Showing results 1621 to 1640 on 1829 in total
Influence des symbiotes secondaires sur l'utilisation et la sélection de la plante hôte chez l'aleurode du tabac, Bemisia tabaci
Interactions entre dispersion et personnalité, et conséquences sur le succès reproducteur. Approches empiriques dans une population fragmentée d'un petit passereau
Étude des interactions entre la punaise de lit et son symbiote nutritionnel obligatoire, Wolbachia
Evaluation de la balance bénéfice-risque des médicaments en néonatologie
Le lundi 27 Novembre à 9H30, Natacha Kremer soutiendra son habilitation à diriger les recherche, nous invitant à concevoir "L'hôte comme écosystème"
De novo analysis of splicing from RNAseq data: models, algorithms and applications
Les ongulés en interaction avec leur environnement et les parasites dans un monde hétérogène et changeant
Effects of global changes on population persistence: an integrative approach
"Les contours flous de l'individu"
Composition du jury :
- Stéphanie Bedhomme, Centre d’écologie fonctionnelle et évolutive, rapporteuse
- Christophe Douady, Laboratoire d'écologie des hydrosystèmes naturels et anthropisés, examinateur
- Pierre-Henri Gouyon, Muséum national d’histoire naturelle, rapporteur
- Thomas Heams, Institut des sciences et industries du vivant et de l'environnement, rapporteur
- Philippe Huneman, Institut d'histoire et de philosophie des sciences et des techniques, examinateur
- Nicolas Lartillot, Laboratoire de biométrie et biologie évolutive, examinateur
- Mylène Weill, Institut des sciences de l'évolution de Montpellier, examinatrice
Infectious diseases account for one fourth of human deaths worldwide. With pathogen collections of ever-increasing sizes, it becomes possible in theory to reconstruct past epidemic events on continental or worldwide scales and to gain actionable insights into the driving forces of pathogen dispersal and evolution. Current population genetics methods excel at this task, however their computational cost hampers their application on massive (n > 1,000) genomic datasets. Here we introduce a novel approach, ancestral state interpolation (AncSI), to reconstruct epidemics through space and time in a computationally efficient fashion. AncSI infers past information (including location, resistance or transmission success) relative to all isolates in the study population in a given time period. By computing series of fine-grained time period, AncSI allows for the visualization of epidemic dispersal in the form of video files. We reconstruct the epidemic progression across Eurasia and Africa of two deadly bacterial pathogens, namely the Mycobacterium tuberculosis Beijing family (n = 4,000 isolates with an evolution on the millenial scale) and the Salmonella Typhi H58 clone (n = 2,000 isolates with an evolution on the decade scale). In both cases, AncSI-inferred epidemic dynamics exhibited a near-perfect match with the conclusions of previous studies based on hypothesis-driven population genetics analyses. Furthermore, AncSI results highlighted previously unreported features of the epidemics such as a Korean (rather than Chinese) emergence of M.tuberculosis Beijing. Our results indicate that an accurate reconstruction of past epidemics can be obtained efficiently from genomic datasets, potentially leading to novel discoveries by leveraging the fast growing collections of pathogen genomes.
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Thèse de Mathieu Fauvernier le 24 septembre 2019 à 14 h, salle des thèses, Rockfeller
Transposable elements (TEs) are now known to occupy a huge proportion of most multicellular eukaryotic genomes, yet the factors influencing their proliferation are only beginning to be investigated. In addition to giving a brief overview of their role in genome evolution, I will discuss recent work on a few important factors (sex, horizontal transfer, and transposition rates) that influence the coevolutionary dynamics between TEs and the host genomes they inhabit.
Basal-like breast cancers are among the breast cancers with the poorest prognoses and patients do not benefit from any targeted therapy yet. We aim to identify the deregulated signaling pathways using genomic, transcriptomic and proteomic (RPPA) data in order to identify therapeutic targets. In this talk, I will focus on the analysis of SNP and CGH data. More specifically, I will discuss several statistical and algorithmic challenges directly related to their statistical analysis.1) Normalisation One important issue when analyzing SNP profiles is their normalisation. Indeed, especially with tumour profiles, it cannot be assumed that most of the genome is normal and it has been shown that not taking these genomic alterations into account while normalising leads to over-correction. We propose a method to estimate the signal (or copy number) and correct technical artefacts simultaneously.2) Exact and Fast segmentation A CGH profile can be viewed as a succession of segments representing regions in the genome that share the same DNA copy number. Multiple-change-point detection methods constitute a natural framework for their analysis and the detection of breakpoints. However, recovering the optimal position of the breakpoints is not an easy task, especially for large SNP profiles such as Affymetrix SNP 6.0. We propose an algorithm to recover quickly the best segmentation (the maximum likelihood estimate).3) Assessing the quality of a segmentation Assessing the quality of a segmentation and in particular the confidence we have in a particular breakpoint is a difficult problem. We propose algorithms and statistical methods to assess and take into account the quality of possible segmentations.