knitr::opts_chunk$set(echo = TRUE, collapse = TRUE)
This package aims to check the strandedness of reads in a bam file, enabling easy detection of any contaminating genomic DNA or other unexpected sources of contamination. It can be applied to quantify and remove reads which correspond to putative double strand DNA within a strand-specific RNA sample. The package uses a sliding window to scan a bam file and find the number of positive/negative reads in each window. It then provides method to plot the proportions of positive/negative stranded alignments within all windows, which allow users to determine how much the sample was contaminated, and to determine an appropriate threshold for filtering. Finally, users can filter putative DNA contamination from any strand-specific RNAseq sample using their selected threshold.
To install the release version from Bioconductor:
install.packages("BiocManager") BiocManager::install("strandCheckR")
To install the development version on github (i.e. this version):
install.packages("BiocManager") BiocManager::install("UofABioinformaticsHub/strandCheckR")
Following are the main functions of the package.
getStrandFromBamFile()
To get the number of +/- stranded reads of all sliding windows across a bam file:
# Load the package and example bam files library(strandCheckR) files <- system.file( "extdata", c("s1.sorted.bam", "s2.sorted.bam"), package = "strandCheckR" ) # Find the read proportions from chromosome 10 for the two files win <- getStrandFromBamFile(files, sequences = "10") # Tidy up the file name for prettier output win$File <- basename(as.character(win$File)) win
plotHist()
The histogram plot shows you the proportion of +/- stranded reads across all windows.
plotHist( windows = win, groupBy = "File", normalizeBy = "File", scales = "free_y" )
In this example, s2.sorted.bam seems to be contaminated with double stranded DNA, as evidenced by many windows containing a roughly equal proportion of reads on both strands, whilst s1.sorted.bam is cleaner.
plotWin()
The output from plotWin()
represents each window as a point.
This plot also has threshold lines which can be used to provide guidance as to
the best threshold to choose when filtering windows.
Given a suitable threshold, reads from a positive (resp. negative) window are
kept if and only if the proportion is above (resp. below) the corresponding
threshold line.
plotWin(win, groupBy = "File")
filterDNA()
The function filterDNA()
removes potential double stranded DNA from a bam
file using a selected threshold.
win2 <- filterDNA( file = files[2], destination = "s2.filtered.bam", sequences = "10", threshold = 0.7, getWin = TRUE )
Comparing the histogram plot of the file before and after filtering shows that reads from the windows with roughly equal proportions of +/- stranded reads have been removed.
win2$File <- basename(as.character(win2$File)) win2$File <- factor(win2$File, levels = c("s2.sorted.bam", "s2.filtered.bam")) library(ggplot2) plotHist(win2, groupBy = "File", normalizeBy = "File", scales = "free_y")
A more comprehensive vignette is available at https://bioconductor.org/packages/release/bioc/vignettes/strandCheckR/inst/doc/strandCheckR.html
We recommend that questions seeking support in using the software are posted to the Bioconductor support forum - https://support.bioconductor.org/ - where they will attract not only our attention but that of the wider Bioconductor community.
Code contributions, bug reports and feature requests are most welcome. Please make any pull requests against the master branch at https://github.com/UofABioinformaticsHub/strandCheckR and file issues at https://github.com/UofABioinformaticsHub/strandCheckR/issues
strandCheckR
is licensed under GPL >= 2.0
## Clean up the files generated by the above file.remove("s2.filtered.bam", "s2.filtered.bam.bai", "out.stat")
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