Alternative pre-messenger RNA splicing affects a majority of human genes and plays important roles in development and disease. Alternative splicing (AS) events conserved since the divergence of human and mouse are likely of primary biological importance, but relatively few such events are known. Here we describe sequence features that distinguish exons subject to evolutionarily conserved AS, which we call 'alternative-conserved exons' (ACEs) from other orthologous human/mouse exons, and integrate these features into an exon classification algorithm, ACEScan. Genome-wide analysis of annotated orthologous human-mouse exon pairs identified ~2,000 predicted ACEs. Alternative splicing was verified in both human and mouse tissues using an RT-PCR-sequencing protocol for 21 of 30 (70%) predicted ACEs tested, supporting the validity of a majority of ACEScan predictions. By contrast, AS was observed in mouse tissues for only 2 of 15 (13%) tested exons which had EST or cDNA evidence of AS in human but were not predicted ACEs, and was never observed for eleven negative control exons in human or mouse tissues. Predicted ACEs were much more likely to preserve reading frame, and less likely to disrupt protein domains than other AS events, and were enriched in genes expressed in the brain and in genes involved in transcriptional regulation, RNA processing and development. Our results also imply that the vast majority of AS events represented in the human EST databases are not conserved in mouse, and therefore may represent aberrant, disease- or allele-specific, or highly lineage-restricted splicing events.

This thesis documents the contribution of my bioinformatics research activities, including novel software development, to a range of research projects aimed at investigating the interactions between bacterial and viral pathogens and their hosts. The focus is largely on farm animal species and their pathogens, although some of the research has a wider scientific impact. RNA interference (RNAi) refers to a variety of related regulatory pathways present in animals, plants and insects. The major pathways are microRNAs (miRNAs), small-interfering RNAs (siRNAs) and PIWI-interacting RNAs (piRNAs). Marek’s disease virus is an important pathogen of poultry, causing T-cell lymphoma. We identified the presence and expression patterns of several MDV-encoded microRNAs, including the identification of 5 novel microRNAs. We also showed that not only do virus-encoded microRNAs dominate the mirNome within chicken cells, but also that specific host-microRNAs are down-regulated. We also identify novel virus-encoded microRNAs in other Herpesviridae and provide the first evidence of miRNA evolution by duplication in viruses. In related work, we present a novel microRNA generated by the canonical miRNA biogenesis pathway in Avian Leukosis Virus, another avian oncogenic virus, and publish data showing the expression pattern of known chicken microRNAs across a range of important avian cells. Two of the other RNAi pathways (siRNA and piRNA) form an important part of the antiviral response in arthropods. We have published work demonstrating an siRNA antiviral response to bluetongue virus and Schmallenberg virus in cells from the Culicoides midge, an important insect vector, as well as work demonstrating the importance of the piRNA pathway in the antiviral response to Semliki forest virus (SFV). Further work on flaviviruses in ticks demonstrates the active suppression of the siRNA response by Langat Virus, as well as a key difference between the siRNA responses in Mosquitos compared to ticks. Salmonella is one of the most important zoonoses, with an estimated 1.4 million cases of human salmonellosis per annum in the USA alone. Salmonella infections of farm animals are an important route into the human food chain. This thesis presents work on the comparative structure and function of 13 fimbrial operons within Salmonella enterica serovar Enteritidis as well as a genomic comparison of that serovar with Salmonella enterica serovar Gallinarum, a chicken-specific serovar. We characterised the global expression profile of Salmonella enterica serovar Typhimurium during colonization of the chicken intestine, and we have published the genomes of four strains of Salmonella eneterica serovars of well-defined virulence in food-producing animals. Our work in this area led to us publishing an important and comprehensive review of the automatic annotation of bacterial genomes. Finally, I present work on novel software development. ProGenExpress, a software tool that allows the easy and accurate integration and visualisation of quantitative data with the genome annotation of bacteria; Meta4 is a web application that allows data sharing of bacterial genome annotations from metagenomes; CORNA, a software tool that allows scientists to link together microRNA targets, gene expression and functional annotation; viRome, a software tool for the analysis of siRNA and piRNA responses in virus-infection studies; DetectiV, a software tool for the analysis of pathogen-detection microarray data; and poRe, a software tool that enables users to organise and analyse nanopore sequencing data.

