Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Large amount of gene expression data has been collected for various environmental and biological conditions. Extracting dense modules that are recurrent in multiple gene coexpression networks has been shown to be promising in functional gene annotation and biomarkers discovery. In this thesis, we propose a biclustering-based approach for mining approximate frequent dense modules. This approach reports a large number of modules with many duplicate modules. Thus, we build on this approach and propose two extended approaches for mining dense modules, which mine set of representative patterns using post-processing and on-line pattern summarization methods. The extended approaches report smaller number of modules and less duplicate modules. Experiments on real gene coexpression networks show that frequent dense modules are biologically interesting as evidenced by the large percentage of biologically enriched frequent dense modules.

During early development of the nervous system, gene expression patterns are known to vary widely depending on the specific developmental trajectories of different structures. Observable changes in gene expression profiles throughout development are determined by an underlying network of precise regulatory interactions between individual genes. Elucidating the organizing principles that shape this gene regulatory network is one of the central goals of developmental biology. Whether the developmental programme is the result of a dynamic driven by a fixed architecture of regulatory interactions, or alternatively, the result of waves of regulatory reorganization is not known. Here we contrast these two alternative models by examining existing expression data derived from the developing human brain in prenatal and postnatal stages. We reveal a sharp change in gene expression profiles at birth across brain areas. This sharp division between foetal and postnatal profiles is not the result of sudden changes in level of expression of existing gene networks. Instead we demonstrate that the perinatal transition is marked by the widespread regulatory rearrangement within and across existing gene clusters, leading to the emergence of new functional groups. This rearrangement is itself organized into discrete blocks of genes, each associated with a particular set of biological functions. Our results provide evidence of an acute modular reorganization of the regulatory architecture of the brain transcriptome occurring at birth, reflecting the reassembly of new functional associations required for the normal transition from prenatal to postnatal brain development.

No description available.

Ferramentas bioinformática têm se tornado a escolha para auxiliar pesquisadores tanto para a anotação de novos genes, como para estudar genes em condições fisiológicas de interesse. Entre essas ferramentas destacam-se os algoritmos de agrupamento filogenético e os algoritmos de predição de padrões curtos de DNA, como, por exemplo, predições de sítios para ligação de fatores de transcrição. Desenvolver uma abordagem mista com o objetivo de agrupar genes baseando-se unicamente nos sinais transcricionais preditos em suas seqüências é um desafio de difícil transposição. No presente trabalho, apresentamos nossos resultados para tentar superar tal limitação que podem ser subdividos em duas seções: a primeira aonde desenvolvemos uma abordagem para a melhoria das predições computacionais de sítios de ligação e a segunda, onde passamos a agrupar genes com base nos seus sinais transcricionais preditos em seqüências conservadas flanqueadoras. A primeira seção de nosso trabalho foi focada no estudo de uma seqüência de indução de transcrição próxima ao gene Aldh1a2 de camundongo aonde foram preditos sítios para fatores de transcrição que foram posteriormente testados biologicamente e se mostraram associados ao controle da expressão desse gene. A partir de uma profunda pesquisa bibliográfica, nós determinamos um grupo de 57 fatores de transcrição já associados com a especialização de subpopulações de neurônios durante o desenvolvimento neuroembrionário de vertebrados. Nossa abordagem de seleção de sítios de alto valor biológico foi agora testada em seqüências conservadas próximas a cada um desses genes que codificam esses fatores de transcrição associados e os sítios de ligação para fatores de transcrição foram preditos. Tais sítios foram contabilizados e utilizados com entrada para nossa abordagem de agrupamento. A análise dos resultados do agrupamento determinou que, nossa abordagem se mostrou suficientemente sensível para construir uma árvore solução com boas relações com os padrões, já conhecidos, de expressão para esses genes agrupados. Essa abordagem poderá ser utilizada tanto para anotar funcionalmente genes de interesse quanto para minerar informações dentro de um grupo de genes previamente selecionado. / Bioinformatics tools are becoming the choice for aiding scientists for gene annotation and for studying gene in physiological conditions of interest. Among those efforts, phylogenetics clustering algorithms and tools for predicting short DNA patterns, such as binding sites for transcription factor, are outlined as essential. To develop a mixture procedure merging this two distant fields of bioinformatics research is a challenge hard to overcome. In the present study, we present our results of trying to overcome such limitation and it be easily subdivided in two distinct sections: initially we develop a procedure to improve the computational prediction of binding site for transcription factors and the second one where genes were grouped based solely in their transcriptional patterns predicted in conserved flanking sequences. The first section of the present study was focused in the study of an enhancer near Aldh1a2 gene in mouse where binding sites were predicted and latter biologically tested and showed strong influence in expression control of this gene. By a comprehensive bibliographic research we determined a group of 57 transcription factors which were already associated with neuron subpopulations specialization during the neuroembryonary development in vertebrates. Our computational procedure for selection of high biological value binding sites was applied in conserved flanking sequence in each of these genes encoding these associated transcription factors and a large group of binding sites were predicted. This sites were counted and use as an input for our clustering procedure. Clustering results analyses determined that our procedure showed to be sufficiently sensible to construct a solution tree showing good relations with, already determined, expression patterns of grouped genes. This procedure could be for functionally annotation of genes and for data mining in a group of already determined genes of interest.

