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

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The aim of this resolutely trans-disciplinary course is to provide the skills needed to effectively manage and exploit data of various origins and types, particularly those with a spatial component. The course is divided into three complementary sections. The first deals with the issues inherent in data compilation and the solutions provided by database management systems (DBMS): from database design to queries. The second covers geographic information systems (GIS): from cartographic representation to geoprocessing. Finally, the third axis presents the diversity of spatial analysis tools for quantitative exploitation of spatial data, from metrics to 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 within species phylogenies, the ways in which evolutionary histories can be represented in tree form, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to build models of character evolution. The maximum parsimony cladistic approach illustrates the use of bootstrapping to estimate the strength of phylogeny nodes, and the impact of taxonomic sampling in detecting multiple substitutions.

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

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