Calculation of read counts from BAM files.

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Description

Generates the read counts from BAM Files. These counts are necessary for CNV detection methods based on depth of coverage information. Note that the function is much faster, if the BAM files have an index file. The index file is assumed to be in the same folder and have an identical file name except that ".bai" is appended.

This function can also be run in a parallel version.

Usage

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getReadCountsFromBAM(BAMFiles, sampleNames, refSeqName, WL, mode,
  parallel = 0)

Arguments

BAMFiles

BAMFiles

sampleNames

The corresponding sample names to the BAM Files.

refSeqName

Name of the reference sequence that should be analyzed. The name must appear in the header of the BAM file. If it is not given the function will select the first reference sequence that appears in the header of the BAM files.

WL

Windowlength. Length of the initial segmentation of the genome in basepairs. Should be chosen such that on the average 100 reads are contained in each segment. If not given, cn.mops will try to find an appropiate window length.

mode

Possible values are "paired" and "unpaired", whether the mapping algorithm was using a "paired" or "unpaired" strategy.

parallel

The number of parallel processes to be used for this function. Default=0.

Value

An instance of "GRanges", that contains the breakpoints of the initial segments and the raw read counts that were extracted from the BAM files. This object can be used as input for cn.mops and other CNV detection methods.

Author(s)

Guenter Klambauer klambauer@bioinf.jku.at

Examples

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BAMFiles <- list.files(system.file("extdata", package="cn.mops"),pattern=".bam$",
	full.names=TRUE)
bamDataRanges <- getReadCountsFromBAM(BAMFiles,
					sampleNames=paste("Sample",1:3),WL=5000,mode="unpaired")
X <- getReadCountsFromBAM(BAMFiles,
					sampleNames=paste("Sample",1:3),WL=5000,mode="unpaired",parallel=2)

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