Regulation of gene transcription plays a major role in mediating cellular responses and physiological behavior in all known organisms. The finding that similar genes are often regulated in a similar manner (co-regulated or "co-expressed") has directed several "guilt-by-association" approaches in order to reverse-engineer the cellular transcriptional networks using gene expression data as a compass. This kind of studies has been considerably assisted in the recent years by the development of high-throughput transcript measurement platforms, specifically gene microarrays and next-generation sequencing. In this thesis, I describe several approaches for improving the extraction and interpretation of the information contained in microarray based gene expression data, through four steps: (1) microarray platform design, (2) microarray data normalization, (3) gene network reverse engineering based on expression data and (4) experimental validation of expression-based guilt-by-association inferences. In the first part test case is shown aimed at the generation of a microarray for Thellungiella salsuginea, a salt and drought resistant close relative to the model plant Arabidopsis thaliana; the transcripts of this organism are generated on the combination of publicly available ESTs and newly generated ad-hoc next-generation sequencing data. Since the design of a microarray platform requires the availability of highly reliable and non-redundant transcript models, these issues are addressed consecutively, proposing several different technical solutions. In the second part I describe how inter-array correlation artifacts are generated by the common microarray normalization methods RMA and GCRMA, together with the technical and mathematical characteristics underlying the problem. A solution is proposed in the form of a novel normalization method, called tRMA. The third part of the thesis deals with the field of expression-based gene network reverse engineering. It is shown how different centrality measures in reverse engineered gene networks can be used to distinguish specific classes of genes, in particular essential genes in Arabidopsis thaliana, and how the use of conditional correlation can add a layer of understanding over the information flow processes underlying transcript regulation. Furthermore, several network reverse engineering approaches are compared, with a particular focus on the LASSO, a linear regression derivative rarely applied before in global gene network reconstruction, despite its theoretical advantages in robustness and interpretability over more standard methods. The performance of LASSO is assessed through several in silico analyses dealing with the reliability of the inferred gene networks. In the final part, LASSO and other reverse engineering methods are used to experimentally identify novel genes involved in two independent scenarios: the seed coat mucilage pathway in Arabidopsis thaliana and the hypoxic tuber development in Solanum tuberosum. In both cases an interesting method complementarity is shown, which strongly suggests a general use of hybrid approaches for transcript expression-based inferences. In conclusion, this work has helped to improve our understanding of gene transcription regulation through a better interpretation of high-throughput expression data. Part of the network reverse engineering methods described in this thesis have been included in a tool (CorTo) for gene network reverse engineering and annotated visualization from custom transcription datasets. / Die Regulation der Gentranskription spielt eine wichtige Rolle bei der Steuerung des physiologischen Verhaltens in allen Organismen. Dass ähnliche Gene oft in gleicher Weise reguliert werden (koreguliert oder koexpimiert), hat zu diversen „guilt-by-association“-Ansätzen zur Rekonstruktion von zellulären Transkriptionsnetzwerken geführt, die Genexpressionsdaten zur Orientierung nutzen. Studien dieser Art wurden in den letzten Jahren durch die Entwicklung von Hochdurchsatzmessungen von Transkriptmengen mittels Mikroarrays und ‚Next Generation‘ Sequenziertechniken stark gefördert. In der vorliegenden Arbeit werden verschiedene Ansätze zur Verbesserung der Extraktion und Interpretation von Mikroarray-basierten Genexpressionsdaten in vier Schritten beschrieben: (1) Mikroarray-Sonden-Design, (2) Mikroarray Datennormalisierung, (3) Rekonstruktion von Gennetzwerken unter Verwendung von Expressionsdaten und (4) experimentelle Überprüfung von expressionsbasierten „guilt-by-association“ Schlussfolgerungen. Im ersten Teil wird ein Beispiel zur Erstellung eines Mikroarrays für Thelungiella salsuginea gezeigt, einem salz- und trockenresistenten Verwandten von Arabidopsis thaliana. Zur Rekonstruktion der Transkripte wurden sowohl öffentliche ESTs (‚expressed sequence tags‘) als auch neu erzeugte ‚Next Generation‘ Sequenzierdaten genutzt. Da das Design von Mikroarrays speziesspezifische, nicht-redundante Transkriptmodelle erfordert, werden diese Aufgaben nacheinander abgearbeitet und verschiedene technische Lösungsmöglichkeiten aufgezeigt. Im zweiten Teil wird beschrieben, wie übliche Mikroarray-Normalisierungsverfahren wie RMA und GCRMA zu Korrelationsartefakten führen können. Technische sowie mathematische Hintergründe werden erläutert und zur Lösung des Problems wird mit tRMA eine neue Normalisierungsmethode vorgestellt. Der dritte Teil der Arbeit beschäftigt sich der expressionsbasierten Rekonstruktion von Gennetzwerken. Es wird demonstriert, wie dabei verschiedene „Zentralitäten“ bei zur Unterscheidung von spezifischen Genklassen, hier beispielhaft essentielle Gene von Arabidopsis thaliana, genutzt werden können und wie die Verwendung von konditioneller Korrelation tieferes Verständnis des der Transkriptionsregulation zugrundeliegenden Informationsflusses ermöglicht. Weiterhin werden Ansätze zur Netzwerkrekonstruktion verglichen. Besonderes Augenmerk liegt dabei auf der LASSO Technik, einer Art linearer Regression, die trotz ihren theoretischen Vorteilen in Robustheit und Interpretierbarkeit gegenüber Standardmethoden bisher selten zur Rekonstruktion von globalen Gennetzwerken genutzt wurde. Die Leistungsfähigkeit von LASSO wird durch in silico Analysen der Zuverlässigkeit der erstellten Gennetzwerke gemessen. Im letzten Teil der Arbeit wurden LASSO und andere Rekonstruktionsmethoden genutzt um experimentell neue Gene der folgenden zwei Szenarien zu identifizieren: im Samenschleim von Arabidopsis thaliana und während der Knollenentwicklung von Solanum tuberosum unter Sauerstoffmangel. In beiden Fällen wird eine interessante Methodenkomplementarität gezeigt, nach welcher eine Mischung mehrerer Ansätze zu empfehlen ist um Schlüsse aufgrund von Transkriptexpression zu ziehen. Zusammenfassend zielt diese Arbeit darauf ab, das Verständnis der Regulation von Gentranskriptionsnetzwerken durch bessere Interpretation von Hochdurchsatzexpressionsdaten zu verbessern. Ein Teil der in dieser Arbeit beschriebenen Methoden wurden im Programm CorTo zur Gennetzwerkrekonstruktion und annotierten Visualisierung von benutzerdefinierten Transkriptionsdaten verarbeitet.

