_3.10.0.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 Package: MVCClass Version: 1.42.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: 16ac8c89d1efa135d6db4af181e83c19 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 source.ver: src/contrib/MVCClass_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MVCClass_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MVCClass_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MVCClass_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MVCClass_1.42.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MVCClass/inst/doc/MVCClass.R dependsOnMe: BioMVCClass Package: mvGST Version: 1.2.0 Depends: R(>= 2.10.0), GO.db, Rgraphviz Imports: gProfileR, stringr, topGO, GOstats, annotate, AnnotationDbi, graph Suggests: hgu133plus2.db, org.Hs.eg.db License: GPL-3 MD5sum: 5a925bd55c680cb2a9dfa22a98a37d2b NeedsCompilation: no Title: Multivariate and directional gene set testing Description: mvGST provides platform-independent tools to identify GO terms (gene sets) that are differentially active (up or down) in multiple contrasts of interest. Given a matrix of one-sided p-values (rows for genes, columns for contrasts), mvGST uses meta-analytic methods to combine p-values for all genes annotated to each gene set, and then classify each gene set as being significantly more active (1), less active (-1), or not significantly differentially active (0) in each contrast of interest. With multiple contrasts of interest, each gene set is assigned to a profile (across contrasts) of differential activity. Tools are also provided for visualizing (in a GO graph) the gene sets classified to a given profile. biocViews: Microarray, OneChannel, RNASeq, DifferentialExpression, GO, Pathways, GeneSetEnrichment, GraphAndNetwork Author: John R. Stevens and Dennis S. Mecham Maintainer: John R. Stevens source.ver: src/contrib/mvGST_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mvGST_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mvGST_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mvGST_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mvGST_1.2.0.tgz vignettes: vignettes/mvGST/inst/doc/mvGST.pdf vignetteTitles: mvGST Tutorial Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mvGST/inst/doc/mvGST.R Package: mygene Version: 1.2.3 Depends: R (>= 3.0.0), GenomicFeatures, Imports: httr (>= 0.3), jsonlite (>= 0.9.7), S4Vectors, Hmisc, sqldf, plyr Suggests: BiocStyle License: Artistic-2.0 MD5sum: 322731849d8b82e427a46a420a1057de 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, Chunlei Wu Maintainer: Adam Mark, Chunlei Wu source.ver: src/contrib/mygene_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/mygene_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.2/mygene_1.2.3.zip mac.binary.ver: bin/macosx/contrib/3.2/mygene_1.2.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mygene_1.2.3.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 Package: mzID Version: 1.6.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 35504a1c630e1770e83885e33b115875 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: DataImport, MassSpectrometry, Proteomics Author: Thomas Lin Pedersen, Vladislav A Petyuk with contributions from Laurent Gatto and Sebastian Gibb. Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr source.ver: src/contrib/mzID_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mzID_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mzID_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mzID_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mzID_1.6.0.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: MSGFgui, MSGFplus, MSnbase, MSnID, Pbase suggestsMe: mzR Package: mzR Version: 2.2.2 Depends: Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics LinkingTo: Rcpp, zlibbioc Suggests: msdata (>= 0.3.5), RUnit, mzID, BiocStyle, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 6bae5e7423d03a5b726716ea57dd20f3 NeedsCompilation: yes Title: parser for netCDF, mzXML, mzData 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 wrapper for the ISB random access parser for mass spectrometry mzXML, mzData and mzML files. The package contains the original code written by the ISB, and a subset of the proteowizard library for mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. biocViews: Infrastructure, DataImport, Proteomics, Metabolomics, MassSpectrometry Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou Maintainer: Bernd Fischer , Steffen Neumann , Laurent Gatto , Qiang Kou URL: https://github.com/sneumann/mzR/ SystemRequirements: GNU make, NetCDF VignetteBuilder: knitr BugReports: https://github.com/sneumann/mzR/issues/new source.ver: src/contrib/mzR_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/mzR_2.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/mzR_2.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/mzR_2.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mzR_2.2.2.tgz vignettes: vignettes/mzR/inst/doc/mzR.pdf vignetteTitles: Accessin raw mass spectrometry and identification data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzR/inst/doc/mzR.R dependsOnMe: MSGFgui, MSnbase, TargetSearch, xcms importsMe: Pbase, SIMAT suggestsMe: qcmetrics Package: NanoStringQCPro Version: 1.0.2 Depends: R (>= 3.2), methods Imports: AnnotationDbi (>= 1.26.0), org.Hs.eg.db (>= 2.14.0), Biobase (>= 2.24.0), knitr (>= 1.6), NMF (>= 0.20.5), RColorBrewer (>= 1.0-5), png (>= 0.1-7) Suggests: roxygen2 (>= 4.0.1), testthat, BiocStyle License: Artistic-2.0 MD5sum: 3972f9c631b7551a0d8a1c97c165b2d7 NeedsCompilation: no Title: Quality metrics and data processing methods for NanoString mRNA gene expression data Description: NanoStringQCPro provides a set of quality metrics that can be used to assess the quality of NanoString mRNA gene expression data -- i.e. to identify outlier probes and outlier samples. It also provides different background subtraction and normalization approaches for this data. It outputs suggestions for flagging samples/probes and an easily sharable html quality control output. biocViews: Microarray, mRNAMicroarray, Preprocessing, Normalization, QualityControl, ReportWriting Author: Dorothee Nickles , Thomas Sandmann , Robert Ziman , Richard Bourgon Maintainer: Robert Ziman source.ver: src/contrib/NanoStringQCPro_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/NanoStringQCPro_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/NanoStringQCPro_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/NanoStringQCPro_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NanoStringQCPro_1.0.2.tgz vignettes: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf vignetteTitles: NanoStringQCPro overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: NarrowPeaks Version: 1.12.0 Depends: R (>= 2.10.0), splines Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, fda, CSAR, ICSNP Suggests: rtracklayer, BiocStyle, GenomicRanges, CSAR License: Artistic-2.0 Archs: i386, x64 MD5sum: a56a78214e6b901e946f92c8b45045df NeedsCompilation: yes Title: Shape-based Analysis of Variation in ChIP-Seq using Functional PCA Description: The package applies a functional version of principal component analysis (FPCA) to: (1) Process data in wiggle track format (WIG) commonly produced by ChIP-Seq peak callers by applying FPCA over a set of read-enriched regions (ChIP-Seq peaks). This is done in order to shorten the genomic locations accounting for a given proportion of variation among the enrichment-score profiles. The function 'narrowpeaks' allows splitting and trimming binding sites in close proximity to each other, narrowing down the length of the putative transcription factor binding sites while preserving the information present in the variability of the dataset and capturing major sources of variation. (2) Analyse differential variation between multiple ChIP-Seq samples with replicates. The function 'narrowpeaksDiff' quantifies differences between the shapes, and uses Hotelling's T2 tests on the functional principal component scores to identify significant differences across conditions. biocViews: Visualization, ChIPSeq, Transcription, Genetics, Sequencing, Sequencing Author: Pedro Madrigal , Pawel Krajewski Maintainer: Pedro Madrigal source.ver: src/contrib/NarrowPeaks_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NarrowPeaks_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NarrowPeaks_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NarrowPeaks_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NarrowPeaks_1.12.0.tgz vignettes: vignettes/NarrowPeaks/inst/doc/NarrowPeaks.pdf vignetteTitles: NarrowPeaks Vignette. Shape-based splitting and trimming ChIP-Seq peaks using functional Principal Components hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NarrowPeaks/inst/doc/NarrowPeaks.R Package: ncdfFlow Version: 2.14.2 Depends: R (>= 2.14.0), flowCore (>= 1.33.13), flowViz, RcppArmadillo, BH Imports: Biobase,flowCore,flowViz,methods,zlibbioc LinkingTo: Rcpp,RcppArmadillo,BH Suggests: testthat,parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: c8895caab02436a2fbb1eccae74e251e 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: FlowCytometry Author: Mike Jiang,Greg Finak,N. Gopalakrishnan Maintainer: Mike Jiang SystemRequirements: hdf5 (>= 1.8.0) source.ver: src/contrib/ncdfFlow_2.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/ncdfFlow_2.14.2.zip win64.binary.ver: bin/windows64/contrib/3.2/ncdfFlow_2.14.2.zip mac.binary.ver: bin/macosx/contrib/3.2/ncdfFlow_2.14.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ncdfFlow_2.14.2.tgz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R Package: NCIgraph Version: 1.16.0 Depends: graph, R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCytoscape, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: c174752d9efc805bb37a9ada5665680b 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 source.ver: src/contrib/NCIgraph_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NCIgraph_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NCIgraph_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NCIgraph_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NCIgraph_1.16.0.tgz 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 Package: neaGUI Version: 1.6.0 Depends: tcltk Imports: hwriter Suggests: AnnotationDbi, org.Hs.eg.db, KEGG.db, GO.db, reactome.db, RUnit, GOstats,hwriter License: GPL-2 MD5sum: 207d9aaf45c41f5049a9d2b09248ff3a NeedsCompilation: no Title: An R package to perform the network enrichment analysis (NEA). Description: neaGUI is an easy to use R package developed to perform the network enrichment analysis (NEA) proposed by Alexeyenko et al. (2012). The NEA method extends the overlap statistics in GSEA to network links between genes in the experimental set and those in the functional categories by exploiting biological information in terms of gene interaction network. The neaGUI requires the following R packages: tcltk, KEGG.db, GO.db, reactome.db, org.Hs.eg.db, AnnotationDbi, and hwriter. biocViews: Microarray, DifferentialExpression, GUI, GeneSetEnrichment, NetworkEnrichment, Pathways, Reactome, Network, GO, KEGG Author: Setia Pramana, Woojoo Lee, Yudi Pawitan Maintainer: Setia Pramana source.ver: src/contrib/neaGUI_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/neaGUI_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/neaGUI_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/neaGUI_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/neaGUI_1.6.0.tgz vignettes: vignettes/neaGUI/inst/doc/neaGUI_vignette.pdf vignetteTitles: neaGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/neaGUI/inst/doc/neaGUI_vignette.R importsMe: EnrichmentBrowser Package: nem Version: 2.42.0 Depends: R (>= 3.0) Imports: boot, e1071, graph, graphics, grDevices, methods, RBGL (>= 1.8.1), RColorBrewer, stats, utils, Rgraphviz, statmod, plotrix, limma Suggests: Biobase (>= 1.10) Enhances: doMC, snow, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 1c1ea757698f48e1a72a00ade64ebba7 NeedsCompilation: yes Title: (Dynamic) Nested Effects Models and Deterministic Effects Propagation Networks to reconstruct phenotypic hierarchies Description: The package 'nem' allows to reconstruct features of pathways from the nested structure of perturbation effects. It takes as input (1.) a set of pathway components, which were perturbed, and (2.) phenotypic readout of these perturbations (e.g. gene expression, protein expression). The output is a directed graph representing the phenotypic hierarchy. biocViews: Microarray, Bioinformatics, GraphsAndNetworks, Pathways, SystemsBiology, NetworkInference Author: Holger Froehlich, Florian Markowetz, Achim Tresch, Theresa Niederberger, Christian Bender, Matthias Maneck, Claudio Lottaz, Tim Beissbarth Maintainer: Holger Froehlich URL: http://www.bioconductor.org source.ver: src/contrib/nem_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/nem_2.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/nem_2.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/nem_2.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nem_2.42.0.tgz vignettes: vignettes/nem/inst/doc/markowetz-thesis-2006.pdf, vignettes/nem/inst/doc/nem.pdf vignetteTitles: markowetz-thesis-2006.pdf, Nested Effects Models - An example in Drosophila immune response hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nem/inst/doc/nem.R dependsOnMe: lpNet importsMe: birte suggestsMe: rBiopaxParser Package: netbenchmark Version: 1.0.3 Depends: grndata (>= 0.99.3) Imports: Rcpp (>= 0.11.0), minet, randomForest, c3net, PCIT, GeneNet, tools, pracma, corpcor, fdrtool LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, knitr License: CC BY-NC-SA 4.0 Archs: i386, x64 MD5sum: 22171ea059a51cdda44745411b3cb6c1 NeedsCompilation: yes Title: Benchmarking of several gene network inference methods Description: This package implements a benchmarking of several gene network inference algorithms from gene expression data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference, GeneExpression Author: Pau Bellot, Catharina Olsen, Patrick Meyer, with contributions from Alexandre Irrthum Maintainer: Pau Bellot URL: https://imatge.upc.edu/netbenchmark/ VignetteBuilder: knitr source.ver: src/contrib/netbenchmark_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/netbenchmark_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.2/netbenchmark_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.2/netbenchmark_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/netbenchmark_1.0.3.tgz vignettes: vignettes/netbenchmark/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netbenchmark/inst/doc/netbenchmark.R htmlDocs: vignettes/netbenchmark/inst/doc/netbenchmark.html htmlTitles: "Netbenchmark" Package: netbiov Version: 1.2.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: 8d1a59e3f1d3f763ba4923078b715aec NeedsCompilation: no Title: A package for visualizing complex biological network Description: A package that provides an effective visualization of large biological networks biocViews: GraphAndNetwork, Network, Software, Visualization Author: Shailesh tripathi and Frank Emmert-Streib Maintainer: Shailesh tripathi URL: http://www.bio-complexity.com source.ver: src/contrib/netbiov_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/netbiov_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/netbiov_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/netbiov_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/netbiov_1.2.0.tgz vignettes: vignettes/netbiov/inst/doc/netbiov-intro.pdf vignetteTitles: netbiov: An R package for visualizing biological networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netbiov/inst/doc/netbiov-intro.R Package: nethet Version: 1.0.1 Imports: glasso, mvtnorm, parcor, GeneNet, huge, CompQuadForm, ggm, mclust, parallel, GSA, limma, multtest, ICSNP, glmnet, network, ggplot2 Suggests: knitr, xtable, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: 899e7a88370d8306af489104bedefc63 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 source.ver: src/contrib/nethet_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/nethet_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/nethet_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/nethet_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nethet_1.0.1.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 Package: NetPathMiner Version: 1.4.1 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, RCytoscape, graph License: GPL (>= 2) Archs: i386, x64 MD5sum: 018f19677534206fdefe6b730a12aa36 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 , Tim Hancock , Ichigaku Takigawa , Nicolas Wicker Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/NetPathMiner SystemRequirements: libxml2, libSBML (>= 5.5) source.ver: src/contrib/NetPathMiner_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/NetPathMiner_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/NetPathMiner_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/NetPathMiner_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NetPathMiner_1.4.1.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.pdf vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R Package: netresponse Version: 1.18.0 Depends: R (>= 2.15.1), Rgraphviz, methods, minet, mclust, reshape Imports: dmt, ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer License: GPL (>=2) Archs: i386, x64 MD5sum: 560660844e75218dd4ad072097351efe 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, Transcription Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso Parkkinen Maintainer: Leo Lahti URL: https://github.com/antagomir/netresponse BugReports: https://github.com/antagomir/netresponse/issues source.ver: src/contrib/netresponse_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/netresponse_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/netresponse_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/netresponse_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/netresponse_1.18.0.tgz vignettes: vignettes/netresponse/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netresponse/inst/doc/netresponse.R Package: NetSAM Version: 1.8.0 Depends: R (>= 2.15.1), methods, igraph (>= 0.6-1), seriation (>= 1.0-6), graph (>= 1.34.0) Imports: methods Suggests: RUnit, BiocGenerics License: LGPL MD5sum: c658acd56cf5da7def51f7f69a60ede8 NeedsCompilation: no Title: Network Seriation And Modularization Description: The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a 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. biocViews: Visualization, Network Author: Jing Wang Maintainer: Bing Zhang source.ver: src/contrib/NetSAM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NetSAM_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NetSAM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NetSAM_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NetSAM_1.8.0.tgz vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf vignetteTitles: NetSAM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R Package: networkBMA Version: 1.10.0 Depends: R (>= 2.15.0), stats, utils, BMA, Rcpp (>= 0.10.3), RcppArmadillo (>= 0.3.810.2), RcppEigen (>= 0.3.1.2.1) LinkingTo: Rcpp, RcppArmadillo, RcppEigen License: GPL (>= 2) Archs: i386, x64 MD5sum: 03155d8ba014243997ba9fc4f78f4521 NeedsCompilation: yes Title: Regression-based network inference using Bayesian Model Averaging Description: An extension of Bayesian Model Averaging (BMA) for network construction using time series gene expression data. Includes assessment functions and sample test data. biocViews: GraphsAndNetwork, NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: Chris Fraley, Wm. Chad Young, Ka Yee Yeung, Adrian Raftery (with contributions from Kenneth Lo) Maintainer: Ka Yee Yeung source.ver: src/contrib/networkBMA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/networkBMA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/networkBMA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/networkBMA_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/networkBMA_1.10.0.tgz vignettes: vignettes/networkBMA/inst/doc/networkBMA.pdf vignetteTitles: networkBMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/networkBMA/inst/doc/networkBMA.R Package: NGScopy Version: 1.2.2 Depends: R (>= 3.1.0) Imports: methods, parallel, Xmisc (>= 0.2.1), rbamtools (>= 2.6.0), changepoint (>= 2.1.1) Suggests: RUnit, NGScopyData, GenomicRanges License: GPL (>=2) MD5sum: 5053daed75dc25f7d9845b2afcd9b60c NeedsCompilation: no Title: NGScopy: Detection of Copy Number Variations in Next Generation Sequencing sequencing Description: NGScopy provides a quantitative caller for detecting copy number variations in next generation sequencing (NGS), including whole genome sequencing (WGS), whole exome sequencing (WES) and targeted panel sequencing (TPS). The caller can be parallelized by chromosomes to use multiple processors/cores on one computer. biocViews: CopyNumberVariation, DNASeq, TargetedResequencing, ExomeSeq, WholeGenome, Sequencing Author: Xiaobei Zhao [aut, cre, cph] Maintainer: Xiaobei Zhao source.ver: src/contrib/NGScopy_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/NGScopy_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/NGScopy_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/NGScopy_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NGScopy_1.2.2.tgz vignettes: vignettes/NGScopy/inst/doc/NGScopy-vignette.pdf vignetteTitles: NGScopy: Detection of copy number variations in next generation sequencing (User's Guide) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NGScopy/inst/doc/NGScopy-vignette.R Package: nnNorm Version: 2.32.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: afc5a05336cb548274c6655f90e9d549 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/ source.ver: src/contrib/nnNorm_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/nnNorm_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/nnNorm_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/nnNorm_2.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nnNorm_2.32.0.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 Package: NOISeq Version: 2.14.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: a2ab803ea432287e23ebd38d66929ff2 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: RNASeq, DifferentialExpression, Visualization, Sequencing Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto Ferrer and Ana Conesa Maintainer: Sonia Tarazona source.ver: src/contrib/NOISeq_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NOISeq_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NOISeq_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NOISeq_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NOISeq_2.14.0.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: metaseqR suggestsMe: compcodeR Package: nondetects Version: 1.4.0 Depends: R (>= 3.0.2), Biobase (>= 2.22.0) Imports: utils, methods, HTqPCR (>= 1.16.0) Suggests: BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL (>= 2) MD5sum: 8e559bd1b31d35b933ac91f04440aeb6 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 Maintainer: Matthew N. McCall source.ver: src/contrib/nondetects_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/nondetects_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/nondetects_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/nondetects_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nondetects_1.4.0.tgz vignettes: vignettes/nondetects/inst/doc/nondetects.pdf vignetteTitles: nondetects - vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nondetects/inst/doc/nondetects.R Package: NormqPCR Version: 1.14.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: dac4fda88b2f289c11810162e4049c7b 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 source.ver: src/contrib/NormqPCR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NormqPCR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NormqPCR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NormqPCR_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NormqPCR_1.14.0.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 Package: npGSEA Version: 1.4.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 3649f82add9ccbfc586f67313d904bcc 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 source.ver: src/contrib/npGSEA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/npGSEA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/npGSEA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/npGSEA_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/npGSEA_1.4.0.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 importsMe: EnrichmentBrowser Package: NTW Version: 1.18.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: 1ee0e64368de3a448bfbd9b04cdbf673 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 source.ver: src/contrib/NTW_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NTW_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NTW_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NTW_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NTW_1.18.0.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 Package: nucleR Version: 2.0.0 Depends: ShortRead Imports: methods, BiocGenerics, IRanges, Biobase, GenomicRanges, Rsamtools, stats, graphics, parallel, S4Vectors Suggests: Starr License: LGPL (>= 3) MD5sum: ae3a9d67d14f3786e038d80379770f91 NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and NGS experiments. biocViews: ChIPSeq, Microarray, Sequencing, Genetics Author: Oscar Flores, Ricard Illa Maintainer: Ricard Illa source.ver: src/contrib/nucleR_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/nucleR_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/nucleR_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/nucleR_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nucleR_2.0.0.tgz vignettes: vignettes/nucleR/inst/doc/nucleR.pdf vignetteTitles: nucleR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleR/inst/doc/nucleR.R Package: nudge Version: 1.34.0 Imports: stats License: GPL-2 MD5sum: 46265e0173a5d33b6fc429c98d6529b0 NeedsCompilation: no Title: Normal Uniform Differential Gene Expression detection Description: Package for normalizing microarray data in single and multiple replicate experiments and fitting a normal-uniform mixture to detect differentially expressed genes in the cases where the two samples are being compared directly or indirectly (via a common reference sample) biocViews: Microarray, TwoChannel, DifferentialExpression Author: N. Dean and A. E. Raftery Maintainer: N. Dean source.ver: src/contrib/nudge_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/nudge_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/nudge_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/nudge_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nudge_1.34.0.tgz vignettes: vignettes/nudge/inst/doc/nudge.vignette.pdf vignetteTitles: nudge Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nudge/inst/doc/nudge.vignette.R Package: NuPoP Version: 1.18.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: c1f0bc4849a5fc314bc5e642891e4838 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 http://nucleosome.stats.northwestern.edu. biocViews: Genetics,Visualization,Classification Author: Ji-Ping Wang ; Liqun Xi Maintainer: Ji-Ping Wang source.ver: src/contrib/NuPoP_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NuPoP_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NuPoP_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NuPoP_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NuPoP_1.18.0.tgz vignettes: vignettes/NuPoP/inst/doc/NuPoP-intro.pdf vignetteTitles: An R package for Nucleosome positioning prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NuPoP/inst/doc/NuPoP-intro.R Package: occugene Version: 1.28.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: 3106caf31265c488e4cc0facd704277a 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 source.ver: src/contrib/occugene_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/occugene_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/occugene_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/occugene_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/occugene_1.28.0.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 Package: OCplus Version: 1.42.0 Depends: R (>= 2.1.0), akima Imports: multtest (>= 1.7.3), graphics, grDevices, stats License: LGPL MD5sum: 54326b0522c9a0990c04a0ce83d74918 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 source.ver: src/contrib/OCplus_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OCplus_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OCplus_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OCplus_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OCplus_1.42.0.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 Package: oligo Version: 1.32.0 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, zlibbioc 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, GenomeGraphs, RCurl, ACME, biomaRt, AnnotationDbi, GenomeGraphs, RCurl Enhances: ff, doMC, doMPI License: LGPL (>= 2) Archs: i386, x64 MD5sum: d455f41efa578b1b403899cebd121ab7 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 VignetteBuilder: knitr source.ver: src/contrib/oligo_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/oligo_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/oligo_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/oligo_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/oligo_1.32.0.tgz vignettes: vignettes/oligo/inst/doc/oug.pdf vignetteTitles: oligo User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oligo/inst/doc/oug.R dependsOnMe: ITALICS, pdInfoBuilder, puma, SCAN.UPC, waveTiling importsMe: charm, cn.farms, frma, ITALICS suggestsMe: BiocGenerics, fastseg, frmaTools Package: oligoClasses Version: 1.30.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 2.1.10), GenomicRanges (>= 1.19.6), Biostrings (>= 2.23.6), affyio (>= 1.23.2), ff, foreach, BiocInstaller, utils, S4Vectors, RSQLite 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, SNPchip, VanillaICE, crlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: GPL (>= 2) MD5sum: 2cc624c19e3b7676009bbccc023b67de 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 source.ver: src/contrib/oligoClasses_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/oligoClasses_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/oligoClasses_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/oligoClasses_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/oligoClasses_1.30.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, waveTiling importsMe: affycoretools, ArrayTV, charm, frma, ITALICS, MinimumDistance, pdInfoBuilder, puma, SNPchip, VanillaICE suggestsMe: BiocGenerics Package: OLIN Version: 1.46.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: 65eab271be908a9342d261aa544f79bd 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://w3.ualg.pt/~mfutschik/software/R/OLIN/index.html source.ver: src/contrib/OLIN_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OLIN_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OLIN_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OLIN_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OLIN_1.46.0.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 suggestsMe: maigesPack Package: OLINgui Version: 1.42.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: 937aaa451875fadf6031b519ac482d1f 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://w3.ualg.pt/~mfutschik/software/R/OLIN/index.html source.ver: src/contrib/OLINgui_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OLINgui_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OLINgui_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OLINgui_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OLINgui_1.42.0.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 Package: omicade4 Version: 1.8.0 Depends: R (>= 3.0.0), ade4 Imports: made4 Suggests: BiocStyle License: GPL-2 MD5sum: a3e424161c6107b95236f7b81b88e3f9 NeedsCompilation: no Title: Multiple co-inertia analysis of omics datasets Description: Multiple co-inertia analysis of omics datasets biocViews: Software, Clustering, Classification, MultipleComparison Author: Chen Meng, Aedin Culhane, Amin M. Gholami. Maintainer: Chen Meng source.ver: src/contrib/omicade4_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/omicade4_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/omicade4_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/omicade4_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/omicade4_1.8.0.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 Package: OmicCircos Version: 1.6.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: 8adabd29bd1b817850215385f0f0c06d 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 source.ver: src/contrib/OmicCircos_1.6.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/OmicCircos_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OmicCircos_1.6.0.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 Package: OmicsMarkeR Version: 1.0.1 Depends: R (>= 3.2.0) Imports: plyr (>= 1.8), data.table (>= 1.9.4), caret (>= 6.0-37), DiscriMiner (>= 0.1-29), e1071 (>= 1.6-1), randomForest (>= 4.6-10), gbm (>= 2.1), pamr (>= 1.54.1), glmnet (>= 1.9-5), caTools (>= 1.14), foreach (>= 1.4.1), permute (>= 0.7-0), assertive (>= 0.3-0), assertive.base (>= 0.0-1) Suggests: testthat, BiocStyle, knitr License: GPL-3 MD5sum: ac6e3aaea7516817d7383cac5133dc0c NeedsCompilation: no Title: Classification and Feature Selection for 'Omics' Datasets Description: Tools for classification and feature selection for 'omics' level datasets. It is a tool to provide multiple multivariate classification and feature selection techniques complete with multiple stability metrics and aggregation techniques. It is primarily designed for analysis of metabolomics datasets but potentially extendable to proteomics and transcriptomics applications. biocViews: Metabolomics, Classification, FeatureExtraction Author: Charles E. Determan Jr. Maintainer: Charles E. Determan Jr. URL: http://github.com/cdeterman/OmicsMarkeR VignetteBuilder: knitr BugReports: http://github.com/cdeterman/OmicsMarkeR/issues/new source.ver: src/contrib/OmicsMarkeR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/OmicsMarkeR_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/OmicsMarkeR_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/OmicsMarkeR_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OmicsMarkeR_1.0.1.tgz vignettes: vignettes/OmicsMarkeR/inst/doc/OmicsMarkeR.pdf vignetteTitles: A Short Introduction to the OmicMarkeR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicsMarkeR/inst/doc/OmicsMarkeR.R Package: OncoSimulR Version: 1.2.0 Depends: R (>= 3.1.0) Imports: Rcpp (>= 0.11.1), parallel, data.table, graph, Rgraphviz LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree License: GPL (>= 3) Archs: i386, x64 MD5sum: 8f1b482fbbee4c46907a90c8d8a86c96 NeedsCompilation: yes Title: Simulation of cancer progresion with order restrictions Description: Functions for simulating and plotting cancer progression data, including drivers and passengers, and allowing for order restrictions. Simulations use continuous-time models (based on Bozic et al., 2010 and McFarland et al., 2013) and fitness functions account for possible restrictions in the order of accumulation of mutations. biocViews: BiologicalQuestion, SomaticMutation Author: Ramon Diaz-Uriarte. Maintainer: Ramon Diaz-Uriarte SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/OncoSimulR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OncoSimulR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OncoSimulR_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OncoSimulR_1.2.0.tgz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.pdf vignetteTitles: OncoSimulR Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R Package: oneChannelGUI Version: 1.34.0 Depends: Biobase, affylmGUI, tkrplot, tkWidgets, IRanges, Rsamtools (>= 1.13.1), Biostrings, siggenes, chimera Suggests: annotate, genefilter, maSigPro, pamr, pdmclass, ChIPpeakAnno, chipseq, BSgenome, Rgraphviz, affy ,annaffy, affyPLM, multtest, ssize, sizepower, RankProd, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, edgeR, metaArray, MergeMaid, biomaRt, GenomeGraphs,AffyCompatible, rtracklayer, Genominator, EDASeq, limma, DESeq, DEXSeq, goseq, hugene10sttranscriptcluster.db, mogene10sttranscriptcluster.db, ragene10sttranscriptcluster.db, GOstats, AnnotationDbi, preprocessCore, baySeq, HuExExonProbesetLocation, MoExExonProbesetLocation, RaExExonProbesetLocation, snow, RmiR, RmiR.Hs.miRNA, BSgenome.Hsapiens.UCSC.hg19, R.utils, cummeRbund, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Rnorvegicus.UCSC.rn4, DESeq2, GenomicAlignments, BiocParallel, KEGG.db, miRNApath, miRNAtap, miRNAtap.db License: Artistic-2.0 MD5sum: c80a0a840f0b8842473a10ae418ae860 NeedsCompilation: no Title: A graphical interface designed to facilitate analysis of microarrays and miRNA/RNA-seq data on laptops Description: This package was developed to simplify the use of Bioconductor tools for beginners having limited or no experience in writing R code. This library provides a graphical interface for microarray gene and exon level analysis as well as miRNA/mRNA-seq data analysis biocViews: Sequencing, RNASeq, Microarray, OneChannel, DataImport, QualityControl, Preprocessing, StatisticalMethod, DifferentialExpression, GUI, MultipleComparison Author: Raffale A Calogero, Bioinformatics and Genomics Unit, Molecular Biotechnology Center, Torino (Italy) Maintainer: Raffaele A Calogero URL: http://www.bioinformatica.unito.it/oneChannelGUI/ source.ver: src/contrib/oneChannelGUI_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/oneChannelGUI_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/oneChannelGUI_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/oneChannelGUI_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/oneChannelGUI_1.34.0.tgz vignettes: vignettes/oneChannelGUI/inst/doc/Exon-level.analysis.pdf, vignettes/oneChannelGUI/inst/doc/gene-level.analysis.pdf, vignettes/oneChannelGUI/inst/doc/install.pdf, vignettes/oneChannelGUI/inst/doc/RNAseq.pdf, vignettes/oneChannelGUI/inst/doc/standAloneFunctions.pdf vignetteTitles: oneChannelGUI microarray exon-level data analysis overview, oneChannelGUI microarray gene-level data analysis overview, oneChannelGUI Installation, oneChannelGUI miRNA and RNA-seq data analysis overview, oneChannelGUI Stand Alone Functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oneChannelGUI/inst/doc/Exon-level.analysis.R, vignettes/oneChannelGUI/inst/doc/gene-level.analysis.R, vignettes/oneChannelGUI/inst/doc/install.R, vignettes/oneChannelGUI/inst/doc/RNAseq.R, vignettes/oneChannelGUI/inst/doc/standAloneFunctions.R Package: ontoCAT Version: 1.20.0 Depends: rJava, methods License: Apache License 2.0 MD5sum: c72d232b6f1f30b79d5bb8e008537e59 NeedsCompilation: no Title: Ontology traversal and search Description: The ontoCAT R package provides a simple interface to ontologies described in widely used standard formats, stored locally in the filesystem or accessible online. biocViews: Classification, DataRepresentation Author: Natalja Kurbatova, Tomasz Adamusiak, Pavel Kurnosov, Morris Swertz, Misha Kapushevsky Maintainer: Natalja Kurbatova source.ver: src/contrib/ontoCAT_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ontoCAT_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ontoCAT_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ontoCAT_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ontoCAT_1.20.0.tgz vignettes: vignettes/ontoCAT/inst/doc/ontoCAT.pdf vignetteTitles: ontoCAT package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ontoCAT/inst/doc/ontoCAT.R suggestsMe: RMassBank Package: openCyto Version: 1.6.0 Depends: flowWorkspace(>= 3.11.10) Imports: methods,Biobase,gtools,flowCore(>= 1.31.17),flowViz,ncdfFlow(>= 2.11.34),flowStats(>= 3.23.7),flowClust,MASS,clue,plyr,RBGL,graph,data.table,ks,RColorBrewer,lattice,rrcov,R.utils LinkingTo: Rcpp Suggests: flowWorkspaceData, knitr, testthat, utils, tools, parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: ebdd92b73c3f86c0ac252e97bbd4dc29 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: FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/openCyto_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/openCyto_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/openCyto_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/openCyto_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/openCyto_1.6.0.tgz vignettes: vignettes/openCyto/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R, vignettes/openCyto/inst/doc/openCytoVignette.R htmlDocs: vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html htmlTitles: "How to write a csv gating template", "An Introduction to the openCyto package" Package: oposSOM Version: 1.4.0 Depends: R (>= 3.0) Imports: som, fastICA, scatterplot3d, pixmap, fdrtool, ape, igraph, KernSmooth, parallel, biomaRt, Biobase License: GPL (>=2) MD5sum: 431ab21fc0ac059123ba222b183fae21 NeedsCompilation: no Title: Comprehensive analysis of transciptome 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 Wirth and Martin Kalcher Maintainer: Henry Wirth URL: http://som.izbi.uni-leipzig.de source.ver: src/contrib/oposSOM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/oposSOM_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/oposSOM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/oposSOM_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/oposSOM_1.4.0.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 Package: OrderedList Version: 1.40.0 Depends: R (>= 2.1.0), Biobase (>= 1.5.12), twilight (>= 1.9.2), methods Imports: Biobase, graphics, methods, stats, twilight License: GPL (>= 2) MD5sum: 55db281f0afdd00f49c5b097169d4c12 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/index.shtml source.ver: src/contrib/OrderedList_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OrderedList_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OrderedList_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OrderedList_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OrderedList_1.40.0.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 Package: OrganismDbi Version: 1.10.0 Depends: R (>= 2.14.0), methods, AnnotationDbi (>= 1.16.10), GenomicFeatures (>= 1.17.13) Imports: BiocGenerics, graph, RBGL, AnnotationDbi, GenomicFeatures, stats Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, RUnit License: Artistic-2.0 MD5sum: a2cd9f58773c33bcf5ef4b3cf9d10c60 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, Herve Pages, Martin Morgan, Valerie Obenchain Maintainer: Biocore Data Team source.ver: src/contrib/OrganismDbi_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OrganismDbi_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OrganismDbi_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OrganismDbi_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OrganismDbi_1.10.0.tgz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.pdf vignetteTitles: OrganismDbi: A meta framework for Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R importsMe: epivizr, ggbio Package: OSAT Version: 1.16.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: a87ccb56e69d99ffd84d1c6e19e94cc2 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 source.ver: src/contrib/OSAT_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OSAT_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OSAT_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OSAT_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OSAT_1.16.0.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 Package: OTUbase Version: 1.18.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: e5aa9fe9b02060efeb6f70bbc9decfb6 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 source.ver: src/contrib/OTUbase_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OTUbase_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OTUbase_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OTUbase_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OTUbase_1.18.0.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 dependsOnMe: mcaGUI Package: OutlierD Version: 1.32.0 Depends: R (>= 2.3.0), Biobase, quantreg License: GPL (>= 2) MD5sum: d4fe52a01d2b4f16350c1a209f130a6d NeedsCompilation: no Title: Outlier detection using quantile regression on the M-A scatterplots of high-throughput data Description: This package detects outliers using quantile regression on the M-A scatterplots of high-throughput data. biocViews: Microarray Author: HyungJun Cho Maintainer: Sukwoo Kim URL: http://www.korea.ac.kr/~stat2242/ source.ver: src/contrib/OutlierD_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OutlierD_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OutlierD_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OutlierD_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OutlierD_1.32.0.tgz vignettes: vignettes/OutlierD/inst/doc/OutlierD.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OutlierD/inst/doc/OutlierD.R Package: PAA Version: 1.3.3 Depends: R (>= 3.2.0), Rcpp (>= 0.11.6) Imports: e1071, limma, MASS, mRMRe, randomForest, ROCR, sva LinkingTo: Rcpp Suggests: BiocStyle, RUnit, BiocGenerics, vsn License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: 2f1ee85c609095bd29d722d629f7fdab 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 pre-processing (background correction, batch filtering, normalization) univariate feature pre-selection 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.medizinisches-proteom-center.de/PAA SystemRequirements: C++ software package Random Jungle source.ver: src/contrib/PAA_1.3.