bedbaser 1.1.3
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())## 128999 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 bbs_stats() to display the total available BED files, BEDsets, and
genomes. Set detailed to TRUE to display the type of BED formats and genomes
available.
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 × 52
## name genome_alias bed_compliance data_format compliant_columns
## <chr> <chr> <chr> <chr> <chr>
## 1 Plasma B cells - T… "hg38" bed6+4 encode_nar… 6
## 2 Young_Daughter 3y3A "mm10" bed5+5 bed_like_rs 5
## 3 DU01: Damage H2AK1… "" bed6+0 ucsc_bed 6
## 4 total RNA at diagn… "hg19" bed4+2 bed_like 4
## 5 encode_16468 "hg38" bed6+4 encode_nar… 6
## 6 Human colon cancer… "hg19" bed6+3 encode_bro… 6
## 7 FGFR1(RA)_ChIP_Seq "mm10" bed6+4 encode_nar… 6
## 8 encode_4362 "hg38" bed6+4 encode_nar… 6
## 9 TF ChIP-seq from H… "hg38" bed6+4 encode_nar… 6
## 10 H3K4me3 Wnt3KO Chi… "mm9" bed6+4 encode_nar… 6
## # ℹ 990 more rows
## # ℹ 47 more variables: non_compliant_columns <chr>, id <chr>,
## # description <chr>, 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>, …bb_list_bedsets(bedbase)## # A tibble: 1,000 × 9
## # Groups: id, name, md5sum, submission_date, last_update_date, description,
## # author, source [1,000]
## id name md5sum submission_date last_update_date description bed_ids
## <chr> <chr> <chr> <chr> <chr> <chr> <list>
## 1 encode_ba… enco… 462ea… 2025-04-25T01:… 2025-04-25T01:4… "Encode pr… <tibble>
## 2 encode_ba… enco… f6f15… 2025-04-24T23:… 2025-04-24T23:3… "Encode pr… <tibble>
## 3 encode_ba… enco… cf9f5… 2025-04-27T03:… 2025-04-27T03:1… "Encode pr… <tibble>
## 4 encode_ba… enco… 50cba… 2025-04-30T05:… 2025-04-30T05:3… "Encode pr… <tibble>
## 5 encode_ba… enco… 6054b… 2025-04-28T22:… 2025-04-28T22:2… "Encode pr… <tibble>
## 6 encode_ba… enco… 59f42… 2025-04-29T06:… 2025-04-29T06:2… "Encode pr… <tibble>
## 7 encode_ba… enco… 1cd93… 2025-05-02T20:… 2025-05-02T20:5… "Encode pr… <tibble>
## 8 excludera… excl… f6826… 2025-05-05T18:… 2025-05-05T18:3… "Exclude r… <tibble>
## 9 gse100000 gse1… 3da66… 2025-06-01T14:… 2025-06-01T14:5… "Data from… <tibble>
## 10 gse100302 gse1… a8df0… 2025-05-23T03:… 2025-05-23T03:4… "Data from… <tibble>
## # ℹ 990 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] "DNase-seq from suppressor macrophage (ENCLB285XKV)"
##
## $genome_alias
## [1] "hg38"
##
## $genome_digest
## NULL
##
## $bed_compliance
## [1] "bed6+4"
##
## $data_format
## [1] "encode_narrowpeak"
##
## $compliant_columns
## [1] 6Use bb_beds_in_bedset() to display the id of BEDs in a BEDset.
bb_beds_in_bedset(bedbase, "excluderanges")## # A tibble: 81 × 32
## name genome_alias genome_digest bed_compliance data_format compliant_columns
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 mm10… mm10 0f10d83b1050… bed4+1 bed_like 4
## 2 hg38… hg38 2230c535660f… bed4+1 bed_like 4
## 3 T2T.… t2t-chm13 <NA> bed4+8 bed_like 4
## 4 TAIR… tair10 <NA> bed4+2 bed_like 4
## 5 mm9.… mm9 <NA> bed4+1 bed_like 4
## 6 T2T.… t2t-chm13 <NA> bed4+1 bed_like 4
## 7 danR… danrer10 <NA> bed4+1 bed_like 4
## 8 mm39… mm39 <NA> bed4+7 bed_like 4
## 9 mm9.… mm9 <NA> bed4+4 bed_like 4
## 10 hg19… hg19 baa91c8f6e27… bed4+7 bed_like 4
## # ℹ 71 more rows
## # ℹ 26 more variables: non_compliant_columns <chr>, id <chr>,
## # description <chr>, 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>, …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 × 44
## id payload.id payload.name payload.description payload.cell_line
## <chr> <chr> <chr> <chr> <chr>
## 1 5fbba3a301e929… 5fbba3a30… WT_T47D_HiC "" T47D-MTVL
## 2 f0f66be64b879d… f0f66be64… WT_T47D_HiC "" T47D-MTVL
## 3 031fcd26012245… 031fcd260… TYUC1_p63_1 "" TYUC-1
## 4 6f0558df8d4bc9… 6f0558df8… TYUC1_p63_2 "" TYUC-1
## 5 4ae0fd71a38e46… 4ae0fd71a… CEBPB_KD1_H… "" A549
## 6 058cc2a31df12f… 058cc2a31… CEBPB_KD2_H… "" A549
## 7 925b77c6c018b8… 925b77c6c… NuMA-C_term… "" HCT116
## 8 4e9845328d8fa5… 4e9845328… NuMA-N_term… "" HCT116
## 9 8ac8cb600dac64… 8ac8cb600… Kai-1 tech … "" HeLa
## 10 994d2d7da44652… 994d2d7da… D7-H23 "Single-nucleus/mi… HeLa
## # ℹ 39 more variables: payload.cell_type <chr>, payload.tissue <chr>,
## # payload.target <chr>, payload.treatment <chr>, payload.assay <chr>,
## # payload.genome_alias <chr>, payload.species_name <chr>, score <chr>,
## # metadata.name <chr>, metadata.genome_alias <chr>,
## # metadata.bed_compliance <chr>, metadata.data_format <chr>,
## # metadata.compliant_columns <chr>, metadata.non_compliant_columns <chr>,
## # metadata.id <chr>, metadata.description <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## GRanges object with 253743 ranges and 6 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 180801-180871 * | <NA> 0
