17 A*STAR researchers are recognised as the world's most influential scientific minds in 2022. Find out more about their work and what drives them to push the frontiers of scientific excellence! A*STAR’s Highly Cited Researchers of 2022
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.
VarNet is freely available for academic use. Commercial usage requires a license.