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/PAA_1.3.3.zip win64.binary.ver: bin/windows64/contrib/3.2/PAA_1.3.3.zip mac.binary.ver: bin/macosx/contrib/3.2/PAA_1.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PAA_1.3.3.tgz vignettes: vignettes/PAA/inst/doc/PAA_vignette.pdf vignetteTitles: PAA tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R Package: PADOG Version: 1.10.0 Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db, hgu133a.db, KEGG.db, nlme Suggests: doParallel, parallel License: GPL (>= 2) MD5sum: 31378bc448f12c8746e94e1ac126c8b6 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 Laurentiu Tarca source.ver: src/contrib/PADOG_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PADOG_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PADOG_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PADOG_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PADOG_1.10.0.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 Package: paircompviz Version: 1.6.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: f72176f98a402d7012bfc52af230ce50 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 source.ver: src/contrib/paircompviz_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/paircompviz_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/paircompviz_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/paircompviz_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/paircompviz_1.6.0.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 Package: pandaR Version: 1.0.0 Depends: R (>= 3.1.3) Suggests: knitr, igraph License: GPL-2 MD5sum: 3d85e74d1757fca1bf01c2a6e29b77af NeedsCompilation: no Title: PANDA algorithm Description: Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complimentary data sources. biocViews: StatisticalMethod, GraphAndNetwork, Microarray, GeneRegulation, NetworkInference, GeneExpression, Transcription, Network Author: "Dan Schlauch " Maintainer: Dan Schlauch VignetteBuilder: knitr source.ver: src/contrib/pandaR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pandaR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pandaR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pandaR_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pandaR_1.0.0.tgz vignettes: vignettes/pandaR/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pandaR/inst/doc/pandaR.R htmlDocs: vignettes/pandaR/inst/doc/pandaR.html htmlTitles: "Introduction" Package: PAnnBuilder Version: 1.32.0 Depends: R (>= 2.7.0), methods, utils, RSQLite, Biobase (>= 1.17.0), AnnotationDbi (>= 1.3.12) Imports: methods, utils, Biobase, DBI, RSQLite, AnnotationDbi Suggests: org.Hs.ipi.db License: LGPL (>= 2.0) MD5sum: 83f55c79c2d455c1dc5af2bf05091efe NeedsCompilation: no Title: Protein annotation data package builder Description: Processing annotation data from public data repositories and building protein-centric annotation data packages. biocViews: Annotation, Proteomics Author: Li Hong lihong@sibs.ac.cn Maintainer: Li Hong URL: http://www.biosino.org/PAnnBuilder source.ver: src/contrib/PAnnBuilder_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PAnnBuilder_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PAnnBuilder_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PAnnBuilder_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PAnnBuilder_1.32.0.tgz vignettes: vignettes/PAnnBuilder/inst/doc/PAnnBuilder.pdf vignetteTitles: Using the PAnnBuilder Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PAnnBuilder/inst/doc/PAnnBuilder.R Package: panp Version: 1.38.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 55801f19f261ff8313b60c7a1d0aace5 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 source.ver: src/contrib/panp_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/panp_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/panp_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/panp_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/panp_1.38.0.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 Package: PANR Version: 1.14.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: ecd8f523fb5e81f56f0be2b7d18a2330 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: NetworkInference, Visualization, GraphAndNetwork, Clustering, CellBasedAssays Author: Xin Wang Maintainer: Xin Wang source.ver: src/contrib/PANR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PANR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PANR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PANR_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PANR_1.14.0.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 suggestsMe: RedeR Package: PAPi Version: 1.8.0 Depends: R (>= 2.15.2), svDialogs, KEGGREST License: GPL(>= 2) MD5sum: bf128d40773953993636e66c25a10ddc NeedsCompilation: no Title: Predict metabolic pathway activity based on metabolomics data Description: The Pathway Activity Profiling - PAPi - is an R package for predicting the activity of metabolic pathways based solely on a metabolomics data set containing a list of metabolites identified and their respective abundances in different biological samples. PAPi generates hypothesis that improves the final biological interpretation. See Aggio, R.B.M; Ruggiero, K. and Villas-Boas, S.G. (2010) - Pathway Activity Profiling (PAPi): from metabolite profile to metabolic pathway activity. Bioinformatics. biocViews: MassSpectrometry, Metabolomics Author: Raphael Aggio Maintainer: Raphael Aggio source.ver: src/contrib/PAPi_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PAPi_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PAPi_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PAPi_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PAPi_1.8.0.tgz vignettes: vignettes/PAPi/inst/doc/PAPiPackage.pdf, vignettes/PAPi/inst/doc/PAPi.pdf vignetteTitles: Applying PAPi, PAPi.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PAPi/inst/doc/PAPiPackage.R Package: parglms Version: 1.0.0 Depends: methods Imports: BiocGenerics, BiocParallel, BatchJobs Suggests: RUnit, sandwich, MASS License: Artistic-2.0 MD5sum: 64442d33aea2dd922a99edad024ab1ca NeedsCompilation: no Title: support for parallelized estimation of GLMs/GEEs Description: support for parallelized estimation of GLMs/GEEs, catering for dispersed data Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/parglms_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/parglms_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/parglms_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/parglms_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/parglms_1.0.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: parody Version: 1.26.0 Depends: R (>= 2.5.0), methods, tools, utils License: Artistic-2.0 MD5sum: 30608361abb1c98e92fe64f51294e69a NeedsCompilation: no Title: Parametric And Resistant Outlier DYtection Description: 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: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/parody_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/parody_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/parody_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/parody_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/parody_1.26.0.tgz vignettes: vignettes/parody/inst/doc/parody.pdf vignetteTitles: parody: parametric and resistant outlier detection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parody/inst/doc/parody.R dependsOnMe: arrayMvout, flowQ Package: pathifier Version: 1.6.0 Imports: R.oo, princurve License: Artistic-1.0 MD5sum: 28626c5725ea2238bf66812b423ee1fc 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 source.ver: src/contrib/pathifier_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pathifier_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pathifier_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pathifier_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pathifier_1.6.0.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 Package: PathNet Version: 1.8.0 Depends: R (>= 1.14.0) Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: 0ad33ad8febf3cdb436a9de5420e0a4c 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: Jason B. Smith source.ver: src/contrib/PathNet_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PathNet_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PathNet_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PathNet_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PathNet_1.8.0.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 importsMe: EnrichmentBrowser Package: pathRender Version: 1.36.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: d670ab6b7d3ddd3c4b664d66bd5b9ad7 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 source.ver: src/contrib/pathRender_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pathRender_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pathRender_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pathRender_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pathRender_1.36.0.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 Package: pathview Version: 1.8.0 Depends: R (>= 2.10), KEGGgraph, org.Hs.eg.db Imports: Rgraphviz, graph, png, AnnotationDbi, KEGGREST, methods, utils Suggests: gage, org.Mm.eg.db, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: 03c9869bb4c624e885223a128cb5fa4c 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: http://pathview.r-forge.r-project.org/ source.ver: src/contrib/pathview_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pathview_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pathview_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pathview_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pathview_1.8.0.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: EnrichmentBrowser importsMe: CompGO suggestsMe: clusterProfiler, gage Package: paxtoolsr Version: 1.2.1 Depends: R (>= 3.1.1), rJava (>= 0.9-4), XML, RCurl, rjson, plyr Suggests: testthat, knitr, BiocStyle, rmarkdown, RColorBrewer, igraph, biomaRt, estrogen, affy, hgu95av2, hgu95av2cdf, limma License: LGPL-3 MD5sum: 8530d225fd7a93ebfe685cc5e916ac37 NeedsCompilation: no Title: PaxtoolsR: Access Pathways from Multiple Databases through BioPAX and Pathway Commons Description: The package provides a set of R functions for interacting with BioPAX OWL files using Paxtools and the querying Pathway Commons (PC) molecular interaction database that are hosted by the Computational Biology Center at Memorial Sloan-Kettering Cancer Center (MSKCC). Pathway Commons databases include: BIND, BioGRID, CORUM, CTD, DIP, DrugBank, HPRD, HumanCyc, IntAct, KEGG, MirTarBase, Panther, PhosphoSitePlus, Reactome, RECON, TRANSFAC. biocViews: GeneSetEnrichment, GraphAndNetwork, Pathways, Software, SystemsBiology, NetworkEnrichment, Network Author: Augustin Luna Maintainer: Augustin Luna URL: https://bitbucket.org/cbio_mskcc/paxtoolsr SystemRequirements: Java (>= 1.5) VignetteBuilder: knitr source.ver: src/contrib/paxtoolsr_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/paxtoolsr_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/paxtoolsr_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/paxtoolsr_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/paxtoolsr_1.2.1.tgz vignettes: vignettes/paxtoolsr/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.R htmlDocs: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html htmlTitles: "Using PaxtoolsR" Package: Pbase Version: 0.8.0 Depends: R (>= 2.10), methods, BiocGenerics, Rcpp, Gviz Imports: cleaver (>= 1.3.6), Biobase, Biostrings, IRanges, S4Vectors, mzID, mzR (>= 1.99.1), MSnbase (>= 1.15.5), Pviz, biomaRt, GenomicRanges, rtracklayer Suggests: testthat (>= 0.8), ggplot2, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationHub, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 540a83598f717f71ab102124c5766b10 NeedsCompilation: no Title: Manipulating and exploring protein and proteomics data Description: A set of classes and functions to investigate and understand protein sequence data in the context of a proteomics experiment. biocViews: Infrastructure, Proteomics, MassSpectrometry, Visualization, DataImport, DataRepresentation Author: Laurent Gatto [aut], Sebastian Gibb [aut, cre] Maintainer: Sebastian Gibb , Laurent Gatto URL: https://github.com/ComputationalProteomicsUnit/Pbase VignetteBuilder: knitr BugReports: https://github.com/ComputationalProteomicsUnit/Pbase/issues source.ver: src/contrib/Pbase_0.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Pbase_0.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Pbase_0.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Pbase_0.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Pbase_0.8.0.tgz vignettes: vignettes/Pbase/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pbase/inst/doc/ensucsc.R, vignettes/Pbase/inst/doc/mapping.R, vignettes/Pbase/inst/doc/Pbase-data.R htmlDocs: vignettes/Pbase/inst/doc/ensucsc.html, vignettes/Pbase/inst/doc/mapping.html, vignettes/Pbase/inst/doc/Pbase-data.html htmlTitles: "Ensembl and UCSC mapping", "mapping", "Pbase-data" Package: pcaGoPromoter Version: 1.12.0 Depends: R (>= 2.14.0) , ellipse Imports: Biobase (>= 2.10.0) , AnnotationDbi Suggests: Rgraphviz, GO.db, hgu133plus2.db, mouse4302.db, rat2302.db, hugene10sttranscriptcluster.db, mogene10sttranscriptcluster.db, Biostrings, pcaGoPromoter.Hs.hg19, pcaGoPromoter.Mm.mm9, pcaGoPromoter.Rn.rn4, serumStimulation, parallel License: GPL (>= 2) MD5sum: fdbd262627345c80733254ff833f1a15 NeedsCompilation: no Title: pcaGoPromoter is used to analyze DNA micro array data Description: This package contains functions to ease the analyses of DNA micro arrays. It utilizes principal component analysis as the initial multivariate analysis, followed by functional interpretation of the principal component dimensions with overrepresentation analysis for GO terms and regulatory interpretations using overrepresentation analysis of predicted transcription factor binding sites with the primo algorithm. biocViews: GeneExpression, Microarray, GO , Visualization Author: Morten Hansen, Jorgen Olsen Maintainer: Morten Hansen source.ver: src/contrib/pcaGoPromoter_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pcaGoPromoter_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pcaGoPromoter_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pcaGoPromoter_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pcaGoPromoter_1.12.0.tgz vignettes: vignettes/pcaGoPromoter/inst/doc/pcaGoPromoter.pdf vignetteTitles: pcaGoPromoter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaGoPromoter/inst/doc/pcaGoPromoter.R Package: pcaMethods Version: 1.58.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice License: GPL (>= 3) Archs: i386, x64 MD5sum: 5c8f278fb931b77d153330419d5b1595 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 SystemRequirements: Rcpp source.ver: src/contrib/pcaMethods_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pcaMethods_1.58.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pcaMethods_1.58.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pcaMethods_1.58.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pcaMethods_1.58.0.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: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R, vignettes/pcaMethods/inst/doc/outliers.R, vignettes/pcaMethods/inst/doc/pcaMethods.R dependsOnMe: DeconRNASeq importsMe: CompGO, MSnbase, SomaticSignatures Package: pcot2 Version: 1.36.0 Depends: R (>= 2.0.0), grDevices, Biobase, amap Suggests: multtest, hu6800.db, KEGG.db, mvtnorm License: GPL (>= 2) MD5sum: f2c725592b44a2e92ba62b5246857322 NeedsCompilation: no Title: Principal Coordinates and Hotelling's T-Square method Description: PCOT2 is a permutation-based method for investigating changes in the activity of multi-gene networks. It utilizes inter-gene correlation information to detect significant alterations in gene network activities. Currently it can be applied to two-sample comparisons. biocViews: Microarray, DifferentialExpression, KEGG, GeneExpression, Network Author: Sarah Song, Mik Black Maintainer: Sarah Song source.ver: src/contrib/pcot2_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pcot2_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pcot2_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pcot2_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pcot2_1.36.0.tgz vignettes: vignettes/pcot2/inst/doc/pcot2.pdf vignetteTitles: PCOT2 Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcot2/inst/doc/pcot2.R Package: PCpheno Version: 1.30.0 Depends: R (>= 2.10), Category, ScISI (>= 1.3.0), SLGI, ppiStats, ppiData, annotate (>= 1.17.4) Imports: AnnotationDbi, Biobase, Category, GO.db, graph, graphics, GSEABase, KEGG.db, methods, ScISI, stats, stats4 Suggests: KEGG.db, GO.db, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 523e104ec583fd94c7e83c0e1b4cd57d NeedsCompilation: no Title: Phenotypes and cellular organizational units Description: Tools to integrate, annotate, and link phenotypes to cellular organizational units such as protein complexes and pathways. biocViews: GraphAndNetwork, Proteomics, Network Author: Nolwenn Le Meur and Robert Gentleman Maintainer: Nolwenn Le Meur source.ver: src/contrib/PCpheno_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PCpheno_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PCpheno_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PCpheno_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PCpheno_1.30.0.tgz vignettes: vignettes/PCpheno/inst/doc/PCpheno.pdf vignetteTitles: PCpheno Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCpheno/inst/doc/PCpheno.R Package: pdInfoBuilder Version: 1.32.1 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 Archs: i386, x64 MD5sum: b8b002ef343b8fe5334032c5fcc604ff 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 source.ver: src/contrib/pdInfoBuilder_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/pdInfoBuilder_1.32.1.zip win64.binary.ver: bin/windows64/contrib/3.2/pdInfoBuilder_1.32.1.zip mac.binary.ver: bin/macosx/contrib/3.2/pdInfoBuilder_1.32.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pdInfoBuilder_1.32.1.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/BuildingPDInfoPkgs.R, vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R Package: pdmclass Version: 1.40.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), fibroEset, mda License: Artistic-2.0 MD5sum: 7ecfa4964918a373162ca7256ee1a7c7 NeedsCompilation: no Title: Classification of Microarray Samples using Penalized Discriminant Methods Description: This package can be used to classify microarray data using one of three penalized regression methods; partial least squares, principal components regression, or ridge regression. biocViews: Classification Author: James W. MacDonald, Debashis Ghosh, based in part on pls code of Mike Denham Maintainer: James W. MacDonald source.ver: src/contrib/pdmclass_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pdmclass_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pdmclass_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pdmclass_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pdmclass_1.40.0.tgz vignettes: vignettes/pdmclass/inst/doc/pdmclass.pdf vignetteTitles: pdmclass Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdmclass/inst/doc/pdmclass.R suggestsMe: oneChannelGUI Package: PECA Version: 1.4.0 Imports: limma, affy, genefilter, preprocessCore Suggests: SpikeIn, ROCR, multtest License: GPL (>= 2) MD5sum: 6026b2875245d44c7a9078d7aa87baac 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 Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo Maintainer: Tomi Suomi source.ver: src/contrib/PECA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PECA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PECA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PECA_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PECA_1.4.0.tgz vignettes: vignettes/PECA/inst/doc/PECA.pdf vignetteTitles: PECA: Probe-level Expression Change Averaging hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PECA/inst/doc/PECA.R Package: pepStat Version: 1.2.0 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: b08c0b4a774585625b5fab98c6a0f3b4 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 source.ver: src/contrib/pepStat_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pepStat_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pepStat_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pepStat_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pepStat_1.2.0.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 Package: pepXMLTab Version: 1.2.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 8c82c99f620080dba412bc32a101de2a 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: Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang source.ver: src/contrib/pepXMLTab_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pepXMLTab_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pepXMLTab_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pepXMLTab_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pepXMLTab_1.2.0.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 Package: PGSEA Version: 1.42.0 Depends: R (>= 2.10), GO.db, KEGG.db, AnnotationDbi, annaffy, methods, Biobase (>= 2.5.5) Suggests: GSEABase, GEOquery, org.Hs.eg.db, hgu95av2.db, limma License: GPL-2 MD5sum: 43af1411fa79ae3e2b82a02465633279 NeedsCompilation: no Title: Parametric Gene Set Enrichment Analysis Description: Parametric Analysis of Gene Set Enrichment biocViews: Microarray Author: Kyle Furge and Karl Dykema Maintainer: Karl Dykema source.ver: src/contrib/PGSEA_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PGSEA_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PGSEA_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PGSEA_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PGSEA_1.42.0.tgz vignettes: vignettes/PGSEA/inst/doc/PGSEA2.pdf, vignettes/PGSEA/inst/doc/PGSEA.pdf vignetteTitles: HOWTO: PGSEA Example Workflow, HOWTO: PGSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PGSEA/inst/doc/PGSEA2.R, vignettes/PGSEA/inst/doc/PGSEA.R dependsOnMe: GeneExpressionSignature Package: phenoDist Version: 1.16.0 Depends: R (>= 2.9.0), imageHTS, e1071 Suggests: GOstats, MASS License: LGPL-2.1 MD5sum: 9427dcdd11d5c32584fdb4f32ff21284 NeedsCompilation: no Title: Phenotypic distance measures Description: PhenoDist is designed for measuring phenotypic distance in image-based high-throughput screening, in order to identify strong phenotypes and to group treatments into functional clusters. biocViews: CellBasedAssays Author: Xian Zhang, Gregoire Pau, Wolfgang Huber, Michael Boutros Maintainer: Xian Zhang URL: http://www.dkfz.de/signaling, http://www.embl.de/research/units/genome_biology/huber/ source.ver: src/contrib/phenoDist_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/phenoDist_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/phenoDist_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/phenoDist_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/phenoDist_1.16.0.tgz vignettes: vignettes/phenoDist/inst/doc/phenoDist.pdf vignetteTitles: Introduction to phenoDist hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoDist/inst/doc/phenoDist.R Package: phenoTest Version: 1.16.0 Depends: R (>= 2.12.0), Biobase, methods, annotate, Heatplus, BMA, ggplot2 Imports: survival, limma, Hmisc, gplots, Category, AnnotationDbi, hopach, biomaRt, GSEABase, genefilter, xtable, annotate, mgcv, SNPchip, hgu133a.db, HTSanalyzeR, ellipse Suggests: GSEABase, KEGG.db, 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: 78410ed1e71f99c9b6265a9ff5201830 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 source.ver: src/contrib/phenoTest_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/phenoTest_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/phenoTest_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/phenoTest_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/phenoTest_1.16.0.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 Package: PhenStat Version: 2.2.0 Depends: R (>= 2.3.0) Imports: methods, car, nlme, nortest, MASS, logistf Suggests: RUnit, BiocGenerics License: file LICENSE MD5sum: 37e70ba9178f04dde28a81dc454189f8 NeedsCompilation: no Title: Statistical analysis of phenotypic data Description: Package contains methods for statistical analysis of phenotypic data such as Mixed Models and Fisher Exact Test. Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason Maintainer: Natasha Karp source.ver: src/contrib/PhenStat_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PhenStat_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PhenStat_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PhenStat_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PhenStat_2.2.0.tgz vignettes: vignettes/PhenStat/inst/doc/PhenStat.pdf, vignettes/PhenStat/inst/doc/PhenStatUsersGuide.pdf vignetteTitles: PhenStat Vignette, PhenStatUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhenStat/inst/doc/PhenStat.R Package: phyloseq Version: 1.12.2 Depends: R (>= 3.1.0) Imports: BiocGenerics (>= 0.14.0), ade4 (>= 1.7.2), ape (>= 3.1.1), biom (>= 0.3.9), Biostrings (>= 2.28.0), cluster (>= 1.14.4), data.table (>= 1.9.4), DESeq2 (>= 1.4.0), foreach (>= 1.4.2), ggplot2 (>= 1.0.0), igraph (>= 0.7.0), methods (>= 3.1.0), multtest (>= 2.16.0), plyr (>= 1.8), reshape2 (>= 1.2.2), scales (>= 0.2.3), vegan (>= 2.0.10) Suggests: genefilter (>= 1.42.0), testthat (>= 0.8), knitr (>= 1.3) Enhances: doParallel (>= 1.0.1) License: AGPL-3 MD5sum: 4fcf9582d1b3b72d9c88407e0570a8c0 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: 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 source.ver: src/contrib/phyloseq_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/phyloseq_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.2/phyloseq_1.12.2.zip mac.binary.ver: bin/macosx/contrib/3.2/phyloseq_1.12.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/phyloseq_1.12.2.tgz vignettes: vignettes/phyloseq/inst/doc/ 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-mixture-models.R htmlDocs: vignettes/phyloseq/inst/doc/phyloseq-analysis.html, vignettes/phyloseq/inst/doc/phyloseq-basics.html, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html htmlTitles: "phyloseq analysis vignette", "phyloseq basics vignette", "phyloseq and DESeq2 on Colorectal Cancer Data" Package: piano Version: 1.8.2 Depends: R (>= 2.14.0) Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM, gtools, biomaRt, snowfall, AnnotationDbi License: GPL (>=2) MD5sum: 409e2cfc8ce001d7ae58f4c6b058fe6c 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 and Intawat Nookaew Maintainer: Leif Varemo URL: http://www.sysbio.se/piano source.ver: src/contrib/piano_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/piano_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.2/piano_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.2/piano_1.8.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/piano_1.8.2.tgz vignettes: vignettes/piano/inst/doc/piano-vignette.pdf vignetteTitles: Piano - Platform for Integrative Analysis of Omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/piano/inst/doc/piano-vignette.R importsMe: saps Package: pickgene Version: 1.40.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: a4c8f431ed22d00381699b4f7f68a4bd 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 source.ver: src/contrib/pickgene_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pickgene_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pickgene_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pickgene_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pickgene_1.40.0.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 Rfiles: vignettes/pickgene/inst/doc/pickgene.R Package: PICS Version: 2.12.0 Depends: R (>= 2.14.0), BiocGenerics (>= 0.1.3) Imports: methods, stats4, IRanges, GenomicRanges, graphics, grDevices, stats, Rsamtools, GenomicAlignments, S4Vectors Suggests: ShortRead, rtracklayer, parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 98b4dbdd26d763975c89599838f5a7ad NeedsCompilation: yes Title: Probabilistic inference of ChIP-seq Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, Visualization, Sequencing, ChIPSeq Author: Xuekui Zhang , Raphael Gottardo Maintainer: Renan Sauteraud source.ver: src/contrib/PICS_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PICS_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PICS_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PICS_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PICS_2.12.0.tgz vignettes: vignettes/PICS/inst/doc/PICS.pdf vignetteTitles: The PICS users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PICS/inst/doc/PICS.R importsMe: PING Package: PING Version: 2.12.0 Depends: R(>= 2.15.0), chipseq, IRanges, GenomicRanges Imports: methods, PICS, graphics, grDevices, stats, Gviz, fda, BSgenome, stats4, BiocGenerics, IRanges, S4Vectors Suggests: parallel, ShortRead, rtracklayer License: Artistic-2.0 Archs: i386, x64 MD5sum: a1bf7513e136108c313176a9c18e4a96 NeedsCompilation: yes Title: Probabilistic inference for Nucleosome Positioning with MNase-based or Sonicated Short-read Data Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, StatisticalMethod, Visualization, Sequencing Author: Xuekui Zhang , Raphael Gottardo , Sangsoon Woo, Maintainer: Renan Sauteraud source.ver: src/contrib/PING_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PING_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PING_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PING_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PING_2.12.0.tgz vignettes: vignettes/PING/inst/doc/PING.pdf, vignettes/PING/inst/doc/PING-PE.pdf vignetteTitles: The PING users guide, Using PING with paired-end sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PING/inst/doc/PING-PE.R, vignettes/PING/inst/doc/PING.R Package: pint Version: 1.18.0 Depends: mvtnorm, methods, graphics, Matrix, dmt License: BSD_2_clause + file LICENSE MD5sum: e49de91e362fd6c4858178f5f4b95e89 NeedsCompilation: no Title: Pairwise INTegration of functional genomics data Description: Pairwise data integration for functional genomics, including tools for DNA/RNA/miRNA dependency screens. biocViews: aCGH, GeneExpression, Genetics, DifferentialExpression, Microarray Author: Olli-Pekka Huovilainen and Leo Lahti Maintainer: Olli-Pekka Huovilainen URL: https://github.com/antagomir/pint BugReports: https://github.com/antagomir/pint/issues source.ver: src/contrib/pint_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pint_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pint_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pint_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pint_1.18.0.tgz vignettes: vignettes/pint/inst/doc/depsearch.pdf vignetteTitles: pint hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pint/inst/doc/depsearch.R Package: pkgDepTools Version: 1.34.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocInstaller License: GPL-2 MD5sum: c8204f77001585f535268f7409e5a68d NeedsCompilation: no Title: Package Dependency Tools Description: This package provides tools for computing and analyzing dependency relationships among R packages. It provides tools for building a graph-based representation of the dependencies among all packages in a list of CRAN-style package repositories. There are also utilities for computing installation order of a given package. If the RCurl package is available, an estimate of the download size required to install a given package and its dependencies can be obtained. biocViews: Infrastructure, GraphAndNetwork Author: Seth Falcon Maintainer: Seth Falcon source.ver: src/contrib/pkgDepTools_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pkgDepTools_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pkgDepTools_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pkgDepTools_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pkgDepTools_1.34.0.tgz vignettes: vignettes/pkgDepTools/inst/doc/pkgDepTools.pdf vignetteTitles: How to Use pkgDepTools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pkgDepTools/inst/doc/pkgDepTools.R Package: plateCore Version: 1.26.0 Depends: R (>= 2.10), flowCore, flowViz, lattice, latticeExtra Imports: Biobase, flowCore, graphics, grDevices, lattice, MASS, methods, robustbase, stats, utils, flowStats Suggests: gplots License: Artistic-2.0 MD5sum: df8b76df2a4d6f35d4c103c361e07a0f NeedsCompilation: no Title: Statistical tools and data structures for plate-based flow cytometry Description: Provides basic S4 data structures and routines for analyzing plate based flow cytometry data. biocViews: FlowCytometry, Infrastructure, CellBasedAssays Author: Errol Strain, Florian Hahne, and Perry Haaland Maintainer: Errol Strain URL: http://www.bioconductor.org source.ver: src/contrib/plateCore_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plateCore_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plateCore_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plateCore_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plateCore_1.26.0.tgz vignettes: vignettes/plateCore/inst/doc/plateCoreVig.pdf vignetteTitles: An R Package for Analysis of High Throughput Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plateCore/inst/doc/plateCoreVig.R Package: plethy Version: 1.6.0 Depends: R (>= 3.1.0), methods, BiocGenerics, S4Vectors Imports: Streamer, DBI, RSQLite (>= 1.0.0), IRanges, reshape2, plyr, RColorBrewer,ggplot2, Biobase Suggests: RUnit, BiocStyle License: GPL-3 MD5sum: de408b19d9a20c2aa1e60c6efc642ad5 NeedsCompilation: no Title: R framework for exploration and analysis of respirometry data Description: This package provides the infrastructure and tools to import, query and perform basic analysis of whole body plethysmography and metabolism data. Currently support is limited to data derived from Buxco respirometry instruments as exported by their FinePointe software. biocViews: DataImport, biocViews, Infastructure, DataRepresentation,TimeCourse Author: Daniel Bottomly [aut, cre], Marty Ferris [ctb], Beth Wilmot [aut], Shannon McWeeney [aut] Maintainer: Daniel Bottomly source.ver: src/contrib/plethy_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plethy_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plethy_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plethy_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plethy_1.6.0.tgz vignettes: vignettes/plethy/inst/doc/plethy.pdf vignetteTitles: plethy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plethy/inst/doc/plethy.R Package: plgem Version: 1.40.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS License: GPL-2 MD5sum: 3f7bc11d41e110b5e9232c4278e8ed0d 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: Microarray, DifferentialExpression, Proteomics, GeneExpression, MassSpectrometry Author: Mattia Pelizzola and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it source.ver: src/contrib/plgem_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plgem_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plgem_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plgem_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plgem_1.40.0.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 Package: plier Version: 1.38.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: c99f5526962fe4d532b9fd0646b1470d 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 source.ver: src/contrib/plier_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plier_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plier_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plier_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plier_1.38.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano Package: PLPE Version: 1.28.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: d16d54fc613d40524fe53ef7361a8469 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/ source.ver: src/contrib/PLPE_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PLPE_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PLPE_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PLPE_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PLPE_1.28.0.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 Package: plrs Version: 1.8.0 Depends: R (>= 2.10), Biobase Imports: BiocGenerics, CGHbase, graphics, grDevices, ic.infer, marray, methods, quadprog, Rcsdp, stats, stats4, utils Suggests: mvtnorm, methods License: GPL (>=2.0) MD5sum: 9fdb5c09d3df394888a541db4c29e17b NeedsCompilation: no Title: Piecewise Linear Regression Splines (PLRS) for the association between DNA copy number and gene expression Description: The present package implements a flexible framework for modeling the relationship between DNA copy number and gene expression data using Piecewise Linear Regression Splines (PLRS). biocViews: Regression Author: Gwenael G.R. Leday Maintainer: Gwenael G.R. Leday to source.ver: src/contrib/plrs_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plrs_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plrs_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plrs_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plrs_1.8.0.tgz vignettes: vignettes/plrs/inst/doc/plrs_vignette.pdf vignetteTitles: plrs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plrs/inst/doc/plrs_vignette.R Package: plw Version: 1.28.0 Depends: R (>= 2.10), affy (>= 1.23.4) Imports: MASS, affy, graphics, splines, stats Suggests: limma License: GPL-2 Archs: i386, x64 MD5sum: 21f365e3c03f94390c6bd1accade3f72 NeedsCompilation: yes Title: Probe level Locally moderated Weighted t-tests. Description: Probe level Locally moderated Weighted median-t (PLW) and Locally Moderated Weighted-t (LMW). biocViews: Microarray, OneChannel, TwoChannel, DifferentialExpression Author: Magnus Astrand Maintainer: Magnus Astrand source.ver: src/contrib/plw_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plw_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plw_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plw_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plw_1.28.0.tgz vignettes: vignettes/plw/inst/doc/HowToPLW.pdf vignetteTitles: HowTo plw hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plw/inst/doc/HowToPLW.R Package: pmm Version: 1.0.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: 10965f96bf12e46fb313bb4ca124da3d 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 source.ver: src/contrib/pmm_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pmm_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pmm_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pmm_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pmm_1.0.0.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 Package: podkat Version: 1.0.3 Depends: R (>= 3.2.0), methods, Rsamtools, GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats, graphics, Biobase, BiocGenerics, Matrix, GenomeInfoDb, IRanges, Biostrings, BSgenome (>= 1.32.0) LinkingTo: Rcpp, Rsamtools Suggests: BSgenome.Hsapiens.UCSC.hg38.masked, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24), VariantAnnotation, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: d2b6a086a0387958da1e97e5dd14a076 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 Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/podkat/ VignetteBuilder: knitr source.ver: src/contrib/podkat_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/podkat_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.2/podkat_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.2/podkat_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/podkat_1.0.3.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 Package: polyester Version: 1.4.0 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma Suggests: knitr, ballgown License: Artistic-2.0 MD5sum: 5eac20e12ab963ac45195aef17dc920f NeedsCompilation: no Title: Simulate RNA-seq reads Description: This package can be used to simulate RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression. biocViews: Sequencing, DifferentialExpression Author: Alyssa C. Frazee, Andrew E. Jaffe, Jeffrey T. Leek Maintainer: Alyssa Frazee , Jeff Leek VignetteBuilder: knitr source.ver: src/contrib/polyester_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/polyester_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/polyester_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/polyester_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/polyester_1.4.0.tgz vignettes: vignettes/polyester/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/polyester/inst/doc/polyester.R htmlDocs: vignettes/polyester/inst/doc/polyester.html htmlTitles: "The Polyester package for simulating RNA-seq reads" Package: Polyfit Version: 1.2.0 Depends: DESeq Suggests: BiocStyle License: GPL (>= 3) MD5sum: 3f6810b86a3ff1831ea0261c75c7e8d8 NeedsCompilation: no Title: Add-on to DESeq to improve p-values and q-values Description: Polyfit is an add-on to the packages DESeq which ensures the p-value distribution is uniform over the interval [0, 1] for data satisfying the null hypothesis of no differential expression, and uses an adpated Storey-Tibshiran method to calculate q-values. biocViews: DifferentialExpression, Sequencing, RNASeq, GeneExpression Author: Conrad Burden Maintainer: Conrad Burden source.ver: src/contrib/Polyfit_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Polyfit_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Polyfit_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Polyfit_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Polyfit_1.2.0.tgz vignettes: vignettes/Polyfit/inst/doc/polyfit.pdf vignetteTitles: Polyfit hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Polyfit/inst/doc/polyfit.R Package: ppiStats Version: 1.34.0 Depends: ScISI (>= 1.13.2), lattice, ppiData (>= 0.1.19) Imports: Biobase, Category, graph, graphics, grDevices, lattice, methods, RColorBrewer, stats Suggests: yeastExpData, xtable License: Artistic-2.0 MD5sum: 797062085fdf5ec78eb4971faad10ff1 NeedsCompilation: no Title: Protein-Protein Interaction Statistical Package Description: Tools for the analysis of protein interaction data. biocViews: Proteomics, GraphAndNetwork, Network, NetworkInference Author: T. Chiang and D. Scholtens with contributions from W. Huber and L. Wang Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/ppiStats_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ppiStats_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ppiStats_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ppiStats_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ppiStats_1.34.0.tgz vignettes: vignettes/ppiStats/inst/doc/ppiStats.pdf vignetteTitles: ppiStats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppiStats/inst/doc/ppiStats.R dependsOnMe: PCpheno suggestsMe: BiocCaseStudies, RpsiXML Package: prada Version: 1.44.0 Depends: R (>= 2.10), Biobase, RColorBrewer, grid, methods, rrcov Imports: Biobase, BiocGenerics, graphics, grDevices, grid, MASS, methods, RColorBrewer, rrcov, stats4, utils Suggests: cellHTS, tcltk License: LGPL Archs: i386, x64 MD5sum: 1b2f56b7841afbb36cf591c56c3e0c0c NeedsCompilation: yes Title: Data analysis for cell-based functional assays Description: Tools for analysing and navigating data from high-throughput phenotyping experiments based on cellular assays and fluorescent detection (flow cytometry (FACS), high-content screening microscopy). biocViews: CellBasedAssays, Visualization Author: Florian Hahne , Wolfgang Huber , Markus Ruschhaupt, Joern Toedling , Joseph Barry Maintainer: Florian Hahne source.ver: src/contrib/prada_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/prada_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/prada_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/prada_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/prada_1.44.0.tgz vignettes: vignettes/prada/inst/doc/norm2.pdf, vignettes/prada/inst/doc/prada2cellHTS.pdf vignetteTitles: Removal of contaminants from FACS data, Combining prada output and cellHTS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prada/inst/doc/norm2.R, vignettes/prada/inst/doc/prada2cellHTS.R dependsOnMe: cellHTS, domainsignatures, RNAither importsMe: cellHTS2 Package: prebs Version: 1.8.1 Depends: R (>= 2.14.0), GenomicAlignments, affy, RPA Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges, Biobase, GenomeInfoDb, S4Vectors Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe License: Artistic-2.0 MD5sum: f08e5df02419dc1fed41656b65f619e7 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: Microarray, RNASeq, Sequencing, GeneExpression, Preprocessing Author: Karolis Uziela and Antti Honkela Maintainer: Karolis Uziela source.ver: src/contrib/prebs_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/prebs_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/prebs_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/prebs_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/prebs_1.8.1.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 Package: PREDA Version: 1.14.0 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, samr, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: 5c90cc6cab414751a3c57fff3f76fe33 NeedsCompilation: no Title: Position RElated Data Anlysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari source.ver: src/contrib/PREDA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PREDA_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PREDA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PREDA_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PREDA_1.14.0.