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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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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315723/
https://www.ncbi.nlm.nih.gov/pubmed/22285562
http://dx.doi.org/10.1093/bioinformatics/bts053
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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.
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
collection PubMed
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. 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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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. Bioinformatics 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. 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. 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.
spellingShingle Original Papers
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
title 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_short 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
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315723/
https://www.ncbi.nlm.nih.gov/pubmed/22285562
http://dx.doi.org/10.1093/bioinformatics/bts053
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