Assigning Mutational Signatures to Individual Samples and Individual Somatic Mutations with SigProfilerAssignment

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2023 Dec 14

PMID 38096571

Authors

Marcos Diaz-Gay

Raviteja Vangara

Mark Barnes

Xi Wang

S M Ashiqul Islam

Ian Vermes

Stephen Duke

Nithish Bharadhwaj Narasimman

Ting Yang

Zichen Jiang

Sarah Moody

Sergey Senkin

Paul Brennan

Michael R Stratton

Ludmil B Alexandrov

Affiliations

Soon will be listed here.

Abstract

Motivation: Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. To evaluate the mutational signatures operative in a cancer genome, one first needs to quantify their activities by estimating the number of mutations imprinted by each signature.

Results: Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures.

Availability And Implementation: SigProfilerAssignment is available under the BSD 2-clause license at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/.

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