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 journalArticlepeer-review

818 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 languageEnglish
Pages (from-to)2283-2285
Number of pages3
JournalBioinformatics
Volume25
Issue number17
DOIs
StatePublished - 2009

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