plot coverage for WES data
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7.5 years ago
koala • 0

Hi! I'd like to plot chromosome coverage for my exome data, possibly distinguish + and - strand!

I've more than one sample. Is it possible to see all samples together (i.e. all chr1 together) ?

Any suggestion?

coverage plot bam • 2.2k views
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I'm not aware of any strand-specific WES library preps, so why (and how) would you distinguish the + and - strand?
In addition, chromosomes are pretty damn big. You either would have to "zoom out" a lot (and as such get low resolution of coverage) or you would have to have a coverage plot so big that it's impossible to get an idea of the coverage.

I'm not sure what the purpose is of your coverage plot and what read-out you aim to obtain. As noted by Vivek you can simply view the coverage in igv.

Another suggestion: when testing things, don't take chromosome 1 but take chromosome 21 ;-)

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I'd like just to have an idea if in my samples (some patients and some healthy people), patients, by chance, have "hot points" in which coverage falls down. I don't know if is possible to verify coverage only in amplicons and not in the entire chomosome: I know that chromosomes are very big !! and I'm already using shorter chromosomes :).

My aim is to answer this question: differences in coverage could suggest me if there is some "problem" in that specific chromosome/ amplicon site only in patients?

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Using some bedtools magic you could check your bam files in predefined regions such as amplicons to get coverage average or per base coverage.

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7.5 years ago
Vivek ★ 2.7k

Simplest way would be to just extract chr1 from all BAM files and view them with IGV.

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7.5 years ago
michael.ante ★ 3.8k

Hi Koala,

you can use e.g. RSeQC's bam2wig tool to create a strand-specific tool. If you have UCSC'S wigToBigWig tool installed, you get than a bigwig file. The bigwig-files can be opened with the IGV browser, as Vivek wrote, or uploaded to the UCSC genome Browser. You can use several files in the two genome browsers. Please be aware of the different chromosome names used in different annotation (chr1 vs. 1 ; chrMT vs. MT vs M).

Cheers,

Michael

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7.5 years ago

I think this is expected if you run the plot on ALL the positions of the chromosome. And what can you see after the plot by plotting all the positions? You should consider sampling from chromosomal positions and producing a plot that would be meaningful and interpretable.

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I removed the link in your post. Since some content of your post is useful I didn't delete it completely, but please don't use answers to spread spam or your account will be suspended.

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