The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the Discovery, Prediction, and Exploration of Mutational Signatures.
The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the Discovery, Prediction, and Exploration of Mutational Signatures.
复制标题
DOI：
10.1158/0008-5472.can-21-0899
复制
发表时间：
2021-12-01
期刊：
影响因子：
11.2
通讯作者：
Campbell JD
中科院分区：
文献类型：
--
作者：
Chevalier A;Yang S;Khurshid Z;Sahelijo N;Tong T;Huggins JH;Yajima M;Campbell JD
musicatk软件包使研究人员能够通过一套全面的预处理工具、发现和预测工具以及用于下游分析和可视化的多种功能来表征突变特征和肿瘤异质性。突变特征是由致癌暴露或异常细胞过程引起的基因组中体细胞改变的模式。为了提供一个全面的工作流程，用于突变签名的预处理，分析和可视化，我们创建了Mutational Signature Comprehensive Analysis Toolkit（musicatk）包。musicatk使用户能够选择不同的模式来计数突变类型，并且能够容易地联合收割机组合来自不同模式的计数表。多种不同的方法可用于解卷积签名和曝光或预测给定预先存在的签名集的单个样本中的曝光。其他探索性功能包括将特征与癌症体细胞突变目录（COSMIC）数据库进行比较的能力，使用均匀流形近似和投影将肿瘤嵌入二维，基于暴露频率将肿瘤聚类为亚组，识别肿瘤亚组之间的差异活性暴露，以及绘制用户定义的注释（如肿瘤类型）的暴露分布。总的来说，musicatk将使用户能够获得对癌症队列中观察到的突变特征模式的新见解。musicatk软件包使研究人员能够通过一套全面的预处理工具、发现和预测工具以及用于下游分析和可视化的多种功能来表征突变特征和肿瘤异质性。
The musicatk package empowers researchers to characterize mutational signatures and tumor heterogeneity with a comprehensive set of preprocessing utilities, discovery and prediction tools, and multiple functions for downstream analysis and visualization. Mutational signatures are patterns of somatic alterations in the genome caused by carcinogenic exposures or aberrant cellular processes. To provide a comprehensive workflow for preprocessing, analysis, and visualization of mutational signatures, we created the Mutational Signature Comprehensive Analysis Toolkit (musicatk) package. musicatk enables users to select different schemas for counting mutation types and to easily combine count tables from different schemas. Multiple distinct methods are available to deconvolute signatures and exposures or to predict exposures in individual samples given a pre-existing set of signatures. Additional exploratory features include the ability to compare signatures to the Catalogue Of Somatic Mutations In Cancer (COSMIC) database, embed tumors in two dimensions with uniform manifold approximation and projection, cluster tumors into subgroups based on exposure frequencies, identify differentially active exposures between tumor subgroups, and plot exposure distributions across user-defined annotations such as tumor type. Overall, musicatk will enable users to gain novel insights into the patterns of mutational signatures observed in cancer cohorts. The musicatk package empowers researchers to characterize mutational signatures and tumor heterogeneity with a comprehensive set of preprocessing utilities, discovery and prediction tools, and multiple functions for downstream analysis and visualization.