Package: AnnotationHub
Authors: Bioconductor Package Maintainer [cre],
Martin Morgan [aut],
Marc Carlson [ctb],
Dan Tenenbaum [ctb],
Sonali Arora [ctb],
Valerie Oberchain [ctb],
Kayla Morrell [ctb],
Lori Shepherd [aut]
Modified: Mon March 18 2024
Compiled: Wed Jun 17 16:19:57 2026
Bioconductor offers pre-built org.* annotation packages for model
organisms, with their use described in the
OrgDb
section of the Annotation work flow. Here we discover available OrgDb
objects for less-model organisms
library(AnnotationHub)
ah <- AnnotationHub()
query(ah, "OrgDb")## AnnotationHub with 19 records
## # snapshotDate(): 2026-06-17
## # $dataprovider: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/, NCBI
## # $species: Escherichia coli, Xenopus laevis, Sus scrofa, Saccharomyces cerevisiae, Rattus norve...
## # $rdataclass: OrgDb, TxDb
## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer,
## # rdatadateadded, preparerclass, tags, rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH111588"]]'
##
## title
## AH111588 | OrgDb Sqlite file for Coffea arabica
## AH121944 | org.Ag.eg.db.sqlite
## AH121945 | org.At.tair.db.sqlite
## AH121946 | org.Bt.eg.db.sqlite
## AH121947 | org.Cf.eg.db.sqlite
## ... ...
## AH121957 | org.Mmu.eg.db.sqlite
## AH121958 | org.Ce.eg.db.sqlite
## AH121959 | org.Xl.eg.db.sqlite
## AH121960 | org.Sc.sgd.db.sqlite
## AH121961 | org.Dr.eg.db.sqliteorgdb <- query(ah, c("OrgDb", "maintainer@bioconductor.org"))[[1]]## Error while performing HEAD request.
## Proceeding without cache information.## loading from cache## Error while performing HEAD request.
## Proceeding without cache information.The object returned by AnnotationHub is directly usable with the
select() interface, e.g., to discover the available keytypes for
querying the object, the columns that these keytypes can map to, and
finally selecting the SYMBOL and GENENAME corresponding to the first 6
ENTREZIDs
library(AnnotationDbi)
AnnotationDbi::keytypes(orgdb)## [1] "ACCNUM" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS" "ENTREZID" "ENZYME"
## [7] "EVIDENCE" "EVIDENCEALL" "GENENAME" "GO" "GOALL" "ONTOLOGY"
## [13] "ONTOLOGYALL" "PATH" "PMID" "REFSEQ" "SYMBOL" "UNIPROT"AnnotationDbi::columns(orgdb)## [1] "ACCNUM" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS" "ENTREZID" "ENZYME"
## [7] "EVIDENCE" "EVIDENCEALL" "GENENAME" "GO" "GOALL" "ONTOLOGY"
## [13] "ONTOLOGYALL" "PATH" "PMID" "REFSEQ" "SYMBOL" "UNIPROT"egid <- head(keys(orgdb, "ENTREZID"))
AnnotationDbi::select(orgdb, egid, c("SYMBOL", "GENENAME"), "ENTREZID")## 'select()' returned 1:1 mapping between keys and columns## ENTREZID SYMBOL GENENAME
## 1 1267439 LOC1267439 uncharacterized LOC1267439
## 2 1267440 LOC1267440 histone H4
## 3 1267450 LOC1267450 zinc finger protein 418
## 4 1267513 Fbxl7 F-box and leucine-rich repeat protein 7
## 5 1267546 Dbp21E2 putative ATP-dependent RNA helicase Dbp21E2
## 6 1267549 LOC1267549 astacinAll Roadmap Epigenomics files are hosted here. If one had to download these files on their own, one would navigate through the web interface to find useful files, then use something like the following R code.
url <- "http://egg2.wustl.edu/roadmap/data/byFileType/peaks/consolidated/broadPeak/E001-H3K4me1.broadPeak.gz"
filename <- basename(url)
download.file(url, destfile=filename)
if (file.exists(filename))
data <- import(filename, format="bed")This would have to be repeated for all files, and the onus would lie on the user to identify, download, import, and manage the local disk location of these files.
