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

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 maximizatio

SUBMITTER: Goya R 

PROVIDER: S-EPMC2832826 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature