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Genome Biol. 2012;13(3):R22. doi: 10.1186/gb-2012-13-3-r22.

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

Author information

  • 1Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA. Suzanne_Sindi@Brown.edu

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 which can analyze multiple alignments of reads. GASVPro outperforms existing methods with a 50-90% improvement in specificity on deletions and a 50% improvement on inversions.

PMID:
22452995
[PubMed - indexed for MEDLINE]
PMCID:
PMC3439973
Free PMC Article

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