Dissertations / Theses on the topic 'Ontologie genowe'

With the proliferation of plant genomes being sequenced, assembled, and annotated, duplicate gene loss from whole genome duplication events, also known in plants as frac- tionation, has shown to have a different pattern from the classic gene duplication models described by Ohno in 1970. Models proposed more recently, the Gene Balance and Gene Dosage hypotheses, try to model this pattern. These models, however, disagree with each other on the relative importance of gene function and gene expression. In this thesis we explore the effects of gene function and gene expression on duplicate gene loss and retention. We use gene sequence similarity and gene order conservation to construct our gene fam- ilies. We applied multiple whole genome comparison methods across various plants in rosids, asterids, and Poaceae in looking for a general pattern. We found that there is great consistency across different plant lineages. Genes categorized as metabolic genes with low level of expression have relatively low fractionation resistance, losing duplicate genes readily, while genes categorized as regulation and response genes with high level of expression have relatively high fractionation resistance, retaining more duplicate gene pairs or triples. Though both gene function and gene expression have important effects on retention pattern, we found that gene function has a bigger effect than gene expression. Our results suggest that both the Gene Balance and Gene Dosage models account to some extent for fractionation resistance.

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Universidade Federal de Sao Carlos
Bioinformatics aims at providing computational tools to the development of genome researches. Among those tools are the annotations systems and the Database Management Systems (DBMS) that, associated to ontologies, allow the formalization of both domain conceptual and the data scheme. The data yielded by genome researches are often textual and with no regular structures and also requires scheme evolution. Due to these aspects, semi-structured DBMS might offer great potential to manipulate those data. Thus, this work presents architecture for biosequence annotation based on XML databases. Considering this architecture, a special attention was given to the database design and also to the manual annotation task performed by researchers. Hence, this architecture presents an interface that uses an ontology-driven model for XML schemas modeling and generation, and also a manual annotation interface prototype that uses molecular biology domain ontologies, such as Gene Ontology and Sequence Ontology. These interfaces were proven by Bioinformatics and Database experienced users, who answered questionnaires to evaluate them. The answers presented good assessments to issues like utility and speeding up the database design. The proposed architecture aims at extending and improving the Bio-TIM, an annotation system developed by the Database Group from the Computer Science Department of the Federal University from São Carlos (UFSCar).
A Bioinformática é uma área da ciência que visa suprir pesquisas de genomas com ferramentas computacionais que permitam o seu desenvolvimento tecnológico. Dentre essas ferramentas estão os ambientes de anotação e os Sistemas Gerenciadores de Bancos de Dados (SGBDs) que, associados a ontologias, permitem a formalização de conceitos do domínio e também dos esquemas de dados. Os dados produzidos em projetos genoma são geralmente textuais e sem uma estrutura de tipo regular, além de requerer evolução de esquemas. Por suas características, SGBDs semi-estruturados oferecem enorme potencial para tratar tais dados. Assim, este trabalho propõe uma arquitetura para um ambiente de anotação de biosseqüências baseada na persistência dos dados anotados em bancos de dados XML. Neste trabalho, priorizou-se o projeto de bancos de dados e também o apoio à anotação manual realizada por pesquisadores. Assim, foi desenvolvida uma interface que utiliza ontologias para guiar a modelagem de dados e a geração de esquemas XML. Adicionalmente, um protótipo de interface de anotação manual foi desenvolvido, o qual faz uso de ontologias do domínio de biologia molecular, como a Gene Ontology e a Sequence Ontology. Essas interfaces foram testadas por usuários com experiências nas áreas de Bioinformática e Banco de Dados, os quais responderam a questionários para avaliá-las. O resultado apresentou qualificações muito boas em diversos quesitos avaliados, como exemplo agilidade e utilidade das ferramentas. A arquitetura proposta visa estender e aperfeiçoar o ambiente de anotação Bio-TIM, desenvolvido pelo grupo de Banco de Dados do Departamento de Computação da Universidade Federal de São Carlos (UFSCar).

The advent of next-generation sequencing technologies is transforming biology by enabling individual researchers to sequence the genomes of individual organisms or cells on a massive scale. In order to realize the translational potential of this technology we will need advanced information systems to integrate and interpret this deluge of data. These systems must be capable of extracting the location and function of genes and biological features from genomic data, requiring the coordinated parallel execution of multiple bioinformatics analyses and intelligent synthesis of the results. The resulting databases must be structured to allow complex biological knowledge to be recorded in a computable way, which requires the development of logic-based knowledge structures called ontologies. To visualise and manipulate the results, new graphical interfaces and knowledge acquisition tools are required. Finally, to help understand complex disease processes, these information systems must be equipped with the capability to integrate and make inferences over multiple data sets derived from numerous sources. RESULTS: Here I describe research, design and implementation of some of the components of such a next-generation information system. I first describe the automated pipeline system used for the annotation of the Drosophila genome, and the application of this system in genomic research. This was succeeded by the development of a flexible graphoriented database system called Chado, which relies on the use of ontologies for structuring data and knowledge. I also describe research to develop, restructure and enhance a number of biological ontologies, adding a layer of logical semantics that increases the computability of these key knowledge sources. The resulting database and ontology collection can be accessed through a suite of tools. Finally I describe how the combination of genome analysis, ontology-based database representation and powerful tools can be combined in order to make inferences about genotype-phenotype relationships within and across species. CONCLUSION: The large volumes of complex data generated by high-throughput genomic and systems biology technology threatens to overwhelm us, unless we can devise better computing tools to assist us with its analysis. Ontologies are key technologies, but many existing ontologies are not interoperable or lack features that make them computable. Here I have shown how concerted ontology, tool and database development can be applied to make inferences of value to translational research.

