Guindon Stéphane, Delsuc Frédéric, Dufayard Jean-François, Gascuel Olivier
Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), UMR 5506-CNRS, Université Montpellier II, Montpellier, France.
Methods Mol Biol. 2009;537:113-37. doi: 10.1007/978-1-59745-251-9_6.
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, 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_montpellier.fr/phyml/).
我们对生物序列的起源、功能和/或结构的理解在很大程度上取决于我们解读分子进化机制的能力。这些复杂的过程可以通过在系统发育框架内比较同源序列来描述。此外，系统发育推断提供了可靠的统计工具，以便从实际序列分析中展现分子进化的主要特征。本章重点关注基于最大似然（ML）原则的系统发育树估计。在此概率标准下推断出的系统发育通常是可靠的，并且重要的生物学假设可以通过比较不同模型来检验。估计最大似然系统发育在计算上要求很高，因此有必要仔细检查结果。本章重点介绍PhyML，这是一个实现了近期最大似然系统发育方法和算法的软件。我们通过分析一个真实数据集来说明该程序的优点和缺陷。PhyML v3.0可从（http://atgc_montpellier.fr/phyml/）获取。
