title: JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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
  
  
  BACKGROUND
  sshah@bccrc.ca
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Molecular Oncology, BC Cancer Agency, BC, Canada.
authors.forename: Andrew
authors.initials: A
authors.lastname: Roth
authors.forename: Jiarui
authors.initials: J
authors.lastname: Ding
authors.forename: Ryan
authors.initials: R
authors.lastname: Morin
authors.forename: Anamaria
authors.initials: A
authors.lastname: Crisan
authors.forename: Gavin
authors.initials: G
authors.lastname: Ha
authors.forename: Ryan
authors.initials: R
authors.lastname: Giuliany
authors.forename: Ali
authors.initials: A
authors.lastname: Bashashati
authors.forename: Martin
authors.initials: M
authors.lastname: Hirst
authors.forename: Gulisa
authors.initials: G
authors.lastname: Turashvili
authors.forename: Arusha
authors.initials: A
authors.lastname: Oloumi
authors.forename: Marco A
authors.initials: MA
authors.lastname: Marra
authors.forename: Samuel
authors.initials: S
authors.lastname: Aparicio
authors.forename: Sohrab P
authors.initials: SP
authors.lastname: Shah
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004252:DNA Mutational Analysis / Q000379:methods*; D004273:DNA, Neoplasm / Q000235:genetics*; D006801:Humans; D015233:Models, Statistical; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*; D012984:Software
chemical_list: D004273:DNA, Neoplasm