An evaluation of copy number variation detection tools from whole-exome sequencing data.

Journal Article

Copy number variation (CNV) has been found to play an important role in human disease. Next-generation sequencing technology, including whole-genome sequencing (WGS) and whole-exome sequencing (WES), has become a primary strategy for studying the genetic basis of human disease. Several CNV calling tools have recently been developed on the basis of WES data. However, the comparative performance of these tools using real data remains unclear. An objective evaluation study of these tools in practical research situations would be beneficial. Here, we evaluated four well-known WES-based CNV detection tools (XHMM, CoNIFER, ExomeDepth, and CONTRA) using real data generated in house. After evaluation using six metrics, we found that the sensitive and accurate detection of CNVs in WES data remains challenging despite the many algorithms available. Each algorithm has its own strengths and weaknesses. None of the exome-based CNV calling methods performed well in all situations; in particular, compared with CNVs identified from high coverage WGS data from the same samples, all tools suffered from limited power. Our evaluation provides a comprehensive and objective comparison of several well-known detection tools designed for WES data, which will assist researchers in choosing the most suitable tools for their research needs.

Full Text

Duke Authors

Cited Authors

  • Tan, R; Wang, Y; Kleinstein, SE; Liu, Y; Zhu, X; Guo, H; Jiang, Q; Allen, AS; Zhu, M

Published Date

  • July 2014

Published In

Volume / Issue

  • 35 / 7

Start / End Page

  • 899 - 907

PubMed ID

  • 24599517

Electronic International Standard Serial Number (EISSN)

  • 1098-1004

International Standard Serial Number (ISSN)

  • 1059-7794

Digital Object Identifier (DOI)

  • 10.1002/humu.22537

Language

  • eng