JointSNVMix: a Probabilistic Model for Accurate Detection of Somatic Mutations in Normal/tumour Paired Next-generation Sequencing Data

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2012 Jan 31

PMID 22285562

Citations 98

Authors

Andrew Roth

Jiarui Ding

Ryan Morin

Anamaria Crisan

Gavin Ha

Ryan Giuliany

Ali Bashashati

Martin Hirst

Gulisa Turashvili

Arusha Oloumi

Marco A Marra

Samuel Aparicio

Sohrab P Shah

Affiliations

Soon will be listed here.

Abstract

Motivation: Identification of somatic single nucleotide variants (SNVs) in tumour genomes is a necessary step in defining the mutational landscapes of cancers. Experimental designs for genome-wide ascertainment of somatic mutations now routinely include next-generation sequencing (NGS) of tumour DNA and matched constitutional DNA from the same individual. This allows investigators to control for germline polymorphisms and distinguish somatic mutations that are unique to the tumour, thus reducing the burden of labour-intensive and expensive downstream experiments needed to verify initial predictions. In order to make full use of such paired datasets, computational tools for simultaneous analysis of tumour-normal paired sequence data are required, but are currently under-developed and under-represented in the bioinformatics literature.

Results: In this contribution, we introduce two novel probabilistic graphical models called JointSNVMix1 and JointSNVMix2 for jointly analysing paired tumour-normal digital allelic count data from NGS experiments. In contrast to independent analysis of the tumour and normal data, our method allows statistical strength to be borrowed across the samples and therefore amplifies the statistical power to identify and distinguish both germline and somatic events in a unified probabilistic framework.

Availability: The JointSNVMix models and four other models discussed in the article are part of the JointSNVMix software package available for download at http://compbio.bccrc.ca

Contact: sshah@bccrc.ca

Supplementary Information: Supplementary data are available at Bioinformatics online.

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