Les gliomes constituent les plus fréquentes des tumeurs malignes primaires du système nerveux central. Les liens qui existent entre ces tumeurs et un certain nombre de cancers rares héréditaires, comme les Neurofibromatoses I et II ou les syndromes de Turcot et de Li-Fraumeni, attestent d’une prédisposition génétique aux gliomes. L’observation d’un risque deux fois plus élevé de développer un gliome chez les parents de premier degré de patients atteints suggère aussi une possible prédisposition génétique dans les gliomes sporadiques. Par ailleurs, l’analyse à haut débit permet de préciser le profil somatique des gliomes et d’identifier des biomarqueurs pronostiques voire prédictifs et s’inscrire dans une démarche de traitement personnalisé du patient. Durant ma thèse, je me suis focalisé sur deux axes de recherches complémentaires; l’identification de gènes de susceptibilité et la découverte de nouveaux gènes fréquemment mutés dans les gliomes, afin de déterminer les voies de signalisation contribuant à la gliomagenèse. Dans leur ensemble, les résultats obtenus dans cette thèse apportent non seulement des informations importantes sur la nature de la prédisposition génétique aux gliomes mais également de son association spécifique pour les différents sous-types de tumeurs. La découverte d’un nouveau gène muté, offre la perspective à plus long terme d’un traitement personnalisé pour chaque patient sur la base du profil génétique de sa tumeur. / Gliomas are the most common adult malignant primary tumour of the central nervous system. Thus far, no environmental exposures has been linked to risk except for ionizing radiation, which only accounts for a very small number of cases. Direct evidence for inherited predisposition to glioma is provided by a number of rare inherited cancer syndromes, such as Turcot's and Li–Fraumeni syndromes, and neurofibromatosis. Even collectively, these diseases however account for little of the twofold increased risk of glioma seen in first-degree relatives of glioma patients. My research was centred on two complementary research activities: Identifying susceptibility genes for glioma to delineate key biological pathways contributing to disease pathogenesis and to identify new recurrent mutated genes for glioma to provide for further insights into glial oncogenesis and suggesting targets for novel therapeutic strategies. Collectively the findings in this thesis provide increased insight into the nature of genetic predisposition to glioma and substantiate the often distinct associations between susceptibility variants and glioma molecular groups. In addition the discovery of a new mutated gene in glioma offers the potential to support drug development and advance precision medicine for this tumours.

Despite major advances in treatment, pediatric cancers in the 5-16 age group remain the most common cause of disease death, and one out of eight children with cancer will not survive. Among children that do survive, some 60% suffer from late effects such as cancer recurrence and increased risk of obesity. This paper will provide a broad overview of pediatric oncology in the context of systems medicine. Systems medicine utilizes an integrative approach that relies on patient information gained from omics technology. A major goal of a systems medicine is to provide personalized medicine that optimizes positive outcomes while minimizing deleterious short and long-term sideeffects. There is an ever increasing development of effective cancer drugs, but a major challenge lies in picking the most effective drug for a particular patient. As detailed below, high-throughput omics technology holds the promise of solving this problem. Omics includes genomics, epigenomics, and proteomics. System medicine integrates omics information and provides detailed insights into disease mechanisms which can then inform the optimal treatment strategy.

