Massively parallel sequencing approaches for characterization of structural variation

Daniel C. Koboldt, David E. Larson, Ken Chen, Li Ding, Richard K. Wilson

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

39 Scopus citations

Abstract

The emergence of next-generation sequencing (NGS) technologies offers an incredible opportunity to comprehensively study DNA sequence variation in human genomes. Commercially available platforms from Roche (454), Illumina (Genome Analyzer and Hiseq 2000), and Applied Biosystems (SOLiD) have the capability to completely sequence individual genomes to high levels of coverage. NGS data is particularly advantageous for the study of structural variation (SV) because it offers the sensitivity to detect variants of various sizes and types, as well as the precision to characterize their breakpoints at base pair resolution. In this chapter, we present methods and software algorithms that have been developed to detect SVs and copy number changes using massively parallel sequencing data. We describe visualization and de novo assembly strategies for characterizing SV breakpoints and removing false positives.

Original languageEnglish
Title of host publicationGenomic Structural Variants
Subtitle of host publicationMethods and Protocols
EditorsLars Feuk
Pages369-384
Number of pages16
DOIs
StatePublished - 2012

Publication series

NameMethods in Molecular Biology
Volume838
ISSN (Print)1064-3745

Keywords

  • 454, Illumina
  • Abi solid
  • Copy number variants
  • Deletions
  • Duplications
  • Indels
  • Insertions
  • Inversions
  • Next-generation sequencing
  • Paired-end sequencing
  • Solexa
  • Translocations

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