mvPPT:A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants  

作　　者：Shi-Yuan Tong Ke Fan Zai-Wei Zhou Lin-Yun Liu Shu-Qing Zhang Yinghui Fu Guang-Zhong Wang Ying Zhu Yong-Chun Yu 

机构地区：[1]Jing'an District Central Hospital of Shanghai,State Key Laboratory of Medical Neurobiology,MOEFrontiers Center for Brain Science,Institutes of Brain Science,Fudan University,Shanghai 200032,China [2]Shanghai Xunyin Biotechnology Co.,Ltd.,Shanghai 201802,China [3]CAS Key Laboratory of Computational Biology,Shanghai Institute of Nutrition and Health,University of Chinese Academy of Sciences,Chinese Academy of Sciences,Shanghai 200031,China [4]Huashan Hospital,State Key Laboratory of Medical Neurobiology,MOE Frontiers Center for Brain Science,Institutes of Brain Science,Fudan University,Shanghai 200032,China

出　　处：《Genomics, Proteomics & Bioinformatics》2023年第2期414-426,共13页基因组蛋白质组与生物信息学报（英文版）

基　　金：supported by the National Key R&D Program of China(Grant No.2021ZD0202500);the Shanghai Natural Science Foundation,China(Grant No.20ZR1403800);the National Natural Science Foundation of China(Grant Nos.31900476,82071259,31930044,and 31725012);the Shanghai Municipal Science and Technology Major Project(Grant No.2018SHZDZX01);ZJ Lab,the Shanghai Center for Brain Science and Brain-Inspired Technology,China,the Foundation of Shanghai Municipal Education Commission,China(Grant No.2019-01-07-00-07-E00062);the Collaborative Innovation Program of Shanghai Municipal Health Commission,China(Grant No.2020CXJQ01).

摘　　要：Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification.In this study,we developed Pathogenicity Prediction Tool for missense variants(mvPPT),a highly sensitive and accurate missense variant classifier based on gradient boosting.mvPPT adopts high-confidence training sets with a wide spectrum of variant profiles,and extracts three categories of features,including scores from existing prediction tools,frequencies(allele frequencies,amino acid frequencies,and genotype frequencies),and genomic context.Compared with established predictors,mvPPT achieves superior performance in all test sets,regardless of data source.In addition,our study also provides guidance for training set and feature selection strategies,as well as reveals highly relevant features,which may further provide biological insights into variant pathogenicity.

关 键 词：Machine learning Missensevariant GENOMICS Computational biology Pathogenicityprediction 

分 类 号：Q811.4[生物学—生物工程]