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Accurate somatic variant detection using weakly supervised deep learning

Computer Science

Accurate somatic variant detection using weakly supervised deep learning

K. Krishnamachari, D. Lu, et al.

Discover VarNet, an innovative deep learning framework developed by Kiran Krishnamachari and colleagues, which revolutionizes the identification of somatic mutations in tumor samples. This groundbreaking research leverages 4.6 million somatic variants to enhance variant calling accuracy, potentially transforming conventional methods in the field.

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Playback language: English
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.
Publisher
NATURE COMMUNICATIONS
Published On
Jul 22, 2022
Authors
Kiran Krishnamachari, Dylan Lu, Alexander Swift-Scott, Anuar Yeraliyev, Kayla Lee, Weitai Huang, Sim Ngak Leng, Anders Jacobsen Skanderup
Tags
deep learning
somatic mutations
variant calling
tumor genome
machine learning
DNA analysis
bioinformatics
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