Accurate Ensemble Prediction of Somatic Mutations with SMuRF2.
Bioinformatics tools
Cancer genomics
Next-generation sequencing
Somatic mutation calling
Supervised machine-learning
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
25
6
2022
pubmed:
26
6
2022
medline:
29
6
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 35751808
doi: 10.1007/978-1-0716-2293-3_4
doi:
Substances chimiques
SMURF2 protein, human
EC 2.3.2.26
Ubiquitin-Protein Ligases
EC 2.3.2.27
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
53-66Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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