AnnotationDbi: Introduction To Bioconductor Annotation Packages
Last modified: November 2022; Compiled: July 20 , 2023
Contents
Figure 1: Annotation Packages: the big picture
Bioconductor provides extensive annotation resources. These can be gene centric, or genome centric. Annotations can be provided in packages curated by Bioconductor, or obtained from web-based resources. This vignette is primarily concerned with describing the annotation resources that are available as packages. More advanced users who wish to learn about how to make new annotation packages should see the vignette titled “Creating select Interfaces for custom Annotation resources” from the AnnotationForge package.
Gene centric AnnotationDbi packages include:
- Organism level: e.g. org.Mm.eg.db.
- Platform level: e.g. hgu133plus2.db, hgu133plus2.probes, hgu133plus2.cdf
- System-biology level: GO.db
Genome centric GenomicFeatures packages include
- Transcriptome level: e.g. TxDb.Hsapiens.UCSC.hg19.knownGene, EnsDb.Hsapiens.v75.
- Generic genome features: Can generate via GenomicFeatures
One web-based resource accesses biomart, via the biomaRt package:
- Query web-based `biomart’ resource for genes, sequence, SNPs, and etc.
The most popular annotation packages have been modified so that they can make
use of a new set of methods to more easily access their contents. These
four methods are named: columns, keytypes, keys and select. And they are
described in this vignette. They can currently be used with all chip, organism,
and TxDb packages along with the popular GO.db package.
For the older less popular packages, there are still convenient ways to retrieve the data. The How to use bimaps from the “.db” annotation packages vignette in the AnnotationDbi package is a key reference for learnign about how to use bimap objects.
Finally, all of the `.db’ (and most other Bioconductor annotation packages) are updated every 6 months corresponding to each release of Bioconductor. Exceptions are made for packages where the actual resources that the packages are based on have not themselves been updated.
1 AnnotationDb objects and the select method
As previously mentioned, a new set of methods have been added that allow a
simpler way of extracting identifier based annotations. All the annotation
packages that support these new methods expose an object named exactly the same
as the package itself. These objects are collectively called AnnotationDb
objects for the class that they all inherit from. The more specific classes (the
ones that you will actually see in the wild) have names like OrgDb, ChipDb
or TxDb objects. These names correspond to the kind of package (and underlying
schema) being represented. The methods that can be applied to all of these
objects are columns, keys, keytypes and select.
In addition, another accessor has recently been added which allows extraction of
one column at a time. the mapIds method allows users to extract data into
either a named character vector, a list or even a SimpleCharacterList. This
method should work with all the different kinds of AnnotationDb objects
described below.
2 ChipDb objects and the select method
An extremely common kind of Annotation package is the so called platform based or chip based package type. This package is intended to make the manufacturer labels for a series of probes or probesets to a wide range of gene-based features. A package of this kind will load an ChipDb object. Below is a set of examples to show how you might use the standard 4 methods to interact with an object of this type.
First we need to load the package:
suppressPackageStartupMessages({
library(hgu95av2.db)
})If we list the contents of this package, we can see that one of the many things loaded is an object named after the package “hgu95av2.db”:
ls("package:hgu95av2.db")## [1] "hgu95av2" "hgu95av2_dbconn" "hgu95av2_dbfile"
## [4] "hgu95av2_dbInfo" "hgu95av2_dbschema" "hgu95av2.db"
## [7] "hgu95av2ACCNUM" "hgu95av2ALIAS2PROBE" "hgu95av2CHR"
## [10] "hgu95av2CHRLENGTHS" "hgu95av2CHRLOC" "hgu95av2CHRLOCEND"
## [13] "hgu95av2ENSEMBL" "hgu95av2ENSEMBL2PROBE" "hgu95av2ENTREZID"
## [16] "hgu95av2ENZYME" "hgu95av2ENZYME2PROBE" "hgu95av2GENENAME"
## [19] "hgu95av2GO" "hgu95av2GO2ALLPROBES" "hgu95av2GO2PROBE"
## [22] "hgu95av2MAP" "hgu95av2MAPCOUNTS" "hgu95av2OMIM"
## [25] "hgu95av2ORGANISM" "hgu95av2ORGPKG" "hgu95av2PATH"
## [28] "hgu95av2PATH2PROBE" "hgu95av2PFAM" "hgu95av2PMID"
## [31] "hgu95av2PMID2PROBE" "hgu95av2PROSITE" "hgu95av2REFSEQ"
## [34] "hgu95av2SYMBOL" "hgu95av2UNIPROT"We can look at this object to learn more about it:
hgu95av2.db## ChipDb object:
## | DBSCHEMAVERSION: 2.1
## | Db type: ChipDb
## | Supporting package: AnnotationDbi
## | DBSCHEMA: HUMANCHIP_DB
## | ORGANISM: Homo sapiens
## | SPECIES: Human
## | MANUFACTURER: Affymetrix
## | CHIPNAME: Affymetrix HG_U95Av2 Array
## | MANUFACTURERURL: http://www.affymetrix.com
## | EGSOURCEDATE: 2021-Apr14
## | EGSOURCENAME: Entrez Gene
## | EGSOURCEURL: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA
## | CENTRALID: ENTREZID
## | TAXID: 9606
## | GOSOURCENAME: Gene Ontology
## | GOSOURCEURL: http://current.geneontology.org/ontology/go-basic.obo
## | GOSOURCEDATE: 2021-02-01
## | GOEGSOURCEDATE: 2021-Apr14
## | 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:
## | GPSOURCEDATE: 2021-Feb16
## | ENSOURCEDATE: 2021-Feb16
## | ENSOURCENAME: Ensembl
## | ENSOURCEURL: ftp://ftp.ensembl.org/pub/current_fasta
## | UPSOURCENAME: Uniprot
## | UPSOURCEURL: http://www.UniProt.org/
## | UPSOURCEDATE: Mon Apr 26 21:53:12 2021##
## Please see: help('select') for usage informationIf we want to know what kinds of data are retriveable via select, then we
should use the columns method like this:
columns(hgu95av2.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" "PROBEID" "PROSITE" "REFSEQ" "SYMBOL"
## [26] "UCSCKG" "UNIPROT"If we are further curious to know more about those values for columns, we can consult the help pages. Asking about any of these values will pull up a manual page describing the different fields and what they mean.
help("SYMBOL")If we are curious about what kinds of fields we could potentially use as keys to
query the database, we can use the keytypes method. In a perfect world, this
method will return values very similar to what was returned by columns, but in
reality, some kinds of values make poor keys and so this list is often shorter.
keytypes(hgu95av2.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" "PROBEID" "PROSITE" "REFSEQ" "SYMBOL"
## [26] "UCSCKG" "UNIPROT"If we want to extract some sample keys of a particular type, we can use the
keys method.
head(keys(hgu95av2.db, keytype="SYMBOL"))## [1] "A1BG" "A2M" "A2MP1" "NAT1" "NAT2" "NATP"And finally, if we have some keys, we can use select to extract them. By
simply using appropriate argument values with select we can specify what keys we
want to look up values for (keys), what we want returned back (columns) and the
type of keys that we are passing in (keytype)
#1st get some example keys
k <- head(keys(hgu95av2.db,keytype="PROBEID"))
# then call select
select(hgu95av2.db, keys=k, columns=c("SYMBOL","GENENAME"), keytype="PROBEID")## 'select()' returned 1:1 mapping between keys and columns## PROBEID SYMBOL
## 1 1000_at MAPK3
## 2 1001_at TIE1
## 3 1002_f_at CYP2C19
## 4 1003_s_at CXCR5
## 5 1004_at CXCR5
## 6 1005_at DUSP1
## GENENAME
## 1 mitogen-activated protein kinase 3
## 2 tyrosine kinase with immunoglobulin like and EGF like domains 1
## 3 cytochrome P450 family 2 subfamily C member 19
## 4 C-X-C motif chemokine receptor 5
## 5 C-X-C motif chemokine receptor 5
## 6 dual specificity phosphatase 1And as you can see, when you call the code above, select will try to return a data.frame with all the things you asked for matched up to each other.
