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

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) | 2016年

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

页码：141 / 144

页数：4

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