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/PREDAclasses.R, vignettes/PREDA/inst/doc/PREDAtutorial.R Package: predictionet Version: 1.14.0 Depends: igraph, catnet Imports: penalized, RBGL, MASS Suggests: network, minet, knitr License: Artistic-2.0 MD5sum: 8bfd513408ae51e2450f26d51abebf7f NeedsCompilation: yes Title: Inference for predictive networks designed for (but not limited to) genomic data Description: This package contains a set of functions related to network inference combining genomic data and prior information extracted from biomedical literature and structured biological databases. The main function is able to generate networks using Bayesian or regression-based inference methods; while the former is limited to < 100 of variables, the latter may infer networks with hundreds of variables. Several statistics at the edge and node levels have been implemented (edge stability, predictive ability of each node, ...) in order to help the user to focus on high quality subnetworks. Ultimately, this package is used in the 'Predictive Networks' web application developed by the Dana-Farber Cancer Institute in collaboration with Entagen. biocViews: GraphAndNetwork, NetworkInference Author: Benjamin Haibe-Kains, Catharina Olsen, Gianluca Bontempi, John Quackenbush Maintainer: Benjamin Haibe-Kains , Catharina Olsen URL: http://compbio.dfci.harvard.edu, http://www.ulb.ac.be/di/mlg source.ver: src/contrib/predictionet_1.14.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/predictionet_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/predictionet_1.14.0.tgz vignettes: vignettes/predictionet/inst/doc/predictionet.pdf vignetteTitles: predictionet hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/predictionet/inst/doc/predictionet.R Package: preprocessCore Version: 1.30.0 Imports: stats License: LGPL (>= 2) Archs: i386, x64 MD5sum: 972a264b6e2236d119f641820b8f1809 NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines biocViews: Infrastructure Author: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad source.ver: src/contrib/preprocessCore_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/preprocessCore_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/preprocessCore_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/preprocessCore_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/preprocessCore_1.30.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, CopyNumber450k, cqn, crlmm, RefPlus importsMe: affy, AffyTiling, ChAMP, charm, cn.farms, ExiMiR, frma, frmaTools, lumi, MBCB, MEDIPS, minfi, MSnbase, MSstats, oligo, PECA, soGGi, waveTiling suggestsMe: oneChannelGUI Package: proBAMr Version: 1.2.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 6c2ea1fb6978bb882ed71f322f9986f7 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: Proteomics, MassSpectrometry, Software, Visualization Author: Xiaojing Wang Maintainer: Xiaojing Wang source.ver: src/contrib/proBAMr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/proBAMr_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/proBAMr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/proBAMr_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/proBAMr_1.2.0.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 Package: PROcess Version: 1.44.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: 6b283757d034ce9cbc3c3f499bfcddc0 NeedsCompilation: no Title: Ciphergen SELDI-TOF Processing Description: A package for processing protein mass spectrometry data. biocViews: MassSpectrometry, Proteomics Author: Xiaochun Li Maintainer: Xiaochun Li source.ver: src/contrib/PROcess_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PROcess_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PROcess_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PROcess_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PROcess_1.44.0.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 Package: procoil Version: 1.18.0 Depends: R (>= 2.12.0) Imports: methods, stats, graphics Suggests: Biostrings License: GPL (>= 2) MD5sum: 6186a98fc90576de675db2ef1c867f83 NeedsCompilation: no Title: Prediction of Oligomerization of Coiled Coil Proteins Description: The procoil package allows to predict 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. The predict function not only computes the prediction itself, but also a profile which allows to determine the strengths to which the individual residues are indicative for either class. Profiles can also be plotted and exported to files. biocViews: Proteomics, Classification Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/procoil/ https://github.com/UBod/procoil source.ver: src/contrib/procoil_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/procoil_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/procoil_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/procoil_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/procoil_1.18.0.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 Package: ProCoNA Version: 1.6.0 Depends: R (>= 2.10), methods, WGCNA, MSnbase, flashClust Imports: BiocGenerics, GOstats Suggests: RUnit License: GPL (>= 2) MD5sum: 82980dfeff12f98969967a17262e7c1f NeedsCompilation: no Title: Protein co-expression network analysis (ProCoNA). Description: Protein co-expression network construction using peptide level data, with statisical analysis. (Journal of Clinical Bioinformatics 2013, 3:11 doi:10.1186/2043-9113-3-11) biocViews: GraphAndNetwork, Software, Proteomics Author: David L Gibbs Maintainer: David L Gibbs source.ver: src/contrib/ProCoNA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ProCoNA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ProCoNA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ProCoNA_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ProCoNA_1.6.0.tgz vignettes: vignettes/ProCoNA/inst/doc/ProCoNA_Vignette.pdf vignetteTitles: De Novo Peptide Network Example hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProCoNA/inst/doc/ProCoNA_Vignette.R Package: pRoloc Version: 1.8.0 Depends: R (>= 2.15), MSnbase (>= 1.13.3), MLInterfaces (>= 1.37.1), methods, Rcpp (>= 0.10.3), BiocParallel Imports: Biobase, mclust (>= 4.3), caret, e1071, sampling, class, kernlab, lattice, nnet, randomForest, proxy, FNN, BiocGenerics, stats, RColorBrewer, scales, MASS, knitr, mvtnorm, gtools, plyr, ggplot2, biomaRt LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, pRolocdata (>= 1.5.8), roxygen2, synapter, xtable, tsne, BiocStyle, hpar, dplyr License: GPL-2 Archs: i386, x64 MD5sum: a36cf2451b671c5674c15f9673cbd1f6 NeedsCompilation: yes Title: A unifying bioinformatics framework for spatial proteomics Description: This package implements pattern recognition techniques on quantitiative mass spectrometry data to infer protein sub-cellular localisation. biocViews: Proteomics, MassSpectrometry, Classification, Clustering, QualityControl Author: Laurent Gatto and Lisa M. Breckels with contributions from Thomas Burger and Samuel Wieczorek Maintainer: Laurent Gatto VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRoloc/issues source.ver: src/contrib/pRoloc_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pRoloc_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pRoloc_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pRoloc_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pRoloc_1.8.0.tgz vignettes: vignettes/pRoloc/inst/doc/HUPO_2011_poster.pdf, vignettes/pRoloc/inst/doc/HUPO_2014_poster.pdf, vignettes/pRoloc/inst/doc/pRoloc-ml.pdf, vignettes/pRoloc/inst/doc/pRoloc-transfer-learning.pdf, vignettes/pRoloc/inst/doc/pRoloc-tutorial.pdf vignetteTitles: HUPO 2011 poster: pRoloc -- A unifying bioinformatics framework for organelle proteomics, HUPO 2014 poster: A state-of-the-art machine learning pipeline for the analysis of spatial proteomics data, Machine learning techniques available in pRoloc, A transfer learning algorithm for spatial proteomics, pRoloc tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRoloc/inst/doc/HUPO_2011_poster.R, vignettes/pRoloc/inst/doc/HUPO_2014_poster.R, vignettes/pRoloc/inst/doc/pRoloc-ml.R, vignettes/pRoloc/inst/doc/pRoloc-transfer-learning.R, vignettes/pRoloc/inst/doc/pRoloc-tutorial.R dependsOnMe: pRolocGUI suggestsMe: MSnbase Package: pRolocGUI Version: 1.2.0 Depends: R (>= 3.1.0), pRoloc (>= 1.5.12), MSnbase (>= 1.13.11), methods Imports: pRolocdata, shiny (>= 0.9.1), tools (>= 3.1.0) Suggests: RUnit, BiocGenerics, knitr, knitrBootstrap, bibtex, knitcitations (>= 1.0-1) License: GPL-2 MD5sum: 66358b4967f8143abcd2fec7f1c70aec NeedsCompilation: no Title: Interactive visualisation of spatial proteomics data Description: The package pRolocGUI comprises functions to interactively visualise organelle (spatial) proteomics data on the basis of pRoloc, pRolocdata and shiny. biocViews: Proteomics, Visualization, GUI Author: Thomas Naake and Laurent Gatto Maintainer: Laurent Gatto , Thomas Naake URL: http://ComputationalProteomicsUnit.github.io/pRolocGUI/ VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/ComputationalProteomicsUnit/pRolocGUI/issues source.ver: src/contrib/pRolocGUI_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pRolocGUI_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pRolocGUI_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pRolocGUI_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pRolocGUI_1.2.0.tgz vignettes: vignettes/pRolocGUI/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R htmlDocs: vignettes/pRolocGUI/inst/doc/pRolocGUI.html htmlTitles: "pRolocVis and pRolocComp application" Package: PROMISE Version: 1.20.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: ca93c4cacdb1c0ec3b5c49d3c9f8d7cc 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 source.ver: src/contrib/PROMISE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PROMISE_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PROMISE_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PROMISE_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PROMISE_1.20.0.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 Package: PROPER Version: 1.0.0 Depends: R (>= 2.10) Imports: edgeR Suggests: BiocStyle,DESeq,DSS License: GPL MD5sum: def90e4afb268d25db77e3759a300485 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: Sequencing, RNASeq, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu source.ver: src/contrib/PROPER_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PROPER_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PROPER_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PROPER_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PROPER_1.0.0.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 Package: prot2D Version: 1.6.0 Depends: R (>= 2.15),fdrtool,st,samr,Biobase,limma,Mulcom,impute,MASS,qvalue Suggests: made4,affy License: GPL (>= 2) MD5sum: b779642a1c783ed3b67dbc32fae09d0f NeedsCompilation: no Title: Statistical Tools for volume data from 2D Gel Electrophoresis Description: The purpose of this package is to analyze (i.e. Normalize and select significant spots) data issued from 2D GEl experiments biocViews: DifferentialExpression, MultipleComparison, Proteomics Author: Sebastien Artigaud Maintainer: Sebastien Artigaud source.ver: src/contrib/prot2D_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/prot2D_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/prot2D_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/prot2D_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/prot2D_1.6.0.tgz vignettes: vignettes/prot2D/inst/doc/prot2D.pdf vignetteTitles: prot2D hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prot2D/inst/doc/prot2D.R Package: proteinProfiles Version: 1.8.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: 1425217510c27cdb94d63035ee83b15f NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring source.ver: src/contrib/proteinProfiles_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/proteinProfiles_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/proteinProfiles_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/proteinProfiles_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/proteinProfiles_1.8.0.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 Package: proteoQC Version: 1.4.0 Depends: R (>= 3.0.0), XML, VennDiagram, MSnbase Imports: rTANDEM, plyr, seqinr, Nozzle.R1, ggplot2, reshape2, parallel, Rcpp (>= 0.11.1) LinkingTo: Rcpp Suggests: RforProteomics, knitr, BiocStyle, rpx, R.utils, RUnit,BiocGenerics License: LGPL-2 Archs: i386, x64 MD5sum: abc34eda9c48353a898bb5c5fd241c14 NeedsCompilation: yes Title: An R package for proteomics data quality control Description: This package creates a HTML format QC report for MS/MS-based proteomics data. The report is intended to allow the user to quickly assess the quality of proteomics data. biocViews: Proteomics, MassSpectrometry, QualityControl, Visualization, ReportWriting Author: Bo Wen , Laurent Gatto Maintainer: Bo Wen VignetteBuilder: knitr source.ver: src/contrib/proteoQC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/proteoQC_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/proteoQC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/proteoQC_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/proteoQC_1.4.0.tgz vignettes: vignettes/proteoQC/inst/doc/proteoQC.pdf vignetteTitles: proteoQC tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteoQC/inst/doc/proteoQC.R Package: ProtGenerics Version: 1.0.0 Depends: methods, BiocGenerics License: Artistic-2.0 MD5sum: 6805f5b52875d39416396527af11480e NeedsCompilation: no Title: S4 generic functions for Bioconductor proteomics infrastructure Description: S4 generic functions needed by Bioconductor proteomics packages. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/ProtGenerics source.ver: src/contrib/ProtGenerics_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ProtGenerics_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ProtGenerics_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ProtGenerics_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ProtGenerics_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MSnbase, xcms importsMe: MSnID, mzR Package: PSEA Version: 1.2.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: 5592e4566889e93c3b54d713a40a09db NeedsCompilation: no Title: Population-Specific Expression Analysis. Description: Deconvolution of gene expression data by Population-Specific Expression Analysis (PSEA). biocViews: Software Author: Alexandre Kuhn Maintainer: Alexandre Kuhn source.ver: src/contrib/PSEA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PSEA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PSEA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PSEA_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PSEA_1.2.0.tgz vignettes: vignettes/PSEA/inst/doc/PSEA.pdf vignetteTitles: PSEA: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSEA/inst/doc/PSEA.R Package: PSICQUIC Version: 1.6.0 Depends: R (>= 2.15.0), methods, IRanges, biomaRt, BiocGenerics, httr, plyr Imports: RCurl Suggests: org.Hs.eg.db License: Apache License 2.0 MD5sum: 97ae70508820be5197591ef50609adb4 NeedsCompilation: no Title: Protemics Standard Initiative Common QUery InterfaCe Description: PSICQUIC is a project within the HUPO Proteomics Standard Initiative (HUPO-PSI). It standardises programmatic access to molecular interaction databases. biocViews: DataImport, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/PSICQUIC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PSICQUIC_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PSICQUIC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PSICQUIC_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PSICQUIC_1.6.0.tgz vignettes: vignettes/PSICQUIC/inst/doc/PSICQUIC.pdf vignetteTitles: PSICQUIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSICQUIC/inst/doc/PSICQUIC.R dependsOnMe: RefNet Package: puma Version: 3.10.1 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 Archs: i386, x64 MD5sum: 3624d56107515f597be688b99cf8d92c 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 source.ver: src/contrib/puma_3.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/puma_3.10.1.zip win64.binary.ver: bin/windows64/contrib/3.2/puma_3.10.1.zip mac.binary.ver: bin/macosx/contrib/3.2/puma_3.10.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/puma_3.10.1.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 Package: pvac Version: 1.16.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: e4a6f6f949bada3b17ec8e2b8dcf8001 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 source.ver: src/contrib/pvac_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pvac_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pvac_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pvac_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pvac_1.16.0.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 Package: pvca Version: 1.8.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: 8f0615eaf8d06a23f5d8b4aa090da633 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 source.ver: src/contrib/pvca_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pvca_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pvca_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pvca_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pvca_1.8.0.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 Package: Pviz Version: 1.2.0 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: 299d2fd8c6db2a6401dba26f0a9ff9ad 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 source.ver: src/contrib/Pviz_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Pviz_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Pviz_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Pviz_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Pviz_1.2.0.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 importsMe: Pbase suggestsMe: pepStat Package: PWMEnrich Version: 4.4.0 Depends: methods, grid, BiocGenerics, Biostrings, Imports: seqLogo, gdata, evd Suggests: MotifDb, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background, BiocStyle, knitr License: LGPL (>= 2) MD5sum: b5bc2615cf63a520a72583c1be9bf4c8 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: SequenceMatching, Software Author: Robert Stojnic, Diego Diez Maintainer: Robert Stojnic VignetteBuilder: knitr source.ver: src/contrib/PWMEnrich_4.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PWMEnrich_4.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PWMEnrich_4.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PWMEnrich_4.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PWMEnrich_4.4.0.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 suggestsMe: rTRM Package: pwOmics Version: 1.1.8 Depends: R (>= 3.2) Imports: data.table, rBiopaxParser, igraph, STRINGdb, graphics, gplots, Biobase, BiocGenerics, AnnotationDbi, biomaRt, AnnotationHub, GenomicRanges Suggests: ebdbNet, longitudinal, Mfuzz License: GPL (>= 2) MD5sum: 9783f76b02c55e40deca62f3252cb324 NeedsCompilation: no Title: Pathway-based data integration of omics data Description: pwOmics performs pathway-based level-specific data comparison of matching omics data sets based on pre-analysed user-specified lists of differential genes/transcripts and proteins. A separate downstream analysis of proteomic data including pathway identification and enrichment analysis, transcription factor identification and target gene identification is opposed to the upstream analysis starting with gene or transcript information as basis for identification of upstream transcription factors and regulators. The cross-platform comparative analysis allows for comprehensive analysis of single time point experiments and time-series experiments by providing static and dynamic analysis tools for data integration. biocViews: SystemsBiology, Transcription, GeneTarget Author: Astrid Wachter Maintainer: Astrid Wachter source.ver: src/contrib/pwOmics_1.1.8.tar.gz win.binary.ver: bin/windows/contrib/3.2/pwOmics_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/pwOmics_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/pwOmics_1.1.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pwOmics_1.1.8.tgz vignettes: vignettes/pwOmics/inst/doc/pwOmics.pdf vignetteTitles: pwOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pwOmics/inst/doc/pwOmics.R Package: qcmetrics Version: 1.6.0 Depends: R (>= 2.10) Imports: Biobase, methods, knitr, tools, Nozzle.R1, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, yaqcaffy, MAQCsubsetAFX, RforProteomics, AnnotationDbi, mzR, hgu133plus2cdf, BiocStyle License: GPL-2 MD5sum: 1036a7805fbe164747c77f443b4f6c50 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: Software, Bioinformatics, QualityControl, Proteomics, Microarray, MassSpectrometry, Visualisation, ReportWriting Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/qcmetrics VignetteBuilder: knitr source.ver: src/contrib/qcmetrics_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/qcmetrics_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/qcmetrics_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/qcmetrics_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qcmetrics_1.6.0.tgz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.pdf vignetteTitles: The 'qcmetrics' infrastructure for quality control and reporting hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R Package: QDNAseq Version: 1.4.2 Depends: R (>= 2.15.0) Imports: graphics, methods, stats, utils, matrixStats (>= 0.13.1), R.utils (>= 1.28.4), Biobase (>= 2.18.0), CGHbase (>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0), Rsamtools (>= 1.19.17) Suggests: R.cache (>= 0.9.0), digest, snowfall, BSgenome, GenomeInfoDb License: GPL MD5sum: 5f937c9ffc9643ddc50797306d4f25ea 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] Maintainer: Daoud Sie URL: https://github.com/ccagc/QDNAseq BugReports: https://github.com/ccagc/QDNAseq/issues source.ver: src/contrib/QDNAseq_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/QDNAseq_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.2/QDNAseq_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.2/QDNAseq_1.4.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/QDNAseq_1.4.2.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 Package: qpcrNorm Version: 1.26.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: 123b84c2b875cdff1b644491886dcd58 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 source.ver: src/contrib/qpcrNorm_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/qpcrNorm_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/qpcrNorm_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/qpcrNorm_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qpcrNorm_1.26.0.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 suggestsMe: EasyqpcR Package: qpgraph Version: 2.2.3 Depends: R (>= 3.0.0) Imports: methods, parallel, Matrix (>= 1.0), grid, annotate, graph (>= 1.45.1), Biobase, S4Vectors, BiocParallel, AnnotationDbi, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, mvtnorm, qtl, Rgraphviz Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db, rlecuyer, snow, Category, GOstats License: GPL (>= 2) Archs: i386, x64 MD5sum: a5f0755466783a396e37b686054997ff NeedsCompilation: yes Title: Estimation of genetic and molecular regulatory networks from high-throughput genomics data Description: Procedures to 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: R. Castelo and A. Roverato Maintainer: Robert Castelo URL: http://functionalgenomics.upf.edu/qpgraph source.ver: src/contrib/qpgraph_2.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/qpgraph_2.2.3.zip win64.binary.ver: bin/windows64/contrib/3.2/qpgraph_2.2.3.zip mac.binary.ver: bin/macosx/contrib/3.2/qpgraph_2.2.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qpgraph_2.2.3.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, ToPASeq Package: qrqc Version: 1.22.0 Depends: reshape, ggplot2, Biostrings, biovizBase, brew, xtable, Rsamtools (>= 1.19.38), testthat Imports: reshape, ggplot2, Biostrings, biovizBase, graphics, methods, plyr, stats LinkingTo: Rsamtools License: GPL (>=2) Archs: i386, x64 MD5sum: e41035fcb337de8adc745322a5ff7c61 NeedsCompilation: yes Title: Quick Read Quality Control Description: Quickly scans reads and gathers statistics on base and quality frequencies, read length, k-mers by position, and frequent sequences. Produces graphical output of statistics for use in quality control pipelines, and an optional HTML quality report. S4 SequenceSummary objects allow specific tests and functionality to be written around the data collected. biocViews: Sequencing, QualityControl, DataImport, Preprocessing, Visualization Author: Vince Buffalo Maintainer: Vince Buffalo URL: http://github.com/vsbuffalo/qrqc source.ver: src/contrib/qrqc_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/qrqc_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/qrqc_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/qrqc_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qrqc_1.22.0.tgz vignettes: vignettes/qrqc/inst/doc/qrqc.pdf vignetteTitles: Using the qrqc package to gather information about sequence qualities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qrqc/inst/doc/qrqc.R Package: QUALIFIER Version: 1.12.1 Depends: R (>= 2.14.0),flowCore,flowViz,ncdfFlow,flowWorkspace, data.table,reshape Imports: MASS,hwriter,lattice,stats4,flowCore,flowViz,methods,flowWorkspace,latticeExtra,grDevices,tools, Biobase,XML,grid Suggests: RSVGTipsDevice, knitr License: Artistic-2.0 MD5sum: 8bd8b1078837e48cef2d890a8697b82d NeedsCompilation: no Title: Quality Control of Gated Flow Cytometry Experiments Description: Provides quality control and quality assessment tools for gated flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: Mike Jiang,Greg Finak,Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/QUALIFIER_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/QUALIFIER_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/QUALIFIER_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/QUALIFIER_1.12.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/QUALIFIER_1.12.1.tgz vignettes: vignettes/QUALIFIER/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QUALIFIER/inst/doc/QUALIFIER.R Package: quantro Version: 1.2.0 Depends: R (>= 3.1.3) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) MD5sum: e19dfbb3ad396cefd434a431b8e6afea 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 and Rafael Irizarry Maintainer: Stephanie Hicks VignetteBuilder: knitr source.ver: src/contrib/quantro_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/quantro_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/quantro_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/quantro_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/quantro_1.2.0.tgz vignettes: vignettes/quantro/inst/doc/quantro-vignette.pdf vignetteTitles: The quantro user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantro/inst/doc/quantro-vignette.R Package: quantsmooth Version: 1.34.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: 2765d35cd49b1ff02ea51d73d6c4ab99 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 source.ver: src/contrib/quantsmooth_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/quantsmooth_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/quantsmooth_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/quantsmooth_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/quantsmooth_1.34.0.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 dependsOnMe: beadarraySNP importsMe: GWASTools, SIM suggestsMe: PREDA Package: QuartPAC Version: 1.0.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 18e167b4bfb415210513e486382069d3 NeedsCompilation: no Title: Identification of mutational clusters in protein quaternary structures. Description: Identifies clustering of somatic mutations in proteins over the entire quaternary structure. biocViews: Clustering, Proteomics, SomaticMutation Author: Gregory Ryslik, Yuwei Cheng, Hongyu Zhao Maintainer: Gregory Ryslik source.ver: src/contrib/QuartPAC_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/QuartPAC_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/QuartPAC_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/QuartPAC_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/QuartPAC_1.0.0.tgz vignettes: vignettes/QuartPAC/inst/doc/QuartPAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuartPAC/inst/doc/QuartPAC.R Package: QuasR Version: 1.8.4 Depends: parallel, GenomicRanges (>= 1.13.3), Rbowtie Imports: methods, zlibbioc, BiocGenerics, S4Vectors, IRanges, BiocInstaller, Biobase, Biostrings, BSgenome, Rsamtools (>= 1.19.38), GenomicFeatures (>= 1.17.13), ShortRead (>= 1.19.1), GenomicAlignments, BiocParallel, GenomeInfoDb LinkingTo: Rsamtools Suggests: rtracklayer, Gviz, RUnit, BiocStyle License: GPL-2 Archs: x64 MD5sum: bc27afeb7b0ec9ddded9e744e3199482 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. biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq, MethylSeq, Coverage, Alignment, QualityControl Author: Anita Lerch, Dimos Gaiditzis and Michael Stadler Maintainer: Michael Stadler source.ver: src/contrib/QuasR_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/QuasR_1.8.4.zip win64.binary.ver: bin/windows64/contrib/3.2/QuasR_1.8.4.zip mac.binary.ver: bin/macosx/contrib/3.2/QuasR_1.8.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/QuasR_1.8.4.tgz vignettes: vignettes/QuasR/inst/doc/QuasR.pdf vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuasR/inst/doc/QuasR.R Package: qusage Version: 1.8.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase License: GPL (>= 2) MD5sum: 9d40a55cba5026568286c680139561bd 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. For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) biocViews: GeneSetEnrichment, Microarray, RNASeq, Software Author: Christopher Bolen and Gur Yaari, with contributions from Juilee Thakar and Steven Kleinstein Maintainer: Christopher Bolen URL: http://clip.med.yale.edu/qusage source.ver: src/contrib/qusage_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/qusage_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/qusage_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/qusage_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qusage_1.8.0.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 Package: qvalue Version: 2.0.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL MD5sum: 1817c221dbf8ae593e2c60cbfef97549 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 Storey [aut, cre], Andrew Bass [ctb], Alan Dabney [ctb], David Robinson [ctb] Maintainer: John D. Storey URL: http://qvalue.princeton.edu/, http://github.com/jdstorey/qvalue VignetteBuilder: knitr source.ver: src/contrib/qvalue_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/qvalue_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/qvalue_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/qvalue_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qvalue_2.0.0.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, CancerMutationAnalysis, DEGseq, DrugVsDisease, metaseqR, r3Cseq, SSPA, webbioc importsMe: anota, clusterProfiler, derfinder, DOSE, edge, erccdashboard, msmsTests, netresponse, Rnits, sRAP, synapter, trigger, webbioc suggestsMe: LBE, maanova, PREDA Package: R3CPET Version: 1.0.0 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: parallel, clues, ggplot2, pheatmap, IRanges, clValid, igraph,data.table,GenomicRanges,S4Vectors, DAVIDQuery, ggbio,reshape2,Hmisc LinkingTo: Rcpp Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene, biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny, ChIPpeakAnno License: GPL (>=2) Archs: i386, x64 MD5sum: 32b9a43419eb8789a5115c694f49786e 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 Author: M. N. Djekidel, Yang Chen et al. Maintainer: Mohamed Nadhir Djekidel VignetteBuilder: knitr source.ver: src/contrib/R3CPET_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/R3CPET_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/R3CPET_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/R3CPET_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/R3CPET_1.0.0.tgz 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 Package: r3Cseq Version: 1.14.0 Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue Imports: methods, GenomeInfoDb, 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 MD5sum: 0c53cdaf7dcf76d1f21ada4747fd5583 NeedsCompilation: no Title: Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq) Description: This package is an implementation of data analysis for the long-range interactions from 3C-seq assay. biocViews: Preprocessing, Sequencing Author: Supat Thongjuea, MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, UK Maintainer: Supat Thongjuea URL: http://r3cseq.genereg.net source.ver: src/contrib/r3Cseq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/r3Cseq_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/r3Cseq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/r3Cseq_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/r3Cseq_1.14.0.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 Package: R453Plus1Toolbox Version: 1.18.0 Depends: R (>= 2.12.0), Biobase, Biostrings, GenomicRanges, IRanges Imports: BiocGenerics (>= 0.1.3), biomaRt, BSgenome, XVector, methods, R2HTML, Rsamtools, ShortRead, VariantAnnotation, xtable, tools, TeachingDemos, S4Vectors Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 Archs: i386, x64 MD5sum: cc09bf2bfc97045db813172ba1d22f51 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 source.ver: src/contrib/R453Plus1Toolbox_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/R453Plus1Toolbox_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/R453Plus1Toolbox_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/R453Plus1Toolbox_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/R453Plus1Toolbox_1.18.0.tgz vignettes: vignettes/R453Plus1Toolbox/inst/doc/vignette.pdf vignetteTitles: A package for importing and analyzing data from Roche's Genome Sequencer System hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R453Plus1Toolbox/inst/doc/vignette.R Package: rain Version: 1.2.1 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: dc633c7645adf79c4a8411b1bf732b67 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 source.ver: src/contrib/rain_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/rain_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/rain_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/rain_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rain_1.2.1.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 Package: rama Version: 1.42.0 Depends: R(>= 2.5.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: 58c2f702bd69851aac76cc12913b03bc NeedsCompilation: yes Title: Robust Analysis of MicroArrays Description: Robust estimation of cDNA microarray intensities with replicates. The package uses a Bayesian hierarchical model for the robust estimation. Outliers are modeled explicitly using a t-distribution, and the model also addresses classical issues such as design effects, normalization, transformation, and nonconstant variance. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Raphael Gottardo Maintainer: Raphael Gottardo source.ver: src/contrib/rama_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rama_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rama_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rama_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rama_1.42.0.tgz vignettes: vignettes/rama/inst/doc/rama.pdf vignetteTitles: rama Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rama/inst/doc/rama.R dependsOnMe: bridge Package: RamiGO Version: 1.14.0 Depends: gsubfn,methods Imports: igraph,RCurl,png,RCytoscape,graph License: Artistic-2.0 MD5sum: 276ff356babef2b940784319fb59ff0c NeedsCompilation: no Title: AmiGO visualize R interface Description: R interface sending requests to AmiGO visualize, retrieving DAG GO trees, parsing GraphViz DOT format files and exporting GML files for Cytoscape. Also uses RCytoscape to interactively display AmiGO trees in Cytoscape. biocViews: GO, Visualization, GraphAndNetwork, Classification, ThirdPartyClient Author: Markus Schroeder, Daniel Gusenleitner, John Quackenbush, Aedin Culhane, Benjamin Haibe-Kains Maintainer: Markus Schroeder source.ver: src/contrib/RamiGO_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RamiGO_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RamiGO_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RamiGO_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RamiGO_1.14.0.tgz vignettes: vignettes/RamiGO/inst/doc/RamiGO.pdf vignetteTitles: RamiGO: An Introduction (HowTo) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RamiGO/inst/doc/RamiGO.R Package: randPack Version: 1.14.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: 2b7c56536c8491d748d288a184d75023 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 source.ver: src/contrib/randPack_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/randPack_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/randPack_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/randPack_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/randPack_1.14.0.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 Package: RankProd Version: 2.40.0 Depends: R (>= 1.9.0) Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: a5300925de4f46bcc729656063db604b 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 Author: Fangxin Hong and Ben Wittner with contribution from Rainer Breitling , Colin Smith , and Florian Battke Maintainer: Fangxin Hong source.ver: src/contrib/RankProd_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RankProd_2.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RankProd_2.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RankProd_2.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RankProd_2.40.0.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: RNAither, tRanslatome importsMe: HTSanalyzeR suggestsMe: oneChannelGUI Package: Rariant Version: 1.4.0 Depends: R (>= 3.0.2), GenomicRanges, VariantAnnotation Imports: IRanges, ggbio, ggplot2, exomeCopy, SomaticSignatures, Rsamtools, shiny, methods, VGAM, dplyr, reshape2, GenomeInfoDb, S4Vectors Suggests: h5vcData, testthat, knitr, optparse, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: dacbf0d35c2cb2074c367c1912f96ecb NeedsCompilation: no Title: Identification and Assessment of Single Nucleotide Variants through Shifts in Non-Consensus Base Call Frequencies Description: The 'Rariant' package identifies single nucleotide variants from sequencing data based on the difference of binomially distributed mismatch rates between matched samples. biocViews: Sequencing, StatisticalMethod, GenomicVariation, SomaticMutation, VariantDetection, Visualization Author: Julian Gehring, Simon Anders, Bernd Klaus (EMBL Heidelberg) Maintainer: Julian Gehring VignetteBuilder: knitr source.ver: src/contrib/Rariant_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rariant_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rariant_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rariant_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rariant_1.4.0.tgz vignettes: vignettes/Rariant/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rariant/inst/doc/Rariant-vignette.R htmlDocs: vignettes/Rariant/inst/doc/Rariant-vignette.html htmlTitles: "Rariant" Package: RbcBook1 Version: 1.36.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: 2adedd9747a819b4ce9d830988dec019 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 source.ver: src/contrib/RbcBook1_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RbcBook1_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RbcBook1_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RbcBook1_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RbcBook1_1.36.0.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 Package: RBGL Version: 1.44.0 Depends: graph, methods Imports: methods Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: a1a23c2231c44d986f33ed71a343f6a8 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 , Li Long , R. Gentleman Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org source.ver: src/contrib/RBGL_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RBGL_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RBGL_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RBGL_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RBGL_1.44.0.tgz vignettes: vignettes/RBGL/inst/doc/RBGL.pdf vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBGL/inst/doc/RBGL.R dependsOnMe: apComplex, BioNet, CellNOptR, joda, pkgDepTools, RpsiXML importsMe: biocViews, CAMERA, Category, ChIPpeakAnno, clipper, DEGraph, flowWorkspace, GeneAnswers, GOSim, GOstats, NCIgraph, nem, OrganismDbi, pkgDepTools, predictionet, RDAVIDWebService, Streamer, ToPASeq, VariantFiltering suggestsMe: BiocCaseStudies, DEGraph, GeneNetworkBuilder, graph, KEGGgraph, rBiopaxParser, VariantTools Package: RBioinf Version: 1.28.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: 17d7ef1afd54e5098c721c63202bae0c NeedsCompilation: yes Title: RBioinf Description: Functions and datasets and examples to accompany the monograph R For Bioinformatics. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl, Classification, Clustering, MultipleComparison, Annotation Author: Robert Gentleman Maintainer: Robert Gentleman source.ver: src/contrib/RBioinf_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RBioinf_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RBioinf_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RBioinf_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RBioinf_1.28.0.tgz vignettes: vignettes/RBioinf/inst/doc/RBioinf.pdf vignetteTitles: RBioinf Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBioinf/inst/doc/RBioinf.R Package: rBiopaxParser Version: 2.6.0 Depends: R (>= 3.0.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, nem, RBGL License: GPL (>= 2) MD5sum: 2cd4244bf0faf79a01b985d691771535 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/rBiopaxParser source.ver: src/contrib/rBiopaxParser_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rBiopaxParser_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rBiopaxParser_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rBiopaxParser_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rBiopaxParser_2.6.0.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 importsMe: pwOmics suggestsMe: AnnotationHub, NetPathMiner Package: RBM Version: 1.0.0 Depends: R (>= 3.0.0), limma, marray License: GPL (>= 2) MD5sum: 9d2cf94bd14b722f3e831c57fd6161a9 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 source.ver: src/contrib/RBM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RBM_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RBM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RBM_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RBM_1.0.0.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 Package: Rbowtie Version: 1.8.0 Suggests: parallel License: Artistic-1.0 | file LICENSE Archs: x64 MD5sum: 904883a2aae87abeec1f0e36c455e629 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, Anita Lerch, Michael B Stadler Maintainer: Michael Stadler SystemRequirements: GNU make source.ver: src/contrib/Rbowtie_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rbowtie_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rbowtie_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rbowtie_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rbowtie_1.8.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.pdf vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR Package: rbsurv Version: 2.26.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: 22a6f1f91154363cd77467523c4b6052 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/ source.ver: src/contrib/rbsurv_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rbsurv_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rbsurv_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rbsurv_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rbsurv_2.26.0.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 Package: Rcade Version: 1.10.0 Depends: R (>= 2.14.0), methods, GenomicRanges, baySeq, Rsamtools Imports: graphics, S4Vectors, rgl, plotrix Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: 29563643d529b5144fafb38e735af49b NeedsCompilation: no Title: R-based analysis of ChIP-seq And Differential Expression - a tool for integrating a count-based ChIP-seq analysis with differential expression summary data. Description: Rcade (which stands for "R-based analysis of ChIP-seq And Differential Expression") is a tool for integrating ChIP-seq data with differential expression summary data, through a Bayesian framework. A key application is in identifing the genes targeted by a transcription factor of interest - that is, we collect genes that are associated with a ChIP-seq peak, and differential expression under some perturbation related to that TF. biocViews: DifferentialExpression, GeneExpression, Transcription, ChIPSeq, Sequencing, Genetics Author: Jonathan Cairns Maintainer: Jonathan Cairns source.ver: src/contrib/Rcade_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rcade_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rcade_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rcade_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rcade_1.10.0.tgz vignettes: vignettes/Rcade/inst/doc/Rcade.pdf vignetteTitles: Rcade Vignette hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcade/inst/doc/Rcade.R Package: RCASPAR Version: 1.14.1 License: GPL (>=3) MD5sum: f53730aa01b4991e2d1d3b3fb438e45f 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 Maintainer: Douaa Mugahid , Lars Kaderali source.ver: src/contrib/RCASPAR_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/RCASPAR_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.2/RCASPAR_1.14.1.zip mac.binary.ver: bin/macosx/contrib/3.2/RCASPAR_1.14.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RCASPAR_1.14.1.