bedbaser 1.5.0
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())## 660029 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 × 51
## name genome_alias genome_digest bed_compliance data_format compliant_columns
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Plas… hg19 GOIHeGSorDrb… bed6+4 encode_nar… 6
## 2 ENCF… hg38 Ba88PY52_qei… bed5+1 bed_like_rs 5
## 3 ENCF… hg38 Ba88PY52_qei… bed4+1 bed_like 4
## 4 ENCF… hg38 Ba88PY52_qei… bed4+1 bed_like 4
## 5 ENCF… hg38 Ba88PY52_qei… bed6+3 encode_bro… 6
## 6 ENCF… hg38 Ba88PY52_qei… bed4+1 bed_like 4
## 7 ENCF… hg38 Ba88PY52_qei… bed4+1 bed_like 4
## 8 ENCF… hg38 Ba88PY52_qei… bed4+0 ucsc_bed 4
## 9 ENCF… hg38 Ba88PY52_qei… bed4+1 bed_like 4
## 10 ENCF… hg38 Ba88PY52_qei… bed4+1 bed_like 4
## # ℹ 990 more rows
## # ℹ 45 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 gse174903 gse17… 29177… 2026-02-09T10:… 2026-02-09T10:2… "Data from… <tibble>
## 2 gse174905 gse17… 1c3b2… 2026-02-09T10:… 2026-02-09T10:4… "Data from… <tibble>
## 3 gse181605 gse18… f2708… 2025-05-24T22:… 2025-05-24T22:2… "Data from… <tibble>
## 4 gse181606 gse18… f2708… 2025-05-24T22:… 2025-05-24T22:3… "Data from… <tibble>
## 5 gse184698 gse18… ffbfd… 2025-05-24T22:… 2025-05-24T22:3… "Data from… <tibble>
## 6 gse262830 gse26… ba57b… 2025-05-24T00:… 2025-05-24T00:3… "Data from… <tibble>
## 7 gse243908 gse24… 3a6e8… 2025-05-24T22:… 2025-05-24T22:2… "Data from… <tibble>
## 8 gse248210 gse24… 76226… 2025-05-24T22:… 2025-05-24T22:2… "Data from… <tibble>
## 9 gse244935 gse24… 15323… 2025-05-24T22:… 2025-05-24T22:3… "Data from… <tibble>
## 10 gse65812 gse65… 85a0d… 2025-05-25T01:… 2025-05-25T01:5… "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] "encode_8320"
##
## $genome_alias
## [1] "hg38"
##
## $genome_digest
## [1] "jFm0Uca8a7vK2cbuIQgBopjBilgCFheD"
##
## $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 × 34
## name genome_alias genome_digest bed_compliance data_format compliant_columns
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 mm10… mm10 hW3Ba5zoufl3… bed4+1 bed_like 4
## 2 hg38… hg38 Ba88PY52_qei… bed4+1 bed_like 4
## 3 T2T.… hg19 GOIHeGSorDrb… bed4+8 bed_like 4
## 4 TAIR… hg18 ieWVCws5MC2Q… bed4+2 bed_like 4
## 5 mm9.… mm9 4mvptys3ckGg… bed4+1 bed_like 4
## 6 T2T.… hg38 Ba88PY52_qei… bed4+1 bed_like 4
## 7 danR… hg18 ieWVCws5MC2Q… bed4+1 bed_like 4
## 8 mm39… mm39 -e70JAQq4NJD… bed4+7 bed_like 4
## 9 mm9.… mm9 4mvptys3ckGg… bed4+4 bed_like 4
## 10 hg19… hg19 GOIHeGSorDrb… bed4+7 bed_like 4
## # ℹ 71 more rows
## # ℹ 28 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 × 43
## id payload.id payload.name payload.description payload.cell_line
## <chr> <chr> <chr> <chr> <chr>
## 1 f0f66be64b879d… f0f66be64… WT_T47D_HiC "" "T47D-MTVL"
## 2 c1ec0ee9b824b3… c1ec0ee9b… VCaP_H3K4me… "Chromatin IP agai… "Vertebral Cance…
## 3 5fbba3a301e929… 