The BioMartGOGeneSets contains pre-compiled GO gene sets for a huge number of organisms supported in BioMart. There are two types of data: 1. genes and 2 gene sets.
To obtain the genes, use the function getBioMartGenes(). You need to provide a proper “dataset”, which can be found with the function supportedOrganisms() (A complete list can be also found from “BioMart Gene Ontology Gene Sets Collections)”. Here we use the dataset "hsapiens_gene_ensembl" as an example which is for human.
## GRanges object with 69299 ranges and 4 metadata columns:
## seqnames ranges strand | ensembl_gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000000003 X 100627108-100639991 - | ENSG00000000003
## ENSG00000000005 X 100584936-100599885 + | ENSG00000000005
## ENSG00000000419 20 50934867-50959140 - | ENSG00000000419
## ENSG00000000457 1 169849631-169894267 - | ENSG00000000457
## ENSG00000000460 1 169662007-169854080 + | ENSG00000000460
## ... ... ... ... . ...
## ENSG00000291313 14 103334237-103335932 + | ENSG00000291313
## ENSG00000291314 X 10566888-10576955 - | ENSG00000291314
## ENSG00000291315 3 40312086-40312214 + | ENSG00000291315
## ENSG00000291316 8 144449582-144465430 - | ENSG00000291316
## ENSG00000291317 8 144463817-144465667 - | ENSG00000291317
## gene_biotype entrezgene_id external_gene_name
## <character> <CharacterList> <character>
## ENSG00000000003 protein_coding 7105 TSPAN6
## ENSG00000000005 protein_coding 64102 TNMD
## ENSG00000000419 protein_coding 8813 DPM1
## ENSG00000000457 protein_coding 57147 SCYL3
## ENSG00000000460 protein_coding 55732 C1orf112
## ... ... ... ...
## ENSG00000291313 protein_coding <NA> <NA>
## ENSG00000291314 protein_coding <NA> <NA>
## ENSG00000291315 protein_coding <NA> <NA>
## ENSG00000291316 protein_coding 157542 <NA>
## ENSG00000291317 protein_coding 84773 TMEM276
## -------
## seqinfo: 445 sequences from an unspecified genome; no seqlengthsThe returned value is a GRanges object which contains the coordinates of genes. The meta columns contain additional information of genes such as different gene IDs and type of genes.
You can also provide a “short name” for dataset and the function will perform a partial matching.
You can try the following command and see what will be printed:
You can also provide the taxon id for the organism:
Chromosome names from Ensembl have no “chr” prefix. You can set add_chr_prefix = TURE to add "chr" prefix to some of the chromosome names. Internally it uses GenomeInfoDb::seqlevelsStyle(gr) = "UCSC" to check and to add "chr" prefix.
## GRanges object with 69299 ranges and 4 metadata columns:
## seqnames ranges strand | ensembl_gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000000003 chrX 100627108-100639991 - | ENSG00000000003
