bedbaser 0.99.9
bedbaser is an R API client for BEDbase that provides access to the BEDbase API and includes convenience functions, such as to create GRanges and GRangesList objects.
Install bedbaser using BiocManager.
if (!"BiocManager" %in% rownames(installed.packages())) {
install.packages("BiocManager")
}
BiocManager::install("bedbaser")Load the package and create a BEDbase instance.
library(bedbaser)
api <- BEDbase()Set the cache path with the argument cache_path; otherwise, bedbaser will
choose the default cache location.
bedbaser includes convenience functions prefixed with bb_ to facilitate
finding BED files, exploring their metadata, downloading files, and creating
GRanges objects.
Use bb_list_beds() and bb_list_bedsets() to browse available resources in
BEDbase. Both functions display the id and names of BED files and BEDsets. An
id can be used to access a specific resource.
bb_list_beds(api)## # A tibble: 1,000 × 26
## name genome_alias genome_digest bed_type bed_format id description
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 encode_7040 hg38 2230c535660f… bed6+4 narrowpeak 0006… "CUX1 TF C…
## 2 encode_12401 hg38 2230c535660f… bed6+4 narrowpeak 000a… "ZBTB2 TF …
## 3 encode_12948 hg38 2230c535660f… bed6+3 broadpeak 0011… "DNase-seq…
## 4 tissue,infi… hg38 2230c535660f… bed3+0 bed 0014… ""
## 5 encode_10146 hg38 2230c535660f… bed6+4 narrowpeak 0019… "H3K9ac Hi…
## 6 hg38.Kundaj… hg38 2230c535660f… bed3+2 bed 0019… "Defined a…
## 7 encode_4782 hg38 2230c535660f… bed6+4 narrowpeak 001d… "FASTKD2 e…
## 8 encode_14119 hg38 2230c535660f… bed6+3 broadpeak 001f… "DNase-seq…
## 9 encode_10920 hg38 2230c535660f… bed6+4 narrowpeak 0020… "ZNF621 TF…
## 10 encode_16747 hg38 2230c535660f… bed6+4 narrowpeak 002b… "POLR2A TF…
## # ℹ 990 more rows
## # ℹ 19 more variables: submission_date <chr>, last_update_date <chr>,
## # is_universe <chr>, license_id <chr>, annotation.organism <chr>,
## # annotation.species_id <chr>, annotation.genotype <chr>,
## # annotation.phenotype <chr>, annotation.description <chr>,
## # annotation.cell_type <chr>, annotation.cell_line <chr>,
## # annotation.tissue <chr>, annotation.library_source <chr>, …bb_list_bedsets(api)## # A tibble: 18,978 × 9
## id name md5sum submission_date last_update_date description bed_ids
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 000a10…
## 2 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 00116c…
## 3 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 00205a…
## 4 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 002f49…
## 5 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 003c20…
## 6 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 00903c…
## 7 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 00a4ac…
## 8 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 00b0c9…
## 9 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 00c021…
## 10 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 00d062…
## # ℹ 18,968 more rows
## # ℹ 2 more variables: author <chr>, source <chr>Use bb_metadata() to learn more about a BED or BEDset associated with an id.
ex_bed <- bb_example(api, "bed")
md <- bb_metadata(api, ex_bed$id)
