<p>Alternative mRNA processing can result in the generation of multiple, qualitatively different RNA transcripts from the same gene and is a powerful engine of complexity in higher organisms. Recent deep sequencing studies have indicated that essentially all human genes containing more than a single exon generate multiple RNA transcripts. Functional roles of alternative processing have been established in virtually all areas of biological regulation, particularly in development and cancer. Changes in alternative mRNA processing can now be detected from over a billion dollars' worth of conventional gene expression microarray data archived over the past 20 years using a program we created called SplicerAV. Application of SplicerAV to publicly available microarray data has granted new insights into previously existing studies of oncogene over-expression and clinical cancer prognosis.</p> <p>Adaptation of SplicerAV to the new Affymetrix Human Exon arrays has resulted in the creation of SplicerEX, the first program that can automatically categorize microarray detected changes in alternative processing into biologically pertinent categories. We use SplicerEX's automatic event categorization to identify changes in global mRNA processing during B cell transformation and show that the conventional U133 platform is able to detect 3' located changes in mRNA processing five times more frequently than the Human Exon array.</p> / Dissertation

<p> Cancer is an ideal target for personal genomics-based medicine that uses high-throughput genome assays such as DNA sequencing, RNA sequencing, and expression analysis (collectively called <i>omics</i>); however, researchers and physicians are overwhelmed by the quantities of big data from these assays and cannot interpret this information accurately without specialized tools. To address this problem, I have created software methods and tools called <i>OCCAM</i> (OmiC&nbsp;data Cancer Analytic Model) and DIPSC (Differential Pathway Signature Correlation) for automatically extracting knowledge from this data and turning it into an actionable knowledge base called the <i>activitome.</i> An activitome signature measures a mutation's effect on the cellular molecular pathway. As well, activitome signatures can also be computed for clinical phenotypes. By comparing the vectors of activitome signatures of different mutations and clinical outcomes, intrinsic relationships between these events may be uncovered. OCCAM identifies activitome signatures that can be used to guide the development and application of therapies. DIPSC overcomes the confounding problem of correlating multiple activitome signatures from the same set of samples. In addition, to support the collection of this big data, I have developed <i>MedBook,</i> a federated distributed social network designed for a medical research and decision support system. OCCAM and DIPSC are two of the many apps that will operate inside of MedBook. MedBook extends the Galaxy system with a signature database, an end-user oriented application platform, a rich data medical knowledge-publishing model, and the Biomedical Evidence Graph (BMEG). The goal of MedBook is to improve the outcomes by learning from every patient.</p>

<p> In the past five years, Single Molecule Real Time (SMRT) sequencing technology has been found to be a reliable indicator of certain epigenetic modifications in bacterial genomes. The genome of the model organism <i>Saccharomyces cerevisiae</i> has long been thought to be free of DNA level modification, but literature surrounding this subject is conflicting. Additionally, the mitochondria of <i>S. cerevisiae</i> control the transition between three distinct chronological life phases &ndash; exponential, postdiauxic, and stationary - as defined by their main metabolic processes. This study attempted to identify base modifications to mtDNA using PacBio sequencing while additionally establishing gene expression changes as a result of altered mitochondrial metabolic capabilities. PacBio results showed intriguing results but statistical analysis proved experimentation with improved protocols were necessary. Multiple genes with unknown or uncharacterized function were also shown to have significant differential expression between metabolic life phases.</p>

Thesis (M. Phil.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 63-69) Also available in print.

