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Analysis and integration of heterogeneous large-scale genomics data

Abstract : 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 (second contribution). 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 (third contribution). 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 (fifth contribution).
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Contributor : Marine Louarn Connect in order to contact the contributor
Submitted on : Friday, January 15, 2021 - 3:49:23 PM
Last modification on : Wednesday, November 3, 2021 - 8:09:53 AM
Long-term archiving on: : Friday, April 16, 2021 - 6:48:39 PM


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  • HAL Id : tel-03111759, version 1


Marine Louarn. Analysis and integration of heterogeneous large-scale genomics data. Bioinformatics [q-bio.QM]. Université Rennes 1, 2020. English. ⟨tel-03111759⟩



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