JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data

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...

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Main 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.
Format:	Online
Language:	English
Published:	Oxford University Press 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315723/

id	pubmed-3315723
recordtype	oai_dc
spelling	pubmed-33157232012-03-30 JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data 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. Original Papers 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. Oxford University Press 2012-04-01 2012-01-27 /pmc/articles/PMC3315723/ /pubmed/22285562 http://dx.doi.org/10.1093/bioinformatics/bts053 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	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.
spellingShingle	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. JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data
author_facet	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.
author_sort	Roth, Andrew
title	JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data
title_short	JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data
title_full	JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data
title_fullStr	JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data
title_full_unstemmed	JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data
title_sort	jointsnvmix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data
description	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.
publisher	Oxford University Press
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315723/
_version_	1611517785322553344

JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data

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