Dissertations / Theses on the topic 'Bioinformatics approach'

<p>Clustering of data is a well-researched topic in computer sciences. Many approaches have been designed for different tasks. In biology many of these approaches are hierarchical and the result is usually represented in dendrograms, e.g. phylogenetic trees. However, many non-hierarchical clustering algorithms are also well-established in biology. The approach in this thesis is based on such common algorithms. The algorithm which was implemented as part of this thesis uses a non-hierarchical graph clustering algorithm to compute a hierarchical clustering in a top-down fashion. It performs the graph clustering iteratively, with a previously computed cluster as input set. The innovation is that it focuses on another feature of the data in each step and clusters the data according to this feature. Common hierarchical approaches cluster e.g. in biology, a set of genes according to the similarity of their sequences. The clustering then reflects a partitioning of the genes according to their sequence similarity. The approach introduced in this thesis uses many features of the same objects. These features can be various, in biology for instance similarities of the sequences, of gene expression or of motif occurences in the promoter region. As part of this thesis not only the algorithm itself was implemented and evaluated, but a whole software also providing a graphical user interface. The software was implemented as a framework providing the basic functionality with the algorithm as a plug-in extending the framework. The software is meant to be extended in the future, integrating a set of algorithms and analysis tools related to the process of clustering and analysing data not necessarily related to biology.</p><p>The thesis deals with topics in biology, data mining and software engineering and is divided into six chapters. The first chapter gives an introduction to the task and the biological background. It gives an overview of common clustering approaches and explains the differences between them. Chapter two shows the idea behind the new clustering approach and points out differences and similarities between it and common clustering approaches. The third chapter discusses the aspects concerning the software, including the algorithm. It illustrates the architecture and analyses the clustering algorithm. After the implementation the software was evaluated, which is described in the fourth chapter, pointing out observations made due to the use of the new algorithm. Furthermore this chapter discusses differences and similarities to related clustering algorithms and software. The thesis ends with the last two chapters, namely conclusions and suggestions for future work. Readers who are interested in repeating the experiments which were made as part of this thesis can contact the author via e-mail, to get the relevant data for the evaluation, scripts or source code.</p>

High-throughput sequencing technologies are allowing the description of genome-wide variation patterns for an ever-growing number of organisms. However, we still lack a thorough comprehension of the relative amount of different types of genetic variation, their phenotypic effects, and the detection and quantification of distinct selection regimes acting on genomes. The recent compilation of more than one thousand of worldwide wild-derived Drosophila melanogaster genome sequences reassembled using a standardized pipeline (Drosophila Genome Nexus, DGN, Lack et al. 2015, 2016) provides a unique resource to test molecular population genetics hypotheses, and ultimately understand the evolutionary dynamics of genetic variation in the populations. Besides, the increasing amount of genomic data available requires the continuous development and optimization of bioinformatics tools able to handle and analyze such information. Thus, the development and implementation of new biologically-oriented software addressing several steps from data acquisition, filtering, processing, display or analysis to the final reporting step is a constantly growing need, especially in fields dealing with large data sets, such as population genomics. This thesis is conceived as a comprehensive bioinformatics and population genomics project. It is centered in the development and application of bioinformatics tools for the analysis and visualization of nucleotide variation patterns and the detection of selective events in the genome of D. melanogaster, using the DGN data. The main goal is accomplished in three sequential steps: (i) capture the evolutionary properties of the analyzed sequences (i.e., create a catalog of population genetics metrics) and implement a tool for the graphical display of such information; (ii) develop a statistical package for the computation of the diverse selection regimes acting on genomes (positive and purifying selection), and finally (iii) perform an initial population genomics analysis in D. melanogaster using the previously developed tools. The common approach applied to process the data, starting at the assembly of genome sequences and ending up at the estimates of population genetics metrics, allows performing, for the first time, a comprehensive comparison and interpretation of results using samples from five continents. Overall, this work provides a global overview of the nucleotide variation and adaptation patterns along the genome, and a general assessment of the relative impact of the major genomic determinants of genetic variation, in Drosophila meta-populations with different geographical origin.

The cluster orchestration tool Kubernetes enables easy deployment and reproducibility of life science research by utilizing the advantages of the container technology. The container technology allows for easy tool creation, sharing and runs on any Linux system once it has been built. The applicability of Kubernetes as an approach to run bioinformatic workflows was evaluated and resulted in some examples of how Kubernetes and containers could be used within the field of life science and how they should not be used. The resulting examples serves as proof of concepts and the general idea of how implementation is done. Kubernetes allows for easy resource management and includes automatic scheduling of workloads. It scales rapidly and has some interesting components that are beneficial when conducting life science research.

Long-range regulatory regions play important functions in the regulation of transcription and are particularly involved in the precise spatio-temporal expression of target genes. Such regions have specific characteristics, among which is their ability to regulate many target genes that can be located up to 1Mb from the transcription start site. The prediction and functional characterization of such regions remains an open problem. Evolutionary approaches have been developed to detect regulatory regions that are under purifying selection. However, little has been done with regards to the impact of long-range regulation on genome evolution.<br>This thesis focuses on three different aspects of long-range regulation: i/ First we develop a method that predicts regions particularly prone to the fixation of evolutionary breakpoints. We discuss the results obtained in the context of long-range regulation and show that this type of regulation is a major factor shaping vertebrate genomes in evolution. ii/ The second project aims at predicting functional interactions between regulatory regions and target genes based on the observation of evolutionary rearrangements in various vertebrate species. We show how this approach produces a biologically meaningful prediction dataset that will be useful to researchers working on regulation. iii/ Third, we focus on the in vivo characterization of regulatory regions. We present a powerful and reliable enhancer detection pipeline composed of an in silico approach to predict putative enhancers and an in vivo method to functionally characterize the expression specificity of predicted regions in the developing medaka fish.<br>The results presented in this thesis contribute to different areas of research such as a better understanding of evolutionary dynamics related to evolutionary rearrangements and to a better in silico and in vivo characterization of cis-regulatory regions.<br>La régulation longue distance a d'importantes fonctions dans la régulation de la transcription et est particulièrement impliquée dans la régulation spatiale et temporelle des gènes cibles. Ces régions ont des caractèristiques spécifiques telles que la capacité de contrôler different gènes à des distances jusqu'a 1Mb du site d'initiation de la transcription. La prédiction et la caractérisation fonctionelle de ces regions restent un problème d'actualité. Des approches évolutionaires ont été d´eveloppées pour détecter les régions sous pression de sélection. En revanche, peu a été fait en rapport avec l'impact de la régulation de longue distance sur l'évolution du génome.<br>Cette thèse se concentre sur trois differents aspects de la régulation longue distance: i/ Premièrement, nous developpons une méthode de prédiction des regions particulièrement sujettes à la fixation des réarrangements de l'évolution. Nous étudions les résultats obtenus dans le contexte de la régulation longue distance et nous montrons que ce type de régulation est un composant majeur dans le façonnement du génome au cours de l'évolution. ii/ Le second projet à pour but de prédire les interactions fonctionnelles entre les régions de régulation et leur gènes cible à partir de l'observation de réarrangements de l'évolution dans differentes espèces. Nous montrons comment une telle approche produit des resultants biologiquement significatifs qui seront particulièrement utiles aux chercheurs travaillant dans le domaine de la régulation. iii/ Troisièmement, nous nous concentrons sur la caractérisation fonctionnelle in vivo des regions régulatrices. Nous présentons une méthode fiable de détection des enhancers composée d'une approche informatique pour la prédiction de ces régions et d'une approche biologique pour caractériser fonctionnellement les spécificités d'expression de ces régions dans le poisson medaka.<br>Les résultats présentés dans cette thèse contribuent à une meilleure comprehension des dynamiques d'évolution en relation avec la régulation longue distance et une meilleure prédiction et caractérisation fonctionnelle de ces régions régulatrices.

