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Through their ability to bind complex glycoconjugates, lectins have unique specificity and potential for biomedical and biotechnological applications. In particular, lectins with short repeated peptides forming carbohydrate-binding domains are not only of high interest for understanding protein evolution but can also be used as scaffold for engineering novel receptors. Synthetic glycobiology now provides the tools for engineering the specificity of lectins as well as their structure, multivalency and topologies. This review focuses on the structure and diversity of two families of tandem-repeat lectins, that is, β-trefoils and β-propellers, demonstrated as the most promising scaffold for engineering novel lectins.

Glycans or carbohydrates represent a major class of biomolecules. They are also the observable result of the most common Post-Translational Modification, known to play a crucial role in the host-pathogen interactions as well as in signaling processes or disease states, among others. Glycoinformatics is the dedicated bioinformatics field supporting glycobiology and its experimental parts, including glycomics and glycoproteomics and any other glycan-oriented studies. The generated data and resulting information drive the discipline. Glycans molecules have tree structures with a characterized topology. This structural complexity induces glycoinformatics problems relative to their representation, encoding formats, processing, and interpretation. Furthermore, the lack of consensus in this respect significantly limits reproducibility and comparability in glycobiology. We suggest that common descriptions, application guidelines, and best practices are solutions to overcome these issues. Over the recent years, Findability, Accessibility, Interoperability, and Reusability (FAIR) principles and minimum information required for a glycomics experiment (MIRAGE) initiatives have driven and ruled data standardization processes. Subsequently, adopting these principles and guidelines improves reproducibility, interoperability, and data reuse, increasing its value.

Le virus d'Epstein-Barr (EBV), ayant contaminé la quasi-totalité de la population humaine, fait partie des agents pathogènes les plus communs et efficaces. Il peut être inclus dans l'étiologie de plusieurs maladies, dont la mononucléose infectieuse, plusieurs lymphomes et divers cancers épithéliaux. Comme pour tous les autres Herpesviridae, le cycle biologique d'EBV est complexe et alterne entre deux phases. Durant sa phase latente, EBV interfère avec les centres germinaux, siège de la production d'anticorps et processus hautement mutagène, afin d'accéder au compartiment de la mémoire immunitaire de son hôte. Sa phase lytique lui permet de produire des virions rapidement et en grandes quantité, lui permettant de se propager vers d'autres tissus ou hôtes. Malgré cela, EBV parvient à se soustraire à la vigilance du système immunitaire et de ce fait, les infections d'EBV restent asymptomatiques la plupart du temps, mais dans tous les cas permanentes. Dès lors, EBV est un candidat idéal pour entreprendre la première étude génome-contre-génome (G2G), afin d'identifier des associations génomiques entre l'hôte humain et le pathogène viral. Par ce biais, nous désirons accroître notre compréhension des pressions sélectives produite par l'humain sur le génome d'EBV. Cependant, notre approche fait appel à des études d'association pangénomique (GWAS), notoirement sensibles au biais de stratification de population. Durant cette thèse, je vais exposer les résultats et difficultés rencontrées lors de ces analyses, en axant mon propos sur la diversité phylogénomique des souches séquencées dans 285 échantillons de sang de patient de la Swiss-HIV cohort (SHCS). Notamment, I) j'ai mis-à-jour la classification actuelle d'EBV en utilisant 272 génomes d'EBV publiés, II) j'ai découvert plusieurs souches d'EBV dans près de la moitié des échantillons de sang et III), j'ai assemblé, en prenant compte de la clonalité mixte des échantillons, 300 nouveaux génomes quasi-complets d'EBV. De plus, les étapes I) et III) comprennent la mise à jour du répertoire des classes de gène importants d'EBV, ainsi qu'une analyse de la conservation des épitopes importants qu'ils contiennent. Pour finir, je vais présenter les résultats de la G2G, avec 6 nouvelles associations entre des polymorphismes humains et des variants viraux.