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L'objectif principal de cette thèse est le développement, l'amélioration et l'évaluation de méthodes de traitement de données massives de séquençage, principalement des lectures de séquençage d'ARN courtes et longues, pour éventuellement aider la communauté à répondre à certaines questions biologiques, en particulier dans les contextes de transcriptomique et d'épissage alternatif. Notre objectif initial était de développer des méthodes pour traiter les données d'ARN-seq de deuxième génération à l'aide de graphes de De Bruijn afin de contribuer à la littérature sur l'épissage alternatif, qui a été exploré dans les trois premiers travaux. Le premier article (Chapitre 3, article [77]) a exploré le problème que les répétitions apportent aux assembleurs de transcriptome si elles ne sont pas correctement traitées. Nous avons montré que la sensibilité et la précision de notre assembleur local d'épissage alternatif augmentaient considérablement lorsque les répétitions étaient formellement modélisées. Le second (Chapitre 4, article [11]) montre que l'annotation d'événements d'épissage alternatifs avec une seule approche conduit à rater un grand nombre de candidats, dont beaucoup sont importants. Ainsi, afin d'explorer de manière exhaustive les événements d'épissage alternatifs dans un échantillon, nous préconisons l'utilisation combinée des approches mapping-first et assembly-first. Étant donné que nous avons une énorme quantité de bulles dans les graphes de De Bruijn construits à partir de données réelles d'ARN-seq, qui est impossible à analyser dans la pratique, dans le troisième travail (Chapitre 5, articles [1, 2]), nous avons exploré théoriquement la manière de représenter efficacement et de manière compacte l'espace des bulles via un générateur des bulles. L'exploration et l'analyse des bulles dans le générateur sont réalisables dans la pratique et peuvent être complémentaires aux algorithmes de l'état de l'art qui analysent un sous-ensemble de l'espace des bulles. Les collaborations et les avancées sur la technologie de séquençage nous ont incités à travailler dans d'autres sous-domaines de la bioinformatique, tels que: études d'association à l'échelle des génomes, correction d'erreur et assemblage hybride. Notre quatrième travail (Chapitre 6, article [48]) décrit une méthode efficace pour trouver et interpréter des unitigs fortement associées à un phénotype, en particulier la résistance aux antibiotiques, ce qui rend les études d'association à l'échelle des génomes plus accessibles aux panels bactériens, surtout ceux qui contiennent des bactéries plastiques. Dans notre cinquième travail (Chapitre 7, article [76]), nous évaluons dans quelle mesure les méthodes existantes de correction d'erreur ADN à lecture longue sont capables de corriger les lectures longues d'ARN-seq à taux d'erreur élevé. Nous concluons qu'aucun outil ne surpasse tous les autres pour tous les indicateurs et est le mieux adapté à toutes les situations, et que le choix devrait être guidé par l'analyse en aval. Les lectures longues d'ARN-seq fournissent une nouvelle perspective sur la manière d'analyser les données transcriptomiques, puisqu'elles sont capables de décrire les séquences complètes des ARN messagers, ce qui n'était pas possible avec des lectures courtes dans plusieurs cas, même en utilisant des assembleurs de transcriptome de l'état de l'art. En tant que tel, dans notre dernier travail (Chapitre 8, article [75]), nous explorons une méthode hybride d'assemblage d'épissages alternatifs qui utilise des lectures à la fois courtes et longues afin de répertorier les événements d'épissage alternatifs de manière complète, grâce aux lectures courtes, guidé par le contexte intégral fourni par les lectures longues / The main goal of this thesis is the development, improvement and evaluation of methods to process massively sequenced data, mainly short and long RNA-sequencing reads, to eventually help the community to answer some biological questions, especially in the transcriptomic and alternative splicing contexts. Our initial objective was to develop methods to process second-generation RNA-seq data through de Bruijn graphs to contribute to the literature of alternative splicing, which was explored in the first three works. The first paper (Chapter 3, paper [77]) explored the issue that repeats bring to transcriptome assemblers if not addressed properly. We showed that the sensitivity and the precision of our local alternative splicing assembler increased significantly when repeats were formally modeled. The second (Chapter 4, paper [11]), shows that annotating alternative splicing events with a single approach leads to missing out a large number of candidates, many of which are significant. Thus, to comprehensively explore the alternative splicing events in a sample, we advocate for the combined use of both mapping-first and assembly-first approaches. Given that we have a huge amount of bubbles in de Bruijn graphs built from real RNA-seq data, which are unfeasible to be analysed in practice, in the third work (Chapter 5, papers [1, 2]), we explored theoretically how to efficiently and compactly represent the bubble space through a bubble generator. Exploring and analysing the bubbles in the generator is feasible in practice and can be complementary to state-of-the-art algorithms that analyse a subset of the bubble space. Collaborations and advances on the sequencing technology encouraged us to work in other subareas of bioinformatics, such as: genome-wide association studies, error correction, and hybrid assembly. Our fourth work (Chapter 6, paper [48]) describes an efficient method to find and interpret unitigs highly associated to a phenotype, especially antibiotic resistance, making genome-wide association studies more amenable to bacterial panels, especially plastic ones. In our fifth work (Chapter 7, paper [76]), we evaluate the extent to which existing long-read DNA error correction methods are capable of correcting high-error-rate RNA-seq long reads. We conclude that no tool outperforms all the others across all metrics and is the most suited in all situations, and that the choice should be guided by the downstream analysis. RNA-seq long reads provide a new perspective on how to analyse transcriptomic data, since they are able to describe the full-length sequences of mRNAs, which was not possible with short reads in several cases, even by using state-of-the-art transcriptome assemblers. As such, in our last work (Chapter 8, paper [75]) we explore a hybrid alternative splicing assembly method, which makes use of both short and long reads, in order to list alternative splicing events in a comprehensive manner, thanks to short reads, guided by the full-length context provided by the long reads

The TRIB1 locus (8q24.13) is a novel locus associated with plasma TGs and CAD risk. Trib1 is a regulator of MAPK activity, and has been shown to regulate hepatic lipogenesis and VLDL production in mice. However, the functional relationship between common SNPs at the TRIB1 locus and plasma lipid traits is unknown; TRIB1 has not been identified as an eQTL. This cluster of SNPs falls within an intergenic region 25kb to 50kb downstream of the TRIB1 coding region. By phylogenetic footprinting analysis and DNA genotyping, we identified an evolutionarily conserved region (CNS1) within the risk locus that harbours two common SNPs in tight LD with GWAS risk SNPs and significantly associated with CAD. We investigated the regulatory function of CNS1 by luciferase reporter assays in HepG2 cells and demonstrate that this region has promoter activity. In addition, the rs2001844 risk allele significantly reduces luciferase activity, suggesting that altered expression of the EST-based gene may be associated with plasma TGs. We identified an EST within the risk locus directly downstream of CNS1. We performed 5'/3' RACE using HepG2 RNA, identified multiple variants of this EST-based gene, and confirmed its transcription start site within CNS1. We hypothesize that this EST is a long noncoding RNA due to low abundance, poor conservation, and absence of significant ORF. Over-expression of a short variant implicates its function in the regulation of target gene transcription, although the mechanism of action remains unknown. We conclude that the risk locus at 8q24.13 harbours a novel EST-based gene that may explain the relationship between GWAS SNPs at this locus and plasma lipid traits.

