Bioinformatic Methods to Identify Mutational Signatures in Cancer

S M Ashiqul Islam; Ludmil B Alexandrov

doi:10.1007/978-1-0716-0810-4_28

Bioinformatic Methods to Identify Mutational Signatures in Cancer

Methods Mol Biol. 2021:2185:447-473. doi: 10.1007/978-1-0716-0810-4_28.

Authors

S M Ashiqul Islam^{1

2}, Ludmil B Alexandrov^{3

4}

Affiliations

¹ Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA.
² Department of Bioengineering and Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA.
³ Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA. l2alexandrov@ucsd.edu.
⁴ Department of Bioengineering and Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA. l2alexandrov@ucsd.edu.

PMID: 33165866
DOI: 10.1007/978-1-0716-0810-4_28

Abstract

The genome of a cancer contains somatic mutations that reflect the activities of endogenous and exogenous mutational processes, with each mutational process imprinting a characteristic mutational signature. Computational analysis of somatic mutations derived from next-generation sequencing data allows revealing the mutational signatures operative in a set of cancer genomes. In this chapter, we briefly review the concept of mutational signatures and the tools available for deciphering mutational signatures. Further, we provide a quick guide as well as an in-depth protocol for deciphering mutational signatures using the tool SigProfilerExtractor and review the results generated from an example dataset of cancer genomes.

Keywords: Bioinformatics; Cancer; Mutational signatures; Mutations; SigProfilerExtractor.

MeSH terms

Computational Biology*
Databases, Nucleic Acid*
Genome, Human*
High-Throughput Nucleotide Sequencing*
Humans
Mutation*
Neoplasms / genetics*