signeR

SigneR is a software tool developed to enhance the understanding of cancer origins by analyzing mutational signatures from high-throughput sequencing data of cancer genomes. Traditional methods using non-negative matrix factorization (NMF) are sensitive to initial conditions and struggle with determining the actual number of signatures that best represent the data.

SigneR addresses these challenges by introducing an empirical Bayesian approach to NMF, allowing for more robust and accurate identification of mutational signatures with minimal user intervention. It directly tackles the model selection problem to determine the optimal number of signatures and introduces clinically relevant concepts for evaluating mutational profiles.

Topic

Genetic variation;Statistics and probability;Data visualisation

Detail

  • Operation: Genetic variation analysis

  • Software interface: Command-line user interface,Library

  • Language: R

  • License: The GNU General Public License v3.0

  • Cost: Free

  • Version name: 2.4.0

  • Credit: Fundação de Apoio a Pesquisa do Estado de São Paulo (FAPESP), Associacão Beneficente Alzira Denise Hertzog Silva (ABADHS), FAPESP.

  • Input: -

  • Output: -

  • Contact: Renan Valieris renan.valieris@accamargo.org.br

  • Collection: -

  • Maturity: Stable

Publications

  • signeR: an empirical Bayesian approach to mutational signature discovery.
  • Rosales RA, et al. signeR: an empirical Bayesian approach to mutational signature discovery. signeR: an empirical Bayesian approach to mutational signature discovery. 2017; 33:8-16. doi: 10.1093/bioinformatics/btw572
  • https://doi.org/10.1093/bioinformatics/btw572
  • PMID: 27591080
  • PMC: -

Download and documentation


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