Mestrado em Biomedicina Molecular<br>A Unfolded Protein Response (UPR) é um mecanismo de defesa crucial que protege as células contra o enrolamento incorreto de proteínas, através da ativação de três sensores principais: ATF6, PERK e IRE1. Cada sensor guia a célula em diferentes mecanismos de transdução de sinal culminando na produção de fatores de transcrição que, por sua vez, regulam genes que aumentam a capacidade da célula corrigir a conformação de proteínas mal enoveladas, impedindo, em último caso, a sua agregação. Nos últimos anos a UPR tem sido associada a várias patologias. Na infertilidade masculina, poucos estudos se têm focado na influência dos componentes da UPR, sendo importante numa primeira abordagem, a identificação destes componentes no sistema reprodutor masculino. Através de pesquisa de bases de dados e com abordagens bioinformáticas, com o objetivo de identificar potenciais candidatos associados a fenótipos de infertilidade, foi realizada uma recolha de proteínas UPR no testículo, espermatozoide e plasma seminal. De forma a determinar possíveis alvos envolvidos na infertilidade masculina, as interações proteínaproteína foram analisadas, destacando-se 6 proteínas com elevado grau de interação: HSP90AA1, HSPA5, SEC61A1, VCP, PERK e ATF4. Considerando ainda a sua importância funcional, as proteínas efetoras da via PERK, a GADD34 e a eIF2 foram destacadas para estudos de deteção experimentais. Neste sentido, foi confirmada pela primeira vez a presença das proteínas PERK e GADD34 em espermatozoides humanos. Estes resultados constituem o primeiro passo fundamental para avançar para estudos mais aprofundados relativamente à expressão e níveis de atividade destes candidatos, procurando perceber a contribuição dos mesmos na via de sinalização UPR e a sua eventual desregulação na infertilidade masculina.<br>The unfolded protein response (UPR) is an essential cell defense response against defects in protein folding and it is mainly triggered by the activation of ATF6, PERK and IRE1. Each sensor leads to different signal transduction mechanisms through the production of transcription factors that, in turn, regulate genes that increase the cell's ability to correct conformation of poorly folded proteins, ultimately hindering their aggregation. The past years shed light on the role of the UPR in several diseases. Regarding male infertility, few studies have focused on the implications of UPR components, hence the need to a prior approach concerning the presence of these components on the male reproductive system. Through a database search and using bioinformatics approaches, with the aim of identifying potential candidates associated with infertility phenotypes, a collection of UPR proteins in the testis, spermatozoa and seminal plasma was performed. To determine potential targets to scrutinize possible involvement in male infertility, a protein-protein interaction network analysis was performed, depicting 6 key proteins highly interconnected: HSP90AA1, HSPA5, SEC61A1, VCP, PERK and ATF4. Considering their functional value, the effector proteins of the PERK pathway, GADD34 and eIF2 were highlighted for experimental studies. Thus, the presence of the PERK and GADD34 were confirmed for the first time in human spermatozoa. These results constitute the first fundamental step towards further studies on the expression and activity levels of these candidates and understand their contribution to the UPR signaling pathway and their possible deregulation in male infertility

During development, the coordinated and sequential action of signals and regulatory factors controls how cells become different from each other and acquire specific fates. This information can be integrated in gene regulatory networks (GRNs) that model these processes over time and consider temporal and spatial changes of gene expression and how these are regulated. During early development, vertebrate sensory organs arise from the pre-placodal region at the border of the neural plate. Subsequently, FGF signalling plays a crucial role in inducing otic-epibranchial progenitors that ultimately give rise to the otic and epibranchial placodes. Downstream of FGF signalling, many transcription factors are activated. However, their regulatory relationships are not very clear. This project uses a bioinformatics approach to establish a GRN to model how multipotent progenitors transit through sequential regulatory states until they are committed to the ear lineage. To this end, using systematic perturbation experiments, new ear-specific genes have been identified some of which respond early to FGF. Focussing on these early genes, I have used phylogenetic footprinting combined with histone ChIP-seq to identify novel enhancers. Subsequently, I have investigated transcription factor binding sites within these enhancers to identify a small group of common regulators. In parallel, using mRNA-seq and perturbation data, I have reverse-engineered GRNs that recapitulate known interactions and predict new ones. Using a combination of these approaches, I have ultimately enriched a preliminary literature-based GRN by placing otic genes and their interactions into a hierarchy. Thus, this network is a resource for identifying key otic regulators and their targets and provides guidelines for future experiments.

A custom-built bioinformatics pipeline is used to costruct a species-level phylogeny for beetles (Coleoptera) using publicly available sequences from Genbank, including 8441 terminals 5 gene loci. 152 of 189 described beetle families are included, representing 2.17% of described species. The overall structure of publicly available data and its fit with Linnaean classifications are discussed. The dataset is further expanded by the inclusion of additional gene loci and relaxation of concatenation conditions, bringing total species to ~12,000. To overcome incomplete or incorrect identifications, a multi-partite matching algorithm is applied, for concatenation of partially conflicting taxon labels between gene loci, using species-level sequence clusters. The method is modified through the addition of country/specimen weighting between loci, and the incorporation of the the GMYC method of sequence-based species delimitation into the bioinformatics pipeline. GMYC and BlastClust approaches are compared, in terms of accuracy of species delimitation, supermatrix structure and topology of resulting trees. GMYC clusters are used as a framework for broad-scale comparisons of intraspecificvariation across the Coleoptera. The Coleoptera tree is used to illustrate a novel method for estimating total extant diversity by extrapolating from higher-taxon diversification rates, generating an estimate of 3.1 million beetle species globally. The sensitivity of the method to phylogenetic uncertainty within the data, and undersampling of families and subfamilies, is examined. Partial and complete mitochondrial genomes are used to generate the largest and most comprehensive phylogeny ever produced fromthis type of data. This tree is used as the basis for a molecular dating analysis, and the quantification of compositional heterogeneity among genes, taxa and sites within protein-coding genes. Non-homogenous substitution models are applied to help resolve problematic regions of the phylogeny, and the effects on topology and phylogenetic diversity of adding a previously unsampled regional fauna from Borneo are assessed.

Inflammatory bowel diseases (IBD) including Crohn’s disease (CD) and ulcerative colitis (UC) are associated with various hepatobiliary disorders. Two of the chronic hepatobiliary disorders that may coexist with inflammatory bowel diseases are: primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC). Previous studies have hypothesized that IBD, PBC, and PSC might share an underlying mechanism which contributes to the pathogenesis of the three conditions. In this study, a module-based network analysis and pathway enrichment analysis was applied on IBD, PSC, and PBC differentially expressed genes (DEGs). The sample data were obtained from the study by Ostrowski et al. (2019). A network module-based approach was applied to examine generated results where additional information about biological processes, pathways and molecular functions can be inferred. FunRich and Enrichr were utilized as functional enrichment tools. A protein interaction network was constructed for the three conditions using STRING. Functional modules and overlapping modules of IBD, PSC, and PBC were identified using different plug-ins in Cytoscape. Some of the results were consistent with the findings of Ostrowski et al. (2019) such as the ATP synthesis and signal transduction that is shared among the overlapping genes in IBD, PBC, and PSC. ModuLand highlighted nodes that have been previously reported to have a role in the pathogenesis of autoimmune diseases. The proposed approach demonstrated that the module-based approach contributes to similar results regarding biological processes and pathway enrichment of generated modules, compared to enrichment analysis of DEGs. In addition, the utilization of the ModuLand plug-in to find hierarchal layers of disease genes is still poorly researched and would benefit from more in-depth comparison with related tools for module discovery. For instance, implementing ModuLand plug-in can potentially support research in elucidating complex diseases.

The WHO classification of brain tumors is based on histological features and the aggressiveness of the tumor is classified from grade I to IV, where grade IV is the most aggressive. Today, the correlation between prognosis and tumor grade is the most important component in tumor classification. High grade gliomas, glioblastomas, are associated with poor prognosis and a median survival of 14 months including all available treatments. Low grade meningiomas, usually benign grade I tumors, are in most cases cured by surgical resection. However despite their benign appearance grade I meningiomas can, without any histopathological signs, in some cases develop bone invasive growth and become lethal. Thus, it is necessary to improve conventional treatment modalities, develop new treatment strategies and improve the knowledge regarding the basic pathophysiology in the classification and treatment of brain tumors. In this thesis, both proteomics and metabolomics have been applied in the search for biomarkers or biomarker patterns in two different types of brain tumors, gliomas and meningiomas. Proteomic studies were carried out mainly by surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS). In one of the studies, isobaric tags for relative and absolute quantitation (iTRAQ) labeling in combination with high-performance liquid chromatography (HPLC) was used for protein detection and identification. For metabolomics, gas-chromatography time-of-flight mass spectrometry (GC-TOF-MS) has been the main platform used throughout this work for generation of robust global metabolite profiles in tissue, blood and cell cultures. To deal with the complexity of the generated data, and to be able to extract relevant biomarker patters or latent biomarkers, for interpretation, prediction and prognosis, bioinformatic strategies based on chemometrics were applied throughout the studies of the thesis. In summary, we detected differentiating protein profiles between invasive and non-invasive meningiomas, in both fibrous and meningothelial tumors. Furthermore, in a different study we discovered treatment induce protein pattern changes in a rat glioma model treated with an angiogenesis inhibitor. We identified a cluster of proteins linked to angiogenesis. One of those proteins, HSP90, was found elevated in relation to treatment in tumors, following ELISA validation. An interesting observation in a separate study was that it was possible to detect metabolite pattern changes in the serum metabolome, as an effect of treatment with radiotherapy, and that these pattern changes differed between different patients, highlighting a possibility for monitoring individual treatment response.  In the fourth study of this work, we investigated tissue and serum from glioma patients that revealed differences in the metabolome between glioblastoma and oligodendroglioma, as well as between oligodendroglioma grade II and grade III. In addition, we discovered metabolite patterns associated to survival in both glioblastoma and oligodendroglioma. In our final work, we identified metabolite pattern differences between cell lines from a subgroup of glioblastomas lacking argininosuccinate synthetase (ASS1) expression, (ASS1 negative glioblastomas), making them auxotrophic for arginine, a metabolite required for tumor growth and proliferation, as compared to glioblastomas with normal ASS1 expression (ASS1 positive). From the identified metabolite pattern differences we could verify the hypothesized alterations in the arginine biosynthetic pathway. We also identified additional interesting metabolites that may provide clues for future diagnostics and treatments. Finally, we were able to verify the specific treatment effect of ASS1 negative cells by means of arginine deprivation on a metabolic level.

L’accumulation exponentielle de données expérimentales générées par les technologies à haut débit a considérablement favorisé l’analyse bioinformatique des séquences promotrices. A ce jour, des approches bioinformatiques ont été utilisées par les biologistes afin de faciliter l’identification des motifs de régulation dans les régions promotrices avant d’entreprendre des caractérisations biochimiques onéreuses en temps. Cependant, l’émergence d’une quantité colossale de données expérimentales, de programmes de prédiction et de méthodes complémentaires souligne l’absolue nécessité de développer des approches intégratives afin d’améliorer l’analyse des promoteurs assistée par ordinateur. Dans ce contexte, nous avons développé PromAn, un outil polyvalent et intégratif qui offre un panel de modules couvrant une grande partie des approches utilisées dans le domaine de l’analyse des promoteurs. Le programme ne requiert aucune connaissance préalable sur la séquence génomique à étudier et inclut une évaluation de la conservation au cours de l’évolution des régions promotrices, une validation des sites d’initiation de la transcription ainsi qu’une prédiction des sites de fixation de facteurs de transcription potentiellement actifs. PromAn implémente deux versions semi-automatiques (en local et sur un serveur web) ainsi qu’une version automatisée dédiée aux analyses à haut débit et utilisée en étroite conjonction avec des groupes de gènes potentiellement co-régulés. Dans le cadre de nombreuses collaborations avec divers groupes de recherche, l’efficacité de PromAn a pu être démontrée en étroite synergie avec des validations expérimentales afin de localiser et d’identifier les sites de fixation de facteurs de transcription biologiquement actifs. La version automatisée de PromAn dédiée à l’analyse à haut débit facilitera la compréhension de réseaux de régulations complexes et surtout leurs impacts sur la santé et les maladies humaines<br>The exponential accumulation of high-throughput experimental data and complete genome sequences has greatly encouraged promoter sequence analysis through bioinformatics. To date, bioinformatics approaches have been used by biologists to facilitate the identification of regulatory motifs in promoter regions before engaging in time-consuming biochemical characterizations. However, the emergence of a huge amount of experimental data, prediction programs and complementary methods means that integrative approaches have become essential to improve in silico promoter analysis. In this context, we have developed PromAn, a versatile and integrative tool which provides a wide range of state-of-the-art promoter analyses. The program requires minimal prior knowledge of the input genomic sequence and includes an evaluation of the evolutionary conservation of promoter regions, a validation of the transcriptional start sites as well as a prediction of potentially active transcription factor binding sites. PromAn has been implemented in two expert-guided versions (local and web server) as well as a high-throughput automatic version that is used in combination with gene groups assumed to be co-regulated. In the context of a number of collaborations with different research groups, the efficiency of PromAn has been demonstrated in strong synergy with experimental validations through the localization and identification of bona-fide transcription factor binding sites. Hopefully, the PromAn high-throughput version will facilitate the understanding of complete regulatory networks and their impact in human health and diseases

Ebolavirus (EBOV) is a negative sense, single stranded RNA virus that causes Ebola hemorrhagic fever. This disease causes substantial morbidity and mortality in humans, with death occurring in 50-90% of cases. Despite years of intensive research, much of the molecular mechanism underlying the entry of EBOV remains unknown. We performed a bioinformatics screen to identify novel entry cofactors by correlating mRNA expression in a panel of human cancer cell lines with permissivity to the EBOV entry glycoprotein. This assay identified several known EBOV entry cofactors such as actin and the tyrosine kinase Axl. In addition, several genes involved in macropinocytosis and endosomal maturation were also correlated with EBOV permissivity. Subsequent evaluation of plasma membrane proteins correlated by this screen showed T-cell immunoglobulin and mucin domain-1 (TIM-1) mRNA expression correlated extremely well with EBOV pseudovirion transduction. Depletion of TIM-1 from highly-permissive cells inhibits EBOV pseudovirion transduction. Conversely, expression of TIM-1 in poorly-permissive cells significantly and specifically enhances EBOV pseudovirion transduction and infection. TIM-1 binds to EBOV GP and this binding is important in the initial interaction between the virus and the host cell. ARD5, a TIM-1 mAb, significantly inhibits EBOV GP-mediated entry into several cell lines and primary human airway epithelia in a dose and time-dependent manner. Therefore, TIM-1 is the first receptor identified for EBOV. Additionally, AMP-activated protein kinase (AMPK) mRNA correlated strongly with EBOV pseudovirion transduction. Compound C, a specific AMPK inhibitor, inhibited EBOV pseudovirion transduction and infection in a time and dose-dependent manner into several cell lines and primary human monocyte derived macrophages. Mouse embryonic fibroblasts (MEFs) lacking functional AMPK were significantly less permissive to EBOV GP-mediated infection that WT MEFs. Visualization of virus entry into these cells revealed that EBOV causes actin polymerization independently of AMPK, but AMPK-/- cells do not form lamellipodia in the presence of EBOV and, consequently, cannot internalize virus into cells by macropinocytosis.

Advances in science and computing technology have accelerated the development and dissemination of a wide range of big data platforms such as bioinformatics into the biomedical and life sciences environments. Bioinformatics brings the promise of enabling life scientists to easily and effectively access large and complex data sets in new ways, thus promoting scientific discoveries by for example generating, validating, and refining hypotheses based on in silico analysis (performed on computer). Meanwhile, life scientists still face challenges in working with big data sets such as difficulties in data extraction and analyses arising from distributed and heterogeneous databases, user interface inconsistencies and discrepancies in results. Moreover, the interdisciplinary nature of modern science adds to significant gaps in scientists' performance caused by limited proficiency levels with bioinformatics resources and a lack of common language across different disciplines. Although developers of bioinformatics platforms are slowly beginning to move away from function-oriented software engineering approaches and towards to user-centered design approaches, they rarely consider users' value, and expectations that embrace different user contexts. Further, there is an absence of research that specifically aims to support the broad range of users from multiple fields of study, including 'wet' (lab-based) and dry' (computational) research communities. Therefore, the ultimate goal of this research is to investigate life scientists' user experiences with knowledge resources and derive design implications for delivering consistent user experiences across different user classes in order to better support data-intensive research communities. To achieve this research goal, we used the theory of distributed cognition as a framework for representing the dynamic interactions among end users and knowledge resources within computer-supported and -mediated environments. To be specific, this research focused on how online bioinformatics resources can be improved in order to both mitigate performance differences among the diverse user classes and better support distributed cognitive activities in data-intensive interdisciplinary research environments. This research consists of three parts: (1) understanding user experience levels with current bioinformatics resources and key determinants to encourage distributed cognitive activities, especially knowledge networking, (2) gaining in-depth understanding of scientists' insight generation behavior and human performance associated with individual differences (i.e., research roles and cognitive styles), and (3) identifying in-context usefulness, and barriers to make better use of bioinformatics resources in real working research contexts and derive design considerations to satisfactorily support positive user experiences. To achieve our research goals, we used a mixed-methods research approach that combines both quantitative (Study 1 and 2) and qualitative (Study 3) methods. First, as a baseline for subsequent studies, we conducted an empirical survey to examine 1) user experience levels with current bioinformatics resources, 2) important criteria to adequately support user requirements, 3) levels of knowledge networking (i.e., knowledge sharing and use) and relationship to users' larger set of distributed cognitive activities, and, 4) key barriers and enablers of knowledge networking. We collected responses from 179 scientists and our findings revealed that lack of integration, inconsistent results and user interfaces across bioinformatics resources, and perceived steep learning curves are current limitations to productive user experiences. Performance-related factors such as speed and responsiveness of resources and ease of use ranked relatively high as important criteria for bioinformatics resources. Our research also confirmed that source credibility, fear of getting scooped, and certain motivation factors (i.e., reciprocal benefit, reputation, and altruism) have an influence on scientists' intention to engage in distributed cognitive activities. Second, we conducted a laboratory experiment with a sample of 16 scientists in the broad area of bench and application sciences. We elicited 1) behavior characteristics, 2) insight characteristics, 3) gaze characteristics, and 4) human errors in relation to individual differences (i.e., research roles such as bench and application scientists, cognitive styles such as field-independent and dependent people) to identify whether human performance gaps exist. Our results (1) confirmed significant differences with respect to insight generation behavior and human performance depending on research roles, and (2) identified some relationships between scientists' cognitive styles and human performance. Third, we collected a rich set of qualitative data from 6 scientists using a longitudinal diary study and a focus group session. The specific objective of this study was to identify in-context usefulness and barriers to using knowledge resources in a real work context to subsequently derive focused design implications. For this work, we examined 1) the types of distributed cognitive activities participants performed, 2) the challenges and alternative actions they faced, 3) important criteria that influenced tasks, and 4) values to support distributed cognitive activities. Based on the empirical findings of this study, we suggest design considerations to support scientists' distributed cognitive activities from user experience perspectives. Overall, this research provides insights and implications for user interface design in order to support data-intensive interdisciplinary communities. Given the importance of today's knowledge-based interdisciplinary society, our findings can also serve as an impetus for accelerating a collaborative culture of scientific discovery in online biomedical and life science research communities. The findings can contribute to the design of online bioinformatics resources to support diverse groups of professionals from different disciplinary backgrounds. Consequently, the implications of these findings can help user experience professionals and system developers working in biomedical and life sciences who seek ways to better support research communities from user experience perspectives.<br>Ph. D.

