title: An integrative probabilistic model for identification of structural variation in sequencing data.
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.
authors.affiliation: Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA. Suzanne_Sindi@Brown.edu
authors.forename: Suzanne S
authors.initials: SS
authors.lastname: Sindi
authors.forename: Selim
authors.initials: S
authors.lastname: Onal
authors.forename: Luke C
authors.initials: LC
authors.lastname: Peng
authors.forename: Hsin-Ta
authors.initials: HT
authors.lastname: Wu
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Raphael
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping; D014644:Genetic Variation; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D015233:Models, Statistical; D012091:Repetitive Sequences, Nucleic Acid / Q000235:genetics; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA; D017384:Sequence Deletion; D057345:Sequence Inversion