## ENSG00000000005 chrX 100584936-100599885 + | ENSG00000000005
## ENSG00000000419 chr20 50934867-50959140 - | ENSG00000000419
## ENSG00000000457 chr1 169849631-169894267 - | ENSG00000000457
## ENSG00000000460 chr1 169662007-169854080 + | ENSG00000000460
## ... ... ... ... . ...
## ENSG00000291313 chr14 103334237-103335932 + | ENSG00000291313
## ENSG00000291314 chrX 10566888-10576955 - | ENSG00000291314
## ENSG00000291315 chr3 40312086-40312214 + | ENSG00000291315
## ENSG00000291316 chr8 144449582-144465430 - | ENSG00000291316
## ENSG00000291317 chr8 144463817-144465667 - | ENSG00000291317
## gene_biotype entrezgene_id external_gene_name
## <character> <CharacterList> <character>
## ENSG00000000003 protein_coding 7105 TSPAN6
## ENSG00000000005 protein_coding 64102 TNMD
## ENSG00000000419 protein_coding 8813 DPM1
## ENSG00000000457 protein_coding 57147 SCYL3
## ENSG00000000460 protein_coding 55732 C1orf112
## ... ... ... ...
## ENSG00000291313 protein_coding <NA> <NA>
## ENSG00000291314 protein_coding <NA> <NA>
## ENSG00000291315 protein_coding <NA> <NA>
## ENSG00000291316 protein_coding 157542 <NA>
## ENSG00000291317 protein_coding 84773 TMEM276
## -------
## seqinfo: 445 sequences from an unspecified genome; no seqlengthsNote add_chr_prefix is just a helper argument. You can basically do the same as:
## GRanges object with 69299 ranges and 4 metadata columns:
## seqnames ranges strand | ensembl_gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000000003 chrX 100627108-100639991 - | ENSG00000000003
## ENSG00000000005 chrX 100584936-100599885 + | ENSG00000000005
## ENSG00000000419 chr20 50934867-50959140 - | ENSG00000000419
## ENSG00000000457 chr1 169849631-169894267 - | ENSG00000000457
## ENSG00000000460 chr1 169662007-169854080 + | ENSG00000000460
## ... ... ... ... . ...
## ENSG00000291313 chr14 103334237-103335932 + | ENSG00000291313
## ENSG00000291314 chrX 10566888-10576955 - | ENSG00000291314
## ENSG00000291315 chr3 40312086-40312214 + | ENSG00000291315
## ENSG00000291316 chr8 144449582-144465430 - | ENSG00000291316
## ENSG00000291317 chr8 144463817-144465667 - | ENSG00000291317
## gene_biotype entrezgene_id external_gene_name
## <character> <CharacterList> <character>
## ENSG00000000003 protein_coding 7105 TSPAN6
## ENSG00000000005 protein_coding 64102 TNMD
## ENSG00000000419 protein_coding 8813 DPM1
## ENSG00000000457 protein_coding 57147 SCYL3
## ENSG00000000460 protein_coding 55732 C1orf112
## ... ... ... ...
## ENSG00000291313 protein_coding <NA> <NA>
## ENSG00000291314 protein_coding <NA> <NA>
## ENSG00000291315 protein_coding <NA> <NA>
## ENSG00000291316 protein_coding 157542 <NA>
## ENSG00000291317 protein_coding 84773 TMEM276
## -------
## seqinfo: 445 sequences from an unspecified genome; no seqlengthsFor some not-well-studied organisms, there might be no “official chromosome name”. For example, "cporcellus_gene_ensembl" for the guinea Pig:
## GRanges object with 26855 ranges and 4 metadata columns:
## seqnames ranges strand | ensembl_gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSCPOG00000000007 DS562868.1 22719983-22745621 - | ENSCPOG00000000007
