Machine-learning-aided precise prediction of deletions with next-generation sequencing

作     者：管瑞 髙敬阳 GUAN Rui;GAO Jing-yang

作者机构：College of Information Science and TechnologyBeijing University of Chemical Technology 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2016年第23卷第12期

页      面：3239-3247页

核心收录：

学科分类：1001[医学-基础医学(可授医学、理学学位)] 10[医学] 

基　　金：Project(61472026)supported by the National Natural Science Foundation of China Project(2014J410081)supported by Guangzhou Scientific Research Program,China 

主　　题：next-generation sequencing deletion prediction sensitivity false discovery rate feature extraction machine learning 

摘      要：When detecting deletions in complex human genomes,split-read approaches using short reads generated with next-generation sequencing still face the challenge that either false discovery rate is high,or sensitivity is *** address the problem,an integrated strategy is *** organically combines the fundamental theories of the three mainstream methods(read-pair approaches,split-read technologies and read-depth analysis) with modern machine learning algorithms,using the recipe of feature extraction as a *** with the state-of-art split-read methods for deletion detection in both low and high sequence coverage,the machine-learning-aided strategy shows great ability in intelligently balancing sensitivity and false discovery rate and getting a both more sensitive and more precise call set at single-base-pair ***,users do not need to rely on former experience to make an unnecessary trade-off beforehand and adjust parameters over and over again any *** should be noted that modern machine learning models can play an important role in the field of structural variation prediction.