title: Empirical profile mixture models for phylogenetic reconstruction.
abstract: BACKGROUND
  Previous studies have shown that accounting for site-specific 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 (ML) 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.
authors: Méthodes et Algorithmes pour la Bioinformatique, LIRMM, CNRS-UM2, Montpellier Cedex 5, France.
authors: Le Si
authors: le S
authors: Quang
authors: Olivier
authors: O
authors: Gascuel
authors: Nicolas
authors: N
authors: Lartillot
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019943:Amino Acid Substitution; D000818:Animals; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D016013:Likelihood Functions; D010802:Phylogeny; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein