SMuRF: Portable and accurate ensemble prediction of somatic mutations.

Bioinformatics. 2019 Jan 12;:

Authors: Huang W, Guo YA, Muthukumar K, Baruah P, Chang MM, Skanderup AJ

Abstract
Summary: 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.
Availability: 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 information: Supplementary data are available at Bioinformatics online.

PMID: 30649191 [PubMed – as supplied by publisher]

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