Les facteurs transcriptionnels (TF) sont des protéines qui contrôlent l'expression des gènes. Leurs motifs de liaison (TFBM, également appelés motifs) sont généralement représentés sous forme de matrices de scores spécifiques de positions (PSSM). L'analyse de motifs est utilisée en routine afin de découvrir des facteurs candidats pour la régulation d'un jeu de séquences d'intérêt. L'avénement des méthodes à haut débit a permis de détecter des centaines de motifs, qui sont disponibles dans des bases de données. Durant ma thèse, j'ai développé deux nouvelles méthodes et implémenté des outils logiciels pour le traitement de collections massives de motifs: matrix-clustering regroupe les motifs par similarité; position-scan détecte les motifs présentant des préférences de position relativement à une coordonnée de référence. Les méthodes que j'ai développées ont été évaluées sur base de cas d'études, et utilisées pour extraire de l'information interprétable à partir de différents jeux de données de Drosophila melanogaster et Homo sapiens. Les résultats démontrent la pertinence de ces méthodes pour l'analyse de données à haut débit, et l'intérêt de les intégrer dans des pipelines d'analyse de motifs. / Transcription Factors (TFs) are DNA-binding proteins that control gene expression. TF binding motifs (TFBMs, simply called “motifs”) are usually represented as Position Specific Scoring Matrices (PSSMs), which can be visualized as sequence logos. The advent of high-throughput methods has allowed the detection of thousands of motifs which are usually stored in databases. In this work I developed two novel methods and implemented software tools to handle large collection of motifs in order to extract interpretable information from high-throughput data: (i) matrix-clustering regroups motifs by similarity and offers a dynamic interface; (2) position-scan detects TFBMs with positional preferences relative to a given reference location (e.g. ChIP-seq peaks, transcription start sites). The methods I developed have been evaluated based on control cases, and applied to extract meaningful information from different datasets from Drosophila melanogaster and Homo sapiens. The results show that these methods enable to analyse motifs in high-throughput datasets, and can be integrated in motif analysis workflows.

In many countries, cancer is one of the biggest threats for citizens’ health, especially among aged people. Genomic mutations play a crucial role in cancer cell development. In previous decades, cancer research has been mainly focused on mutations in coding regions. These mutations can directly change the encoded protein sequences and influence their functions. In recent years, as the function of non-coding regions has been gradually understood, a growing number of studies have focused on the role of non-coding mutations in cancer. Transcription factor (TF) is an important group of gene regulatory factors. These factors only bind to specific sequences called transcription factor binding motifs (TFBMs) in the genome. Mutations in these motifs can disrupt the TF binding and thus influence gene regulation. A framework called funMotifs was made to predict and annotate functional TFBMs in the human genome. And a research has been made to intersect the mutation information from Pan-Cancer Analysis of Whole Genomes (PCAWG) to motifs in funMotifs, aiming to give a general view of influence of cancer-related mutations on functional TF motifs. But the research only focused on the existing motifs that were identified previously from the normal genome, while de novo motifs that could be potentially created by mutations were disregarded. An instance near the TERT promoter has been found, showing that mutations create a de novo ETS binding site and up-regulate the TERT expression.  My study aims to extend the borderline of funMotifs, from existing motifs to de novo motifs created by cancer-related mutations. I extended the original motifs in funMotifs database and merged the overlapping motifs into longer regulatory elements. Then I mutated these elements according to the mutation data from PCAWG. Next I scan through the mutated elements and identify TF motifs. These motifs were then intersected with original motifs in funMotifs database to remove the redundant results. After intersection and filtering, 2,525,771 de novo motifs were retained. These motifs mainly come from C2H2 zinc finger factors, tryptophan cluster factors, STAT domain factors, fork head/winged helix factors, MADS box factors and homeo domain factors. Even though the de novo motifs I found in this study still need further verification and analysis, for example the change of information content in the mutated sites of the motifs, the result I obtained can be a useful data source for further research on regulatory impact from cancer-related mutations. / <p></p><p></p><p></p><p></p><p></p>