Sequence features play an important role in the regulation of translation initiation. This thesis focuses on the sequence features affecting eukaryotic initiation. The characteristics of 5' untranslated region in Saccharomyces cerevisiae were explored. It is found that the 40 nucleotides upstream of the start codon is the critical region for translation initiation in yeast. Moreover, this thesis attempted to solve some controversies related to the start codon context. Two key nucleotides in the start codon context are the third nucleotide upstream of the start codon (-3 site) and the nucleotide immediately following the start codon (+4 site). Two hypotheses regarding +4G (G at +4 site) in Kozak consensus, the translation initiation hypothesis and the amino acid constraint hypothesis, were tested. The relationship between the -3 and +4 sites in seven eukaryotic species does not support the translation initiation hypothesis. The amino acid usage at the position after the initiator (P1' position) compared to other positions in the coding sequences of seven eukaryotic species was examined. The result is consistent with the amino acid constraint hypothesis. In addition, this thesis explored the relationship between +4 nucleotide and translation efficiency in yeast. The result shows that +4 nucleotide is not important for translation efficiency, which does not support the translation initiation hypothesis. This work improves our current understanding of eukaryotic translation initiation process.

Includes abstract.<br>Includes bibliographical references.<br>In this project, we review some existing pathway-based approaches for GWA study analyses, by exploring different implemented methods for combining effects of multiple modest genetic variants at gene and pathway levels. We then propose a graph-based method, ancGWAS, that incorporates the signal from GWA study, and the locus-specific ancestry into the human protein-protein interaction (PPI) network to identify significant sub-networks or pathways associated with the trait of interest. This network-based method applies centrality measures within linkage disequilibrium (LD) on the network to search for pathways and applies a scoring summary statistic on the resulting pathways to identify the most enriched pathways associated with complex diseases.

One of the key goals of current cancer research is the identification of biologic molecules that allow non-invasive detection of existing cancers or cancer precursors. One way to begin this process of biomarker discovery is by using time-of-flight mass spectroscopy to identify proteins or other molecules in tissue or serum that correlate to certain cancers. However, there are many difficulties associated with the output of such experiments. The distribution of protein abundances in a population is unknown, the mass spectroscopy measurements have high variability, and high correlations between variables cause problems with popular methods of data mining. to mitigate these issues, Bayesian inductive methods, combined with non-model dependent information theory scoring, are used to find feature sets and build classifiers for mass spectroscopy data from blood serum Such methods show improvement over existing measures, and naturally incorporate measurement uncertainties. Resulting Bayesian network models are applied to three blood serum data sets: one artificially generated, one from a 2004 leukemia study, and another from a 2007 prostate cancer study. Feature sets obtained appear to show sufficient stability under cross-validation to provide not only biomarker candidates but also families of features for further biochemical analysis.

Radical SAM enzymes are ancient, essential enzymes. They perform radical chemical reactions in virtually all living organisms and are involved in producing antibiotics, generating greenhouse gases, human health, and likely many other essential roles that have yet to be established. A wide variety of reactions have been characterized from this group of enzymes, including hydrogen abstractions, the transferring of methylthio groups, complex cyclization and rearrangement reactions, and others. However, many radical SAM enzymes have yet to be identified or characterized. There have been great leaps forward in the amount of enzyme sequences that are available in public databases, but experiments to investigate what chemical reactions the enzymes perform take a great deal of time. In our work, we utilize Hidden Markov Models to identify possible radical SAM enzymes and predict their possible functions through BLAST alignments and homology modelling. We also explore their distribution across the tree of life and determine how it is correlated with organism oxygen tolerances, because the core iron-sulfur cluster is oxygen sensitive. Trends in the abundances of radical SAM enzymes depending on oxygen tolerances were more apparent in prokaryotes than in eukaryotes. Although eukaryotes tend to have fewer radical SAM enzymes than prokaryotes, we were able to analyze uncharacterized radical SAM enzymes from both an aerobic eukaryote (Entamoeba histolytica) and a eukaryote capable of oxygenic photosynthesis (Gossypium barbadense), and predict the reactions they catalyze. This work sets the stage for the functional characterization of these essential yet elusive enzymes in future laboratory experiments.<br>Master of Science in Life Sciences<br>Radical SAM enzymes are ancient, essential enzymes that perform chemical reactions in virtually all living organisms. We do know that they are involved in producing antibiotics, human health, and generating greenhouse gases. We also know that there are many radical SAM enzymes whose functions remain a mystery. There have been great leaps forward in the amount of enzyme sequences that are available in public databases, but experiments to investigate what chemical reactions enzymes perform take a great deal of time. The experiments are especially difficult for radical SAM enzymes because the oxygen we breathe can break the enzymes down in a laboratory. In our work, we utilize computational techniques to identify possible radical SAM enzymes and predict what reactions they might catalyze. Because these enzymes are vulnerable to oxygen in laboratory environments, we also explore whether organisms that breathe oxygen have fewer of these enzymes than organisms that perform anaerobic respiration instead. We found that does seem to be the case in microbes like bacteria and archaea, but the results were not as consistent for eukaryotes. We then chose radical SAM enzymes we had identified from both an aerobic eukaryote (Entamoeba histolytica) and a eukaryote capable of producing oxygen (Gossypium barbadense), and predicted the reactions they catalyze. This work sets the stage for the functional characterization of these essential yet elusive enzymes in future laboratory experiments.

Estudios clínicos y epidemiológicos muestran que la comorbilidad, la coexistencia de varias enfermedades en un mismo paciente, tiene un gran impacto en la evolución de su estado de salud. Por lo tanto, el análisis de comorbilidades es clave para identificar nuevas estrategias preventivas y terapéuticas, trabajando hacia una medicina más personalizada. Con el fin de aprovechar el potencial del creciente volumen de información de salud disponible en la época del “big data”, esta tesis presenta el desarrollo de nuevas herramientas y recursos para la identificación de patrones de comorbilidad, basados en la información clínica y molecular. Las herramientas comoRbidity y psygenet2r presentados en esta tesis permiten analizar las comorbilidades de forma amplia y completa, y en particular, ofrecen a los usuarios la posibilidad de diseñar su propio estudio de comorbilidad según sus necesidades y especificaciones. Por otra parte, debido al importante papel que juega la información molecular en la interpretación de la causa de comorbilidades y la falta de recursos para recopilar esta información en el área específica de los trastornos mentales, una nueva base de datos, PsyGeNET, se ha desarrollado centrada en las asociaciones gen-enfermedad. En resumen, todas las herramientas desarrolladas en esta tesis, disponibles en el dominio público y aplicadas ya en estudios del campo biomédico, son de gran valor práctico para el análisis de la comorbilidad y puede ayudar a transformar la información clínica en conocimiento que puede ser analizado, interpretado por los investigadores y aplicado para lograr una práctica de la medicina más personalizada.<br>Clinical and epidemiological studies show that comorbidity, the coexistence of disorders in a patient, has a great impact on the evolution of the health status of patients. Therefore, comorbidity analysis is key to identify new preventive and therapeutic strategies, walking through a more personalized medicine. In order to harness the power of the increasing volume of available health information in the era of big data, this thesis presents the development of new tools and resources for the identification of comorbidity patterns, based on the clinical and molecular information. The comoRbidity package and the psygenet2r one presented in this thesis provide an adequately complete and comprehensive analysis of comorbidities and in particular, offer the users the possibility to design their own comorbidity study according to their needs and specifications. Moreover, due to the significant role that plays the molecular information in interpreting the cause of disease comorbidities and the lack of resources to collect that information in the specific area of mental disorders, a new manual curated database, PsyGeNET, focus on gene-disease association has also been developed. In summary, all the tools developed in this thesis, available to the scientific community and already applied to several studies in the biomedical field, are of immense practical value for the comorbidity analysis and can aid to transform clinical information in a form of knowledge that can be analyzed, interpreted by researchers and applied leading overall, to more personalized medicine.

