Making and Utilizing TxDb Objects

Introduction

The GenomicFeatures package retrieves and manages transcript-related features from the UCSC Genome Bioinformatics\footnote{(http://genome.ucsc.edu/)} and BioMart\footnote{(http://www.biomart.org/)} data resources. The package is useful for ChIP-chip, ChIP-seq, and RNA-seq analyses.

suppressPackageStartupMessages(library('GenomicFeatures'))

## Error in library("GenomicFeatures"): there is no package called 'GenomicFeatures'

`TxDb` Objects

The GenomicFeatures package uses TxDb objects to store transcript metadata. This class maps the 5’ and 3’ untranslated regions (UTRs), protein coding sequences (CDSs) and exons for a set of mRNA transcripts to their associated genome. 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 manages genomic locations and the relationships between pre-processed mRNA transcripts, exons, protein coding sequences, and their related gene identifiers.

Retrieving Data from `TxDb` objects

Loading Transcript Data

There are two ways that users can load pre-existing data to generate a TxDb object. One method 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)

## Error in loadDb(samplefile): could not find function "loadDb"

txdb

## Error in eval(expr, envir, enclos): object 'txdb' not found

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)

## Error in library(TxDb.Hsapiens.UCSC.hg19.knownGene): there is no package called 'TxDb.Hsapiens.UCSC.hg19.knownGene'

txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene #shorthand (for convenience)

## Error in eval(expr, envir, enclos): object 'TxDb.Hsapiens.UCSC.hg19.knownGene' not found

txdb

## Error in eval(expr, envir, enclos): object 'txdb' not found

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.

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(txdb))

## Error in seqlevels(txdb): could not find function "seqlevels"

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(txdb) <- "chr1"

## Error: object 'txdb' not found

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(txdb).

seqlevels(txdb) <- seqlevels0(txdb)

## Error in seqlevels0(txdb): could not find function "seqlevels0"

\begin{Exercise} Use seqlevelsto set only chromsome 15 to be active. BTW, the rest of this vignette will assume you have succeeded at this. \end{Exercise} `wl\begin{Solution}


seqlevels(txdb) <- "chr15"

## Error: object 'txdb' not found

seqlevels(txdb)

## Error in seqlevels(txdb): could not find function "seqlevels"

\end{Solution}wl`
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(txdb)

## Error in columns(txdb): could not find function "columns"

keytypes(txdb)

## Error in keytypes(txdb): could not find function "keytypes"

select(txdb, keys = keys, columns="TXNAME", keytype="GENEID")

## Error in select(txdb, keys = keys, columns = "TXNAME", keytype = "GENEID"): could not find function "select"

\begin{Exercise} For the genes in the example above, find the chromosome and strand information that will go with each of the transcript names. \end{Exercise}
`wl\begin{Solution}
columns(txdb)

## Error in columns(txdb): could not find function "columns"

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

## Error in select(txdb, keys = keys, columns = cols, keytype = "GENEID"): could not find function "select"

\end{Solution}wl`
Methods for returning GRanges objects
Retrieving data with select is useful, but sometimes it is more
convenient to extract the result as GRanges objects.  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(txdb)

## Error in transcripts(txdb): could not find function "transcripts"

GR[1:3]

## Error in eval(expr, envir, enclos): object 'GR' not found

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.  At the bottom, the seqlengths display all the possible seqnames
along with the length of each sequence.
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)

## Error in strand(GR): could not find function "strand"

tx_strand

## Error in eval(expr, envir, enclos): object 'tx_strand' not found

sum(runLength(tx_strand))

## Error in runLength(tx_strand): could not find function "runLength"

length(GR)

## Error in eval(expr, envir, enclos): object 'GR' not found

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(txdb, filter=list(tx_chrom = "chr15", tx_strand = "+"))

## Error in transcripts(txdb, filter = list(tx_chrom = "chr15", tx_strand = "+")): could not find function "transcripts"

length(GR)

## Error in eval(expr, envir, enclos): object 'GR' not found

unique(strand(GR))

## Error in strand(GR): could not find function "strand"

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(txdb, upstream=2000, downstream=400)

## Error in promoters(txdb, upstream = 2000, downstream = 400): could not find function "promoters"

PR

## Error in eval(expr, envir, enclos): object 'PR' not found

The exons and cds functions can also be used
in a similar fashion to retrive genomic coordinates for exons and
coding sequences.

\begin{Exercise} Use exonsto retrieve all the exons from chromosome 15. How does the length of this compare to the value returned bytranscripts? \end{Exercise} `wl\begin{Solution}


EX <- exons(txdb)

## Error in exons(txdb): could not find function "exons"

EX[1:4]

## Error in eval(expr, envir, enclos): object 'EX' not found

length(EX)