Increased exposure to antibiotics has led to the dissemination of genes conferring resistance to antimicrobial metabolites throughout human microbiomes globally via horizontal gene transfer (HGT). This has resulted in the emergence of new resistant strains leading to a rising epidemic of deaths from previously treatable infections. Evidence suggests that before the age of anthropogenic antibiotic use, microbes living within a community produced antibiotic metabolites and, subsequently, maintained such genes for several useful functions and a balance of diversity in nature. The question of the origin of these resistant genes is difficult to answer, but with continued advancements in ancient genomic analysis, researchers have developed methods of acquiring a more accurate representation of the microbiome associated with our human ancestors by extracting fossilized microbial specimens from dental calculus and directly sequencing the metagenomes. This thesis outlines the production of taxonomic and functional profiles of 20 different human and non-human oral microbiome samples using metagenomics tools originally developed for living individuals, altered for use with ancient microbial specimens. Putative antimicrobial resistant (AMR) genes derived from these profiles were reconstructed and conserved functional regions were identified. From the data that is available regarding the human microbiome from a range of time points throughout history dating back to Neanderthal specimens, it is possible to elucidate relationships between these AMR genes and to better understand the evolutionary trajectory of antibiotic resistance.

Bacterial evolution has been shaped by the acquisition of clusters of genes called genomic islands through means other than vertical inheritance. These gene clusters provide beneficial traits to the recipient bacteria such as virulence, resistance and the ability to utilize different metabolites, thereby facilitating bacterial adaptation to diverse environments and leading to the emergence of multi-drug resistant pathogens. As identification of genomic islands are of immense biomedical importance, we have developed a novel genomic island detection method, DICEP, to robustly identify genomic islands in bacterial genomes. Once genomic islands were identified, we focused on functional characterization of genes harbored by these islands as an essential step towards understanding their role in providing fitness to the recipient bacterium. We have used a gene co-expression network-based approach to gain insights into the functional association of genes within an island. The network analysis revealed novel pathogenicity associated genes and helped in functional characterization of island genes.

Left/right (L/R) asymmetries, differences in morphology between the otherwise mirrored left- and right-hand sides of the body, are found in animals across the Bilateria. For many years it was thought that the mechanisms for establishment of these asymmetries had evolved separately in the three superphyla that constitute the Bilateria, but the discovery in 2009 that the TGF-beta ligand Nodal shares a conserved role in the Deuterostomia and Lophotrochozoa has re-ignited debate and interest in this field. In this thesis, work examining the establishment and maintenance of L/R asymmetries in the lophotrochozoan superphylum is presented, aimed at uncovering the wider conservation of these pathways across the Bilateria. Illumina sequencing and a range of de novo assembly techniques were used to derive genomic and transcriptomic data respectively for two primary model organisms, the limpet Patella vulgata and the serpulid annelid Pomatoceros lamarckii. Additionally, collaborative work lead to the derivation of transcriptomes for two other mollusc species and the genome of the monogont rotifer Brachionus plicatilis. A range of analysis was performed on these novel resources and is detailed here, with particular reference to the transcription factor cassettes contained in these datasets. These sequence resources formed the basis for examination of the breaking of initial symmetry in these model organisms. Known read-outs of correct establishment of L/R asymmetry, the expression of genes Nodal and Pitx on the right of the body, were codified in the course of normal development in P. vulgata. Pharmacological inhibitors of genes implicated in the establishment of L/R asymmetry, particularly ATPase ion channels, were then applied to embryos. After development, markers of normal development were assayed for signs of bilateral inversion. Although radialised phenotypes were observed, it is unclear whether these are specifically the result of L/R asymmetry defects. The localisation of ATPase mRNA and serotonin, often posited as a small molecule potential morphogen, were also assayed, although no conclusions could be drawn as to a role in the establishment of L/R asymmetry for these molecules, counter to some evidence from vertebrates. Once symmetry is broken, the TGF-beta pathway is responsible for the communication, specification and maintenance of tissue identity across the L/R axis. The novel sequence resources described in this thesis provided a comprehensive window into this signalling cassette, and detailed here is a treatment of the TGF-beta pathway within the Lophotrochozoa. Ligand diversity has increased markedly in some clades, while signal transduction and regulatory steps are relatively unchanged. This work has increased our knowledge of lophotrochozoan biology and particularly the mechanisms underpinning the establishment of asymmetry in this under-researched clade, however, much remains to be discovered about the ultimate origin of asymmetry itself.

