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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f5eef99 | Dave Tang | 2024-11-08 | Output info from org.Hs.eg.db |
| html | def5ab0 | Dave Tang | 2024-10-23 | Build site. |
| Rmd | 58216ae | Dave Tang | 2024-10-23 | Entrez Gene IDs are species specific |
| html | 4ee2a0f | Dave Tang | 2024-10-23 | Build site. |
| Rmd | b3485f3 | Dave Tang | 2024-10-23 | reactome.db |
| html | de5bf16 | Dave Tang | 2024-10-23 | Build site. |
| Rmd | 2506265 | Dave Tang | 2024-10-23 | Using AnnotationDbi |
The Bioconductor annotation packages are an extensive collection of annotations. For this post I simply illustrate the basics of probing these annotation packages. For the first example I will use the org.Hs.eg.db package, which provides genome wide annotations for the human genome.
To begin, install the {org.Hs.eg.db} package.
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("org.Hs.eg.db")Load package.
packageVersion("org.Hs.eg.db")[1] '3.20.0'suppressPackageStartupMessages(library(org.Hs.eg.db))
class(org.Hs.eg.db)[1] "OrgDb"
attr(,"package")
[1] "AnnotationDbi"{org.Hs.eg.db} info.
org.Hs.eg.dbOrgDb object:
| DBSCHEMAVERSION: 2.1
| Db type: OrgDb
| Supporting package: AnnotationDbi
| DBSCHEMA: HUMAN_DB
| ORGANISM: Homo sapiens
| SPECIES: Human
| EGSOURCEDATE: 2024-Sep20
| EGSOURCENAME: Entrez Gene
| EGSOURCEURL: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA
| CENTRALID: EG
| TAXID: 9606
| GOSOURCENAME:
| GOSOURCEURL:
| GOSOURCEDATE:
| GOEGSOURCEDATE: 2024-Sep20
| GOEGSOURCENAME: Entrez Gene
| GOEGSOURCEURL: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA
| KEGGSOURCENAME: KEGG GENOME
| KEGGSOURCEURL: ftp://ftp.genome.jp/pub/kegg/genomes
| KEGGSOURCEDATE: 2011-Mar15
| GPSOURCENAME: UCSC Genome Bioinformatics (Homo sapiens)
| GPSOURCEURL: ftp://hgdownload.cse.ucsc.edu/goldenPath/hg38/database
| GPSOURCEDATE: 2024-Sep22
| ENSOURCEDATE: 2024-May14
| ENSOURCENAME: Ensembl
| ENSOURCEURL: ftp://ftp.ensembl.org/pub/current_fasta
| UPSOURCENAME: Uniprot
| UPSOURCEURL: http://www.UniProt.org/
| UPSOURCEDATE: Mon Sep 23 15:46:45 2024
Please see: help('select') for usage informationWe can query the package by using the
AnnotationDbi::select() function; to find out what we can
select and return we can use the keys(),
columns(), and keytypes() functions.
head(keys(org.Hs.eg.db))[1] "1" "2" "3" "9" "10" "11"#store the first six keys
my_keys <- head(keys(org.Hs.eg.db))
keytypes(org.Hs.eg.db) [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
[6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
[11] "GENETYPE" "GO" "GOALL" "IPI" "MAP"
[16] "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM"
[21] "PMID" "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG"
[26] "UNIPROT" columns(org.Hs.eg.db) [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
[6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
[11] "GENETYPE" "GO" "GOALL" "IPI" "MAP"
[16] "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM"
[21] "PMID" "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG"
[26] "UNIPROT" AnnotationDbi::select(
org.Hs.eg.db,
keys = my_keys,
columns=c("ENTREZID","SYMBOL","GENENAME"),
keytype="ENTREZID"
)'select()' returned 1:1 mapping between keys and columns ENTREZID SYMBOL GENENAME
1 1 A1BG alpha-1-B glycoprotein
2 2 A2M alpha-2-macroglobulin
3 3 A2MP1 alpha-2-macroglobulin pseudogene 1
4 9 NAT1 N-acetyltransferase 1
5 10 NAT2 N-acetyltransferase 2
6 11 NATP N-acetyltransferase pseudogeneWithin the annotation package are many objects, which we can also probe.