## ENSCPOG00000000011 DS562863.1 58475996-58484540 + | ENSCPOG00000000011
## ENSCPOG00000000012 DS562864.1 51072990-51073943 + | ENSCPOG00000000012
## ENSCPOG00000000013 DS563093.1 409300-421802 + | ENSCPOG00000000013
## ENSCPOG00000000014 DS562923.1 9324624-9325632 + | ENSCPOG00000000014
## ... ... ... ... . ...
## ENSCPOG00000040808 DS562855.1 547865-551436 + | ENSCPOG00000040808
## ENSCPOG00000040809 DS562862.1 50369714-50415125 - | ENSCPOG00000040809
## ENSCPOG00000040810 DS562864.1 43092199-43093149 - | ENSCPOG00000040810
## ENSCPOG00000040811 DS562863.1 28315518-28533596 + | ENSCPOG00000040811
## ENSCPOG00000040812 DS562897.1 10154287-10157556 - | ENSCPOG00000040812
## gene_biotype entrezgene_id external_gene_name
## <character> <CharacterList> <character>
## ENSCPOG00000000007 protein_coding 100730010 D2HGDH
## ENSCPOG00000000011 protein_coding 100730666 OTOL1
## ENSCPOG00000000012 protein_coding 100720088 <NA>
## ENSCPOG00000000013 protein_coding <NA> TRIM10
## ENSCPOG00000000014 protein_coding <NA> <NA>
## ... ... ... ...
## ENSCPOG00000040808 protein_coding 100735988 TMEM74
## ENSCPOG00000040809 protein_coding <NA> <NA>
## ENSCPOG00000040810 pseudogene <NA> <NA>
## ENSCPOG00000040811 protein_coding <NA> <NA>
## ENSCPOG00000040812 lincRNA <NA> <NA>
## -------
## seqinfo: 449 sequences from an unspecified genome; no seqlengthsThe sequence names are in a special format of DS\d+. The source of the format can be obtained by getBioMartGenomeInfo(). In the seqname_style element of the returned list, there are several examples that you can compare to.
## $dataset
## [1] "cporcellus_gene_ensembl"
##
## $version
## [1] "Cavpor3.0"
##
## $name
## [1] "Cavia porcellus (Guinea Pig)"
##
## $taxon_id
## [1] 10141
##
## $genbank_accession
## [1] "GCA_000151735.1"
##
## $mart
## [1] "genes_mart"
##
## $seqname_style
## $seqname_style$`Sequence-Name`
## [1] "supercont2_0" "supercont2_1" "supercont2_2" "supercont2_3" "supercont2_4"
##
## $seqname_style$`GenBank-Accn`
## [1] "DS562855.1" "DS562856.1" "DS562857.1" "DS562858.1" "DS562859.1"
##
## $seqname_style$`RefSeq-Accn`
## [1] "NT_176419.1" "NT_176418.1" "NT_176417.1" "NT_176416.1" "NT_176415.1"Now we know they are the GenBank accession IDs. Next we might want to change them to the "Sequence-Name" style. Simply use changeSeqnameStyle().
gr2 = changeSeqnameStyle(gr, "cporcellus_gene_ensembl",
seqname_style_from = "GenBank-Accn",
seqname_style_to = "Sequence-Name")
gr2## GRanges object with 26818 ranges and 5 metadata columns:
## seqnames ranges strand |
## <Rle> <IRanges> <Rle> |
## ENSCPOG00000000007 supercont2_13 22719983-22745621 - |
## ENSCPOG00000000011 supercont2_8 58475996-58484540 + |
## ENSCPOG00000000012 supercont2_9 51072990-51073943 + |
## ENSCPOG00000000013 supercont2_238 409300-421802 + |
## ENSCPOG00000000014 supercont2_68 9324624-9325632 + |
## ... ... ... ... .
## ENSCPOG00000040808 supercont2_0 547865-551436 + |
## ENSCPOG00000040809 supercont2_7 50369714-50415125 - |
## ENSCPOG00000040810 supercont2_9 43092199-43093149 - |
## ENSCPOG00000040811 supercont2_8 28315518-28533596 + |
## ENSCPOG00000040812 supercont2_42 10154287-10157556 - |
## ensembl_gene_id gene_biotype entrezgene_id
## <character> <character> <CharacterList>
## ENSCPOG00000000007 ENSCPOG00000000007 protein_coding 100730010
## ENSCPOG00000000011 ENSCPOG00000000011 protein_coding 100730666
## ENSCPOG00000000012 ENSCPOG00000000012 protein_coding 100720088
## ENSCPOG00000000013 ENSCPOG00000000013 protein_coding <NA>
## ENSCPOG00000000014 ENSCPOG00000000014 protein_coding <NA>
## ... ... ... ...
## ENSCPOG00000040808 ENSCPOG00000040808 protein_coding 100735988
## ENSCPOG00000040809 ENSCPOG00000040809 protein_coding <NA>
## ENSCPOG00000040810 ENSCPOG00000040810 pseudogene <NA>
## ENSCPOG00000040811 ENSCPOG00000040811 protein_coding <NA>
## ENSCPOG00000040812 ENSCPOG00000040812 lincRNA <NA>
## external_gene_name .original_seqname
## <character> <character>
## ENSCPOG00000000007 D2HGDH DS562868.1
## ENSCPOG00000000011 OTOL1 DS562863.1
## ENSCPOG00000000012 <NA> DS562864.1
## ENSCPOG00000000013 TRIM10 DS563093.1
## ENSCPOG00000000014 <NA> DS562923.1
## ... ... ...
## ENSCPOG00000040808 TMEM74 DS562855.1
## ENSCPOG00000040809 <NA> DS562862.1
## ENSCPOG00000040810 <NA> DS562864.1
## ENSCPOG00000040811 <NA> DS562863.1
## ENSCPOG00000040812 <NA> DS562897.1
## -------
## seqinfo: 448 sequences from an unspecified genome; no seqlengthsSometimes the internal sequence names need to be reformatted to fit the input gr. In the second example ( "apercula_gene_ensembl" for the orange clownfish), the sequence names are in format of 1, 2, 3, ..., while internally they are represented as chr1, chr2, ....
## GRanges object with 24840 ranges and 3 metadata columns:
## seqnames ranges strand | ensembl_gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSAPEG00000000002 1 24225-56222 - | ENSAPEG00000000002
## ENSAPEG00000000003 1 84299-92425 - | ENSAPEG00000000003
## ENSAPEG00000000004 1 128917-153166 - | ENSAPEG00000000004
## ENSAPEG00000000005 1 159517-165941 + | ENSAPEG00000000005
## ENSAPEG00000000006 1 169070-172981 - | ENSAPEG00000000006
## ... ... ... ... . ...
## ENSAPEG00000024837 1 42572675-42577642 + | ENSAPEG00000024837
## ENSAPEG00000024838 1 42576978-42651838 - | ENSAPEG00000024838
## ENSAPEG00000024839 1 42687220-42701205 - | ENSAPEG00000024839
## ENSAPEG00000024840 1 42704049-42726377 - | ENSAPEG00000024840
## ENSAPEG00000024841 1 42727223-42735871 - | ENSAPEG00000024841
## gene_biotype external_gene_name
## <character> <character>
## ENSAPEG00000000002 protein_coding emilin1b
## ENSAPEG00000000003 protein_coding <NA>
## ENSAPEG00000000004 protein_coding <NA>
## ENSAPEG00000000005 protein_coding <NA>
## ENSAPEG00000000006 protein_coding ctrb1
## ... ... ...
## ENSAPEG00000024837 protein_coding dut
## ENSAPEG00000024838 protein_coding FBN1
## ENSAPEG00000024839 protein_coding <NA>
## ENSAPEG00000024840 protein_coding SECISBP2L
## ENSAPEG00000024841 protein_coding cops2
## -------
## seqinfo: 72 sequences from an unspecified genome; no seqlengths## $dataset
## [1] "apercula_gene_ensembl"
##
## $version
## [1] "Nemo_v1"
##
## $name
## [1] "Amphiprion percula (Orange clownfish)"
##
## $taxon_id
## [1] 161767
##
## $genbank_accession
## [1] "GCA_003047355.1"
##
## $mart
## [1] "genes_mart"
##
## $seqname_style
## $seqname_style$`Sequence-Name`
## [1] "chr1" "chr2" "chr3" "chr4" "chr5"
##
## $seqname_style$`GenBank-Accn`
## [1] "CM009708.1" "CM009709.1" "CM009710.1" "CM009711.1" "CM009712.1"
##
## $seqname_style$`RefSeq-Accn`
## [1] "na" "na" "na" "na" "na"In this case, we need to set the argument reformat_from as a function to reformat the internal format to fit the sequence names in gr. Also you can set reformat_to as a function to reformat the converted sequence names.