AnnotationHub reduces this task to just a few lines of R code
library(AnnotationHub)
ah = AnnotationHub()
epiFiles <- query(ah, "EpigenomeRoadMap")A look at the value returned by epiFiles shows us that
18250 roadmap resources are available via
AnnotationHub. Additional information about
the files is also available, e.g., where the files came from
(dataprovider), genome, species, sourceurl, sourcetypes.
epiFiles## AnnotationHub with 18250 records
## # snapshotDate(): 2026-06-17
## # $dataprovider: BroadInstitute, NA
## # $species: Homo sapiens
## # $rdataclass: BigWigFile, GRanges, data.frame
## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer,
## # rdatadateadded, preparerclass, tags, rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH28856"]]'
##
## title
## AH28856 | E001-H3K4me1.broadPeak.gz
## AH28857 | E001-H3K4me3.broadPeak.gz
## AH28858 | E001-H3K9ac.broadPeak.gz
## AH28859 | E001-H3K9me3.broadPeak.gz
## AH28860 | E001-H3K27me3.broadPeak.gz
## ... ...
## AH49542 | E061_mCRF_FractionalMethylation.bigwig
## AH49543 | E081_mCRF_FractionalMethylation.bigwig
## AH49544 | E082_mCRF_FractionalMethylation.bigwig
## AH116724 | TENET_consensus_enhancer_regions
## AH116726 | TENET_consensus_promoter_regionsA good sanity check to ensure that we have files only from the Roadmap Epigenomics project is to check that all the files in the returned smaller hub object come from Homo sapiens and the hg19, hg38 genome
unique(epiFiles$species)## [1] "Homo sapiens"unique(epiFiles$genome)## [1] "hg19" "hg38"Broadly, one can get an idea of the different files from this project looking at the sourcetype
table(epiFiles$sourcetype)##
## BED BigWig GTF Multiple Zip tab
## 8298 9932 3 2 14 1To get a more descriptive idea of these different files one can use:
sort(table(epiFiles$description), decreasing=TRUE)##
## Bigwig File containing -log10(p-value) signal tracks from EpigenomeRoadMap Project
## 6881
## Bigwig File containing fold enrichment signal tracks from EpigenomeRoadMap Project
## 2947
## Narrow ChIP-seq peaks for consolidated epigenomes from EpigenomeRoadMap Project
## 2894
## Broad ChIP-seq peaks for consolidated epigenomes from EpigenomeRoadMap Project
## 2534
## Gapped ChIP-seq peaks for consolidated epigenomes from EpigenomeRoadMap Project
## 2534
## Narrow DNasePeaks for consolidated epigenomes from EpigenomeRoadMap Project
## 131
## 15 state chromatin segmentations from EpigenomeRoadMap Project
## 127
## Broad domains on enrichment for DNase-seq for consolidated epigenomes from EpigenomeRoadMap Project
## 78
## RRBS fractional methylation calls from EpigenomeRoadMap Project
## 51
## Whole genome bisulphite fractional methylation calls from EpigenomeRoadMap Project
## 37
## MeDIP/MRE(mCRF) fractional methylation calls from EpigenomeRoadMap Project
## 16
## GencodeV10 gene/transcript coordinates and annotations corresponding to hg19 version of the human genome
## 3
## RNA-seq read count matrix for intronic protein-coding RNA elements
## 2
## RNA-seq read counts matrix for ribosomal gene exons
## 2
## RPKM expression matrix for ribosomal gene exons
## 2
## A composite GRanges object containing regions of putative enhancer elements from a variety of sources, primarily for use in the TENET Bioconductor package. This dataset is composed of regions of strong enhancers as annotated by the Roadmap Epigenomics ChromHMM expanded 18-state model based on 98 reference epigenomes, lifted over to the hg38 genome, as well as regions of human permissive enhancers identified by the FANTOM5 project. For additional information on component datasets, see the manifest file hosted at https://github.com/rhielab/TENET.AnnotationHub/blob/devel/data-raw/TENET_consensus_datasets_manifest.tsv
## 1
## A composite GRanges object containing regions of putative promoter elements from a variety of sources, primarily for use in the TENET Bioconductor package. This dataset is composed of regions flanking