Accurate somatic variant detection using weakly supervised deep learning.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 35869060.
- Also identified by DOI 10.1038/s41467-022-31765-8 and PMC identifier 9307817.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Deep Learning
- Neoplasms