GenomicFeatures 1.57.1
The GenomicFeatures
package implements the TxDb
container for
storing transcript metadata for a given organism. A TxDb
object
stores the genomic positions of the 5’ and 3’ untranslated regions
(UTRs), protein coding sequences (CDSs), and exons for a set of mRNA
transcripts. The genomic positions are stored and reported with respect
to a given genome assembly. TxDb
objects have numerous accessors
functions to allow such features to be retrieved individually or grouped
together in a way that reflects the underlying biology.
All TxDb
objects are backed by a SQLite database that stores
the genomic positions and relationships between pre-processed mRNA
transcripts, exons, protein coding sequences, and their related
gene identifiers.
GenomicFeatures
packageInstall the package with:
if (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("GenomicFeatures")
Then load it with:
suppressPackageStartupMessages(library(GenomicFeatures))
TxDb
objectThere are three ways that users can obtain a TxDb
object.
One way is to use the loadDb
method to load the object directly
from an appropriate .sqlite
database file.
Here we are loading a previously created TxDb
object
based on UCSC known gene data. This database only contains a small
subset of the possible annotations for human and is only included to
demonstrate and test the functionality of the
GenomicFeatures
package as a demonstration.
samplefile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package="GenomicFeatures")
txdb <- loadDb(samplefile)
txdb
## TxDb object:
## # Db type: TxDb
## # Supporting package: GenomicFeatures
## # Data source: UCSC
## # Genome: hg19
## # Organism: Homo sapiens
## # UCSC Table: knownGene
## # Resource URL: http://genome.ucsc.edu/
## # Type of Gene ID: Entrez Gene ID
## # Full dataset: no
## # miRBase build ID: NA
## # transcript_nrow: 178
## # exon_nrow: 620
## # cds_nrow: 523
## # Db created by: GenomicFeatures package from Bioconductor
## # Creation time: 2014-10-08 10:31:15 -0700 (Wed, 08 Oct 2014)
## # GenomicFeatures version at creation time: 1.17.21
## # RSQLite version at creation time: 0.11.4
## # DBSCHEMAVERSION: 1.0
In this case, the TxDb
object has been returned by
the loadDb
method.
More commonly however, we expect that users will just load a TxDb annotation package like this:
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
hg19_txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene # shorthand (for convenience)
hg19_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: 1.1
Loading the package like this will also create a TxDb
object, and by default that object will have the same name as the
package itself.
Finally, the third way to obtain a TxDb
object is to use one of
the numerous tools defined in the txdbmaker
package. txdbmaker
provides a set of tools for making TxDb
objects from genomic
annotations from various sources (e.g. UCSC, Ensembl, and GFF files).
See the vignette in the txdbmaker
package for more information.
TxDb
objectIt is possible to filter the data that is returned from a
TxDb
object based on it’s chromosome. This can be a
useful way to limit the things that are returned if you are only
interested in studying a handful of chromosomes.
To determine which chromosomes are currently active, use the
seqlevels
method. For example:
head(seqlevels(hg19_txdb))
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6"
Will tell you all the chromosomes that are active for the
TxDb.Hsapiens.UCSC.hg19.knownGene TxDb
object (by
default it will be all of them).
If you then wanted to only set Chromosome 1 to be active you could do it like this:
seqlevels(hg19_txdb) <- "chr1"
So if you ran this, then from this point on in your R session only
chromosome 1 would be consulted when you call the various retrieval
methods… If you need to reset back to the original seqlevels (i.e.
to the seqlevels stored in the db), then set the seqlevels to
seqlevels0(hg19_txdb)
.
seqlevels(hg19_txdb) <- seqlevels0(hg19_txdb)
Exercise:
Use seqlevels
to set only chromsome 15 to be active. BTW,
the rest of this vignette will assume you have succeeded at this.
Solution:
seqlevels(hg19_txdb) <- "chr15"
seqlevels(hg19_txdb)
## [1] "chr15"
select()
methodThe TxDb
objects inherit from AnnotationDb
objects (just as the ChipDb
and OrgDb
objects do).
One of the implications of this relationship is that these object
ought to be used in similar ways to each other. Therefore we have
written supporting columns
, keytypes
, keys
and select
methods for TxDb
objects.
These methods can be a useful way of extracting data from a
TxDb
object. And they are used in the same way that
they would be used to extract information about a ChipDb
or
an OrgDb
object. Here is a simple example of how to find the
UCSC transcript names that match with a set of gene IDs.
keys <- c("100033416", "100033417", "100033420")
columns(hg19_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(hg19_txdb)
## [1] "CDSID" "CDSNAME" "EXONID" "EXONNAME" "GENEID" "TXID" "TXNAME"
select(hg19_txdb, keys = keys, columns="TXNAME", keytype="GENEID")
## 'select()' returned 1:1 mapping between keys and columns
## GENEID TXNAME
## 1 100033416 uc001yxl.4
## 2 100033417 uc001yxo.3
## 3 100033420 uc001yxr.3
Exercise: For the genes in the example above, find the chromosome and strand information that will go with each of the transcript names.
Solution:
columns(hg19_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"
cols <- c("TXNAME", "TXSTRAND", "TXCHROM")
select(hg19_txdb, keys=keys, columns=cols, keytype="GENEID")
## 'select()' returned 1:1 mapping between keys and columns
## GENEID TXNAME TXCHROM TXSTRAND
## 1 100033416 uc001yxl.4 chr15 +
## 2 100033417 uc001yxo.3 chr15 +
## 3 100033420 uc001yxr.3 chr15 +
GRanges
objectsRetrieving data with select is useful, but sometimes it is more
convenient to extract the result as a GRanges
object. This is
often the case when you are doing counting or specialized overlap
operations downstream. For these use cases there is another family of
methods available.
Perhaps the most common operations for a TxDb
object
is to retrieve the genomic coordinates or ranges for exons,
transcripts or coding sequences. The functions
transcripts
, exons
, and cds
return
the coordinate information as a GRanges
object.
As an example, all transcripts present in a TxDb
object
can be obtained as follows:
GR <- transcripts(hg19_txdb)
GR[1:3]
## GRanges object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr15 20362688-20364420 + | 53552 uc001yte.1
## [2] chr15 20487997-20496811 + | 53553 uc001ytf.1
## [3] chr15 20723929-20727150 + | 53554 uc001ytj.3
## -------
## seqinfo: 1 sequence from hg19 genome
The transcripts
function returns a GRanges
class
object. You can learn a lot more about the manipulation of these
objects by reading the GenomicRanges
introductory
vignette. The show
method for a GRanges
object
will display the ranges, seqnames (a chromosome or a contig), and
strand on the left side and then present related metadata on the right
side.
The strand
function is used to obtain the strand
information from the transcripts. The sum of the Lengths of the
Rle
object that strand
returns is equal to the
length of the GRanges
object.
tx_strand <- strand(GR)
tx_strand
## factor-Rle of length 3337 with 2 runs
## Lengths: 1732 1605
## Values : + -
## Levels(3): + - *
sum(runLength(tx_strand))
## [1] 3337
length(GR)
## [1] 3337
In addition, the transcripts
function can also be used to
retrieve a subset of the transcripts available such as those on the
+
-strand of chromosome 1.
GR <- transcripts(hg19_txdb, filter=list(tx_chrom = "chr15", tx_strand = "+"))
length(GR)
## [1] 1732
unique(strand(GR))
## [1] +
## Levels: + - *
The exons
and cds
functions can also be used
in a similar fashion to retrive genomic coordinates for exons and
coding sequences.
The promoters
function computes a GRanges
object
that spans the promoter region around the transcription start site
for the transcripts in a TxDb
object. The upstream
and downstream
arguments define the number of bases upstream
and downstream from the transcription start site that make up the
promoter region.
