Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2010 Mar 15;26(6):730-6. doi: 10.1093/bioinformatics/btq040. Epub 2010 Feb 3.

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

Author information

  • 1Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.

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 genome sequence, can be interpreted for the presence of SNVs. Although tools exist for SNV discovery from NGS data, none are specifically suited to work with data from tumors, where altered ploidy and tumor cellularity impact the statistical expectations of SNV discovery.

RESULTS:

We developed three implementations of a probabilistic Binomial mixture model, called SNVMix, designed to infer SNVs from NGS data from tumors to address this problem. The first models allelic counts as observations and infers SNVs and model parameters using an expectation maximization (EM) algorithm and is therefore capable of adjusting to deviation of allelic frequencies inherent in genomically unstable tumor genomes. The second models nucleotide and mapping qualities of the reads by probabilistically weighting the contribution of a read/nucleotide to the inference of a SNV based on the confidence we have in the base call and the read alignment. The third combines filtering out low-quality data in addition to probabilistic weighting of the qualities. We quantitatively evaluated these approaches on 16 ovarian cancer RNASeq datasets with matched genotyping arrays and a human breast cancer genome sequenced to >40x (haploid) coverage with ground truth data and show systematically that the SNVMix models outperform competing approaches.

AVAILABILITY:

Software and data are available at http://compbio.bccrc.ca

CONTACT:

sshah@bccrc.ca SUPPLEMANTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:
20130035
[PubMed - indexed for MEDLINE]
PMCID:
PMC2832826
Free PMC Article

Images from this publication.See all images (4)Free text

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Write to the Help Desk