MutSignatures: an R package for extraction and analysis of cancer mutational signatures.

MutSignatures: an R package for extraction and analysis of cancer mutational signatures.
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mutsignatures:用于提取和分析癌症突变特征的R包装。

DOI:
10.1038/s41598-020-75062-0
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发表时间:
2020-10-26
期刊:
影响因子:
4.6
通讯作者:
Meeks JJ
Meeks JJ
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Fantini D;Vidimar V;Yu Y;Condello S;Meeks JJ

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由于DNA损伤、不准确的修复等机制,癌细胞积累了体细胞突变。不同的遗传不稳定过程导致DNA突变的特征非随机模式,也称为突变特征。我们开发了MutSigNatures,这是一个基于R的集成计算框架,旨在破译DNA突变签名。我们的软件提供了先进的功能,用于导入DNA变体,计算突变类型,并通过非负矩阵分解提取突变特征。具体地说,MutSigNatures接受多种类型的输入数据,与非人类基因组兼容,并支持非标准突变类型的分析,如四核苷酸突变类型。我们应用突变签名来分析在吸烟相关癌症数据集中发现的体细胞突变。我们描述了与之前在独立调查中报道的一致的突变特征。我们的工作表明,选定的突变特征与不同癌症类型的特定临床和分子特征相关,并揭示了以前尚未发现的特定突变模式的互补性。总而言之,我们建议将muSignatures作为一种强大的开源工具,用于检测癌症的分子决定因素,并收集对癌症生物学和治疗的见解。
Cancer cells accumulate somatic mutations as result of DNA damage, inaccurate repair and other mechanisms. Different genetic instability processes result in characteristic non-random patterns of DNA mutations, also known as mutational signatures. We developed mutSignatures, an integrated R-based computational framework aimed at deciphering DNA mutational signatures. Our software provides advanced functions for importing DNA variants, computing mutation types, and extracting mutational signatures via non-negative matrix factorization. Specifically, mutSignatures accepts multiple types of input data, is compatible with non-human genomes, and supports the analysis of non-standard mutation types, such as tetra-nucleotide mutation types. We applied mutSignatures to analyze somatic mutations found in smoking-related cancer datasets. We characterized mutational signatures that were consistent with those reported before in independent investigations. Our work demonstrates that selected mutational signatures correlated with specific clinical and molecular features across different cancer types, and revealed complementarity of specific mutational patterns that has not previously been identified. In conclusion, we propose mutSignatures as a powerful open-source tool for detecting the molecular determinants of cancer and gathering insights into cancer biology and treatment.
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