AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples - Jeon, Hyeonseong; Ahn, Junhak; Na, Byunggook; Hong, Soona; Sael, Lee; Kim, Sun; Yoon, Sungroh; Baek, Daehyun (Experimental and Molecular Medicine, 2023)
The detection of somatic DNA variants in tumor samples with low tumor purity or sequencing depth remains a daunting challenge despite numerous attempts to address this problem. In this study, we constructed a substantially extended set of actual positive variants originating from a wide range of tumor purities and sequencing depths, as well as actual negative variants derived from sequencer-specific sequencing errors. A deep learning model named AIVariant, trained on this extended dataset, outperforms previously reported methods when tested under various tumor purities and sequencing depths, especially low tumor purity and sequencing depth.
Keywords: Algorithms; Computational Biology; Deep Learning; Gene Frequency; Humans; Mutation; Neoplasms