The Mutational signature comprehensive analysis toolkit (musicatk) for the discovery, prediction, and exploration of mutational signatures
Files
Published version
Date
2021-12-01
Authors
Chevalier, Aaron
Yang, Shiyi
Khurshid, Zainab
Sahelijo, Nathan
Tong, Tong
Huggins, Jonathan H.
Yajima, Masanao
Campbell, Joshua David
Version
OA Version
Citation
A. Chevalier, S. Yang, Z. Khurshid, N. Sahelijo, T. Tong, J.H. Huggins, M. Yajima, J.D. Campbell. 2021. "The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the Discovery, Prediction, and Exploration of Mutational Signatures.." Cancer Res, Volume 81, Issue 23, pp. 5813 - 5817. https://doi.org/10.1158/0008-5472.CAN-21-0899
Abstract
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. SIGNIFICANCE: 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.
Description
License
©2021 The Authors; Published by the American Association for Cancer Research. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs International 4.0 License.