VarScan: Variant detection in massively parallel sequencing of individual and pooled samples

Daniel C. Koboldt; Ken Chen; Todd Wylie; David E. Larson; Michael D. McLellan; Elaine R. Mardis; George M. Weinstock; Richard K. Wilson; Li Ding

doi:10.1093/bioinformatics/btp373

VarScan: Variant detection in massively parallel sequencing of individual and pooled samples

Daniel C. Koboldt, Ken Chen, Todd Wylie, David E. Larson, Michael D. McLellan, Elaine R. Mardis, George M. Weinstock, Richard K. Wilson, Li Ding

Research output: Contribution to journal › Article › peer-review

962 Scopus citations

Abstract

Massively parallel sequencing technologies hold incredible promise for the study of DNA sequence variation, particularly the identification of variants affecting human disease. The unprecedented throughput and relatively short read lengths of Roche/454, Illumina/Solexa, and other platforms have spurred development of a new generation of sequence alignment algorithms. Yet detection of sequence variants based on short read alignments remains challenging, and most currently available tools are limited to a single platform or aligner type. We present VarScan, an open source tool for variant detection that is compatible with several short read aligners. We demonstrate VarScan's ability to detect SNPs and indels with high sensitivity and specificity, in both Roche/454 sequencing of individuals and deep Illumina/Solexa sequencing of pooled samples.

Original language	English (US)
Pages (from-to)	2283-2285
Number of pages	3
Journal	Bioinformatics
Volume	25
Issue number	17
DOIs	https://doi.org/10.1093/bioinformatics/btp373
State	Published - 2009
Externally published	Yes

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/btp373

Cite this

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title = "VarScan: Variant detection in massively parallel sequencing of individual and pooled samples",

abstract = "Massively parallel sequencing technologies hold incredible promise for the study of DNA sequence variation, particularly the identification of variants affecting human disease. The unprecedented throughput and relatively short read lengths of Roche/454, Illumina/Solexa, and other platforms have spurred development of a new generation of sequence alignment algorithms. Yet detection of sequence variants based on short read alignments remains challenging, and most currently available tools are limited to a single platform or aligner type. We present VarScan, an open source tool for variant detection that is compatible with several short read aligners. We demonstrate VarScan's ability to detect SNPs and indels with high sensitivity and specificity, in both Roche/454 sequencing of individuals and deep Illumina/Solexa sequencing of pooled samples.",

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AU - Koboldt, Daniel C.

AU - Chen, Ken

AU - Wylie, Todd

AU - Larson, David E.

AU - McLellan, Michael D.

AU - Mardis, Elaine R.

AU - Weinstock, George M.

AU - Wilson, Richard K.

AU - Ding, Li

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AB - Massively parallel sequencing technologies hold incredible promise for the study of DNA sequence variation, particularly the identification of variants affecting human disease. The unprecedented throughput and relatively short read lengths of Roche/454, Illumina/Solexa, and other platforms have spurred development of a new generation of sequence alignment algorithms. Yet detection of sequence variants based on short read alignments remains challenging, and most currently available tools are limited to a single platform or aligner type. We present VarScan, an open source tool for variant detection that is compatible with several short read aligners. We demonstrate VarScan's ability to detect SNPs and indels with high sensitivity and specificity, in both Roche/454 sequencing of individuals and deep Illumina/Solexa sequencing of pooled samples.

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VarScan: Variant detection in massively parallel sequencing of individual and pooled samples

Abstract

ASJC Scopus subject areas

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