Bioinformatics uses mathematical concepts and informatics tools to unravel the knowledge hidden in biological data. When bioinformatics is applied to glycans and glycobiology, it is called glycoinformatics. New technologies allow mass sequencing of new species genomes and of environmental samples metagenomes. But all newly discovered genomes and encoded proteins are only partially annotated with biological function assessed by similarities to reference organisms. Glycobiology is the research field dedicated to the study of glycan/carbohydrate compounds, composed of one or multiple monosaccharides. Lectins are proteins able to bind reversibly to glycans, and without enzymatic functions. Lectins are powerful tools for the recognition of glycans in samples, and they are also targets for therapeutic compounds due to their involvement in cancer, immunology and infections. This thesis aims to use bioinformatics for developing new in silico tools for the study of lectins. More specifically, it addresses the need for a new online database covering curated information on lectins for both reference organisms and newly sequenced genomes belonging to other organisms. To provide a curated classification of lectin 3D structures and their annotation in genomes, a dedicated web portal called UniLectin, was developed and includes several modules. The UniLectin3D module provides manually curated and classified 3D structures together with their interacting glycans. Due to the difficulty of identifying tandem repeated lectins in genomes, a specific method has been developed for the prediction of those particular lectins, now available in the PropLec and TrefLec modules. Finally, the LectomeXplore module includes lectin predictions based on 107 classes defined on the basis of UniLectin3D content, and resulting from screening available sequences stored in the reference protein databases NCBI-nr and UniProt. This made the study of lectomes in different environments possible as collaborative work described in the last part of the thesis.

Les approches métagénomiques nous donnent un aperçu en profondeur du microbiome humain en une seule analyse, sans la nécessité d'avoir une connaissance a priori des contenus d'un échantillon. De plus en plus, elles deviennent un complément aux tests de routine, car leur approche ouverte est capable de détecter des organismes divergents qui ne font pas partie des tests de diagnostic standards. Les virus jouent un rôle important dans la santé humaine, étant à l'origine de plusieurs infections et maladies cliniquement relevantes. Pour les études présentées dans cette thèse, j'ai collaboré avec le laboratoire de virologie de l'Hôpital de l'Université de Genève pour élaborer des méthodes d'analyse permettant de mettre en évidence des séquences virales dans des données métagénomiques cliniques humaines (ezVIR2), dans le but de caractériser des maladies d'étiologie inconnue, suspectées d'être d'origine virale. Afin de mieux comprendre le rôle de chaque virus, nous avons d'abord évalué le virome sain d'un individu en analysant des échantillons métagénomiques de plasma, globules rouges et plaquettes de donneurs sains issus de la banque du sang de l'hôpital, ainsi que le liquide céphalorachidien d'individus sains, révélant que les anellovirus, pegivirus, et papillomavirus font partie d'un microbiome sain. Nous avons ensuite comparé ces résultats avec ceux obtenus à partir du virome du sang, du liquide céphalorachidien et des voies respiratoires de patients atteints d'une maladie d'étiologie inconnue, montrant la présence de pathogènes viraux. Plusieurs souches d'astrovirus ont été décrites en dehors de leur site d'infection typique (gastro-intestinal), suggérant leur implication probable dans une maladie de type méningite et infection disséminée. Nous avons également récupéré plusieurs dicistrovirus dans le sang d'enfants fébriles. Nous avons aussi évalué le rôle potentiel des virus ont dans la récupération des patients fortement immunodéprimés, en analysant le métagénome des transplantés de cellules-souches hématopoïétiques, contenant des infections de pegivirus à la fois persistantes et ayant une charge virale élevée. Ces infections n'ont pas interféré avec la guérison des patients. La dynamique entre humain et virus a également été au centre de notre étude, révélant des signatures spécifiques pour les souches de rhabdovirus lors d'une infection de l'épithélium des voies aériennes. Nous avons aussi évalué les signatures virales des patients atteints de la maladie de Kawasaki, ne montrant aucun lien entre les patients et décrivant uniquement des virus commensaux. Finalement, j'ai détaillé un cas de contamination complexe dans des échantillons cliniques, où un rhabdovirus a été identifié chez des patients souffrant de symptômes de méningite, une maladie généralement associée à des infections virales. Cependant, le vrai hôte de l'infection virale s'est avéré être une petite mouche qui contaminait ces échantillons. En résumé, nous avons développé le potentiel des approches métagénomiques dans un milieu clinique et l'avons utilisé avec succès pour décrire plusieurs cas d'infection, ainsi que pour comprendre ce qu'est le virome normal d'un individu.