head(md)## $name
## [1] "encode_3676"
##
## $genome_alias
## [1] "hg38"
##
## $genome_digest
## [1] "2230c535660fb4774114bfa966a62f823fdb6d21acf138d4"
##
## $bed_type
## [1] "bed6+4"
##
## $bed_format
## [1] "narrowpeak"
##
## $id
## [1] "95900d67ed6411a322af35098e445eb0"Use bb_beds_in_bedset() to display the id of BEDs in a BEDset.
bb_beds_in_bedset(api, "excluderanges")## # A tibble: 80 × 26
## name genome_alias genome_digest bed_type bed_format id description
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 hg38.Kundaj… hg38 2230c535660f… bed3+2 bed 0019… Defined as…
## 2 mm10.UCSC.s… mm10 0f10d83b1050… bed3+8 bed 027d… Gaps on th…
## 3 mm9.Lareau.… mm9 <NA> bed3+2 bed 04db… ENCODE exc…
## 4 mm39.exclud… mm39 <NA> bed3+3 bed 0c37… Defined by…
## 5 TAIR10.UCSC… tair10 <NA> bed3+3 bed 0f77… Gaps in th…
## 6 mm10.Lareau… mm10 0f10d83b1050… bed3+8 bed 1139… Regions of…
## 7 mm39.UCSC.s… mm39 <NA> bed3+8 bed 18ff… Gaps betwe…
## 8 mm9.UCSC.fr… mm9 <NA> bed3+8 bed 1ae4… Gaps betwe…
## 9 dm3.UCSC.co… dm3 <NA> bed3+8 bed 1dab… Gaps betwe…
## 10 hg19.UCSC.c… hg19 baa91c8f6e27… bed3+8 bed 254e… Gaps betwe…
## # ℹ 70 more rows
## # ℹ 19 more variables: submission_date <chr>, last_update_date <chr>,
## # is_universe <chr>, license_id <chr>, annotation.species_name <chr>,
## # annotation.species_id <chr>, annotation.genotype <chr>,
## # annotation.phenotype <chr>, annotation.description <chr>,
## # annotation.cell_type <chr>, annotation.cell_line <chr>,
## # annotation.tissue <chr>, annotation.library_source <chr>, …Search for BED files by keywords. bb_bed_text_search() returns all BED files
scored against a keyword query.
bb_bed_text_search(api, "cancer", limit = 10)## # A tibble: 10 × 43
## id payload.species_name payload.species_id payload.genotype
## <chr> <chr> <chr> <chr>
## 1 9455677c-9039-928b-… Homo sapiens 9606 ""
## 2 3919e978-9020-690d-… Homo sapiens 9606 ""
## 3 26fb0de5-5b10-9a0d-… Homo sapiens 9606 ""
## 4 ffc1e5ac-45d9-2313-… Homo sapiens 9606 ""
## 5 a07d627d-d3d7-cff9-… Homo sapiens 9606 ""
## 6 f2f0eee0-0aaa-4629-… Homo sapiens 9606 ""
## 7 cfefafeb-002e-c744-… Homo sapiens 9606 ""
## 8 b4857063-a3fb-f9e2-… Homo sapiens 9606 ""
## 9 e0b3c20c-f147-29d8-… Homo sapiens 9606 ""
## 10 2f11d929-c18a-b99b-… Homo sapiens 9606 ""
## # ℹ 39 more variables: payload.phenotype <chr>, payload.description <chr>,
## # payload.cell_type <chr>, payload.cell_line <chr>, payload.tissue <chr>,
## # payload.library_source <chr>, payload.assay <chr>, payload.antibody <chr>,
## # payload.target <chr>, payload.treatment <chr>,
## # payload.global_sample_id <chr>, payload.global_experiment_id <chr>,
## # score <chr>, metadata.name <chr>, metadata.genome_alias <chr>,
## # metadata.bed_type <chr>, metadata.bed_format <chr>, metadata.id <chr>, …Create a GRanges object with a BED id with bb_to_granges, which
downloads and imports a BED file using rtracklayer.
ex_bed <- bb_example(api, "bed")
head(ex_bed)## $name
## [1] "encode_3676"
##
## $genome_alias
## [1] "hg38"
##
## $genome_digest
## [1] "2230c535660fb4774114bfa966a62f823fdb6d21acf138d4"
##
## $bed_type
## [1] "bed6+4"
##
## $bed_format
## [1] "narrowpeak"
##
## $id
## [1] "95900d67ed6411a322af35098e445eb0"# Allow bedbaser to assign column names and types
bb_to_granges(api, ex_bed$id, quietly = FALSE)## GRanges object with 57761 ranges and 6 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr7 99556889-99560178 * | Peak_1 1000