Penstemon Mitchell (Plantaginaceae) is one of the largest, most diverse plant genera in North America. Their unique diversity, paired with their drought-tolerance and overall hardiness, give Penstemon a vast amount of potential in the landscaping industry—especially in the more arid western United States where they naturally thrive. In order to develop Penstemon lines for more widespread commercial and private landscaping use, we must improve our understanding of the vast genetic diversity of the genus on a molecular level. In this study we utilize genome reduction and barcoding to optimize 454-pyrosequencing in four target species of Penstemon (P. cyananthus, P. davidsonii, P. dissectus and P. fruticosus). Sequencing and assembly produced contigs representing an average of 0.5% of the Penstemon species. From the sequence, SNP information and microsatellite markers were extracted. One hundred and thirty-three interspecific microsatellite markers were discovered, of which 50 met desired primer parameters, and were of high quality with readable bands on 3% Metaphor gels. Of the microsatellite markers, 82% were polymorphic with an average heterozygosity value of 0.51. An average of one SNP in 2,890 bp per species was found within the individual species assemblies and one SNP in 97 bp were found between any two supposed homologous sequences of the four species. An average of 21.5% of the assembled contigs were associated with putative genes involved in cellular components, biological processes, and molecular functions. On average 19.7% of the assembled contigs were identified as repetitive elements of which LTRs, DNA transposons and other unclassified repeats, were discovered. Our study demonstrates the effectiveness of using the GR-RSC technique to selectively reduce the genome size to putative homologous sequence in different species of Penstemon. It has also enabled us the ability to gain greater insights into microsatellite, SNP, putative gene and repetitive element content in the Penstemon genome which provide essential tools for further genetic work including plant breeding and phylogenetics.

