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SMS: Smart Model Selection in PhyML

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 ex...

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Detalles Bibliográficos
Autores principales: Lefort, Vincent, Longueville, Jean-Emmanuel, Gascuel, Olivier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5850602/
https://www.ncbi.nlm.nih.gov/pubmed/28472384
http://dx.doi.org/10.1093/molbev/msx149
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author Lefort, Vincent
Longueville, Jean-Emmanuel
Gascuel, Olivier
author_facet Lefort, Vincent
Longueville, Jean-Emmanuel
Gascuel, Olivier
author_sort Lefort, Vincent
collection PubMed
description 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/).
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spelling pubmed-58506022018-03-23 SMS: Smart Model Selection in PhyML Lefort, Vincent Longueville, Jean-Emmanuel Gascuel, Olivier Mol Biol Evol Resources 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/). Oxford University Press 2017-09 2017-05-11 /pmc/articles/PMC5850602/ /pubmed/28472384 http://dx.doi.org/10.1093/molbev/msx149 Text en © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Resources
Lefort, Vincent
Longueville, Jean-Emmanuel
Gascuel, Olivier
SMS: Smart Model Selection in PhyML
title SMS: Smart Model Selection in PhyML
title_full SMS: Smart Model Selection in PhyML
title_fullStr SMS: Smart Model Selection in PhyML
title_full_unstemmed SMS: Smart Model Selection in PhyML
title_short SMS: Smart Model Selection in PhyML
title_sort sms: smart model selection in phyml
topic Resources
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5850602/
https://www.ncbi.nlm.nih.gov/pubmed/28472384
http://dx.doi.org/10.1093/molbev/msx149
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