Get reads from indexed bam files for defined regions
This function collects all short reads from bam files that map
to pre-defined regions of interest. Note, that it fetches the exact start and
end positions of mapped fragments, not the coverage. In the case of
single-end reads, the left most postions of fragments mapping to the positive
strands and the right most positions of fragments
mapping to the negative strands are stored. To find centers of fragments use
estimateFragmentCenters(). Positions are given relative to the start
of the peak. Also computed are TotalCounts, i.e. number of fragments mapping to a peak
region, as well as number of fragments mapping to forward and reverse strand.
DBAmmd Object. This Object can be created using
Defines flanking regions of peaks. The estimated fragment length is a good guess (DEFAULT: 200).
whether the reads are paired-end (paired-end is currently not fully tested) (DEFAULT: FALSE)
whether to run in parallel (currently no parallelization implemented) (DEFAULT: FALSE)
DBAmmd object with updated slots
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## Example using a small data set provided in the MMDiffBamSubset package # setting the Experiment meta data: ExpData <- list(dataDir=system.file("extdata", package="MMDiffBamSubset"), sampleSheet="Cfp1.csv") MetaData <- list('ExpData' = ExpData) # Creating a DBAmmd data set: MMD <- DBAmmd(MetaData) # defining a small Region for which to get reads: Regions <- GRanges(seqnames=c('chr1'), IRanges(start = c(4560912,4677889), end = c(4562680,4679681))) MMD <- setRegions(MMD,Regions) MMD <- getPeakReads(MMD) # To access Left ends of fragments mapping to positive strand: Reads.L <- Reads(MMD,'Left.p') # To access Right ends of fragments mapping to negative strand: Reads.R <- Reads(MMD,'Right.n') # To access Matrix of TotalCounts: C.t <- Counts(MMD,whichCounts='T') # Counts on positive strand: C.p <- Counts(MMD,whichCounts='p') # Counts on negative strand: C.n <- Counts(MMD,whichCounts='n')
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