Unit

UNIT 7.19 A Survey of Copy-Number Variation Detection Tools Based on High-Throughput Sequencing Data

  1. Ruibin Xi1,2,
  2. Semin Lee1,
  3. Peter J. Park1,3,4

Published Online: 1 OCT 2012

DOI: 10.1002/0471142905.hg0719s75

Current Protocols in Human Genetics

Current Protocols in Human Genetics

How to Cite

Xi, R., Lee, S. and Park, P. J. 2012. A Survey of Copy-Number Variation Detection Tools Based on High-Throughput Sequencing Data. Current Protocols in Human Genetics. 75:7.19:7.19.1–7.19.15.

Author Information

  1. 1

    Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts

  2. 2

    School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijng, China

  3. 3

    Children's Hospital Informatics Program, Boston, Massachusetts

  4. 4

    Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts

Publication History

  1. Published Online: 1 OCT 2012

Abstract

Copy-number variation (CNV) is a major class of genomic variation with potentially important functional consequences in both normal and diseased populations. Remarkable advances in development of next-generation sequencing (NGS) platforms provide an unprecedented opportunity for accurate, high-resolution characterization of CNVs. In this unit, we give an overview of available computational tools for detection of CNVs and discuss comparative advantages and disadvantages of different approaches. Curr. Protoc. Hum. Genet. 75:7.19.1-7.19.15. © 2012 by John Wiley & Sons, Inc.

Keywords:

  • structural variation;
  • insertion;
  • deletion;
  • indel;
  • inversion;
  • translocation