Sequencing B and T cell receptor genes allows for detailed characterisation of the genetic diversity underlying adaptive immune responses in health and disease. In the context of infectious diseases this can act as a powerful tool for identification of pathogen-specific immune signatures and genetic determinants of immune memory, protection and response to re-exposure. As part of my PhD I developed and optimised a method for high-resolution profiling of B cell receptor (BCR) immune repertoires based on the barcoded sequencing of the human immunoglobulin genes. The use of molecular barcodes allowed for reduction of technical noise, which can lead to erroneous assignment of lymphocyte function. I applied this methodology to the study of natural infection with measles virus in unvaccinated children. Childhood measles causes a profound immune suppression, which can last for weeks to months post infection, with large reductions in numbers of circulating B cells. Interestingly, long-term consequences of measles immune suppression result in increased incidence of secondary infections up to 3 years after resolution of measles. Vaccination against measles virus with the MMR vaccine has been a major factor in reducing direct and secondary childhood morbidity and mortality. The maintenance of sufficient global vaccine coverage, however, has been challenging due to the refusal of vaccination, mainly in religious communities, resulting in increasing number of outbreaks worldwide. In addition to the overall drop in measles virus herd immunity, measles-induced immune suppression can compromise immunity to other infectious pathogens, thus complicating global vaccination and surveillance efforts. The exact mechanisms underlying the prolonged immune-suppression associated with measles remain elusive and have not been investigated in humans. I applied BCR sequencing to characterise the long-term immunological effects of natural measles virus infection in a cohort of unvaccinated children. Specifically, I addressed the restructuring of immune memory and the possible loss of immunity to non-measles pathogens. My work provided evidence for previously hypothesised depletion of B cell memory pools, referred to as ‘immunological amnesia’. Loss of clonally expanded B memory populations lead to immune re-setting and convergence in repertoire diversity between measles-infected and control groups. In addition to the loss of individual-specific variation in immune memory, a subset of measles-infected individuals exhibited dramatic collapse in the diversity of their naïve B cell compartment, despite the recovery of normal B naïve cell counts. An effect of measles on serological immunity was also demonstrated in a ferret model of measles, where lymphotropic challenge lead to significant loss of vaccine-acquired immunity to influenza virus. The work presented in this dissertation demonstrates the utility of BCR sequencing for understanding adaptive immune responses in the context of infectious diseases and highlights the potential of this approach to uncover novel mechanisms of immune (dys)function.

Made available in DSpace on 2015-03-03T11:52:29Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-06-26Bitstream added on 2015-03-03T12:06:39Z : No. of bitstreams: 1 000811102.pdf: 563228 bytes, checksum: eaa482c1af9524645e59e24b54093f07 (MD5) / No melhoramento genético animal a forma tradicional de realizar seleção é com base no fenótipo dos indivíduos e na informação do parentesco entre estes, porém é um processo lento, sendo assim, programas de melhoramento estão procurando identificar os genes responsáveis pela característica de interesse e assim realizar a seleção dos animais que carregam a informação desejada. Com as informações dos indivíduos genotipados, tornou-se possível a utilização da informação de genes idênticos em estado tornando viável a utilização de uma matriz de parentesco (G) permitindo aumentar a precisão das avaliações genéticas, porém, devido à dificuldade de se obter o genótipo de todos os animais de uma população, foi proposto um método que realiza a integração da matriz G com a matriz de parentesco (A) em uma matriz de parentesco-genômica (H). Embora tenham trabalhos que indiquem uma similaridade no progresso genético utilizando estas diferentes matrizes é importante a avaliação da contribuição da avaliação genômica nos processos de avaliação genética em populações com estruturas de parentesco diferentes, bem como avaliar a metodologia de seleção genômica em populações multirraciais, a fim de atender o sistema de criação de animais cruzados. Assim, o objetivo geral deste trabalho foi estudar os efeitos da informação genômica na avaliação genética animal por meio de diferentes matrizes genômicas, utilizando dados de bovinos de corte com diferentes estruturas populacionais e composições raciais. Primeiramente avaliou-se 3 diferentes metodologias de se obter a matriz H, com a frequência alélica observada (HGOF), menor frequência alélica (HGMF) e uma frequência de 0,5 para todos os SNPs (HG50). Foram feitas comparações entre estas matrizes genômicas e a matriz de parentesco tradicional (A) utilizando uma população de 1695 animais da raça Brahman (BB). De acordo com os ... / In animal breeding methodologies, the traditional method of performing selection is based on the phenotype of individuals and information of relationship between them, but it is a slow process, so breeding programs are trying to identify the genes responsible for the trait of interest and thus achieve selection of animals that carry the interesting genes. With the information of genotyped individuals, it became possible to use the information of genes identical in state making it feasible to use a relationship matrix (G) which increase the accuracy of genetic evaluations, however, due to difficulty of obtaining the genotype of all animals in a population, we propose a method that performs the integration of the G matrix with the relationship matrix (A) in a pedigree-genomic relationship matrix (H). Although studies indicating a similarity in genetic progress using these matrices is important to evaluate the contribution of genomic evaluation in the process of genetic evaluation in populations with different structures of kinship, as well as evaluating the methodology of genomic selection in multiracial populations in order to cater to the creation of crossbred system. Thus the objective of this work was to study the effects of genomic information in genetic evaluation through different genomic arrays using data from beef cattle with different population structures and racial compositions. First we evaluated three different methods of obtaining the H matrix with the observed allele frequency (HGOF), lower allele frequency (HGMF) and a frequency of 0.5 for all SNPs (HG50). Comparisons between these genomic arrays and traditional kinship (A) using a population of 1695 animals breed Brahman (BB) matrix were made. According to the results , the HGOF was a matrix that showed the greatest similarity to the matrix A but the greatest differences were found in the classification of animals, when we evaluated the classification of animals ...

Proteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-individual variation in protein levels (protein quantitative trait loci (pQTLs)). Limitations in high-throughput proteomic measurement technology have meant well-powered genome-wide association studies for large number of proteins so far have lagged behind many of the other "omic" studies such as transcriptomics and metabolomics. This is made more challenging by the complexity of human plasma, characterised by high dynamic range spanning several magnitudes of concentrations and a large number of low abundance proteins. By using an expanded high-throughput multiplex aptamer-based proteomic assay with more than twice the proteome coverage of previous studies, I am able to greatly expand on existing knowledge on genetic determinants of human plasma proteins through testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 individuals. I identify 1,927 genetic associations with 1,478 proteins, replicating many previous associations as well as gaining novel insights into the genetic architecture of the human plasma proteome. I use several approaches to highlight the application of pQTLs to biology and disease. I show several examples linking distant pQTLs to biologically plausible genes and demonstrate the mediation of distant pQTL by local protein levels, highlighting the role of protein-protein interactions. In addition, I find epistatic effects of genetically determined phenotypes (blood group and secretor status) on protein levels. Through linking previous disease associations, I show that disease associated variants are enriched for pQTLs and I provide insights into possible mechanisms underpinning some of the disease loci. Finally, I identify causal roles for protein biomarkers in disease through multivariable Mendelian randomisation (MR) analysis, leveraging on the simultaneous measurement of multiple functionally related proteins in a locus to account for potential pleiotropic effects. Whereas MR studies of plasma proteins have been constrained by availability of few suitable genetic instruments, the data generated here remedy this bottleneck by furnishing an extensive toolkit. Overall, the work within this thesis foreshadows major advances in post-genomic science through increasing application of novel bioassay technologies to major population biobanks.

abstract: The processes of a human somatic cell are very complex with various genetic mechanisms governing its fate. Such cells undergo various genetic mutations, which translate to the genetic aberrations that we see in cancer. There are more than 100 types of cancer, each having many more subtypes with aberrations being unique to each. In the past two decades, the widespread application of high-throughput genomic technologies, such as micro-arrays and next-generation sequencing, has led to the revelation of many such aberrations. Known types and subtypes can be readily identified using gene-expression profiling and more importantly, high-throughput genomic datasets have helped identify novel sub-types with distinct signatures. Recent studies showing usage of gene-expression profiling in clinical decision making in breast cancer patients underscore the utility of high-throughput datasets. Beyond prognosis, understanding the underlying cellular processes is essential for effective cancer treatment. Various high-throughput techniques are now available to look at a particular aspect of a genetic mechanism in cancer tissue. To look at these mechanisms individually is akin to looking at a broken watch; taking apart each of its parts, looking at them individually and finally making a list of all the faulty ones. Integrative approaches are needed to transform one-dimensional cancer signatures into multi-dimensional interaction and regulatory networks, consequently bettering our understanding of cellular processes in cancer. Here, I attempt to (i) address ways to effectively identify high quality variants when multiple assays on the same sample samples are available through two novel tools, snpSniffer and NGSPE; (ii) glean new biological insight into multiple myeloma through two novel integrative analysis approaches making use of disparate high-throughput datasets. While these methods focus on multiple myeloma datasets, the informatics approaches are applicable to all cancer datasets and will thus help advance cancer genomics. / Dissertation/Thesis / Ph.D. Biomedical Informatics 2014

