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      SMS: Smart Model Selection in PhyML

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          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/).

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          ProtTest: selection of best-fit models of protein evolution.

          F. Abascal, R Zardoya, D Posada (2005)
          Using an appropriate model of amino acid replacement is very important for the study of protein evolution and phylogenetic inference. We have built a tool for the selection of the best-fit model of evolution, among a set of candidate models, for a given protein sequence alignment. ProtTest is available under the GNU license from http://darwin.uvigo.es
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            BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data.

            O. Gascuel (1997)
            We propose an improved version of the neighbor-joining (NJ) algorithm of Saitou and Nei. This new algorithm, BIONJ, follows the same agglomerative scheme as NJ, which consists of iteratively picking a pair of taxa, creating a new mode which represents the cluster of these taxa, and reducing the distance matrix by replacing both taxa by this node. Moreover, BIONJ uses a simple first-order model of the variances and covariances of evolutionary distance estimates. This model is well adapted when these estimates are obtained from aligned sequences. At each step it permits the selection, from the class of admissible reductions, of the reduction which minimizes the variance of the new distance matrix. In this way, we obtain better estimates to choose the pair of taxa to be agglomerated during the next steps. Moreover, in comparison with NJ's estimates, these estimates become better and better as the algorithm proceeds. BIONJ retains the good properties of NJ--especially its low run time. Computer simulations have been performed with 12-taxon model trees to determine BIONJ's efficiency. When the substitution rates are low (maximum pairwise divergence approximately 0.1 substitutions per site) or when they are constant among lineages, BIONJ is only slightly better than NJ. When the substitution rates are higher and vary among lineages,BIONJ clearly has better topological accuracy. In the latter case, for the model trees and the conditions of evolution tested, the topological error reduction is on the average around 20%. With highly-varying-rate trees and with high substitution rates (maximum pairwise divergence approximately 1.0 substitutions per site), the error reduction may even rise above 50%, while the probability of finding the correct tree may be augmented by as much as 15%.
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              jModelTest 2: More models, new heuristics and parallel computing

              D Darriba, GL Taboada, R Doallo … (2012)
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                Author and article information

                Journal
                Journal ID (nlm-ta): Mol Biol Evol
                Journal ID (iso-abbrev): Mol. Biol. Evol
                Journal ID (publisher-id): molbev
                Title: Molecular Biology and Evolution
                Publisher: Oxford University Press
                ISSN (Print): 0737-4038
                ISSN (Electronic): 1537-1719
                Publication date (Print): September 2017
                Publication date (Electronic): 11 May 2017
                Publication date PMC-release: 11 May 2017
                Volume: 34
                Issue: 9
                Pages: 2422-2424
                Affiliations
                [1 ]Institut de Biologie Computationnelle, LIRMM, UMR 5506 - CNRS et Université de Montpellier, Montpellier, France
                [2 ]Unité de Bioinformatique Evolutive, C3BI, USR 3756 - Institut Pasteur et CNRS, Paris, France
                Author notes
                [* ] Corresponding author: E-mail: olivier.gascuel@ 123456pasteur.fr .

                Associate editor: Tal Pupko

                Article
                Publisher ID: msx149
                DOI: 10.1093/molbev/msx149
                PMC ID: 5850602
                PubMed ID: 28472384
                SO-VID: 8a23c4c0-4d22-4536-9d36-e051b91159ec
                Copyright © © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
                License:

                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

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                Page count
                Pages: 3
                Categories
                Subject: Resources

                ScienceOpen disciplines: Molecular biology
                Keywords: model selection,heuristic procedure,aic and bic criteria,web server,phyml
                Data availability:
                ScienceOpen disciplines: Molecular biology
                Keywords: model selection, heuristic procedure, aic and bic criteria, web server, phyml

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