[ES] Uno de los aspectos más apasionantes de la transcripción es la plasticidad transcriptómica y proteómica mediada por los procesos de regulación post-transcripcional (PTR). Los mecanismos PTR como el splicing alternativo (AS) y la poliadenilación alternativa (APA) han emergido como procesos estrechamente regulados que juegan un papel clave en la generación de la complejidad transcriptómica y están asociados con la coordinación de la diferenciación celular o el desarrollo de tejidos. Sin embargo nuestro conocimiento sobre cómo estos mecanismos regulan las propiedades de los productos resultantes para definir el fenotipo es aún muy reducido. La cantidad de variantes existentes y el amplio rango de posibles consecuencias funcionales, hacen su validación funcional una tarea impracticable si se realiza caso por caso. Además, la falta de herramientas para la evaluación funcional orientada a isoformas ha provocado que gran parte del trabajo computacional haya empleado pipelines ad-hoc aplicadas a sistemas biológicos específicos o simplemente hayan confiado en análisis de enriquecimiento GO, los cuales no son informativos del impacto en las propiedades de las isoformas que hay detrás de la regulación PTR. De hecho, a pesar de las más de sesenta mil publicaciones relativas al AS, muy pocas isoformas se han asociado con propiedades específicas, mientras que el número de nuevas variantes AS/APA con function desconocida crece exponencialmente debido a las técnicas de secuenciación de segunda generación (NGS). Además, y debido a limitaciones técnicas de las NGS para reconstruir la estructura de los transcritos, las tecnologías de secuenciación de tercera generación (TGS) están definiendo una nueva era en la que, por primera vez, es posible conocer la secuencia de elementos estructurales y funcionales en los mRNAs. En esta tesis se han abordado tres propósitos principales para poder avanzar en el estudio funcional de las isoformas. En primer lugar, con las TGS siendo cada vez más utilizadas, la evaluación de la calidad de los transcriptomas \textit{de novo} es esencial para asegurar la fiabilidad de la diversidad transcriptómica encontrada. La falta de análisis de calidad orientados a secuencias largas ha motivado el desarrollo de SQANTI, una pipeline automatizado para la exhaustiva evaluación de TGS transcriptomas. En segundo lugar, la información a nivel de gen de la mayoría de bases de datos funcionales sigue siendo el principal escollo para el estudio de la variabilidad entre isoformas, especialmente en el caso de las isoformas nuevas, en las que las bases de datos estáticas impiden su caracterización. Así, hemos diseñado IsoAnnot, que construye una base de datos de anotaciones funcionales con resolución a nivel de isoformas integrando información diseminada por múltiples bases de datos y métodos de predicción. Finalmente, la indisponibilidad de métodos para estudiar el impacto funcional de la regulación de isoformas, nos ha motivado a desarrollar tappAS, una herramienta dinámica, flexible y diseñada para facilitar el abordaje de este tipo de estudios. Por lo tanto, durante esta tesis hemos desarrollado una infraestructura que resuelve los retos principales del análisis funcional de isoformas, proporcionando un conjunto de nuevos métodos y herramientas que ofrecen una oportunidad única para explorar cómo el fenotipo se especifica post-transcripcionalmente, mediante la alteración de las propiedades funcionales de las isoformas expresadas. La aplicación de nuestro análisis a un doble sistema de diferenciación neuronal en ratón definió el efecto de la regulación de isoformas entre la diferenciación de motoneuronas y oligodendrocitos para múltiples elementos funcionales. Entre ellos, hemos descubierto regiones transmembrana que son diferencialmente incluidas en las isoformas expresadas entre ambos tipos celulares y cuya regulación podría estar contribuyendo al control de<br>[CAT] Un dels aspectes més emocionants de la biologia del transcriptoma és l'adaptabilitat contextual de transcriptomes i proteomes eucariotes mitjançant la regulació post-transcripcional (PTR). Els mecanismes PTR, com el splicing alternatiu (AS) i la poliadenilació alternativa (APA), s'han convertit en processos molt regulats que juguen un paper clau en la generació de la complexitat del transcriptoma i en la coordinació de la diferenciació cel·lular o del desenvolupament de teixits. No obstant això, el nostre coneixement de com aquests mecanismes imprimeixen característiques funcionals diferents al conjunt resultant d'isoformes per definir el fenotip observat és encara escàs. El nombre de variants de PTR i les seues conseqüències potencialment funcionals fa que la validació funcional sigui una tasca poc pràctica si es fa cas per cas. A més, la manca d'enfocaments funcionals orientats a isoformes ha fet que gran part del treballs computacionals per esbrinar qüestions funcionals a nivell de transcriptoma siguen estratègies computacionals ad hoc aplicades a sistemes biològics específics o bé basats en un simple anàlisi d'enriquiment GO, que no aporten informació sobre l'impacte de la PTR sobre les propietats de les isoformes. Així, malgrat les més de 60.000 publicacions existents sobre AS, poques de les isoformes existents s'han associat a propietats específiques, mentre que el nombre de noves variants AS/APA amb funcions desconegudes i fins i tot inexplorades augmenta de manera exponencial gràcies a la seqüenciació de nova generació (NGS). A causa de les limitacions tècniques del NGS per reconstruir l'estructura dels transcrits, la seqüenciació d'alt rendiment de transcrits de longitud completa mitjançant tecnologies de tercera generació (TGS) obre una nova era en la transcriptòmica, ja que millora la definició dels models genètics i, per primera vegada, permet associar amb precisió esdeveniments funcionals dins de la molècula d'ARN. Aquesta tesi aborda tres grans reptes per a progressar en l'estudi de la funció de les isoformes. En primer lloc, amb l'aparició i la popularitat creixent del TGS, la definició precisa i la caracterització completa dels transcriptomes de novo són essencials per garantir la qualitat de qualsevol conclusió sobre la diversitat del transcriptoma. La manca d'anàlisis de qualitat orientats a lectures llargues va motivar el desenvolupament de SQANTI (https://bitbucket.org/ ConesaLab / sqanti), una estratègia computacional automatitzada per a la caracterització estructural i l'avaluació de la qualitat dels transcriptomes de longitud completa. En segon lloc, els recursos funcionals existents centrats en el gen suposen una gran limitació per a l'estudi extensiu de la variabilitat funcional de les isoformes, especialment en les noves isoformes, que no es poden caracteritzar per bases de dades estàtiques. Per tant, vam dissenyar IsoAnnot, que construeix dinàmicament una base de dades amb anotacions funcionals a nivell d'isoforma, que utilitza com a informació d'entrada les seqüències dels transcrits i integra informació de diverses bases de dades i mètodes de predicció. Finalment, com no hi havia cap mètode per interrogar l'impacte funcional del PTR, vam desenvolupar nous enfocaments i eines fàcils d'utilitzar, com ara tappAS (http://tappas.org/), dissenyada per facilitar als investigadors els estudis funcionals de transcriptoma complet i de regulació d'isoformes en contexts específics. Per tant, aquesta tesi descriu el desenvolupament d'un marc d'anàlisi que aborda els reptes fonamentals de l'anàlisi funcional d'isoformes. Aplicada a un sistema de diferenciació neuronal murina, vam descobrir regions transmembrana específiques d'isoformes, la modulació de les quals per PTR podria contribuir a controlar la dinàmica mitocondrial específica del tipus cel·lular durant la determinació del destí neuronal.<br>[EN] One of the most exciting aspects of transcriptome biology is the contextual adaptability of eukaryotic transcriptomes and proteomes by post-transcriptional regulation (PTR). PTR mechanisms such as alternative splicing (AS) and alternative polyadenylation (APA) have emerged as tightly regulated processes playing a key role in generating transcriptome complexity and coordinating cell differentiation or tissue development. However, how these mechanisms imprint distinct functional characteristics on the resulting set of isoforms to define the observed phenotype remains poorly understood. The number of PTR variants and their resulting range of potentially functional consequences makes their functional validation an impractical task if done on a case-by-case basis. Besides, the lack of isoform-oriented functional profiling approaches has made that much of the computational work done to elucidate transcriptome-wide functional questions has either involved ad hoc computational pipelines applied to specific biological systems or has relied on simple GO-enrichment analysis that are not informative about the PTR impact on isoform properties. Thus, even though more than 60,000 publications on AS, a few number of existing isoforms have been associated with specific properties while the number of novel AS/APA variants with unknown and even unexplored functions is exponentially increasing thanks to the use of next-generation sequencing (NGS). Due to the technical limitations of NGS to reconstruct the transcript structure, high-throughput sequencing of full-length transcripts using third-generation technologies (TGS) is opening up a new transcriptomics era that enhances the definition of gene models and, for the first time, enables to precisely associate functional events within the RNA molecule. This thesis addresses three major challenges to the progression of the study of isoform function. First, with the emergence and increasing popularity of TGS, the accurate definition and comprehensive characterisation of de novo transcriptomes is essential to ensure the quality of any conclusions on transcriptome diversity drawn from these data. The lack of long-read oriented quality aware analysis motivated the development of SQANTI \url{(https://bitbucket.org/ConesaLab/sqanti)}, an automated pipeline for the structural characterization and quality assessment of full-length transcriptomes. Secondly, the gene-centric nature of functional resources remained the major limitation to the extended study of functional isoform variability, especially for novel isoforms, which cannot be characterised by static databases. Thus, we designed IsoAnnot, which dynamically constructs an isoform-resolved rich database of functional annotations by using as input transcript sequences and integrating information disseminated across several databases and prediction methods. Finally, because no methods to interrogate the functional impact of PTR were available, we developed novel approaches and user-friendly tools such as tappAS \url{(http://tappas.org/)}, designed to facilitate researchers the transcriptome-wide functional study of context-specific isoform regulation. Thereby, this thesis describes the development of an analysis framework that tackles the fundamental challenges of the isoform functional analysis by providing a set of novel methods and tools that offer an unique opportunity to explore how the phenotype is specified by altering the functional characteristics of expressed isoforms. Applied to a murine neural differentiation system, our pipeline profiled the effect of isoform regulation on the inclusion of several functional elements within transcripts between motor-neuron and oligodendrocyte differentiation systems and specifically, we discovered isoform-specific transmembrane regions whose modulation by PTR might contribute to control cell type-specific mitochondrial dynamics during neural fate determination.<br>This work was funded by the following grants: From 2014 to 2018. FPU: Training programme for Academic Staff. Spanish Ministry of Education, FPU2013/02348. From 2016 to 2019. NOVELSEQ: Novel methods for new challenges in the analysis of high-throughput sequencing data. MINECO, BIO2015-1658-R. From 2014 to 2017. DEANN: Developing a European American NGS Network. EU Marie Curie IRSES, GA-612583.<br>Fuente Lorente, LDL. (2019). Development of a bioinformatics approach for the functional analysis of alternative splicing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/124974<br>TESIS