gr2 = changeSeqnameStyle(gr, "apercula_gene_ensembl",
seqname_style_from = "Sequence-Name",
seqname_style_to = "GenBank-Accn",
reformat_from =function(x) gsub("chr", "", x),
reformat_to = function(x) gsub("\\.\\d+$", "", x)
)
gr2## GRanges object with 24678 ranges and 4 metadata columns:
## seqnames ranges strand | ensembl_gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSAPEG00000000002 CM009708 24225-56222 - | ENSAPEG00000000002
## ENSAPEG00000000003 CM009708 84299-92425 - | ENSAPEG00000000003
## ENSAPEG00000000004 CM009708 128917-153166 - | ENSAPEG00000000004
## ENSAPEG00000000005 CM009708 159517-165941 + | ENSAPEG00000000005
## ENSAPEG00000000006 CM009708 169070-172981 - | ENSAPEG00000000006
## ... ... ... ... . ...
## ENSAPEG00000024837 CM009708 42572675-42577642 + | ENSAPEG00000024837
## ENSAPEG00000024838 CM009708 42576978-42651838 - | ENSAPEG00000024838
## ENSAPEG00000024839 CM009708 42687220-42701205 - | ENSAPEG00000024839
## ENSAPEG00000024840 CM009708 42704049-42726377 - | ENSAPEG00000024840
## ENSAPEG00000024841 CM009708 42727223-42735871 - | ENSAPEG00000024841
## gene_biotype external_gene_name .original_seqname
## <character> <character> <character>
## ENSAPEG00000000002 protein_coding emilin1b 1
## ENSAPEG00000000003 protein_coding <NA> 1
## ENSAPEG00000000004 protein_coding <NA> 1
## ENSAPEG00000000005 protein_coding <NA> 1
## ENSAPEG00000000006 protein_coding ctrb1 1
## ... ... ... ...
## ENSAPEG00000024837 protein_coding dut 1
## ENSAPEG00000024838 protein_coding FBN1 1
## ENSAPEG00000024839 protein_coding <NA> 1
## ENSAPEG00000024840 protein_coding SECISBP2L 1
## ENSAPEG00000024841 protein_coding cops2 1
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengthsTo obtain the gene sets, use the function getBioMartGOGeneSets(). Also you need to provide the “dataset”. Here we use a different dataset: "mmusculus_gene_ensembl" (mouse).
## [1] 16060## $`GO:0000002`
## [1] "ENSMUSG00000019699" "ENSMUSG00000028982" "ENSMUSG00000022615"
## [4] "ENSMUSG00000039264" "ENSMUSG00000030557" "ENSMUSG00000030879"
## [7] "ENSMUSG00000032449" "ENSMUSG00000038084" "ENSMUSG00000022889"
## [10] "ENSMUSG00000041064" "ENSMUSG00000033845" "ENSMUSG00000107283"
## [13] "ENSMUSG00000027424" "ENSMUSG00000036875" "ENSMUSG00000038225"
## [16] "ENSMUSG00000022292" "ENSMUSG00000004069" "ENSMUSG00000039176"
## [19] "ENSMUSG00000025209" "ENSMUSG00000029911" "ENSMUSG00000020718"
## [22] "ENSMUSG00000030978" "ENSMUSG00000026496" "ENSMUSG00000028893"
## [25] "ENSMUSG00000002814" "ENSMUSG00000026365" "ENSMUSG00000015337"
## [28] "ENSMUSG00000034203" "ENSMUSG00000059552" "ENSMUSG00000055660"
## [31] "ENSMUSG00000028455" "ENSMUSG00000030314" "ENSMUSG00000020697"
## [34] "ENSMUSG00000036923" "ENSMUSG00000032633" "ENSMUSG00000029167"The variable lt is a list of vectors where each vector corresponds to a GO gene set with Ensembl IDs as gene identifiers.
You can try the following command and see what will be printed:
Remember you can also set the taxon ID:
In getBioMartGOGeneSets(), argument as_table can be set to TRUE, then the function returns a data frame.
