Advanced Phylogenetics: Methods and Applications in Evolutionary Biology

Phylogeny is a search for evolutionary clues. The goal of this module is to highlight the existence of gene phylogenies within species phylogenies, the methods for representing evolutionary histories as trees, and the concept of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the core of this course unit. Distance-based methods highlight the difficulties in distinguishing between homology and homoplasy, as well as the need to construct models of character evolution. The cladistic approach based on maximum parsimony illustrates, on the one hand, the use of bootstrapping to estimate the robustness of phylogenetic nodes and, on the other hand, the impact of taxonomic sampling on the detection of multiple substitutions.

Probabilistic approaches are introduced and then explored in greater depth. The long-branch attraction artifact leads to the introduction of probabilistic reasoning. The maximum likelihood method allows us to address the calculation of likelihood, the estimation of model parameters using optimality criteria, the construction of various models of character evolution, and the comparison of models. Bayesian inference, in turn, introduces the distinction between density-based and optimality-based approaches. It then covers the a priori use of probability densities, the estimation of posterior distributions of model parameters based on the data, their approximation using Markov chains with Monte Carlo and Metropolis coupling (MCMCMC), the initialization and convergence phases, and the calculation and interpretation of posterior probabilities for trees and clades. The importance of evolutionary models for DNA, RNA, and protein sequences and their refinement is emphasized.

"In addition to the description above, the course also covers the applications of phylogenetic inference methods: the analysis of matrix representations of source trees using the principle of parsimony to infer supertrees, trait conservatism, phylogenetic inertia, indices of phylogenetic diversity, the estimation of ancestral character states, the comparison of trees and evolutionary scenarios, and co-phylogenies with evaluation of hypotheses of coevolution and cospeciation."

These skills involve the practical application of bioinformatics to the concepts discussed above: data assembly (reads, sequences); sequence alignment; tree inference using distance-based methods, maximum parsimony, maximum likelihood, and Bayesian inference, including the detection of long-branch attraction; model parameter estimation; model comparison; and the use of multigene corroboration. The software programs students should be able to use are SeaView, NJPlot, PhyML, IQTree, PAUP, and MrBayes."

"Elective: EU course M1 S7 "Phylogeny and Evolution" (HAB708B).

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

Comprehensive ongoing assessment: 100%