AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples - Hyeonseong Jeon; Junhak Ahn; Byunggook Na; Soona Hong; Lee Sael; Sun Kim; Sungroh Yoon; Daehyun Baek (Experimental and Molecular Medicine)
Abstract 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.