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Comparative study of exome copy number variation estimation tools using array comparative genomic hybridization as

Yan Guo1, Quanghu Sheng, David C Samuels

  • 1Center for Quantitative Sciences, Vanderbilt University, Nashville, TN 37027, USA.

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|December 5, 2013
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Summary

Exome sequencing can detect copy number variations (CNVs), but current tools show limitations like false positives. CoNIFER and cn.MOPS are recommended for CNV detection over other exome CNV tools.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Medical Genetics

Background:

  • Exome sequencing is a cost-effective method for analyzing human genome coding regions.
  • Identifying copy number variations (CNVs) is an important application of exome sequencing data.
  • Numerous exome CNV detection tools exist, but their performance lacks comprehensive evaluation.

Purpose of the Study:

  • To systematically compare the performance and accuracy of four popular exome CNV tools.
  • To evaluate the effectiveness of exome CNV tools against array comparative genome hybridization (array CGH).

Main Methods:

  • Systematic comparison of four exome CNV tools: CoNIFER, cn.MOPS, exomeCopy, and ExomeDepth.
  • Performance evaluation against array CGH platforms.

Main Results:

  • Exome CNV tools can identify CNVs but exhibit issues like high false positives, low sensitivity, and duplication bias compared to array CGH.
  • CoNIFER and cn.MOPS showed lower false-positive rates than exomeCopy and ExomeDepth.
  • No exome CNV tool achieved outstanding performance when benchmarked against array CGH.

Conclusions:

  • Exome CNV tools are useful for data mining but require careful validation.
  • CoNIFER and cn.MOPS are recommended for non-paired exome CNV detection due to better performance.
  • Further research and validation are crucial for reliable exome CNV analysis.