Comparative modular analysis of gene expression in vertebrate development

Détails

Ressource 1Télécharger: BIB_CB3081439982.P001.pdf (4603.92 [Ko])
Etat: Serval
Version: Après imprimatur
ID Serval
serval:BIB_CB3081439982
Type
Thèse: thèse de doctorat.
Collection
Publications
Titre
Comparative modular analysis of gene expression in vertebrate development
Auteur(s)
Piasecka B.
Directeur(s)
Robinson-Rechavi  M.
Codirecteur(s)
Bergmann  S.
Institution
Université de Lausanne, Faculté de biologie et médecine
Adresse
Department of Ecology and Evolution, Biophore, University of Lausanne, CH-1015 Lausanne, Switzerland
Statut éditorial
Acceptée
Date de publication
11/2012
Langue
anglais
Résumé
The focus of my PhD research was the concept of modularity. In the last 15 years, modularity has become a classic term in different fields of biology. On the conceptual level, a module is a set of interacting elements that remain mostly independent from the elements outside of the module. I used modular analysis techniques to study gene expression evolution in vertebrates. In particular, I identified ``natural'' modules of gene expression in mouse and human, and I showed that expression of organ-specific and system-specific genes tends to be conserved between such distance vertebrates as mammals and fishes. Also with a modular approach, I studied patterns of developmental constraints on transcriptome evolution. I showed that none of the two commonly accepted models of the evolution of embryonic development (``evo-devo'') are exclusively valid. In particular, I found that the conservation of the sequences of regulatory regions is highest during mid-development of zebrafish, and thus it supports the ``hourglass model''. In contrast, events of gene duplication and new gene introduction are most rare in early development, which supports the ``early conservation model''. In addition to the biological insights on transcriptome evolution, I have also discussed in detail the advantages of modular approaches in large-scale data analysis. Moreover, I re-analyzed several studies (published in high-ranking journals), and showed that their conclusions do not hold out under a detailed analysis. This demonstrates that complex analysis of high-throughput data requires a co-operation between biologists, bioinformaticians, and statisticians.
Création de la notice
11/02/2013 13:17
Dernière modification de la notice
03/03/2018 21:26
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