5fbba3a30… WT_T47D_HiC "" "T47D-MTVL"
## 4 6247ec77ee9a4f… 6247ec77e… PAX 139 "" ""
## 5 0815d601135466… 0815d6011… PAX 217 "" ""
## 6 d1d29207cdb6a6… d1d29207c… HeLa_1k_1D_… "" "HeLa"
## 7 6a508f798160d9… 6a508f798… PAX 265 "" ""
## 8 4e870164bcc0a4… 4e870164b… HeLa_1k_1D_… "" "HeLa"
## 9 57291034c3664d… 57291034c… 786-O H3K27… "" "786-O"
## 10 7d929f0062483c… 7d929f006… HeLa_1k_1D_… "" "HeLa"
## # ℹ 38 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.genome_digest <chr>, metadata.bed_compliance <chr>,
## # metadata.data_format <chr>, metadata.compliant_columns <chr>,
## # metadata.non_compliant_columns <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## GRanges object with 134214 ranges and 6 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 1011559-1011907 * | Peak_17877 94
## [2] chr1 1100362-1100829 * | Peak_128709 21
## [3] chr1 1113217-1113860 * | Peak_477 285
## [4] chr1 1192152-1192369 * | Peak_80934 43
## [5] chr1 1365846-1366946 * | Peak_1628 212
## ... ... ... ... . ... ...
## [134210] chrY 56863009-56863326 * | Peak_9430 120
## [134211] chrY 56865410-56865614 * | Peak_106191 35
## [134212] chrY 56868733-56868946 * | Peak_57076 56
## [134213] chrY 56869876-56870126 * | Peak_6376 140
## [134214] chrY 56872109-56872328 * | Peak_29026 77
## signalValue pValue qValue peak
## <numeric> <numeric> <numeric> <integer>
## [1] 3.43081 8.07173 5.08229 208
## [2] 1.80252 2.89222 1.02526 144
## [3] 5.81182 21.63969 16.85817 182
## [4] 2.60721 4.48469 2.17283 143
## [5] 4.22880 16.47118 12.31763 396
## ... ... ... ... ...
## [134210] 3.07474 9.99596 6.71214 284
## [134211] 2.27692 3.86663 1.72586 185
## [134212] 2.26984 5.37840 2.88900 187
## [134213] 4.09058 11.31119 7.83814 191
## [134214] 3.16690 6.88420 4.09865 144
## -------
## 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] "Ba88PY52_qeifhJrgUXyin6UITdXNsg3"
##
## $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: 50 × 3
## tag operation summary
## <chr> <chr> <chr>
## 1 base get_assays_list_v1_assays_get Get available assa…
## 2 base get_bedbase_db_stats_v1_stats_get Get summary statis…
## 3 base get_detailed_stats_v1_detailed_stats_get Get detailed stati…
## 4 base get_detailed_usage_v1_detailed_usage_get Get detailed usage…
## 5 base get_genomes_list_v1_genomes_get Get available geno…
## 6 base redirect_to_download_v1_files__file_path__get Redirect To Downlo…
## 7 base service_info_v1_service_info_get GA4GH service info
## 8 bed analyze_reference_genome_v1_bed_analyze_genome_post Analyze reference …
## 9 bed bed_to_bed_search_v1_bed_search_bed_post Search for similar…
## 10 bed embed_bed_file_v1_bed_embed_post Get embeddings for…
## # ℹ 40 more rows
## tag values:
## base, bed, bedset, home, objects, search, NA
## schemas():
## AccessMethod, AccessURL, BaseListResponse, BedClassification,
## BedEmbeddingResult
