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Method to detect CNV in a large set of coverage profile ?

Hi all,

I've got the profile of coverage for >10000 BAM files for the same 16Kb region , all normalized on the median depth of the region. The data are available as a binary file containing an array of integers but I can change this.

Of course, the profile of coverage is more or less close to '1.0' , but if there is a deletion a local part of the coverage will be close to '0.5' , etc...

Do you known any method (machine learning ?) to detect a rare CNV in those files ? something unusual (>50bp) in the profile ?

Thanks!

P

bam cnv coverage machine-learning
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updated by jared.andrews07 jared.andrews07 • written by Pierre Lindenbaum Pierre Lindenbaum

2 answers

I don't know if your data are appropriate (short reads?), but this one might fit the bill. https://github.com/PopicLab/cue

I must admit I haven't tried it yet.

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written by colindaven colindaven

I'd probably just try circular binary segmentation first and see what that looks like. You may have to play with bin size a bit to get the resolution you want, but I don't see why it wouldn't work for your purposes.

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written by jared.andrews07 jared.andrews07

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