View source: R/countBamInGranges.R
count.everted.reads | R Documentation |
This is the ExomeDepth high level function that takes a GenomicRanges object, a list of indexed/sorted BAM files, and compute the number of everted reads in each of the defined bins.
count.everted.reads( bed.frame = NULL, bed.file = NULL, bam.files, index.files = bam.files, min.mapq = 20, include.chr = FALSE )
bed.frame |
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bed.file |
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bam.files |
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index.files |
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min.mapq |
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include.chr |
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Everted reads are characteristic of the presence of duplications in a BAM files. This routine will parse a BAM files and the suggested use is to provide relatively large bins (for example gene based, and ExomeDepth has a genes.hg19 object that is appropriate for this) to flag the genes that contain such reads suggestive of a duplication. A manual check of the data using IGV is recommended to confirm that these reads are all located in the same DNA region, which would confirm the presence of a copy number variant.
A data frame that contains the region and the number of identified reads in each bin.
This function calls a lower level function called XXX that works on each single BAM file.
Medvedev et al (2009) <https://doi.org/10.1038/nmeth.1374> "Computational methods for discovering structural variation with next-generation sequencing"
getBAMCounts
data(genes.hg19) bam_file <- system.file('extdata/minimum_1_25630000_25650000.bam', package = 'ExomeDepth') genes.hg19.TTC <- subset(genes.hg19, grepl(pattern = '^TTC34', genes.hg19[['name']])) print(count.everted.reads (bed.frame = genes.hg19.TTC, bam.files = bam_file, min.mapq = 0)) print(count.everted.reads (bed.frame = genes.hg19.TTC, bam.files = bam_file, min.mapq = 35))
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