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Kumar, Sushant; Harmanci, Arif; Vytheeswaran, Jagath; Gerstein, Mark B
Genome Biology, 11/2020, Letnik: 21, Številka: 1Journal Article
There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity scores to somatic and germline SVs. In particular, we generate somatic and germline training models, which include genomic, epigenomic, and conservation-based features, for SV call sets in diseased and healthy individuals. We then apply SVFX to SVs in cancer and other diseases; SVFX achieves high accuracy in identifying pathogenic SVs. Predicted pathogenic SVs in cancer cohorts are enriched among known cancer genes and many cancer-related pathways.
