countShiftReads: Apply an offset on the read start along the transcript and...

In RiboProfiling: Ribosome Profiling Data Analysis: from BAM to Data Representation and Interpretation

Description

Apply an offset on the read start along the transcript and returns the coverage on the 5pUTR, CDS, 3pUTR, as well as a matrix of codon coverage per ORF.

Usage

countShiftReads(exonGRanges, cdsPosTransc, alnGRanges, shiftValue, motifSize)

Arguments

exonGRanges	a GRangesList. It contains the exon coordinates grouped by transcript.
cdsPosTransc	a list. It contains the relative positions of the start and end of the ORFs. The transcript names in exonGRanges and cdsPosTransc should be the same.
alnGRanges	A GRanges object containing the alignment information. In order to improve the performance the GAlignments BAM object should be transformed into a GRanges object with cigar match size metadata.
shiftValue	integer. The offset for recalibrating reads on transcripts when computing coverage. The default value for this parameter is 0, no offset should be performed.
motifSize	an integer. The number of nucleotides in each motif on which to compute coverage and usage. Either 3, 6, or 9. Default 3 nucleotides (codon).

Value

a list with 2 objects. The first object in the list is a data.frame containing: information on ORFs (names, chromosomal position, length) as well as the counts on the 5pUTR, CDS and 3pUTR once the offset is applied. The second object in the list is a list in itself. It contains: for each ORF in the cdsPosTransc, for each codon the sum of read starts covering the 3 codon nucleotides. For motifs of size 6 nucleotides, the motif coverage is computed only for the first codon in the motif, considered as the codon in the P-site. For motifs of size 9 nucleotides, the motif coverage is computed only for the second codon in the motif, considered as the codon in the P-site. This per codon coverage does not contain information on the codon type, just its position in the ORF and its coverage.

Examples

#read the BAM file into a GAlignments object using
#GenomicAlignments::readGAlignments
#the GAlignments object should be similar to ctrlGAlignments
data(ctrlGAlignments)
aln <- ctrlGAlignments

#transform the GAlignments object into a GRanges object (faster processing)
alnGRanges <- readsToStartOrEnd(aln, what="start")

#make a txdb object containing the annotations for the specified species.
#In this case hg19.
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene
#Please make sure that seqnames of txdb correspond to
#the seqnames of the alignment files ("chr" particle)
#if not rename the txdb seqlevels
#renameSeqlevels(txdb, sub("chr", "", seqlevels(txdb)))

#get all CDSs by transcript
cds <- GenomicFeatures::cdsBy(txdb, by="tx", use.names=TRUE)

#get all exons by transcript
exonGRanges <- GenomicFeatures::exonsBy(txdb, by="tx", use.names=TRUE)
#get the per transcript relative position of start and end codons
#cdsPosTransc <- orfRelativePos(cds, exonGRanges)
data(cdsPosTransc)

#compute the counts on the different features after applying
#the specified shift value on the read start along the transcript
countsData <- countShiftReads(exonGRanges[names(cdsPosTransc)], cdsPosTransc,
           alnGRanges, -14)

RiboProfiling documentation built on Nov. 8, 2020, 5:26 p.m.