VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants. Issue 1 (18th December 2022)

• Record Type: Journal Article
• Authors: Ge, Fang; Li, Chen; Iqbal, Shahid; Muhammad, Arif; Li, Fuyi; Thafar, Maha A; Yan, Zihao; Worachartcheewan, Apilak; Xu, Xiaofeng; Song, Jiangning; Yu, Dong-Jun
• Is Part Of: Briefings in bioinformatics. Volume 24:Issue 1(2023)
• Publication Date: 2022-12-18
• Subjects: weighted-loss function -- random under-sampling -- 1D-ResNet -- 2D-ResNet -- gnomAD variants -- pathogenic GOF/LOF; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac535
• Languages: English
• ISSNs: 1467-5463

Abstract

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: (i) a random under-sampling multi-scale residual neural network (ResNet) with a newly defined weighted-loss function (RUS-Wg-MSResNet) was proposed to predict variants' pathogenicity on the gnomAD_NV + GOF/LOF dataset; and (ii) an XGBOD model was constructed to predict the functional impact of the given variants. Benchmarking experiments demonstrated that RUS-Wg-MSResNet achieved the highest prediction performance with the weights calculated based on the ratios of neutral versus pathogenic variants. Independent tests showed that both RUS-Wg-MSResNet and XGBOD achieved outstanding performance. Moreover, assessed using variants from the CAGI6 competition, RUS-Wg-MSResNet achieved superior performance compared to state-of-the-art predictors. The fine-trained XGBOD models were further used to blind test the whole LOF data downloaded from gnomAD and accordingly, we identified 31 nonLOF variants that were previously labeled as LOF/uncertain variants. As an implementation of the developed approach, a webserver of VPatho is made publicly available at http://csbio.njust.edu.cn/bioinf/vpatho/ to facilitate community-wide efforts for profiling and prioritizing the query variants with respect to their pathogenicity and functional impact.