## [2] chr1 181421-181560 * | <NA> 0
## [3] chr1 268012-268080 * | <NA> 0
## [4] chr1 727181-727287 * | <NA> 0
## [5] chr1 778661-778860 * | <NA> 0
## ... ... ... ... . ... ...
## [253739] chrY 26315181-26315340 * | <NA> 0
## [253740] chrY 26372441-26372620 * | <NA> 0
## [253741] chrY 26670501-26670760 * | <NA> 0
## [253742] chrY 26671021-26671400 * | <NA> 0
## [253743] chrY 26671621-26671699 * | <NA> 0
## signalValue pValue qValue peak
## <numeric> <numeric> <numeric> <integer>
## [1] 0.3595720 -1 -1 75
## [2] 1.5477200 -1 -1 75
## [3] 1.5946300 -1 -1 75
## [4] 0.0625343 -1 -1 75
## [5] 14.3412000 -1 -1 75
## ... ... ... ... ...
## [253739] 0.901537 -1 -1 75
## [253740] 0.130280 -1 -1 75
## [253741] 0.333516 -1 -1 75
## [253742] 0.416896 -1 -1 75
## [253743] 0.166758 -1 -1 75
## -------
## 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(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_compliance
## [1] "bed6+4"
##
## $data_format
## [1] "encode_narrowpeak_rs"
##
## $compliant_columns
## [1] 6bb_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 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 864 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 690078-6272609 *
## [2] chr1 690078-2326424 *
## [3] chr1 771707-6806566 *
## [4] chr1 771707-3153758 *
## [5] chr1 805477-4942653 *
## ... ... ... ...
## [860] chrY 23762211-26011096 *
## [861] chrY 23762211-26011096 *
## [862] chrY 23774007-25910251 *
## [863] chrY 26011096-26174983 *
## [864] chrY 26312489-26653776 *
## -------
## 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: 47 × 3
## tag operation summary
## <chr> <chr> <chr>
## 1 base get_bedbase_db_stats_v1_assays_get Get available assays
## 2 base get_bedbase_db_stats_v1_genomes_get Get available genomes
## 3 base get_bedbase_db_stats_v1_stats_get Get summary statistics f…
## 4 base get_detailed_stats_v1_detailed_stats_get Get detailed statistics …
## 5 base get_detailed_usage_v1_detailed_usage_get Get detailed usage stati…
## 6 base redirect_to_download_v1_files__file_path__get Redirect To Download
## 7 base service_info_v1_service_info_get GA4GH service info
## 8 bed bed_to_bed_search_v1_bed_search_bed_post Search for similar bed f…
## 9 bed embed_bed_file_v1_bed_embed_post Get embeddings for a bed…
## 10 bed exact_search_v1_bed_search_exact_get Search for exact match o…
## # ℹ 37 more rows
## tag values:
## base, bed, bedset, home, objects, search, NA
## schemas():
## AccessMethod, AccessURL, BaseListResponse, BedClassification,
## BedEmbeddingResult
## # ... with 44 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 <- "f2a5b06011706376560514c3f39648ea"
bedbase <- BEDbase()
gro <- bb_to_granges(bedbase, id)
gro## GRanges object with 132610 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 3132268-3132768 + | chr1-21633 1
## [2] chr1 3185464-3185964 + | chr1-21634 1
## [3] chr1 3221560-3222060 + | chr1-6085 1
## [4] chr1 3476307-3476807 + | chr1-21635 1
## [5] chr1 3560226-3561000 + | chr1-4747 1
## ... ... ... ... . ... ...
## [132606] chrY 90737580-90739215 + | chrY-23 1
## [132607] chrY 90742758-90744732 + | chrY-35 1
## [132608] chrY 90810972-90814119 + | chrY-47 1
## [132609] chrY 90819248-90819748 + | chrY-131 1
## [132610] chrY 90828312-90828949 + | chrY-103 1
## -------
## 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 132675 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 3202491-3202991 + | chr1-21633 1
## [2] chr1 3255687-3256187 + | chr1-21634 1
## [3] chr1 3291783-3292283 + | chr1-6085 1
## [4] chr1 3546530-3547030 + | chr1-21635 1
## [5] chr1 3630449-3631223 + | chr1-4747 1
## ... ... ... ... . ... ...
## [132671] chrY 90748849-90750484 + | chrY-23 1
## [132672] chrY 90754027-90756001 + | chrY-35 1
## [132673] chrY 90822241-90825388 + | chrY-47 1
## [132674] chrY 90830517-90831017 + | chrY-131 1
## [132675] chrY 90839581-90840218 + | chrY-103 1
## -------
## seqinfo: 21 sequences from mm39 genome; no seqlengthssessionInfo()## R version 4.5.1 Patched (2025-08-23 r88802)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-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_1.77.2
## [5] BiocIO_1.19.0
## [6] Biostrings_2.77.2
## [7] XVector_0.49.1
## [8] bedbaser_1.1.3
## [9] liftOver_1.33.1
## [10] Homo.sapiens_1.3.1
## [11] TxDb.Hsapiens.UCSC.hg19.knownGene_3.22.0
## [12] org.Hs.eg.db_3.22.0
## [13] GO.db_3.22.0
## [14] OrganismDbi_1.51.4
## [15] GenomicFeatures_1.61.6
## [16] AnnotationDbi_1.71.1
## [17] Biobase_2.69.1
## [18] GenomeInfoDb_1.45.12
## [19] gwascat_2.41.1
## [20] R.utils_2.13.0
## [21] R.oo_1.27.1
## [22] R.methodsS3_1.8.2
## [23] rtracklayer_1.69.1
## [24] GenomicRanges_1.61.5
## [25] Seqinfo_0.99.2
## [26] IRanges_2.43.5
## [27] S4Vectors_0.47.4
## [28] BiocGenerics_0.55.3
## [29] generics_0.1.4
## [30] BiocStyle_2.37.1
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 bitops_1.0-9
## [3] RBGL_1.85.0 httr2_1.2.1
## [5] formatR_1.14 AnVILBase_1.3.1
## [7] rlang_1.1.6 magrittr_2.0.4
## [9] matrixStats_1.5.0 compiler_4.5.1
## [11] RSQLite_2.4.3 png_0.1-8
## [13] vctrs_0.6.5 stringr_1.5.2
## [15] pkgconfig_2.0.3 crayon_1.5.3
## [17] fastmap_1.2.0 dbplyr_2.5.1
## [19] utf8_1.2.6 promises_1.3.3
## [21] Rsamtools_2.25.3 rmarkdown_2.30
## [23] graph_1.87.0 UCSC.utils_1.5.0
## [25] purrr_1.1.0 bit_4.6.0
## [27] xfun_0.53 cachem_1.1.0
## [29] jsonlite_2.0.0 blob_1.2.4
## [31] later_1.4.4 DelayedArray_0.35.3
## [33] BiocParallel_1.43.4 parallel_4.5.1
## [35] R6_2.6.1 VariantAnnotation_1.55.1
## [37] stringi_1.8.7 bslib_0.9.0
## [39] jquerylib_0.1.4 Rcpp_1.1.0
## [41] bookdown_0.45 SummarizedExperiment_1.39.2
## [43] knitr_1.50 BiocBaseUtils_1.11.2
## [45] httpuv_1.6.16 Matrix_1.7-4
## [47] splines_4.5.1 tidyselect_1.2.1
## [49] abind_1.4-8 yaml_2.3.10
## [51] miniUI_0.1.2 codetools_0.2-20
## [53] curl_7.0.0 lattice_0.22-7
## [55] tibble_3.3.0 withr_3.0.2
## [57] shiny_1.11.1 KEGGREST_1.49.2
## [59] evaluate_1.0.5 lambda.r_1.2.4
## [61] survival_3.8-3 futile.logger_1.4.3
## [63] BiocFileCache_2.99.6 snpStats_1.59.2
## [65] pillar_1.11.1 BiocManager_1.30.26
## [67] filelock_1.0.3 MatrixGenerics_1.21.0
## [69] DT_0.34.0 AnVIL_1.21.10
## [71] RCurl_1.98-1.17 xtable_1.8-4
## [73] glue_1.8.0 tools_4.5.1
## [75] data.table_1.17.8 GenomicAlignments_1.45.4
## [77] rapiclient_0.1.8 XML_3.99-0.19
## [79] grid_4.5.1 tidyr_1.3.1
## [81] restfulr_0.0.16 cli_3.6.5
## [83] rappdirs_0.3.3 futile.options_1.0.1
## [85] S4Arrays_1.9.1 GCPtools_0.99.4
## [87] dplyr_1.1.4 sass_0.4.10
## [89] digest_0.6.37 SparseArray_1.9.1
## [91] htmlwidgets_1.6.4 rjson_0.2.23
## [93] memoise_2.0.1 htmltools_0.5.8.1
## [95] lifecycle_1.0.4 mime_0.13
## [97] bit64_4.6.0-1