transcription start sites as annotated by the Roadmap Epigenomics ChromHMM expanded 18-state model based on 98 reference epigenomes, lifted over to the hg38 genome. For additional information on component datasets, see the manifest file hosted at https://github.com/rhielab/TENET.AnnotationHub/blob/devel/data-raw/TENET_consensus_datasets_manifest.tsv
## 1
## Metadata for EpigenomeRoadMap Project
## 1
## RNA-seq read counts matrix for non-coding RNAs
## 1
## RNA-seq read counts matrix for protein coding exons
## 1
## RNA-seq read counts matrix for protein coding genes
## 1
## RNA-seq read counts matrix for ribosomal genes
## 1
## RPKM expression matrix for non-coding RNAs
## 1
## RPKM expression matrix for protein coding exons
## 1
## RPKM expression matrix for protein coding genes
## 1
## RPKM expression matrix for ribosomal RNAs
## 1The ‘metadata’ provided by the Roadmap Epigenomics Project is also available. Note that the information displayed about a hub with a single resource is quite different from the information displayed when the hub references more than one resource.
metadata.tab <- query(ah , c("EpigenomeRoadMap", "Metadata"))
metadata.tab## AnnotationHub with 1 record
## # snapshotDate(): 2026-06-17
## # names(): AH41830
## # $dataprovider: BroadInstitute
## # $species: Homo sapiens
## # $rdataclass: data.frame
## # $rdatadateadded: 2015-05-11
## # $title: EID_metadata.tab
## # $description: Metadata for EpigenomeRoadMap Project
## # $taxonomyid: 9606
## # $genome: hg19
## # $sourcetype: tab
## # $sourceurl: http://egg2.wustl.edu/roadmap/data/byFileType/metadata/EID_metadata.tab
## # $sourcesize: 18035
## # $tags: c("EpigenomeRoadMap", "Metadata")
## # retrieve record with 'object[["AH41830"]]'So far we have been exploring information about resources, without
downloading the resource to a local cache and importing it into R.
One can retrieve the resource using [[ as indicated at the
end of the show method
## loading from cachemetadata.tab <- ah[["AH41830"]]## loading from cacheThe metadata.tab file is returned as a data.frame. The first 6 rows of the first 5 columns are shown here:
metadata.tab[1:6, 1:5]## EID GROUP COLOR MNEMONIC STD_NAME
## 1 E001 ESC #924965 ESC.I3 ES-I3 Cells
## 2 E002 ESC #924965 ESC.WA7 ES-WA7 Cells
## 3 E003 ESC #924965 ESC.H1 H1 Cells
## 4 E004 ES-deriv #4178AE ESDR.H1.BMP4.MESO H1 BMP4 Derived Mesendoderm Cultured Cells
## 5 E005 ES-deriv #4178AE ESDR.H1.BMP4.TROP H1 BMP4 Derived Trophoblast Cultured Cells
## 6 E006 ES-deriv #4178AE ESDR.H1.MSC H1 Derived Mesenchymal Stem CellsOne can keep constructing different queries using multiple arguments to trim down these 18250 to get the files one wants. For example, to get the ChIP-Seq files for consolidated epigenomes, one could use
bpChipEpi <- query(ah , c("EpigenomeRoadMap", "broadPeak", "chip", "consolidated"))To get all the bigWig signal files, one can query the hub using
allBigWigFiles <- query(ah, c("EpigenomeRoadMap", "BigWig"))To access the 15 state chromatin segmentations, one can use
seg <- query(ah, c("EpigenomeRoadMap", "segmentations"))If one is interested in getting all the files related to one sample
E126 <- query(ah , c("EpigenomeRoadMap", "E126", "H3K4ME2"))
E126## AnnotationHub with 6 records
## # snapshotDate(): 2026-06-17
## # $dataprovider: BroadInstitute
## # $species: Homo sapiens
## # $rdataclass: GRanges, BigWigFile
## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer,
## # rdatadateadded, preparerclass, tags, rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH29817"]]'
##
## title
## AH29817 | E126-H3K4me2.broadPeak.gz
## AH30868 | E126-H3K4me2.narrowPeak.gz
## AH31801 | E126-H3K4me2.gappedPeak.gz
## AH32990 | E126-H3K4me2.fc.signal.bigwig
## AH34022 | E126-H3K4me2.pval.signal.bigwig
## AH40177 | E126-H3K4me2.imputed.pval.signal.bigwigHub resources can also be selected using $, subset(), and
BiocHubsShiny(); see the main
AnnotationHub vignette for additional detail.
