MuSiC: identifying mutational significance in cancer genomes.
MuSiC: identifying mutational significance in cancer genomes.
复制标题
DOI：
10.1101/gr.134635.111
复制
发表时间：
2012-08
期刊：
影响因子：
7
通讯作者：
Ding L
中科院分区：
文献类型：
--
作者：
Dees ND;Zhang Q;Kandoth C;Wendl MC;Schierding W;Koboldt DC;Mooney TB;Callaway MB;Dooling D;Mardis ER;Wilson RK;Ding L
大规模并行测序技术和相关的快速降低的测序成本使得能够在大量癌症病例中系统分析体细胞突变。在这里，我们介绍了一个全面的突变分析管道，使用标准化的基于序列的输入沿着与多种类型的临床数据，以建立突变位点之间的相关性，受影响的基因和途径，并最终从真正重要的事件中分离出通常丰富的乘客突变。换句话说，我们的目标是确定这些大型数据集的癌症突变意义（MuSiC）。MuSiC框架中分析操作的集成广泛适用于广泛的肿瘤类型，并提供自动化和标准化的好处。在此，我们描述了MuSiC管道的计算结构和统计基础，并使用来自TCGA卵巢癌项目的316个卵巢癌样本证明了其性能。MuSiC正确地证实了许多预期的结果，并确定了几个潜在的新发现途径。
Massively parallel sequencing technology and the associated rapidly decreasing sequencing costs have enabled systemic analyses of somatic mutations in large cohorts of cancer cases. Here we introduce a comprehensive mutational analysis pipeline that uses standardized sequence-based inputs along with multiple types of clinical data to establish correlations among mutation sites, affected genes and pathways, and to ultimately separate the commonly abundant passenger mutations from the truly significant events. In other words, we aim to determine the Mutational Significance in Cancer (MuSiC) for these large data sets. The integration of analytical operations in the MuSiC framework is widely applicable to a broad set of tumor types and offers the benefits of automation as well as standardization. Herein, we describe the computational structure and statistical underpinnings of the MuSiC pipeline and demonstrate its performance using 316 ovarian cancer samples from the TCGA ovarian cancer project. MuSiC correctly confirms many expected results, and identifies several potentially novel avenues for discovery.
登录
查看更多内容
影响因子：
7.3
作者：
Ahmed, Ashour Ahmed;Etemadmoghadam, Dariush;Temple, Jillian;Lynch, Andy G.;Riad, Mohamed;Sharma, Raghwa;Stewart, Colin;Fereday, Sian;Caldas, Carlos;DeFazio, Anna;Bowtell, David;Brenton, James D.
通讯作者：
Brenton, James D.
影响因子：
14.8
作者：
Kumar, Prateek;Henikoff, Steven;Ng, Pauline C.
通讯作者：
Ng, Pauline C.
影响因子：
14.9
作者：
Schultz, J;Copley, RR;Bork, P
通讯作者：
Bork, P
预测蛋白质突变的功能影响：在癌症基因组学中的应用。
影响因子：
14.9
作者：
Reva B;Antipin Y;Sander C
通讯作者：
Sander C
影响因子：
3.6
作者：
Carter, Hannah;Samayoa, Josue;Karchin, Rachel
通讯作者：
Karchin, Rachel