A growing body of evidence suggests that genetic variants that alter gene expression are responsible for many phenotypic differences across individuals, particularly for the risk of developing common diseases. However, the molecular mechanisms that underlie the vast majority of associations between genetic variants and their phenotypes remain unknown. An important limiting factor is that genetic variants remain difficult to interpret, particularly in noncoding sequences. Developing truly systematic approaches for characterizing regulatory variants will require: (a) improved annotations for the genomic sequences that control gene expression, (b) a more complete understanding of the molecular mechanisms through which genetic variants, both coding and noncoding, can affect gene expression, and (c) better experimental tools for testing hypotheses about regulatory variants. In this dissertation, I present conceptual and methodological advances that directly contribute to each of these goals. A recurring theme in all of these developments is the statistical modeling of protein-DNA interactions and its integration with other data types. First, I describe enhancer-FACS-Seq, a high-throughput experimental approach for screening candidate enhancer sequences to test for in vivo, tissue-specific activity. Second, I present an integrative computational analysis of the in vivo binding of NF-kappaB, a key regulator of the immune system, yielding new insights into how genetic variants can affect NF-kappaB binding. Next, I describe the first comprehensive survey of coding variation in human transcription factors and what it reveals about additional sources of genetic variation that can affect gene expression. Finally, I present SIFTED, a statistical framework and web tool for the optimal design of TAL effectors, which have been used successfully in genome editing and can thus be used to test hypotheses about regulatory variants. Together, these developments help fulfill key needs in the quest to understand the molecular basis of human phenotypic variation.<br>Biophysics

The pathogenic species of trypanosomatids, including Trypanosoma brucei, T. cruzi, and Leishmania spp, cause serious human as well as animal diseases, with a very high incidence and mortality rate if untreated. Although the genome sequences of several trypanosomatids have been known for several years, many aspects of gene function and gene regulation are still unclear in these organisms. Most importantly, the lack of similarity between the majority of their genes and characterized genes of other organisms has limited our understanding of the gene functions in trypanosomatids. Not only the functions of many genes are unknown, the factors that are involved in their regulation are mostly uncharacterized. Trypanosomatids primarily rely on post-transcriptional programs for regulation of gene expression, and transcriptional regulation is of least importance. The genomes of these organisms harbour a large number of RNA-binding proteins with potential role in regulating mRNA stability and translation; however, the sequence specificity of these RNA-binding proteins and their function is mostly unknown. The focus of this thesis is on development of new methods for homology-independent functional characterization of genes in trypanosomatids, and deciphering the programs that are involved in their regulation. First, I describe a novel universal relationship between codon usage and gene function, and show the utility of this relationship for functional characterization of genes in various organisms, including trypanosomatids. This relationship most probably points to the role of codon usage in dynamic regulation of protein expression in different conditions, and helps the cell to adapt to new environments and conditions by synchronously regulating proteins with required functions. Then, I introduce a computational approach for identification of function-specific cis-acting regulatory elements, and demonstrate the utility of this approach for identification of potential regulatory elements in trypanosomatids, as well as for prediction of gene function based on the flanking regulatory sequences. I also show that combination of cis-regulatory elements and codon usage is a strong predictor of gene function in trypanosomatids. In addition to these methods, which can identify biological processes and pathways, a new method for identification of protein molecular functions based on short sequence signatures is introduced in this thesis. I show that this new method is able to identify function-specific protein short motifs that present functional sites on proteins, and demonstrate the utility of these motifs in predicting protein molecular function in trypanosomatids. In addition to these sequence-based approaches, I also explore the possibility of predicting trypanosomatid gene functions based on co-expression. I present the first co-expression network of T. brucei, which is constructed by combining several microarray datasets from different studies, and use it for predicting new components of several essential pathways in this organism. This analysis suggested the presence of a conserved post-transcriptional regulatory network in trypanosomatids, which encouraged us to develop a novel framework for identification of regulatory programs with high network-level conservation across multiple species. This framework revealed an extensive set of conserved regulatory programs in trypanosomatids, many of which could be validated using available expression datasets as well as our microarray profiles of chemical perturbations. The studies described here contribute significantly to functional annotation of genes in trypanosomatids, and identify the regulatory mechanisms that govern gene expression in these organisms. Furthermore, the introduced methods can be used for functional annotation of many uncharacterized genes and identification of gene regulatory programs in virtually all organisms with available genome sequences.<br>Les espèces pathogènes de l'ordre des trypanosomatida, incluant Trypanosoma brucei, T. cruzi, et différentes espèces de Leishmania sont responsables de sérieuses maladies humaines et animales, avec une très forte incidence et taux de mortalité élevé lorsque non soignées. Bien que les génomes de plusieurs trypanosomatida soient disponibles depuis plusieurs années, de nombreux aspects de la fonction et de la régulation génique restent inexplorés chez ces organismes. Les trypanosomatida se reposent principalement sur des mécanismes post-transcriptionels pour la régulation de l'expression génique, et la régulation de la transcription n'a que peu d'importance. Les génomes de ces organismes hébergent un grand nombre de protéine se liant à l'ARN avec des rôles potentiels dans la régulation de la stabilité et de la traduction des ARNm. Néanmoins, les séquences spécifiques de ces protéines se liant à l'ARN et leurs fonctions restent principalement méconnues. L'objectif de cette thèse se situe au niveau du développement de nouvelles méthodes indépendantes de l'homologie pour permettre la caractérisation fonctionnelles de gènes chez les trypanosomatida, et de déchiffrer les mécanismes impliqués dans cette régulation. Premièrement, je décris une nouvelle relation universelle entre l'utilisation des codons et la fonction génique, et montre l'utilité de cette relation pour la caractérisation de gènes dans divers organismes, incluant les trypanosomatida. Cette relation pointe probablement vers un rôle de l'utilisation des codons dans la régulation dynamique de l'expression protéique sous diverses conditions, et aide la cellule à s'adapter à de nouveaux environnements et conditions en synchronisant la régulation des protéines avec les fonctions requises. J'ai introduis une approche computationnelle pour l'identification d'éléments cis-régulateurs fonction-spécifiques et démontré l'utilité de cette approche pour l'identification d'éléments régulateurs potentiels chez les trypanosomatida, ainsi que pour la prédiction de fonctions géniques basées sur les séquences régulatrices flanquantes. En plus de ces méthodes, qui peuvent identifier biologiquement des phénomènes et des voies métaboliques, une nouvelle procédure pour l'identification des fonctions moléculaires des protéines, basée sur de courtes signatures de séquences, est introduite dans cette thèse. Outre cette approche basée sur les séquences, j'explore également la possibilité de prédire la fonction de certains gènes des trypanosomatida en me basant sur la co-expression. Je présente le premier réseau de co-expression de T. brucei, élaboré en combinant plusieurs jeux de données de microarray provenant de différentes études, et les utilise pour prédire de nouveaux éléments de multiples voies métaboliques essentielles dans cet organisme. Cette analyse suggère la présence de réseaux post-transcriptionels conservés chez les trypanosomatida, ce qui nous encourage à mettre au point un nouveau cadre expérimental pour l'identification de mécanismes régulateurs avec un fort niveau de conservation au sein de multiples espèces. Ce cadre expérimental a révélé une somme importante de mécanismes régulateurs conservés chez les trypanosomatida, dont beaucoup pourraient êtres validés en utilisant des données d'expression disponibles ainsi qu'avec des profils de perturbations chimiques de microarrays. Les études décrites ici contribuent significativement à l'annotation génique fonctionnelle chez les trypanosomatida, et permet d'identifier des mécanismes de régulation qui gouvernent l'expression génique de ces organismes. De plus, les méthodes introduites peuvent être utilisée pour l'annotation fonctionnelle de nombreux gènes non-caractérisés et l'identification de programmes de régulation génique dans virtuellement n'importe quel organisme dont le génome est disponible.

