SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors

• DOI: 10.1093/bioinformatics/btq040
• Publication Date: 2010-02-04T01:55:22Z
• Authors: Rodrigo Goya; Mark G.F. Sun; Ryan D. Morin; Gillian Leung; Gavin Ha; Kimberley C. Wiegand; Janine Senz; Anamaria Crisan; Marco A. Marra; Martin Hirst; David Huntsman; Kevin P. Murphy; Sam Aparicio; Sohrab P. Shah
• Keywords: 0303 health sciences; Base Sequence; Genome, Human; Gene Expression Profiling; Molecular Sequence Data; Genetic Variation; Sequence Analysis, DNA; Original Papers; 3. Good health; 03 medical and health sciences; Neoplasms; Databases, Genetic; Humans; Sequence Alignment; Algorithms; Software

Abstract Motivation: Next-generation sequencing (NGS) has enabled whole genome and transcriptome single nucleotide variant (SNV) discovery in cancer. NGS produces millions of short sequence reads that, once aligned to a reference sequence, can be interpreted for the presence SNVs. Although tools exist SNV from data, none are specifically suited work with data tumors, where altered ploidy tumor cellularity impact statistical expectations discovery. Results: We developed three implementations probabilistic Binomial mixture model, called SNVMix, designed infer SNVs tumors address this problem. The first models allelic counts as observations infers model parameters using an expectation maximization (EM) algorithm is therefore capable adjusting deviation frequencies inherent genomically unstable genomes. second mapping qualities by probabilistically weighting contribution read/nucleotide inference based on confidence we have base call read alignment. third combines filtering out low-quality addition qualities. quantitatively evaluated these approaches 16 ovarian cancer RNASeq datasets matched genotyping arrays human breast sequenced >40× (haploid) coverage ground truth show systematically that SNVMix outperform competing approaches. Availability: Software available at http://compbio.bccrc.ca Contact: [e-mail] Supplemantary information: Supplementary Bioinformatics online.