breakTranscriptsByPeaks {transcriptR} | R Documentation

## breakTranscriptsByPeaks

### Description

The function divides closely spaced transcripts into individually transcribed units using the detected active transcription start sites.

### Usage

    breakTranscriptsByPeaks(tdsObj, cdsObj, estimate.params = TRUE)

    ## S4 method for signature 'TranscriptionDataSet,ChipDataSet'
    breakTranscriptsByPeaks(tdsObj,
      cdsObj, estimate.params = TRUE)

### Arguments

`tdsObj` | A `TranscriptionDataSet` object.
`cdsObj` | A `ChipDataSet` object.
`estimate.params` | `Logical`. Whether to estimate expression level and coverage density of the newly detected transcripts. Default: TRUE.

### Details

One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. `transcriptR` combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample. Transcription initiation within a peak region is investigated by comparing RNA-seq read densities upstream and downstream of empirically determined TSSs. Closely spaced transcripts are divided into individually transcribed units using the detected active TSSs.

### Value

The slot `transcripts` of the provided `TranscriptionDataSet` object will be updated by the `GRanges` object, containing transcripts and, if estimated, corresponding expression levels.

### Author(s)

Armen R. Karapetyan

### See Also

`predictStrand`

### Examples

    ### Load TranscriptionDataSet object
    data(tds)

    ### Load ChipDataSet object
    data(cds)

    ### Load reference annotations (knownGene from UCSC)
    data(annot)

    ### Detect transcripts
    detectTranscripts(object = tds, coverage.cutoff = 5, gap.dist = 4000,
    estimate.params = TRUE, combine.by.annot = FALSE, annot = annot)

    ### Classify peaks on gene associated and background
    predictTssOverlap(object = cds, feature = "pileup", p = 0.75)

    ### Predict peak 'strand'
    predictStrand(cdsObj = cds, tdsObj = tds, coverage.cutoff = 5,
    quant.cutoff = 0.1, win.size = 2500)

    ### If `estimate.params = TRUE`, FPKM and coverage density will be re-calculated
    breakTranscriptsByPeaks(tdsObj = tds, cdsObj = cds, estimate.params = TRUE)

    ### View detected transcripts
    getTranscripts(tds)

[Package transcriptR version 1.10.1 Index]