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 Package: rcellminer Version: 1.0.1 Depends: R (>= 3.1.1), Biobase, rcdk, fingerprint Imports: stringr, gplots, methods, shiny Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat, rcellminerData, BiocStyle License: LGPL-3 MD5sum: 28bc17b3edced86fea48079504b0b25f NeedsCompilation: no Title: rcellminer: Molecular Profiles and Drug Response 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 URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr source.ver: src/contrib/rcellminer_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/rcellminer_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/rcellminer_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/rcellminer_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rcellminer_1.0.1.tgz vignettes: vignettes/rcellminer/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R htmlDocs: vignettes/rcellminer/inst/doc/rcellminerUsage.html htmlTitles: "Using rcellminer" Package: Rchemcpp Version: 2.6.1 Depends: R (>= 2.15.0) Imports: Rcpp (>= 0.11.1), methods, ChemmineR LinkingTo: Rcpp Suggests: apcluster, kernlab License: GPL (>= 2.1) Archs: i386, x64 MD5sum: 47a05f1cb5874a607e1f2356b65f975d NeedsCompilation: yes Title: Similarity measures for chemical compounds Description: The Rchemcpp package implements the marginalized graph kernel and extensions, Tanimoto kernels, graph kernels, pharmacophore and 3D kernels suggested for measuring the similarity of molecules. biocViews: Bioinformatics, CellBasedAssays, Clustering, DataImport, Infrastructure, MicrotitrePlateAssay, Proteomics, Software, Visualization Author: Michael Mahr, Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/Rchemcpp SystemRequirements: GNU make source.ver: src/contrib/Rchemcpp_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rchemcpp_2.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/Rchemcpp_2.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/Rchemcpp_2.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rchemcpp_2.6.1.tgz vignettes: vignettes/Rchemcpp/inst/doc/Rchemcpp.pdf vignetteTitles: Rchemcpp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rchemcpp/inst/doc/Rchemcpp.R Package: RchyOptimyx Version: 2.8.0 Depends: R (>= 2.10) Imports: Rgraphviz, sfsmisc, graphics, methods, graph, grDevices, flowType (>= 2.0.0) Suggests: flowCore License: Artistic-2.0 Archs: i386, x64 MD5sum: 32f1b4aa5e4b9ef4da6148d15e13e211 NeedsCompilation: yes Title: Optimyzed Cellular Hierarchies for Flow Cytometry Description: Constructs a hierarchy of cells using flow cytometry for maximization of an external variable (e.g., a clinical outcome or a cytokine response). biocViews: FlowCytometry Author: Adrin Jalali, Nima Aghaeepour Maintainer: Adrin Jalali , Nima Aghaeepour source.ver: src/contrib/RchyOptimyx_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RchyOptimyx_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RchyOptimyx_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RchyOptimyx_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RchyOptimyx_2.8.0.tgz vignettes: vignettes/RchyOptimyx/inst/doc/RchyOptimyx.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RchyOptimyx/inst/doc/RchyOptimyx.R Package: Rcpi Version: 1.4.0 Imports: RCurl, rjson, rcdk, foreach, doParallel, Biostrings, GOSemSim, ChemmineR, fmcsR Suggests: RUnit, BiocGenerics Enhances: ChemmineOB License: Artistic-2.0 MD5sum: 283403b6fef4e9c6627d3da1b51c85a6 NeedsCompilation: no Title: Toolkit for Compound-Protein Interaction in Drug Discovery Description: The Rcpi package offers an R/Bioconductor package emphasizing the comprehensive integration of bioinformatics and chemoinformatics into a molecular informatics platform for drug discovery. biocViews: Software, DataImport, DataRepresentation, FeatureExtraction, Cheminformatics, BiomedicalInformatics, Proteomics, GO, GraphAndNetwork, SystemsBiology Author: Nan Xiao , Dongsheng Cao , Qingsong Xu Maintainer: Nan Xiao URL: https://github.com/road2stat/Rcpi BugReports: https://github.com/road2stat/Rcpi/issues source.ver: src/contrib/Rcpi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rcpi_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rcpi_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rcpi_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rcpi_1.4.0.tgz vignettes: vignettes/Rcpi/inst/doc/Rcpi.pdf, vignettes/Rcpi/inst/doc/Rcpi-quickref.pdf vignetteTitles: Rcpi: R/Bioconductor Package as an Integrated Informatics Platform in Drug Discovery, Rcpi Quick Reference Card hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcpi/inst/doc/Rcpi-quickref.R, vignettes/Rcpi/inst/doc/Rcpi.R Package: RCyjs Version: 1.0.0 Depends: R (>= 3.1.0), BrowserViz, graph (>= 1.44.0) Imports: methods, httpuv (>= 1.3.2), Rcpp (>= 0.11.5), jsonlite (>= 0.9.15), igraph, BiocGenerics Suggests: RUnit, BiocStyle, RefNet License: GPL-2 MD5sum: 1e3e981cad9d6b604351be50028de43b NeedsCompilation: no Title: Display and manipulate graphs in Cytoscape.js Description: Interactvive viewing and exploration of graphs, connecting R to Cytoscape.js biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon URL: http://rcytoscape.systemsbiology.net source.ver: src/contrib/RCyjs_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/RCyjs_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RCyjs_1.0.0.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.pdf vignetteTitles: RCyjs hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R Package: RCytoscape Version: 1.18.0 Depends: R (>= 2.14.0), graph (>= 1.31.0), XMLRPC (>= 0.2.4) Imports: methods, XMLRPC, BiocGenerics Suggests: RUnit License: GPL-2 MD5sum: e91293f09be171624cc2b592e78ad8b8 NeedsCompilation: no Title: Display and manipulate graphs in Cytoscape Description: Interactvive viewing and exploration of graphs, connecting R to Cytoscape. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon URL: http://rcytoscape.systemsbiology.net source.ver: src/contrib/RCytoscape_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RCytoscape_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RCytoscape_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RCytoscape_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RCytoscape_1.18.0.tgz vignettes: vignettes/RCytoscape/inst/doc/RCytoscape.pdf vignetteTitles: RCytoscape Overview hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCytoscape/inst/doc/RCytoscape.R importsMe: categoryCompare, NCIgraph suggestsMe: clipper, GeneNetworkBuilder, graphite, mmnet, NetPathMiner Package: RDAVIDWebService Version: 1.6.0 Depends: R (>= 2.14.1), methods, graph, GOstats, ggplot2 Imports: Category, GO.db, RBGL, rJava Suggests: Rgraphviz License: GPL (>=2) MD5sum: 74142db96c8d305a061cc68dd58435a7 NeedsCompilation: no Title: An R Package for retrieving data from DAVID into R objects using Web Services API. Description: Tools for retrieving data from the Database for Annotation, Visualization and Integrated Discovery (DAVID) using Web Services into R objects. This package offers the main functionalities of DAVID website including: i) user friendly connectivity to upload gene/background list/s, change gene/background position, select current specie/s, select annotations, etc. ii) Reports of the submitted Gene List, Annotation Category Summary, Gene/Term Clusters, Functional Annotation Chart, Functional Annotation Table biocViews: Visualization, DifferentialExpression, GraphAndNetwork Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar, http://david.abcc.ncifcrf.gov/ source.ver: src/contrib/RDAVIDWebService_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RDAVIDWebService_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RDAVIDWebService_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RDAVIDWebService_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RDAVIDWebService_1.6.0.tgz vignettes: vignettes/RDAVIDWebService/inst/doc/RDavidWS-vignette.pdf vignetteTitles: RDAVIDWebService: a versatile R interface to DAVID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDAVIDWebService/inst/doc/RDavidWS-vignette.R dependsOnMe: CompGO suggestsMe: clusterProfiler, FGNet Package: Rdisop Version: 1.28.0 Depends: R (>= 2.0.0), RcppClassic LinkingTo: RcppClassic, Rcpp Suggests: RUnit License: GPL-2 Archs: i386, x64 MD5sum: cadc60dcccb6cf79b90e0a78e173ef2c NeedsCompilation: yes Title: Decomposition of Isotopic Patterns Description: Identification of metabolites using high precision mass spectrometry. MS Peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists. biocViews: MassSpectrometry, Metabolomics Author: Anton Pervukhin , Steffen Neumann Maintainer: Steffen Neumann URL: https://github.com/sneumann/Rdisop SystemRequirements: None BugReports: https://github.com/sneumann/Rdisop/issues/new source.ver: src/contrib/Rdisop_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rdisop_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rdisop_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rdisop_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rdisop_1.28.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf vignetteTitles: Molecule Identification with Rdisop hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/Rdisop/inst/doc/Rdisop.R suggestsMe: MSnbase Package: RDRToolbox Version: 1.18.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: 7629a34b08ec3d292f28cdda157626cd 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 source.ver: src/contrib/RDRToolbox_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RDRToolbox_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RDRToolbox_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RDRToolbox_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RDRToolbox_1.18.0.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 Package: ReactomePA Version: 1.12.3 Imports: DOSE, AnnotationDbi, reactome.db, igraph, graphite Suggests: clusterProfiler, knitr, BiocStyle License: GPL-2 MD5sum: 54896281b772ab24a24793dbb1b38d93 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. biocViews: Pathways, Visualization, Annotation, MultipleComparison, GeneSetEnrichment, Reactome Author: Guangchuang Yu Maintainer: Guangchuang Yu VignetteBuilder: knitr source.ver: src/contrib/ReactomePA_1.12.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/ReactomePA_1.12.3.zip win64.binary.ver: bin/windows64/contrib/3.2/ReactomePA_1.12.3.zip mac.binary.ver: bin/macosx/contrib/3.2/ReactomePA_1.12.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ReactomePA_1.12.3.tgz vignettes: vignettes/ReactomePA/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R htmlDocs: vignettes/ReactomePA/inst/doc/ReactomePA.html htmlTitles: "An R package for Reactome Pathway Analysis" suggestsMe: ChIPseeker, clusterProfiler Package: ReadqPCR Version: 1.14.0 Depends: R(>= 2.14.0), Biobase, methods, affy Imports: Biobase Suggests: qpcR License: LGPL-3 MD5sum: 038fa5b9ef181c2aeaa1d1cd6d013eb0 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 source.ver: src/contrib/ReadqPCR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ReadqPCR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ReadqPCR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ReadqPCR_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ReadqPCR_1.14.0.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 Package: reb Version: 1.46.0 Depends: R (>= 2.0), Biobase, idiogram (>= 1.5.3) License: GPL-2 Archs: i386, x64 MD5sum: 24cdc8595b25001f78b28ae23f595cd3 NeedsCompilation: yes Title: Regional Expression Biases Description: A set of functions to dentify regional expression biases biocViews: Microarray, CopyNumberVariation, Visualization Author: Kyle A. Furge and Karl Dykema Maintainer: Karl J. Dykema source.ver: src/contrib/reb_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/reb_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/reb_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/reb_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/reb_1.46.0.tgz vignettes: vignettes/reb/inst/doc/reb.pdf vignetteTitles: Smoothing of Microarray Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/reb/inst/doc/reb.R Package: RedeR Version: 1.16.0 Depends: R (>= 2.15), methods, igraph Imports: RCurl, XML Suggests: PANR, pvclust License: GPL (>= 2) MD5sum: 685e7d91565b3c3916396990a4227bdc 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 modular structures, nested networks and multiple levels of hierarchical associations. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Mauro Castro, Xin Wang, Florian Markowetz Maintainer: Mauro Castro URL: http://genomebiology.com/2012/13/4/R29 SystemRequirements: Java Runtime Environment (>= 6) source.ver: src/contrib/RedeR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RedeR_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RedeR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RedeR_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RedeR_1.16.0.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.pdf vignetteTitles: Main vignette: interactive visualization and manipulation of nested networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R importsMe: PANR, RTN Package: REDseq Version: 1.14.1 Depends: R (>= 2.15.0), BiocGenerics (>= 0.1.0), BSgenome.Celegans.UCSC.ce2, multtest, Biostrings, BSgenome, ChIPpeakAnno Imports: BiocGenerics, AnnotationDbi, Biostrings, ChIPpeakAnno, graphics, IRanges (>= 1.13.5), multtest, stats, utils License: GPL (>=2) MD5sum: d2445738216d78078917f29589a15343 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 and Thomas Fazzio Maintainer: Lihua Julie Zhu source.ver: src/contrib/REDseq_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/REDseq_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.2/REDseq_1.14.1.zip mac.binary.ver: bin/macosx/contrib/3.2/REDseq_1.14.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/REDseq_1.14.1.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 Package: RefNet Version: 1.4.0 Depends: R (>= 2.15.0), methods, IRanges, PSICQUIC, AnnotationHub, RCurl, shiny Imports: BiocGenerics Suggests: RUnit, BiocStyle, org.Hs.eg.db License: Artistic-2.0 MD5sum: 1d9bcf59180b4582dd7cbb38b4e32dea NeedsCompilation: no Title: A queryable collection of molecular interactions, from many sources Description: Molecular interactions with metadata, some archived, some dynamically obtained biocViews: GraphAndNetwork Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/RefNet_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RefNet_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RefNet_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RefNet_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RefNet_1.4.0.tgz vignettes: vignettes/RefNet/inst/doc/RefNet.pdf vignetteTitles: RefNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefNet/inst/doc/RefNet.R suggestsMe: RCyjs Package: RefPlus Version: 1.38.0 Depends: R (>= 2.8.0), Biobase (>= 2.1.0), affy (>= 1.20.0), affyPLM (>= 1.18.0), preprocessCore (>= 1.4.0) Suggests: affydata License: GPL (>= 2) MD5sum: 3362eac7a9f2d24e324ab6f686a2fbc6 NeedsCompilation: no Title: A function set for the Extrapolation Strategy (RMA+) and Extrapolation Averaging (RMA++) methods. Description: The package contains functions for pre-processing Affymetrix data using the RMA+ and the RMA++ methods. biocViews: Microarray, OneChannel, Preprocessing Author: Kai-Ming Chang , Chris Harbron , Marie C South Maintainer: Kai-Ming Chang source.ver: src/contrib/RefPlus_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RefPlus_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RefPlus_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RefPlus_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RefPlus_1.38.0.tgz vignettes: vignettes/RefPlus/inst/doc/RefPlus.pdf vignetteTitles: RefPlus Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefPlus/inst/doc/RefPlus.R Package: regioneR Version: 1.0.3 Depends: memoise, GenomicRanges, BSgenome, rtracklayer, parallel Imports: memoise, GenomicRanges, BSgenome, rtracklayer, parallel Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19.masked License: Artistic-2.0 MD5sum: 4258bc16e4c3648981c058f94d2d66ff 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 source.ver: src/contrib/regioneR_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/regioneR_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.2/regioneR_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.2/regioneR_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/regioneR_1.0.3.tgz vignettes: vignettes/regioneR/inst/doc/regioneR.pdf vignetteTitles: regioneR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneR/inst/doc/regioneR.R Package: regionReport Version: 1.2.1 Depends: R(>= 3.2) Imports: bumphunter (>= 1.7.6), derfinder (>= 1.1.0), derfinderPlot (>= 1.1.0), devtools (>= 1.6), GenomeInfoDb, GenomicRanges, ggbio (>= 1.13.13), ggplot2, grid, gridExtra, IRanges, knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), mgcv, RColorBrewer, rmarkdown (>= 0.3.3), whisker Suggests: biovizBase, Cairo, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: a0d82fea5e419173aa3103ce0c3141b0 NeedsCompilation: no Title: Generate HTML reports for exploring a set of regions Description: Generate HTML 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. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, Transcription, Coverage Author: Leonardo Collado-Torres [aut, cre], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/regionReport VignetteBuilder: knitr BugReports: https://github.com/lcolladotor/regionReport/issues source.ver: src/contrib/regionReport_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/regionReport_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/regionReport_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/regionReport_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/regionReport_1.2.1.tgz vignettes: vignettes/regionReport/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regionReport/inst/doc/regionReport.R htmlDocs: vignettes/regionReport/inst/doc/regionReport.html htmlTitles: "Introduction to regionReport" Package: Repitools Version: 1.14.0 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.8.0) Imports: S4Vectors, IRanges (>= 1.20.0), GenomeInfoDb, GenomicRanges, GenomicAlignments, BSgenome, gplots, grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Ringo, aroma.affymetrix, Rsolnp, parallel, Biostrings, Rsamtools, cluster, rtracklayer Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) Archs: i386, x64 MD5sum: 66eec88d826fb62febccaf87ee39e3fb NeedsCompilation: yes 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 source.ver: src/contrib/Repitools_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Repitools_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Repitools_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Repitools_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Repitools_1.14.0.tgz vignettes: vignettes/Repitools/inst/doc/Repitools_vignette.pdf vignetteTitles: Using Repitools for Epigenomic Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Repitools/inst/doc/Repitools_vignette.R Package: ReportingTools Version: 2.8.0 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, License: Artistic-2.0 MD5sum: 29f395b202482b545400df615aa3d298 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: 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, knitr source.ver: src/contrib/ReportingTools_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ReportingTools_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ReportingTools_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ReportingTools_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ReportingTools_2.8.0.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/knitr.R, vignettes/ReportingTools/inst/doc/microarrayAnalysis.R, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R, vignettes/ReportingTools/inst/doc/shiny.R htmlDocs: vignettes/ReportingTools/inst/doc/knitr.html htmlTitles: "Knitr and ReportingTools" importsMe: affycoretools, EnrichmentBrowser suggestsMe: cpvSNP, GSEABase, npGSEA Package: ReQON Version: 1.14.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: e6226c3e9cf91f8611ffd33c9b5945c7 NeedsCompilation: no Title: Recalibrating Quality Of Nucleotides Description: Algorithm for recalibrating the base quality scores for aligned sequencing data in BAM format. biocViews: Sequencing, Preprocessing, QualityControl Author: Christopher Cabanski, Keary Cavin, Chris Bizon Maintainer: Christopher Cabanski SystemRequirements: Java version >= 1.6 source.ver: src/contrib/ReQON_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ReQON_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ReQON_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ReQON_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ReQON_1.14.0.tgz vignettes: vignettes/ReQON/inst/doc/ReQON.pdf vignetteTitles: ReQON Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReQON/inst/doc/ReQON.R Package: rfPred Version: 1.6.0 Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel Suggests: BiocStyle License: GPL (>=2 ) Archs: i386, x64 MD5sum: dc67b7327d31d184906a4f547a048086 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 be connected on the Internet or 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 URL: http://www.sbim.fr/rfPred source.ver: src/contrib/rfPred_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rfPred_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rfPred_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rfPred_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rfPred_1.6.0.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 Package: rGADEM Version: 2.16.0 Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo Imports: Biostrings, IRanges, methods, graphics, seqLogo Suggests: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 Archs: i386, x64 MD5sum: 4c7d7c4fac3bf825afd11e46e46527fe 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 source.ver: src/contrib/rGADEM_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rGADEM_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rGADEM_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rGADEM_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rGADEM_2.16.0.tgz vignettes: vignettes/rGADEM/inst/doc/rGADEM.pdf vignetteTitles: The rGADEM users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGADEM/inst/doc/rGADEM.R importsMe: MotIV Package: RGalaxy Version: 1.12.0 Depends: XML, methods, tools, optparse, digest, Imports: BiocGenerics, Biobase, roxygen2 Suggests: RUnit, hgu95av2.db, knitr, formatR, Rserve Enhances: RSclient License: Artistic-2.0 MD5sum: cfe7277c87dd7b533595381ef6eb922c NeedsCompilation: no Title: Make an R function available in the Galaxy web platform Description: Given an R function and its manual page, make the documented function available in Galaxy. biocViews: Infrastructure Author: Dan Tenenbaum Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/RGalaxy_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RGalaxy_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RGalaxy_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RGalaxy_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RGalaxy_1.12.0.tgz vignettes: vignettes/RGalaxy/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.R htmlDocs: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html htmlTitles: "Introduction to RGalaxy" Package: Rgraphviz Version: 2.12.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL Archs: i386, x64 MD5sum: 47f9d0732cbab72a1d7ab61589d185b1 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) source.ver: src/contrib/Rgraphviz_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rgraphviz_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rgraphviz_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rgraphviz_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rgraphviz_2.12.0.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, flowCL, gaucho, GOFunction, MineICA, mvGST, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, TDARACNE, ToPASeq, TRONCO importsMe: apComplex, biocGraph, CompGO, DEGraph, EnrichmentBrowser, facopy, GOFunction, hyperdraw, nem, OncoSimulR, paircompviz, pathview, qpgraph, RchyOptimyx, SplicingGraphs suggestsMe: altcdfenvs, annotate, BiocCaseStudies, Category, CNORfeeder, CNORfuzzy, ddgraph, DEGraph, flowCore, GeneNetworkBuilder, geneplotter, GlobalAncova, globaltest, GOstats, GSEABase, KEGGgraph, MLP, NCIgraph, oneChannelGUI, pcaGoPromoter, pkgDepTools, RBGL, RBioinf, rBiopaxParser, RDAVIDWebService, Rtreemix, safe, SPIA, SRAdb, Streamer, topGO, vtpnet Package: rGREAT Version: 1.0.0 Depends: R (>= 2.10.0), GenomicRanges, IRanges Imports: methods, rjson, GetoptLong (>= 0.0.9), RCurl Suggests: testthat (>= 0.3), knitr, circlize License: GPL (>= 2) MD5sum: c7d0668d2166e342217540dbe53246e9 NeedsCompilation: no Title: Client for GREAT Analysis Description: This package makes GREAT (Genomic Regions Enrichment of Annotations Tool) analysis automatic by constructing a HTTP POST request according to user's input and automatically retrieving results from GREAT web server. biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing, WholeGenome, GenomeAnnotation Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/rGREAT VignetteBuilder: knitr source.ver: src/contrib/rGREAT_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rGREAT_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rGREAT_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rGREAT_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rGREAT_1.0.0.tgz vignettes: vignettes/rGREAT/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGREAT/inst/doc/rGREAT.R htmlDocs: vignettes/rGREAT/inst/doc/rGREAT.html htmlTitles: "Analyze with GREAT" Package: RGSEA Version: 1.2.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: 27965f0fdc7345e88b4e18423d775c0c 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 source.ver: src/contrib/RGSEA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RGSEA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RGSEA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RGSEA_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RGSEA_1.2.0.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 Package: rgsepd Version: 1.0.0 Depends: R (>= 3.0.0), DESeq2, goseq (>= 1.17) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, GenomicRanges, hash, AnnotationDbi Suggests: boot, tools, RUnit, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: 3455ac1303f15d6c8ad0e82fa8386a98 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 RefSeq 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: Software, DifferentialExpression, GeneSetEnrichment, RNASeq Author: Karl Stamm Maintainer: Karl Stamm VignetteBuilder: knitr source.ver: src/contrib/rgsepd_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rgsepd_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rgsepd_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rgsepd_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rgsepd_1.0.0.tgz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R Package: rhdf5 Version: 2.12.0 Depends: methods Imports: zlibbioc Suggests: bit64,BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 849379d3e552baf612a678307871d135 NeedsCompilation: yes Title: HDF5 interface to R Description: This R/Bioconductor 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, Gregoire Pau Maintainer: Bernd Fischer SystemRequirements: GNU make source.ver: src/contrib/rhdf5_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rhdf5_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rhdf5_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rhdf5_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rhdf5_2.12.0.tgz vignettes: vignettes/rhdf5/inst/doc/rhdf5.pdf vignetteTitles: rhdf5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GENE.E, GSCA importsMe: diffHic, GENE.E, h5vc Package: Rhtslib Version: 1.0.0 Imports: zlibbioc LinkingTo: zlibbioc Suggests: BiocStyle, knitr License: LGPL (>= 2) Archs: i386, x64 MD5sum: 8dd39941828765eb462262255d8bbfb1 NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.1 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 [cre, aut], Martin Morgan [aut] Maintainer: Nathaniel Hayden URL: https://github.com/nhayden/Rhtslib, http://www.htslib.org/ VignetteBuilder: knitr BugReports: https://github.com/nhayden/Rhtslib source.ver: src/contrib/Rhtslib_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rhtslib_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rhtslib_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rhtslib_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rhtslib_1.0.0.tgz vignettes: vignettes/Rhtslib/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R htmlDocs: vignettes/Rhtslib/inst/doc/Rhtslib.html htmlTitles: "Motivation and Use of Rhtslib" dependsOnMe: deepSNV importsMe: deepSNV Package: rHVDM Version: 1.34.0 Depends: R (>= 2.10), R2HTML (>= 1.5), affy (>= 1.23.4), minpack.lm (>= 1.0-5), Biobase (>= 2.5.5) License: GPL-2 MD5sum: 0eea50af515bf17c389fb150b90040f2 NeedsCompilation: no Title: Hidden Variable Dynamic Modeling Description: A R implementation of HVDM (Genome Biol 2006, V7(3) R25) biocViews: Microarray, GraphAndNetwork, Transcription, Classification, NetworkInference Author: Martino Barenco Maintainer: Martino Barenco source.ver: src/contrib/rHVDM_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rHVDM_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rHVDM_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rHVDM_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rHVDM_1.34.0.tgz vignettes: vignettes/rHVDM/inst/doc/rHVDM.pdf vignetteTitles: rHVDM primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rHVDM/inst/doc/rHVDM.R Package: riboSeqR Version: 1.2.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Suggests: baySeq, BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: 34a59918c7fa6e3e1ab331a6226ecfb1 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 Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle source.ver: src/contrib/riboSeqR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/riboSeqR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/riboSeqR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/riboSeqR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/riboSeqR_1.2.0.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 Package: Ringo Version: 1.32.0 Depends: methods, Biobase (>= 1.14.1), RColorBrewer, limma, Matrix, grid, lattice Imports: BiocGenerics (>= 0.1.11), genefilter, limma, vsn, stats4 Suggests: rtracklayer (>= 1.3.1), mclust, topGO (>= 1.15.0) License: Artistic-2.0 Archs: i386, x64 MD5sum: 4a00fbde0f4ca32ec3f6e0a071730167 NeedsCompilation: yes Title: R Investigation of ChIP-chip Oligoarrays Description: The package Ringo facilitates the primary analysis of ChIP-chip data. The main functionalities of the package are data read-in, quality assessment, data visualisation and identification of genomic regions showing enrichment in ChIP-chip. The package has functions to deal with two-color oligonucleotide microarrays from NimbleGen used in ChIP-chip projects, but also contains more general functions for ChIP-chip data analysis, given that the data is supplied as RGList (raw) or ExpressionSet (pre- processed). The package employs functions from various other packages of the Bioconductor project and provides additional ChIP-chip-specific and NimbleGen-specific functionalities. biocViews: Microarray,TwoChannel,DataImport,QualityControl,Preprocessing Author: Joern Toedling, Oleg Sklyar, Tammo Krueger, Matt Ritchie, Wolfgang Huber Maintainer: J. Toedling source.ver: src/contrib/Ringo_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Ringo_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Ringo_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Ringo_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Ringo_1.32.0.tgz vignettes: vignettes/Ringo/inst/doc/Ringo.pdf vignetteTitles: R Investigation of NimbleGen Oligoarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Ringo/inst/doc/Ringo.R dependsOnMe: SimBindProfiles, Starr importsMe: Repitools Package: RIPSeeker Version: 1.8.0 Depends: R (>= 2.15), methods, IRanges, GenomicRanges, Rsamtools, GenomicAlignments, rtracklayer Suggests: biomaRt, ChIPpeakAnno, parallel, GenomicFeatures License: GPL-2 MD5sum: 1d01df901891132b1eb19f2a55c4dc3d NeedsCompilation: no Title: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments Description: Infer and discriminate RIP peaks from RIP-seq alignments using two-state HMM with negative binomial emission probability. While RIPSeeker is specifically tailored for RIP-seq data analysis, it also provides a suite of bioinformatics tools integrated within this self-contained software package comprehensively addressing issues ranging from post-alignments processing to visualization and annotation. biocViews: Sequencing, RIPSeq Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html source.ver: src/contrib/RIPSeeker_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RIPSeeker_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RIPSeeker_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RIPSeeker_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RIPSeeker_1.8.0.tgz vignettes: vignettes/RIPSeeker/inst/doc/RIPSeeker.pdf vignetteTitles: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIPSeeker/inst/doc/RIPSeeker.R Package: Risa Version: 1.10.0 Depends: R (>= 2.0.9), Biobase (>= 2.4.0), methods, Rcpp (>= 0.9.13), biocViews, affy Imports: xcms Suggests: faahKO (>= 1.2.11) License: LGPL MD5sum: 3f0ab572dceec8304aad652a6146d832 NeedsCompilation: no Title: Converting experimental metadata from ISA-tab into Bioconductor data structures Description: The Investigation / Study / Assay (ISA) tab-delimited format is a general purpose framework with which to collect and communicate complex metadata (i.e. sample characteristics, technologies used, type of measurements made) from experiments employing a combination of technologies, spanning from traditional approaches to high-throughput techniques. Risa allows to access metadata/data in ISA-Tab format and build Bioconductor data structures. Currently, data generated from microarray, flow cytometry and metabolomics-based (i.e. mass spectrometry) assays are supported. The package is extendable and efforts are undergoing to support metadata associated to proteomics assays. biocViews: Annotation, DataImport, MassSpectrometry Author: Alejandra Gonzalez-Beltran, Audrey Kauffmann, Steffen Neumann, Gabriella Rustici, ISA Team Maintainer: Alejandra Gonzalez-Beltran, ISA Team URL: source.ver: src/contrib/Risa_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Risa_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Risa_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Risa_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Risa_1.10.0.tgz vignettes: vignettes/Risa/inst/doc/Risa.pdf vignetteTitles: Risa: converts experimental metadata from ISA-tab into Bioconductor data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Risa/inst/doc/Risa.R Package: RLMM Version: 1.30.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: 4b2f164a391639c6411a20ded09d0261 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) source.ver: src/contrib/RLMM_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RLMM_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RLMM_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RLMM_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RLMM_1.30.0.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 Package: Rmagpie Version: 1.24.0 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: 000f329b4d68a54477c1ee15c031da64 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/ source.ver: src/contrib/Rmagpie_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rmagpie_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rmagpie_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rmagpie_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rmagpie_1.24.0.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 Package: RMassBank Version: 1.10.0 Depends: Rcpp Imports: XML,RCurl,rjson, rcdk,yaml,mzR,methods Suggests: gplots,RMassBankData, xcms (>= 1.37.1), CAMERA, ontoCAT, RUnit License: Artistic-2.0 MD5sum: 0a249db530946eba06cc26bc751675f7 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: Bioinformatics, MassSpectrometry, Metabolomics, Software Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, with contributions from Tobias Schulze Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel source.ver: src/contrib/RMassBank_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RMassBank_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RMassBank_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RMassBank_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RMassBank_1.10.0.tgz vignettes: vignettes/RMassBank/inst/doc/RMassBankNonstandard.pdf, vignettes/RMassBank/inst/doc/RMassBank.pdf, vignettes/RMassBank/inst/doc/RMassBankXCMS.pdf vignetteTitles: RMassBank non-standard usage, RMassBank walkthrough, RMassBank using XCMS walkthrough hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RMassBank/inst/doc/RMassBankNonstandard.R, vignettes/RMassBank/inst/doc/RMassBank.R, vignettes/RMassBank/inst/doc/RMassBankXCMS.R Package: rMAT Version: 3.18.0 Depends: R(>= 2.9.0), BiocGenerics (>= 0.1.3), IRanges (>= 1.13.10), Biobase (>= 2.15.1), affxparser Imports: stats, methods, BiocGenerics, IRanges, Biobase, affxparser, stats4 Suggests: GenomeGraphs, rtracklayer License: Artistic-2.0 MD5sum: 744fea2d842deb09e2b46181df685e12 NeedsCompilation: yes Title: R implementation from MAT program to normalize and analyze tiling arrays and ChIP-chip data. Description: This package is an R version of the package MAT and contains functions to parse and merge Affymetrix BPMAP and CEL tiling array files (using C++ based Fusion SDK and Bioconductor package affxparser), normalize tiling arrays using sequence specific models, detect enriched regions from ChIP-chip experiments. Note: users should have GSL and GenomeGraphs installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. Snow Leopard users can take advantage of increase speed with Grand Central Dispatch! biocViews: Microarray, Preprocessing Author: Charles Cheung and Arnaud Droit and Raphael Gottardo Maintainer: Arnaud Droit and Raphael Gottardo URL: http://www.rglab.org source.ver: src/contrib/rMAT_3.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/rMAT_3.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rMAT_3.18.0.tgz vignettes: vignettes/rMAT/inst/doc/rMAT.pdf vignetteTitles: The rMAT users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rMAT/inst/doc/rMAT.R Package: RmiR Version: 1.24.0 Depends: R (>= 2.7.0), RmiR.Hs.miRNA, RSVGTipsDevice Imports: DBI, methods, stats Suggests: hgug4112a.db,org.Hs.eg.db License: Artistic-2.0 MD5sum: 0793db4ccc8a24d58467b467678b6c72 NeedsCompilation: no Title: Package to work with miRNAs and miRNA targets with R Description: Useful functions to merge microRNA and respective targets using differents databases biocViews: Software,GeneExpression,Microarray,TimeCourse,Visualization Author: Francesco Favero Maintainer: Francesco Favero source.ver: src/contrib/RmiR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RmiR_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RmiR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RmiR_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RmiR_1.24.0.tgz vignettes: vignettes/RmiR/inst/doc/RmiR.pdf vignetteTitles: RmiR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RmiR/inst/doc/RmiR.R suggestsMe: oneChannelGUI Package: RNAinteract Version: 1.16.0 Depends: R (>= 2.12.0), abind, locfit, Biobase Imports: RColorBrewer, ICS, ICSNP, cellHTS2, geneplotter, gplots, grid, hwriter, lattice, latticeExtra, limma, methods, splots (>= 1.13.12) License: Artistic-2.0 MD5sum: e00ff291acf3b1900492fcc50339a9c9 NeedsCompilation: no Title: Estimate Pairwise Interactions from multidimensional features Description: RNAinteract estimates genetic interactions from multi-dimensional read-outs like features extracted from images. The screen is assumed to be performed in multi-well plates or similar designs. Starting from a list of features (e.g. cell number, area, fluorescence intensity) per well, genetic interactions are estimated. The packages provides functions for reporting interacting gene pairs, plotting heatmaps and double RNAi plots. An HTML report can be written for quality control and analysis. biocViews: CellBasedAssays, QualityControl, Preprocessing, Visualization Author: Bernd Fischer Maintainer: Bernd Fischer source.ver: src/contrib/RNAinteract_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RNAinteract_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RNAinteract_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RNAinteract_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RNAinteract_1.16.0.tgz vignettes: vignettes/RNAinteract/inst/doc/RNAinteract.pdf vignetteTitles: RNAinteract hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAinteract/inst/doc/RNAinteract.R Package: RNAither Version: 2.16.0 Depends: R (>= 2.10), topGO, RankProd, prada Imports: geneplotter, limma, biomaRt, car, splots, methods License: Artistic-2.0 MD5sum: 042070d198124bf76c90d3998186e088 NeedsCompilation: no Title: Statistical analysis of high-throughput RNAi screens Description: RNAither analyzes cell-based RNAi screens, and includes quality assessment, customizable normalization and statistical tests, leading to lists of significant genes and biological processes. biocViews: CellBasedAssays, QualityControl, Preprocessing, Visualization, Annotation, GO Author: Nora Rieber and Lars Kaderali, University of Heidelberg, Viroquant Research Group Modeling, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Maintainer: Lars Kaderali source.ver: src/contrib/RNAither_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RNAither_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RNAither_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RNAither_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RNAither_2.16.0.tgz vignettes: vignettes/RNAither/inst/doc/vignetteRNAither.pdf vignetteTitles: RNAither,, an automated pipeline for the statistical analysis of high-throughput RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAither/inst/doc/vignetteRNAither.R Package: RNAprobR Version: 1.1.2 Depends: R (>= 3.1.1), GenomicFeatures(>= 1.16.3), plyr(>= 1.8.1), BiocGenerics(>= 0.10.0) Imports: Biostrings(>= 2.32.1), GenomicRanges(>= 1.16.4), Rsamtools(>= 1.16.1), rtracklayer(>= 1.24.2) Suggests: BiocStyle License: GPL (>=2) MD5sum: f2738908cc115b6dd2d81ad77fb68d0f NeedsCompilation: no Title: An R package for analysis of massive parallel sequencing based RNA structure probing data Description: This package facilitates analysis of Next Generation Sequencing data for which positional information with a single nucleotide resolution is a key. It allows for applying different types of relevant normalizations, data visualization and export in a table or UCSC compatible bedgraph file. biocViews: Coverage, Normalization, Sequencing, GenomeAnnotation Author: Lukasz Jan Kielpinski [aut], Nikos Sidiropoulos [cre, aut], Jeppe Vinther [aut] Maintainer: Nikos Sidiropoulos source.ver: src/contrib/RNAprobR_1.1.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/RNAprobR_1.1.2.zip win64.binary.ver: bin/windows64/contrib/3.2/RNAprobR_1.1.2.zip mac.binary.ver: bin/macosx/contrib/3.2/RNAprobR_1.1.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RNAprobR_1.1.2.tgz vignettes: vignettes/RNAprobR/inst/doc/RNAprobR.pdf vignetteTitles: RNAprobR: An R package for analysis of the massive parallel sequencing based methods of RNA structure probing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAprobR/inst/doc/RNAprobR.R Package: rnaSeqMap Version: 2.26.0 Depends: R (>= 2.11.0), methods, Biobase, Rsamtools, GenomicAlignments Imports: GenomicRanges , IRanges, edgeR, DESeq, DBI License: GPL-2 Archs: i386, x64 MD5sum: b82b315d974694a3864530593dd8f76b NeedsCompilation: yes Title: rnaSeq secondary analyses Description: The rnaSeqMap library provides classes and functions to analyze the RNA-sequencing data using the coverage profiles in multiple samples at a time biocViews: Annotation, ReportWriting, Transcription, GeneExpression, DifferentialExpression, Sequencing, RNASeq, SAGE, Visualization Author: Anna Lesniewska ; Michal Okoniewski Maintainer: Michal Okoniewski source.ver: src/contrib/rnaSeqMap_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rnaSeqMap_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rnaSeqMap_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rnaSeqMap_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rnaSeqMap_2.26.0.tgz vignettes: vignettes/rnaSeqMap/inst/doc/rnaSeqMap.pdf vignetteTitles: rnaSeqMap primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaSeqMap/inst/doc/rnaSeqMap.R dependsOnMe: ampliQueso Package: RNASeqPower Version: 1.8.0 License: LGPL (>=2) MD5sum: 3a745f227dd9a00ba76ae84b543f95f3 NeedsCompilation: no Title: Sample size for RNAseq studies Description: RNA-seq, sample size biocViews: RNASeq Author: Terry M Therneau [aut, cre], Hart Stephen [ctb] Maintainer: Terry M Therneau source.ver: src/contrib/RNASeqPower_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RNASeqPower_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RNASeqPower_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RNASeqPower_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RNASeqPower_1.8.0.