Cell lines became an essential asset for biomedical research through the numerous practical advantages they offer over other types of cell cultures. They are used in a wide range of experiments in the different omics fields, which entered in a high-throughput era in recent years with the emergence of new technology and instrumentation. Cell line cross-contamination and misidentification is however known to impede the reliability and reproducibility of experimental results. The large-scale generation of biological data also raises many bioinformatics challenges mainly spanning issues of formats, storage, representation, and interpretation. This thesis focuses on the development of bioinformatics software to address these problems, providing tools for the analysis and interpretation of omics data to the scientific community. Three distinct applications were developed in the course of this thesis, which led to the publication of four related scientific articles.

La quantité d'informations obtenue lors de l'étude de génomes augmente de façon constante. La complexité des processus de séquençage conduit à des données imparfaites avec un niveau élevé de bruit. Celles-ci nécessitent une variété de méthodes pour donner une interprétation biologique à leur contenu. Comme certains résultats reflètent mieux la réalité que d'autres, des stratégies pour juger de la qualité des données et des performances des méthodes informatiques sont nécessaires pour guider les choix expérimentaux. Ces évaluations doivent être systématiques et reproductibles. J'explore ici deux aspects liés à l'évaluation de processus informatiques dédiés à la recherche biologique, ainsi que de leurs résultats. Ils sont abordés au travers de deux ressources bio-informatiques distinctes dédiées à l'évaluation des performances (benchmark). i) La plate-forme LEMMI fournit une évaluation continue des outils de classification taxonomique. ii) Le logiciel BUSCO utilise des gènes conservés universellement pour évaluer le niveau de complétion de séquences génomiques.

Peptidic natural products (PNPs) and non ribosomal peptides (NRPs) are chemical compounds produced by living organisms. These peptides often exhibit interesting bioactivities. In this thesis I present a collection of innovative bioinformatic platforms dedicated to PNPs with the aim of facilitating the structural analysis of these compounds, especially for MS experiments. First, I introduce rBAN, a retro-biosynthesis tool mainly dedicated to the monomeric annotation of NRP chemical structures. Secondly, I present the KFP application, a straightforward program for NRP chemical formula detection and MS peak annotation, which implements a prediction method based on the Kendrick mass defect. Lastly, the thesis culminates with NRPro, a new platform for the MS/MS analysis of PNPs through automatic annotation and dereplication. These three tools were integrated in the Norine database, the only resource that is exclusively dedicated to NRPs.

La glycosylation des protéines est essentielle dans de nombreux mécanismes cellulaires. Au cours de la cancérogenèse, l'expression des glycanes varie et ces changements peuvent modifier leur structure et leur fonction. Ces dernières années, la glycobiologie a pris de l'importance dans la recherche sur le cancer en raison de l'association potentielle de l'expression aberrante des glycanes avec diverses pathologies. Les variations sont principalement liées aux modifications structurelles des N-glycanes et des O-glycanes et plus particulièrement visibles dans leurs motifs terminaux (glycoépitopes/déterminants des glycanes). La relation entre les déterminants des glycanes et le cancer avait déjà été identifiée au début du 20e siècle, mais leur pertinence en tant que cibles thérapeutiques potentielles n'a été concrètement révélée que récemment. Afin d'évaluer le rôle des déterminants des glycanes, nous avons d'abord extrait les informations fonctionnelles de la littérature, puis comparé les données expérimentales issues des études publiées sur le profil des lignées cellulaires néoplasiques pour analyser l'expression des glycanes et par la suite identifier leurs caractéristiques discriminantes. Nous avons utilisé un logiciel récemment publié dédié au support du glyco-profilage des protéines et des tissus. Les résultats de ce logiciel fournissent des liens potentiels permettant la découverte de nouveaux biomarqueurs pour la détection précoce du cancer. Dans le présent travail, l'application est centrée sur des données du cancer des ovaires.