ls("package:org.Hs.eg.db") [1] "org.Hs.eg" "org.Hs.eg_dbconn"
[3] "org.Hs.eg_dbfile" "org.Hs.eg_dbInfo"
[5] "org.Hs.eg_dbschema" "org.Hs.eg.db"
[7] "org.Hs.egACCNUM" "org.Hs.egACCNUM2EG"
[9] "org.Hs.egALIAS2EG" "org.Hs.egCHR"
[11] "org.Hs.egCHRLENGTHS" "org.Hs.egCHRLOC"
[13] "org.Hs.egCHRLOCEND" "org.Hs.egENSEMBL"
[15] "org.Hs.egENSEMBL2EG" "org.Hs.egENSEMBLPROT"
[17] "org.Hs.egENSEMBLPROT2EG" "org.Hs.egENSEMBLTRANS"
[19] "org.Hs.egENSEMBLTRANS2EG" "org.Hs.egENZYME"
[21] "org.Hs.egENZYME2EG" "org.Hs.egGENENAME"
[23] "org.Hs.egGENETYPE" "org.Hs.egGO"
[25] "org.Hs.egGO2ALLEGS" "org.Hs.egGO2EG"
[27] "org.Hs.egMAP" "org.Hs.egMAP2EG"
[29] "org.Hs.egMAPCOUNTS" "org.Hs.egOMIM"
[31] "org.Hs.egOMIM2EG" "org.Hs.egORGANISM"
[33] "org.Hs.egPATH" "org.Hs.egPATH2EG"
[35] "org.Hs.egPFAM" "org.Hs.egPMID"
[37] "org.Hs.egPMID2EG" "org.Hs.egPROSITE"
[39] "org.Hs.egREFSEQ" "org.Hs.egREFSEQ2EG"
[41] "org.Hs.egSYMBOL" "org.Hs.egSYMBOL2EG"
[43] "org.Hs.egUCSCKG" "org.Hs.egUNIPROT" Ensembl Gene IDs to Entrez Gene IDs.
class(org.Hs.egENSEMBL2EG)[1] "AnnDbBimap"
attr(,"package")
[1] "AnnotationDbi"head(keys(org.Hs.egENSEMBL2EG))[1] "ENSG00000121410" "ENSG00000175899" "ENSG00000291190" "ENSG00000171428"
[5] "ENSG00000156006" "ENSG00000196136"Create a lookup table.
my_ensg_keys <- head(keys(org.Hs.egENSEMBL2EG))
AnnotationDbi::select(
org.Hs.eg.db,
keys = my_ensg_keys,
columns=c("ENSEMBL","ENTREZID","SYMBOL","GENENAME"),
keytype="ENSEMBL"
)'select()' returned 1:1 mapping between keys and columns ENSEMBL ENTREZID SYMBOL GENENAME
1 ENSG00000121410 1 A1BG alpha-1-B glycoprotein
2 ENSG00000175899 2 A2M alpha-2-macroglobulin
3 ENSG00000291190 3 A2MP1 alpha-2-macroglobulin pseudogene 1
4 ENSG00000171428 9 NAT1 N-acetyltransferase 1
5 ENSG00000156006 10 NAT2 N-acetyltransferase 2
6 ENSG00000196136 12 SERPINA3 serpin family A member 3Using the annotation package {org.Hs.eg.db} we can easily convert different gene identifiers, obtain their gene symbols, and descriptions (as well as all other keytypes).
reactome.db is a set of annotation maps for Reactome, assembled using data from Reactome. This package has been created by a third-party developer, and is not affiliated with the Reactome team.
To begin, install the {reactome.db} package.
