JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.
JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.
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DOI：
10.1093/bioinformatics/bts053
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发表时间：
2012-04-01
期刊：
影响因子：
--
通讯作者：
Shah SP
中科院分区：
文献类型：
--
作者：
Roth A;Ding J;Morin R;Crisan A;Ha G;Giuliany R;Bashashati A;Hirst M;Turashvili G;Oloumi A;Marra MA;Aparicio S;Shah SP
动机：在肿瘤基因组中识别体细胞单核苷酸变异（SNV）是定义癌症突变景观的必要步骤。用于全基因组确定体细胞突变的实验设计现在通常包括肿瘤DNA的下一代测序（NGS）和来自同一个体的匹配的体质DNA。这使研究人员能够控制生殖系多态性并区分肿瘤特有的体细胞突变，从而减少验证初始预测所需的劳动密集型和昂贵的下游实验的负担。为了充分利用这样的配对数据集，需要用于同时分析肿瘤-正常配对序列数据的计算工具，但目前在生物信息学文献中开发不足且代表性不足。结果如下：在这篇文章中，我们介绍了两种新的概率图形模型，称为JointSNVMix 1和JointSNVMix 2，用于联合分析NGS实验中配对的肿瘤正常数字等位基因计数数据。与肿瘤和正常数据的独立分析相反，我们的方法允许在样本中借用统计强度，因此放大了在统一的概率框架中识别和区分种系和体细胞事件的统计能力。可用性：本文中讨论的JointSNVMix模型和其他四个模型是JointSNVMix软件包的一部分，可在www.example.com下载http://compbio.bccrc.ca方式：sshah@bccrc.ca补充信息：补充数据可在生物信息学在线获得。
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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影响因子：
64.8
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影响因子：
64.8
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影响因子：
64.8
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影响因子：
64.8
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影响因子：
158.5
作者：
Shah, Sohrab P.;Koebel, Martin;Huntsman, David G.
通讯作者：
Huntsman, David G.