## Error in eval(expr, envir, enclos): object 'EX' not found

length(GR)

## Error in eval(expr, envir, enclos): object 'GR' not found

\end{Solution}wl`
Working with Grouped Features
Often one is interested in how particular genomic features relate to
each other, and not just their location.  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(txdb, by = "gene")

## Error in transcriptsBy(txdb, by = "gene"): could not find function "transcriptsBy"

length(GRList)

## Error in eval(expr, envir, enclos): object 'GRList' not found

names(GRList)[10:13]

## Error in eval(expr, envir, enclos): object 'GRList' not found

GRList[11:12]

## Error in eval(expr, envir, enclos): object 'GRList' not found

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(txdb, by = "tx")

## Error in exonsBy(txdb, by = "tx"): could not find function "exonsBy"

length(GRList)

## Error in eval(expr, envir, enclos): object 'GRList' not found

names(GRList)[10:13]

## Error in eval(expr, envir, enclos): object 'GRList' not found

GRList[[12]]

## Error in eval(expr, envir, enclos): object 'GRList' not found

As you can see, the GRangesList objects returned from each
function contain locations 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.

\begin{Exercise} Starting with the tx_ids that are the names of the GRList object we just made, use selectto retrieve that matching transcript names. Remember that the list used abyargument = "tx", so the list is grouped by transcript IDs. \end{Exercise} `wl\begin{Solution}


GRList <- exonsBy(txdb, by = "tx")

## Error in exonsBy(txdb, by = "tx"): could not find function "exonsBy"

tx_ids <- names(GRList)

## Error in eval(expr, envir, enclos): object 'GRList' not found

head(select(txdb, keys=tx_ids, columns="TXNAME", keytype="TXID"))

## Error in select(txdb, keys = tx_ids, columns = "TXNAME", keytype = "TXID"): could not find function "select"

\end{Solution}wl`
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 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(txdb))

## Error in intronsByTranscript(txdb): could not find function "intronsByTranscript"

length(fiveUTRsByTranscript(txdb))

## Error in fiveUTRsByTranscript(txdb): could not find function "fiveUTRsByTranscript"

length(threeUTRsByTranscript(txdb))

## Error in threeUTRsByTranscript(txdb): could not find function "threeUTRsByTranscript"

Getting the actual sequence data
The GenomicFeatures package also provides provides
functions for converting from ranges to actual sequence (when paired
with an appropriate BSgenome package).
library(BSgenome.Hsapiens.UCSC.hg19)

## Error in library(BSgenome.Hsapiens.UCSC.hg19): there is no package called 'BSgenome.Hsapiens.UCSC.hg19'

tx_seqs1 <- extractTranscriptSeqs(Hsapiens, TxDb.Hsapiens.UCSC.hg19.knownGene,
                                  use.names=TRUE)

## Error in extractTranscriptSeqs(Hsapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, : could not find function "extractTranscriptSeqs"

And, once these sequences have been extracted, you can translate them
into proteins with translate:
suppressWarnings(translate(tx_seqs1))

## Error in translate(tx_seqs1): could not find function "translate"

\begin{Exercise} But of course this is not a meaningful translation, because the call to extractTranscriptSeqswill have extracted all the transcribed regions of the genome regardless of whether or not they are translated. Look at the manual page forextractTranscriptSeqsand see how you can use cdsBy to only translate only the coding regions. \end{Exercise} `wl\begin{Solution}


cds_seqs <- extractTranscriptSeqs(Hsapiens,
                                  cdsBy(txdb, by="tx", use.names=TRUE))

## Error in extractTranscriptSeqs(Hsapiens, cdsBy(txdb, by = "tx", use.names = TRUE)): could not find function "extractTranscriptSeqs"

translate(cds_seqs)

## Error in translate(cds_seqs): could not find function "translate"

\end{Solution}wl`
Creating New TxDb Objects or Packages
The GenomicFeatures package provides functions to create
TxDb objects based on data downloaded from UCSC
Genome Bioinformatics or BioMart. The following subsections
demonstrate the use of these functions.  There is also support for
creating TxDb objects from custom data sources using
makeTxDb; see the help page for this function for
details.
Using makeTxDbFromUCSC
The function makeTxDbFromUCSC downloads UCSC
Genome Bioinformatics transcript tables (e.g. knownGene,
refGene, ensGene) for a genome build (e.g.
mm9, hg19).  Use the supportedUCSCtables
utility function to get the list of tables known to work with
makeTxDbFromUCSC.
supportedUCSCtables(genome="mm9")

## Error in supportedUCSCtables(genome = "mm9"): could not find function "supportedUCSCtables"

mm9KG_txdb <- makeTxDbFromUCSC(genome="mm9", tablename="knownGene")