BiocManager::install("reactome.db")Load the package.
packageVersion('reactome.db')[1] '1.89.0'suppressPackageStartupMessages(library(reactome.db))
class(reactome.db)[1] "ReactomeDb"
attr(,"package")
[1] "AnnotationDbi"Demo.
keytypes(reactome.db)[1] "ENTREZID" "GO" "PATHID" "PATHNAME" "REACTOMEID"columns(reactome.db)[1] "ENTREZID" "GO" "PATHID" "PATHNAME" "REACTOMEID"head(keys(reactome.db))[1] "1" "10" "100" "1000" "10000" "100000160"my_keys <- head(keys(reactome.db))
AnnotationDbi::select(
reactome.db,
keys = my_keys,
columns="REACTOMEID",
keytype="ENTREZID"
) |>
head()'select()' returned 1:many mapping between keys and columns ENTREZID REACTOMEID
1 1 R-HSA-109582
2 1 R-HSA-114608
3 1 R-HSA-168249
4 1 R-HSA-168256
5 1 R-HSA-6798695
6 1 R-HSA-76002Note that Entrez Gene IDs are species-specific; TP53 in humans has Entrez Gene ID 7157 and TP53 in mice has Entrez Gene ID 22059.
AnnotationDbi::select(
reactome.db,
keys = '7157',
columns=c("REACTOMEID", "PATHNAME"),
keytype="ENTREZID"
) |>
head()'select()' returned 1:many mapping between keys and columns ENTREZID REACTOMEID
1 7157 R-HSA-109581
2 7157 R-HSA-109582
3 7157 R-HSA-109606
4 7157 R-HSA-111448
5 7157 R-HSA-114452
6 7157 R-HSA-1169410
PATHNAME
1 Homo sapiens: Apoptosis
2 Homo sapiens: Hemostasis
3 Homo sapiens: Intrinsic Pathway for Apoptosis
4 Homo sapiens: Activation of NOXA and translocation to mitochondria
5 Homo sapiens: Activation of BH3-only proteins
6 Homo sapiens: Antiviral mechanism by IFN-stimulated genesAnnotationDbi::select(
org.Hs.eg.db,
keys = '7157',
columns=c("ENTREZID","SYMBOL","GENENAME"),
keytype="ENTREZID"
)'select()' returned 1:1 mapping between keys and columns ENTREZID SYMBOL GENENAME
1 7157 TP53 tumor protein p53AnnotationDbi::select(
reactome.db,
keys = '22059',
columns=c("REACTOMEID", "PATHNAME"),
keytype="ENTREZID"
) |>
head()'select()' returned 1:many mapping between keys and columns ENTREZID REACTOMEID
1 22059 R-MMU-1169410
2 22059 R-MMU-1280215
3 22059 R-MMU-1640170
4 22059 R-MMU-168256
5 22059 R-MMU-212436
6 22059 R-MMU-2262752
PATHNAME
1 Mus musculus: Antiviral mechanism by IFN-stimulated genes
2 Mus musculus: Cytokine Signaling in Immune system
3 Mus musculus: Cell Cycle
4 Mus musculus: Immune System
5 Mus musculus: Generic Transcription Pathway
6 Mus musculus: Cellular responses to stressThere are also other annotation packages, such as GO.db, which contains a set of annotation maps describing the entire Gene Ontology that we can probe in the same manner.
To begin, install the {GO.db} package.
BiocManager::install("GO.db")Load the package.
packageVersion('GO.db')[1] '3.20.0'suppressPackageStartupMessages(library(GO.db))
class(GO.db)[1] "GODb"
attr(,"package")
[1] "AnnotationDbi"Use the same functions as before on the AnnotationDbi
object.
keytypes(GO.db)[1] "DEFINITION" "GOID" "ONTOLOGY" "TERM" my_go_keys <- head(keys(GO.db))
my_go_keys[1] "GO:0000001" "GO:0000002" "GO:0000006" "GO:0000007" "GO:0000009"
[6] "GO:0000010"AnnotationDbi::select(
GO.db,
keys = my_go_keys,
columns=c("GOID", "TERM", "ONTOLOGY"),
keytype="GOID"
)'select()' returned 1:1 mapping between keys and columns GOID TERM ONTOLOGY
1 GO:0000001 mitochondrion inheritance BP
2 GO:0000002 mitochondrial genome maintenance BP
3 GO:0000006 high-affinity zinc transmembrane transporter activity MF
4 GO:0000007 low-affinity zinc ion transmembrane transporter activity MF
5 GO:0000009 alpha-1,6-mannosyltransferase activity MF
6 GO:0000010 heptaprenyl diphosphate synthase activity MFFor genome annotation purposes, one may be interested in the gene models, which are provided in the TxDb.Hsapiens.UCSC.hg38.knownGene package.
