In-depth phylogeny: methods and application in evolution

Phylogeny is a quest for evolutionary clues. The aim of this module is to recall the existence of gene phylogenies in species phylogenies, the ways of representing 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 the knowledge of this course. The distance methods allow to underline the difficulties of separating homology and homoplasy, and the necessity to build models of character evolution. The cladistic approach with maximum parsimony allows to illustrate on the one hand the use of bootstrap to estimate the strength of the nodes of phylogenies, and on the other hand the impact of taxonomic sampling to detect multiple substitutions.

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

"In addition to the above description, the knowledge also concerns applications of phylogenetic inference methods: analysis of matrix representation with parsimony 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-evolutionary and co-speciation hypotheses.

Skills involve bioinformatics application of the above concepts: data assembly (reads, sequences); sequence alignment; tree inference using distance, maximum parsimony, maximum likelihood, and Bayesian inference methods, with detection of long branch attraction; estimation of model parameters; model comparison; and use of multigene corroboration. Software to be handled includes SeaView, NJPlot, PhyML, IQTree, PAUP, and MrBayes."

" Optional: follow the M1 S7 UE ""Phylogeny and Evolution"" (HAB708B).

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

Continuous assessment : 100%.