## # ... with 46 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 4.6.0 RC (2026-04-17 r89917)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.24-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_1.4.8
## [3] BSgenome.Hsapiens.UCSC.hg38_1.4.5
## [4] BSgenome_1.81.0
## [5] BiocIO_1.23.0
## [6] Biostrings_2.81.0
## [7] XVector_0.53.0
## [8] bedbaser_1.5.0
## [9] liftOver_1.35.0
## [10] Homo.sapiens_1.3.1
## [11] TxDb.Hsapiens.UCSC.hg19.knownGene_3.22.1
## [12] org.Hs.eg.db_3.23.1
## [13] GO.db_3.23.1
## [14] OrganismDbi_1.55.0
## [15] GenomicFeatures_1.65.0
## [16] AnnotationDbi_1.75.0
## [17] Biobase_2.73.0
## [18] GenomeInfoDb_1.49.0
## [19] gwascat_2.45.0
## [20] R.utils_2.13.0
## [21] R.oo_1.27.1
## [22] R.methodsS3_1.8.2
## [23] rtracklayer_1.73.0
## [24] GenomicRanges_1.65.0
## [25] Seqinfo_1.3.0
## [26] IRanges_2.47.0
## [27] S4Vectors_0.51.0
## [28] BiocGenerics_0.59.0
## [29] generics_0.1.4
## [30] BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_2.0.0 magrittr_2.0.5
## [3] rmarkdown_2.31 vctrs_0.7.3
## [5] memoise_2.0.1 Rsamtools_2.29.0
## [7] RCurl_1.98-1.18 htmltools_0.5.9
## [9] S4Arrays_1.13.0 BiocBaseUtils_1.15.0
## [11] lambda.r_1.2.4 curl_7.1.0
## [13] SparseArray_1.13.0 sass_0.4.10
## [15] bslib_0.10.0 htmlwidgets_1.6.4
## [17] keyring_1.4.1 httr2_1.2.2
## [19] futile.options_1.0.1 cachem_1.1.0
## [21] GenomicAlignments_1.49.0 mime_0.13
## [23] lifecycle_1.0.5 pkgconfig_2.0.3
## [25] Matrix_1.7-5 R6_2.6.1
## [27] fastmap_1.2.0 MatrixGenerics_1.25.0
## [29] shiny_1.13.0 digest_0.6.39
## [31] GCPtools_1.3.0 RSQLite_2.4.6
## [33] filelock_1.0.3 abind_1.4-8
## [35] compiler_4.6.0 bit64_4.8.0
## [37] withr_3.0.2 BiocParallel_1.47.0
## [39] DBI_1.3.0 rappdirs_0.3.4
## [41] DelayedArray_0.39.0 rjson_0.2.23
## [43] tools_4.6.0 otel_0.2.0
## [45] httpuv_1.6.17 glue_1.8.1
## [47] restfulr_0.0.16 promises_1.5.0
## [49] grid_4.6.0 tidyr_1.3.2
## [51] data.table_1.18.2.1 utf8_1.2.6
## [53] pillar_1.11.1 stringr_1.6.0
## [55] later_1.4.8 splines_4.6.0
## [57] dplyr_1.2.1 BiocFileCache_3.3.0
## [59] lattice_0.22-9 survival_3.8-6
## [61] bit_4.6.0 tidyselect_1.2.1
## [63] RBGL_1.89.0 miniUI_0.1.2
## [65] knitr_1.51 bookdown_0.46
## [67] SummarizedExperiment_1.43.0 snpStats_1.63.0
## [69] futile.logger_1.4.9 xfun_0.57
## [71] matrixStats_1.5.0 DT_0.34.0
## [73] stringi_1.8.7 UCSC.utils_1.9.0
## [75] yaml_2.3.12 evaluate_1.0.5
## [77] codetools_0.2-20 cigarillo_1.3.0
## [79] tibble_3.3.1 AnVILBase_1.7.0
## [81] BiocManager_1.30.27 graph_1.91.0
## [83] cli_3.6.6 AnVIL_1.25.0
## [85] xtable_1.8-8 jquerylib_0.1.4
## [87] Rcpp_1.1.1-1.1 dbplyr_2.5.2
## [89] png_0.1-9 XML_3.99-0.23
## [91] rapiclient_0.1.8 parallel_4.6.0
## [93] blob_1.3.0 bitops_1.0-9
## [95] VariantAnnotation_1.59.0 purrr_1.2.2
## [97] crayon_1.5.3 rlang_1.2.0
## [99] KEGGREST_1.53.0 formatR_1.14