ERDS-Exome: A Hybrid Approach for Copy Number Variant Detection from Whole-Exome Sequencing Data

作者：Tan, Renjie; Wang, Jixuan; Wu, Xiaoliang; Juan, Liran; Zhang, Tianjiao; Ma, Rui; Zhan, Qing; Wang, Tao; Jin, Shuilin; Jiang, Qinghua^*; Wang, Yadong^*

来源：IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020, 17(3): 796-803.

DOI：10.1109/TCBB.2017.2758779

• Bioinformatics
• Sequential analysis
• Hidden Markov models
• Genomics
• Tools
• Matrix decomposition
• Sensitivity
• Copy number variation
• whole-exome sequencing
• principal component analysis
• hidden Markov model

Copy number variants (CNVs) play important roles in human disease and evolution. With the rapid development of next-generation sequencing technologies, many tools have been developed for inferring CNVs based on whole-exome sequencing (WES) data. However, as a result of the sparse distribution of exons in the genome, the limitations of the WES technique, and the nature of high-level signal noises in WES data, the efficacy of these variants remains less than desirable. Thus, there is need for the development of an effective tool to achieve a considerable power in WES CNVs discovery. In the present study, we describe a novel method, Estimation by Read Depth (RD) with Single-nucleotide variants from exome sequencing data (ERDS-exome). ERDS-exome employs a hybrid normalization approach to normalize WES data and to incorporate RD and single-nucleotide variation information together as a hybrid signal into a paired hidden Markov model to infer CNVs from WES data. Based on systematic evaluations of real data from the 1000 Genomes Project using other state-of-the-art tools, we observed that ERDS-exome demonstrates higher sensitivity and provides comparable or even better specificity than other tools. ERDS-exome is publicly available at: https://erds-exome.github.io.

• 出版日期2020-5
• 单位哈尔滨工业大学