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

doi:10.1186/gb-2012-13-3-r22

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 journal › Article › peer-review

104 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 language	English (US)
Article number	R22
Journal	Genome biology
Volume	13
Issue number	3
DOIs	https://doi.org/10.1186/gb-2012-13-3-r22
State	Published - Mar 27 2012
Externally published	Yes

All Science Journal Classification (ASJC) codes

Ecology, Evolution, Behavior and Systematics
Genetics
Cell Biology

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10.1186/gb-2012-13-3-r22

Cite this

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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 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.",

author = "Sindi, {Suzanne S.} and Selim {\"O}nal and Peng, {Luke C.} and Wu, {Hsin Ta} and Raphael, {Benjamin J.}",

note = "Funding Information: We would like to thank Paul Medvedev for his assistance with running CNVer and Anna Ritz for helpful discussions regarding the manuscript. This work is supported by National Institutes of Health (R01 HG5690) and Burroughs Wellcome Career Award at the Scientific Interface to BJR. Some read alignments were obtained using the free version of Novoalign [48].",

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doi = "10.1186/gb-2012-13-3-r22",

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AU - Önal, Selim

AU - Peng, Luke C.

AU - Wu, Hsin Ta

AU - Raphael, Benjamin J.

N1 - Funding Information: We would like to thank Paul Medvedev for his assistance with running CNVer and Anna Ritz for helpful discussions regarding the manuscript. This work is supported by National Institutes of Health (R01 HG5690) and Burroughs Wellcome Career Award at the Scientific Interface to BJR. Some read alignments were obtained using the free version of Novoalign [48].

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Y1 - 2012/3/27

N2 - 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.

AB - 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.

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

Abstract

All Science Journal Classification (ASJC) codes

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