## [2] chr7 105530992-105534384 * | Peak_2 976
## [3] chr7 44982405-44988303 * | Peak_3 969
## [4] chr4 48830246-48833027 * | Peak_4 953
## [5] chr7 74209131-74211593 * | Peak_5 952
## ... ... ... ... . ... ...
## [57757] chr8 8881307-8881578 * | Peak_57757 10
## [57758] chr9 589353-589606 * | Peak_57758 10
## [57759] chr19 51138137-51138357 * | Peak_57759 10
## [57760] chr6 156659590-156660000 * | Peak_57760 10
## [57761] chr7 154976160-154976403 * | Peak_57761 10
## signalValue pValue qValue peak
## <numeric> <numeric> <numeric> <integer>
## [1] 50.4952 587.814 579.279 1420
## [2] 47.9550 574.182 566.517 1362
## [3] 42.1230 569.985 562.559 3866
## [4] 58.4641 560.289 553.038 1109
## [5] 50.5395 559.634 552.401 1172
## ... ... ... ... ...
## [57757] 1.73028 2.03201 0.42519 108
## [57758] 1.73028 2.03201 0.42519 203
## [57759] 2.01695 2.03088 0.42430 78
## [57760] 1.64774 2.03023 0.42430 35
## [57761] 1.64774 2.03023 0.42430 223
## -------
## seqinfo: 711 sequences (1 circular) from hg38 genomeFor BEDX+Y formats, a named list with column types may be passed through
extra_cols if the column name and type are known. Otherwise, bb_to_granges
guesses the column types and assigns column names.
# Manually assign column name and type using `extra_cols`
bb_to_granges(api, ex_bed$id, extra_cols = c("column_name" = "character"))bb_to_granges automatically assigns the column names and types for broad peak
and narrow peak files.
bed_id <- "bbad85f21962bb8d972444f7f9a3a932"
md <- bb_metadata(api, bed_id)
head(md)## $name
## [1] "PM_137_NPC_CTCF_ChIP"
##
## $genome_alias
## [1] "hg38"
##
## $genome_digest
## [1] "2230c535660fb4774114bfa966a62f823fdb6d21acf138d4"
##
## $bed_type
## [1] "bed6+4"
##
## $bed_format
## [1] "narrowpeak"
##
## $id
## [1] "bbad85f21962bb8d972444f7f9a3a932"bb_to_granges(api, bed_id)## GRanges object with 26210 ranges and 6 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 869762-870077 * | 111-11-DSP-NPC-CTCF-.. 587
## [2] chr1 904638-904908 * | 111-11-DSP-NPC-CTCF-.. 848
## [3] chr1 921139-921331 * | 111-11-DSP-NPC-CTCF-.. 177
## [4] chr1 939191-939364 * | 111-11-DSP-NPC-CTCF-.. 139
## [5] chr1 976105-976282 * | 111-11-DSP-NPC-CTCF-.. 185
## ... ... ... ... . ... ...
## [26206] chrY 18445992-18446211 * | 111-11-DSP-NPC-CTCF-.. 203
## [26207] chrY 18608331-18608547 * | 111-11-DSP-NPC-CTCF-.. 203
## [26208] chrY 18669820-18670062 * | 111-11-DSP-NPC-CTCF-.. 244
## [26209] chrY 18997783-18997956 * | 111-11-DSP-NPC-CTCF-.. 191
## [26210] chrY 19433165-19433380 * | 111-11-DSP-NPC-CTCF-.. 275
## signalValue pValue qValue peak
## <numeric> <numeric> <numeric> <integer>
## [1] 20.94161 58.7971 54.9321 152
## [2] 30.90682 84.8282 80.3102 118
## [3] 9.62671 17.7065 14.8446 69
## [4] 8.10671 13.9033 11.1352 49
## [5] 9.26375 18.5796 15.6985 129
## ... ... ... ... ...
## [26206] 10.64005 20.3549 17.4328 106
## [26207] 8.00064 20.3991 17.4753 149
## [26208] 12.16006 24.4764 21.4585 119
## [26209] 8.97342 19.1163 16.2230 69
## [26210] 12.21130 27.5139 24.4211 89
## -------
## seqinfo: 711 sequences (1 circular) from hg38 genomebb_to_granges can also import big BED files.
ex_bed <- bb_example(api, "bed")
bb_to_granges(api, ex_bed$id, "bigbed", quietly = FALSE)## Downloading
## https://data2.bedbase.org/files/9/5/95900d67ed6411a322af35098e445eb0.bigBed ...## GRanges object with 57761 ranges and 6 metadata columns:
## seqnames ranges strand | name score field8
## <Rle> <IRanges> <Rle> | <character> <integer> <character>
## [1] chr1 777827-778195 * | Peak_33839 27 5.26766
## [2] chr1 778361-778761 * | Peak_15349 142 17.67560
## [3] chr1 778884-779471 * | Peak_18464 96 13.26751
## [4] chr1 779527-780059 * | Peak_22729 61 9.51248
## [5] chr1 826634-827013 * | Peak_26078 45 7.71659
## ... ... ... ... . ... ... ...
## [57757] chrY 20726456-20727106 * | Peak_26091 45 7.71659
## [57758] chrY 20731782-20732102 * | Peak_43849 17 3.67835
## [57759] chrY 20732243-20732510 * | Peak_46498 16 3.19705
## [57760] chrY 20734468-20736884 * | Peak_20414 78 10.64534
## [57761] chrY 20798030-20798986 * | Peak_22217 65 10.00227
## field9 field10 field11
## <character> <character> <character>
## [1] 12.17603 10.32837 187
## [2] 80.41011 78.17186 195
## [3] 53.33309 51.20127 389
## [4] 32.48199 30.45850 200
## [5] 23.13210 21.17438 186
## ... ... ... ...
## [57757] 23.13210 21.17438 378
## [57758] 6.60524 4.84712 177
## [57759] 5.85335 4.11228 209
## [57760] 42.32733 40.24815 1383
## [57761] 34.66776 32.63152 397
## -------
## seqinfo: 28 sequences from an unspecified genomeCreate a GRangesList given a BEDset id with bb_to_grangeslist.
bedset_id <- "lola_hg38_ucsc_features"
bb_to_grangeslist(api, bedset_id)## GRangesList object of length 11:
## [[1]]
## GRanges object with 28633 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 28736-29810 *
## [2] chr1 135125-135563 *
## [3] chr1 491108-491546 *
## [4] chr1 381173-382185 *
## [5] chr1 368793-370063 *
## ... ... ... ...
## [28629] chrY 25463969-25464941 *
## [28630] chrY 26409389-26409785 *
## [28631] chrY 26627169-26627397 *
## [28632] chrY 57067646-57068034 *
## [28633] chrY 57203116-57203423 *
## -------
## seqinfo: 711 sequences (1 circular) from hg38 genome
##
## ...
## <10 more elements>Because bedbaser uses the AnVIL Service class, it’s possible to access any endpoint of the BEDbase API.
show(api)## service: bedbase
## tags(); use bedbase$<tab completion>:
## # A tibble: 38 × 3
## tag operation summary
## <chr> <chr> <chr>
## 1 base get_bedbase_db_stats_v1_genomes_get Get av…
## 2 base get_bedbase_db_stats_v1_stats_get Get su…
## 3 base service_info_v1_service_info_get GA4GH …
## 4 bed bed_to_bed_search_v1_bed_search_bed_post Search…
## 5 bed embed_bed_file_v1_bed_embed_post Get em…
## 6 bed get_bed_classification_v1_bed__bed_id__metadata_classification… Get cl…
## 7 bed get_bed_embedding_v1_bed__bed_id__embedding_get Get em…
## 8 bed get_bed_files_v1_bed__bed_id__metadata_files_get Get me…
## 9 bed get_bed_metadata_v1_bed__bed_id__metadata_get Get me…
## 10 bed get_bed_pephub_v1_bed__bed_id__metadata_raw_get Get ra…
## # ℹ 28 more rows
## tag values:
## base, bed, bedset, home, objects, search, NA
## schemas():
## AccessMethod, AccessURL, BaseListResponse, BedClassification,
## BedEmbeddingResult
## # ... with 37 more elementsFor example, to access a BED file’s stats, access the endpoint with $ and use
httr to get the result. show will display information about the
endpoint.
library(httr)##
## Attaching package: 'httr'## The following object is masked from 'package:Biobase':
##
## contentshow(api$get_bed_stats_v1_bed__bed_id__metadata_stats_get)## get_bed_stats_v1_bed__bed_id__metadata_stats_get
## Get stats for a single BED record
## Description:
## Example bed_id: bbad85f21962bb8d972444f7f9a3a932
##
## Parameters:
## bed_id (string)
## BED digestid <- "bbad85f21962bb8d972444f7f9a3a932"
rsp <- api$get_bed_stats_v1_bed__bed_id__metadata_stats_get(id)
content(rsp)## $number_of_regions
## [1] 26210
##
## $gc_content
## NULL
##
## $median_tss_dist
## [1] 31480
##
## $mean_region_width
## [1] 276.3
##
## $exon_frequency
## [1] 1358
##
## $exon_percentage
## [1] 0.0518
##
## $intron_frequency
## [1] 9390
##
## $intron_percentage
## [1] 0.3583
##
## $intergenic_percentage
## [1] 0.4441
##
## $intergenic_frequency
## [1] 11639
##
## $promotercore_frequency
## [1] 985
##
## $promotercore_percentage
## [1] 0.0376
##
## $fiveutr_frequency
## [1] 720
##
## $fiveutr_percentage
## [1] 0.0275
##
## $threeutr_frequency
## [1] 1074
##
## $threeutr_percentage
## [1] 0.041
##
## $promoterprox_frequency
## [1] 1044
##
## $promoterprox_percentage
## [1] 0.0398Given a BED id, we can use liftOver to convert one genomic coordinate system to another.
Install liftOver and rtracklayer then load the packages.
