Accurate somatic variant detection using weakly supervised deep learning

Kiran Krishnamachari, Dylan Lu, Alexander Swift-Scott, Anuar Yeraliyev, Kayla Lee, Weitai Huang, Sim Ngak Leng and Anders Jacobsen Skanderup ()
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Kiran Krishnamachari: Genome Institute of Singapore
Dylan Lu: Genome Institute of Singapore
Alexander Swift-Scott: Genome Institute of Singapore
Anuar Yeraliyev: Genome Institute of Singapore
Kayla Lee: Genome Institute of Singapore
Weitai Huang: Genome Institute of Singapore
Sim Ngak Leng: Genome Institute of Singapore
Anders Jacobsen Skanderup: Genome Institute of Singapore

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract Identification of somatic mutations in tumor samples is commonly based on statistical methods in combination with heuristic filters. Here we develop VarNet, an end-to-end deep learning approach for identification of somatic variants from aligned tumor and matched normal DNA reads. VarNet is trained using image representations of 4.6 million high-confidence somatic variants annotated in 356 tumor whole genomes. We benchmark VarNet across a range of publicly available datasets, demonstrating performance often exceeding current state-of-the-art methods. Overall, our results demonstrate how a scalable deep learning approach could augment and potentially supplant human engineered features and heuristic filters in somatic variant calling.

Date: 2022
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DOI: 10.1038/s41467-022-31765-8

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