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 Package: RnaSeqSampleSize Version: 1.0.0 Depends: R (>= 2.10), RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,Rcpp (>= 0.11.2) LinkingTo: Rcpp Suggests: BiocStyle, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: eb86fd45ea79299596c8ff344ada5bb5 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 biocViews: ExperimentalDesign, Sequencing, RNASeq, GeneExpression, DifferentialExpression Author: Shilin Zhao, Chung-I Li, Yan Guo, Quanhu Sheng, Yu Shyr Maintainer: Shilin Zhao VignetteBuilder: knitr source.ver: src/contrib/RnaSeqSampleSize_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RnaSeqSampleSize_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RnaSeqSampleSize_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RnaSeqSampleSize_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RnaSeqSampleSize_1.0.0.tgz 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 Package: RnBeads Version: 1.0.0 Depends: R (>= 3.0.0), BiocGenerics, GenomicRanges, MASS, RColorBrewer, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr Imports: IRanges Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Rnorvegicus.UCSC.rn5, Category, GEOquery, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RefFreeEWAS, RnBeads.hg19, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5, 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, sva, wateRmelon, wordcloud, argparse License: GPL-3 MD5sum: 2375cb148f027f2e4efa73a54fa7440d 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, TwoChannel, DataImport Author: Yassen Assenov [aut], Pavlo Lutsik [aut], Fabian Mueller [aut, cre] Maintainer: Fabian Mueller source.ver: src/contrib/RnBeads_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RnBeads_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RnBeads_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RnBeads_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RnBeads_1.0.0.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 Package: Rnits Version: 1.2.0 Depends: R (>= 3.1.0), Biobase, ggplot2, limma, methods Imports: affy, boot, impute, splines, graphics, qvalue, reshape2 Suggests: BiocStyle, knitr, GEOquery, stringr License: GPL-3 MD5sum: 6316b3015b0208777d83b8566890dfe3 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 source.ver: src/contrib/Rnits_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rnits_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rnits_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rnits_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rnits_1.2.0.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 Package: roar Version: 1.4.0 Depends: R (>= 3.0.1) Imports: GenomicRanges, GenomicAlignments(>= 0.99.4), methods, rtracklayer, S4Vectors Suggests: RUnit, BiocGenerics, RNAseqData.HNRNPC.bam.chr14 License: GPL-3 MD5sum: 7f1e98213d92b8a1eb64f2c57b90ff2c 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/ source.ver: src/contrib/roar_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/roar_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/roar_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/roar_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/roar_1.4.0.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 Package: ROC Version: 1.44.0 Depends: R (>= 1.9.0), utils, methods Suggests: Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 3420b7061c47f66a7c5f25f5a08ddd56 NeedsCompilation: yes Title: utilities for ROC, with uarray focus Description: utilities for ROC, with uarray focus biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: http://www.bioconductor.org source.ver: src/contrib/ROC_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ROC_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ROC_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ROC_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ROC_1.44.0.tgz vignettes: vignettes/ROC/inst/doc/ROCnotes.pdf vignetteTitles: ROC notes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROC/inst/doc/ROCnotes.R dependsOnMe: TCC, wateRmelon importsMe: clst suggestsMe: genefilter, MCRestimate Package: Roleswitch Version: 1.6.0 Depends: R (>= 2.10), pracma, reshape, plotrix, microRNA, biomaRt, Biostrings, Biobase, DBI Suggests: ggplot2 License: GPL-2 MD5sum: 3929c5accc8a13b4b36b71515059d64e NeedsCompilation: no Title: Infer miRNA-mRNA interactions using paired expression data from a single sample Description: Infer Probabilities of MiRNA-mRNA Interaction Signature (ProMISe) using paired expression data from a single sample. Roleswitch operates in two phases by inferring the probability of mRNA (miRNA) being the targets ("targets") of miRNA (mRNA), taking into account the expression of all of the mRNAs (miRNAs) due to their potential competition for the same miRNA (mRNA). Due to dynamic miRNA repression in the cell, Roleswitch assumes that the total transcribed mRNA levels are higher than the observed (equilibrium) mRNA levels and iteratively updates the total transcription of each mRNA targets based on the above inference. NB: in the paper, we used ProMISe as both the model name and inferred score name. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/roleswitch.html source.ver: src/contrib/Roleswitch_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Roleswitch_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Roleswitch_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Roleswitch_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Roleswitch_1.6.0.tgz vignettes: vignettes/Roleswitch/inst/doc/Roleswitch.pdf vignetteTitles: Roleswitch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Roleswitch/inst/doc/Roleswitch.R Package: Rolexa Version: 1.24.0 Depends: R (>= 2.9.0), graphics, grDevices, methods, ShortRead Imports: mclust, Biostrings, graphics, grDevices, IRanges, methods, ShortRead, stats Enhances: fork License: GPL-2 MD5sum: 7d7d9462e8f71351bf6f235fdaf8cae6 NeedsCompilation: no Title: Statistical analysis of Solexa sequencing data Description: Provides probabilistic base calling, quality checks and diagnostic plots for Solexa sequencing data biocViews: Sequencing, DataImport, Preprocessing, QualityControl Author: Jacques Rougemont, Arnaud Amzallag, Christian Iseli, Laurent Farinelli, Ioannis Xenarios, Felix Naef Maintainer: Jacques Rougemont source.ver: src/contrib/Rolexa_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rolexa_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rolexa_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rolexa_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rolexa_1.24.0.tgz vignettes: vignettes/Rolexa/inst/doc/Rolexa-vignette.pdf vignetteTitles: Rolexa hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rolexa/inst/doc/Rolexa-vignette-batchplot.R, vignettes/Rolexa/inst/doc/Rolexa-vignette.R, vignettes/Rolexa/inst/doc/Rolexa-vignette-tileimage.R Package: rols Version: 1.10.0 Depends: methods Imports: XML, XMLSchema (>= 0.6.0), SSOAP (>= 0.8.0), Biobase, utils Suggests: xtable, GO.db, knitr (>= 1.1.0), BiocStyle License: GPL-2 MD5sum: fe2bc985b056b4811dec8995cc1d9c8a NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: This package allows to query EBI's Ontology Lookup Service (OLS) using Simple Object Access Protocol (SOAP). biocViews: Software, Annotation, MassSpectrometry, GO Author: Laurent Gatto Maintainer: Laurent Gatto URL: http://lgatto.github.com/rols/ VignetteBuilder: knitr source.ver: src/contrib/rols_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rols_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rols_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rols_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rols_1.10.0.tgz vignettes: vignettes/rols/inst/doc/rols.pdf vignetteTitles: The rols interface to the Ontology Lookup Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rols/inst/doc/rols.R suggestsMe: MSnbase Package: ROntoTools Version: 1.8.1 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: 65100408fdc3310977c116f25e6e7373 NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sorin Draghici Maintainer: Calin Voichita source.ver: src/contrib/ROntoTools_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ROntoTools_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ROntoTools_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ROntoTools_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ROntoTools_1.8.1.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 Package: RPA Version: 1.24.0 Depends: R (>= 3.1.1), parallel, affy, methods Suggests: affydata License: BSD_2_clause + file LICENSE MD5sum: e9ff82c31bbc55fed52130f2e8f1b5c2 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. Lahti et al. "Probabilistic Analysis of Probe Reliability in Differential Gene Expression Studies with Short Oligonucleotide Arrays", TCBB/IEEE, 2011. http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.38 biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA BugReports: https://github.com/antagomir/RPA source.ver: src/contrib/RPA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RPA_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RPA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RPA_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RPA_1.24.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs Package: RpsiXML Version: 2.10.0 Depends: methods, annotate (>= 1.21.0), graph (>= 1.21.0), Biobase, RBGL (>= 1.17.0), XML (>= 2.4.0), hypergraph (>= 1.15.2), AnnotationDbi Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db,hom.Hs.inp.db, hom.Mm.inp.db, hom.Dm.inp.db, hom.Rn.inp.db, hom.Sc.inp.db,Rgraphviz, ppiStats, ScISI License: LGPL-3 MD5sum: 64f2c4bb82f631dbb9705a03938e4fab NeedsCompilation: no Title: R interface to PSI-MI 2.5 files Description: Queries, data structure and interface to visualization of interaction datasets. This package inplements the PSI-MI 2.5 standard and supports up to now 8 databases. Further databases supporting PSI-MI 2.5 standard will be added continuously. biocViews: Infrastructure, Proteomics Author: Jitao David Zhang, Stefan Wiemann, Marc Carlson, with contributions from Tony Chiang Maintainer: Jitao David Zhang URL: http://www.bioconductor.org source.ver: src/contrib/RpsiXML_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RpsiXML_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RpsiXML_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RpsiXML_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RpsiXML_2.10.0.tgz vignettes: vignettes/RpsiXML/inst/doc/RpsiXMLApp.pdf, vignettes/RpsiXML/inst/doc/RpsiXML.pdf vignetteTitles: Application Examples of RpsiXML package, Reading PSI-25 XML files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RpsiXML/inst/doc/RpsiXMLApp.R, vignettes/RpsiXML/inst/doc/RpsiXML.R dependsOnMe: ScISI importsMe: ScISI Package: rpx Version: 1.4.0 Depends: methods Imports: XML, RCurl, utils Suggests: MSnbase, Biostrings, BiocStyle, BiocGenerics, RUnit, knitr License: GPL-2 MD5sum: 48c02e9f82946a2683db8551816bb0fd NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: This package implements an interface to proteomics data submitted to the ProteomeXchange consortium. biocViews: Proteomics, MassSpectrometry, DataImport, ThirdPartyClient Author: Laurent Gatto Maintainer: Laurent Gatto VignetteBuilder: knitr source.ver: src/contrib/rpx_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rpx_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rpx_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rpx_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rpx_1.4.0.tgz vignettes: vignettes/rpx/inst/doc/rpx.pdf vignetteTitles: An interface to proteomics data repositories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R suggestsMe: proteoQC Package: Rqc Version: 1.2.0 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics, Biostrings, IRanges, methods, S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid, reshape2 License: GPL (>= 2) MD5sum: bb2f47008c375a15bcfd9cc01f37693f NeedsCompilation: no 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 source.ver: src/contrib/Rqc_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rqc_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rqc_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rqc_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rqc_1.2.0.tgz vignettes: vignettes/Rqc/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R htmlDocs: vignettes/Rqc/inst/doc/Rqc.html htmlTitles: "Rqc - Quality Control Tool for High-Throughput Sequencing Data" Package: rqubic Version: 1.14.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 Archs: i386, x64 MD5sum: a0436f66ee22f37a6651f01fa701a820 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: Microarray, Clustering Author: Jitao David Zhang, with inputs from Laura Badi and Martin Ebeling Maintainer: Jitao David Zhang source.ver: src/contrib/rqubic_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rqubic_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rqubic_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rqubic_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rqubic_1.14.0.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 Package: rRDP Version: 1.2.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE MD5sum: 3e5b85b05ad06a62e724ccfe2ef5f6ad NeedsCompilation: no Title: Interface to the RDP Classifier Description: Seamlessly interfaces RDP classifier (version 2.9). biocViews: Genetics, Sequencing, Infrastructure, Classification, Microbiome Author: Michael Hahsler, Anurag Nagar Maintainer: Michael Hahsler SystemRequirements: Java source.ver: src/contrib/rRDP_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rRDP_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rRDP_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rRDP_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rRDP_1.2.0.tgz vignettes: vignettes/rRDP/inst/doc/rRDP.pdf vignetteTitles: rRDP: Interface to the RDP Classifier hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rRDP/inst/doc/rRDP.R Package: RRHO Version: 1.8.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: 2000af6f4ae82547a0f0cbb9923af8b7 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 source.ver: src/contrib/RRHO_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RRHO_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RRHO_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RRHO_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RRHO_1.8.0.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 Package: Rsamtools Version: 1.20.5 Depends: methods, S4Vectors (>= 0.5.11), IRanges (>= 1.99.17), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.19), XVector (>= 0.5.3), Biostrings (>= 2.33.11) Imports: utils, BiocGenerics (>= 0.1.3), zlibbioc, bitops LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, KEGG.db, TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: 73f4242b4e958452890baad202680c26 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 (see 'LICENCE') 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, Herv\'e Pag\`es, Valerie Obenchain, Nathaniel Hayden Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/release/bioc/html/Rsamtools.html Video: https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q source.ver: src/contrib/Rsamtools_1.20.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rsamtools_1.20.5.zip win64.binary.ver: bin/windows64/contrib/3.2/Rsamtools_1.20.5.zip mac.binary.ver: bin/macosx/contrib/3.2/Rsamtools_1.20.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rsamtools_1.20.5.tgz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.pdf, vignettes/Rsamtools/inst/doc/Rsamtools-UsingCLibraries.pdf vignetteTitles: An introduction to Rsamtools, Using samtools C libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R, vignettes/Rsamtools/inst/doc/Rsamtools-UsingCLibraries.R dependsOnMe: ArrayExpressHTS, BitSeq, chimera, CODEX, exomeCopy, exomePeak, GenomicAlignments, GenomicFiles, girafe, MEDIPS, methylPipe, oneChannelGUI, podkat, qrqc, r3Cseq, Rcade, ReQON, rfPred, RIPSeeker, rnaSeqMap, ShortRead, systemPipeR, TEQC, TitanCNA, VariantAnnotation, wavClusteR importsMe: AllelicImbalance, annmap, ArrayExpressHTS, biovizBase, BSgenome, CAGEr, casper, CexoR, ChIPQC, cn.mops, CNVrd2, compEpiTools, CopywriteR, csaw, customProDB, derfinder, DEXSeq, diffHic, DOQTL, easyRNASeq, EDASeq, epigenomix, FourCSeq, FunciSNP, genomation, GenomicAlignments, GenomicInteractions, ggbio, GGtools, gmapR, GoogleGenomics, GreyListChIP, Gviz, gwascat, h5vc, HTSeqGenie, metagene, nucleR, PICS, QDNAseq, QuasR, R453Plus1Toolbox, Rariant, Repitools, RNAprobR, rtracklayer, SGSeq, similaRpeak, soGGi, SplicingGraphs, tracktables, trackViewer, TransView, VariantFiltering, VariantTools suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocParallel, biomvRCNS, DiffBind, gage, GenomeInfoDb, GenomicFeatures, GenomicRanges, gQTLstats, metaseqR, seqbias, SigFuge, Streamer Package: rsbml Version: 2.26.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: abfac35a1f7eb337f7010515f473c8dc 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) source.ver: src/contrib/rsbml_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rsbml_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rsbml_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rsbml_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rsbml_2.26.0.tgz vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rsbml/inst/doc/quick-start.R dependsOnMe: BiGGR suggestsMe: piano, SBMLR Package: rSFFreader Version: 0.16.0 Depends: ShortRead (>= 1.23.17) Imports: methods, Biostrings, IRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: xtable License: Artistic-2.0 MD5sum: 5e26c009840328cba691a26061113eca NeedsCompilation: yes Title: rSFFreader reads in sff files generated by Roche 454 and Life Sciences Ion Torrent sequencers Description: rSFFreader reads sequence, qualities and clip point values from sff files generated by Roche 454 and Life Sciences Ion Torrent sequencers into similar classes as are present for fastq files. biocViews: DataImport, Sequencing Author: Matt Settles , Sam Hunter, Brice Sarver, Ilia Zhbannikov, Kyu-Chul Cho Maintainer: Matt Settles source.ver: src/contrib/rSFFreader_0.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/rSFFreader_0.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rSFFreader_0.16.0.tgz vignettes: vignettes/rSFFreader/inst/doc/rSFFreader.pdf vignetteTitles: An introduction to rSFFreader hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rSFFreader/inst/doc/rSFFreader.R importsMe: hiReadsProcessor Package: Rsubread Version: 1.18.0 License: GPL-3 MD5sum: 2a6726c3175402838ba5f5dccb91f173 NeedsCompilation: yes Title: Rsubread package: high-performance read alignment, quantification and mutation discovery Description: This R package provides powerful and easy-to-use tools for analyzing next-gen sequencing read data. Functions of this package include quality assessment of sequence reads, read alignment, read summarization, exon-exon junction detection, fusion detection, detection of short and long indels, absolute expression calling and SNP calling. This package can be used to anlayze data generated from all major sequencing platforms such as Illumina GA/HiSeq/MiSeq, Roche GS-FLX, ABI SOLiD and LifeTech Ion PGM/Proton sequencers. It supports multiple operating systems incluidng Linux, Mac OS X, FreeBSD and Solaris. biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq, GeneExpression, GeneRegulation, Genetics, SNP, GeneticVariability, Preprocessing, QualityControl, GenomeAnnotation, Software Author: Wei Shi and Yang Liao with contributions from Jenny Zhiyin Dai and Timothy Triche, Jr. Maintainer: Wei Shi URL: http://bioconductor.org/packages/release/bioc/html/Rsubread.html source.ver: src/contrib/Rsubread_1.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/Rsubread_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rsubread_1.18.0.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf vignetteTitles: Rsubread Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R Package: RSVSim Version: 1.8.0 Depends: R (>= 3.0.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer License: LGPL-3 MD5sum: f9f2adef8a6bfc9a75e2dcaf7549007f 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 source.ver: src/contrib/RSVSim_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RSVSim_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RSVSim_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RSVSim_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RSVSim_1.8.0.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 Package: rTANDEM Version: 1.8.0 Depends: XML, Rcpp, data.table (>= 1.8.8) Imports: methods LinkingTo: Rcpp Suggests: biomaRt License: Artistic-1.0 | file LICENSE Archs: i386, x64 MD5sum: 12c8024ed13cac0f06231d1f92d3b449 NeedsCompilation: yes Title: Interfaces the tandem protein identification algorithm in R Description: This package interfaces the tandem protein identification algorithm in R. Identification can be launched in the X!Tandem style, by using as sole parameter the path to a parameter file. But rTANDEM aslo provides extended syntax and functions to streamline launching analyses, as well as function to convert results, parameters and taxonomy to/from R. A related package, shinyTANDEM, provides visualization interface for result objects. biocViews: MassSpectrometry, Proteomics Author: Frederic Fournier , Charles Joly Beauparlant , Rene Paradis , Arnaud Droit Maintainer: Frederic Fournier SystemRequirements: rTANDEM uses expat and pthread libraries. See the README file for details. source.ver: src/contrib/rTANDEM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rTANDEM_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rTANDEM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rTANDEM_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rTANDEM_1.8.0.tgz vignettes: vignettes/rTANDEM/inst/doc/rTANDEM.pdf vignetteTitles: The rTANDEM users guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rTANDEM/inst/doc/rTANDEM.R dependsOnMe: shinyTANDEM importsMe: proteoQC Package: RTCA Version: 1.20.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: cce5e3ca7cc0b57d7101726de557a349 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: 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 source.ver: src/contrib/RTCA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RTCA_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RTCA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RTCA_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RTCA_1.20.0.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: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R Package: RTN Version: 1.6.2 Depends: R (>= 2.15), methods, igraph Imports: RedeR, minet, snow, limma, data.table, ff, car, IRanges Suggests: HTSanalyzeR, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 422e1b48356deb2906da63f07ba75f5f NeedsCompilation: no Title: Reconstruction of transcriptional networks and analysis of master regulators Description: This package provides classes and methods for transcriptional network inference and analysis. Modulators of transcription factor activity are assessed by conditional mutual information, and master regulators are mapped to phenotypes using different strategies, e.g., gene set enrichment, shadow and synergy analyses. Additionally, master regulators can be linked to genetic markers using eQTL/VSE analysis, taking advantage of the haplotype block structure mapped to the human genome in order to explore risk-associated SNPs identified in GWAS studies. biocViews: NetworkInference, NetworkAnalysis, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork, GeneSetEnrichment,GeneticVariability,SNP Author: Mauro Castro, Xin Wang, Michael Fletcher, Florian Markowetz and Kerstin Meyer Maintainer: Mauro Castro URL: http://dx.doi.org/10.1038/ncomms3464 source.ver: src/contrib/RTN_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/RTN_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.2/RTN_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.2/RTN_1.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RTN_1.6.2.tgz vignettes: vignettes/RTN/inst/doc/RTN.pdf vignetteTitles: Main vignette: reconstruction and analysis of transcriptional networks in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R Package: RTopper Version: 1.14.0 Depends: R (>= 2.11.0), Biobase Imports: limma, multtest Suggests: limma, org.Hs.eg.db, KEGG.db, GO.db License: GPL (>= 3) MD5sum: 658fd7f9f0d87d5b245146fbd2f79e06 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 source.ver: src/contrib/RTopper_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RTopper_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RTopper_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RTopper_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RTopper_1.14.0.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 Package: rtracklayer Version: 1.28.10 Depends: R (>= 2.10), methods, GenomicRanges (>= 1.19.3) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.13.8), S4Vectors (>= 0.5.11), IRanges (>= 2.1.41), XVector (>= 0.5.8), GenomeInfoDb (>= 1.3.14), Biostrings (>= 2.33.14), zlibbioc, RCurl (>= 1.4-2), Rsamtools (>= 1.17.8), GenomicAlignments (>= 1.3.23), tools LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 Archs: i386, x64 MD5sum: 3063491473638503b533eb4c1d3f06aa NeedsCompilation: yes Title: R interface to genome browsers and their annotation tracks 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 source.ver: src/contrib/rtracklayer_1.28.10.tar.gz win.binary.ver: bin/windows/contrib/3.2/rtracklayer_1.28.10.zip win64.binary.ver: bin/windows64/contrib/3.2/rtracklayer_1.28.10.zip mac.binary.ver: bin/macosx/contrib/3.2/rtracklayer_1.28.10.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rtracklayer_1.28.10.tgz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BSgenome, cummeRbund, epivizr, exomePeak, GenomicFiles, groHMM, MethylSeekR, r3Cseq, regioneR, RIPSeeker, spliceR importsMe: ballgown, BiSeq, BSgenome, CAGEr, casper, CexoR, ChIPseeker, ChromHeatMap, CNEr, coMET, CompGO, conumee, customProDB, derfinder, ensembldb, FourCSeq, FunciSNP, genomation, GenomicFeatures, GenomicInteractions, ggbio, GGtools, gmapR, GOTHiC, GreyListChIP, Gviz, gwascat, hiAnnotator, HiTC, HTSeqGenie, MEDIPS, metagene, methyAnalysis, MotifDb, Pbase, proBAMr, regioneR, Repitools, RNAprobR, roar, seqplots, SGSeq, similaRpeak, soGGi, TFBSTools, trackViewer, VariantAnnotation, VariantTools, wavClusteR suggestsMe: AnnotationHub, biovizBase, ChIPpeakAnno, compEpiTools, GenomicAlignments, GenomicRanges, goseq, InPAS, interactiveDisplay, metaseqR, methylumi, MotIV, NarrowPeaks, oneChannelGUI, PICS, PING, QuasR, R453Plus1Toolbox, Ringo, rMAT, RnBeads, RSVSim, triplex, TSSi Package: Rtreemix Version: 1.30.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL Archs: i386, x64 MD5sum: 3f295e22dddeaff7e81aa263c5c01cec NeedsCompilation: yes Title: Rtreemix: Mutagenetic trees mixture models. Description: Rtreemix is a package that offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. It includes functions for fitting the trees mixture models, likelihood computations, model comparisons, waiting time estimations, stability analysis, etc. biocViews: StatisticalMethod Author: Jasmina Bogojeska Maintainer: Jasmina Bogojeska source.ver: src/contrib/Rtreemix_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rtreemix_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rtreemix_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rtreemix_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rtreemix_1.30.0.tgz vignettes: vignettes/Rtreemix/inst/doc/Rtreemix.pdf vignetteTitles: Rtreemix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtreemix/inst/doc/Rtreemix.R Package: rTRM Version: 1.6.0 Depends: R (>= 2.10), igraph (>= 0.7), RSQLite Imports: AnnotationDbi Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt, knitr, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db, org.Mm.eg.db, ggplot2 License: GPL-3 MD5sum: 6e9b28501c4ff0f0f1e1d534c386c57c NeedsCompilation: no Title: Identification of transcriptional regulatory modules from PPI networks Description: rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork Author: Diego Diez Maintainer: Diego Diez VignetteBuilder: knitr source.ver: src/contrib/rTRM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rTRM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rTRM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rTRM_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rTRM_1.6.0.tgz vignettes: vignettes/rTRM/inst/doc/rTRM_Introduction.pdf vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRM/inst/doc/rTRM_Introduction.R importsMe: rTRMui Package: rTRMui Version: 1.6.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 80ee1d67c6c6fbf9d1b5c5f3a2e437bb 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 source.ver: src/contrib/rTRMui_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rTRMui_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rTRMui_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rTRMui_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rTRMui_1.6.0.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 Package: RUVcorr Version: 1.0.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2 Suggests: knitr, BiocStyle, hgu133a2.db License: GPL-2 MD5sum: 24403cc2df28c8ddf1967d715cd4e44e 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) to real and simulated gene expression data. biocViews: GeneExpression, Normalization Author: Saskia Freytag Maintainer: Saskia Freytag VignetteBuilder: knitr source.ver: src/contrib/RUVcorr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RUVcorr_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RUVcorr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RUVcorr_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RUVcorr_1.0.0.tgz vignettes: vignettes/RUVcorr/inst/doc/RUVcorrVignetteNew.pdf vignetteTitles: RUVcorr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVcorr/inst/doc/RUVcorrVignetteNew.R Package: RUVnormalize Version: 1.2.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: 1d10e50366cfc60069a480ba76b3ec71 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 source.ver: src/contrib/RUVnormalize_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RUVnormalize_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RUVnormalize_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RUVnormalize_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RUVnormalize_1.2.0.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 Package: RUVSeq Version: 1.2.0 Depends: EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq License: Artistic-2.0 MD5sum: ce54f0e92952f3bb728b0a9887e61b31 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: DifferentialExpression, Preprocessing, RNASeq, Software Author: Davide Risso and Sandrine Dudoit Maintainer: Davide Risso VignetteBuilder: knitr source.ver: src/contrib/RUVSeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RUVSeq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RUVSeq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RUVSeq_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RUVSeq_1.2.0.tgz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.pdf vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R Package: RWebServices Version: 1.32.0 Depends: SJava (>= 0.85), TypeInfo, methods, tools (>= 2.10.0), R (>= 2.5.0) Imports: RCurl License: file LICENSE License_restricts_use: no MD5sum: 30fc28a23054031704e99da19d66360c NeedsCompilation: yes Title: Expose R functions as web services through Java/Axis/Apache Description: This package provides mechanisms for automatic function prototyping and exposure of R functionality in a web services environment. biocViews: Infrastructure Author: Nianhua Li, MT Morgan Maintainer: Martin Morgan source.ver: src/contrib/RWebServices_1.32.0.tar.gz vignettes: vignettes/RWebServices/inst/doc/EnablingPackages.pdf, vignettes/RWebServices/inst/doc/InstallingAndTesting.pdf, vignettes/RWebServices/inst/doc/LessonsLearned.pdf, vignettes/RWebServices/inst/doc/RelatedWork.pdf, vignettes/RWebServices/inst/doc/RToJava.pdf vignetteTitles: Enabling packages as web services, Installing and testing RWebServices and enabled packages, Lessons learned exposing web services, RelatedWork, From R to Java hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RWebServices/inst/doc/EnablingPackages.R, vignettes/RWebServices/inst/doc/InstallingAndTesting.R, vignettes/RWebServices/inst/doc/LessonsLearned.R, vignettes/RWebServices/inst/doc/RelatedWork.R, vignettes/RWebServices/inst/doc/RToJava.R Package: S4Vectors Version: 0.6.6 Depends: R (>= 3.1.0), methods, utils, stats, stats4, BiocGenerics (>= 0.11.3) Imports: methods, utils, stats, stats4, BiocGenerics Suggests: IRanges, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 3f443aa8c3e91f7c99e43ebdf99f73fe NeedsCompilation: yes Title: S4 implementation of vectors and lists 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, 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: H. Pages, M. Lawrence and P. Aboyoun Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/S4Vectors_0.6.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/S4Vectors_0.6.6.zip win64.binary.ver: bin/windows64/contrib/3.2/S4Vectors_0.6.6.zip mac.binary.ver: bin/macosx/contrib/3.2/S4Vectors_0.6.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/S4Vectors_0.6.6.tgz vignettes: vignettes/S4Vectors/inst/doc/RleTricks.pdf vignetteTitles: Rle Tips and Tricks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R dependsOnMe: Biostrings, BiSeq, BSgenome, bsseq, bumphunter, CexoR, chipseq, ChIPseqR, CSAR, DESeq2, DirichletMultinomial, DMRcaller, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges, girafe, groHMM, Gviz, InPAS, IRanges, meshr, methyAnalysis, MotifDb, OTUbase, plethy, Rsamtools, segmentSeq, triplex, VariantTools, XVector importsMe: AllelicImbalance, AnnotationDbi, AnnotationForge, AnnotationHub, ballgown, biovizBase, BiSeq, BitSeq, BSgenome, casper, ChIPQC, ChIPseeker, CNEr, coMET, compEpiTools, copynumber, CopywriteR, csaw, DECIPHER, derfinder, derfinderHelper, diffHic, DOQTL, easyRNASeq, EnrichmentBrowser, ensembldb, epivizr, GenomeInfoDb, GenomicAlignments, GenomicInteractions, GenomicTuples, genoset, ggbio, GGtools, gmapR, GoogleGenomics, GOTHiC, gQTLBase, gQTLstats, gwascat, h5vc, IVAS, kebabs, methylPipe, methylumi, minfi, MinimumDistance, msa, MSnbase, mygene, NarrowPeaks, nucleR, oligoClasses, Pbase, pdInfoBuilder, PICS, PING, polyester, prebs, qcmetrics, qpgraph, QuasR, R453Plus1Toolbox, Rariant, Rcade, Repitools, roar, Rqc, rtracklayer, SeqArray, seqplots, SeqVarTools, SGSeq, ShortRead, soGGi, SomaticSignatures, SplicingGraphs, TFBSTools, TSSi, VanillaICE, VariantAnnotation, VariantFiltering, XVector suggestsMe: BiocGenerics Package: safe Version: 3.8.0 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: c749a6d4919b655ed98fa2d09e86eee2 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: William T. Barry source.ver: src/contrib/safe_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/safe_3.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/safe_3.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/safe_3.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/safe_3.8.0.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: EnrichmentBrowser Package: sagenhaft Version: 1.38.1 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, methods, SparseM, stats, utils License: GPL (>= 2) MD5sum: b19045b442dd1740c9d4c982de36ab56 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 and Lavinia Hyde . Maintainer: Tim Beissbarth URL: http://tagcalling.mbgproject.org source.ver: src/contrib/sagenhaft_1.38.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/sagenhaft_1.38.1.zip win64.binary.ver: bin/windows64/contrib/3.2/sagenhaft_1.38.1.zip mac.binary.ver: bin/macosx/contrib/3.2/sagenhaft_1.38.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sagenhaft_1.38.1.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 Package: SAGx Version: 1.42.0 Depends: R (>= 2.5.0), stats, multtest, methods Imports: Biobase, stats4 Suggests: KEGG.db, hu6800.db, MASS License: GPL-3 Archs: i386, x64 MD5sum: 427d7411b8a69330f93509911a0f114e NeedsCompilation: yes Title: Statistical Analysis of the GeneChip Description: A package for retrieval, preparation and analysis of data from the Affymetrix GeneChip. In particular the issue of identifying differentially expressed genes is addressed. biocViews: Microarray, OneChannel, Preprocessing, DataImport, DifferentialExpression, Clustering, MultipleComparison, GeneExpression, GeneSetEnrichment, Pathways, Regression, KEGG Author: Per Broberg Maintainer: Per Broberg, URL: http://home.swipnet.se/pibroberg/expression_hemsida1.html source.ver: src/contrib/SAGx_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SAGx_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SAGx_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SAGx_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SAGx_1.42.0.tgz vignettes: vignettes/SAGx/inst/doc/samroc-ex.pdf vignetteTitles: samroc - example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAGx/inst/doc/samroc-ex.R Package: SamSPECTRAL Version: 1.22.0 Depends: R (>= 2.10) Imports: methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 092f7dc102a0e5d0c2c285da20648c89 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 sample. For instructions on manual installation, refer to the PDF file provided in the following documentation. biocViews: FlowCytometry, CellBiology, Clustering, Cancer, FlowCytometry, StemCells, HIV Author: Habil Zare and Parisa Shooshtari Maintainer: Habil Zare source.ver: src/contrib/SamSPECTRAL_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SamSPECTRAL_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SamSPECTRAL_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SamSPECTRAL_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SamSPECTRAL_1.22.0.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 Package: sangerseqR Version: 1.4.0 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: b33a1fb1926513245c6178b89e7f7510 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 source.ver: src/contrib/sangerseqR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sangerseqR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sangerseqR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sangerseqR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sangerseqR_1.4.0.tgz vignettes: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.pdf vignetteTitles: sangerseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.R Package: SANTA Version: 2.6.0 Depends: R (>= 2.14), igraph Imports: Matrix, snow Suggests: RUnit, BiocGenerics, knitr, knitcitations, formatR, org.Sc.sgd.db, BioNet, DLBCL, msm License: GPL (>= 2) Archs: i386, x64 MD5sum: 89a65c97977cf19118e494984835d943 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 J. Cornish and Florian Markowetz Maintainer: Alex J. Cornish VignetteBuilder: knitr source.ver: src/contrib/SANTA_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SANTA_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SANTA_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SANTA_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SANTA_2.6.0.tgz vignettes: vignettes/SANTA/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R htmlDocs: vignettes/SANTA/inst/doc/SANTA-vignette.html htmlTitles: "Introduction to SANTA" Package: sapFinder Version: 1.6.0 Depends: R (>= 3.0.0),rTANDEM (>= 1.3.5) Imports: pheatmap,Rcpp (>= 0.10.6),graphics,grDevices,stats, utils LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: 80bd7f9057c965f4f7c29054d1587757 NeedsCompilation: yes Title: A package for variant peptides detection and visualization in shotgun proteomics. Description: sapFinder is developed to automate (1) variation-associated database construction, (2) database searching, (3) post-processing, (4) HTML-based report generation in shotgun proteomics. biocViews: MassSpectrometry, Proteomics, SNP, RNASeq, Visualization, ReportWriting Author: Shaohang Xu, Bo Wen Maintainer: Shaohang Xu , Bo Wen source.ver: src/contrib/sapFinder_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sapFinder_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sapFinder_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sapFinder_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sapFinder_1.6.0.tgz vignettes: vignettes/sapFinder/inst/doc/sapFinder.pdf vignetteTitles: sapFinder Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sapFinder/inst/doc/sapFinder.R Package: saps Version: 2.0.0 Depends: R (>= 2.14.0), survival Imports: piano, survcomp, reshape2 Suggests: snowfall, knitr License: MIT + file LICENSE MD5sum: e5cf4554f028dfbd4d46285d60b7c747 NeedsCompilation: no Title: Significance Analysis of Prognostic Signatures Description: Functions implementing the Significance Analysis of Prognostic Signatures method (SAPS). SAPS provides a robust method for identifying biologically significant gene sets associated with patient survival. Three basic statistics are computed. First, patients are clustered into two survival groups based on differential expression of a candidate gene set. P_pure is calculated as the probability of no survival difference between the two groups. Next, the same procedure is applied to randomly generated gene sets, and P_random is calculated as the proportion achieving a P_pure as significant as the candidate gene set. Finally, a pre-ranked Gene Set Enrichment Analysis (GSEA) is performed by ranking all genes by concordance index, and P_enrich is computed to indicate the degree to which the candidate gene set is enriched for genes with univariate prognostic significance. A SAPS_score is calculated to summarize the three statistics, and optionally a Q-value is computed to estimate the significance of the SAPS_score by calculating SAPS_scores for random gene sets. biocViews: BiomedicalInformatics, GeneExpression, GeneSetEnrichment, DifferentialExpression, Survival Author: Daniel Schmolze [aut, cre], Andrew Beck [aut], Benjamin Haibe-Kains [aut] Maintainer: Daniel Schmolze VignetteBuilder: knitr source.ver: src/contrib/saps_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/saps_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/saps_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/saps_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/saps_2.0.0.tgz vignettes: vignettes/saps/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/saps/inst/doc/saps.R htmlDocs: vignettes/saps/inst/doc/saps.html htmlTitles: "SAPS Vignette" Package: savR Version: 1.7.5 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo, testthat License: AGPL-3 MD5sum: 74388d1f9b46bf03a0cb065b9e0731ec NeedsCompilation: no Title: Parse and analyze Illumina SAV files Description: Parse Illumina Sequence Analysis Viewer (SAV) files, access data, and generate QC plots. biocViews: Sequencing Author: R. Brent Calder Maintainer: R. Brent Calder URL: https://github.com/bcalder/savR BugReports: https://github.com/bcalder/savR/issues source.ver: src/contrib/savR_1.7.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/savR_1.7.5.zip win64.binary.ver: bin/windows64/contrib/3.2/savR_1.7.5.zip mac.binary.ver: bin/macosx/contrib/3.2/savR_1.7.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/savR_1.7.5.tgz vignettes: vignettes/savR/inst/doc/savR.pdf vignetteTitles: Using savR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/savR/inst/doc/savR.R Package: SBMLR Version: 1.64.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: 881102ebed0c89923881632f97bb455b 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 source.ver: src/contrib/SBMLR_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SBMLR_1.64.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SBMLR_1.64.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SBMLR_1.64.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SBMLR_1.64.0.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 Package: SCAN.UPC Version: 2.10.9 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: f4fe72947546ebf408552dcc5fc20b1f 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: 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 source.ver: src/contrib/SCAN.UPC_2.10.9.tar.gz win.binary.ver: bin/windows/contrib/3.2/SCAN.UPC_2.10.9.zip win64.binary.ver: bin/windows64/contrib/3.2/SCAN.UPC_2.10.9.zip mac.binary.ver: bin/macosx/contrib/3.2/SCAN.UPC_2.10.9.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SCAN.UPC_2.10.9.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 Package: ScISI Version: 1.40.0 Depends: R (>= 2.10), GO.db, RpsiXML, annotate, apComplex Imports: AnnotationDbi, GO.db, RpsiXML, annotate, methods, org.Sc.sgd.db, utils Suggests: ppiData, xtable License: LGPL MD5sum: e8b1926269eca00df352ef1022fea609 NeedsCompilation: no Title: In Silico Interactome Description: Package to create In Silico Interactomes biocViews: GraphAndNetwork, Proteomics, NetworkInference, DecisionTree Author: Tony Chiang Maintainer: Tony Chiang source.ver: src/contrib/ScISI_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ScISI_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ScISI_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ScISI_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ScISI_1.40.0.tgz vignettes: vignettes/ScISI/inst/doc/vignette.pdf vignetteTitles: ScISI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ScISI/inst/doc/vignette.R dependsOnMe: PCpheno, ppiStats, SLGI importsMe: PCpheno, SLGI suggestsMe: RpsiXML Package: scsR Version: 1.4.0 Depends: R (>= 2.14.0), STRINGdb, methods, BiocGenerics, Biostrings, IRanges, plyr, tcltk Imports: sqldf, hash, ggplot2, graphics,grDevices, RColorBrewer Suggests: RUnit License: GPL-2 MD5sum: 15fd9428e57a85acca8c61342dba4181 NeedsCompilation: no Title: SiRNA correction for seed mediated off-target effect Description: Corrects genome-wide siRNA screens for seed mediated off-target effect. Suitable functions to identify the effective seeds/miRNAs and to visualize their effect are also provided in the package. biocViews: Preprocessing Author: Andrea Franceschini Maintainer: Andrea Franceschini , Roger Meier , Christian von Mering source.ver: src/contrib/scsR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/scsR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/scsR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/scsR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/scsR_1.4.0.tgz vignettes: vignettes/scsR/inst/doc/scsR.pdf vignetteTitles: scsR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scsR/inst/doc/scsR.R Package: segmentSeq Version: 2.2.2 Depends: R (>= 2.3.0), methods, baySeq (>= 1.99.0), ShortRead, GenomicRanges, IRanges, S4Vectors Imports: graphics, grDevices, utils Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 5a3ffb22c368030e57067c68ff05b0f7 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 Maintainer: Thomas J. Hardcastle source.ver: src/contrib/segmentSeq_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/segmentSeq_2.