Les introns sont des portions de gènes transcrites dans l’ARN messager, mais retirées pendant l’épissage avant la synthèse des produits du gène. Chez les eucaryotes, on rencontre les introns splicéosomaux, qui sont retirés de l’ARN messager par des splicéosomes. Les introns permettent plusieurs processus importants, tels que l'épissage alternatif, la dégradation des ARNs messagers non-sens, et l'encodage d'ARNs fonctionnels. Leurs rôles nous interrogent sur l'influence de la sélection naturelle sur leur évolution. Nous nous intéressons aux mutations qui peuvent modifier les produits d'un gène en changeant les sites d'épissage des introns. Ces mutations peuvent influencer le fonctionnement d'un organisme, et constituent donc un sujet d'étude intéressant, mais il n'existe actuellement pas de logiciels permettant de les étudier convenablement. Le but de notre projet était donc de concevoir une méthode pour détecter et analyser les changements des sites d'épissage des introns splicéosomaux. Nous avons finalement développé une méthode qui repère les évènements évolutifs qui affectent les introns splicéosomaux dans un jeu d'espèces données. La méthode a été exécutée sur un ensemble d'espèces d'oomycètes. Plusieurs évènements détectés ont changé les sites d’épissage et les protéines, mais de nombreux évènements trouvés ont modifié les introns sans affecter les produits des gènes. Il manque à notre méthode une étape finale d'analyse approfondie des données récoltées. Cependant, la méthode actuelle est facilement reproductible et automatise l'analyse des génomes pour la détection des évènements. Les fichiers produits peuvent ensuite être analysés dans chaque étude pour répondre à des questions spécifiques. / Introns are portions of genes transcribed into messenger RNA, but removed during RNA splicing. In eukaryotes, they are called spliceosomal introns as they are removed by spliceosomes. Introns allow many important processes such as alternative splicing, nonsense-mediated decay and functional-RNA coding. These roles leads to the question of the influence of natural selection on evolution of introns. We focus on mutations that are able to change gene products by modifing introns splice sites. These mutations seems to be an interesting topic as they can affect proteins, but there is currently no software to study them properly. The aim of our project was to design a method to detect and analyze changes in splice sites of spliceosomal introns. We finally developed a method that locates the evolutionary events on splice sites of spliceosomal introns in a given species set. The method was performed on a set of oomycetes. Several detected events change splice sites and proteins, but there is also many events that seems to modify introns without affecting gene products. Our method lacks a final step for thorough analysis of the collected events. However, the current method is easily reusable and automates genome analysis for the detection of events. The resulting files can then be analyzed in each study to answer specific questions.