Hub resources are imported as the appropriate Bioconductor object for use in further analysis. For example, peak files are returned as GRanges objects.
## loading from cache## require("rtracklayer")peaks <- E126[['AH29817']]## loading from cacheseqinfo(peaks)## Seqinfo object with 298 sequences (2 circular) from hg19 genome:
## seqnames seqlengths isCircular genome
## chr1 249250621 FALSE hg19
## chr2 243199373 FALSE hg19
## chr3 198022430 FALSE hg19
## chr4 191154276 FALSE hg19
## chr5 180915260 FALSE hg19
## ... ... ... ...
## chrUn_gl000245 36651 FALSE hg19
## chrUn_gl000246 38154 FALSE hg19
## chrUn_gl000247 36422 FALSE hg19
## chrUn_gl000248 39786 FALSE hg19
## chrUn_gl000249 38502 FALSE hg19BigWig files are returned as BigWigFile objects. A BigWigFile is a
reference to a file on disk; the data in the file can be read in using
rtracklayer::import(), perhaps querying these large files for
particular genomic regions of interest as described on the help page
?import.bw.
Each record inside AnnotationHub is associated with a unique identifier. Most GRanges objects returned by AnnotationHub contain the unique AnnotationHub identifier of the resource from which the GRanges is derived. This can come handy when working with the GRanges object for a while, and additional information about the object (e.g., the name of the file in the cache, or the original sourceurl for the data underlying the resource) that is being worked with.
metadata(peaks)## $AnnotationHubName
## [1] "AH29817"
##
## $`File Name`
## [1] "E126-H3K4me2.broadPeak.gz"
##
## $`Data Source`
## [1] "http://egg2.wustl.edu/roadmap/data/byFileType/peaks/consolidated/broadPeak/E126-H3K4me2.broadPeak.gz"
##
## $Provider
## [1] "BroadInstitute"
##
## $Organism
## [1] "Homo sapiens"
##
## $`Taxonomy ID`
## [1] 9606ah[metadata(peaks)$AnnotationHubName]$sourceurl## [1] "http://egg2.wustl.edu/roadmap/data/byFileType/peaks/consolidated/broadPeak/E126-H3K4me2.broadPeak.gz"Bioconductor represents gene models using ‘transcript’
databases. These are available via packages such as
TxDb.Hsapiens.UCSC.hg38.knownGene
or can be constructed using functions such as
txdbmaker::makeTxDbFromBiomart().
AnnotationHub provides an easy way to work with gene models published by Ensembl. Let’s see what Ensembl’s Release-94 has in terms of data for pufferfish, Takifugu rubripes.
query(ah, c("Takifugu", "release-94"))## AnnotationHub with 7 records
## # snapshotDate(): 2026-06-17
## # $dataprovider: Ensembl
## # $species: Takifugu rubripes
## # $rdataclass: TwoBitFile, GRanges
## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer,
## # rdatadateadded, preparerclass, tags, rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH64856"]]'
##
## title
## AH64856 | Takifugu_rubripes.FUGU5.94.abinitio.gtf
## AH64857 | Takifugu_rubripes.FUGU5.94.chr.gtf
## AH64858 | Takifugu_rubripes.FUGU5.94.gtf
## AH66114 | Takifugu_rubripes.FUGU5.cdna.all.2bit
## AH66115 | Takifugu_rubripes.FUGU5.dna_rm.toplevel.2bit
## AH66116 | Takifugu_rubripes.FUGU5.dna_sm.toplevel.2bit
## AH66117 | Takifugu_rubripes.FUGU5.ncrna.2bitWe see that there is a GTF file descrbing gene models, as well as various DNA sequences. Let’s retrieve the GTF and top-level DNA sequence files. The GTF file is imported as a GRanges instance, the DNA sequence as a twobit file.