Breast cancers fall into two major classes depending on their estrogen receptor (ER) status. ER+ and ER- tumors have very different molecular phenotypes, and may have distinct cells of origin. ER- tumors generally fail to respond to endocrine therapy and have a poorer prognosis. To develop a comprehensive understanding of the gene networks active in ER+ compared to ER- breast cancers, we performed a meta-analysis of Grade 3 breast cancers from five published datasets. A measure of association with ER status taking into account intra- and inter-study variability was calculated for every probe set. The meta-analysis revealed that ER-/Grade 3 tumors show increased expression of proliferation-associated functional categories when compared to ER+/Grade 3 tumors. Using Gene Set Enrichment Analysis we show that transcript levels of direct transcriptional targets of ER are lower in ER- tumors, but that expression of other estrogen-induced genes is higher in ER- tumors. Transcript levels of both direct and other targets of the estrogen-regulated MYC gene and the E2F family of genes are significantly higher in ER- tumors. The increased expression of targets of MYC and E2F is particularly pronounced in the "basal" subgroup of ER- tumors. This suggests that a study assessing the association of these genes with clinical outcome in ER- patients is warranted, but is not currently feasible due to lack of suitable publicly available data. The contribution of genes regulated or bound by estrogen, MYC or E2F to increased risk of relapse in ER+ tamoxifen-treated patients was assessed in a pilot study using Cox proportional hazards models and Gene Set Enrichment Analysis. The high expression of several gene sets containing genes induced by estrogen and/or MYC and direct targets of MYC and E2F was correlated with poor outcome in these patients. We conclude that over-expression or constitutive activation of MYC, possibly in conjunction with elevated E2F activity, may lead to the induction of a set of genes characteristic of the estrogen response thereby contributing to increased proliferation in ER- breast tumors, particularly in the basal subgroup. A pilot survival study indicated that MYC- and E2F-activity may play a role in tamoxifen-resistance.

The endoplasmic reticulum (ER) is a cellular organelle responsible for lipid biosynthesis, protein folding, drug detoxification and regulation of cellular calcium levels. One third of all cellular proteins are folded and assembled in the ER, including most membrane-bound and secreted proteins that are responsible for inter-cellular signaling. As such, the ER has evolved a series of pathways collectively called Endoplasmic Reticulum Quality Control (ERQC) that ensure proteins that are properly folded, as errantly folded proteins can be toxic to the cell. These pathways play a diverse role in many human diseases. In neurodegenerative diseases such as Huntington's disease, the accumulation of protein plaques can be prevented by over-expression of protein chaperones, suggesting that weakened folding machinery causes the disease phenotype. In the case of diseases such as Cystic Fibrosis that are caused by genetic mutation to cell surface proteins, ERQC machinery degrades these mutated proteins despite their ability to function properly if they were allowed to exit the ER. In cancer, the ability of ERQC machinery to protect cells from stress enables tumor cells to survive and thrive in hypoxic and nutrient-poor environments. Systems biology methods have enabled the study of signaling pathways in human disease across the cell. However, with this breadth comes a limited ability to focus on particular areas of interest such as the ER. To address this, this thesis applies systems biology methods specifically to ER and ERQC pathways to better understand their role in human disease. We first characterize the proteins that reside in the ER and Golgi through comprehensive analysis of peptides identified in ER and Golgi fractions via mass spectrometry, providing the first experimentally-derived ER proteome. We then use this list of ER proteins to identify ER signaling pathways that distinguish between breast cancer subtypes to provide novel therapeutic approaches to treating<br>Le réticulum endoplasmique (RE) est un organelle cellulaire responsable de la biosynth`ese des lipides, du repliement des protéines, de la désintoxication et de la régulation des niveaux cellulaires de calcium. Un tiers des protéines cellulaires est plié et assemblé dans le RE, y compris la plupart des protéines liées à la membrane et des protéines sécrétées responsables de la signalisation inter-cellulaire. Ainsi, le RE a mis au point une série de voies de signalisation collectivement appelées Contrôle de Qualité du Réticulum Endoplasmique (CQRE) qui assurent que les protéines soient correctement pliées, étant donné que les protéines incorrectement pliées peuvent être toxiques pour la cellule. Ces voies jouent divers rôles dans de nombreuses maladies humaines. Dans les maladies neurod égénératives telles que la maladie de Huntington, l'accumulation de plaques de protéines peut être évitée par la sur-expression de protéines chaperons, ce qui sugg`ere qu'un affaiblissement de la machinerie de pliage cause le phénotype de cette maladie. Dans le cas de maladies comme la Fibrose Cystique qui sont causées par une mutation génétique des protéines de la membrane cellulaire, le CQRE dégrade ces protéines mutées bien que celles-ci fonctionneraient correctement si elles avaient été autorisées à quitter le RE. Dans le cancer, la capacité du CQRE à protéger les cellules contre le stress permet aux cellules tumorales de survivre et de se développer dans des environnements hypoxiques et pauvres en éléments nutritifs. Les méthodes de biologie des syst`emes ont permis l'étude des voies de signalisation dans les maladies humaines à travers la cellule. Cependant, avec cette large étendue, il devient difficile de se concentrer sur certains domaines d'intérêt tels que le RE. Pour résoudre ce problme, cette thse applique les méthodes de la biologie des syst`emes spécifiquement au RE et aux voies de signalisation du CQRE po

A hallmark of the joint disease osteoarthritis (OA) is the degradation of the articular cartilage in the affected joint, debilitating pain and decreased mobility. At present there are no disease modifying drugs for treatment of osteoarthritis. This represents a significant, unmet medical need as there is a large and increasing prevalence of OA. Using a systems biology approach, we aimed to better understand the pathogenic mechanisms of OA and ultimately aid development of therapeutics. This thesis focuses on the analysis of gene expression data from human OA cartilage obtained at total knee replacement (TKR). This transcriptomics approach gives a genome-wide overview of changes, but can be challenging to interpret. Network-based algorithms provide a framework for the fusion of knowledge so allowing effective interpretation. The PhenomeExpress algorithm was developed as part of this thesis to aid the interpretation of gene expression data. PhenomeExpress uses known disease gene associations to identify relevant dysregulated pathways in the data. PhenomeExpress was further developed into an 'app' for Cytoscape, the widely used network analysis and visualisation platform. To investigate the processes that occur during the degradation of cartilage we examined the gene expression of damaged and intact OA cartilage using RNA-Seq and identified key altered pathways with PhenomeExpress. A regulatory network driven by four transcription factors accounts for a significant proportion of the observed differential expression of damage-associated genes in the PhenomeExpress identified pathways. We further explored the role of the cytokines IL-1 and TNF that have been reported to β drive the progression of OA. Comparison of the expression response of in vitro cytokine-treated explants with the in vivo damage response revealed major differences, providing little evidence for any significant role of IL-1 and TNF as drivers of OA β damage in vivo. Finally, we examined the heterogeneity of OA through analysis of cartilage expression profiles at TKR. Through a network-based clustering method, we found two subgroups of patients on the basis of their gene expression profiles. These subgroups were found to have distinct OA expression perturbations and we identified TGF and S100A8/9 β signalling as potentially explaining the observed differential expression. We developeda RT-qPCR based classifier that allowed classification of new samples into these subgroups so allowing future assessment of the clinical significance of these subgroups. The work presented in this thesis includes a novel, widely-accessible tool for the analysis of disease gene expression data, which we used to give new insights into the pathogenesis of osteoarthritis. We have produced a rich dataset for future research and our analysis of this data has increased our understanding of cartilage damage processes and the heterogeneity of OA.