PR <- promoters(hg19_txdb, upstream=2000, downstream=400)
PR
## GRanges object with 3337 ranges and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## uc001yte.1 chr15 20360688-20363087 + | 53552 uc001yte.1
## uc001ytf.1 chr15 20485997-20488396 + | 53553 uc001ytf.1
## uc001ytj.3 chr15 20721929-20724328 + | 53554 uc001ytj.3
## uc021sex.1 chr15 20737312-20739711 + | 53555 uc021sex.1
## uc010tzb.1 chr15 20740235-20742634 + | 53556 uc010tzb.1
## ... ... ... ... . ... ...
## uc021syy.1 chr15 102302656-102305055 - | 56884 uc021syy.1
## uc002cdf.1 chr15 102462863-102465262 - | 56885 uc002cdf.1
## uc002cds.2 chr15 102518897-102521296 - | 56886 uc002cds.2
## uc010utv.1 chr15 102518897-102521296 - | 56887 uc010utv.1
## uc010utw.1 chr15 102518897-102521296 - | 56888 uc010utw.1
## -------
## seqinfo: 1 sequence from hg19 genome
A similar function (terminators
) is provided to compute the
terminator region around the transcription end site for the
transcripts in a TxDb
object.
Exercise:
Use exons
to retrieve all the exons from chromosome 15.
How does the length of this compare to the value returned by
transcripts
?
Solution:
EX <- exons(hg19_txdb)
EX[1:4]
## GRanges object with 4 ranges and 1 metadata column:
## seqnames ranges strand | exon_id
## <Rle> <IRanges> <Rle> | <integer>
## [1] chr15 20362688-20362858 + | 192986
## [2] chr15 20362943-20363123 + | 192987
## [3] chr15 20364397-20364420 + | 192988
## [4] chr15 20487997-20488227 + | 192989
## -------
## seqinfo: 1 sequence from hg19 genome
length(EX)
## [1] 10771
length(GR)
## [1] 1732
Often one is interested in how particular genomic features relate to
each other, and not just their genomic positions. For example, it might
be of interest to group transcripts by gene or to group exons by transcript.
Such groupings are supported by the transcriptsBy
,
exonsBy
, and cdsBy
functions.
The following call can be used to group transcripts by genes:
GRList <- transcriptsBy(hg19_txdb, by = "gene")
length(GRList)
## [1] 799
names(GRList)[10:13]
## [1] "100033424" "100033425" "100033427" "100033428"
GRList[11:12]
## GRangesList object of length 2:
## $`100033425`
## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr15 25324204-25325381 + | 53638 uc001yxw.4
## -------
## seqinfo: 1 sequence from hg19 genome
##
## $`100033427`
## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr15 25326433-25326526 + | 53640 uc001yxz.3
## -------
## seqinfo: 1 sequence from hg19 genome
The transcriptsBy
function returns a GRangesList
class object. As with GRanges
objects, you can learn more
about these objects by reading the GenomicRanges
introductory vignette. The show
method for a
GRangesList
object will display as a list of GRanges
objects. And, at the bottom the seqinfo will be displayed once for
the entire list.
For each of these three functions, there is a limited set of options
that can be passed into the by
argument to allow grouping.
For the transcriptsBy
function, you can group by gene,
exon or cds, whereas for the exonsBy
and cdsBy
functions can only be grouped by transcript (tx) or gene.
So as a further example, to extract all the exons for each transcript you can call:
GRList <- exonsBy(hg19_txdb, by = "tx")
length(GRList)
## [1] 3337
names(GRList)[10:13]
## [1] "53561" "53562" "53563" "53564"
GRList[[12]]
## GRanges object with 1 range and 3 metadata columns:
## seqnames ranges strand | exon_id exon_name exon_rank
## <Rle> <IRanges> <Rle> | <integer> <character> <integer>
## [1] chr15 22043463-22043502 + | 193028 <NA> 1
## -------
## seqinfo: 1 sequence from hg19 genome
As you can see, the GRangesList
objects returned from each
function contain genomic positions and identifiers grouped into a
list-like object according to the type of feature specified in the by
argument. The object returned can then be used by functions like
findOverlaps
to contextualize alignments from
high-throughput sequencing.