The TRIB1 locus (8q24.13) is a novel locus associated with plasma TGs and CAD risk. Trib1 is a regulator of MAPK activity, and has been shown to regulate hepatic lipogenesis and VLDL production in mice. However, the functional relationship between common SNPs at the TRIB1 locus and plasma lipid traits is unknown; TRIB1 has not been identified as an eQTL. This cluster of SNPs falls within an intergenic region 25kb to 50kb downstream of the TRIB1 coding region. By phylogenetic footprinting analysis and DNA genotyping, we identified an evolutionarily conserved region (CNS1) within the risk locus that harbours two common SNPs in tight LD with GWAS risk SNPs and significantly associated with CAD. We investigated the regulatory function of CNS1 by luciferase reporter assays in HepG2 cells and demonstrate that this region has promoter activity. In addition, the rs2001844 risk allele significantly reduces luciferase activity, suggesting that altered expression of the EST-based gene may be associated with plasma TGs. We identified an EST within the risk locus directly downstream of CNS1. We performed 5'/3' RACE using HepG2 RNA, identified multiple variants of this EST-based gene, and confirmed its transcription start site within CNS1. We hypothesize that this EST is a long noncoding RNA due to low abundance, poor conservation, and absence of significant ORF. Over-expression of a short variant implicates its function in the regulation of target gene transcription, although the mechanism of action remains unknown. We conclude that the risk locus at 8q24.13 harbours a novel EST-based gene that may explain the relationship between GWAS SNPs at this locus and plasma lipid traits.

The TRIB1 locus (8q24.13) is a novel locus associated with plasma TGs and CAD risk. Trib1 is a regulator of MAPK activity, and has been shown to regulate hepatic lipogenesis and VLDL production in mice. However, the functional relationship between common SNPs at the TRIB1 locus and plasma lipid traits is unknown; TRIB1 has not been identified as an eQTL. This cluster of SNPs falls within an intergenic region 25kb to 50kb downstream of the TRIB1 coding region. By phylogenetic footprinting analysis and DNA genotyping, we identified an evolutionarily conserved region (CNS1) within the risk locus that harbours two common SNPs in tight LD with GWAS risk SNPs and significantly associated with CAD. We investigated the regulatory function of CNS1 by luciferase reporter assays in HepG2 cells and demonstrate that this region has promoter activity. In addition, the rs2001844 risk allele significantly reduces luciferase activity, suggesting that altered expression of the EST-based gene may be associated with plasma TGs. We identified an EST within the risk locus directly downstream of CNS1. We performed 5'/3' RACE using HepG2 RNA, identified multiple variants of this EST-based gene, and confirmed its transcription start site within CNS1. We hypothesize that this EST is a long noncoding RNA due to low abundance, poor conservation, and absence of significant ORF. Over-expression of a short variant implicates its function in the regulation of target gene transcription, although the mechanism of action remains unknown. We conclude that the risk locus at 8q24.13 harbours a novel EST-based gene that may explain the relationship between GWAS SNPs at this locus and plasma lipid traits.

The aetiology of common diseases is shaped by the effects of genetic and environmental factors. Big efforts have been devoted to unravel the genetic basis of disease with the hope that it will help to develop new therapeutic treatments and to achieve personalized medicine. With the development of high-throughput genotyping technologies, hundreds of association studies have described many loci associated to disease. However, the depiction of disease architecture remains incomplete. The aim of this work is to perform exhaustive comparisons across human populations to evaluate pressing questions. Our results provide new insights in the allele frequency of risk variants, their sharing across populations and the likely architecture of disease / La etiología de las enfermedades comunes está formada por factores genéticos y ambientales. Se ha puesto mucho empeño en describir sus bases genéticas. Este conocimiento será útil para desarrollar nuevas terapias y la medicina personalizada. Gracias a las técnicas de genotipado masivo, centenares de estudios de asociación han descrito una infinidad de genes asociados a enfermedad. Pese a ello, la arquitectura genética de las enfermedades no ha sido totalmente descrita. Esta tesis pretende llevar a cabo exhaustivas comparaciones entre poblaciones para responder diversas preguntas candentes. Nuestros resultados dan pistas sobre la frecuencia de los alelos de riesgo, su presencia entre poblaciones y la probable arquitectura de las enfermedades.