Empirical Profile Mixture Models for Phylogenetic Reconstruction

Quang Le 1 Olivier Gascuel 2, * Nicolas Lartillot 2
* Auteur correspondant
2 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : MOTIVATION: Previous studies have shown that accounting for sitespecific amino acid replacement patterns using mixtures of stationary probability profiles offers a promising approach for improving the robustness of phylogenetic reconstructions in the presence of saturation. However, such profile mixture models were introduced only in a Bayesian context, and are not yet available in a Maximum Likelihood framework. In addition, these mixture models only perform well on large alignments, from which they can reliably learn the shapes of profiles, and their associated weights. RESULTS: In this work, we introduce an expectation-maximization algorithm for estimating amino-acid profile mixtures from alignment databases. We apply it, learning on the HSSP database, and observe that a set of 20 profiles is enough to provide a better statistical fit than currently available empirical matrices (WAG, JTT), in particular on saturated data. AVAILABILITY: We have implemented these models into two currently available Bayesian and Maximum Likelihood phylogenetic reconstruction programs. The two implementations, PhyloBayes, and PhyML, are freely available on our web site (http://atgc.lirmm.fr/cat). They run under Linux and MaxOSX operating systems. CONTACT: nicolas.lartillot@lirmm.fr
Type de document :
Article dans une revue
Bioinformatics, Oxford University Press (OUP), 2008, 29, pp.2317-2323. 〈http://atgc.lirmm.fr/cat/〉
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Contributeur : Olivier Gascuel <>
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Dernière modification le : jeudi 11 janvier 2018 - 06:26:12
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Quang Le, Olivier Gascuel, Nicolas Lartillot. Empirical Profile Mixture Models for Phylogenetic Reconstruction. Bioinformatics, Oxford University Press (OUP), 2008, 29, pp.2317-2323. 〈http://atgc.lirmm.fr/cat/〉. 〈lirmm-00324090〉



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