In Bioconductor/GenomicFeatures: Conveniently import and query gene models

Introduction

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.

Installing the GenomicFeatures package

Install the package with:

if (!require("BiocManager", quietly=TRUE))
    install.packages("BiocManager")

BiocManager::install("GenomicFeatures")

Then load it with:

suppressPackageStartupMessages(library(GenomicFeatures))

Obtaining a TxDb object

There 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

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

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.

Retrieving Data from a TxDb object

Pre-filtering data based on Chromosomes

It 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))

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)

Retrieving data using the select() method

The 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)
keytypes(hg19_txdb)
select(hg19_txdb, keys = keys, columns="TXNAME", keytype="GENEID")

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)
cols <- c("TXNAME", "TXSTRAND", "TXCHROM")
select(hg19_txdb, keys=keys, columns=cols, keytype="GENEID")

Methods for returning GRanges objects

Retrieving 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]

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
sum(runLength(tx_strand))
length(GR)

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)
unique(strand(GR))

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

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]
length(EX)
length(GR)

Working with Grouped Features

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)
names(GRList)[10:13]
GRList[11:12]

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)
names(GRList)[10:13]
GRList[[12]]

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"))

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.

Predefined grouping functions

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))
length(fiveUTRsByTranscript(hg19_txdb))
length(threeUTRsByTranscript(hg19_txdb))

Getting the actual sequence data

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))

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)

Session Information

sessionInfo()

Bioconductor/GenomicFeatures documentation built on April 29, 2024, 11:23 a.m.