BiocManager::install("TxDb.Hsapiens.UCSC.hg38.knownGene")Load the package.
packageVersion('TxDb.Hsapiens.UCSC.hg38.knownGene')[1] '3.20.0'suppressPackageStartupMessages(library(TxDb.Hsapiens.UCSC.hg38.knownGene))
class(TxDb.Hsapiens.UCSC.hg38.knownGene)[1] "TxDb"
attr(,"package")
[1] "GenomicFeatures"To see what columns can be returned use the columns()
function
columns(TxDb.Hsapiens.UCSC.hg38.knownGene) [1] "CDSCHROM" "CDSEND" "CDSID" "CDSNAME" "CDSPHASE"
[6] "CDSSTART" "CDSSTRAND" "EXONCHROM" "EXONEND" "EXONID"
[11] "EXONNAME" "EXONRANK" "EXONSTART" "EXONSTRAND" "GENEID"
[16] "TXCHROM" "TXEND" "TXID" "TXNAME" "TXSTART"
[21] "TXSTRAND" "TXTYPE" Selecting.
AnnotationDbi::select(
TxDb.Hsapiens.UCSC.hg38.knownGene,
keys = head(keys(TxDb.Hsapiens.UCSC.hg38.knownGene), 2),
columns=c('GENEID', 'TXCHROM', 'TXSTART', 'TXEND', 'TXID'),
keytype="GENEID"
)'select()' returned 1:many mapping between keys and columns GENEID TXID TXCHROM TXSTART TXEND
1 1 230133 chr19 58345178 58347634
2 1 230134 chr19 58345183 58353492
3 1 230135 chr19 58346854 58356225
4 1 230136 chr19 58346858 58353491
5 1 230137 chr19 58346860 58347657
6 1 230138 chr19 58348466 58362751
7 1 230139 chr19 58350594 58353129
8 1 230140 chr19 58353021 58356083
9 10 104998 chr8 18386311 18388323
10 10 105000 chr8 18391282 18401218
11 10 105001 chr8 18391287 18400993We can combine different annotation packages too; say I have two genes LRRK2 and PINK1, and I want their genomic locations as well as OMIM ids.
my_genes <- c("LRRK2","PINK1")
AnnotationDbi::select(
org.Hs.eg.db,
keys = my_genes,
columns=c("ENTREZID", "SYMBOL","OMIM"),
keytype="SYMBOL"
) -> a'select()' returned 1:many mapping between keys and columnsa SYMBOL ENTREZID OMIM
1 LRRK2 120892 607060
2 LRRK2 120892 609007
3 PINK1 65018 605909
4 PINK1 65018 608309# symbol and transcript location
AnnotationDbi::select(
TxDb.Hsapiens.UCSC.hg38.knownGene,
keys = a$ENTREZID,
columns=c('GENEID', 'TXCHROM', 'TXSTART', 'TXEND', 'TXID'),
keytype="GENEID"
) -> b'select()' returned many:many mapping between keys and columnshead(b) GENEID TXID TXCHROM TXSTART TXEND
1 120892 149454 chr12 40196744 40283952
2 120892 149456 chr12 40224977 40242782
3 120892 149457 chr12 40224997 40367077
4 120892 149458 chr12 40224997 40369285
5 120892 149459 chr12 40225011 40304976
6 120892 149460 chr12 40225080 40369259# change GENEID column to ENTREZID for consistency and for merging
names(b) <- c('ENTREZID', 'TXID', 'TXCHROM', 'TXSTART', 'TXEND')
c <- merge(a, b, 'ENTREZID')
head(c) ENTREZID SYMBOL OMIM TXID TXCHROM TXSTART TXEND
1 120892 LRRK2 607060 149454 chr12 40196744 40283952
2 120892 LRRK2 607060 149456 chr12 40224977 40242782
3 120892 LRRK2 607060 149457 chr12 40224997 40367077
4 120892 LRRK2 607060 149458 chr12 40224997 40369285
5 120892 LRRK2 607060 149459 chr12 40225011 40304976
6 120892 LRRK2 607060 149460 chr12 40225080 40369259One important point to be aware of when using the Bioconductor annotation packages is to appreciate the lag between updates to the packages. From the FAQ on the Bioconductor website:
Different sources take different approaches to managing annotations. The annotation packages in Bioconductor are based on downloads obtained shortly before each Bioconductor release, and so can lag by six months (at the end of the release cycle) compared to on-line resources. The advantage of this approach is that the annotations do not change unexpectedly during development of an analysis, while the disadvantage is that the resource is not quite up-to-date with current understanding.