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/segmentSeq_2.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/segmentSeq_2.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/segmentSeq_2.2.2.tgz vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.pdf, vignettes/segmentSeq/inst/doc/segmentSeq.pdf 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 Package: SELEX Version: 1.0.0 Depends: R (>= 2.7.0), rJava (>= 0.5-0), Biostrings (>= 2.26.0) License: GPL (>=2) MD5sum: fa2416761c9b1a526549f5687e2d60b3 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, and Harmen Bussemaker Maintainer: Harmen Bussemaker URL: http://bussemakerlab.org/software/SELEX/ SystemRequirements: Java (>= 1.5) source.ver: src/contrib/SELEX_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SELEX_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SELEX_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SELEX_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SELEX_1.0.0.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 Package: SemDist Version: 1.2.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: f3b2b53c74b720b8daccafffe27ab122 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 source.ver: src/contrib/SemDist_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SemDist_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SemDist_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SemDist_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SemDist_1.2.0.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 Package: seq2pathway Version: 1.0.1 Depends: R (>= 2.10.0) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: a929c2100d20ddc75bd27ccded03c42c 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: Xinan Yang with contribution from Lorenzo Pesce and Ana Marija Sokovic source.ver: src/contrib/seq2pathway_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/seq2pathway_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/seq2pathway_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/seq2pathway_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seq2pathway_1.0.1.tgz vignettes: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.pdf vignetteTitles: An R package for sequence hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.R Package: SeqArray Version: 1.8.0 Depends: gdsfmt (>= 1.2.2) Imports: methods, Biostrings, GenomicRanges, IRanges, S4Vectors, VariantAnnotation LinkingTo: gdsfmt Suggests: parallel, BiocStyle, BiocGenerics, knitr, RUnit, Rcpp License: GPL-3 Archs: i386, x64 MD5sum: 5c6f07ee83ab3ce82937c890a88a2b92 NeedsCompilation: yes Title: Big Data Management of Genome-wide Sequencing Variants Description: Big data management of genome-wide variants using the CoreArray C++ library: genotypic data and annotations are stored in an array-oriented manner, offering efficient access of genetic variants using the R programming language. biocViews: Infrastructure, Sequencing, Genetics Author: Xiuwen Zheng [aut, cre], Stephanie Gogarten [aut], Cathy Laurie [ctb] Maintainer: Xiuwen Zheng URL: http://corearray.sourceforge.net/tutorials/SeqArray/, http://github.com/zhengxwen/SeqArray VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SeqArray/issues source.ver: src/contrib/SeqArray_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SeqArray_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SeqArray_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SeqArray_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SeqArray_1.8.0.tgz vignettes: vignettes/SeqArray/inst/doc/SeqArray-JSM2013.pdf, vignettes/SeqArray/inst/doc/SeqArrayTutorial.pdf vignetteTitles: SeqArray-JSM2013.pdf, SeqArray Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/SeqArrayTutorial.R dependsOnMe: SeqVarTools Package: seqbias Version: 1.16.0 Depends: R (>= 2.13.0), GenomicRanges (>= 0.1.0), Biostrings (>= 2.15.0), methods Imports: zlibbioc LinkingTo: Rsamtools (>= 1.19.38) Suggests: Rsamtools, ggplot2 License: LGPL-3 Archs: i386, x64 MD5sum: 2d07699d2fcc32be4a71cc4bc81255cb NeedsCompilation: yes Title: Estimation of per-position bias in high-throughput sequencing data Description: This package implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence. biocViews: Sequencing Author: Daniel Jones Maintainer: Daniel Jones source.ver: src/contrib/seqbias_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqbias_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/seqbias_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/seqbias_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqbias_1.16.0.tgz vignettes: vignettes/seqbias/inst/doc/overview.pdf vignetteTitles: Assessing and Adjusting for Technical Bias in High Throughput Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqbias/inst/doc/overview.R dependsOnMe: ReQON Package: seqCNA Version: 1.14.0 Depends: R (>= 3.0), GLAD (>= 2.14), doSNOW (>= 1.0.5), adehabitatLT (>= 0.3.4), seqCNA.annot (>= 0.99), methods License: GPL-3 Archs: i386, x64 MD5sum: 76b38d8676c20e35f93aae108d3b9fe3 NeedsCompilation: yes Title: Copy number analysis of high-throughput sequencing cancer data Description: Copy number analysis of high-throughput sequencing cancer data with fast summarization, extensive filtering and improved normalization biocViews: CopyNumberVariation, Genetics, Sequencing Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena SystemRequirements: samtools source.ver: src/contrib/seqCNA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqCNA_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/seqCNA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/seqCNA_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqCNA_1.14.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf vignetteTitles: seqCNA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R Package: SeqGSEA Version: 1.8.1 Depends: Biobase, doParallel, DESeq Imports: methods, biomaRt Suggests: easyRNASeq, GenomicRanges License: GPL (>= 3) MD5sum: f23c361ddb7018cd0cebb0be8022e08b 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 Author: Xi Wang Maintainer: Xi Wang source.ver: src/contrib/SeqGSEA_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/SeqGSEA_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/SeqGSEA_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/SeqGSEA_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SeqGSEA_1.8.1.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 Package: seqLogo Version: 1.34.0 Depends: methods, grid Imports: stats4 License: LGPL (>= 2) MD5sum: b826133a8b15d71848c16f5c658439b7 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 Maintainer: Oliver Bembom source.ver: src/contrib/seqLogo_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqLogo_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/seqLogo_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/seqLogo_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqLogo_1.34.0.tgz vignettes: vignettes/seqLogo/inst/doc/seqLogo.pdf vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R dependsOnMe: motifRG, rGADEM importsMe: PWMEnrich, rGADEM, TFBSTools suggestsMe: BCRANK, MotifDb Package: seqPattern Version: 1.0.1 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: 16228b612f3e6eedb5614309db4d2202 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 source.ver: src/contrib/seqPattern_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqPattern_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/seqPattern_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/seqPattern_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqPattern_1.0.1.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 Package: seqplots Version: 1.6.7 Depends: R (>= 3.1.0) Imports: methods, IRanges, BSgenome, digest, rtracklayer, GenomicRanges, Biostrings, shiny (>= 0.11.0), DBI, RSQLite, plotrix, fields, grid, kohonen, Cairo, parallel, GenomeInfoDb, class, S4Vectors, ggplot2, reshape2, gridExtra, jsonlite, DT, RColorBrewer Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: c8982c6cca59a26650ae310781a69f73 NeedsCompilation: no Title: An interactive tool for visualizing NGS signals and sequence motif densities along genomic features using average plots and heatmaps Description: SeqPlots is a tool for plotting next generation sequencing (NGS) based experiments' signal tracks, e.g. reads coverage from ChIP-seq, RNA-seq and DNA accessibility assays like DNase-seq and MNase-seq, over user specified genomic features, e.g. promoters, gene bodies, etc. It can also calculate sequence motif density profiles from reference genome. The data are visualized as average signal profile plot, with error estimates (standard error and 95% confidence interval) shown as fields, or as series of heatmaps that can be sorted and clustered using hierarchical clustering, k-means algorithm and self organising maps. Plots can be prepared using R programming language or web browser based graphical user interface (GUI) implemented using Shiny framework. The dual-purpose implementation allows running the software locally on desktop or deploying it on server. SeqPlots is useful for both for exploratory data analyses and preparing replicable, publication quality plots. Other features of the software include collaboration and data sharing capabilities, as well as ability to store pre-calculated result matrixes, that combine many sequencing experiments and in-silico generated tracks with multiple different features. These binaries can be further used to generate new combination plots on fly, run automated batch operations or share with colleagues, who can adjust their plotting parameters without loading actual tracks and recalculating numeric values. SeqPlots relays on Bioconductor packages, mainly on rtracklayer for data input and BSgenome packages for reference genome sequence and annotations. biocViews: ChIPSeq, RNASeq, Sequencing, Software, Visualization Author: Przemyslaw Stempor Maintainer: Przemyslaw Stempor URL: http://github.com/przemol/seqplots VignetteBuilder: knitr BugReports: http://github.com/przemol/seqplots/issues source.ver: src/contrib/seqplots_1.6.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqplots_1.6.7.zip win64.binary.ver: bin/windows64/contrib/3.2/seqplots_1.6.7.zip mac.binary.ver: bin/macosx/contrib/3.2/seqplots_1.6.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqplots_1.6.7.tgz vignettes: vignettes/seqplots/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqplots/inst/doc/QuickStart.R, vignettes/seqplots/inst/doc/SeqPlotsGUI.R htmlDocs: vignettes/seqplots/inst/doc/QuickStart.html, vignettes/seqplots/inst/doc/SeqPlotsGUI.html htmlTitles: "SeqPlots R workflow", "SeqPlots R workflow" Package: seqTools Version: 1.2.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: af9d1f051143b3aae2776b7f12db406d 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 source.ver: src/contrib/seqTools_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqTools_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/seqTools_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/seqTools_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqTools_1.2.0.tgz vignettes: vignettes/seqTools/inst/doc/seqTools.pdf, vignettes/seqTools/inst/doc/seqTools_qual_report.pdf vignetteTitles: Introduction, seqTools_qual_report hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqTools/inst/doc/seqTools_qual_report.R, vignettes/seqTools/inst/doc/seqTools.R Package: SeqVarTools Version: 1.6.0 Depends: SeqArray (>= 1.1.1) Imports: methods, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, VariantAnnotation Suggests: BiocGenerics, BiocStyle, RUnit License: GPL-3 MD5sum: fc17df4d9593c9c72a6b1ea7c38b94c1 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 Maintainer: Stephanie M. Gogarten , Xiuwen Zheng source.ver: src/contrib/SeqVarTools_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SeqVarTools_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SeqVarTools_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SeqVarTools_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SeqVarTools_1.6.0.tgz vignettes: vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Introduction to SeqVarTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqVarTools/inst/doc/SeqVarTools.R Package: SGSeq Version: 1.2.2 Depends: GenomicRanges, IRanges, methods Imports: AnnotationDbi, BiocGenerics, Biostrings, GenomicAlignments, GenomicFeatures, GenomeInfoDb, igraph, parallel, Rsamtools, rtracklayer, S4Vectors Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 6951b77fbe571036f6fb5bc59f22711b NeedsCompilation: no Title: Splice event detection and quantification from RNA-seq data Description: Predict splice junctions and exons from BAM files and obtain compatible read counts and FPKMs. Identify splice events and estimate relative usage of splice variants based on compatible read counts at event boundaries. biocViews: AlternativeSplicing, RNASeq, Transcription Author: Leonard Goldstein Maintainer: Leonard Goldstein VignetteBuilder: knitr source.ver: src/contrib/SGSeq_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/SGSeq_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/SGSeq_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/SGSeq_1.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SGSeq_1.2.2.tgz vignettes: vignettes/SGSeq/inst/doc/SGSeq.pdf vignetteTitles: SGSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R Package: shinyMethyl Version: 1.2.0 Depends: methods, BiocGenerics (>= 0.3.2), shiny (>= 0.9.1), minfi (>= 1.6.0), IlluminaHumanMethylation450kmanifest, matrixStats, R (>= 3.0.0) Imports: RColorBrewer Suggests: shinyMethylData, minfiData, BiocStyle, RUnit, digest, knitr License: Artistic-2.0 MD5sum: e620baf9c22d08bc17097d092fe11aea NeedsCompilation: no Title: Interactive visualization for Illumina's 450k methylation arrays Description: Interactive tool for visualizing Illumina's 450k array data biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin VignetteBuilder: knitr source.ver: src/contrib/shinyMethyl_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/shinyMethyl_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/shinyMethyl_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/shinyMethyl_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/shinyMethyl_1.2.0.tgz vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.pdf vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K methylation arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinyMethyl/inst/doc/shinyMethyl.R Package: shinyTANDEM Version: 1.6.0 Depends: rTANDEM (>= 1.3.5), shiny, mixtools, methods, xtable License: GPL-3 MD5sum: a445b6cc732de2786ce4f90d0a9ad965 NeedsCompilation: no Title: Provides a GUI for rTANDEM Description: This package provides a GUI interface for rTANDEM. The GUI is primarily designed to visualize rTANDEM result object or result xml files. But it will also provides an interface for creating parameter objects, launching searches or performing conversions between R objects and xml files. biocViews: MassSpectrometry, Proteomics Author: Frederic Fournier , Arnaud Droit Maintainer: Frederic Fournier source.ver: src/contrib/shinyTANDEM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/shinyTANDEM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/shinyTANDEM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/shinyTANDEM_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/shinyTANDEM_1.6.0.tgz vignettes: vignettes/shinyTANDEM/inst/doc/shinyTANDEM.pdf vignetteTitles: shinyTANDEM user guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinyTANDEM/inst/doc/shinyTANDEM.R Package: ShortRead Version: 1.26.0 Depends: BiocGenerics (>= 0.11.3), BiocParallel, Biostrings (>= 2.33.14), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.28) Imports: Biobase, S4Vectors (>= 0.5.11), IRanges (>= 1.99.27), GenomeInfoDb (>= 1.1.19), GenomicRanges (>= 1.17.39), hwriter, methods, zlibbioc, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi License: Artistic-2.0 Archs: i386, x64 MD5sum: 7572d49ee4a8449f8a880fc81a347157 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: Martin Morgan, Michael Lawrence, Simon Anders Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/ShortRead_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ShortRead_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ShortRead_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ShortRead_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ShortRead_1.26.0.tgz vignettes: vignettes/ShortRead/inst/doc/Overview.pdf vignetteTitles: An introduction to ShortRead hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ShortRead/inst/doc/Overview.R dependsOnMe: Basic4Cseq, chipseq, EDASeq, girafe, HTSeqGenie, nucleR, OTUbase, Rolexa, Rqc, rSFFreader, segmentSeq, systemPipeR importsMe: ArrayExpressHTS, BEAT, chipseq, ChIPseqR, ChIPsim, easyRNASeq, GOTHiC, QuasR, R453Plus1Toolbox, Rolexa, RSVSim suggestsMe: BiocParallel, CSAR, DBChIP, GenomicAlignments, Genominator, PICS, PING, Repitools, Rsamtools Package: sigaR Version: 1.12.0 Depends: Biobase, CGHbase, methods, mvtnorm, penalized Imports: corpcor (>= 1.6.2), graphics, igraph, marray, MASS, mvtnorm, quadprog, penalized (>= 0.9-39), snowfall, stats License: GPL (>= 2) MD5sum: b6118f3740a271582d19ed877e94c8bf NeedsCompilation: no Title: statistics for integrative genomics analyses in R Description: Facilites the joint analysis of high-throughput data from multiple molecular levels. Contains functions for manipulation of objects, various analysis types, and some visualization. biocViews: Microarray, DifferentialExpression, aCGH, GeneExpression, Pathways Author: Wessel N. van Wieringen Maintainer: Wessel N. van Wieringen URL: http://www.few.vu.nl/~wvanwie source.ver: src/contrib/sigaR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sigaR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sigaR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sigaR_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sigaR_1.12.0.tgz vignettes: vignettes/sigaR/inst/doc/sigaR.pdf vignetteTitles: sigaR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigaR/inst/doc/sigaR.R dependsOnMe: HCsnip Package: SigCheck Version: 2.0.2 Depends: R (>= 3.1.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI License: Artistic-2.0 MD5sum: 44f08af6b64392b2636a88d7fe8438c6 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 source.ver: src/contrib/SigCheck_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/SigCheck_2.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/SigCheck_2.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/SigCheck_2.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SigCheck_2.0.2.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 Package: SigFuge Version: 1.6.0 Depends: R (>= 3.1.1), 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 MD5sum: cc2c5846016305622000d91468d3dd9d NeedsCompilation: no Title: SigFuge Description: Algorithm for testing significance of clustering in RNA-seq data. biocViews: Clustering, Visualization, RNASeq Author: Patrick Kimes, Christopher Cabanski Maintainer: Patrick Kimes source.ver: src/contrib/SigFuge_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SigFuge_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SigFuge_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SigFuge_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SigFuge_1.6.0.tgz vignettes: vignettes/SigFuge/inst/doc/SigFuge.pdf vignetteTitles: SigFuge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigFuge/inst/doc/SigFuge.R Package: siggenes Version: 1.42.0 Depends: methods, Biobase, multtest, splines, graphics Imports: stats4 Suggests: affy, annotate, genefilter, KernSmooth, scrime (>= 1.2.5) License: LGPL (>= 2) MD5sum: 7b7327bf887f26920d8d978721f945fe 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 source.ver: src/contrib/siggenes_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/siggenes_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/siggenes_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/siggenes_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/siggenes_1.42.0.tgz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf vignetteTitles: siggenes Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/siggenes/inst/doc/siggenes.R dependsOnMe: KCsmart, oneChannelGUI importsMe: charm, GeneSelector, minfi suggestsMe: GeneSelector, logicFS, trio, XDE Package: sigPathway Version: 1.36.0 Depends: R (>= 2.10) Suggests: hgu133a.db (>= 1.10.0), XML (>= 1.6-3), AnnotationDbi (>= 1.3.12) License: GPL-2 Archs: i386, x64 MD5sum: 8404465b6e542dbab55d69aff43fceed NeedsCompilation: yes Title: Pathway Analysis Description: Conducts pathway analysis by calculating the NT_k and NE_k statistics as described in Tian et al. (2005) biocViews: DifferentialExpression, MultipleComparison Author: Weil Lai (optimized R and C code), Lu Tian and Peter Park (algorithm development and initial R code) Maintainer: Weil Lai URL: http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102, http://www.chip.org/~ppark/Supplements/PNAS05.html source.ver: src/contrib/sigPathway_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sigPathway_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sigPathway_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sigPathway_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sigPathway_1.36.0.tgz vignettes: vignettes/sigPathway/inst/doc/sigPathway-vignette.pdf vignetteTitles: sigPathway hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigPathway/inst/doc/sigPathway-vignette.R dependsOnMe: tRanslatome Package: sigsquared Version: 1.0.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: 63ed5f102c823aeaec0afcfb3679574b 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 source.ver: src/contrib/sigsquared_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sigsquared_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sigsquared_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sigsquared_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sigsquared_1.0.0.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 Package: SIM Version: 1.38.0 Depends: R (>= 2.4), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) Archs: i386, x64 MD5sum: 0d5590edf3d0cb9c3a08d3a4d73f86ef 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 source.ver: src/contrib/SIM_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SIM_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SIM_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SIM_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SIM_1.38.0.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 Package: SIMAT Version: 1.0.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2 Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: a824c8d94f8f251c1392d95635b1e5c8 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: Software, Metabolomics, MassSpectrometry Author: Mo R. Nezami Ranjbar Maintainer: Mo R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html source.ver: src/contrib/SIMAT_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SIMAT_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SIMAT_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SIMAT_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SIMAT_1.0.0.tgz vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf vignetteTitles: UNDO Demo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMAT/inst/doc/SIMAT-vignette.R Package: SimBindProfiles Version: 1.6.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 MD5sum: 4d778a3dbb9259ea80fbb4ae45047a1e NeedsCompilation: no Title: Similar Binding Profiles Description: SimBindProfiles identifies common and unique binding regions in genome tiling array data. This package does not rely on peak calling, but directly compares binding profiles processed on the same array platform. It implements a simple threshold approach, thus allowing retrieval of commonly and differentially bound regions between datasets as well as events of compensation and increased binding. biocViews: Microarray, Software Author: Bettina Fischer, Enrico Ferrero, Robert Stojnic, Steve Russell Maintainer: Bettina Fischer source.ver: src/contrib/SimBindProfiles_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SimBindProfiles_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SimBindProfiles_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SimBindProfiles_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SimBindProfiles_1.6.0.tgz vignettes: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.pdf vignetteTitles: SimBindProfiles: Similar Binding Profiles,, identifies common and unique regions in array genome tiling array data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.R Package: similaRpeak Version: 1.0.0 Depends: R6 (>= 2.0) Imports: rtracklayer, GenomicAlignments, Rsamtools Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 | file LICENSE MD5sum: f5d6ed5fc3969006329e415788539b4d NeedsCompilation: no Title: similaRpeak: Metrics to estimate a level of similarity between two ChIP-Seq profiles Description: This package calculates metrics which assign a level of similarity between ChIP-Seq profiles. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, DifferentialExpression Author: Astrid Louise Deschenes [cre, aut], Elsa Bernatchez [aut], Charles Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb [aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Louise Deschenes URL: https://github.com/adeschen/similaRpeak VignetteBuilder: knitr BugReports: https://github.com/adeschen/similaRpeak/issues source.ver: src/contrib/similaRpeak_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/similaRpeak_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/similaRpeak_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/similaRpeak_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/similaRpeak_1.0.0.tgz vignettes: vignettes/similaRpeak/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R htmlDocs: vignettes/similaRpeak/inst/doc/similaRpeak.html htmlTitles: "similaRpeak: similarity between two ChIP-Seq profiles" Package: simpleaffy Version: 2.44.0 Depends: R (>= 2.0.0), methods, utils, grDevices, graphics, stats, BiocGenerics (>= 0.1.12), Biobase, affy (>= 1.33.6), genefilter, gcrma Imports: methods, utils, grDevices, graphics, stats, BiocGenerics, Biobase, affy, genefilter, gcrma License: GPL (>= 2) Archs: i386, x64 MD5sum: 24bf0e012120e64a84f418f653f052de NeedsCompilation: yes Title: Very simple high level analysis of Affymetrix data Description: Provides high level functions for reading Affy .CEL files, phenotypic data, and then computing simple things with it, such as t-tests, fold changes and the like. Makes heavy use of the affy library. Also has some basic scatter plot functions and mechanisms for generating high resolution journal figures... biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Transcription, DataImport, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Crispin J Miller Maintainer: Crispin Miller URL: http://www.bioconductor.org, http://bioinformatics.picr.man.ac.uk/simpleaffy/ source.ver: src/contrib/simpleaffy_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/simpleaffy_2.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/simpleaffy_2.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/simpleaffy_2.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/simpleaffy_2.44.0.tgz vignettes: vignettes/simpleaffy/inst/doc/simpleAffy.pdf vignetteTitles: simpleaffy primer hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleaffy/inst/doc/simpleAffy.R dependsOnMe: yaqcaffy importsMe: affyQCReport, arrayMvout suggestsMe: AffyExpress, ArrayTools, ELBOW Package: simulatorZ Version: 1.2.0 Depends: R (>= 3.1), Biobase, survival, CoxBoost Imports: GenomicRanges, gbm, Hmisc, stats, graphics Suggests: BiocGenerics, RUnit, BiocStyle, curatedOvarianData, parathyroidSE, superpc License: Artistic-2.0 Archs: i386, x64 MD5sum: a26112b17be50bf05b4ef776fe0e8c32 NeedsCompilation: yes Title: Simulator for Collections of Independent Genomic Data Sets Description: simulatorZ is a package intended primarily to simulate collections of independent genomic data sets, as well as performing training and validation with predicting algorithms. It supports ExpressionSets and SummarizedExperiment objects. biocViews: Survival Author: Yuqing Zhang, Christoph Bernau, Levi Waldron Maintainer: Yuqing Zhang URL: https://github.com/zhangyuqing/simulatorZ BugReports: https://github.com/zhangyuqing/simulatorZ source.ver: src/contrib/simulatorZ_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/simulatorZ_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/simulatorZ_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/simulatorZ_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/simulatorZ_1.2.0.tgz vignettes: vignettes/simulatorZ/inst/doc/simulatorZ-vignette.pdf vignetteTitles: SimulatorZ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simulatorZ/inst/doc/simulatorZ-vignette.R Package: sincell Version: 1.0.0 Depends: R (>= 3.0.2), igraph Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2, reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster LinkingTo: Rcpp Suggests: BiocStyle, knitr, biomaRt, stringr, monocle License: GPL (>= 2) Archs: i386, x64 MD5sum: d67331d140803b032e0f25c9bca94311 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: Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology Author: Miguel Juliá , Amalio Telenti , Antonio Rausell Maintainer: Miguel Juliá , Antonio Rausell URL: http://bioconductor.org/ VignetteBuilder: knitr source.ver: src/contrib/sincell_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sincell_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sincell_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sincell_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sincell_1.0.0.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 Package: sizepower Version: 1.38.0 Depends: stats License: LGPL MD5sum: 659ad5e38bfb14e258873cf6464c86c7 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 source.ver: src/contrib/sizepower_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sizepower_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sizepower_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sizepower_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sizepower_1.38.0.tgz vignettes: vignettes/sizepower/inst/doc/sizepower.pdf vignetteTitles: package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sizepower/inst/doc/sizepower.R suggestsMe: oneChannelGUI Package: SJava Version: 0.94.0 Depends: R (>= 2.10.0), methods License: GPL (>= 2) MD5sum: e305fbc48b366a79fcecb4364f791855 NeedsCompilation: yes Title: The Omegahat interface for R and Java Description: An interface from R to Java to create and call Java objects and methods. biocViews: Infrastructure Author: Duncan Temple Lang, John Chambers Maintainer: Martin Morgan source.ver: src/contrib/SJava_0.94.0.tar.gz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RWebServices Package: skewr Version: 1.0.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, IRanges, RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 MD5sum: e31eb7c3d99c3aec501b39acb415d8cc 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 source.ver: src/contrib/skewr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/skewr_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/skewr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/skewr_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/skewr_1.0.0.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 Package: SLGI Version: 1.28.0 Depends: R (>= 2.10), ScISI, lattice Imports: AnnotationDbi, Biobase, GO.db, ScISI, graphics, lattice, methods, stats, BiocGenerics Suggests: GO.db, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 2771680b0601f30f2941386cd278f798 NeedsCompilation: no Title: Synthetic Lethal Genetic Interaction Description: A variety of data files and functions for the analysis of genetic interactions biocViews: GraphAndNetwork, Proteomics, Genetics, Network Author: Nolwenn LeMeur, Zhen Jiang, Ting-Yuan Liu, Jess Mar and Robert Gentleman Maintainer: Nolwenn Le Meur source.ver: src/contrib/SLGI_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SLGI_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SLGI_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SLGI_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SLGI_1.28.0.tgz vignettes: vignettes/SLGI/inst/doc/SLGI.pdf vignetteTitles: SLGI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLGI/inst/doc/SLGI.R dependsOnMe: PCpheno Package: SLqPCR Version: 1.34.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: d995e645a7bdbbb14185e3c0e7673f03 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 source.ver: src/contrib/SLqPCR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SLqPCR_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SLqPCR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SLqPCR_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SLqPCR_1.34.0.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 suggestsMe: EasyqpcR Package: SMAP Version: 1.32.0 Depends: R (>= 2.10), methods License: GPL-2 Archs: i386, x64 MD5sum: b12b8f356b6fb4f713a26e278ac74acb NeedsCompilation: yes Title: A Segmental Maximum A Posteriori Approach to Array-CGH Copy Number Profiling Description: Functions and classes for DNA copy number profiling of array-CGH data biocViews: Microarray, TwoChannel, CopyNumberVariation Author: Robin Andersson Maintainer: Robin Andersson source.ver: src/contrib/SMAP_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SMAP_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SMAP_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SMAP_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SMAP_1.32.0.tgz vignettes: vignettes/SMAP/inst/doc/SMAP.pdf vignetteTitles: SMAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMAP/inst/doc/SMAP.R Package: SNAGEE Version: 1.8.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: da8a66443dad40410fb037b32bd5e721 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/ source.ver: src/contrib/SNAGEE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SNAGEE_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SNAGEE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SNAGEE_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SNAGEE_1.8.0.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 Package: snapCGH Version: 1.38.0 Depends: limma, DNAcopy, methods Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL Archs: i386, x64 MD5sum: 33b5bf7fc289ac12bbf8563f2b7cabf7 NeedsCompilation: yes Title: Segmentation, normalisation and processing of aCGH data. Description: Methods for segmenting, normalising and processing aCGH data; including plotting functions for visualising raw and segmented data for individual and multiple arrays. biocViews: Microarray, CopyNumberVariation, TwoChannel, Preprocessing Author: Mike L. Smith, John C. Marioni, Steven McKinney, Thomas Hardcastle, Natalie P. Thorne Maintainer: John Marioni source.ver: src/contrib/snapCGH_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/snapCGH_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/snapCGH_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/snapCGH_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/snapCGH_1.38.0.tgz vignettes: vignettes/snapCGH/inst/doc/snapCGHguide.pdf vignetteTitles: Segmentation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snapCGH/inst/doc/snapCGHguide.R importsMe: ADaCGH2 suggestsMe: beadarraySNP Package: snm Version: 1.16.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: 0945444147bcaace04fed5cecd178c09 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 source.ver: src/contrib/snm_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/snm_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/snm_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/snm_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/snm_1.16.0.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: edge Package: SNPchip Version: 2.14.0 Depends: R (>= 2.14.0) Imports: graphics, lattice, grid, foreach, utils, methods, oligoClasses (>= 1.21.12), Biobase, GenomicRanges, IRanges, GenomeInfoDb Suggests: crlmm (>= 1.17.14), RUnit Enhances: doSNOW, VanillaICE (>= 1.21.24), RColorBrewer License: LGPL (>= 2) MD5sum: 7b9cca9cb77ca0c7b03c7cd0627d60ef NeedsCompilation: no Title: Visualizations for copy number alterations Description: This package defines methods for visualizing high-throughput genomic data biocViews: CopyNumberVariation, SNP, GeneticVariability, Visualization Author: Robert Scharpf and Ingo Ruczinski Maintainer: Robert Scharpf URL: http://www.biostat.jhsph.edu/~iruczins/software/snpchip.html source.ver: src/contrib/SNPchip_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SNPchip_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SNPchip_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SNPchip_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SNPchip_2.14.0.tgz vignettes: vignettes/SNPchip/inst/doc/PlottingIdiograms.pdf vignetteTitles: Plotting Idiograms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPchip/inst/doc/PlottingIdiograms.R dependsOnMe: mBPCR importsMe: crlmm, phenoTest suggestsMe: Category, MinimumDistance, oligoClasses, VanillaICE Package: SNPRelate Version: 1.2.0 Depends: R (>= 2.14), gdsfmt (>= 1.2.2) LinkingTo: gdsfmt Suggests: parallel, RUnit, knitr, MASS, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: bd520cc306fa4beb59f49de3a946b84b 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 format in this package is also used by the GWASTools package with the support of S4 classes and generic functions. biocViews: Infrastructure, Genetics, StatisticalMethod, PrincipalComponent Author: Xiuwen Zheng [aut, cre, cph], Stephanie Gogarten [ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/SNPRelate, http://corearray.sourceforge.net/tutorials/SNPRelate/ VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SNPRelate/issues source.ver: src/contrib/SNPRelate_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SNPRelate_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SNPRelate_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SNPRelate_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SNPRelate_1.2.0.tgz vignettes: vignettes/SNPRelate/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelateTutorial.R htmlDocs: vignettes/SNPRelate/inst/doc/SNPRelateTutorial.html htmlTitles: "Tutorials for the R/Bioconductor Package SNPRelate" suggestsMe: GENESIS, GWASTools, HIBAG Package: snpStats Version: 1.18.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: 19b4a427abfa0f336d324866878d44b1 NeedsCompilation: yes Title: SnpMatrix and XSnpMatrix classes and methods Description: Classes and statistical methods for large SNP association studies, extending the snpMatrix package biocViews: Microarray, SNP, GeneticVariability Author: David Clayton Maintainer: David Clayton source.ver: src/contrib/snpStats_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/snpStats_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/snpStats_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/snpStats_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/snpStats_1.18.0.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/differences.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: GGBase importsMe: FunciSNP, GGtools, gQTLstats, gwascat suggestsMe: crlmm, GWASTools, VariantAnnotation Package: soGGi Version: 1.0.0 Depends: R (>= 3.2.0),BiocGenerics Imports: IRanges, ggplot2, rtracklayer, GenomicAlignments, GenomicRanges, GenomeInfoDb, Rsamtools, reshape2, Biostrings, preprocessCore, chipseq, methods, BiocParallel, S4Vectors Suggests: testthat, BiocStyle, knitr License: GPL (>= 3) MD5sum: 824ca7e31ea1f749f70d37c81f9aab2a 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, Tom Carroll Maintainer: Tom Carroll VignetteBuilder: knitr source.ver: src/contrib/soGGi_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/soGGi_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/soGGi_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/soGGi_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/soGGi_1.0.0.tgz vignettes: vignettes/soGGi/inst/doc/soggi.pdf vignetteTitles: soggi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/soGGi/inst/doc/soggi.R Package: SomatiCA Version: 1.12.0 Depends: R (>= 2.14.0), lars, DNAcopy, foreach, methods, rebmix, GenomicRanges, IRanges, doParallel Imports: foreach, lars, sn, DNAcopy, methods, rebmix, GenomicRanges, IRanges Enhances: sn, SomatiCAData License: GPL (>=2) MD5sum: 900cd0c36f24d012c0d974278dae9451 NeedsCompilation: no Title: SomatiCA: identifying, characterizing, and quantifying somatic copy number aberrations from cancer genome sequencing Description: SomatiCA is a software suite that is capable of identifying, characterizing, and quantifying somatic CNAs from cancer genome sequencing. First, it uses read depths and lesser allele frequencies (LAF) from mapped short sequence reads to segment the genome and identify candidate CNAs. Second, SomatiCA estimates the admixture rate from the relative copy-number profile of tumor-normal pair by a Bayesian finite mixture model. Third, SomatiCA quantifies absolute somatic copy-number and subclonality for each genomic segment to guide its characterization. Results from SomatiCA can be further integrated with single nucleotide variations (SNVs) to get a better understanding of the tumor evolution. biocViews: Sequencing, CopyNumberVariation Author: Mengjie Chen , Hongyu Zhao Maintainer: Mengjie Chen source.ver: src/contrib/SomatiCA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SomatiCA_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SomatiCA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SomatiCA_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SomatiCA_1.12.0.tgz vignettes: vignettes/SomatiCA/inst/doc/SomatiCA.pdf, vignettes/SomatiCA/inst/doc/SomatiCAUserGuide.pdf vignetteTitles: SomatiCA Vignette, SomatiCAUserGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SomatiCA/inst/doc/SomatiCA.R Package: SomaticSignatures Version: 2.4.8 Depends: R (>= 3.1.0), VariantAnnotation, GenomicRanges Imports: S4Vectors, IRanges, GenomeInfoDb, Biostrings, ggplot2, ggbio, reshape2, NMF, pcaMethods, Biobase, methods, proxy Suggests: testthat, knitr, parallel, BSgenome.Hsapiens.UCSC.hg19, SomaticCancerAlterations, ggdendro, fastICA, sva License: GPL-3 MD5sum: 00ef9527236553ead4d4b8c94e90381c 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 (EMBL Heidelberg) Maintainer: Julian Gehring URL: http://bioconductor.org/packages/release/bioc/html/SomaticSignatures.html, https://github.com/julian-gehring/SomaticSignatures-release VignetteBuilder: knitr source.ver: src/contrib/SomaticSignatures_2.4.8.tar.gz win.binary.ver: bin/windows/contrib/3.2/SomaticSignatures_2.4.8.zip win64.binary.ver: bin/windows64/contrib/3.2/SomaticSignatures_2.4.8.zip mac.binary.ver: bin/macosx/contrib/3.2/SomaticSignatures_2.4.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SomaticSignatures_2.4.8.tgz vignettes: vignettes/SomaticSignatures/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R htmlDocs: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html htmlTitles: "SomaticSignatures" importsMe: Rariant Package: SpacePAC Version: 1.6.