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      PHYML Online—a web server for fast maximum likelihood-based phylogenetic inference

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          Abstract

          PHYML Online is a web interface to PHYML, a software that implements a fast and accurate heuristic for estimating maximum likelihood phylogenies from DNA and protein sequences. This tool provides the user with a number of options, e.g. nonparametric bootstrap and estimation of various evolutionary parameters, in order to perform comprehensive phylogenetic analyses on large datasets in reasonable computing time. The server and its documentation are available at http://atgc.lirmm.fr/phyml.

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          MRBAYES: Bayesian inference of phylogenetic trees.

          J P Huelsenbeck, F Ronquist (2001)
          The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo. MRBAYES, including the source code, documentation, sample data files, and an executable, is available at http://brahms.biology.rochester.edu/software.html.
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            ATV: display and manipulation of annotated phylogenetic trees.

            C. M. Zmasek, S. Eddy (2001)
            A Tree Viewer (ATV) is a Java tool for the display and manipulation of annotated phylogenetic trees. It can be utilized both as a standalone application and as an applet in a web browser.
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              Different versions of the Dayhoff rate matrix.

              Nick Goldman, Carolin Kosiol (2005)
              Many phylogenetic inference methods are based on Markov models of sequence evolution. These are usually expressed in terms of a matrix (Q) of instantaneous rates of change but some models of amino acid replacement, most notably the PAM model of Dayhoff and colleagues, were originally published only in terms of time-dependent probability matrices (P(t)). Previously published methods for deriving Q have used eigen-decomposition of an approximation to P(t). We show that the commonly used value of t is too large to ensure convergence of the estimates of elements of Q. We describe two simpler alternative methods for deriving Q from information such as that published by Dayhoff and colleagues. Neither of these methods requires approximation or eigen-decomposition. We identify the methods used to derive various different versions of the Dayhoff model in current software, perform a comparison of existing and new implementations, and, to facilitate agreement among scientists using supposedly identical models, recommend that one of the new methods be used as a standard.
              Article 0 comments Cited 59 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (publisher-id): Nucleic Acids Research
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: 01 July 2005
                Publication date (Print): 01 July 2005
                Publication date (Electronic): 27 June 2005
                Volume: 33
                Issue: Web Server issue
                Pages: W557-W559
                Affiliations
                Bioinformatics Institute and Allan Wilson Centre, University of Auckland Private Bag 92019, Auckland, New Zealand
                1Projet Méthodes et Algorithmes pour la Bioinformatique, LIRMM-CNRS 161 Rue Ada, 34392-Montpellier Cedex 5, France
                Author notes
                *To whom correspondence should be addressed. Tel: +33 4 67 41 85 47; Fax: +33 4 67 41 85 00; Email: gascuel@ 123456lirmm.fr
                Article
                DOI: 10.1093/nar/gki352
                PMC ID: 1160113
                PubMed ID: 15980534
                SO-VID: 12002493-376a-457b-bfe3-28337d1f1b39
                Copyright © © The Author 2005. Published by Oxford University Press. All rights reserved
                License:

                The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@ 123456oupjournals.org

                History
                Date received : 08 December 2004
                Date accepted : 20 December 2004
                Categories
                Subject: Article

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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