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 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: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[2] GenomicFeatures_1.58.0
[3] GenomicRanges_1.58.0
[4] GenomeInfoDb_1.42.0
[5] GO.db_3.20.0
[6] reactome.db_1.89.0
[7] org.Hs.eg.db_3.20.0
[8] AnnotationDbi_1.68.0
[9] IRanges_2.40.0
[10] S4Vectors_0.44.0
[11] Biobase_2.66.0
[12] BiocGenerics_0.52.0
[13] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] blob_1.2.4 Biostrings_2.74.0
[3] bitops_1.0-9 fastmap_1.2.0
[5] RCurl_1.98-1.16 GenomicAlignments_1.42.0
[7] promises_1.3.0 XML_3.99-0.17
[9] digest_0.6.37 lifecycle_1.0.4
[11] processx_3.8.4 KEGGREST_1.46.0
[13] RSQLite_2.3.7 magrittr_2.0.3
[15] compiler_4.4.1 rlang_1.1.4
[17] sass_0.4.9 tools_4.4.1
[19] utf8_1.2.4 yaml_2.3.10
[21] rtracklayer_1.66.0 knitr_1.48
[23] S4Arrays_1.6.0 bit_4.5.0
[25] curl_6.0.0 DelayedArray_0.32.0
[27] abind_1.4-8 BiocParallel_1.40.0
[29] grid_4.4.1 fansi_1.0.6
[31] git2r_0.35.0 SummarizedExperiment_1.36.0
[33] cli_3.6.3 rmarkdown_2.29
[35] crayon_1.5.3 rstudioapi_0.17.1
[37] httr_1.4.7 rjson_0.2.23
[39] DBI_1.2.3 cachem_1.1.0
[41] stringr_1.5.1 zlibbioc_1.52.0
[43] parallel_4.4.1 XVector_0.46.0
[45] restfulr_0.0.15 matrixStats_1.4.1
[47] vctrs_0.6.5 Matrix_1.7-0
[49] jsonlite_1.8.9 callr_3.7.6
[51] bit64_4.5.2 jquerylib_0.1.4
[53] glue_1.8.0 codetools_0.2-20
[55] ps_1.8.1 stringi_1.8.4
[57] later_1.3.2 BiocIO_1.16.0
[59] UCSC.utils_1.2.0 tibble_3.2.1
[61] pillar_1.9.0 htmltools_0.5.8.1
[63] GenomeInfoDbData_1.2.13 R6_2.5.1
[65] rprojroot_2.0.4 evaluate_1.0.1
[67] lattice_0.22-6 png_0.1-8
[69] Rsamtools_2.22.0 memoise_2.0.1
[71] httpuv_1.6.15 bslib_0.8.0
[73] Rcpp_1.0.13-1 SparseArray_1.6.0
[75] whisker_0.4.1 xfun_0.49
[77] fs_1.6.5 MatrixGenerics_1.18.0
[79] getPass_0.2-4 pkgconfig_2.0.3