ERDS-pe:从全外显子组测序数据中检测拷贝数变异的配对隐马尔可夫模型

Renjie Tan, Jixuan Wang, Xiaoliang Wu, Guoqiang Wan, Rongjie Wang, Rui Ma, Zhijie Han, Wenyang Zhou, Shuilin Jin, Qinghua Jiang, Yadong Wang
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引用次数: 2

摘要

拷贝数变异的检测是变异调用过程中的一个重要环节。在这里,我们描述了一种从全外显子组测序(WES)数据中检测CNVs的新方法ERDS-pe。erds - type首先采用主成分分析对WES数据进行归一化处理。然后,ERDS-pe将读取深度信号和单核苷酸变异信息作为混合信号结合到配对隐马尔可夫模型中,从WES数据中推断CNVs。在真实人体WES数据上的实验结果表明,ERDS-pe具有更高的灵敏度,并且具有与其他工具相当甚至更好的特异性。ERDS-pe可在:https://github.com/microtan0902/erds-pe公开获取。
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ERDS-pe: A paired hidden Markov model for copy number variant detection from whole-exome sequencing data
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.
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