The identifiers used to label the GRanges
objects depend upon
the data source used to create the TxDb
object. So
the list identifiers will not always be Entrez Gene IDs, as they were
in the first example. Furthermore, some data sources do not provide a
unique identifier for all features. In this situation, the group
label will be a synthetic ID created by GenomicFeatures
to
keep the relations between features consistent in the database this
was the case in the 2nd example. Even though the results will
sometimes have to come back to you as synthetic IDs, you can still
always retrieve the original IDs.
Exercise:
Starting with the tx_ids that are the names of the GRList object we
just made, use select
to retrieve that matching transcript
names. Remember that the list used a by
argument = “tx”, so
the list is grouped by transcript IDs.
Solution:
GRList <- exonsBy(hg19_txdb, by = "tx")
tx_ids <- names(GRList)
head(select(hg19_txdb, keys=tx_ids, columns="TXNAME", keytype="TXID"))
## 'select()' returned 1:1 mapping between keys and columns
## TXID TXNAME
## 1 53552 uc001yte.1
## 2 53553 uc001ytf.1
## 3 53554 uc001ytj.3
## 4 53555 uc021sex.1
## 5 53556 uc010tzb.1
## 6 53557 uc021sey.1
Finally, the order of the results in a GRangesList
object can
vary with the way in which things were grouped. In most cases the
grouped elements of the GRangesList
object will be listed in
the order that they occurred along the chromosome. However, when
exons or CDS parts are grouped by transcript, they will instead be
grouped according to their position along the transcript itself.
This is important because alternative splicing can mean that the
order along the transcript can be different from that along the
chromosome.
The intronsByTranscript
, fiveUTRsByTranscript
and threeUTRsByTranscript
are convenience functions that
provide behavior equivalent to the grouping functions, but in
prespecified form. These functions return a GRangesList
object grouped by transcript for introns, 5’ UTR’s, and 3’ UTR’s,
respectively. Below are examples of how you can call these methods.
length(intronsByTranscript(hg19_txdb))
## [1] 3337
length(fiveUTRsByTranscript(hg19_txdb))
## [1] 1825
length(threeUTRsByTranscript(hg19_txdb))
## [1] 1803
The GenomicFeatures
package also provides functions for
converting from ranges to actual sequence (when paired
with an appropriate BSgenome
package).
suppressPackageStartupMessages(library(BSgenome.Hsapiens.UCSC.hg19))
genome <- BSgenome.Hsapiens.UCSC.hg19 # shorthand (for convenience)
tx_seqs1 <- extractTranscriptSeqs(genome, hg19_txdb, use.names=TRUE)
And, once these sequences have been extracted, you can translate them
into proteins with translate
:
suppressWarnings(translate(tx_seqs1))
## AAStringSet object of length 3337:
## width seq names
## [1] 125 EDQDDEARVQYEGFRPGMYVRV...YTPQHMHCGAAFWA*FSDSCH uc001yte.1
## [2] 288 RIAS*GRAEFSSAQTSEIQRRR...ESVFYSVYFNYGNNCFFTVTD uc001ytf.1
## [3] 588 RSGQRLPEQPEAEGGDPGKQRR...RDLLENETHLYLCSIKICFSS uc001ytj.3
## [4] 10 HHLNCRPQTG uc021sex.1
## [5] 9 STVTLPHSQ uc010tzb.1
## ... ... ...
## [3333] 10 QVPMRVQVGQ uc021syy.1
## [3334] 306 MVTEFIFLGLSDSQELQTFLFM...DMKTAIRRLRKWDAHSSVKF* uc002cdf.1
## [3335] 550 LAVSLFFDLFFLFMCICCLLAQ...TPRRLHPAQLEILY*KHTVGF uc002cds.2
## [3336] 496 LAVSLFFDLFFLFMCICCLLAQ...PETFASCTARDPLLKAHCWFL uc010utv.1
## [3337] 531 LAVSLFFDLFFLFMCICCLLAQ...TPRRLHPAQLEILY*KHTVGF uc010utw.1
Exercise:
But of course this is not a meaningful translation, because the call
to extractTranscriptSeqs
will have extracted all
the transcribed regions of the genome regardless of whether or not
they are translated. Look at the manual page for
extractTranscriptSeqs
and see how you can use cdsBy
to only translate only the coding regions.
