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:
## Warning: multiple methods tables found for 'setequal'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'IRanges'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'GenomeInfoDb'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'GenomicRanges'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'XVector'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'AnnotationDbi'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'Biostrings'## Warning: multiple methods tables found for 'setequal'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'GenomicFeatures'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'rtracklayer'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'GenomicAlignments'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'SummarizedExperiment'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'S4Arrays'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'DelayedArray'## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'SparseArray'## Warning: replacing previous import 'S4Arrays::read_block' by
## 'DelayedArray::read_block' when loading 'SummarizedExperiment'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: 1.0In 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: 1.1Loading 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:
## [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:
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).
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:
## [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.
## [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"## [1] "CDSID" "CDSNAME" "EXONID" "EXONNAME" "GENEID" "TXID" "TXNAME"## 'select()' returned 1:1 mapping between keys and columns## GENEID TXNAME
## 1 100033416 uc001yxl.4
## 2 100033417 uc001yxo.3
## 3 100033420 uc001yxr.3Exercise: For the genes in the example above, find the chromosome and strand information that will go with each of the transcript names.
Solution:
## [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:
## 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 genomeThe 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.
## factor-Rle of length 3337 with 2 runs
## Lengths: 1732 1605
## Values : + -
## Levels(3): + - *## [1] 3337## [1] 3337In 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.
## [1] 1732## [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.
## 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 genomeA 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:
## 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## [1] 10771## [1] 1732Often 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:
## [1] 799## [1] "100033424" "100033425" "100033427" "100033428"## 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 genomeThe 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:
## [1] 3337## [1] "53561" "53562" "53563" "53564"## 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 genomeAs 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.1Finally, 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.
## [1] 3337## [1] 1825## [1] 1803The GenomicFeatures package also provides functions for
converting from ranges to actual sequence (when paired with an
appropriate BSgenome package).
## Warning: replacing previous import 'BiocGenerics::setequal' by
## 'S4Vectors::setequal' when loading 'BSgenome'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:
## 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.1Exercise: 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 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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
## [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] BSgenome.Hsapiens.UCSC.hg19_1.4.3
## [2] BSgenome_1.75.0
## [3] rtracklayer_1.67.0
## [4] BiocIO_1.17.0
## [5] Biostrings_2.75.1
## [6] XVector_0.47.0
## [7] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [8] GenomicFeatures_1.59.1
## [9] AnnotationDbi_1.69.0
## [10] Biobase_2.67.0
## [11] GenomicRanges_1.59.0
## [12] GenomeInfoDb_1.43.0
## [13] IRanges_2.41.0
## [14] S4Vectors_0.45.0
## [15] BiocGenerics_0.53.1
## [16] generics_0.1.3
## [17] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.47.0 SummarizedExperiment_1.37.0
## [3] rjson_0.2.23 xfun_0.49
## [5] bslib_0.8.0 lattice_0.22-6
## [7] vctrs_0.6.5 tools_4.4.2
## [9] bitops_1.0-9 curl_6.0.0
## [11] parallel_4.4.2 RSQLite_2.3.7
## [13] blob_1.2.4 pkgconfig_2.0.3
## [15] Matrix_1.7-1 lifecycle_1.0.4
## [17] GenomeInfoDbData_1.2.13 compiler_4.4.2
## [19] Rsamtools_2.23.0 codetools_0.2-20
## [21] htmltools_0.5.8.1 sys_3.4.3
## [23] buildtools_1.0.0 sass_0.4.9
## [25] RCurl_1.98-1.16 yaml_2.3.10
## [27] crayon_1.5.3 jquerylib_0.1.4
## [29] BiocParallel_1.41.0 DelayedArray_0.33.1
## [31] cachem_1.1.0 abind_1.4-8
## [33] digest_0.6.37 restfulr_0.0.15
## [35] maketools_1.3.1 grid_4.4.2
## [37] fastmap_1.2.0 SparseArray_1.7.1
## [39] cli_3.6.3 S4Arrays_1.7.1
## [41] XML_3.99-0.17 UCSC.utils_1.3.0
## [43] bit64_4.5.2 rmarkdown_2.29
## [45] httr_1.4.7 matrixStats_1.4.1
## [47] bit_4.5.0 png_0.1-8
## [49] memoise_2.0.1 evaluate_1.0.1
## [51] knitr_1.48 rlang_1.1.4
## [53] DBI_1.2.3 BiocManager_1.30.25
## [55] jsonlite_1.8.9 R6_2.5.1
## [57] MatrixGenerics_1.19.0 GenomicAlignments_1.43.0
## [59] zlibbioc_1.52.0