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

Suzanne S Sindi12*, Selim Önal3, Luke C Peng3, Hsin-Ta Wu13 and Benjamin J Raphael13*

Author affiliations

1 Center for Computational Molecular Biology, Brown University, Box 1910, Providence, RI 02912, USA

2 Department for Molecular Biology, Cellular Biology and Biochemistry, Brown University, 185 Meeting St, Providence, RI 02912, USA

3 Department of Computer Science, Brown University, 115 Waterman St. Providence, RI 20912, USA

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Citation and License

Genome Biology 2012, 13:R22  doi:10.1186/gb-2012-13-3-r22

Published: 27 March 2012


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