The extreme diversity of arthropods provides many opportunities for understating how ecological and genetic factors have interacted over the course of evolution. However, there is a marked imbalance on the genomic sampling of the arthropod tree of life due to a variety of reasons. Entire orders of basal arthropods have gone entirely unsampled in terms of genomic data, although they would be very informative for evolutionarily comparisons. This sparseness of available sequencing data is partially explained due to the inherent difficulties in sequencing organisms possessing both large genome sizes and high levels of heterozygosis, further compounded by an absence of inbred laboratory colonies for most of these clades. To enable genomic evolutionarily comparisons of early radiated arthropod lineages, we sequenced, de novo assembled, and annotated the genomes of several basal insects, including some previously unsampled arthropod orders, such Notoptera and Odonata.

Glycosylation is one of the most abundant post-translational modifications (PTM) and the primary cause of microheterogeneity in proteins (glycoforms). It consists of the addition of a sugar moiety on the surface of a protein, and it has been suggested that over 50% of mammalian cellular proteins are typically glycosylated. Despite its importance, glycobiology is still lacking concrete bioinformatics support which, in the last two decades, has boosted other major 'omics' like proteomics, transcriptomics and genomics. Additionally, the inherent complexity of carbohydrates and the use of many orthogonal experimental techniques to elucidate a structure have slowed down the annotation of novel glycans leaving glycobiology fragmented internally. This thesis aims at the creation of a collection of integrative, explorative and knowledge-based tools to reconstitute the puzzle of biological evidence produced by different glycomics experiments and, at the same time, fill the gap with other 'omics'.

Mammalian sex determination is a particularly interesting model to study cell fate decision. Depending on the genetic sex, the common gonadal primordium is able to differentiate as two different organs, the testis and the ovary. This PhD project aimed to dissect with single-cell RNA-sequencing the process of sex determination in mouse by characterising the transcriptomic changes occurring in the different somatic cell lineages from the bipotential state to advanced developing testes and ovaries. For this purpose, we proceeded to single-cell RNA sequencing of the gonadal somatic cells (NR5A1 expressing cells) from XX and XY mice at key stages of the gonad development. With cell lineage predictive algorithms and pseudotime ordering, we were able to reconstruct the chronology of events driving testis and ovary development, as well as sex fate decision in both supporting and steroidogenic cell lineages.

It is important to investigate how genetic variation affects different cellular phenotypes and what are the mechanistic relationships among them in order to better understand the genetic basis of disease. The objective of this thesis is to gain insights into human gene regulation in the context of natural variation of cellular traits and DNA sequence. In this thesis I present the results of two main studies in which we have examined in an integrative manner all the pair wise relationships among genetic variation, DNA methylation and gene expression to: (a) study the participation of DNA methylation in gene regulation; and (b) examine the tissue- specificity of genetic and epigenetic regulatory effects in gene expression and alternative splicing. Additionally, I present my contribution to a third study that has assessed the allelic specificity and allelic coordination across multiple regulatory layers. Overall, these studies have yielded insights into the regulatory architecture of the genome across tissues, and highlight a scenario in which the epigenome is a marker for regulatory activity that is mainly driven by sequence-specific transcription factors.

Many algorithms for protein/peptide identification from Tandem Mass Spectrometry (MS/MS) data have been developed. The majority of such methods are based on sequence or spectral database search: experimental spectra are matched against spectra characterizing known peptide sequences. Hits define candidate peptides. In this context, the development of reliable and fast candidate peptide/spectrum filtering or ranking techniques would enhance the pre-processing of large scale datasets. The present work introduces IndiMax, a multi-stage, fast and accurate peptide/spectrum candidate filtering and ranking methodology for peptide MS/MS data. Based on a low complexity hierarchical search approach, IndiMax can be applicable to pre-process multiply charged (up to +5), de-isotoped, experimental MS/MS spectra of large quantity and variable quality. The developed methodology can be integrated into the general workflows to increase the identification rate and the consistency of the results. Thus, the potential user would get an opportunity to optimize the previously established data analysis strategy.

