AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples

Autor: Hyeonseong Jeon, Junhak Ahn, Byunggook Na, Soona Hong, Lee Sael, Sun Kim, Sungroh Yoon, Daehyun Baek
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Experimental and Molecular Medicine, Vol 55, Iss 8, Pp 1734-1742 (2023)
Druh dokumentu: article
ISSN: 2092-6413
DOI: 10.1038/s12276-023-01049-2
Popis: 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.
Databáze: Directory of Open Access Journals
Externí odkaz: