Dataset Information
SMuRF: portable and accurate ensemble prediction of somatic mutations.
Ontology highlight
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 and implementation
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.
SUBMITTER: Huang W
PROVIDER: S-EPMC6735703 | biostudies-literature | 2019 Sep
REPOSITORIES: biostudies-literature
Publications
SMuRF: portable and accurate ensemble prediction of somatic mutations.
Bioinformatics (Oxford, England) 20190901 17
<h4>Summary</h4>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 frequen ...[more]