multiSNV: a probabilistic approach for improving detection of somatic point mutations from multiple related tumour samples.
multiSNV: a probabilistic approach for improving detection of somatic point mutations from multiple related tumour samples.
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MultiSNV：一种改善从多个相关肿瘤样品的体细胞突变检测的概率方法。
DOI：
10.1093/nar/gkv135
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发表时间：
2015-05-19
影响因子：
14.9
通讯作者：
Tavaré S
中科院分区：
文献类型：
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作者：
Josephidou M;Lynch AG;Tavaré S
肿瘤样本及其匹配正常样本的体细胞变异分析已广泛用于癌症研究，以区分生殖系多态性与体细胞突变。然而，由于癌症的广泛的肿瘤内异质性，来自单个肿瘤样本的测序数据可能大大低估了整体突变景观。在最近的研究中，对来自同一患者的多个空间或时间上分离的肿瘤样本进行测序，以确定体细胞突变的区域分布并研究肿瘤内异质性。有许多工具可以从匹配的肿瘤正常下一代测序（NGS）数据中进行体细胞变异识别;然而，这些工具都不允许对多个相同患者样本进行联合分析。我们讨论了多样本体细胞变异调用的好处和挑战，并提出了multiSNV，一个软件包调用单核苷酸变异（SNV）使用NGS数据从多个相同的病人样本。multiSNV不是对单个肿瘤样本和匹配的正常样本进行多个成对分析，而是在贝叶斯框架下联合考虑所有可用样本，以提高调用共享SNV的灵敏度。通过利用来自所有可用样本的信息，multiSNV能够从全外显子组测序实验中检测到变异等位基因频率低至3%的罕见突变。
Somatic variant analysis of a tumour sample and its matched normal has been widely used in cancer research to distinguish germline polymorphisms from somatic mutations. However, due to the extensive intratumour heterogeneity of cancer, sequencing data from a single tumour sample may greatly underestimate the overall mutational landscape. In recent studies, multiple spatially or temporally separated tumour samples from the same patient were sequenced to identify the regional distribution of somatic mutations and study intratumour heterogeneity. There are a number of tools to perform somatic variant calling from matched tumour-normal next-generation sequencing (NGS) data; however none of these allow joint analysis of multiple same-patient samples. We discuss the benefits and challenges of multisample somatic variant calling and present multiSNV, a software package for calling single nucleotide variants (SNVs) using NGS data from multiple same-patient samples. Instead of performing multiple pairwise analyses of a single tumour sample and a matched normal, multiSNV jointly considers all available samples under a Bayesian framework to increase sensitivity of calling shared SNVs. By leveraging information from all available samples, multiSNV is able to detect rare mutations with variant allele frequencies down to 3% from whole-exome sequencing experiments.
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DOI：
10.1093/bioinformatics/bts053
发表时间：
2012-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
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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
通讯作者：
Shah SP
使用下一代 DNA 测序数据进行变异发现和基因分型的框架。
影响因子：
30.8
作者：
通讯作者：
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DOI：
10.1126/science.1253462
发表时间：
2014-10-10
期刊：
Science (New York, N.Y.)
影响因子：
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作者：
de Bruin EC;McGranahan N;Mitter R;Salm M;Wedge DC;Yates L;Jamal-Hanjani M;Shafi S;Murugaesu N;Rowan AJ;Grönroos E;Muhammad MA;Horswell S;Gerlinger M;Varela I;Jones D;Marshall J;Voet T;Van Loo P;Rassl DM;Rintoul RC;Janes SM;Lee SM;Forster M;Ahmad T;Lawrence D;Falzon M;Capitanio A;Harkins TT;Lee CC;Tom W;Teefe E;Chen SC;Begum S;Rabinowitz A;Phillimore B;Spencer-Dene B;Stamp G;Szallasi Z;Matthews N;Stewart A;Campbell P;Swanton C
通讯作者：
Swanton C
影响因子：
64.8
作者：
Shah, Sohrab P.;Morin, Ryan D.;Aparicio, Samuel
通讯作者：
Aparicio, Samuel
影响因子：
46.9
作者：
通讯作者：
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