countShiftReads {RiboProfiling} | R Documentation

## 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.

### 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)

[Package RiboProfiling version 1.18.0 Index]