The genome of a cancer cell carries somatic mutations that are the cumulative consequence of the DNA damage and repair processes. Until now there have been no theoretical models describing the signatures of mutational processes operative in cancer genomes and no systematic computational approaches are available to decipher these mutational signatures. Here, we introduce a MATLAB based computational framework that effectively addresses these questions. Our approach provides a basis for characterizing mutational signatures from cancer-derived somatic mutational catalogues, paving the way to insights into the common pathogenetic mechanism underlying all cancers. Please see the included readme.pdf file and the supporting article for more information about the theoretical model and how to use the provided computational framework.
Minor updates to the documentation.
Minor bugs fixed.
Several plotting bugs were fixed. Minor updates to the documentation. A new version of SigProfilerSingleSample has been released.
Updated SigProfilerSingleSample for a novel set of signatures. Update example.
Added sparse assignments of the activity of mutational signatures in the tool SigProfilerSingleSample.
Added sparse assignments of the final activities of mutational signatures. Added another plotting function to one of the examples. Updated documentation and comments in the code.
Provided code and an example for performing hierarchical signatures analysis.
The framework now includes a tool for assigning known mutational signatures in individual samples. An example for running the tool on a set of bladder cancer exomes is provided.
Updated name, description, and documentation.
The framework was updated to improve its accuracy for extracting mutational signatures. Bugs to parallel execution were fixed. Completely new set of examples was provided. The format of output files was changed. Archive folder has been updated.
The code was updated to work with R2017b. The previous version was using commands from R2008b which were deprecated in recent MATLB releases. Several minor bugs have also been fixed.
Data has been separated in folders based on publications. Functions for plotting individual samples have been provided.
Several minor plotting bugs were fixed. Additionally, we have included extensive cancer genomics data allowing mutational signature analysis of multiple cancer types.
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