## go_geneset ensembl_gene
## 1 GO:0000002 ENSMUSG00000019699
## 2 GO:0000002 ENSMUSG00000028982
## 3 GO:0000002 ENSMUSG00000022615
## 4 GO:0000002 ENSMUSG00000039264
## 5 GO:0000002 ENSMUSG00000030557
## 6 GO:0000002 ENSMUSG00000030879Argument ontology controls which category of GO gene sets. Possible values should be "BP", "CC" and "MF".
getBioMartGOGeneSets("mmusculus_gene_ensembl", ontology = "BP") # the default one
getBioMartGOGeneSets("mmusculus_gene_ensembl", ontology = "CC")
getBioMartGOGeneSets("mmusculus_gene_ensembl", ontology = "MF")Last, argument gene_id_type can be set to "entrez_gene" or "gene_symbol", then genes in the gene sets are in Entrez IDs or gene symbols. Note this depends on specific organisms, that not every organism supports Entrez IDs or gene symbols.
## $`GO:0000002`
## [1] "23797" "70556" "72962" "83408" "17258" "27397" "192287" "74143"
## [9] "27393" "208084" "27395" "17527" "74528" "327762" "408022" "382985"
## [17] "83945" "18975" "226153" "381760" "50776" "20133" "11545" "230784"
## [25] "21975" "12628" "13804" "72170" "22059" "76781" "66592" "74244"
## [33] "16882" "216021" "216805" "19017"The object BioMartGOGeneSets contains the version and source of data.
## BioMart Gene Ontology gene sets
## Source: https://www.ensembl.org/info/data/biomart/index.html
## Number of organisms: 670
## Marts: fungi_mart, genes_mart, metazoa_mart, plants_mart, protists_mart
## Built date: 2023-03-25, with biomaRt (2.54.0), GO.db (3.17.0)## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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] BioMartGOGeneSets_0.99.10 GO.db_3.17.0
## [3] AnnotationDbi_1.62.2 IRanges_2.34.1
## [5] S4Vectors_0.38.1 Biobase_2.60.0
## [7] BiocGenerics_0.46.0 biomaRt_2.56.1
## [9] knitr_1.43
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.40.0 xfun_0.40 bslib_0.5.1
## [4] vctrs_0.6.3 tools_4.3.1 bitops_1.0-7
## [7] generics_0.1.3 curl_5.0.2 tibble_3.2.1
## [10] fansi_1.0.4 RSQLite_2.3.1 blob_1.2.4
## [13] pkgconfig_2.0.3 dbplyr_2.3.3 lifecycle_1.0.3
## [16] GenomeInfoDbData_1.2.10 compiler_4.3.1 stringr_1.5.0
## [19] Biostrings_2.68.1 progress_1.2.2 GenomeInfoDb_1.36.2
## [22] htmltools_0.5.6 sass_0.4.7 RCurl_1.98-1.12
## [25] yaml_2.3.7 pillar_1.9.0 crayon_1.5.2
## [28] jquerylib_0.1.4 cachem_1.0.8 tidyselect_1.2.0
## [31] digest_0.6.33 stringi_1.7.12 purrr_1.0.2
## [34] dplyr_1.1.3 fastmap_1.1.1 cli_3.6.1
## [37] magrittr_2.0.3 XML_3.99-0.14 utf8_1.2.3
## [40] withr_2.5.0 prettyunits_1.1.1 filelock_1.0.2
## [43] rappdirs_0.3.3 bit64_4.0.5 rmarkdown_2.24
## [46] XVector_0.40.0 httr_1.4.7 bit_4.0.5
## [49] png_0.1-8 hms_1.1.3 memoise_2.0.1
## [52] evaluate_0.21 GenomicRanges_1.52.0 BiocFileCache_2.8.0
## [55] rlang_1.1.1 glue_1.6.2 DBI_1.1.3
## [58] xml2_1.3.5 jsonlite_1.8.7 R6_2.5.1
## [61] zlibbioc_1.46.0