bedbaser 0.99.23
bedbaser is an R API client for BEDbase that provides access to the bedhost 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, optionally setting the cache
to cache_path. If cache_path is not set, bedbaser will
choose the default location.
library(bedbaser)
bedbase <- BEDbase(tempdir())## 21141 BED files available.bedbaser can use the same cache as
geniml’s BBClient by setting the
cache_path to the same location. It will create the following structure:
cache_path
bedfiles
a/f/afile.bed.gz
bedsets
a/s/aset.txtbedbaser 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 DNase-seq f… mm10 <NA> bed4+1 bed 0014… "https://w…
## 5 tissue,infi… hg38 2230c535660f… bed3+0 bed 0014… ""
## 6 encode_10146 hg38 2230c535660f… bed6+4 narrowpeak 0019… "H3K9ac Hi…
## 7 hg38.Kundaj… hg38 2230c535660f… bed3+2 bed 0019… "Defined a…
## 8 encode_4782 hg38 2230c535660f… bed6+4 narrowpeak 001d… "FASTKD2 e…
## 9 encode_14119 hg38 2230c535660f… bed6+3 broadpeak 001f… "DNase-seq…
## 10 encode_10920 hg38 2230c535660f… bed6+4 narrowpeak 0020… "ZNF621 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: 21,373 × 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… d0585a…
## 2 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… aa41f6…
## 3 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… f248f0…
## 4 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 6efa1a…
## 5 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 54d9df…
## 6 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… d4dae8…
## 7 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… d7882f…
## 8 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… bfead5…
## 9 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 7f77a2…
## 10 encode_bat… enco… 6dc4c… 2024-11-03T18:… 2024-11-03T18:2… "Encode pr… 4e4735…
## # ℹ 21,363 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")
# 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>Save BED files or BEDsets with bb_save:
bb_save(bedbase, ex_bed$id, tempdir())Because bedbaser uses the AnVIL Service class, it’s possible to access any endpoint of the BEDbase API.
show(bedbase)## service: bedbase
## host: api.bedbase.org
## 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) (2025-01-20 r87609)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 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 LAPACK version 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_1.75.1
## [5] BiocIO_1.17.1
## [6] Biostrings_2.75.3
## [7] XVector_0.47.2
## [8] bedbaser_0.99.23
## [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_1.67.1
## [23] GenomicRanges_1.59.1
## [24] GenomeInfoDb_1.43.4
## [25] IRanges_2.41.3
## [26] S4Vectors_0.45.4
## [27] BiocGenerics_0.53.6
## [28] generics_0.1.3
## [29] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_1.9.0 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_1.7.3
## [11] BiocBaseUtils_1.9.0 progress_1.2.3
## [13] lambda.r_1.2.4 curl_6.2.1
## [15] SparseArray_1.7.6 sass_0.4.9
## [17] bslib_0.9.0 htmlwidgets_1.6.4
## [19] httr2_1.1.0 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_1.7-2
## [27] R6_2.6.1 fastmap_1.2.0
## [29] GenomeInfoDbData_1.2.13 MatrixGenerics_1.19.1
## [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_4.6.0-1
## [39] BiocParallel_1.41.2 DBI_1.2.3
## [41] biomaRt_2.63.1 rappdirs_0.3.3
## [43] DelayedArray_0.33.6 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_1.10.1
## [57] stringr_1.5.1 later_1.4.1
## [59] splines_4.5.0 dplyr_1.1.4
## [61] BiocFileCache_2.15.1 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_0.42 SummarizedExperiment_1.37.0
## [71] snpStats_1.57.0 futile.logger_1.4.3
## [73] xfun_0.51 matrixStats_1.5.0
## [75] DT_0.33 stringi_1.8.4
## [77] UCSC.utils_1.3.1 yaml_2.3.10
## [79] evaluate_1.0.3 codetools_0.2-20
## [81] tibble_3.2.1 AnVILBase_1.1.0
## [83] BiocManager_1.30.25 graph_1.85.1
## [85] cli_3.6.4 AnVIL_1.19.8
## [87] xtable_1.8-4 jquerylib_0.1.4
## [89] Rcpp_1.0.14 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_1.53.1
## [101] purrr_1.0.4 crayon_1.5.3
## [103] rlang_1.1.5 KEGGREST_1.47.0
## [105] formatR_1.14