Thesis (MSc)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: Described as one of the costliest and most debilitating disorders, schizophrenia has proven to be among the greatest challenges for medical researchers. The disorder poses difficulties on all levels: from genotype to phenotype. Even though it is known that there is a substantial genetic contribution to schizophrenia susceptibility (~80%), it is unknown whether this is due to common variants, rare variants, epigenetic factors, polymorphisms in regulatory regions of the genome or a combination of all these factors. Over the past few decades, many approaches have been employed to elucidate the genetic architecture of schizophrenia, with the latest and most promising being genome wide association studies (GWAS). However, nearly a decade after the first GWAS, the limitations are increasingly being recognised and new avenues need to be explored. Studies have recently started to focus on the analysis of non-coding regions of the genome since these regions harbour the majority of variants identified in GWAS thus far. This study aimed to use recently developed programs that utilize data from large scale studies such as previous GWAS, the Encyclopaedia of DNA Elements (ENCODE), 1000 Genomes, HapMap and Functional Annotation of the Mammalian Genome (FANTOM) to establish a simple, yet effective bioinformatics pipeline for the identification and assessment of variants in regulatory regions. Using the established workflow, 149 single nucleotide polymorphisms (SNPs) in regulatory regions were implicated in schizophrenia susceptibility, with the most significant SNP being rs200981. Pathway and network analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and GeneMANIA respectively indicated that the most frequently affected genes were involved in immune responses or neurodevelopmental processes, which support previous findings. Yet, novel findings of this study implicated processes crucial for DNA packaging (from DNA level to chromatin level). The second part of the study used restriction fragment length polymorphism analysis of polymerase chain reaction-amplified fragments (PCR-RFLP) to genotype ten of the most significant SNPs (identified by bioinformatic analyses in the first part of the study) in a South African Xhosa cohort of 100 cases and 100 controls, while bi-directional Sanger sequencing was used to confirm the presence of these SNPs. Statistical analyses revealed two haplotypes of regulatory variants, rs200483-rs200485-rs2517611 (p = 0.0385; OR = 1.71; 95% CI = 1.01-2.91) and rs200981-rs2517611-rs3129701 (p = 0.041; OR = 0.51; 95% CI = 0.27-0.98) associated with schizophrenia susceptibility. Bioinformatic analysis indicated that these haplotypes affect DNA packaging, which supported the findings of the first part of the study and could implicate epigenetic processes. The findings of this study support the importance of regulatory variants in schizophrenia susceptibility. This study also showed the importance of combining GWAS data with additional analyses in order to better understand complex diseases. It is hoped that these findings could fuel future research, specifically in genetically unique populations.<br>AFRIKAANSE OPSOMMING: Skisofrenie kan beskryf word as een van die duurste en mees ernstige siektes en bly steeds een van die grootste uitdagings vir mediese navorsers. Hierdie versteuring behels probleme op alle vlakke: van genotipe tot fenotipe. Alhoewel dit bekend is dat daar 'n aansienlike genetiese bydrae tot skisofrenie vatbaarheid is (~ 80%), is dit onbekend of dit is as gevolg van algemene variasies, skaars variasies, epigenetiese faktore, variasies in regulerende gebiede van die genoom of 'n kombinasie van al hierdie faktore. Oor die afgelope paar dekades is verskeie benaderings gebruik om die genetiese samestelling van skisofrenie te bestudeer, met die nuutste en mees belowende synde genoom-wye assosiasie studies (GWAS). Byna 'n dekade na die eerste GWAS, word die beperkinge egter toenemend erken en nuwe navorsingstrategieë moet gebruik word. Studies het onlangs begin om meer te fokus op die analise van nie-koderende areas van die genoom aangesien hierdie areas die meerderheid van die variasies behels wat tot dusver in GWAS geïdentifiseer is. Hierdie studie het gepoog om onlangs ontwikkelde programme, wat gebruik maak van die data van grootskaalse studies soos vorige GWAS, die “Encyclopaedia of DNA Elements” (ENCODE), “1000 Genomes”, “HapMap” en “Functional Annotation of the Mammalian Genome” (FANTOM), te implementeer om sodoende 'n eenvoudige, maar doeltreffende bioinformatika pyplyn vir die identifisering en evaluering van variante in regulerende gebiede, te vestig. Deur die gebruik van die gevestigde bioinformatika pyplyn, is 149 enkel nukleotied polimorfismes (SNPs) in regulerende gebiede in skisofrenie vatbaarheid betrek, met rs200981 wat die mees betekenisvol was. Pad- en netwerk-analise met die onderskeidelike hulp van die “Database for Annotation, Visualization and Integrated Discovery” (DAVID) en “GeneMANIA”, het aangedui dat die gene wat die meeste geaffekteer was, betrokke is by immuunreaksies en neuro-ontwikkeling. Hierdie bevindinge ondersteun vorige studies. Tog het nuwe bevindinge van hierdie studie prosesse geïmpliseer wat uiters noodsaaklik is vir DNS verpakking (van DNS- tot chromatien-vlak). Die tweede deel van die studie het restriksie fragment lengte polimorfisme analise van polimerase ketting reaksie geamplifiseerde fragmente (PKR-RFLP) gebruik om tien van die belangrikste SNPs (wat geïdentifiseer is deur bioinformatiese ontledings in die eerste deel van die studie) in `n Suid-Afrikaanse Xhosa studiegroep van 100 skisofrenie gevalle en 100 kontroles te genotipeer, terwyl tweerigting Sanger volgordebepaling gebruik is om die teenwoordigheid van hierdie SNPs te bevestig. Statistiese analise het aangedui dat twee<br>National Research Foundation (DAAD-NRF)

Long non-coding RNA (lncRNAs) show a wide range of regulatory functions at the transcriptional and post-transcripltional levels both in the nucleus and cytoplasm. Recently, antisense lncRNAs (ASlncRNAs) were reported to up-regulate protein synthesis post-transcriptionally through a mechanism depending on an embedded inverted SINE B2 and 5' overlap to the target mRNAs. Such ASlncRNAs are also referred as SINEUPs. Synthetic SINEUPs with identical modular organization were also demonstrated to exert the same activity suggesting a functional relationship between SINE repetitive elements and ASlncRNAs. In order to gain a broader insight on the contribution of transposable elements (TEs) in the sequence composition of ASlncRNAs, I have developed a bioinformatic pipeline that can identify and characterize. transcripts containing TEs and analyze TEs coverage for different classes of coding/non-coding sense/antisense (S/AS) pairs. I aimed at identifying if the functional activity of SINEUPs could be a widespread phenomenon across multiple similar natural ASlnRNAs in the transcriptomes of the extensively studied model organisms that have a well annotated catalog of lncRNAs. From my initial analysis I identified human and mouse are the two species that showed a significant coverage enrichment of SINE repeats among ASlncRNAs. I further performed several functional enrichment analysis for the sense coding genes overlapping to ASlncRNAs taking into consideration of different characteristics of the 5' binding domain and the 3' embedded SINE repetitive elements. This permitted me to identify the effect of these modular features over the functional associations of sense coding genes. The results of the analysis showed that the products of coding genes associated to ASlncRNAs containing SINEs are significantly enriched for rnitochondriallocalization. Further, to determine if these ASlncRNAs could exert SINEUP-like activity during stress, I analyzed the data from a published custom rnicroarray experiment study, that were associated to the polysome fractions of MRCS cell lysates in control and oxidative stress condition. The results revealed that the ASlncRNA carrying inverted or direct SINE repeats and their corresponding sense coding genes do not show any significant differential polysome loading in stress with respect to normal conditions, which is not a desired characteristic of a potential SINEUP. However, ASlncRNAs with inverted and direct SINE repeats corresponding to high translating polysome fractions showed a significantly higher ratio of means for RNA levels in stress over control, in contrast to noASlncRNA. This suggests that the ASlncRNA containing SINE elements are the key RNA molecules that are active during stress, although to determine if they are also involved in the increased polysome loading of their respective sense coding mRNAs, there is a need of further experimentation and exploration. Altogether, the work presented in this thesis provides a novel bioinformatics approach to study

<p> <b>petal</b> is a network analysis method that includes and takes advantage of precise Mathematics, Statistics, and Graph Theory, but remains practical to the life scientist. <b>petal</b> is built upon the assumption that large complex systems follow a scale-free and small-world network topology. One main intention of creating this program is to eliminate unnecessary noise and imprecision introduced by the user. Consequently, no user input parameters are required, and the program is designed to allow the two structural properties, scale-free and small-world, to govern the construction of network models. </p><p> The program is implemented in the statistical language <b>R</b> and is freely available as a package for download. Its package includes several simple <b>R</b> functions that the researcher can use to construct co-expression networks and extract gene groupings from a biologically meaningful network model. More advanced <b>R</b> users may use other functions for further downstream analyses, if desired. </p><p> The <b>petal</b> algorithm is discussed and its application demonstrated on several datasets. <b>petal</b> results show that the technique is capable of detecting biologically meaningful network modules from co-expression networks. That is, scientists can use this technique to identify groups of genes with possible similar function based on their expression information. </p><p> While this approach is motivated by whole-system gene expression data, the fundamental components of the method are transparent and can be applied to large datasets of many types, sizes, and stemming from various fields. </p>

The brain – the organ that allows us to be aware of our surroundings – consists of a complex network of neurons, which seemingly allows the human brain to be able of abstract thinking, emotions, and cognitive function. To learn how the brain is capable of this, the two main branches of neuroscience study either neurons in detail, or how they communicate within neuronal networks. Both these branches often tackle the complexity using a combination of experiments and mathematical modelling. A third and less studied aspect of neuroscience concerns the neurovascular coupling (NVC), for which my research group has previously developed mathematical models. However, these NVC models have still not integrated valuable data from rodents and primates, and the NVC models are also not connected to existing neuronal network models. In this project, I address both of these two shortcomings. First, an existing model for the NVC was connected with a simple model for neuronal networks, establishing a connection between the NVC models and the software NEURON. Second, we established a way to preserved information from NVC data from rodents and mice into NVC models humans. This work thus connects the previously developed NVC model both with data from other species and with other types of models. This brings us one step closer to a more holistic and interconnected understanding of the brain and its many intriguing cognitive and physiological functions.