SMuRF: portable and accurate ensemble prediction of somatic mutations.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 09 2019
01 09 2019
Historique:
received:
01
08
2018
revised:
26
11
2018
accepted:
07
01
2019
pubmed:
17
1
2019
medline:
18
6
2020
entrez:
17
1
2019
Statut:
ppublish
Résumé
Somatic Mutation calling method using a Random Forest (SMuRF) integrates predictions and auxiliary features from multiple somatic mutation callers using a supervised machine learning approach. SMuRF is trained on community-curated matched tumor and normal whole genome sequencing data. SMuRF predicts both SNVs and indels with high accuracy in genome or exome-level sequencing data. Furthermore, the method is robust across multiple tested cancer types and predicts low allele frequency variants with high accuracy. In contrast to existing ensemble-based somatic mutation calling approaches, SMuRF works out-of-the-box and is orders of magnitudes faster. The method is implemented in R and available at https://github.com/skandlab/SMuRF. SMuRF operates as an add-on to the community-developed bcbio-nextgen somatic variant calling pipeline. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 30649191
pii: 5288515
doi: 10.1093/bioinformatics/btz018
pmc: PMC6735703
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3157-3159Informations de copyright
© The Author(s) 2019. Published by Oxford University Press.
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