ERDS-pe: a paired hidden Markov model for copy number variant detection from whole-exome sequencing data

被引:0
|
作者
Tan, Renjie [1 ]
Wang, Jixuan [2 ]
Wu, Xiaoliang [1 ]
Wan, Guoqiang [3 ]
Wang, Rongjie [1 ]
Ma, Rui [1 ]
Han, Zhijie [3 ]
Zhou, Wenyang [3 ]
Jin, Shuilin [4 ]
Jiang, Qinghua [3 ]
Wang, Yadong [1 ]
机构
[1] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China
[2] Harbin Inst Technol, Sch Software, Harbin 150001, Heilongjiang, Peoples R China
[3] Harbin Inst Technol, Sch Life Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China
[4] Harbin Inst Technol, Dept Math, Harbin, Heilongjiang, Peoples R China
基金
中国国家自然科学基金;
关键词
copy number variation; whole-exome sequencing; principal component analysis; hidden Markov model; DISEASE;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Detecting copy number variants (CNVs) is an essential part in variant calling process. Here, we describe a novel method ERDS-pe to detect CNVs from whole-exome sequencing (WES) data. ERDS-pe first employs principal component analysis to normalize WES data. Then, ERDS-pe incorporates read depth signal and single-nucleotide variation information together as a hybrid signal into a paired hidden Markov model to infer CNVs from WES data. Experimental results on real human WES data show that ERDS-pe demonstrates higher sensitivity and provides comparable or even better specificity than other tools. ERDS-pe is publicly available at: https://github.com/microtan0902/erds-pe.
引用
收藏
页码:141 / 144
页数:4
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