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
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

3157-3159

Informations de copyright

© The Author(s) 2019. Published by Oxford University Press.

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Auteurs

Weitai Huang (W)

Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.
Graduate School of Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore.

Yu Amanda Guo (YA)

Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.

Karthik Muthukumar (K)

Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.

Probhonjon Baruah (P)

Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.

Mei Mei Chang (MM)

Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.

Anders Jacobsen Skanderup (A)

Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.

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Classifications MeSH