Solution:
cds_seqs <- extractTranscriptSeqs(Hsapiens,
cdsBy(hg19_txdb, by="tx", use.names=TRUE))
translate(cds_seqs)
## AAStringSet object of length 1875:
## width seq names
## [1] 102 MYVRVEIENVPCEFVQNIDPHY...RQRLLKYTPQHMHCGAAFWA* uc001yte.1
## [2] 435 MEWKLEQSMREQALLKAQLTQL...LGSNCCVPFFCWAWPPRRRR* uc010tzc.1
## [3] 317 MKIANNTVVTEFILLGLTQSQD...SMKRLLSRHVVCQVDFIIRN* uc001yuc.1
## [4] 314 METANYTKVTEFVLTGLSQTPE...KEVKAAMRKLVTKYILCKEK* uc010tzu.2
## [5] 317 MKIANNTVVTEFILLGLTQSQD...SMKRLLSRHVVCQVDFIIRN* uc010tzv.2
## ... ... ...
## [1871] 186 MAGGVLPLRGLRALCRVLLFLS...CLGRSEFKDICQQNVFLQVY* uc010ush.1
## [1872] 258 MYNSKLWEASGHWQHYSENMFT...PVNFLKKDLWLTLTWITVVH* uc002bxl.3
## [1873] 803 MAAEALAAEAVASRLERQEEDI...AIDKLKNLRKTRTLNAEEAF* uc002bxm.3
## [1874] 306 MVTEFIFLGLSDSQELQTFLFM...DMKTAIRRLRKWDAHSSVKF* uc002cdf.1
## [1875] 134 MSESINFSHNLGQLLSPPRCVV...KGETQESVESRVLPGPRHRH* uc010utv.1
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /media/volume/teran2_disk/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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
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## [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.Hsapiens.UCSC.hg19_1.4.3
## [2] BSgenome_1.73.1
## [3] rtracklayer_1.65.0
## [4] BiocIO_1.15.2
## [5] Biostrings_2.73.2
## [6] XVector_0.45.0
## [7] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [8] GenomicFeatures_1.57.1
## [9] AnnotationDbi_1.67.0
## [10] Biobase_2.65.1
## [11] GenomicRanges_1.57.1
## [12] GenomeInfoDb_1.41.1
## [13] IRanges_2.39.2
## [14] S4Vectors_0.43.2
## [15] BiocGenerics_0.51.2
## [16] BiocStyle_2.33.1
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.45.1 SummarizedExperiment_1.35.2
## [3] rjson_0.2.23 xfun_0.47
## [5] bslib_0.8.0 lattice_0.22-6
## [7] vctrs_0.6.5 tools_4.4.1
## [9] bitops_1.0-8 curl_5.2.3
## [11] parallel_4.4.1 RSQLite_2.3.7
## [13] blob_1.2.4 pkgconfig_2.0.3
## [15] Matrix_1.7-0 lifecycle_1.0.4
## [17] GenomeInfoDbData_1.2.12 compiler_4.4.1
## [19] Rsamtools_2.21.2 codetools_0.2-20
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## [31] digest_0.6.37 restfulr_0.0.15
## [33] bookdown_0.40 fastmap_1.2.0
## [35] grid_4.4.1 SparseArray_1.5.41
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## [51] DBI_1.2.3 BiocManager_1.30.25
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## [55] MatrixGenerics_1.17.0 GenomicAlignments_1.41.0
## [57] zlibbioc_1.51.1