Ferramentas bioinformática têm se tornado a escolha para auxiliar pesquisadores tanto para a anotação de novos genes, como para estudar genes em condições fisiológicas de interesse. Entre essas ferramentas destacam-se os algoritmos de agrupamento filogenético e os algoritmos de predição de padrões curtos de DNA, como, por exemplo, predições de sítios para ligação de fatores de transcrição. Desenvolver uma abordagem mista com o objetivo de agrupar genes baseando-se unicamente nos sinais transcricionais preditos em suas seqüências é um desafio de difícil transposição. No presente trabalho, apresentamos nossos resultados para tentar superar tal limitação que podem ser subdividos em duas seções: a primeira aonde desenvolvemos uma abordagem para a melhoria das predições computacionais de sítios de ligação e a segunda, onde passamos a agrupar genes com base nos seus sinais transcricionais preditos em seqüências conservadas flanqueadoras. A primeira seção de nosso trabalho foi focada no estudo de uma seqüência de indução de transcrição próxima ao gene Aldh1a2 de camundongo aonde foram preditos sítios para fatores de transcrição que foram posteriormente testados biologicamente e se mostraram associados ao controle da expressão desse gene. A partir de uma profunda pesquisa bibliográfica, nós determinamos um grupo de 57 fatores de transcrição já associados com a especialização de subpopulações de neurônios durante o desenvolvimento neuroembrionário de vertebrados. Nossa abordagem de seleção de sítios de alto valor biológico foi agora testada em seqüências conservadas próximas a cada um desses genes que codificam esses fatores de transcrição associados e os sítios de ligação para fatores de transcrição foram preditos. Tais sítios foram contabilizados e utilizados com entrada para nossa abordagem de agrupamento. A análise dos resultados do agrupamento determinou que, nossa abordagem se mostrou suficientemente sensível para construir uma árvore solução com boas relações com os padrões, já conhecidos, de expressão para esses genes agrupados. Essa abordagem poderá ser utilizada tanto para anotar funcionalmente genes de interesse quanto para minerar informações dentro de um grupo de genes previamente selecionado. / Bioinformatics tools are becoming the choice for aiding scientists for gene annotation and for studying gene in physiological conditions of interest. Among those efforts, phylogenetics clustering algorithms and tools for predicting short DNA patterns, such as binding sites for transcription factor, are outlined as essential. To develop a mixture procedure merging this two distant fields of bioinformatics research is a challenge hard to overcome. In the present study, we present our results of trying to overcome such limitation and it be easily subdivided in two distinct sections: initially we develop a procedure to improve the computational prediction of binding site for transcription factors and the second one where genes were grouped based solely in their transcriptional patterns predicted in conserved flanking sequences. The first section of the present study was focused in the study of an enhancer near Aldh1a2 gene in mouse where binding sites were predicted and latter biologically tested and showed strong influence in expression control of this gene. By a comprehensive bibliographic research we determined a group of 57 transcription factors which were already associated with neuron subpopulations specialization during the neuroembryonary development in vertebrates. Our computational procedure for selection of high biological value binding sites was applied in conserved flanking sequence in each of these genes encoding these associated transcription factors and a large group of binding sites were predicted. This sites were counted and use as an input for our clustering procedure. Clustering results analyses determined that our procedure showed to be sufficiently sensible to construct a solution tree showing good relations with, already determined, expression patterns of grouped genes. This procedure could be for functionally annotation of genes and for data mining in a group of already determined genes of interest.

