An integrative probabilistic model for identification of structural variation in sequencing data

Suzanne S. Sindi, Selim Önal, Luke C. Peng, Hsin Ta Wu, Benjamin J. Raphael

Research output: Contribution to journalArticle

92 Scopus citations

Abstract

Paired-end sequencing is a common approach for identifying structural variation (SV) in genomes. Discrepancies between the observed and expected alignments indicate potential SVs. Most SV detection algorithms use only one of the possible signals and ignore reads with multiple alignments. This results in reduced sensitivity to detect SVs, especially in repetitive regions. We introduce GASVPro, an algorithm combining both paired read and read depth signals into a probabilistic model that can analyze multiple alignments of reads. GASVPro outperforms existing methods with a 50 to 90% improvement in specificity on deletions and a 50% improvement on inversions. GASVPro is available at http://compbio.cs.brown.edu/software.

Original languageEnglish (US)
Article numberR22
JournalGenome biology
Volume13
Issue number3
DOIs
StatePublished - Mar 27 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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