if (!"BiocManager" %in% rownames(installed.packages())) {
install.packages("BiocManager")
}
BiocManager::install(c("liftOver", "rtracklayer"))
library(liftOver)
library(rtracklayer)Create a GRanges object from a
mouse genome.
Create a BEDbase Service instance. Use the instance to create a GRanges
object from the BEDbase id.
id <- "7816f807ffe1022f438e1f5b094acf1a"
api <- BEDbase()
gro <- bb_to_granges(api, id)
gro## GRanges object with 3435 ranges and 3 metadata columns:
## seqnames ranges strand | V4 V5
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr1 8628601-8719100 * | 90501 *
## [2] chr1 12038301-12041400 * | 3101 *
## [3] chr1 14958601-14992600 * | 34001 *
## [4] chr1 17466801-17479900 * | 13101 *
## [5] chr1 18872501-18901300 * | 28801 *
## ... ... ... ... . ... ...
## [3431] chrY 6530201-6663200 * | 133001 *
## [3432] chrY 6760201-6835800 * | 75601 *
## [3433] chrY 6984101-8985400 * | 2001301 *
## [3434] chrY 10638501-41003800 * | 30365301 *
## [3435] chrY 41159201-91744600 * | 50585401 *
## V6
## <character>
## [1] High Signal Region
## [2] High Signal Region
## [3] High Signal Region
## [4] High Signal Region
## [5] High Signal Region
## ... ...
## [3431] High Signal Region
## [3432] High Signal Region
## [3433] High Signal Region
## [3434] High Signal Region
## [3435] High Signal Region
## -------
## seqinfo: 239 sequences (1 circular) from mm10 genomeDownload the chain file from UCSC.
chain_url <- paste0(
"https://hgdownload.cse.ucsc.edu/goldenPath/mm10/liftOver/",
"mm10ToMm39.over.chain.gz"
)
tmpdir <- tempdir()
gz <- file.path(tmpdir, "mm10ToMm39.over.chain.gz")
download.file(chain_url, gz)
gunzip(gz, remove = FALSE)Import the chain, set the sequence levels style, and set the genome for the GRanges object.
ch <- import.chain(file.path(tmpdir, "mm10ToMm39.over.chain"))
seqlevelsStyle(gro) <- "UCSC"
gro39 <- liftOver(gro, ch)
gro39 <- unlist(gro39)
genome(gro39) <- "mm39"
gro39## GRanges object with 6435 ranges and 3 metadata columns:
## seqnames ranges strand | V4 V5
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr1 8698825-8789324 * | 90501 *