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: e6472901a0ff3af85e0a82a81967804f NeedsCompilation: no Title: Identification of Mutational Clusters in 3D Protein Space via Simulation. Description: Identifies clustering of somatic mutations in proteins via a simulation approach while considering the protein's tertiary structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik source.ver: src/contrib/SpacePAC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SpacePAC_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SpacePAC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SpacePAC_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SpacePAC_1.6.0.tgz vignettes: vignettes/SpacePAC/inst/doc/SpacePAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpacePAC/inst/doc/SpacePAC.R dependsOnMe: QuartPAC Package: spade Version: 1.16.0 Depends: R (>= 2.11), igraph, Rclusterpp Imports: Biobase, flowCore Suggests: flowViz License: GPL-2 Archs: i386, x64 MD5sum: 3cf63916a8bc0fe8674c36ad92dad115 NeedsCompilation: yes Title: SPADE -- An analysis and visualization tool for Flow Cytometry Description: SPADE, or Spanning tree Progression of Density normalized Events, is an analysis and visualization tool for high dimensional flow cytometry data that organizes cells into hierarchies of related phenotypes. biocViews: FlowCytometry, GraphAndNetwork, GUI, Visualization, Clustering Author: M. Linderman, P. Qiu, E. Simonds, Z. Bjornson Maintainer: Zach Bjornson URL: http://cytospade.org source.ver: src/contrib/spade_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/spade_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/spade_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/spade_1.16.0.tgz vignettes: vignettes/spade/inst/doc/SPADE.pdf vignetteTitles: spade package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spade/inst/doc/SPADE.R Package: specL Version: 1.2.0 Depends: R (>= 3.0.2), methods, DBI, RSQLite, seqinr, protViz (>= 0.2.5), LinkingTo: Rcpp (>= 0.9.9) Suggests: RUnit, BiocGenerics, BiocStyle, BiocParallel, plotrix License: GPL-3 Archs: i386, x64 MD5sum: 820ecc40dd7e3de2b06c443f0fc3fd02 NeedsCompilation: yes Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: specL provides a function for generating spectra libraries which 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. biocViews: MassSpectrometry, Proteomics Author: Christian Trachsel , Christian Panse , Jonas Grossmann Maintainer: Christian Panse URL: http://www.bioconductor.org/packages/devel/bioc/html/specL.html source.ver: src/contrib/specL_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/specL_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/specL_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/specL_1.2.0.tgz vignettes: vignettes/specL/inst/doc/specL.pdf vignetteTitles: Introduction to specL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/specL.R Package: SpeCond Version: 1.22.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: 79e9b01afbf7a919f057b0be73a5cced 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 source.ver: src/contrib/SpeCond_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SpeCond_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SpeCond_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SpeCond_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SpeCond_1.22.0.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 Package: SPEM Version: 1.8.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 78bc9e2bcf4a9899b3b7455a61a29272 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 source.ver: src/contrib/SPEM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SPEM_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SPEM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SPEM_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SPEM_1.8.0.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 Package: SPIA Version: 2.20.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: GPL (>= 2) MD5sum: 8423ff604914ab96f46b16b414046e86 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 source.ver: src/contrib/SPIA_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SPIA_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SPIA_2.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SPIA_2.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SPIA_2.20.0.tgz vignettes: vignettes/SPIA/inst/doc/SPIA.pdf vignetteTitles: SPIA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPIA/inst/doc/SPIA.R importsMe: EnrichmentBrowser suggestsMe: graphite, KEGGgraph Package: spikeLI Version: 2.28.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: 0234628e4af84fdadcd11375ce86bc41 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 source.ver: src/contrib/spikeLI_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/spikeLI_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/spikeLI_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/spikeLI_2.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/spikeLI_2.28.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spikeLI/inst/doc/spikeLI.R Package: spkTools Version: 1.24.0 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: 07c0586bf53613eda541f13df581e4f7 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 source.ver: src/contrib/spkTools_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/spkTools_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/spkTools_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/spkTools_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/spkTools_1.24.0.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 Package: splicegear Version: 1.40.0 Depends: R (>= 2.6.0), methods, Biobase(>= 2.5.5) Imports: annotate, Biobase, graphics, grDevices, grid, methods, utils, XML License: LGPL MD5sum: c2eac6dd7e777856f0902c1cf2cde041 NeedsCompilation: no Title: splicegear Description: A set of tools to work with alternative splicing biocViews: Infrastructure, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier source.ver: src/contrib/splicegear_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/splicegear_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/splicegear_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/splicegear_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/splicegear_1.40.0.tgz vignettes: vignettes/splicegear/inst/doc/splicegear.pdf vignetteTitles: splicegear Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splicegear/inst/doc/splicegear.R Package: spliceR Version: 1.10.0 Depends: R (>= 2.15.0), methods, cummeRbund, rtracklayer, VennDiagram, RColorBrewer, plyr Imports: GenomicRanges, IRanges Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome License: GPL (>=2) Archs: i386, x64 MD5sum: 0f92f3fd7af608f7fcbb0e636418bc93 NeedsCompilation: yes Title: Classification of alternative splicing and prediction of coding potential from RNA-seq data. Description: An R package for classification of alternative splicing and prediction of coding potential from RNA-seq data. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, Visualization Author: Johannes Waage , Kristoffer Vitting-Seerup Maintainer: Johannes Waage , Kristoffer Vitting-Seerup source.ver: src/contrib/spliceR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/spliceR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/spliceR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/spliceR_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/spliceR_1.10.0.tgz vignettes: vignettes/spliceR/inst/doc/spliceR.pdf vignetteTitles: spliceR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spliceR/inst/doc/spliceR.R Package: spliceSites Version: 1.6.0 Depends: methods,rbamtools,refGenome (>= 1.1.2),doBy,Biobase,Biostrings (>= 2.28.0),seqLogo Imports: BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: f7ee3f2267dd1c269636585ec35286d8 NeedsCompilation: yes Title: Manages align gap positions from RNA-seq data Description: Align gap positions from RNA-seq data biocViews: RNASeq, GeneExpression, DifferentialExpression, Proteomics Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers source.ver: src/contrib/spliceSites_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/spliceSites_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/spliceSites_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/spliceSites_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/spliceSites_1.6.0.tgz vignettes: vignettes/spliceSites/inst/doc/spliceSites.pdf vignetteTitles: RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spliceSites/inst/doc/spliceSites.R Package: SplicingGraphs Version: 1.8.1 Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, igraph, BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, Rsamtools, GenomicAlignments, graph, Rgraphviz Suggests: igraph, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: 546a3dc0ee486b3b9621196fff283ee1 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 Author: D. Bindreither, M. Carlson, M. Morgan, H. Pages Maintainer: H. Pages source.ver: src/contrib/SplicingGraphs_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/SplicingGraphs_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/SplicingGraphs_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/SplicingGraphs_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SplicingGraphs_1.8.1.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 Package: splots Version: 1.34.0 Imports: grid, RColorBrewer License: LGPL MD5sum: 58aaa966079817bdf6b66aa38ab031c9 NeedsCompilation: no Title: Visualization of high-throughput assays in microtitre plate or slide format Description: The splots package provides the plotScreen function for visualising data in microtitre plate or slide format. biocViews: Visualization, Sequencing, MicrotitrePlateAssay Author: Wolfgang Huber, Oleg Sklyar Maintainer: Wolfgang Huber source.ver: src/contrib/splots_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/splots_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/splots_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/splots_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/splots_1.34.0.tgz vignettes: vignettes/splots/inst/doc/splotsHOWTO.pdf vignetteTitles: Visualization of data from assays in microtitre plate or slide format hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splots/inst/doc/splotsHOWTO.R dependsOnMe: cellHTS2 importsMe: RNAinteract, RNAither Package: spotSegmentation Version: 1.42.0 Depends: R (>= 2.10), mclust License: GPL (>= 2) MD5sum: 2923f1d3ef43344662a517a7dd920d68 NeedsCompilation: no Title: Microarray Spot Segmentation and Gridding for Blocks of Microarray Spots Description: Spot segmentation via model-based clustering and gridding for blocks within microarray slides, as described in Li et al, Robust Model-Based Segmentation of Microarray Images, Technical Report no. 473, Department of Statistics, University of Washington. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Qunhua Li, Chris Fraley, Adrian Raftery Department of Statistics, University of Washington Maintainer: Chris Fraley URL: http://www.stat.washington.edu/fraley source.ver: src/contrib/spotSegmentation_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/spotSegmentation_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/spotSegmentation_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/spotSegmentation_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/spotSegmentation_1.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SQUADD Version: 1.18.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: 24d822ed61e9a42976383292bfd0de27 NeedsCompilation: no Title: Add-on of the SQUAD Software Description: This package SQUADD is a SQUAD add-on. It permits to generate SQUAD simulation matrix, prediction Heat-Map and Correlation Circle from PCA analysis. biocViews: GraphAndNetwork, Network, Visualization Author: Martial Sankar, supervised by Christian Hardtke and Ioannis Xenarios Maintainer: Martial Sankar URL: http://www.unil.ch/dbmv/page21142_en.html source.ver: src/contrib/SQUADD_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SQUADD_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SQUADD_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SQUADD_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SQUADD_1.18.0.tgz vignettes: vignettes/SQUADD/inst/doc/SQUADD_ERK.pdf, vignettes/SQUADD/inst/doc/SQUADD.pdf vignetteTitles: SQUADD package, SQUADD package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQUADD/inst/doc/SQUADD_ERK.R, vignettes/SQUADD/inst/doc/SQUADD.R Package: SRAdb Version: 1.26.0 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: 399b9d3c718bdc7974530a4950931466 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 URL: http://gbnci.abcc.ncifcrf.gov/sra/ BugReports: https://github.com/seandavi/SRAdb/issues/new source.ver: src/contrib/SRAdb_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SRAdb_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SRAdb_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SRAdb_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SRAdb_1.26.0.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 Package: sRAP Version: 1.8.0 Depends: WriteXLS Imports: gplots, pls, ROCR, qvalue License: GPL-3 MD5sum: 460bd774b0d01f226a56c607d27c4dd0 NeedsCompilation: no Title: Simplified RNA-Seq Analysis Pipeline Description: This package provides a pipeline for gene expression analysis (primarily for RNA-Seq data). The normalization function is specific for RNA-Seq analysis, but all other functions (Quality Control Figures, Differential Expression and Visualization, and Functional Enrichment via BD-Func) will work with any type of gene expression data. biocViews: GeneExpression, RNAseq, Microarray, Preprocessing, QualityControl, Statistics, DifferentialExpression, Visualization, GeneSetEnrichment, GO Author: Charles Warden Maintainer: Charles Warden source.ver: src/contrib/sRAP_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sRAP_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sRAP_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sRAP_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sRAP_1.8.0.tgz vignettes: vignettes/sRAP/inst/doc/sRAP.pdf vignetteTitles: sRAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sRAP/inst/doc/sRAP.R Package: sscore Version: 1.40.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) MD5sum: fa7eddf41deaa86f60c2022f47afe970 NeedsCompilation: no Title: S-Score Algorithm for Affymetrix Oligonucleotide Microarrays Description: This package contains an implementation of the S-Score algorithm as described by Zhang et al (2002). biocViews: DifferentialExpression Author: Richard Kennedy , based on C++ code from Li Zhang and Borland Delphi code from Robnet Kerns . Maintainer: Richard Kennedy URL: http://home.att.net/~richard-kennedy/professional.html source.ver: src/contrib/sscore_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sscore_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sscore_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sscore_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sscore_1.40.0.tgz vignettes: vignettes/sscore/inst/doc/sscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sscore/inst/doc/sscore.R Package: sSeq Version: 1.6.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: 0543cbcded721742a844e53e9a21338d 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: RNASeq Author: Danni Yu , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu source.ver: src/contrib/sSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sSeq_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sSeq_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sSeq_1.6.0.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 Package: ssize Version: 1.42.0 Depends: gdata, xtable License: LGPL MD5sum: 59a6cd1d4f977c95b3bea5311da6d04e 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 source.ver: src/contrib/ssize_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ssize_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ssize_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ssize_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ssize_1.42.0.tgz vignettes: vignettes/ssize/inst/doc/ssize.pdf vignetteTitles: package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssize/inst/doc/ssize.R suggestsMe: oneChannelGUI Package: SSPA Version: 2.8.0 Depends: R (>= 2.12), methods, qvalue, lattice, limma Imports: graphics, stats Suggests: BiocStyle, genefilter, edgeR, DESeq License: GPL (>= 2) Archs: i386, x64 MD5sum: 27f0dd4302fca8a3371a8268caeae1ab NeedsCompilation: yes Title: General Sample Size and Power Analysis for Microarray and Next-Generation Sequencing Data Description: General Sample size and power analysis for microarray and next-generation sequencing data. biocViews: GeneExpression, RNASeq, Microarray, StatisticalMethod Author: Maarten van Iterson Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html source.ver: src/contrib/SSPA_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SSPA_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SSPA_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SSPA_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SSPA_2.8.0.tgz vignettes: vignettes/SSPA/inst/doc/SSPA.pdf vignetteTitles: SSPA Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SSPA/inst/doc/SSPA.R Package: ssviz Version: 1.2.1 Depends: R (>= 2.15.1),methods,Rsamtools,Biostrings,reshape,ggplot2,RColorBrewer Suggests: knitr License: GPL-2 MD5sum: 182414c966537fab250de1cc5a304f02 NeedsCompilation: no Title: A small RNA-seq visualizer and analysis toolkit Description: Small RNA sequencing viewer biocViews: Sequencing,RNASeq,Visualization,MultipleComparison,Genetics Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr source.ver: src/contrib/ssviz_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ssviz_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ssviz_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ssviz_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ssviz_1.2.1.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 Package: STAN Version: 1.2.0 Depends: Rsolnp, methods Suggests: BiocStyle, Gviz, GenomicRanges, IRanges, gplots, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 379203c0576828f98034f8afc0d83662 NeedsCompilation: yes Title: STrand-specific ANnotation of genomic data Description: STAN (STrand-specic ANnotation of genomic data) implements bidirectional Hidden Markov Models (bdHMM), which are designed for studying directed genomic processes, such as gene transcription, DNA replication, recombination or DNA repair by integrating genomic data. bdHMMs model a sequence of successive observations (e.g. ChIP or RNA measurements along the genome) by a discrete number of 'directed genomic states', which e.g. reflect distinct genome-associated complexes. Unlike standard HMM approaches, bdHMMs allow the integration of strand-specific (e.g. RNA) and non strand-specific data (e.g. ChIP). biocViews: HiddenMarkovModel, GenomeAnnotation, Microarray, Sequencing Author: Benedikt Zacher, Julien Gagneur, Achim Tresch Maintainer: Benedikt Zacher VignetteBuilder: knitr source.ver: src/contrib/STAN_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/STAN_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/STAN_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/STAN_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/STAN_1.2.0.tgz vignettes: vignettes/STAN/inst/doc/STAN.pdf vignetteTitles: STrand-specific ANnotation of genomic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STAN/inst/doc/STAN.R Package: staRank Version: 1.10.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL MD5sum: f20a6e8d409e84d10bc96b12246f9814 NeedsCompilation: no Title: Stability Ranking Description: Detecting all relevant variables from a data set is challenging, especially when only few samples are available and data is noisy. Stability ranking provides improved variable rankings of increased robustness using resampling or subsampling. biocViews: MultipleComparison, CellBiology, CellBasedAssays, MicrotitrePlateAssay Author: Juliane Siebourg, Niko Beerenwinkel Maintainer: Juliane Siebourg source.ver: src/contrib/staRank_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/staRank_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/staRank_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/staRank_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/staRank_1.10.0.tgz vignettes: vignettes/staRank/inst/doc/staRank.pdf vignetteTitles: Using staRank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/staRank/inst/doc/staRank.R Package: Starr Version: 1.24.0 Depends: Ringo, affy, affxparser Imports: pspline, MASS, zlibbioc License: Artistic-2.0 Archs: i386, x64 MD5sum: ab2c54bdc79891bcebe14577b3a02138 NeedsCompilation: yes Title: Simple tiling array analysis of Affymetrix ChIP-chip data Description: Starr facilitates the analysis of ChIP-chip data, in particular that of Affymetrix tiling arrays. The package provides functions for data import, quality assessment, data visualization and exploration. Furthermore, it includes high-level analysis features like association of ChIP signals with annotated features, correlation analysis of ChIP signals and other genomic data (e.g. gene expression), peak-finding with the CMARRT algorithm and comparative display of multiple clusters of ChIP-profiles. It uses the basic Bioconductor classes ExpressionSet and probeAnno for maximum compatibility with other software on Bioconductor. All functions from Starr can be used to investigate preprocessed data from the Ringo package, and vice versa. An important novel tool is the the automated generation of correct, up-to-date microarray probe annotation (bpmap) files, which relies on an efficient mapping of short sequences (e.g. the probe sequences on a microarray) to an arbitrary genome. biocViews: Microarray,OneChannel,DataImport,QualityControl,Preprocessing,ChIPchip Author: Benedikt Zacher, Johannes Soeding, Pei Fen Kuan, Matthias Siebert, Achim Tresch Maintainer: Benedikt Zacher source.ver: src/contrib/Starr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Starr_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Starr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Starr_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Starr_1.24.0.tgz vignettes: vignettes/Starr/inst/doc/Starr.pdf vignetteTitles: Simple tiling array analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Starr/inst/doc/Starr.R suggestsMe: nucleR Package: STATegRa Version: 1.2.1 Depends: R (>= 2.10) Imports: Biobase, gridExtra, ggplot2, methods, grid, MASS, calibrate, gplots, edgeR, limma, foreach, affy Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>= 1.3), roxygen2, doSNOW License: GPL-2 MD5sum: 52c322fdad0b2cad94bf661ca355e466 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 , Patricia Sebastián-León , Gordon Ball VignetteBuilder: knitr source.ver: src/contrib/STATegRa_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/STATegRa_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/STATegRa_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/STATegRa_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/STATegRa_1.2.1.tgz vignettes: vignettes/STATegRa/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R htmlDocs: vignettes/STATegRa/inst/doc/STATegRa.html htmlTitles: "STATegRa User’s Guide" Package: stepNorm Version: 1.40.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: aa08a3df63e6023283ba867e1e6210e8 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/ source.ver: src/contrib/stepNorm_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/stepNorm_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/stepNorm_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/stepNorm_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/stepNorm_1.40.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: stepwiseCM Version: 1.14.0 Depends: R (>= 2.14), randomForest, MAclinical, tspair, pamr, snowfall, glmpath, penalized, e1071, Biobase License: GPL (>2) MD5sum: 7ee25c4bd22854aada366a051f022567 NeedsCompilation: no Title: Stepwise Classification of Cancer Samples using High-dimensional Data Sets Description: Stepwise classification of cancer samples using multiple data sets. This package implements the classification strategy using two heterogeneous data sets without actually combining them. Package uses the data type for which full measurements are available at the first stage, and the data type for which only partial measurements are available at the second stage. For incoming new samples package quantifies how much improvement will be obtained if covariates of new samples for the data types at the second stage are measured. This packages suits for the application where study goal is not only obtain high classification accuracy, but also requires economically cheap classifier. biocViews: Classification, Microarray Author: Askar Obulkasim Maintainer: Askar Obulkasim source.ver: src/contrib/stepwiseCM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/stepwiseCM_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/stepwiseCM_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/stepwiseCM_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/stepwiseCM_1.14.0.tgz vignettes: vignettes/stepwiseCM/inst/doc/stepwiseCM.pdf vignetteTitles: stepwiseCM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stepwiseCM/inst/doc/stepwiseCM.R Package: Streamer Version: 1.14.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: 46ce423050639dc4fbc336bdbb9b7329 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 source.ver: src/contrib/Streamer_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Streamer_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Streamer_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Streamer_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Streamer_1.14.0.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 importsMe: plethy Package: STRINGdb Version: 1.8.1 Depends: R (>= 2.14.0), png, sqldf, plyr, igraph, RCurl, plotrix, methods, RColorBrewer, gplots, hash Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: d8d5760cb8284a4b6b7c744da76d6475 NeedsCompilation: no Title: STRINGdb (Search Tool for the Retrieval of Interacting proteins database) Description: The STRINGdb package provides a user-friendly interface to the STRING protein-protein interactions database ( http://www.string-db.org ). biocViews: Network Author: Andrea Franceschini Maintainer: Andrea Franceschini source.ver: src/contrib/STRINGdb_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/STRINGdb_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/STRINGdb_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/STRINGdb_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/STRINGdb_1.8.1.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: scsR importsMe: pwOmics Package: supraHex Version: 1.6.0 Depends: R (>= 3.0.2), hexbin Imports: ape, MASS License: GPL-2 MD5sum: e27c86fb249d06dcfc80a26b37ebf3eb NeedsCompilation: no Title: A supra-hexagonal map for analysing tabular omics data Description: A supra-hexagonal map is a giant hexagon on a 2-dimensional grid seamlessly consisting of smaller hexagons. It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. Uniquely to this package, users can ultrafastly understand any tabular omics data, both scientifically and artistically, especially in a sample-specific fashion but without loss of information on large genes (see http://www.ncbi.nlm.nih.gov/pubmed/24309102). biocViews: Bioinformatics, Clustering, Visualization, GeneExpression Author: Hai Fang and Julian Gough Maintainer: Hai Fang URL: http://supfam.org/supraHex source.ver: src/contrib/supraHex_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/supraHex_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/supraHex_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/supraHex_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/supraHex_1.6.0.tgz vignettes: vignettes/supraHex/inst/doc/supraHex_vignettes.pdf vignetteTitles: supraHex User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supraHex/inst/doc/supraHex_vignettes.R Package: survcomp Version: 1.18.0 Depends: survival, prodlim, R (>= 2.10) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta Suggests: Hmisc, CPE, clinfun, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: e7dc310a1c501c6e1e0bec9ecdf50d1a NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: R package providing functions to assess and to compare the performance of risk prediction (survival) models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains, Markus Schroeder, Catharina Olsen, Christos Sotiriou, Gianluca Bontempi, John Quackenbush Maintainer: Benjamin Haibe-Kains , Markus Schroeder , Catharina Olsen URL: http://www.pmgenomics.ca/bhklab/ source.ver: src/contrib/survcomp_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/survcomp_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/survcomp_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/survcomp_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/survcomp_1.18.0.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: saps suggestsMe: metaseqR Package: Sushi Version: 1.4.0 Depends: R (>= 2.10), zoo,biomaRt Imports: graphics, grDevices License: GPL (>= 2) MD5sum: fddfa1fbf63dab6430461ceb536f81a3 NeedsCompilation: no Title: Tools for visualizing genomics data Description: Flexible, quantitative, and integrative genomic visualizations for publication-quality multi-panel figures biocViews: DataRepresentation, Visualization, Genetics, Sequencing, Infrastructure, HiC Author: Douglas H Phanstiel Maintainer: Douglas H Phanstiel source.ver: src/contrib/Sushi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Sushi_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Sushi_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Sushi_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Sushi_1.4.0.tgz vignettes: vignettes/Sushi/inst/doc/Sushi.pdf vignetteTitles: Sushi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sushi/inst/doc/Sushi.R Package: sva Version: 3.14.0 Depends: R (>= 2.8), mgcv, genefilter Suggests: limma, pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 9945720f23b028ef3ac60a1075c5ea73 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: Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , John D. Storey Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson source.ver: src/contrib/sva_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sva_3.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sva_3.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sva_3.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sva_3.14.0.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: SCAN.UPC importsMe: ballgown, ChAMP, charm, edge, PAA, trigger suggestsMe: RnBeads, SomaticSignatures Package: SVM2CRM Version: 1.0.0 Depends: R (>= 3.2.0), LiblineaR, SVM2CRMdata Imports: AnnotationDbi, mclust, GenomicRanges, IRanges, zoo, squash, pls,rtracklayer,ROCR,verification License: GPL-3 MD5sum: adaa7d18882a5eacb458cfb0ffc66438 NeedsCompilation: no Title: SVM2CRM: support vector machine for cis-regulatory elements detections Description: Detection of cis-regulatory elements using svm implemented in LiblineaR. biocViews: ChIPSeq, SupportVectorMachine, Software, Preprocessing, ChipOnChip Author: Guidantonio Malagoli Tagliazucchi Maintainer: Guidantonio Malagoli Tagliazucchi source.ver: src/contrib/SVM2CRM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SVM2CRM_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SVM2CRM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SVM2CRM_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SVM2CRM_1.0.0.tgz vignettes: vignettes/SVM2CRM/inst/doc/SVM2CRM.pdf vignetteTitles: Package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SVM2CRM/inst/doc/SVM2CRM.R Package: SwimR Version: 1.6.0 Depends: R (>= 3.0.0), methods, gplots (>= 2.10.1), heatmap.plus (>= 1.3), signal (>= 0.7), R2HTML (>= 2.2.1) Imports: methods License: LGPL-2 MD5sum: 3b5b257c9457bd56ba7dd2e534183dd1 NeedsCompilation: no Title: SwimR: A Suite of Analytical Tools for Quantification of C. elegans Swimming Behavior Description: SwimR is an R-based suite that calculates, analyses, and plots the frequency of C. elegans swimming behavior over time. It places a particular emphasis on identifying paralysis and quantifying the kinetic elements of paralysis during swimming. Data is input to SwipR from a custom built program that fits a 5 point morphometric spine to videos of single worms swimming in a buffer called Worm Tracker. biocViews: Visualization Author: Jing Wang , Andrew Hardaway and Bing Zhang Maintainer: Randy Blakely source.ver: src/contrib/SwimR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SwimR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SwimR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SwimR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SwimR_1.6.0.tgz vignettes: vignettes/SwimR/inst/doc/SwimR.pdf vignetteTitles: SwimR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwimR/inst/doc/SwimR.R Package: switchBox Version: 1.2.0 Depends: R (>= 2.13.1) License: GPL-2 Archs: i386, x64 MD5sum: dc659dc84c12c9a94ebcf4bddbd74009 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 Maintainer: Bahman Afsari , Luigi Marchionni source.ver: src/contrib/switchBox_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/switchBox_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/switchBox_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/switchBox_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/switchBox_1.2.0.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 Package: synapter Version: 1.10.0 Depends: R (>= 2.15), methods, MSnbase Imports: hwriter, RColorBrewer, lattice, qvalue, multtest, utils, Biobase, knitr, Biostrings, cleaver, BiocParallel Suggests: synapterdata, xtable, tcltk, tcltk2, BiocStyle License: GPL-2 MD5sum: 1ab41f34c94070e79f17665d1638eef6 NeedsCompilation: no Title: Label-free data analysis pipeline for optimal identification and quantitation Description: The synapter package provides functionality to reanalyse label-free proteomics data acquired on a Synapt G2 mass spectrometer. One or several runs, possibly processed with additional ion mobility separation to increase identification accuracy can be combined to other quantitation files to maximise identification and quantitation accuracy. biocViews: MassSpectrometry, Proteomics, GUI Author: Laurent Gatto, Nick J. Bond and Pavel V. Shliaha and Sebastian Gibb. Maintainer: Laurent Gatto and Sebastian Gibb URL: http://lgatto.github.com/synapter/ VignetteBuilder: knitr source.ver: src/contrib/synapter_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/synapter_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/synapter_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/synapter_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/synapter_1.10.0.tgz vignettes: vignettes/synapter/inst/doc/synapter.pdf vignetteTitles: Combining HDMSe/MSe data using 'synapter' to optimise identification and quantitation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synapter/inst/doc/synapter.R suggestsMe: pRoloc Package: systemPipeR Version: 1.2.23 Depends: Rsamtools, Biostrings, ShortRead, methods Imports: BiocGenerics, GenomicRanges, GenomicAlignments, VariantAnnotation, rjson, grid, ggplot2, limma, edgeR, DESeq2, GOstats, GO.db, annotate, pheatmap, BatchJobs Suggests: ape, RUnit, BiocStyle, knitr, rmarkdown, biomaRt, GenomicFeatures, BiocParallel License: Artistic-2.0 MD5sum: 945e8a9fa9b129165ff2d25e3b7f2f39 NeedsCompilation: no Title: systemPipeR: NGS workflow and report generation environment Description: R package for building end-to-end analysis pipelines with automated report generation for next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. An important feature is support for running command-line software, such as NGS aligners, on both single machines or compute clusters. Instructions for using systemPipeR are given in the Overview Vignette (HTML). The remaining Vignettes, linked below, are workflow templates for common NGS use cases. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage, GeneSetEnrichment, Alignment, QualityControl 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 source.ver: src/contrib/systemPipeR_1.2.23.tar.gz win.binary.ver: bin/windows/contrib/3.2/systemPipeR_1.2.23.zip win64.binary.ver: bin/windows64/contrib/3.2/systemPipeR_1.2.23.zip mac.binary.ver: bin/macosx/contrib/3.2/systemPipeR_1.2.23.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/systemPipeR_1.2.23.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeChIPseq.pdf, vignettes/systemPipeR/inst/doc/systemPipeRNAseq.pdf, vignettes/systemPipeR/inst/doc/systemPipeVARseq.pdf vignetteTitles: ChIP-Seq Report Template, RNA-Seq Report Template, VAR-Seq Report Template hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeChIPseq.R, vignettes/systemPipeR/inst/doc/systemPipeRNAseq.R, vignettes/systemPipeR/inst/doc/systemPipeR.R, vignettes/systemPipeR/inst/doc/systemPipeVARseq.R htmlDocs: vignettes/systemPipeR/inst/doc/systemPipeR.html htmlTitles: "Overview Vignette" importsMe: DiffBind Package: TargetScore Version: 1.6.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 0b6db0fb32e6e9d66aa6d6e1f40ed49d 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 source.ver: src/contrib/TargetScore_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TargetScore_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TargetScore_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TargetScore_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TargetScore_1.6.0.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 Package: TargetSearch Version: 1.24.0 Depends: R (>= 2.7.0), mzR Imports: graphics, grDevices, methods, stats, tcltk, utils Suggests: TargetSearchData License: GPL (>= 2) Archs: i386, x64 MD5sum: 0456fc0c10eee45d7251fb62f47e22a3 NeedsCompilation: yes Title: A package for the analysis of GC-MS metabolite profiling data. Description: This packages provides a targeted pre-processing method for GC-MS data. biocViews: MassSpectrometry, Preprocessing, DecisionTree Author: Alvaro Cuadros-Inostroza , Jan Lisec , Henning Redestig , Matt Hannah Maintainer: Alvaro Cuadros-Inostroza source.ver: src/contrib/TargetSearch_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TargetSearch_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TargetSearch_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TargetSearch_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TargetSearch_1.24.0.tgz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R Package: TCC Version: 1.8.5 Depends: R (>= 2.15), methods, DESeq, DESeq2, edgeR, baySeq, ROC Imports: samr Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 MD5sum: f7d06528de44fb70df243374d7b3f210 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: Sequencing, DifferentialExpression, RNASeq Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer: Jianqiang Sun , Tomoaki Nishiyama source.ver: src/contrib/TCC_1.8.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/TCC_1.8.5.zip win64.binary.ver: bin/windows64/contrib/3.2/TCC_1.8.5.zip mac.binary.ver: bin/macosx/contrib/3.2/TCC_1.8.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TCC_1.8.5.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 Package: TDARACNE Version: 1.18.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 MD5sum: fad4f8e1cec251a41425c04137a7676f NeedsCompilation: no Title: Network reverse engineering from time course data. Description: To infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data. biocViews: Microarray, TimeCourse Author: Zoppoli P.,Morganella S., Ceccarelli M. Maintainer: Zoppoli Pietro source.ver: src/contrib/TDARACNE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TDARACNE_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TDARACNE_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TDARACNE_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TDARACNE_1.18.0.tgz vignettes: vignettes/TDARACNE/inst/doc/TDARACNE.pdf vignetteTitles: TDARACNE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDARACNE/inst/doc/TDARACNE.R Package: TEQC Version: 3.8.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: 4806cbe8afbb795762c33891e2babc43 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: Manuela Hummel source.ver: src/contrib/TEQC_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TEQC_3.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TEQC_3.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TEQC_3.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TEQC_3.8.0.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 Package: ternarynet Version: 1.12.0 Depends: R (>= 2.10.0), methods Imports: utils, igraph License: GPL (>= 2) Archs: i386, x64 MD5sum: 756d7b9b362a715df8dbe981196aa1cb NeedsCompilation: yes Title: Ternary Network Estimation Description: A computational Bayesian approach to ternary gene regulatory network estimation from gene perturbation experiments. biocViews: Software, CellBiology, GraphAndNetwork Author: Matthew N. McCall , Anthony Almudevar Maintainer: Matthew N. McCall source.ver: src/contrib/ternarynet_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ternarynet_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ternarynet_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ternarynet_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ternarynet_1.12.0.tgz vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf vignetteTitles: ternarynet: A Computational Bayesian Approach to Ternary Network Estimation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R Package: TFBSTools Version: 1.6.1 Depends: R (>= 3.0.1) Imports: Biostrings(>= 2.33.3), RSQLite(>= 0.11.4), seqLogo, GenomicRanges(>= 1.17.7), caTools(>= 1.14), XVector(>= 0.5.8), rtracklayer(>= 1.25.3), BSgenome(>= 1.30.0), S4Vectors(>= 0.2.3), IRanges(>= 1.99.28), methods, gtools(>= 3.0.0), CNEr(>= 0.99.8), BiocParallel(>= 0.5.6), DirichletMultinomial(>= 1.7.1), TFMPvalue(>= 0.0.5), BiocGenerics Suggests: JASPAR2014(>= 0.99.3), RUnit, BiocStyle, knitr License: GPL-2 Archs: i386, x64 MD5sum: 3acdf968e4e8314848b999121495441f 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 and transcription factor profile matrices. biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery Author: Ge Tan Maintainer: Ge Tan URL: http://jaspar.genereg.net/ SystemRequirements: meme VignetteBuilder: knitr source.ver: src/contrib/TFBSTools_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/TFBSTools_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/TFBSTools_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/TFBSTools_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TFBSTools_1.6.1.tgz vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.pdf 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: MatrixRider Package: tigre Version: 1.22.0 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle License: AGPL-3 Archs: i386, x64 MD5sum: b08470e67669d2ac24e18b7047793319 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: http://www.bioinf.manchester.ac.uk/resources/tiger/ source.ver: src/contrib/tigre_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/tigre_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/tigre_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/tigre_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tigre_1.22.0.tgz vignettes: vignettes/tigre/inst/doc/tigre.pdf, vignettes/tigre/inst/doc/tigre_quick.pdf vignetteTitles: tigre User Guide, tigre Quick Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tigre/inst/doc/tigre_quick.R, vignettes/tigre/inst/doc/tigre.R Package: tilingArray Version: 1.46.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 Archs: i386, x64 MD5sum: 4d147e16910cdd1769e78a6a3f8b932f 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 source.ver: src/contrib/tilingArray_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/tilingArray_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/tilingArray_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/tilingArray_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tilingArray_1.46.0.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/assessNorm.R, vignettes/tilingArray/inst/doc/costMatrix.R, vignettes/tilingArray/inst/doc/findsegments.R, vignettes/tilingArray/inst/doc/plotAlongChrom.R, vignettes/tilingArray/inst/doc/segmentation.R importsMe: ADaCGH2, snapCGH Package: timecourse Version: 1.40.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 68ee3541ec43666df359ef5ff6f3f145 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 source.ver: src/contrib/timecourse_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/timecourse_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/timecourse_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/timecourse_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/timecourse_1.40.0.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 Package: TIN Version: 1.0.0 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: 0082187be63c58632fe016c52e3a58e5 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 source.ver: src/contrib/TIN_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TIN_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TIN_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TIN_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TIN_1.0.0.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 Package: TitanCNA Version: 1.6.0 Depends: R (>= 3.1.0), foreach (>= 1.4.0), IRanges (>= 1.99.1), Rsamtools (>= 1.17.11), GenomeInfoDb (>= 1.1.3) License: file LICENSE Archs: i386, x64 MD5sum: 090cc8d946e03bc93dc805359049ed5b NeedsCompilation: yes 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 prevalenece of clonal clusters in tumour whole genome sequencing data. biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq, StatisticalMethod, CopyNumberVariation, HiddenMarkovModel, Genetics, GenomicVariation Author: Gavin Ha, Sohrab P Shah Maintainer: Gavin Ha , Sohrab P Shah URL: https://github.com/gavinha/TitanCNA source.ver: src/contrib/TitanCNA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TitanCNA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TitanCNA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TitanCNA_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TitanCNA_1.6.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf vignetteTitles: TitanCNA hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R Package: tkWidgets Version: 1.46.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: 6bb819061599a0b7bec8717a62a241d4 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 source.ver: src/contrib/tkWidgets_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/tkWidgets_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/tkWidgets_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/tkWidgets_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tkWidgets_1.46.0.