gtf <- ah[["AH64858"]]## loading from cache## Importing File into R ..dna <- ah[["AH66116"]]## Error while performing HEAD request.
## Proceeding without cache information.## loading from cache## Error while performing HEAD request.
## Proceeding without cache information.head(gtf, 3)## GRanges object with 3 ranges and 22 metadata columns:
## seqnames ranges strand | source type score phase gene_id
## <Rle> <IRanges> <Rle> | <factor> <factor> <numeric> <integer> <character>
## [1] 1 217531-252954 + | ensembl gene NA <NA> ENSTRUG00000009922
## [2] 1 217531-252954 + | ensembl transcript NA <NA> ENSTRUG00000009922
## [3] 1 217531-217702 + | ensembl exon NA <NA> ENSTRUG00000009922
## gene_version gene_name gene_source gene_biotype transcript_id transcript_version
## <character> <character> <character> <character> <character> <character>
## [1] 2 sdk2b ensembl protein_coding <NA> <NA>
## [2] 2 sdk2b ensembl protein_coding ENSTRUT00000025027 2
## [3] 2 sdk2b ensembl protein_coding ENSTRUT00000025027 2
## transcript_name transcript_source transcript_biotype exon_number exon_id
## <character> <character> <character> <character> <character>
## [1] <NA> <NA> <NA> <NA> <NA>
## [2] sdk2b-201 ensembl protein_coding <NA> <NA>
## [3] sdk2b-201 ensembl protein_coding 1 ENSTRUE00000325931
## exon_version protein_id protein_version projection_parent_gene projection_parent_transcript
## <character> <character> <character> <character> <character>
## [1] <NA> <NA> <NA> <NA> <NA>
## [2] <NA> <NA> <NA> <NA> <NA>
## [3] 1 <NA> <NA> <NA> <NA>
## tag
## <character>
## [1] <NA>
## [2] <NA>
## [3] <NA>
## -------
## seqinfo: 1627 sequences (1 circular) from FUGU5 genome; no seqlengthsdna## TwoBitFile object
## resource: /home/biocbuild/.cache/R/AnnotationHub/1ea1e535ad8f06_72862head(seqlevels(dna))## [1] "1" "2" "3" "4" "5" "6"Let’s identify the 25 longest DNA sequences, and keep just the annotations on these scaffolds.
keep <- names(tail(sort(seqlengths(dna)), 25))
gtf_subset <- gtf[seqnames(gtf) %in% keep]It is trivial to make a TxDb instance of this subset (or of the entire gtf)
library(txdbmaker) # for makeTxDbFromGRanges
txdb <- makeTxDbFromGRanges(gtf_subset)## Warning in .get_cds_IDX(mcols0$type, mcols0$phase): The "phase" metadata column contains non-NA values for features of type stop_codon. This
## information was ignored.## Warning in .makeTxDb_normarg_chrominfo(chrominfo): genome version information is not available for
## this TxDb objectand to use that in conjunction with the DNA sequences, e.g., to find exon sequences of all annotated genes.