One of the main goals of genetics has been to understand the link between genotype and phenotype. Using yeast (Saccharomyces cerevisiae) as our model organism, we take a closer look at the connection between genetic variation and gene expression to learn more about the mechanisms of gene regulation. We propose an algorithm based on ANOVA to detect causal relationships between coexpressed genes. We first identify expression quantitative trait loci (eQTLs) with strong effects on gene expression. The algorithm then uses these eQTLs with strong effects and the expression of all genes to identify how genes are affecting each other. This is done by analysing coexpressed gene pairs where both genes have an eQTL and finding if the eQTL of one gene affects the expression of the other. Genes that were found to affect the expression of other genes were named “causal genes”. We evaluate our method by comparing its results with known causal genes and conclude that it is a good predictor of known interactions. Using this algorithm, we found 741 genes having causal effects on gene expression, many of which affected the gene expression of many other genes across the genome (2278 total affected genes). Some of the causal genes clustered at six hotspot regions in the genome. Genes in hotspot regions were found to have lower heritability than genes outside these regions. We hypothesize that hotspot regions may be enriched for essential and/or fitness related genes.

No description available.

A Biologia de Sistemas proporciona um olhar holístico sobre os processos biológicos, integrando os diversos componentes intracelulares através de redes altamente complexas. Em particular, redes de co-expressão tem permitido nos últimos anos uma compreensão cada vez maior dos sistemas biológicos e dos mecanismos moleculares que os regem. Por outro lado, as ferramentas matemáticas e estatísticas já desenvolvidas para a análise destas redes e sistemas são, em geral, densas e pouco familiares para profissionais das áreas biológicas e da saúde. Portanto, a fim de possibilitar uma análise ao mesmo tempo relevante e facilitada, nosso grupo criou a ferramenta CEMiTool, que tem por objetivo identificar módulos de coexpressão de genes de modo automático, de maneira fácil e intuitiva para usuários com pouca ou nenhuma experiência com linguagens de programação. A fim de demonstrar a facilidade de uso da ferramenta, aplicamos o CEMiTool a mais de 1000 estudos de transcriptômica, cujos resultados foram utilizados para a confecção de um banco de dados, permitindo a integração de informações entre estudos. Além disso, para facilitar ainda mais o acesso a este tipo de análises, foi criada uma versão online da ferramenta, denominada webCEMiTool, que permite realizar as análises no navegador. Finalmente, criou-se também a ferramenta annotator, permitindo a definição automática de grupos de amostras de estudos de transcriptômica a partir do agrupamento de cadeias de caracteres presentes em dados de anotação. Todo o código está livremente disponível à comunidade. / System biology methods provide a holistic view of biological processes, integrating the several intracellular molecular components via the use of highly complex networks. In particular, co-expression networks have allowed for an increasing understanding of biological systems and the complex molecular mechanisms driving them. On the other hand, previously described tools for the analysis of biological networks are in general relatively difficult to use for life and health scientists given their high mathematical and computational demand. Therefore, in order to provide at the same time a relevant and easy-to-use analysis, we have developed the CEMiTool package, which aims to identify gene coexpression modules in an automatic, easy and intuitive way for users with little to no prior computational expertise. We applied CEMiTool to over 1000 transcriptomics studies and used the results to create a new gene coexpression database, which allows users to integrate information across analyses. Moreover, to further facilitate analyses we developed an online version of the tool named webCEMiTool, which permits users to run coexpression analysis easily via browser. Finally, we also developed annotator, a package for automatically determining experimental groups based on sample annotation string similarity. All code is freely available to the community.