MutationalPatterns: comprehensive genome-wide analysis of mutational processes.
MutationalPatterns: comprehensive genome-wide analysis of mutational processes.
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DOI：
10.1186/s13073-018-0539-0
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发表时间：
2018-04-25
期刊：
影响因子：
12.3
通讯作者：
Cuppen E
中科院分区：
文献类型：
--
作者：
Blokzijl F;Janssen R;van Boxtel R;Cuppen E
碱基替换目录代表细胞中活跃的突变过程的历史记录。这些过程可以通过各种特征来区分，如突变类型、序列背景、转录和复制链偏好、基因组分布和与（表位）基因组特征的关联。我们已经创建了MutationalPatterns，这是一个R/Bioconductor软件包，允许研究人员在碱基取代目录中描述广泛的模式，以剖析潜在的分子机制。此外，它提供了一种有效的方法来量化单个样品中已知突变特征的贡献。这种分析可用于确定某些DNA修复机制是否受到干扰，并进一步表征已知突变特征的过程。MutationalPatterns允许突变模式的简单表征和可视化。这些分析将支持对突变机制的基础研究，并可能最终改善癌症诊断和治疗策略。MutationalPatterns可在http://bioconductor.org/packages/MutationalPatterns上免费获得。本文的在线版本（10.1186/s13073-018-0539-0）包含补充材料，可供授权用户使用。
Base substitution catalogues represent historical records of mutational processes that have been active in a cell. Such processes can be distinguished by various characteristics, like mutation type, sequence context, transcriptional and replicative strand bias, genomic distribution and association with (epi)-genomic features. We have created MutationalPatterns, an R/Bioconductor package that allows researchers to characterize a broad range of patterns in base substitution catalogues to dissect the underlying molecular mechanisms. Furthermore, it offers an efficient method to quantify the contribution of known mutational signatures within single samples. This analysis can be used to determine whether certain DNA repair mechanisms are perturbed and to further characterize the processes underlying known mutational signatures. MutationalPatterns allows for easy characterization and visualization of mutational patterns. These analyses willsupport fundamental research into mutational mechanisms and may ultimately improve cancer diagnosis and treatment strategies. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns. The online version of this article (10.1186/s13073-018-0539-0) contains supplementary material, which is available to authorized users.
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影响因子：
7
作者：
Shinbrot E;Henninger EE;Weinhold N;Covington KR;Göksenin AY;Schultz N;Chao H;Doddapaneni H;Muzny DM;Gibbs RA;Sander C;Pursell ZF;Wheeler DA
通讯作者：
Wheeler DA
DOI：
10.1126/science.aao3130
发表时间：
2017-10-13
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Drost J;van Boxtel R;Blokzijl F;Mizutani T;Sasaki N;Sasselli V;de Ligt J;Behjati S;Grolleman JE;van Wezel T;Nik-Zainal S;Kuiper RP;Cuppen E;Clevers H
通讯作者：
Clevers H
影响因子：
64.5
作者：
Haradhvala NJ;Polak P;Stojanov P;Covington KR;Shinbrot E;Hess JM;Rheinbay E;Kim J;Maruvka YE;Braunstein LZ;Kamburov A;Hanawalt PC;Wheeler DA;Koren A;Lawrence MS;Getz G
通讯作者：
Getz G
影响因子：
13.6
作者：
Law EK;Sieuwerts AM;LaPara K;Leonard B;Starrett GJ;Molan AM;Temiz NA;Vogel RI;Meijer-van Gelder ME;Sweep FC;Span PN;Foekens JA;Martens JW;Yee D;Harris RS
通讯作者：
Harris RS
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J