library(Rsamtools) # for getSeq,FaFile-method
exons <- exons(txdb)
length(exons)## [1] 178769getSeq(dna, exons)## DNAStringSet object of length 178769:
## width seq
## [1] 172 CGATACGGCGCGCTCCGTTTGCCTCCGCCCCCCCCGTGGCG...GCGTTTCTGGGCCCCGCCCCCCTCGCCTCCCTCCGTGGCAG
## [2] 28 TTGGGATTATTCTCACACGCTGATCGGT
## [3] 160 ACGACGTGCCCCCCTACTTCAAGACGGAGCCGGCCCGGAGC...CACAACAACACGGAGCTGACGCGCTTCTCGCTGGAGTACAG
## [4] 107 GTACGTGATCCCGTCTTTGGACCGCTCCCACGCCGGATTCT...GGGCGCCCTGCTGCAGAGACGCACCGAAGTCCAGGTGGTCT
## [5] 148 TTATGGGAAGCTTCGAGGAGGGCGAGCGAGCCCAGTCCGTC...TGGTACCGGGATGGACGCAAGATTCCCCCGAGCAGCCGCAT
## ... ... ...
## [178765] 54 ATGCCCTCAATTACACTACCGCAGAAGGAGAACGCTCTCTTCAAAAGAATATTG
## [178766] 863 CTCTTGGTGAGGGGAAGGATGAATTTATCCGATGTCCAGTG...GTGATATAAGTTTTAGGGAAGAGCCCCATAGGCTGATGTAG
## [178767] 270 TTTGTGCAATGGGTGGCACCAGCAGCACCAGCAGGTTGTTT...CCCGTCTATCCGGATCATGCAGTGGAACATACTGGCACAAG
## [178768] 982 CAGTTGTACAGAAATCGTTGGAGCAGACCTGGAGGCTGTTG...CCCGTCTATCCGGATCATGCAGTGGAACATACTGGCACAAG
## [178769] 627 GGGGGAGATTCCGATGGTGGTATATTTAAAAAGTTGAAACT...GCCAAAGTGTTCCAGTTCCACCCATCGTGGCGGCCCGCCAGThere is a one-to-one mapping between the genomic ranges contained in
exons and the DNA sequences returned by getSeq().
Some difficulties arise when working with this partly assembled genome that require more advanced GenomicRanges skills, see the GenomicRanges vignettes, especially “GenomicRanges HOWTOs” and “An Introduction to GenomicRanges”.
Suppose we wanted to lift features from one genome build to another, e.g., because annotations were generated for hg19 but our experimental analysis used hg18. We know that UCSC provides ‘liftover’ files for mapping between genome builds.
In this example, we will take our broad Peak GRanges from E126 which comes from the ‘hg19’ genome, and lift over these features to their ‘hg38’ coordinates.
chainfiles <- query(ah , c("hg38", "hg19", "chainfile"))
chainfiles## AnnotationHub with 4 records
## # snapshotDate(): 2026-06-17
## # $dataprovider: UCSC, NCBI
## # $species: Homo sapiens
## # $rdataclass: ChainFile
## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer,
## # rdatadateadded, preparerclass, tags, rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH14108"]]'
##
## title
## AH14108 | hg38ToHg19.over.chain.gz
## AH14150 | hg19ToHg38.over.chain.gz
## AH78915 | Chain file for Homo sapiens rRNA hg19 to hg38
## AH78916 | Chain file for Homo sapiens rRNA hg38 to hg19We are interested in the file that lifts over features from hg19 to hg38 so lets download that using
## loading from cachechain <- chainfiles[['AH14150']]## loading from cachechain## Chain of length 25
## names(25): chr1 chr2 chr3 chr4 chr5 chr6 chr7 chr8 ... chr18 chr19 chr20 chr21 chr22 chrX chrY chrMPerform the liftOver operation using rtracklayer::liftOver():
library(rtracklayer)
gr38 <- liftOver(peaks, chain)This returns a GRangeslist; update the genome of the result to get the final result
genome(gr38) <- "hg38"
gr38## GRangesList object of length 153266:
## [[1]]
## GRanges object with 1 range and 5 metadata columns:
## seqnames ranges strand | name score signalValue pValue qValue
## <Rle> <IRanges> <Rle> | <character> <numeric> <numeric> <numeric> <numeric>
## [1] chr1 28667912-28670147 * | Rank_1 189 10.5585 22.0132 18.9991
## -------
## seqinfo: 23 sequences from hg38 genome; no seqlengths
##
## [[2]]
## GRanges object with 1 range and 5 metadata columns:
## seqnames ranges strand | name score signalValue pValue qValue
## <Rle> <IRanges> <Rle> | <character> <numeric> <numeric> <numeric> <numeric>
## [1] chr4 54090990-54092984 * | Rank_2 188 8.11483 21.8044 18.8066
## -------
## seqinfo: 23 sequences from hg38 genome; no seqlengths
##
## [[3]]
## GRanges object with 1 range and 5 metadata columns:
## seqnames ranges strand | name score signalValue pValue qValue
## <Rle> <IRanges> <Rle> | <character> <numeric> <numeric> <numeric> <numeric>
## [1] chr14 75293392-75296621 * | Rank_3 180 8.89834 20.9771 18.0282
## -------
## seqinfo: 23 sequences from hg38 genome; no seqlengths
##
## ...
## <153263 more elements>sessionInfo()## 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 LC_TIME=en_GB
## [4] LC_COLLATE=C LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C 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 base
##
## other attached packages:
## [1] BSgenome.Hsapiens.UCSC.hg19_1.4.3 BSgenome_1.81.0
## [3] BiocIO_1.23.3 rtracklayer_1.73.0
## [5] VariantAnnotation_1.59.0 SummarizedExperiment_1.43.0
## [7] MatrixGenerics_1.25.0 matrixStats_1.5.0
## [9] Rsamtools_2.29.0 Biostrings_2.81.3
## [11] XVector_0.53.0 txdbmaker_1.9.0
## [13] GenomicFeatures_1.65.0 AnnotationDbi_1.75.0
## [15] Biobase_2.73.1 GenomicRanges_1.65.0
## [17] IRanges_2.47.2 Seqinfo_1.3.0
## [19] S4Vectors_0.51.3 AnnotationHub_4.3.1
## [21] BiocFileCache_3.3.0 dbplyr_2.5.2
## [23] BiocGenerics_0.59.7 generics_0.1.4
## [25] BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 dplyr_1.2.1 blob_1.3.0
## [4] filelock_1.0.3 bitops_1.0-9 fastmap_1.2.0
## [7] RCurl_1.98-1.19 GenomicAlignments_1.49.0 XML_3.99-0.23
## [10] digest_0.6.39 lifecycle_1.0.5 KEGGREST_1.53.4
## [13] RSQLite_3.53.2 magrittr_2.0.5 compiler_4.6.0
## [16] rlang_1.2.0 sass_0.4.10 progress_1.2.3
## [19] tools_4.6.0 yaml_2.3.12 knitr_1.51
## [22] prettyunits_1.2.0 S4Arrays_1.13.0 bit_4.6.0
## [25] curl_7.1.0 DelayedArray_0.39.3 abind_1.4-8
## [28] BiocParallel_1.47.0 withr_3.0.2 purrr_1.2.2
## [31] grid_4.6.0 biomaRt_2.69.0 cli_3.6.6
## [34] rmarkdown_2.31 crayon_1.5.3 otel_0.2.0
## [37] httr_1.4.8 rjson_0.2.23 BiocBaseUtils_1.15.1
## [40] DBI_1.3.0 cachem_1.1.0 stringr_1.6.0
## [43] parallel_4.6.0 BiocManager_1.30.27 restfulr_0.0.17
## [46] vctrs_0.7.3 Matrix_1.7-5 jsonlite_2.0.0
## [49] bookdown_0.47 hms_1.1.4 bit64_4.8.2
## [52] jquerylib_0.1.4 glue_1.8.1 codetools_0.2-20
## [55] stringi_1.8.7 BiocVersion_3.24.0 GenomeInfoDb_1.49.1
## [58] UCSC.utils_1.9.0 tibble_3.3.1 pillar_1.11.1
## [61] rappdirs_0.3.4 htmltools_0.5.9 R6_2.6.1
## [64] httr2_1.2.2 evaluate_1.0.5 lattice_0.22-9
## [67] png_0.1-9 cigarillo_1.3.0 memoise_2.0.1
## [70] bslib_0.11.0 SparseArray_1.13.2 xfun_0.58
## [73] pkgconfig_2.0.3