Journal article
VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants
F Ge, C Li, S Iqbal, A Muhammad, F Li, MA Thafar, Z Yan, A Worachartcheewan, X Xu, J Song, DJ Yu
Briefings in Bioinformatics | OXFORD UNIV PRESS | Published : 2023
DOI: 10.1093/bib/bbac535
Open access
Abstract
Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a 'one-stop' framework for the accurate identification of pathogenicity and functional impact of variants, we developed a two-stage deep-learning-based computational solution, termed VPatho, which was trained using a total of 9619 pathogenic GOF/LOF and 138 026 neutral variants curated from various databases. A total number of 138 variant-level, 262 protein-level and 103 genome-level features were extracted for constructing the models of VPatho. The development of VPatho consists of two stages..
View full abstractGrants
Awarded by National Institutes of Health
Funding Acknowledgements
National Natural Science Foundation of China (62072243, 61772273, and 61872186), the Natural Science Foundation of Jiangsu (BK20201304), the Foundation of National Defense Key Laboratory of Science and Technology (JZX7Y202001SY000901), the National Health and Medical Research Council of Australia (NHMRC) (1144652, 1127948), the Australian Research Council (ARC) (LP110200333 and DP120104460), the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (R01 AI111965) and a Major Inter-Disciplinary Research (IDR) project awarded by Monash University, Taif University Researchers Supporting Project number (TURSP-2020/280), Taif University, Taif, Saudi Arabia, Mahidol University (Basic Research Fund: fiscal year 2022), the Natural Science Foundation of Anhui Province of China (KJ2018A0572) and the Provincial Natural Science Foundation of Anhui (2108085QF268).