Poxviruses are double stranded (ds) DNA viruses with large brick-shaped virions (~200x300nm) that can be seen by light microscopy. The Chordopoxvirus (ChPV) subfamily demonstrates a vast genetic diversity in poxvirus virulence and evolution, and infects a wide range of vertebrate hosts including human/primates, rodents, birds, squirrels, and many economically important ruminants. There are at least 14 distinct ChPV genera, whose members have genomes that range between 127-360 kbp, and can be either GC-rich (33-38% A+T base composition) or AT-rich (up to 76% A+T). My work in the assembly and annotation of novel poxviruses serves to enrich the poxvirus sequence repository and further virulence characterization, comparative analysis, and phylogenetic studies. Using a variety of programs, as well as tools developed by the Virus Bioinformatics Research Centre, a protocol is created, refined, and applied to the assembly and annotation of novel poxviruses: Pteropox virus (PTPV) from a south Australian megabat Pteropus scapulatus, Eptesipox virus (EPTV) from a north American microbat Eptesicus fuscus, sea otter poxvirus (SOPV) from the north American Enhydra lutris, and two Kangaroopox viruses western and eastern Kangaroopox viruses (WKPV, EKPV) from the Australian Macropus fuliginosus and Macropus giganteus. This is the first time poxviruses from these vertebrate hosts are assembled in full, and the result supports the establishment of 4 new ChPV genera. The two bat-isolated poxviruses, PTPV and EPTV, likely did not co-speciate with their hosts despite infection of related host species. Instead, EPTV forms a sister clade with the Clade II virus, and together forms a sister group with the orthopoxviruses. On the other hand, PTPV and SOPV are each other’s closest extant relatives despite the distant geographical location from which they were isolated; together they share a novel homolog of TRAIL (Tumor necrosis factor-Related Apoptosis-Inducing Ligand) never before seen in poxviruses. SOPV additionally encodes distinct interleukin (IL)-18 binding protein and tumor necrosis factor (TNF) receptor-like protein that could have novel immune-evasion roles. The KPVs present the first case of a putative viral cullin-like protein, which might be involved in regulating the host ubiquitination pathway. Altogether, these novel proteins can potentially serve as new virokines and viroceptors in the form of viromimicry pathogenesis; they demonstrate the capacity and diversity with which poxviruses modulate host immune responses in their favour, and should be studied further. / Graduate / 2019-04-11

No description available.

Cette thèse a pour sujet les méthodes informatiques traitant les séquences ADN provenant des séquenceurs haut débit. Nous nous concentrons essentiellement sur la reconstruction de génomes à partir de fragments ADN (assemblage génomique) et sur des problèmes connexes. Ces tâches combinent de très grandes quantités de données et des problèmes combinatoires. Différentes structures de graphe sont utilisées pour répondre à ces problèmes, présentant des compromis entre passage à l'échelle et qualité d'assemblage. Ce document introduit plusieurs contributions pour répondre à ces problèmes. De nouvelles représentations de graphes d'assemblage sont proposées pour autoriser un meilleur passage à l'échelle. Nous présentons également de nouveaux usages de ces graphes, différent de l'assemblage, ainsi que des outils pour utiliser ceux-ci comme références dans les cas où un génome de référence n'est pas disponible. Pour finir nous montrons comment utiliser ces méthodes pour produire un meilleur assemblage en utilisant des ressources raisonnables. / Novel approaches for the exploitation of high throughput sequencing data In this thesis we discuss computational methods to deal with DNA sequences provided by high throughput sequencers. We will mostly focus on the reconstruction of genomes from DNA fragments (genome assembly) and closely related problems. These tasks combine huge amounts of data with combinatorial problems. Various graph structures are used to handle this problem, presenting trade-off between scalability and assembly quality. This thesis introduces several contributions in order to cope with these tasks. First, novel representations of assembly graphs are proposed to allow a better scaling. We also present novel uses of those graphs apart from assembly and we propose tools to use such graphs as references when a fully assembled genome is not available. Finally we show how to use those methods to produce less fragmented assembly while remaining tractable.

No description available.