The rapidly advancing high-throughput and next generation sequencing technologies facilitate deeper insights into the molecular mechanisms underlying the expression of phenotypes in living organisms. Experimental data and scientific publications following this technological advancement have rapidly accumulated in public databases. Meaningful analysis of currently available data in genomic databases requires sophisticated computational tools and algorithms, and presents considerable challenges to molecular biologists without specialized training in bioinformatics. To study their phenotype of interest molecular biologists must prioritize large lists of poorly characterized genes generated in high-throughput experiments. To date, prioritization tools have primarily been designed to work with phenotypes of human diseases as defined by the genes known to be associated with those diseases. There is therefore a need for more prioritization tools for phenotypes which are not related with diseases generally or diseases with which no genes have yet been associated in particular. Chromatin immunoprecipitation followed by next generation sequencing (ChIP-Seq) is a method of choice to study the gene regulation processes responsible for the expression of cellular phenotypes. Among publicly available computational pipelines for the processing of ChIP-Seq data, there is a lack of tools for the downstream analysis of composite motifs and preferred binding distances of the DNA binding proteins. This thesis is aimed to address the gap existing in the tools available to process high-throughput ChIP-Seq data to provide rapid analysis and interpretation of large lists of poorly characterized genes. Additionally, programs for the analysis of preferred binding distances of transcription factors were integrated into the pipeline for expedited results. A gene prioritization algorithm linking genes to non-disease phenotypes described by meaningful keywords was developed. This algorithm can be used to process candidate genetic targets of a transcription factor produced by a computational pipeline for ChIP-Seq data analysis.

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

The cell differentiation process is associated with activation and repression of different genes, whereby the formation of heterochromatin is mediated by spreading of repressor proteins along large chromatin domains. Some of these proteins are methyltransferases, including GLP and G9a that are implicated in the addition of mono- and dimethyl groups to lysine 9 at Histone 3. Despite extensive research the exact mechanism of binding and spreading of G9a and GLP is unclear. To better understand the molecular mechanisms through which G9a and GLP bind to chromatin we tested the in vivo binding of a mutant G9a that is unable to bind to H3K9me2 histone marks via its Ankyrin domain. Murine erythroleukemia (MEL) cell line with expression of mutant G9a was generated using recombinant DNA technologies; G9a binding targets genome-wide were detected by the analysis of ChIP-sequencing data. We validated ChIP-sequencing data providing a reliable tool to visualize G9a targets in MEL cells. We also found that G9a Ankyrin mutant bound to all tested regions suggesting that the Ankyrin domain is not the only factor that contributes to the binding of G9a on chromatin in vivo.

Le glioblastome est la tumeur cérébrale de mauvais pronostic la plus fréquente et la plus agressive. Les traitements actuels combinent la chirurgie à la radio thérapie et la chimiothérapie. Cependant ces traitements sont peu efficaces. Le taux de récidive est élevé et la survie moyenne est de 15 mois.La récidive s'explique en partie par la présence de cellules initiatrices de glioblastomes (CIG). Ces cellules possèdent des propriétés de cellules souches adultes. Elles s'auto-renouvellent en maintenant un pool de cellules tumorales et se différencient en différents types cellulaires. Elles sont aussi résistantes aux thérapies par l'activation de mécanismes d'élimination des molécules destinées à les détruire. L'engagement des CIGs vers un état tumoral différencié diminue fortement leur potentiel tumorigénique les rendant plus vulnérables.Le facteur de transcription EGR1 est impliqué dans des processus biologiques comme la prolifération et la différenciation. Dans les CIG l'expression d'EGR1 est anormalement élevée. Ce niveau diminue lorsque les cellules se différencient. L'expression d'EGR1 est donc corrélée avec un état souche suggérant sa contribution dans la régulation de la prolifération des CIG ou dans le maintien de cet état.Mon objectif est de caractériser le rôle d'EGR1 dans la régulation de l'état proliférant des CIG.Nous avons démontré l'implication d'EGR1 dans une cascade de régulation impliquant le mir18a* et les gènes SHH et GLI1. Il contribue ainsi à l'autorenouvellement, à la prolifération et au maintien de l'état souche des CiGs. De plus en régulant directement le gène PDGFa, EGR1 entretient ce système régulatoire par une deuxième boucle moléculaire. / Glioblastoma is the most commun and agressive cerebral tumor. The current treatments combine surgery with chemotherapy and radiotherapy. However these treatments are poor effective. The relapse is frequent and the rate survival is less than 18 months.The relapse is in part due to the presence of glioblastoma initiating cells (GIC). The cells have stem cell properties. They can self-renew to maintain a pool of tumor cells and they can differentiate in different kind of tumor cells. They are also able to resist to the therapies by activating mechanisms of drug efflux. The commitment of GIC toward a differentiated tumor state decreases strongly their tumorigenic potential.EGR1 transcription factor is involved in many biological processes such as proliferation and differentiation. In the GIC EGR1 expression is abnormally elevated. This level decreases when cells are differentiated. EGR1 expression is strongly correlated with stem state suggesting its contribution in the proliferation regulation of GIC or in the maintenance of this state.My aim is to characterize the role of EGR1 in the regulation of proliferating state of the GIC.We have demonstrated the involvement of EGR1 in the pathway involving the mir18a* and the genes SHH and GLI1. It contributes so to the self-renewal, to the proliferation and to the maintenance of the stem state of GIC. In addition by directly regulating the gene PDGFa EGR1 maintains this system by a second molecular loop.

No description available.