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bioRxiv · 10.1101/2020.05.22.111260

Learning What a Good Structural Variant Looks Like.

Chowdhury, M.·Layer, R. M.
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Abstract

Structural variations (SVs) are an important class of genetic mutations, yet SV detectors still suffer from high false-positive rates. In many cases, humans can quickly determine whether a putative SV is real by merely looking at a visualization of the SVs coverage profile. To that end, we developed Samplot-ML, a convolutional neural network (CNN) trained to genotype genomic deletions using Samplot visualizations that incorporate various forms of evidence such as genome coverage, discordant pairs, and split reads. Using Samplot-ML, we were able to reduce false positives by 47% while keeping 97% of true positives on average across several test samples.

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Chowdhury, M., Layer, R. M.. 2020-05-23. Learning What a Good Structural Variant Looks Like.. https://doi.org/10.1101/2020.05.22.111260

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