Méline Wéry’s PhD defense on “Identification of causal pathologic signature by multi-omic data integration” will take place on Thursday 16th december at 14:00 (CEST+1).
The defense will be broadcasted live on youtube at: https://youtu.be/5aL92INe2NI
– Emmanuelle BECKER (Université de Rennes 1)
– Charles BETTEMBOURG (Sanofi, Chilly-Mazarin)
– Laurence CALZONE (Institut Curie, Paris)
– Olivier DAMERON (Université de Rennes 1)
– Franck DELAPLACE (Université de Paris-Saclay, Évry)
– Fleur MOUGIN (LaBRI Bordeaux)
– Anne SIEGEL (IRISA Rennes)
– Vassili SOUMELIS (Hôpital Saint Louis, Paris)
– Emmanuel OGER (Université de Rennes 1)
Systematic erythematosus lupus is an example of a complex, heterogeneous and multifactorial disease. The identification of signature that can explain the cause of a disease remains an important challenge for the stratification of patients. Classic statistical analysis can hardly be applied when population of interest are heterogeneous and they do not highlight the cause. This thesis presents two methods that answer those issues. First, a transomic model is described in order to structure all the omic data, using semantic Web (RDF). Its supplying is based on a patient-centric approach. SPARQL query interrogates this model and allow the identification of expression Individually-Consistent Trait Loci (eICTLs). It a reasoning association between a SNP and a gene whose the presence of the SNP impact the variation of its gene expression. Those elements provide a reduction of omics data dimension and show a more informative contribution than genomic data. This first method are omics data-driven. Then, the second method is based on the existing regulation dependancies in biological networks. By combining the dynamic of biological system with the formal concept analysis, the generated stable states are automatically classified. This classification enables the enrichment of biological signature, which caracterised a phenotype. Moreover, new hybrid phenotype is identified.
Marine Louarn’s PhD defense on “Analysis and integration of heterogeneous large-scale genomics data: application to B cell differentiation and Follicular Lymphoma non coding mutations” will take place on Thursday 26th november at 09:30 (CEST+1).
The defense will be broadcasted live on Youtube: https://youtu.be/9kPjm6yERMM
– Adrien COULET (MCU) LORIA, Nancy
– Lydie LANE (Researcher), SIB Lausanne, Swiss
– Salvatore SPICUGLIA (DR) INSERM U1090 Marseilles
– Alexandre TERMIER (Professor) University Rennes 1 Rennes
– Sarah COHEN-BOULAKIA (Professor) LRI Orsay
– Fabrice CHATONNET (IR) CHU Rennes
– Olivier DAMERON (Professor) University Rennes 1 Rennes
– Anne SIEGEL (DR CNRS) IRISA Rennes
– Thierry FEST (PU-PH) INSERM / CHU Rennes
Regulatory networks inference from heterogeneous data is a computational step aiming at identifying key regulators involved in differentiation processes leading to cancer. In this thesis I focus on B cell differentiation, from which follicular lymphoma emerges. The first contribution outlines the reproducibility and reusability limitations of a state-of-the-art method for network inference from genomic data. To overcome these limitations, I demonstrated that Semantic Web technologies can structure and integrate large-scale heterogeneous datasets in a systematic way. The original analysis workflow outputs could be reproduced as queries on a graph of data, which could itself be layered and enriched with public databases. This demonstrates the technical relevance of this approach and underlines its benefits in improving reusability and reproducibility. As a fourth contribution, a new method for network inference was designed to take expert knowledge into account – both to extend the previous framework to the analysis of smaller, closely-related datasets and to enrich the inferred networks with signs, therefore including inhibitory regulatory processes. Finally, the method was applied to B cell differentiation, leading to the discovery of 146 TF with potential large impact on the network.
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