SVision: a deep learning approach to resolve complex structural variants - Lin, Jiadong; Wang, Songbo; Audano, Peter A.; Meng, Deyu; Flores, Jacob I.; Kosters, Walter; Yang, Xiaofei; Jia, Peng; Marschall, Tobias; Beck, Christine R.; Ye, Kai (Nature methods, 2022)
Complex structural variants (CSVs) encompass multiple breakpoints and are often missed or misinterpreted. We developed SVision, a deep-learning-based multi-object-recognition framework, to automatically detect and characterize CSVs from long-read sequencing data. SVision outperforms current callers at identifying the internal structure of complex events and has revealed 80 high-quality CSVs with 25 distinct structures from an individual genome. SVision directly detects CSVs without matching known structures, allowing sensitive detection of both common and previously uncharacterized complex rearrangements. SVision is a deep-learning-based method that can sensitively and accurately detect and characterize complex structural variants using long-read sequencing data.