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SMS: Smart Model Selection in PhyML

Molecular Biology and Evolution · 2017 · Vol. 34(9) · pp. 2422–2424
Vincent LefortJean-Emmanuel LonguevilleOlivier Gascuel

Abstract

Model selection using likelihood-based criteria (e.g., AIC) is one of the first steps in phylogenetic analysis. One must select both a substitution matrix and a model for rates across sites. A simple method is to test all combinations and select the best one. We describe heuristics to avoid these extensive calculations. Runtime is divided by ∼2 with results remaining nearly the same, and the method performs well compared with ProtTest and jModelTest2. Our software, "Smart Model Selection" (SMS), is implemented in the PhyML environment and available using two interfaces: command-line (to be integrated in pipelines) and a web server (http://www.atgc-montpellier.fr/phyml-sms/).

Genomics and Phylogenetic StudiesBiomedical Text Mining and OntologiesGenetic diversity and population structureBiologyHeuristicsSelection (genetic algorithm)SoftwareModel selectionSimple (philosophy)Computer scienceSubstitution (logic)Data miningMachine learning

MeSH terms

AlgorithmsModels, GeneticPhylogenySoftwareLikelihood FunctionsComputational Biology

Funding

  • Institut Français de Bioinformatique
Citations
1,956
FWCI
80.26
field-weighted impact
References
8
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References
BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data
Molecular Biology and Evolution · 1997 · 1,809 citations
MODELTEST: testing the model of DNA substitution.
Bioinformatics · 1998 · 20,063 citations
An Improved General Amino Acid Replacement Matrix
Molecular Biology and Evolution · 2008 · 3,248 citations
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