Name of software VarNet 
Purpose Somatic mutation caller using weakly supervised deep learning
Name of Contact Kiran Krishnamachari & Anders Skanderup
Email of technical contact
Summary of software function

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.

Publications describing software & its application Krishnamachari, K., Lu, D., Swift-Scott, A. et al. Accurate somatic variant detection using weakly supervised deep learning. Nat Commun 13, 4248 (2022).