Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment.
Bioinformatics
; 39(12)2023 12 01.
Article
en En
| MEDLINE
| ID: mdl-38096571
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/.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias
Límite:
Humans
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos