Estimating Maximum Likelihood Phylogenies with PhyML

Abstract

Our understanding of the origins, the functions and/or the structures of biological sequences strongly depends on our ability to decipher the mechanisms of molecular evolution. These complex processes can be described through the comparison of homologous sequences in a phylogenetic framework. Moreover, phylogenetic inference provides sound statistical tools to exhibit the main features of molecular evolution from the analysis of actual sequences. This chapter focuses on phylogenetic tree estimation under the maximum likelihood (ML) principle. Phylogenies inferred under this probabilistic criterion are usually reliable and important biological hypotheses can be tested through the comparison of different models. Estimating ML phylogenies is computationally demanding though and careful examination of the results is warranted. This chapter focuses on PhyML, a software that implements recent ML phylogenetic methods and algorithms. We illustrate the strengths and pitfalls of this program through the analysis of a real data set. PhyML v3.0 is available from http://atgc.lirmm.fr/phyml 2 1 Introduction.

The Role of Palmitoylation for Protein Recruitment to the Inner Membrane Complex of the Malaria Parasite

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Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

“…All ML analyses were conducted in PhyMLv3.0. 24 Trees were inferred for datasets (1) and (2) in two ways: first, by only using one variant from each individual/ time point (46 sequences from 23 pairs), and second, by using all of the NGS variants (2,199 sequences from 23 pairs). Trees were inferred for dataset (3) with 1,022 Sanger sequences from 1,022 individuals.…”

Evolutionary history of the mammalian synaptonemal complex

“…Each site of the alignment is considered as independent event and assumed to evolve uniformly during this procedure. ML trees were inferred with the ML option implemented in the Seaview program or with PhyML version 3.0.1 (Guindon et al 2009) with the LG model , option NNI+SPR and a gamma distribution to take into account heterogeneous evolutionary rates of different sites with four categories of sites and an estimated alpha parameter. The robustness of the resulting ML tree was assessed with the bootstrap procedure.…”