L'acide désoxyribonucléique (ADN) et l'acide ribonucléique (ARN) sont des polymères de nucléotides essentiels à la cellule. À l'inverse de l'ADN qui sert principalement à stocker l'information génétique, les ARN sont impliqués dans plusieurs processus métaboliques. Par exemple, ils transmettent l’information génétique codée dans l’ADN. Ils sont essentiels pour la maturation des autres ARN, la régulation de l’expression génétique, la prévention de la dégradation des chromosomes et le ciblage des protéines dans la cellule. La polyvalence fonctionnelle de l'ARN résulte de sa plus grande diversité structurale. Notre laboratoire a développé MC-Fold, un algorithme pour prédire la structure des ARN qu'on représente avec des graphes d'interactions inter-nucléotidiques. Les sommets de ces graphes représentent les nucléotides et les arêtes leurs interactions. Notre laboratoire a aussi observé qu'un petit ensemble de cycles d'interactions à lui seul définit la structure de n'importe quel motif d'ARN. La formation de ces cycles dépend de la séquence de nucléotides et MC-Fold détermine les cycles les plus probables étant donnée cette séquence. Mon projet de maîtrise a été, dans un premier temps, de définir une base de données des motifs structuraux et fonctionnels d'ARN, bdMotifs, en terme de ces cycles. Par la suite, j’ai implanté un algorithme, MC-Motifs, qui recherche ces motifs dans des graphes d'interactions et, entre autres, ceux générés par MC-Fold. Finalement, j’ai validé mon algorithme sur des ARN dont la structure est connue, tels que les ARN ribosomaux (ARNr) 5S, 16S et 23S, et l'ARN utilisé pour prédire la structure des riborégulateurs. Le mémoire est divisé en cinq chapitres. Le premier chapitre présente la structure chimique, les fonctions cellulaires de l'ARN et le repliement structural du polymère. Dans le deuxième chapitre, je décris la base de données bdMotifs. Dans le troisième chapitre, l’algorithme de recherche MC-Motifs est introduit. Le quatrième chapitre présente les résultats de la validation et des prédictions. Finalement, le dernier chapitre porte sur la discussion des résultats suivis d’une conclusion sur le travail. / Deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) are polymers of nucleotides essential for the survival of the cell. Contrary to DNA, whose main role is to store genetic information, RNA is involved in multiple metabolic processes. For example, RNA is involved in the transfer of information from DNA to protein, the processing and modification of other RNAs, the regulation of gene expression, the end-maintenance of chromosomes, and the sorting of proteins within the cell. This functional versatility of RNA comes from its structural diversity. Our laboratory developed MC-Fold, an algorithm that predicts RNA structures by representing them with nucleotide interaction graphs. The nodes in these graphs represent the nucleotides, and the edges the interactions between them. Our laboratory also observed that a limited number of interaction cycles can define the structure of any RNA motif. The formation of these cycles is determined by the nucleotide sequence and MC-Fold determines the most likely cycles based on that sequence. In this Master Degree project, I first built a database of structural and functional RNA motifs, bdMotifs, based on their constituent cycles. Then, I implemented an algorithm, MC-Motifs, which detects motifs within interaction graphs generated either by MC-Fold or by any other method. Finally, I validated my algorithm on known RNA structures such as the 5S, 16S and 23S ribosomal RNA (rRNA) and predicted structure of riboswitches. The Master thesis is divided into five chapters. The first chapter presents the chemical structure of RNA, its cellular functions and the structural folding of the polymer. In the second chapter, the database bdMotifs is described. In the third chapter, the MC-Motifs algorithm is introduced. In the fourth chapter, I present the results of MC-Motifs. Finally, in the last chapter, I discuss theses results and I give a conclusion on the project.

The understanding of cellular metabolism has been an intriguing challenge in classical cellular biology for decades. Essentially, cellular metabolism can be viewed as a complex system of enzyme-catalysed biochemical reactions that produces the energy and material necessary for the maintenance of life. In modern biochemistry, it is well-known that these reactions group into metabolic pathways so as to accomplish a particular function in the cell. The identification of these metabolic pathways is a key step to fully understanding the metabolic capabilities of a given organism. Typically, metabolic pathways have been elucidated via experimentation on different organisms. However, experimental findings are generally limited and fail to provide a complete description of all pathways. For this reason it is important to have mathematical models that allow us to identify and analyze metabolic pathways in a computational fashion. This is precisely the main theme of this thesis. We firstly describe, review and discuss existent mathematical/computational approaches to metabolic pathways, namely stoichiometric and path finding approaches. Then, we present our initial mathematical model named the Beasley-Planes (BP) model, which significantly improves on previous stoichiometric approaches. We also illustrate a successful application of the BP model to optimally disrupt metabolic pathways. The main drawback of the BP model is that it needs as input extra pathway knowledge. This is especially inappropriate if we wish to detect unknown metabolic pathways. As opposed to the BP model and stoichoimetric approaches, this issue is not found in path finding approaches. For this reason a novel path finding approach is built and examined in detail. This analysis serves us as inspiration to build the Improved Beasley-Planes (IBP) model. The IBP model incorporates elements of both stoichometric and path finding approaches. Though somewhat less accurate than the BP model, the IBP model solves the issue of extra pathway knowledge. Our research clearly demonstrates that there is a significant chance of developing a mathematical optimisation model that underlies many/all metabolic pathways.