Finally if you wanted to extract only one column of data you could instead use the mapIds method like this:
#1st get some example keys
k <- head(keys(hgu95av2.db,keytype="PROBEID"))
# then call mapIds
mapIds(hgu95av2.db, keys=k, column=c("GENENAME"), keytype="PROBEID")## 'select()' returned 1:1 mapping between keys and columns## 1000_at
## "mitogen-activated protein kinase 3"
## 1001_at
## "tyrosine kinase with immunoglobulin like and EGF like domains 1"
## 1002_f_at
## "cytochrome P450 family 2 subfamily C member 19"
## 1003_s_at
## "C-X-C motif chemokine receptor 5"
## 1004_at
## "C-X-C motif chemokine receptor 5"
## 1005_at
## "dual specificity phosphatase 1"3 OrgDb objects and the select method
An organism level package (an ‘org’ package) uses a central gene identifier (e.g. Entrez Gene id) and contains mappings between this identifier and other kinds of identifiers (e.g. GenBank or Uniprot accession number, RefSeq id, etc.). The name of an org package is always of the form org.<Ab>.<id>.db (e.g.org.Sc.sgd.dbwhere <Ab> is a 2-letter abbreviation of the organism (e.g.Sc for Saccharomyces cerevisiae) and <id> is an abbreviation (in lower-case) describing the type of central identifier (e.g. sgd for gene identifiers assigned by the Saccharomyces Genome Database, or eg for Entrez Gene ids).
Just as the chip packages load a ChipDb object, the org packages will load a OrgDb object. The following exercise should acquaint you with the use of these methods in the context of an organism package.
Exercise 1
Display the OrgDb object for the org.Hs.eg.db package.
Use the columns method to discover which sorts of annotations can be extracted
from it. Is this the same as the result from the keytypes method? Use the
keytypes method to find out.
Finally, use the keys method to extract UNIPROT identifiers and then pass
those keys in to the select method in such a way that you extract the gene
symbol and KEGG pathway information for each. Use the help system as needed to
learn which values to pass in to columns in order to achieve this.
Solution:
library(org.Hs.eg.db)
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"help("SYMBOL") ## for explanation of these columns and keytypes values
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"uniKeys <- head(keys(org.Hs.eg.db, keytype="UNIPROT"))
cols <- c("SYMBOL", "PATH")
select(org.Hs.eg.db, keys=uniKeys, columns=cols, keytype="UNIPROT")## 'select()' returned 1:many mapping between keys and columns## UNIPROT SYMBOL PATH
## 1 P04217 A1BG <NA>
## 2 V9HWD8 A1BG <NA>
## 3 P01023 A2M 04610
## 4 P18440 NAT1 00232
## 5 P18440 NAT1 00983
## 6 P18440 NAT1 01100
## 7 Q400J6 NAT1 00232
## 8 Q400J6 NAT1 00983
## 9 Q400J6 NAT1 01100
## 10 F5H5R8 NAT1 00232
## 11 F5H5R8 NAT1 00983
## 12 F5H5R8 NAT1 01100So how could you use select to annotate your results? This next exercise should hlep you to understand how that should generally work.
Exercise 2
Please run the following code snippet (which will load a fake data result that I have provided for the purposes of illustration):
load(system.file("extdata", "resultTable.Rda", package="AnnotationDbi"))
head(resultTable)## logConc logFC LR.statistic PValue FDR
## 100418920 -9.639471 -4.679498 378.0732 3.269307e-84 2.613484e-80
## 100419779 -10.638865 -4.264830 291.1028 2.859424e-65 1.142912e-61
## 100271867 -11.448981 -4.009603 222.3653 2.757135e-50 7.346846e-47
## 100287169 -11.026699 -3.486593 206.7771 6.934967e-47 1.385953e-43
## 100287735 -11.036862 3.064980 204.1235 2.630432e-46 4.205535e-43
## 100421986 -12.276297 -4.695736 190.5368 2.427556e-43 3.234314e-40The rownames of this table happen to provide entrez gene identifiers for each row (for human). Find the gene symbol and gene name for each of the rows in resultTable and then use the merge method to attach those annotations to it.
Solution:
annots <- select(org.Hs.eg.db, keys=rownames(resultTable),
columns=c("SYMBOL","GENENAME"), keytype="ENTREZID")## 'select()' returned 1:1 mapping between keys and columnsresultTable <- merge(resultTable, annots, by.x=0, by.y="ENTREZID")
head(resultTable)## Row.names logConc logFC LR.statistic PValue FDR
## 1 100127888 -10.57050 2.758937 182.8937 1.131473e-41 1.130624e-38
## 2 100131223 -12.37808 -4.654318 179.2331 7.126423e-41 6.329847e-38
## 3 100271381 -12.06340 3.511937 188.4824 6.817155e-43 7.785191e-40
## 4 100271867 -11.44898 -4.009603 222.3653 2.757135e-50 7.346846e-47
## 5 100287169 -11.02670 -3.486593 206.7771 6.934967e-47 1.385953e-43
## 6 100287735 -11.03686 3.064980 204.1235 2.630432e-46 4.205535e-43
## SYMBOL GENENAME
## 1 SLCO4A1-AS1 SLCO4A1 antisense RNA 1
## 2 ARL8BP2 ARL8B pseudogene 2
## 3 RPS28P8 ribosomal protein S28 pseudogene 8
## 4 MPVQTL1 Mean platelet volume QTL1
## 5 <NA> <NA>
## 6 TTTY13B testis-specific transcript, Y-linked 13B4 Using select with GO.db
When you load the r Biocpkg("GO.db") package, a GODb object is also loaded.
This allows you to use the columns, keys, keytypes and select methods on
the contents of the GO ontology. So if for example, you had a few GO IDs and
wanted to know more about it, you could do it like this:
library(GO.db)## GOIDs <- c("GO:0042254","GO:0044183")
select(GO.db, keys=GOIDs, columns="DEFINITION", keytype="GOID")## 'select()' returned 1:1 mapping between keys and columns## GOID
## 1 GO:0042254
## 2 GO:0044183
## DEFINITION
## 1 A cellular process that results in the biosynthesis of constituent macromolecules, assembly, and arrangement of constituent parts of ribosome subunits; includes transport to the sites of protein synthesis.
## 2 Binding to a protein or a protein-containing complex to assist the protein folding process.5 Using select with TxDb packages
A TxDb package (a ‘TxDb’ package) connects a set of genomic coordinates to various transcript oriented features. The package can also contain Identifiers to features such as genes and transcripts, and the internal schema describes the relationships between these different elements. All TxDb containing packages follow a specific naming scheme that tells where the data came from as well as which build of the genome it comes from.
Exercise 3
Display the TxDb object for the TxDb.Hsapiens.UCSC.hg19.knownGene package.
As before, use the columns and keytypes methods to discover which sorts of
annotations can be extracted from it.
Use the keys method to extract just a few gene identifiers and then pass those
keys in to the select method in such a way that you extract the transcript ids
and transcript starts for each.
