SMuRFv2

Name of software	SMuRFv2
Purpose	Somatic mutation calling using a RandomForest machine learning approach
Name of Contact	Huang Weitai & Anders Skanderup
Email of technical contact	huangwt@gis.a-star.edu.sg skanderupamj@gis.a-star.edu.sg
Summary of software function	Somatic mutation calling using a RandomForest machine learning approach SMuRFv2 is freely available for academic use. Commercial usage requires a license. https://github.com/skandlab/SMuRF
Publications describing software & its application	Huang et al. (2022) Accurate ensemble prediction of somatic mutations with SMuRF2. Methods in Molecular Biology: Variant Calling.https://pubmed.ncbi.nlm.nih.gov/35751808/ Huang et al. (2020) Ensemble-Based Somatic Mutation Calling in Cancer Genomes. Methods in Molecular Biology: Bioinformatics for Cancer Immunotherapy. https://pubmed.ncbi.nlm.nih.gov/32124310/ Huang et al. (2019) SMuRF: Portable and accurate ensemble prediction of somatic mutations. Bioinformatics. https://pubmed.ncbi.nlm.nih.gov/30649191/

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