M2 - Integrative Study of Parasitic and Infectious Emergencies EI-EPI

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The objective of this resolutely transdisciplinary course is to provide skills useful for an effective management and a relevant exploitation of data of various origins and nature, and in particular with spatial component. The UE is composed of three successive complementary axes. The first one deals with the issues inherent to data compilation and the solutions provided by database management systems (DBMS): from database design to queries. The second deals with geographic information systems (GIS): from cartographic representation to geoprocessing. Finally, the third axis presents the diversity of spatial analysis tools that allow the quantitative exploitation of spatial data, whether it be metrics or statistical tests.

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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.

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