Solution:
library(TxDb.Hsapiens.UCSC.hg19.knownGene)## Loading required package: GenomicFeatures## Loading required package: GenomeInfoDb## Loading required package: GenomicRangestxdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
txdb## TxDb object:
## # Db type: TxDb
## # Supporting package: GenomicFeatures
## # Data source: UCSC
## # Genome: hg19
## # Organism: Homo sapiens
## # Taxonomy ID: 9606
## # UCSC Table: knownGene
## # Resource URL: http://genome.ucsc.edu/
## # Type of Gene ID: Entrez Gene ID
## # Full dataset: yes
## # miRBase build ID: GRCh37
## # transcript_nrow: 82960
## # exon_nrow: 289969
## # cds_nrow: 237533
## # Db created by: GenomicFeatures package from Bioconductor
## # Creation time: 2015-10-07 18:11:28 +0000 (Wed, 07 Oct 2015)
## # GenomicFeatures version at creation time: 1.21.30
## # RSQLite version at creation time: 1.0.0
## # DBSCHEMAVERSION: 1.1columns(txdb)## [1] "CDSCHROM" "CDSEND" "CDSID" "CDSNAME" "CDSSTART"
## [6] "CDSSTRAND" "EXONCHROM" "EXONEND" "EXONID" "EXONNAME"
## [11] "EXONRANK" "EXONSTART" "EXONSTRAND" "GENEID" "TXCHROM"
## [16] "TXEND" "TXID" "TXNAME" "TXSTART" "TXSTRAND"
## [21] "TXTYPE"keytypes(txdb)## [1] "CDSID" "CDSNAME" "EXONID" "EXONNAME" "GENEID" "TXID" "TXNAME"keys <- head(keys(txdb, keytype="GENEID"))
cols <- c("TXID", "TXSTART")
select(txdb, keys=keys, columns=cols, keytype="GENEID")## 'select()' returned 1:many mapping between keys and columns## GENEID TXID TXSTART
## 1 1 70455 58858172
## 2 1 70456 58859832
## 3 10 31944 18248755
## 4 100 72132 43248163
## 5 1000 65378 25530930
## 6 1000 65379 25530930
## 7 10000 7895 243651535
## 8 10000 7896 243663021
## 9 10000 7897 243663021
## 10 100008586 75890 49217763As is widely known, in addition to providing access via the select method,
TxDb objects also provide access via the more familiar transcripts, exons,
cds, transcriptsBy, exonsBy and cdsBy methods. For those who do not yet
know about these other methods, more can be learned by seeing the vignette
called: Making and Utilizing TxDb Objects in the
GenomicFeatures package.
6 Using select with EnsDb packages
Similar to the TxDb objects/packages discussed in the previous section,
EnsDb objects/packages provide genomic coordinates of gene models along with
additional annotations (e.g. gene names, biotypes etc) but are tailored to
annotations provided by Ensembl. The central methods columns, keys,
keytypes and select are all implemented for EnsD objects. In addition,
these methods allow also the use of the EnsDb specific filtering framework to
retrieve only selected information from the database (see vignette of the
ensembldb package for more information).
In the example below we first evaluate which columns, keys and keytypes are available for EnsDb objects and fetch then the transcript ids, genomic start coordinate and transcript biotype for some genes.
library(EnsDb.Hsapiens.v75)## Loading required package: ensembldb## Loading required package: AnnotationFilter##
## Attaching package: 'ensembldb'## The following object is masked from 'package:stats':
##
## filteredb <- EnsDb.Hsapiens.v75
edb## EnsDb for Ensembl:
## |Backend: SQLite
## |Db type: EnsDb
## |Type of Gene ID: Ensembl Gene ID
## |Supporting package: ensembldb
## |Db created by: ensembldb package from Bioconductor
## |script_version: 0.3.0
## |Creation time: Thu May 18 09:15:45 2017
## |ensembl_version: 75
## |ensembl_host: localhost
## |Organism: homo_sapiens
## |taxonomy_id: 9606
## |genome_build: GRCh37
## |DBSCHEMAVERSION: 2.0
## | No. of genes: 64102.
## | No. of transcripts: 215647.
## |Protein data available.## List all columns
columns(edb)## [1] "ENTREZID" "EXONID" "EXONIDX"
## [4] "EXONSEQEND" "EXONSEQSTART" "GENEBIOTYPE"
## [7] "GENEID" "GENENAME" "GENESEQEND"
## [10] "GENESEQSTART" "INTERPROACCESSION" "ISCIRCULAR"
## [13] "PROTDOMEND" "PROTDOMSTART" "PROTEINDOMAINID"
## [16] "PROTEINDOMAINSOURCE" "PROTEINID" "PROTEINSEQUENCE"
## [19] "SEQCOORDSYSTEM" "SEQLENGTH" "SEQNAME"
## [22] "SEQSTRAND" "SYMBOL" "TXBIOTYPE"
## [25] "TXCDSSEQEND" "TXCDSSEQSTART" "TXID"
## [28] "TXNAME" "TXSEQEND" "TXSEQSTART"
## [31] "UNIPROTDB" "UNIPROTID" "UNIPROTMAPPINGTYPE"## List all keytypes
keytypes(edb)## [1] "ENTREZID" "EXONID" "GENEBIOTYPE"
## [4] "GENEID" "GENENAME" "PROTDOMID"
## [7] "PROTEINDOMAINID" "PROTEINDOMAINSOURCE" "PROTEINID"
## [10] "SEQNAME" "SEQSTRAND" "SYMBOL"
## [13] "TXBIOTYPE" "TXID" "TXNAME"
## [16] "UNIPROTID"## Get the first
keys <- head(keys(edb, keytype="GENEID"))
## Get the data
select(edb, keys=keys, columns=c("TXID", "TXSEQSTART", "TXBIOTYPE"),
keytype="GENEID")## GENEID TXID TXSEQSTART TXBIOTYPE
## 1 ENSG00000000003 ENST00000373020 99883667 protein_coding
## 2 ENSG00000000003 ENST00000496771 99887538 processed_transcript
## 3 ENSG00000000003 ENST00000494424 99888439 processed_transcript
## 4 ENSG00000000005 ENST00000373031 99839799 protein_coding
## 5 ENSG00000000005 ENST00000485971 99848621 processed_transcript
## 6 ENSG00000000419 ENST00000371588 49551404 protein_coding
## 7 ENSG00000000419 ENST00000466152 49551404 processed_transcript
## 8 ENSG00000000419 ENST00000371582 49551404 protein_coding
## 9 ENSG00000000419 ENST00000494752 49551433 processed_transcript
## 10 ENSG00000000419 ENST00000371584 49551482 protein_coding
## 11 ENSG00000000419 ENST00000413082 49552685 protein_coding
## 12 ENSG00000000419 ENST00000371583 49551490 protein_coding
## 13 ENSG00000000457 ENST00000367771 169818772 protein_coding
## 14 ENSG00000000457 ENST00000367770 169822215 protein_coding
## 15 ENSG00000000457 ENST00000423670 169823652 protein_coding
## 16 ENSG00000000457 ENST00000470238 169828260 processed_transcript
## 17 ENSG00000000457 ENST00000367772 169821804 protein_coding
## 18 ENSG00000000460 ENST00000498289 169631245 processed_transcript
## 19 ENSG00000000460 ENST00000472795 169763871 processed_transcript
## 20 ENSG00000000460 ENST00000496973 169764184 processed_transcript
## 21 ENSG00000000460 ENST00000481744 169764184 processed_transcript
## 22 ENSG00000000460 ENST00000359326 169764190 protein_coding
## 23 ENSG00000000460 ENST00000466580 169764220 processed_transcript
## 24 ENSG00000000460 ENST00000459772 169764220 processed_transcript
## 25 ENSG00000000460 ENST00000286031 169764550 protein_coding
## 26 ENSG00000000460 ENST00000456684 169764220 protein_coding
## 27 ENSG00000000460 ENST00000413811 169764181 protein_coding
## 28 ENSG00000000938 ENST00000374005 27938575 protein_coding
## 29 ENSG00000000938 ENST00000374004 27938811 protein_coding
## 30 ENSG00000000938 ENST00000374003 27939180 protein_coding
## 31 ENSG00000000938 ENST00000457296 27942025 protein_coding
## 32 ENSG00000000938 ENST00000475472 27949895 processed_transcript
## 33 ENSG00000000938 ENST00000468038 27949918 processed_transcript
## 34 ENSG00000000938 ENST00000545953 27938803 protein_coding
## 35 ENSG00000000938 ENST00000399173 27938803 protein_codingWe can modify the queries above to retrieve only genes encoded on chromosome Y. To this end we use filter objects available for EnsDb objects and its methods.
