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Genomic Structural Variants

Volume 838 of the series Methods in Molecular Biology pp 369-384

Date:

Massively Parallel Sequencing Approaches for Characterization of Structural Variation

  • Daniel C. KoboldtAffiliated withThe Genome Institute at Washington University School of Medicine
  • , David E. LarsonAffiliated withThe Genome Institute at Washington University School of Medicine
  • , Ken ChenAffiliated withThe Genome Institute at Washington University School of Medicine
  • , Li DingAffiliated withThe Genome Institute at Washington University School of Medicine
  • , Richard K. WilsonAffiliated withThe Genome Institute at Washington University School of Medicine Email author 

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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.

Key words

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