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 dependsOnMe: oneChannelGUI importsMe: Mfuzz, OLINgui suggestsMe: affy, affyQCReport, annotate, Biobase, genefilter, marray Package: ToPASeq Version: 1.2.0 Depends: graphite, gRbase, graph, locfit, Rgraphviz Imports: R.utils, methods, Biobase, parallel, edgeR, DESeq2, GenomicRanges, RBGL, DESeq, fields, limma, TeachingDemos, KEGGgraph, qpgraph, clipper LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, gageData, DEGraph, plotrix License: AGPL-3 Archs: i386, x64 MD5sum: 10c1204d611da9fc84c6fe2b0be99dab NeedsCompilation: yes Title: Package for Topology-based Pathway Analysis of RNASeq data Description: Implementation of seven methods for topology-based pathway analysis of both RNASeq and microarray data: SPIA, DEGraph, TopologyGSA, TAPPA, TBS, PWEA and a visualization tool for a single pathway. biocViews: Software, GeneExpression, NetworkEnrichment, GraphAndNetwork, RNASeq, Visualization, Microarray, Pathways, DifferentialExpression, Author: Ivana Ihnatova, Eva Budinska Maintainer: Ivana Ihnatova source.ver: src/contrib/ToPASeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ToPASeq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ToPASeq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ToPASeq_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ToPASeq_1.2.0.tgz vignettes: vignettes/ToPASeq/inst/doc/ToPASeq.pdf vignetteTitles: An R Package for topology-based pathway analysis of microaray and RNA-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ToPASeq/inst/doc/ToPASeq.R Package: topGO Version: 2.20.0 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: methods, graph, Biobase, SparseM, AnnotationDbi, lattice Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest, Rgraphviz, globaltest License: LGPL MD5sum: a5746dfe4287c2089cf0ee542ed997b5 NeedsCompilation: no Title: topGO: 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: Microarray, Visualization Author: Adrian Alexa, Jorg Rahnenfuhrer Maintainer: Adrian Alexa source.ver: src/contrib/topGO_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/topGO_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/topGO_2.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/topGO_2.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/topGO_2.20.0.tgz vignettes: vignettes/topGO/inst/doc/topGO.pdf vignetteTitles: topGO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topGO/inst/doc/topGO.R dependsOnMe: compEpiTools, RNAither, tRanslatome importsMe: EnrichmentBrowser, GOSim, mvGST suggestsMe: FGNet, miRNAtap, Ringo Package: TPP Version: 1.0.3 Depends: R (>= 3.2.0), Biobase, ggplot2, openxlsx, plyr, gridExtra, grid Imports: VGAM, VennDiagram, reshape2, nls2, foreach, doParallel, parallel Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: f58d99164f8293e17d6a5e8104d4bf3c 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: Proteomics, MassSpectrometry Author: Dorothee Childs, Holger Franken, Mikhail Savitski and Wolfgang Huber Maintainer: Dorothee Childs VignetteBuilder: knitr source.ver: src/contrib/TPP_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/TPP_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.2/TPP_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.2/TPP_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TPP_1.0.3.tgz vignettes: vignettes/TPP/inst/doc/TPP_introduction.pdf vignetteTitles: TPP_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP/inst/doc/TPP_introduction.R Package: tracktables Version: 1.2.3 Depends: R (>= 3.0.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: 754b435b57338c90a349c36890526deb 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, QualityControl Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang Maintainer: Tom Carroll VignetteBuilder: knitr source.ver: src/contrib/tracktables_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/tracktables_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.2/tracktables_1.2.3.zip mac.binary.ver: bin/macosx/contrib/3.2/tracktables_1.2.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tracktables_1.2.3.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 htmlDocs: vignettes/tracktables/inst/doc/EBFGI.html, vignettes/tracktables/inst/doc/IGVEx3.html, vignettes/tracktables/inst/doc/IGV_Example.html, vignettes/tracktables/inst/doc/index.html, vignettes/tracktables/inst/doc/markdownexample.html htmlTitles: "EBFGI.html", "IGVEx3.html", "IGV_Example.html", "Tracktables user guide and examples", "Supplementary Materials" Package: trackViewer Version: 1.4.0 Depends: R (>= 3.1.0), methods, GenomicRanges, grid, gWidgetstcltk Imports: GenomicAlignments, GenomicFeatures, Gviz, pbapply, Rsamtools, rtracklayer, scales Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 04fced116f1889c8c1726f1e24f0fe3b NeedsCompilation: no Title: A bioconductor package with minimalist design for plotting elegant track layers Description: visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq. biocViews: Visualization Author: Jianhong Ou, Yong-Xu Wang, Lihua Julie Zhu Maintainer: Jianhong Ou source.ver: src/contrib/trackViewer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/trackViewer_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/trackViewer_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/trackViewer_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/trackViewer_1.4.0.tgz vignettes: vignettes/trackViewer/inst/doc/trackViewer.pdf vignetteTitles: trackViewer Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trackViewer/inst/doc/trackViewer.R importsMe: coMET Package: tRanslatome Version: 1.6.0 Depends: R (>= 2.15.0), methods, limma, sigPathway, samr, anota, DESeq, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 MD5sum: d06d79c8e14106e18a98e70925632fd2 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, SAM, 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 source.ver: src/contrib/tRanslatome_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/tRanslatome_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/tRanslatome_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/tRanslatome_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tRanslatome_1.6.0.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 Package: TransView Version: 1.12.0 Depends: methods,GenomicRanges Imports: Rsamtools (>= 1.19.38), zlibbioc, gplots, IRanges LinkingTo: Rsamtools Suggests: RUnit, pasillaBamSubset License: GPL-3 Archs: i386, x64 MD5sum: 8940be8739aba95b1bd1572562906210 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: 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 source.ver: src/contrib/TransView_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TransView_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TransView_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TransView_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TransView_1.12.0.tgz vignettes: vignettes/TransView/inst/doc/TransView.pdf vignetteTitles: An introduction to TransView hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TransView/inst/doc/TransView.R Package: triform Version: 1.10.0 Depends: R (>= 2.11.0), IRanges, yaml Imports: IRanges, yaml, BiocGenerics Suggests: RUnit License: GPL-2 MD5sum: fcc1824388135fd4eb12e049d1b8e428 NeedsCompilation: no Title: Triform finds enriched regions (peaks) in transcription factor ChIP-sequencing data Description: The Triform algorithm uses model-free statistics to identify peak-like distributions of TF ChIP sequencing reads, taking advantage of an improved peak definition in combination with known profile characteristics. biocViews: Sequencing, ChIPSeq Author: Karl Kornacker Developer [aut], Tony Handstad Developer [aut, cre] Maintainer: Tony Handstad Developer source.ver: src/contrib/triform_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/triform_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/triform_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/triform_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/triform_1.10.0.tgz vignettes: vignettes/triform/inst/doc/triform.pdf vignetteTitles: Triform users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/triform/inst/doc/triform.R Package: trigger Version: 1.14.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 Archs: i386, x64 MD5sum: 1c68a1bebf56e2ab728a7b5a8b08be1c NeedsCompilation: yes Title: Transcriptional Regulatory Inference from Genetics of Gene ExpRession Description: This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest. biocViews: GeneExpression, SNP, GeneticVariability, Microarray, Genetics Author: Lin S. Chen , Dipen P. Sangurdekar and John D. Storey Maintainer: John D. Storey source.ver: src/contrib/trigger_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/trigger_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/trigger_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/trigger_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/trigger_1.14.0.tgz vignettes: vignettes/trigger/inst/doc/trigger.pdf vignetteTitles: Trigger Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trigger/inst/doc/trigger.R Package: trio Version: 3.6.0 Depends: R (>= 3.0.1) Suggests: survival, haplo.stats, mcbiopi, siggenes, splines, LogicReg (>= 1.5.3), logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 MD5sum: a3de32c1381a8fdeb7a672ebc7993c3c 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 source.ver: src/contrib/trio_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/trio_3.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/trio_3.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/trio_3.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/trio_3.6.0.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 Package: triplex Version: 1.8.0 Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 1.99.1), XVector (>= 0.7.3), Biostrings (>= 2.33.3) Imports: methods, grid, GenomicRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer, GenomeGraphs License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: 9aff92525147d71294b8e31427e45693 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/ source.ver: src/contrib/triplex_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/triplex_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/triplex_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/triplex_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/triplex_1.8.0.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 Package: TRONCO Version: 1.0.0 Depends: R (>= 2.10), methods, Rgraphviz, lattice, graph Suggests: RUnit, BiocGenerics License: EPL (>= 1.0) MD5sum: 7c2843cf4a4967c98398c0232b621164 NeedsCompilation: no Title: TRONCO, a package for TRanslational ONCOlogy Description: Genotype-level cancer progression models describe the ordering of accumulating mutations, e.g., somatic mutations / copy number variations, during cancer development. These graphical models help understand the causal structure involving events promoting cancer progression, possibly predicting complex patterns characterising genomic progression of a cancer. Reconstructed models can be used to better characterise genotype-phenotype relation, and suggest novel targets for therapy design. TRONCO (TRanslational ONCOlogy) is a R package aimed at collecting state-of-the-art algorithms to infer progression models from cross-sectional data, i.e., data collected from independent patients which does not necessarily incorporate any evident temporal information. These algorithms require a binary input matrix where: (i) each row represents a patient genome, (ii) each column an event relevant to the progression (a priori selected) and a 0/1 value models the absence/presence of a certain mutation in a certain patient. The current first version of TRONCO implements the CAPRESE algorithm (Cancer PRogression Extraction with Single Edges) to infer possible progression models arranged as trees; cfr. Inferring tree causal models of cancer progression with probability raising, L. Olde Loohuis, G. Caravagna, A. Graudenzi, D. Ramazzotti, G. Mauri, M. Antoniotti and B. Mishra. PLoS One, to appear. This vignette shows how to use TRONCO to infer a tree model of ovarian cancer progression from CGH data of copy number alterations (classified as gains or losses over chromosome's arms). The dataset used is available in the SKY/M-FISH database. biocViews: Cancer Author: Marco Antoniotti, Giulio Caravagna, Alex Graudenzi, Ilya Korsunsky, Mattia Longoni, Loes Olde Loohuis, Giancarlo Mauri, Bud Mishra, Daniele Ramazzotti Maintainer: Giulio Caravagna , Alex Graudenzi , Daniele Ramazzotti URL: http://bimib.disco.unimib.it source.ver: src/contrib/TRONCO_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TRONCO_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TRONCO_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TRONCO_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TRONCO_1.0.0.tgz vignettes: vignettes/TRONCO/inst/doc/TRONCO-manual.pdf, vignettes/TRONCO/inst/doc/vignette.pdf vignetteTitles: TRONCO-manual.pdf, TRONCO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TRONCO/inst/doc/vignette.R Package: TSCAN Version: 1.4.1 Depends: R(>= 2.10.0) Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots Suggests: knitr License: GPL(>=2) MD5sum: 9a0abb7528abb2a5c83e64884314c6c4 NeedsCompilation: no Title: TSCAN: Tools for Single-Cell ANalysis Description: TSCAN enables users to easily construct and tune pseudotemporal cell ordering as well as analyzing differentially expressed genes. TSCAN comes with a user-friendly GUI written in shiny. More features will come in the future. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji VignetteBuilder: knitr source.ver: src/contrib/TSCAN_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/TSCAN_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/TSCAN_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/TSCAN_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TSCAN_1.4.1.tgz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R Package: tspair Version: 1.26.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 Archs: i386, x64 MD5sum: 4b9dd812b126ab2acb30f136d5c59528 NeedsCompilation: yes Title: Top Scoring Pairs for Microarray Classification Description: These functions calculate the pair of genes that show the maximum difference in ranking between two user specified groups. This "top scoring pair" maximizes the average of sensitivity and specificity over all rank based classifiers using a pair of genes in the data set. The advantage of classifying samples based on only the relative rank of a pair of genes is (a) the classifiers are much simpler and often more interpretable than more complicated classification schemes and (b) if arrays can be classified using only a pair of genes, PCR based tests could be used for classification of samples. See the references for the tspcalc() function for references regarding TSP classifiers. biocViews: Microarray Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek source.ver: src/contrib/tspair_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/tspair_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/tspair_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/tspair_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tspair_1.26.0.tgz vignettes: vignettes/tspair/inst/doc/tsp.pdf vignetteTitles: tspTutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tspair/inst/doc/tsp.R dependsOnMe: stepwiseCM Package: TSSi Version: 1.14.0 Depends: R (>= 2.13.2) Imports: methods, BiocGenerics (>= 0.3.2), S4Vectors, Hmisc, minqa, stats, Biobase (>= 0.3.2), plyr, IRanges Suggests: rtracklayer Enhances: parallel License: GPL-3 Archs: i386, x64 MD5sum: 1527c454592832cc281cd136ffad1f2a NeedsCompilation: yes Title: Transcription Start Site Identification Description: Identify and normalize transcription start sites in high-throughput sequencing data. biocViews: Sequencing, RNASeq, Genetics, Preprocessing Author: Julian Gehring, Clemens Kreutz Maintainer: Julian Gehring URL: http://julian-gehring.github.com/TSSi/ source.ver: src/contrib/TSSi_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TSSi_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TSSi_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TSSi_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TSSi_1.14.0.tgz vignettes: vignettes/TSSi/inst/doc/TSSi.pdf vignetteTitles: Introduction to the TSSi package: Identification of Transcription Start Sites hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSSi/inst/doc/TSSi.R Package: TurboNorm Version: 1.16.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata License: LGPL Archs: i386, x64 MD5sum: 9206c26ab6ed360436b869d932c959cf 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 source.ver: src/contrib/TurboNorm_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TurboNorm_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TurboNorm_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TurboNorm_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TurboNorm_1.16.0.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 Package: tweeDEseq Version: 1.14.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) Archs: i386, x64 MD5sum: 6de34577c495e025c1c6a7ac204efbeb 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 family of distributions. biocViews: StatisticalMethod, DifferentialExpression, Sequencing, RNASeq Author: Juan R Gonzalez and Mikel Esnaola (with contributions from Robert Castelo ) Maintainer: Juan R Gonzalez URL: http://www.creal.cat/jrgonzalez/software.htm source.ver: src/contrib/tweeDEseq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/tweeDEseq_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/tweeDEseq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/tweeDEseq_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tweeDEseq_1.14.0.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 Package: twilight Version: 1.44.0 Depends: R (>= 2.10), splines (>= 2.2.0), stats (>= 2.2.0), Biobase(>= 1.12.0) Imports: Biobase, graphics, grDevices, stats Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2) License: GPL (>= 2) Archs: i386, x64 MD5sum: b9aa216443325cf19a2ef1e5bcedc894 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 Scheid Maintainer: Stefanie Scheid URL: http://compdiag.molgen.mpg.de/software/twilight.shtml source.ver: src/contrib/twilight_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/twilight_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/twilight_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/twilight_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/twilight_1.44.0.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 importsMe: OrderedList Package: TypeInfo Version: 1.34.0 Depends: methods Suggests: Biobase License: BSD MD5sum: 7ad6a20b499628a7df10a45dc28527c0 NeedsCompilation: no Title: Optional Type Specification Prototype Description: A prototype for a mechanism for specifying the types of parameters and the return value for an R function. This is meta-information that can be used to generate stubs for servers and various interfaces to these functions. Additionally, the arguments in a call to a typed function can be validated using the type specifications. We allow types to be specified as either i) by class name using either inheritance - is(x, className), or strict instance of - class(x) %in% className, or ii) a dynamic test given as an R expression which is evaluated at run-time. More precise information and interesting tests can be done via ii), but it is harder to use this information as meta-data as it requires more effort to interpret it and it is of course run-time information. It is typically more meaningful. biocViews: Infrastructure Author: Duncan Temple Lang Robert Gentleman () Maintainer: Duncan Temple Lang source.ver: src/contrib/TypeInfo_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TypeInfo_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TypeInfo_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TypeInfo_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TypeInfo_1.34.0.tgz vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf vignetteTitles: TypeInfo R News hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TypeInfo/inst/doc/TypeInfoNews.R dependsOnMe: RWebServices Package: UNDO Version: 1.10.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: d402515c4332b113dfa6031e3b28505b 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 source.ver: src/contrib/UNDO_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/UNDO_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/UNDO_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/UNDO_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/UNDO_1.10.0.tgz vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf vignetteTitles: UNDO Demo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R Package: unifiedWMWqPCR Version: 1.4.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: c6f48cce35abf083fe7c3dfb47fcf6ec 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 source.ver: src/contrib/unifiedWMWqPCR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/unifiedWMWqPCR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/unifiedWMWqPCR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/unifiedWMWqPCR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/unifiedWMWqPCR_1.4.0.tgz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R Package: UniProt.ws Version: 2.8.0 Depends: methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8) Imports: BiocGenerics, AnnotationDbi Suggests: RUnit License: Artistic License 2.0 MD5sum: 912915710ccf2002f9d4530b1dcc0cb0 NeedsCompilation: no Title: R Interface to UniProt Web Services Description: A collection of functions for retrieving, processing and repackaging the Uniprot web services. biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta Author: Marc Carlson Maintainer: Marc Carlson source.ver: src/contrib/UniProt.ws_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/UniProt.ws_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/UniProt.ws_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/UniProt.ws_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/UniProt.ws_2.8.0.tgz vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.pdf 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 suggestsMe: cleaver, dagLogo Package: VanillaICE Version: 1.30.1 Depends: R (>= 3.0.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.19.47) Imports: Biobase, oligoClasses (>= 1.24.0), IRanges (>= 1.14.0), S4Vectors, foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb, crlmm, tools Suggests: RUnit, SNPchip, human610quadv1bCrlmm, BSgenome.Hsapiens.UCSC.hg18, ArrayTV Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 Archs: i386, x64 MD5sum: f0679ff57bc0435ca9d584457a8af340 NeedsCompilation: yes Title: A Hidden Markov Model for high throughput genotyping arrays Description: Hidden Markov Models for characterizing chromosomal alterations in high throughput SNP arrays biocViews: CopyNumberVariation Author: Robert Scharpf , Kevin Scharpf, and Ingo Ruczinski Maintainer: Robert Scharpf source.ver: src/contrib/VanillaICE_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/VanillaICE_1.30.1.zip win64.binary.ver: bin/windows64/contrib/3.2/VanillaICE_1.30.1.zip mac.binary.ver: bin/macosx/contrib/3.2/VanillaICE_1.30.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/VanillaICE_1.30.1.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 importsMe: MinimumDistance suggestsMe: oligoClasses Package: VariantAnnotation Version: 1.14.13 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.7.7), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.19.47), Rsamtools (>= 1.19.52) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.5.14), IRanges (>= 2.1.27), XVector (>= 0.5.6), Biostrings (>= 2.33.5), AnnotationDbi (>= 1.27.9), BSgenome, rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.19.17) LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rsamtools Suggests: RUnit, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20110815, SNPlocs.Hsapiens.dbSNP.20101109, SIFT.Hsapiens.dbSNP132, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 7f47671a1feb35d7fe414ba3e0d7368f 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 Obenchain [aut, cre], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb] Maintainer: Valerie Obenchain Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q source.ver: src/contrib/VariantAnnotation_1.14.13.tar.gz win.binary.ver: bin/windows/contrib/3.2/VariantAnnotation_1.14.13.zip win64.binary.ver: bin/windows64/contrib/3.2/VariantAnnotation_1.14.13.zip mac.binary.ver: bin/macosx/contrib/3.2/VariantAnnotation_1.14.13.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/VariantAnnotation_1.14.13.tgz vignettes: vignettes/VariantAnnotation/inst/doc/filterVcf.pdf, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf vignetteTitles: filterVcf Overview, Introduction to VariantAnnotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantAnnotation/inst/doc/filterVcf.R, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R dependsOnMe: AllelicImbalance, CNVrd2, deepSNV, ensemblVEP, GoogleGenomics, HTSeqGenie, Rariant, SomaticSignatures, VariantFiltering, VariantTools importsMe: AllelicImbalance, biovizBase, customProDB, FunciSNP, ggbio, GGtools, gmapR, gQTLstats, gwascat, methyAnalysis, R453Plus1Toolbox, SeqArray, SeqVarTools, systemPipeR suggestsMe: AnnotationHub, GenomicRanges, GWASTools, podkat, trio, vtpnet Package: VariantFiltering Version: 1.4.3 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.13.8), VariantAnnotation (>= 1.13.29) Imports: DBI, RSQLite (>= 1.0.0), Biobase, S4Vectors, IRanges (>= 2.1.18), RBGL, graph, AnnotationDbi, BiocParallel, Biostrings (>= 2.33.11), GenomeInfoDb (>= 1.3.6), GenomicRanges (>= 1.19.13), GenomicFeatures, Rsamtools (>= 1.17.8), BSgenome, Gviz, shiny LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: BiocStyle, org.Hs.eg.db, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20120608, MafDb.ALL.wgs.phase1.release.v3.20101123, MafDb.ESP6500SI.V2.SSA137, MafDb.ExAC.r0.3.sites, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 Archs: i386, x64 MD5sum: 390cb4bd14cf65e42474b66aae6438ad 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, minimum 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] Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering/issues source.ver: src/contrib/VariantFiltering_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/VariantFiltering_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.2/VariantFiltering_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.2/VariantFiltering_1.4.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/VariantFiltering_1.4.3.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 Package: VariantTools Version: 1.10.0 Depends: S4Vectors (>= 0.0.2), IRanges (>= 1.99.2), GenomicRanges (>= 1.17.7), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.17.6), BiocGenerics, Biostrings, parallel, gmapR (>= 1.7.2), GenomicFeatures (>= 1.17.13), Matrix, rtracklayer (>= 1.25.3), BiocParallel, GenomeInfoDb, BSgenome, Biobase Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph License: Artistic-2.0 MD5sum: 68e5d222a4024909af65fdb4c1a2d29c NeedsCompilation: no Title: Tools for Working with Genetic Variants Description: Tools for detecting, filtering, calling, comparing and plotting variants. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence source.ver: src/contrib/VariantTools_1.10.0.tar.gz vignettes: vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R importsMe: HTSeqGenie Package: vbmp Version: 1.36.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) MD5sum: 80b2c33fe491729cca65eb06a2d9ba94 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 source.ver: src/contrib/vbmp_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/vbmp_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/vbmp_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/vbmp_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/vbmp_1.36.0.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 Package: Vega Version: 1.16.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 96bf897aa7d726376d157db6ab4d49ac NeedsCompilation: yes Title: An R package for copy number data segmentation Description: Vega (Variational Estimator for Genomic Aberrations) is an algorithm that adapts a very popular variational model (Mumford and Shah) used in image segmentation so that chromosomal aberrant regions can be efficiently detected. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella Maintainer: Sandro Morganella source.ver: src/contrib/Vega_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Vega_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Vega_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Vega_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Vega_1.16.0.tgz vignettes: vignettes/Vega/inst/doc/Vega.pdf vignetteTitles: Vega hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Vega/inst/doc/Vega.R Package: VegaMC Version: 3.6.0 Depends: R (>= 2.10.0), biomaRt, Biobase, genoset Imports: methods License: GPL-2 Archs: i386, x64 MD5sum: dc6f13a5aa470b0347fa118d1f968e6c 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 source.ver: src/contrib/VegaMC_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/VegaMC_3.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/VegaMC_3.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/VegaMC_3.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/VegaMC_3.6.0.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 Package: viper Version: 1.4.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: GPL (>=2) MD5sum: 97e2e7af37d1fcad44b7c58ed8784d70 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 source.ver: src/contrib/viper_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/viper_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/viper_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/viper_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/viper_1.4.0.tgz vignettes: vignettes/viper/inst/doc/viper.pdf vignetteTitles: Using VIPER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/viper/inst/doc/viper.R importsMe: diggit Package: vsn Version: 3.36.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: methods, affy (>= 1.23.4), limma, lattice Suggests: affydata, hgu95av2cdf License: Artistic-2.0 Archs: i386, x64 MD5sum: 9e05bf830029b3d97c75ee45cc2b7e47 NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities, both between colours within array, and between arrays. The method uses a robust variant of the maximum-likelihood estimator for the stochastic model of microarray data described in the references (see vignette). The model incorporates data calibration (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 source.ver: src/contrib/vsn_3.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/vsn_3.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/vsn_3.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/vsn_3.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/vsn_3.36.0.tgz vignettes: vignettes/vsn/inst/doc/convergence2.pdf, vignettes/vsn/inst/doc/likelihoodcomputations.pdf, vignettes/vsn/inst/doc/vsn.pdf vignetteTitles: Verifying and assessing the performance with simulated data, Likelihood Calculations for vsn, Introduction to vsn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/convergence2.R, vignettes/vsn/inst/doc/likelihoodcomputations.R, vignettes/vsn/inst/doc/vsn.R dependsOnMe: affyPara, cellHTS2, MmPalateMiRNA, webbioc importsMe: arrayQualityMetrics, imageHTS, LVSmiRNA, metaseqR, MSnbase, pvca, Ringo, tilingArray suggestsMe: adSplit, beadarray, BiocCaseStudies, cellHTS, DESeq, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, PAA, twilight Package: vtpnet Version: 0.8.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 4502573eebc3d80406cd7163c482b59b 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 source.ver: src/contrib/vtpnet_0.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/vtpnet_0.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/vtpnet_0.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/vtpnet_0.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/vtpnet_0.8.0.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 Package: wateRmelon Version: 1.8.0 Depends: R (>= 2.10), limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19 Suggests: RPMM Enhances: minfi, methylumi, IMA License: GPL-3 MD5sum: a634d9c1504470a38cce30ae778443c8 NeedsCompilation: no Title: Illumina 450 methylation array normalization and metrics Description: 15 flavours of betas and three performance metrics, with methods for objects produced by methylumi, minfi and IMA packages. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Leonard C Schalkwyk, Ruth Pidsley, Chloe CY Wong, with functions contributed by Nizar Touleimat, Matthieu Defrance, Andrew Teschendorff, Jovana Maksimovic Maintainer: Leo source.ver: src/contrib/wateRmelon_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/wateRmelon_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/wateRmelon_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/wateRmelon_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/wateRmelon_1.8.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R dependsOnMe: skewr importsMe: ChAMP suggestsMe: RnBeads Package: wavClusteR Version: 2.2.0 Depends: R (>= 3.0.0), GenomicRanges, Rsamtools Imports: Biostrings, foreach, GenomicFeatures, ggplot2, Hmisc, IRanges, mclust, rtracklayer, seqinr, stringr, wmtsa Suggests: BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: 5ab853c5b7f7f91139794e2aeb2cf95c NeedsCompilation: no Title: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data Description: A comprehensive 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: HighThroughputSequencing, Software Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio source.ver: src/contrib/wavClusteR_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/wavClusteR_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/wavClusteR_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/wavClusteR_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/wavClusteR_2.2.0.tgz vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.pdf vignetteTitles: wavClusteR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R Package: waveTiling Version: 1.10.0 Depends: oligo, oligoClasses, Biobase, Biostrings, GenomeGraphs Imports: methods, affy, preprocessCore, GenomicRanges, waveslim, IRanges Suggests: BSgenome, BSgenome.Athaliana.TAIR.TAIR9, waveTilingData, pd.atdschip.tiling, TxDb.Athaliana.BioMart.plantsmart22 License: GPL (>=2) Archs: i386, x64 MD5sum: 5cf30d40ccc557450992cb5c622683a6 NeedsCompilation: yes Title: Wavelet-Based Models for Tiling Array Transcriptome Analysis Description: This package is designed to conduct transcriptome analysis for tiling arrays based on fast wavelet-based functional models. biocViews: Microarray, DifferentialExpression, TimeCourse, GeneExpression Author: Kristof De Beuf , Peter Pipelers and Lieven Clement Maintainer: Kristof De Beuf URL: https://r-forge.r-project.org/projects/wavetiling/ source.ver: src/contrib/waveTiling_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/waveTiling_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/waveTiling_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/waveTiling_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/waveTiling_1.10.0.tgz vignettes: vignettes/waveTiling/inst/doc/waveTiling-vignette.pdf vignetteTitles: The waveTiling package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/waveTiling/inst/doc/waveTiling-vignette.R Package: weaver Version: 1.34.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: cfbbd8151c35be51d2012b739290766a 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 source.ver: src/contrib/weaver_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/weaver_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/weaver_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/weaver_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/weaver_1.34.0.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 suggestsMe: BiocCaseStudies Package: webbioc Version: 1.40.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocInstaller License: GPL (>= 2) MD5sum: 4c4dadaaf2a2f05338fa7e03475960b3 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 source.ver: src/contrib/webbioc_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/webbioc_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/webbioc_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/webbioc_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/webbioc_1.40.0.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 Rfiles: vignettes/webbioc/inst/doc/demoscript.R, vignettes/webbioc/inst/doc/webbioc.R Package: widgetTools Version: 1.46.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: 25c0b1f01deba90207ff73ec814570e1 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 source.ver: src/contrib/widgetTools_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/widgetTools_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/widgetTools_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/widgetTools_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/widgetTools_1.46.0.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 suggestsMe: affy Package: xcms Version: 1.44.0 Depends: R (>= 2.14.0), methods, mzR (>= 1.1.6), BiocGenerics, ProtGenerics, Biobase Suggests: faahKO, msdata, ncdf, multtest, rgl, MassSpecWavelet (>= 1.5.2), RANN, RUnit Enhances: Rgraphviz, Rmpi, XML License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 4959ea6d59f9a4b0a7fe5e6d15136281 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: MassSpectrometry, Metabolomics Author: Colin A. Smith , Ralf Tautenhahn , Steffen Neumann , Paul Benton , Christopher Conley Maintainer: Steffen Neumann URL: http://metlin.scripps.edu/download/ and https://github.com/sneumann/xcms BugReports: https://github.com/sneumann/xcms/issues/new source.ver: src/contrib/xcms_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/xcms_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/xcms_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/xcms_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/xcms_1.44.0.tgz vignettes: vignettes/xcms/inst/doc/xcmsDirect.pdf, vignettes/xcms/inst/doc/xcmsInstall.pdf, vignettes/xcms/inst/doc/xcmsMSn.pdf, vignettes/xcms/inst/doc/xcmsPreprocess.pdf vignetteTitles: Grouping FTICR-MS data with xcms, Installation Instructions for xcms, Processing Tandem-MS and MS$^n$ data with xcms, LC/MS Preprocessing and Analysis with xcms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/xcms/inst/doc/xcmsDirect.R, vignettes/xcms/inst/doc/xcmsInstall.R, vignettes/xcms/inst/doc/xcmsMSn.R, vignettes/xcms/inst/doc/xcmsPreprocess.R dependsOnMe: CAMERA, flagme, Metab, metaMS importsMe: CAMERA, cosmiq, Risa suggestsMe: MassSpecWavelet, RMassBank Package: XDE Version: 2.14.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5), methods, graphics Imports: Biobase, BiocGenerics, genefilter, graphics, grDevices, gtools, MergeMaid, methods, stats, utils, mvtnorm Suggests: siggenes, genefilter, MASS, RColorBrewer, GeneMeta, RUnit Enhances: coda License: LGPL-2 Archs: i386, x64 MD5sum: a7b75e70fd61473ad0c33f4d96d9b799 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 source.ver: src/contrib/XDE_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/XDE_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/XDE_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/XDE_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/XDE_2.14.0.tgz vignettes: vignettes/XDE/inst/doc/XdeParameterClass.pdf, vignettes/XDE/inst/doc/XDE.pdf vignetteTitles: XdeParameterClass Vignette, XDE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XDE/inst/doc/XdeParameterClass.R, vignettes/XDE/inst/doc/XDE.R Package: xmapbridge Version: 1.26.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: dd13655fe9dd8b32061656dcab83c113 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 source.ver: src/contrib/xmapbridge_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/xmapbridge_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/xmapbridge_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/xmapbridge_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/xmapbridge_1.26.0.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 Package: xps Version: 1.28.0 Depends: R (>= 2.6.0), methods, utils Suggests: tools License: GPL (>= 2.0) Archs: i386 MD5sum: b43f83d6f0fce126c72eb770001f92a6 NeedsCompilation: yes Title: Processing and Analysis of Affymetrix Oligonucleotide Arrays including Exon Arrays, Whole Genome Arrays and Plate Arrays Description: The package handles pre-processing, normalization, filtering and analysis of Affymetrix GeneChip expression arrays, including exon arrays (Exon 1.0 ST: core, extended, full probesets), gene arrays (Gene 1.0 ST) and plate arrays on computers with 1 GB RAM only. It imports Affymetrix .CDF, .CLF, .PGF and .CEL as well as annotation files, and computes e.g. RMA, MAS5, FARMS, DFW, FIRMA, tRMA, MAS5-calls, DABG-calls, I/NI-calls. It is an R wrapper to XPS (eXpression Profiling System), which is based on ROOT, an object-oriented framework developed at CERN. Thus, the prior installation of ROOT is a prerequisite for the usage of this package, however, no knowledge of ROOT is required. ROOT is licensed under LGPL and can be downloaded from http://root.cern.ch. biocViews: ExonArray, GeneExpression, Microarray, OneChannel, DataImport, Preprocessing, Transcription, DifferentialExpression Author: Christian Stratowa, Vienna, Austria Maintainer: Christian Stratowa SystemRequirements: GNU make, root_v5.34.05 - See README file for installation instructions. source.ver: src/contrib/xps_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/xps_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/xps_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/xps_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/xps_1.28.0.tgz vignettes: vignettes/xps/inst/doc/APTvsXPS.pdf, vignettes/xps/inst/doc/xpsClasses.pdf, vignettes/xps/inst/doc/xps.pdf, vignettes/xps/inst/doc/xpsPreprocess.pdf vignetteTitles: 3. XPS Vignette: Comparison APT vs XPS, 2. XPS Vignette: Classes, 1. XPS Vignette: Overview, 4. XPS Vignette: Function express() hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xps/inst/doc/APTvsXPS.R, vignettes/xps/inst/doc/xpsClasses.R, vignettes/xps/inst/doc/xpsPreprocess.R, vignettes/xps/inst/doc/xps.R Package: XVector Version: 0.8.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.5.19), IRanges (>= 2.1.38) Imports: methods, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: a1cfa2b1f4529cf95ca4ef9f5991a998 NeedsCompilation: yes Title: Representation and manpulation of external sequences Description: Memory efficient S4 classes for storing sequences "externally" (behind an R external pointer, or on disk). biocViews: Infrastructure, DataRepresentation Author: H. Pages and P. Aboyoun Maintainer: H. Pages source.ver: src/contrib/XVector_0.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/XVector_0.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/XVector_0.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/XVector_0.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/XVector_0.8.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, motifRG, Rsamtools, triplex importsMe: Biostrings, BSgenome, ChIPsim, CNEr, compEpiTools, DECIPHER, gcrma, GenomicFeatures, GenomicRanges, Gviz, kebabs, MatrixRider, R453Plus1Toolbox, rtracklayer, TFBSTools, tracktables, VariantAnnotation suggestsMe: IRanges Package: yaqcaffy Version: 1.28.0 Depends: simpleaffy (>= 2.19.3), methods Imports: stats4 Suggests: MAQCsubsetAFX, affydata, xtable, tcltk2, tcltk License: Artistic-2.0 MD5sum: dd6fe1c06b35fa1e78110c047323ef1e NeedsCompilation: no Title: Affymetrix expression data quality control and reproducibility analysis Description: Quality control of Affymetrix GeneChip expression data and reproducibility analysis of human whole genome chips with the MAQC reference datasets. biocViews: Microarray,OneChannel,QualityControl,ReportWriting Author: Laurent Gatto Maintainer: Laurent Gatto source.ver: src/contrib/yaqcaffy_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/yaqcaffy_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/yaqcaffy_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/yaqcaffy_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/yaqcaffy_1.28.0.tgz vignettes: vignettes/yaqcaffy/inst/doc/yaqcaffy.pdf vignetteTitles: yaqcaffy: Affymetrix quality control and MAQC reproducibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yaqcaffy/inst/doc/yaqcaffy.R suggestsMe: qcmetrics Package: zlibbioc Version: 1.14.0 License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: 7866ce05336c75c9a47b7e549a51406b NeedsCompilation: yes Title: An R packaged zlib-1.2.5 Description: This package uses the source code of zlib-1.2.5 to create libraries for systems that do not have these available via other means (most Linux and Mac users should have system-level access to zlib, and no direct need for this package). See the vignette for instructions on use. biocViews: Infrastructure Author: Martin Morgan Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/release/bioc/html/Zlibbioc.html source.ver: src/contrib/zlibbioc_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/zlibbioc_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/zlibbioc_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/zlibbioc_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/zlibbioc_1.14.0.tgz vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.pdf vignetteTitles: Using zlibbioc C libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zlibbioc/inst/doc/UsingZlibbioc.R dependsOnMe: BitSeq importsMe: affy, affyio, affyPLM, bamsignals, Biostrings, ChemmineOB, DiffBind, LVSmiRNA, makecdfenv, oligo, QuasR, rhdf5, Rhtslib, Rsamtools, rtracklayer, seqbias, ShortRead, snpStats, Starr, TransView, VariantAnnotation