## Retrieve all gene IDs of all lincRNAs encoded on chromosome Y
linkY <- keys(edb,
filter=list(GeneBiotypeFilter("lincRNA"), SeqNameFilter("Y")))
length(linkY)## [1] 48## We get now all transcripts for these genes.
txs <- select(edb, keys=linkY, columns=c("TXID", "TXSEQSTART", "TXBIOTYPE"),
keytype="GENEID")
nrow(txs)## [1] 66## Alternatively, we could specify/pass the filters with the keys argument.
txs <- select(edb, keys=list(GeneBiotypeFilter("lincRNA"), SeqNameFilter("Y")),
columns=c("TXID", "TXSEQSTART", "TXBIOTYPE"))## Note: ordering of the results might not match ordering of keys!nrow(txs)## [1] 66The version number of R and packages loaded for generating the vignette were:
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## 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] EnsDb.Hsapiens.v75_2.99.0
## [2] ensembldb_2.25.0
## [3] AnnotationFilter_1.25.0
## [4] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [5] GenomicFeatures_1.53.1
## [6] GenomicRanges_1.53.1
## [7] GenomeInfoDb_1.37.2
## [8] GO.db_3.17.0
## [9] hgu95av2.db_3.13.0
## [10] org.Hs.eg.db_3.17.0
## [11] AnnotationDbi_1.63.2
## [12] IRanges_2.35.2
## [13] S4Vectors_0.39.1
## [14] Biobase_2.61.0
## [15] BiocGenerics_0.47.0
## [16] BiocStyle_2.29.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 dplyr_1.1.2
## [3] blob_1.2.4 filelock_1.0.2
## [5] Biostrings_2.69.2 bitops_1.0-7
## [7] lazyeval_0.2.2 fastmap_1.1.1
## [9] RCurl_1.98-1.12 BiocFileCache_2.9.1
## [11] GenomicAlignments_1.37.0 XML_3.99-0.14
## [13] digest_0.6.33 lifecycle_1.0.3
## [15] ProtGenerics_1.33.1 KEGGREST_1.41.0
## [17] RSQLite_2.3.1 magrittr_2.0.3
## [19] compiler_4.3.1 rlang_1.1.1
## [21] sass_0.4.7 progress_1.2.2
## [23] tools_4.3.1 utf8_1.2.3
## [25] yaml_2.3.7 rtracklayer_1.61.0
## [27] knitr_1.43 S4Arrays_1.1.4
## [29] prettyunits_1.1.1 bit_4.0.5
## [31] curl_5.0.1 DelayedArray_0.27.9
## [33] xml2_1.3.5 BiocParallel_1.35.3
## [35] grid_4.3.1 fansi_1.0.4
## [37] biomaRt_2.57.1 SummarizedExperiment_1.31.1
## [39] cli_3.6.1 rmarkdown_2.23
## [41] crayon_1.5.2 generics_0.1.3
## [43] rstudioapi_0.15.0 httr_1.4.6
## [45] rjson_0.2.21 DBI_1.1.3
## [47] cachem_1.0.8 stringr_1.5.0
## [49] zlibbioc_1.47.0 parallel_4.3.1
## [51] BiocManager_1.30.21.1 XVector_0.41.1
## [53] restfulr_0.0.15 matrixStats_1.0.0
## [55] vctrs_0.6.3 Matrix_1.6-0
## [57] jsonlite_1.8.7 bookdown_0.34
## [59] hms_1.1.3 bit64_4.0.5
## [61] jquerylib_0.1.4 glue_1.6.2
## [63] codetools_0.2-19 stringi_1.7.12
## [65] BiocIO_1.11.0 tibble_3.2.1
## [67] pillar_1.9.0 rappdirs_0.3.3
## [69] htmltools_0.5.5 GenomeInfoDbData_1.2.10
## [71] R6_2.5.1 dbplyr_2.3.3
## [73] lattice_0.21-8 evaluate_0.21
## [75] highr_0.10 png_0.1-8
## [77] Rsamtools_2.17.0 memoise_2.0.1
## [79] bslib_0.5.0 SparseArray_1.1.10
## [81] xfun_0.39 MatrixGenerics_1.13.0
## [83] pkgconfig_2.0.3