Accurate Ensemble Prediction of Somatic Mutations with SMuRF2.
Methods Mol Biol
; 2493: 53-66, 2022.
Article
em En
| MEDLINE
| ID: mdl-35751808
ABSTRACT
Accurate identification of somatic mutations is crucial for discovery and identification of driver mutations in cancer tumors. Here, we describe the updated Somatic Mutation calling method using a Random Forest (SMuRF2), an ensemble method that combines the predictions and auxiliary features from individual mutation callers using supervised machine learning. SMuRF2 provides an efficient workflow to predict both somatic point mutations (SNVs) and small insertions/deletions (indels) in cancer genomes and exomes. We describe the latest method and provide a detailed tutorial for running SMuRF2.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Sequenciamento de Nucleotídeos em Larga Escala
/
Neoplasias
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Methods Mol Biol
Assunto da revista:
BIOLOGIA MOLECULAR
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Singapura