Advanced phylogenetics: methods and applications in evolution

Phylogeny is a quest for evolutionary clues. The aim of this module is to highlight the existence of gene phylogenies within species phylogenies, the methods used to represent evolutionary histories in the form of trees, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course unit. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to construct models of character evolution. The cladistic approach with maximum parsimony illustrates, on the one hand, the use of bootstrapping to estimate the robustness of phylogeny nodes and, on the other hand, the impact of taxonomic sampling on the detection of multiple substitutions.

Probabilistic approaches are presented and explored in depth. The artifact of attraction to long branches leads to the introduction of probabilistic reasoning. The maximum likelihood method allows us to address likelihood calculation, model parameter estimation by optimality, the construction of different character evolution models, and model comparison. Bayesian inference introduces the distinction between density-based and optimality-based approaches. It then shows the a priori use of probability densities, the estimation of the posterior distributions of model parameters based on the data, their approximation by Markov chains with Monte Carlo techniques and Metropolis coupling (MCMCMC), the ignition and convergence phases, and the calculation and interpretation of the posterior probabilities of trees and clades. The importance of DNA, RNA, and protein sequence evolution models and their improvement is emphasized.

"In addition to the above description, the knowledge also concerns the applications of phylogenetic inference methods: the analysis of the parsimonious matrix representation of source trees to infer supertrees, trait conservatism, phylogenetic inertia, phylogenetic diversity indices, estimation of ancestral character states, comparison of trees and evolutionary scenarios, and co-phylogenies with evaluation of co-evolution and co-speciation hypotheses.

The skills involved require the bioinformatic application of the above concepts: data assembly (readings, sequences); sequence alignment; tree inference using distance methods, maximum parsimony, maximum likelihood, and Bayesian inference, with detection of long branch attraction; model parameter estimation; model comparison; and use of multigene corroboration. The software to be familiar with is SeaView, NJPlot, PhyML, IQTree, PAUP, and MrBayes.

Optional: EU follow-up to M1 S7 "Phylogeny and Evolution" (HAB708B).

Recommended: Sequence alignment; tree reading and reconstruction: distances, cladistics and parsimony, probabilistic approaches (likelihood).

Continuous assessment: 100%