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- Title
JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.
- Authors
Roth, Andrew; Ding, Jiarui; Morin, Ryan; Crisan, Anamaria; Ha, Gavin; Giuliany, Ryan; Bashashati, Ali; Hirst, Martin; Turashvili, Gulisa; Oloumi, Arusha; Marra, Marco A; Aparicio, Samuel; Shah, Sohrab P
- Abstract
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.
- Publication
Bioinformatics (Oxford, England), 2012, Vol 28, Issue 7, p907
- ISSN
1367-4811
- Publication type
Journal Article
- DOI
10.1093/bioinformatics/bts053