bedbaser 0.99.17
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)
bedbase <- BEDbase()## 20331 BED files available.Set the cache path with the argument cache_path. If cache_path is not set,
bedbaser will choose the default location. bedbaser can use the same cache as
bbclient available through the genomic interval machine learning toolkit
geniml by setting the cache_path to the same location.
library(bedbaser)
bedbase <- BEDbase(cache_path = "/path/to/cache")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(bedbase)## # 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(bedbase)## # A tibble: 20,301 × 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 20,291 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(bedbase, "bed")
md <- bb_metadata(bedbase, ex_bed$id)
head(md)## $name
## [1] "LNCaP_AR_NSD2KO"
##
## $genome_alias
## [1] "hg38"
##
## $genome_digest
## [1] "2230c535660fb4774114bfa966a62f823fdb6d21acf138d4"
##
## $bed_type
## [1] "bed3+0"
##
## $bed_format
## [1] "bed"
##
## $id
## [1] "233479aab145cffe46221475d5af5fae"Use bb_beds_in_bedset() to display the id of BEDs in a BEDset.
bb_beds_in_bedset(bedbase, "excluderanges")## # A tibble: 81 × 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 71 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(bedbase, "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(bedbase, "bed")
head(ex_bed)## $name
## [1] "LNCaP_AR_NSD2KO"
##
## $genome_alias
## [1] "hg38"
##
## $genome_digest
## [1] "2230c535660fb4774114bfa966a62f823fdb6d21acf138d4"
##
## $bed_type
## [1] "bed3+0"
##
## $bed_format
## [1] "bed"
##
## $id
## [1] "233479aab145cffe46221475d5af5fae"# Allow bedbaser to assign column names and types
bb_to_granges(bedbase, ex_bed$id, quietly = FALSE)##
## Attaching package: 'Biostrings'## The following object is masked from 'package:base':
##
## strsplit##
## Attaching package: 'BiocIO'## The following object is masked from 'package:rtracklayer':
##
## FileForFormat## GRanges object with 51701 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 9998-10232 *
## [2] chr1 778492-778869 *
## [3] chr1 815471-815747 *
## [4] chr1 827327-827614 *
## [5] chr1 865483-865708 *
## ... ... ... ...
## [51697] chrY 11643569-11643878 *
## [51698] chrY 15948275-15948667 *
## [51699] chrY 26670300-26671222 *
## [51700] chrY 26671523-26671759 *
## [51701] chrY 56685447-56685717 *
## -------
## seqinfo: 97 sequences from hg38 genome; no seqlengthsFor 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(bedbase, 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(bedbase, 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(bedbase, 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.
bed_id <- "ffc1e5ac45d923135500bdd825177356"
bb_to_granges(bedbase, bed_id, "bigbed", quietly = FALSE)## GRanges object with 300000 ranges and 6 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <integer>
## [1] chr1 16125-16495 * | . 0
## [2] chr1 778466-778836 * | . 0
## [3] chr1 827302-827672 * | . 0
## [4] chr1 831317-831687 * | . 0
## [5] chr1 833404-833774 * | . 0
## ... ... ... ... . ... ...
## [299996] chrX 155949138-155949508 * | . 0
## [299997] chrX 155956062-155956432 * | . 0
## [299998] chrX 155980144-155980514 * | . 0
## [299999] chrX 155995383-155995753 * | . 0
## [300000] chrX 156001705-156002075 * | . 0
## field8 field9 field10 field11
## <character> <character> <character> <character>
## [1] 16.3207258990882 -1 0.854018473960329 185
## [2] 24.351219597285 -1 1.45742438962178 185
## [3] 9.91444319802196 -1 0.374586115852685 185
## [4] 10.1721186217002 -1 0.393410941697021 185
## [5] 12.8366426014557 -1 0.589311857454583 185
## ... ... ... ... ...
## [299996] 10.2287080749905 -1 0.396322586637046 185
## [299997] 13.2124210374009 -1 0.617919098619241 185
## [299998] 11.6850933554246 -1 0.505069997531904 185
## [299999] 13.5427435989866 -1 0.643122806955742 185
## [300000] 9.94858883577123 -1 0.377302396460228 185
## -------
## seqinfo: 82 sequences from hg38 genomeCreate a GRangesList given a BEDset id with bb_to_grangeslist.
bedset_id <- "lola_hg38_ucsc_features"
bb_to_grangeslist(bedbase, 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(bedbase)## 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(bedbase$get_bed_stats_v1_bed__bed_id__metadata_stats_get)## get_bed_stats_v1_bed__bed_id__metadata_stats_get
## Get stats for a single BED record
## Description:
## Example bed_id: bbad85f21962bb8d972444f7f9a3a932
##
## Parameters:
## bed_id (string)
## BED digestid <- "bbad85f21962bb8d972444f7f9a3a932"
rsp <- bedbase$get_bed_stats_v1_bed__bed_id__metadata_stats_get(id)
content(rsp)## $number_of_regions
## [1] 26210
##
## $gc_content
## [1] 0.5
##
## $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"
bedbase <- BEDbase()
gro <- bb_to_granges(bedbase, 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_1.17.1
## [6] Biostrings_2.75.3
## [7] XVector_0.47.1
## [8] bedbaser_0.99.17
## [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_1.59.1
## [24] GenomeInfoDb_1.43.2
## [25] IRanges_2.41.2
## [26] S4Vectors_0.45.2
## [27] BiocGenerics_0.53.3
## [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_2.23.1 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_6.0.1
## [15] SparseArray_1.7.2 sass_0.4.9
## [17] bslib_0.8.0 htmlwidgets_1.6.4
## [19] httr2_1.0.7 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_1.10.0 digest_0.6.37
## [33] RSQLite_2.3.9 filelock_1.0.3
## [35] abind_1.4-8 compiler_4.5.0
## [37] withr_3.0.2 bit64_4.5.2
## [39] BiocParallel_1.41.0 DBI_1.2.3
## [41] biomaRt_2.63.0 rappdirs_0.3.3
## [43] DelayedArray_0.33.3 rjson_0.2.23
## [45] tools_4.5.0 httpuv_1.6.15
## [47] glue_1.8.0 restfulr_0.0.15
## [49] promises_1.3.2 grid_4.5.0
## [51] tzdb_0.4.0 tidyr_1.3.1
## [53] hms_1.1.3 utf8_1.2.4
## [55] xml2_1.3.6 pillar_1.10.0
## [57] stringr_1.5.1 later_1.4.1
## [59] splines_4.5.0 dplyr_1.1.4
## [61] BiocFileCache_2.15.0 lattice_0.22-6
## [63] survival_3.8-3 bit_4.5.0.1
## [65] tidyselect_1.2.1 RBGL_1.83.0
## [67] miniUI_0.1.1.1 knitr_1.49
## [69] bookdown_0.41 SummarizedExperiment_1.37.0
## [71] snpStats_1.57.0 futile.logger_1.4.3
## [73] xfun_0.49 matrixStats_1.4.1
## [75] DT_0.33 stringi_1.8.4
## [77] UCSC.utils_1.3.0 yaml_2.3.10
## [79] evaluate_1.0.1 codetools_0.2-20
## [81] tibble_3.2.1 AnVILBase_1.1.0
## [83] BiocManager_1.30.25 graph_1.85.1
## [85] cli_3.6.3 AnVIL_1.19.4
## [87] xtable_1.8-4 jquerylib_0.1.4
## [89] Rcpp_1.0.13-1 dbplyr_2.5.0
## [91] png_0.1-8 rapiclient_0.1.8
## [93] XML_3.99-0.18 parallel_4.5.0
## [95] readr_2.1.5 blob_1.2.4
## [97] prettyunits_1.2.0 bitops_1.0-9
## [99] txdbmaker_1.3.1 VariantAnnotation_1.53.0
## [101] purrr_1.0.2 crayon_1.5.3
## [103] rlang_1.1.4 KEGGREST_1.47.0
## [105] formatR_1.14