Current pharmaceutical research is actively exploring the field of natural peptides. Venomics addresses this issue with the study of toxins. The concomitant development of sequencing techniques is opening new perspectives of understanding biological mechanisms. Transcriptome sequencing of specific tissues is undertaken to better understand and characterize the context of gene expression. In this framework, transcriptomic data made available require automated processing workflows and user-friendly interfaces for data exploitation and comprehension. We present TATools, a bioinformatic platform that provides a unique management environment for understanding transcriptome data by merging results of diverse classical sequence analysis. Additional features and dedicated viewer pages makes TATools a valuable solution for highlighting novelty in a single transcriptome as well as cross-analysis of several transcriptomes in the same environment. TATools is validated in the context of venomics. This thesis reports the genesis of the design of TATools as exposed in two published articles and a manuscript (at this stage under revision) and it describes the final outcome of this work with the support of a submitted manuscript detailing the analysis workflow. The use of TATools is illustrated with the study of the Conus consors venom gland transcriptome and subsequent conopeptide identification and classification. Other applications of parts of the TATools platform are shown in another two published articles.

The use of tandem mass spectrometry (MS/MS) is a well established method to identify and characterize proteins from complex samples. Various tools are designed to map MS/MS spectra to peptides and proteins. The most commonly used software are Sequest, Mascot, and Phenyx, often referred to as sequence search tools, as they employ a spectrum identification algorithm where the experimental spectra are compared to theoretical spectra generated in silico from a protein sequence database. In recent years, a different method for protein identification based on spectral library search has shown promising results. In this approach the sequence database is replaced by a collection of high quality MS/MS spectra confidently identified in previous analysis. This thesis is made up of four papers contributing to the development of an analysis workflow, combining the two identification strategies, to improve upon protein identification and characterization.

LLC-MSMS combined with library search approaches is getting popular as an alternative to existing methods in clinical toxicology, for the screening of toxics. Such screenings are traditionally operated in emergency units, where they act as an early diagnostic test of the patient condition. As a result, routine laboratories need robust and easy to use software solutions that can handle the complexity of spectra generated by LCMSMS as well as deliver high quality results in the shortest time possible. In this thesis, we present a small molecule identification platform called SmileMS. This platform was developed to be used in applications based on LC-MSMS identification but also to act as an exploration platform for further investigation in the field of small molecule identification. Throughout the conception of SmileMS, these investigations concerned four areas: the creation scoring models, the handling of spectral libraries, the theoretical fragmentation of molecules, and the setting of engineering strategies enabling software exploration of data.

Identification of small molecules is of major importance for many applications. Liquid Chromatography Tandem Mass spectrometry (LC- MSMS) is gaining increasing interest in the field of small molecule identification. LC-MSMS has a broad range of detection, is sensitive and does not need special sample pre-processing. As a major chal- lenge, spectra of the same compound can show great variability across acquisitions. High spectra variability limits the use of LC-MSMS for library search identifications. Dedicated identification tools such as MS Search from NIST show insufficient performances when it comes to cross-platform identification. In this thesis, we present the new library search scoring model X- Rank. X-Rank matches conserved properties of spectra and proposes a robust probability scoring model. Scoring parameters can be opti- mized from a training set. A re-training of X-Rank for a specific data set, was shown to essentially improve the results. The efficiency of X-Rank was compared to existing solutions, using two test-sets from different machine types. Overall X-Rank showed better results in terms of sensitivity and specificity. Especially in the case of cross-platform identification, X-Rank could better discriminate correct from wrong matches. Furthermore, X-Rank could correctly identify and top rank eight chemical compounds in a test mix. Even though these results confirm an important improvement for cross-platform identification, filters before and after the X-Rank scor- ing are still useful. In this perspective, a new approach to confidently use the retention time information is presented. Furthermore, a spec- tra filtering approach is applied, which improves the identification in terms of quality and speed. Finally, using a specific training configuration, X-Rank was adapted for proteomics data. Combined with the peptide identification tool Phenyx, X-Rank helped matching additional peptides. X-Rank was implemented into the small molecule identification platform SmileMS. SmileMS is designed for a routine use in laborato- ries. It is a multi-user platform, which provides a simple identification workflow and intuitive result visualization. Thanks to the generic software architecture and the mutual inte- gration with the open-source project Java Proteomic Library (JPL), the addition of new methods to SmileMS is facilitated. Such methods in- clude quantification, the combined use of several algorithms, GC-MS and exact mass identification.