Using makeTxDbFromBiomart
Retrieve data from BioMart by specifying the mart and the data set to
the makeTxDbFromBiomart function (not all BioMart
data sets are currently supported):
mmusculusEnsembl <- makeTxDbFromBiomart(dataset="mmusculus_gene_ensembl")

As with the makeTxDbFromUCSC function, the
makeTxDbFromBiomart function also has a
circ_seqs argument that will default to using the contents
of the DEFAULT_CIRC_SEQS vector.  And just like those UCSC
sources, there is also a helper function called
getChromInfoFromBiomart that can show what the different
chromosomes are called for a given source.
Using the makeTxDbFromBiomart
makeTxDbFromUCSC functions can take a while and
may also require some bandwidth as these methods have to download and
then assemble a database from their respective sources.  It is not
expected that most users will want to do this step every time.
Instead, we suggest that you save your annotation objects and label
them with an appropriate time stamp so as to facilitate reproducible
research.
Using makeTxDbFromEnsembl
The makeTxDbFromEnsembl function creates a TxDb object
for a given organism by importing the genomic locations of its transcripts,
exons, CDS, and genes from an Ensembl database.
See ?makeTxDbFromEnsembl for more information.
Using makeTxDbFromGFF
You can also extract transcript information from either GFF3 or GTF
files by using the makeTxDbFromGFF function.
Usage is similar to makeTxDbFromBiomart and
makeTxDbFromUCSC.
Saving and Loading a TxDb Object
Once a TxDb object has been created, it can be saved
to avoid the time and bandwidth costs of recreating it and to make it
possible to reproduce results with identical genomic feature data at a
later date.  Since TxDb objects are backed by a
SQLite database, the save format is a SQLite database file (which
could be accessed from programs other than R if desired).  Note that
it is not possible to serialize a TxDb object using
R’s save function.
saveDb(mm9KG_txdb, file="fileName.sqlite")

And as was mentioned earlier, a saved TxDb object can
be initialized from a .sqlite file by simply using loadDb.
mm9KG_txdb <- loadDb("fileName.sqlite")

Using makeTxDbPackageFromUCSC and
`makeTxDbPackageFromBiomart`

It is often much more convenient to just make an annotation package
out of your annotations.  If you are finding that this is the case,
then you should consider the convenience functions:
makeTxDbPackageFromUCSC and
makeTxDbPackageFromBiomart.  These functions are similar
to makeTxDbFromUCSC and
makeTxDbFromBiomart except that they will take the
extra step of actually wrapping the database up into an annotation
package for you.  This package can then be installed and used as of
the standard TxDb packages found on in the Bioconductor
repository.
Session Information
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux trixie/sid
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] markdown_1.7 knitr_1.43  
## 
## loaded via a namespace (and not attached):
## [1] compiler_4.3.1 tools_4.3.1    xfun_0.39      evaluate_0.21

Making and Utilizing TxDb Objects

Marc Carlson, Patrick Aboyoun, Hervé Pagès, Seth Falcon, Martin Morgan

Thursday, July 27, 2023

Introduction

`TxDb` Objects

Retrieving Data from `TxDb` objects

Loading Transcript Data

Pre-filtering data based on Chromosomes

Retrieving data using the select method

Methods for returning `GRanges` objects

Working with Grouped Features

Predefined grouping functions

Getting the actual sequence data

Creating New `TxDb` Objects or Packages

Using `makeTxDbFromUCSC`

Using `makeTxDbFromBiomart`

Using `makeTxDbFromEnsembl`

Using `makeTxDbFromGFF`

Saving and Loading a `TxDb` Object

Using `makeTxDbPackageFromUCSC` and

Session Information

Making and Utilizing TxDb Objects

Marc Carlson, Patrick Aboyoun, Hervé Pagès, Seth Falcon, Martin Morgan

Thursday, July 27, 2023

Introduction

TxDb Objects

Retrieving Data from TxDb objects

Loading Transcript Data

Pre-filtering data based on Chromosomes

Retrieving data using the select method

Methods for returning GRanges objects

Working with Grouped Features

Predefined grouping functions

Getting the actual sequence data

Creating New TxDb Objects or Packages

Using makeTxDbFromUCSC

Using makeTxDbFromBiomart

Using makeTxDbFromEnsembl

Using makeTxDbFromGFF

Saving and Loading a TxDb Object

Using makeTxDbPackageFromUCSC and

Session Information

`TxDb` Objects

Retrieving Data from `TxDb` objects

Methods for returning `GRanges` objects

Creating New `TxDb` Objects or Packages

Using `makeTxDbFromUCSC`

Using `makeTxDbFromBiomart`

Using `makeTxDbFromEnsembl`

Using `makeTxDbFromGFF`

Saving and Loading a `TxDb` Object

Using `makeTxDbPackageFromUCSC` and