## [2] chr1 12108525-12111624 * | 3101 *
## [3] chr1 15028825-15062824 * | 34001 *
## [4] chr1 17537025-17550124 * | 13101 *
## [5] chr1 18942725-18971524 * | 28801 *
## ... ... ... ... . ... ...
## [6431] chrY 78211533-78211575 * | 50585401 *
## [6432] chrY 78170295-78170413 * | 50585401 *
## [6433] chrY 78151769-78152688 * | 50585401 *
## [6434] chrY 78149461-78151766 * | 50585401 *
## [6435] chrY 72066439-72066462 * | 50585401 *
## V6
## <character>
## [1] High Signal Region
## [2] High Signal Region
## [3] High Signal Region
## [4] High Signal Region
## [5] High Signal Region
## ... ...
## [6431] High Signal Region
## [6432] High Signal Region
## [6433] High Signal Region
## [6434] High Signal Region
## [6435] High Signal Region
## -------
## seqinfo: 21 sequences from mm39 genome; no seqlengthssessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BSgenome.Mmusculus.UCSC.mm10_1.4.3
## [2] httr_1.4.7
## [3] BSgenome.Hsapiens.UCSC.hg38_1.4.5
## [4] BSgenome_1.75.0
## [5] BiocIO_1.17.0
## [6] Biostrings_2.75.1
## [7] XVector_0.47.0
## [8] bedbaser_0.99.9
## [9] liftOver_1.31.0
## [10] Homo.sapiens_1.3.1
## [11] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [12] org.Hs.eg.db_3.20.0
## [13] GO.db_3.20.0
## [14] OrganismDbi_1.49.0
## [15] GenomicFeatures_1.59.1
## [16] AnnotationDbi_1.69.0
## [17] Biobase_2.67.0
## [18] gwascat_2.39.0
## [19] R.utils_2.12.3
## [20] R.oo_1.27.0
## [21] R.methodsS3_1.8.2
## [22] rtracklayer_1.67.0
## [23] GenomicRanges_1.59.0
## [24] GenomeInfoDb_1.43.0
## [25] IRanges_2.41.0
## [26] S4Vectors_0.45.1
## [27] BiocGenerics_0.53.2
## [28] generics_0.1.3
## [29] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_1.8.9 magrittr_2.0.3
## [3] rmarkdown_2.29 zlibbioc_1.53.0
## [5] vctrs_0.6.5 memoise_2.0.1
## [7] Rsamtools_2.23.0 RCurl_1.98-1.16
## [9] htmltools_0.5.8.1 S4Arrays_1.7.1
## [11] BiocBaseUtils_1.9.0 progress_1.2.3
## [13] lambda.r_1.2.4 curl_6.0.0
## [15] SparseArray_1.7.1 sass_0.4.9
## [17] bslib_0.8.0 htmlwidgets_1.6.4
## [19] httr2_1.0.6 futile.options_1.0.1
## [21] cachem_1.1.0 GenomicAlignments_1.43.0
## [23] mime_0.12 lifecycle_1.0.4
## [25] pkgconfig_2.0.3 Matrix_1.7-1
## [27] R6_2.5.1 fastmap_1.2.0
## [29] GenomeInfoDbData_1.2.13 MatrixGenerics_1.19.0
## [31] shiny_1.9.1 digest_0.6.37
## [33] RSQLite_2.3.7 filelock_1.0.3
## [35] fansi_1.0.6 abind_1.4-8
## [37] compiler_4.5.0 withr_3.0.2
## [39] bit64_4.5.2 BiocParallel_1.41.0
## [41] DBI_1.2.3 biomaRt_2.63.0
## [43] rappdirs_0.3.3 DelayedArray_0.33.1
## [45] rjson_0.2.23 tools_4.5.0
## [47] httpuv_1.6.15 glue_1.8.0
## [49] restfulr_0.0.15 promises_1.3.0
## [51] grid_4.5.0 tzdb_0.4.0
## [53] tidyr_1.3.1 hms_1.1.3
## [55] xml2_1.3.6 utf8_1.2.4
## [57] pillar_1.9.0 stringr_1.5.1
## [59] later_1.3.2 splines_4.5.0
## [61] dplyr_1.1.4 BiocFileCache_2.15.0
## [63] lattice_0.22-6 survival_3.7-0
## [65] bit_4.5.0 tidyselect_1.2.1
## [67] RBGL_1.83.0 miniUI_0.1.1.1
## [69] knitr_1.49 bookdown_0.41
## [71] SummarizedExperiment_1.37.0 snpStats_1.57.0
## [73] futile.logger_1.4.3 xfun_0.49
## [75] matrixStats_1.4.1 DT_0.33
## [77] stringi_1.8.4 UCSC.utils_1.3.0
## [79] yaml_2.3.10 evaluate_1.0.1
## [81] codetools_0.2-20 tibble_3.2.1
## [83] AnVILBase_1.1.0 BiocManager_1.30.25
## [85] graph_1.85.0 cli_3.6.3
## [87] AnVIL_1.19.3 xtable_1.8-4
## [89] jquerylib_0.1.4 Rcpp_1.0.13-1
## [91] dbplyr_2.5.0 png_0.1-8
## [93] rapiclient_0.1.8 XML_3.99-0.17
## [95] parallel_4.5.0 readr_2.1.5
## [97] blob_1.2.4 prettyunits_1.2.0
## [99] bitops_1.0-9 txdbmaker_1.3.0
## [101] VariantAnnotation_1.53.0 purrr_1.0.2
## [103] crayon_1.5.3 rlang_1.1.4
## [105] KEGGREST_1.47.0 formatR_1.14