In eukaryotic genomes ranging from plants to mammals, DNA methylation is a major epigenetic modification of DNA by adding a methyl group exclusively to cytosine residuals. In mammalian genomes such as humans, these cytosine bases are usually followed by guanine. Although it does not change the primary DNA sequence, this covalent modification plays critical roles in several regulatory processes and can impact gene activity in a heritable fashion. What is more important, DNA methylation is essential for mammalian embryonic development and aberrant DNA methylation is implicated in several human diseases, in particular in neuro-developmental syndromes (such as the fragile X and Rett syndromes) and cancer. These biological significances disclose the importance of understanding genomic patterns and function role of DNA methylation in human, as a initial step to get to know the epigenotype and its manner in connecting the phenotype and genotype. Two key papers back in 1975 independently suggested that methylation of CpG dinucleotides in vertebrates could be established de novo and inherited through somatic cell divisions by protein machineries of DNA methyltransferases that recognizes hemi-methylated CpG palindromes. They also indicated that the methyl group could be recognized by DNA-binding proteins and that DNA methylation directly silences gene expression. After almost four decades, several key points in these foundation papers are proved to be true. Take the mammalian genome for example, there are several findings indicating the epigenetic repression of gene expression by DNA methylation. These include X-chromosome inactivation, gene imprinting and suppressing the proliferation of transposable elements and repeat elements of viral or retroviral origin. In addition to these, many novel roles of DNA methylation have also been revealed. For example, DNA methylation can regulate alternative splicing by preventing CTCF, an evolutionarily conserved zinc-finger protein, binding to DNA. By using the technique of fluorescence resonance energy transfer (FRET) and fluorescence polarization, DNA methylation has also been shown to increase nucleosome compaction through DNA-histone contacts. What is more important, DNA methylation is essential for mammalian embryonic development and aberrant change of DNA methylation has been related to disease such as cancer. However, it is also notable there are several lines of evidence contradicting the relationship between DNA methylation and gene silencing. For example, comparison of DNA methylation levels in human genome on the active and inactive X chromosomes showed reduced methylation specifically over gene bodies on inactive X chromosomes. Not only in human, DNA methylation is found to be usually targeted to the transcription units of actively transcribed genes in invertebrate species. These results prove that the function of DNA methylation is challenging to be unravel. Besides, due to the development of sequencing technique, whole genome DNA methylation profiles have been detected in diverse species. Comparing genomic patterns of DNA methylation shows considerable variation among taxa, especially between vertebrates and invertebrates. However, even though extensive studies reveal the patterns and functions of DNA methylation in different species, in the mean time, they also highlight the limits to our understanding of this complex epigenetic system. During my Ph.D., in order to perform in-depth studies of DNA methylation in diverse animals as a way to understand the complexity of DNA methylation and its functions, I dedicated my efforts in investigating and analyzing the DNA methylation profiles in diverse species, ranging from insects to primates, including both model and non-model organisms. This dissertation, which constitutes an important part of my research, mainly focuses on the DNA methylation profile in primates including human and chimpanzee. In general, I will use three chapters to elucidate my work in generating and interpreting the whole genome DNA methylation data. Firstly, we generated nucleotide-resolution whole-genome methylation maps of the prefrontal cortex of multiple humans and chimpanzees, then comprehensive comparative studies for these DNA methylation maps have been performed, by integrating data on gene expression as well. This work demonstrates that differential DNA methylation might be an important molecular mechanism driving gene-expression divergence between human and chimpanzee brains and also potentially contribute to the human-specific traits, such as evolution of disease vulnerabilities. Secondly , we performed global analyses of CpG islands (CGIs) methylation across multiple methylomes of distinctive cellular origins in human. The results from this work show that the human CpG islands can be distinctly classified into different clusters solely based upon the DNA methylation profiles, and these CpG islands clusters reflect their distinctive nature at many biological levels, including both genomic characteristics and evolutionary features. Moreover, these CpG islands clusters are non-randomly associated with several important biological phenomena and processes such as diseases, aging, and gene imprinting. These new findings shed lights in deciphering the regulatory mechanisms of CpG islands in human health and diseases. At last, by utilizing the DNA methylome from human sperm and genetic map generated from the International HapMap Consortium project, we investigated the hypothesis suggesting a potential role of germ line DNA methylation in affecting meiotic recombination, which is essential for successful meiosis and various evolutionary processes. Even thought the results imply that DNA methylation is a important factor affecting regional recombination rate, the strength of correlation between these two is not as strong as the previous report. Besides, high-throughput analyses indicate that other epigenetic modifications, tri-methylation of histone 3 lysine 4 and histone 3 lysine 27 are also global features at the recombination hotspots, and may interact with methylation to affect the recombination pattern simultaneously. This work suggests epigenetic mechanisms as additional factors affecting recombination, which cannot be fully explained by the DNA sequence itself. In summary, I hope the results from these work can expand our knowledge regarding the common and variable patterns of DNA methylation in different taxa, and shed light about the function role and its major change during animal evolution.

This project compared the performance of the correlation coefficient to show similarities in annotations between a predictive automated bacterial annotation database and the curated EcoCyc database. EcoCyc is a conservative multidimensional annotation system that is exclusively based on experimentally validated findings by over 15,000 publications. The automated annotation system, used in the comparison was BASys. It is often used as a first pass annotation tool that tries to add as many annotations as possible by drawing upon over 30 information sources. Gene ontology served as one basis of comparison between these databases because of the limited common terms in the ontology annotations. Translation libraries were used to extend the number of BASys terms that could be compared to the gene ontology terms in EcoCyc. Additional, non-ontology terms and metadata in BASys were compared to EcoCyc terms after parsing them into root words. The different term sources were quantitatively compared by using the correlation coefficient as the evaluation metric. The direct gene ontology comparison gave the lowest correlation coefficient. The addition of gene ontology terms to BASys by using translation tables of metadata greatly increased the correlation coefficient, which was comparable to the parsed word comparison. The combination of enhanced gene ontology and parsed word methods gave the highest correlation coefficient of 0.16. The controlled vocabulary system of gene ontology was not sufficient to compare two annotated databases. The addition of gene ontology terms from translation libraries greatly increased the performance of these comparisons. In general, as the number of comparison terms increased the correlation coefficient increased. Future comparisons should include the enhanced gene ontology dataset in order to monitor the organization pertaining to formal nomenclature and the datasets generated from Word parsing can be used to monitor the degree of additional terms might be incorporated with translation libraries.

Dizertační práce se zabývá predikcí vysokodimenzionálních dat genových expresí. Množství dostupných genomických dat významně vzrostlo v průběhu posledního desetiletí. Kombinování dat genových expresí s dalšími daty nachází uplatnění v mnoha oblastech. Například v klinickém řízení rakoviny (clinical cancer management) může přispět k přesnějšímu určení prognózy nemocí. Hlavní část této dizertační práce je zaměřena na kombinování dat genových expresí a klinických dat. Používáme logistické regresní modely vytvořené prostřednictvím různých regularizačních technik. Generalizované lineární modely umožňují kombinování modelů s různou strukturou dat. V dizertační práci je ukázáno, že kombinování modelu dat genových expresí a klinických dat může vést ke zpřesnění výsledku predikce oproti vytvoření modelu pouze z dat genových expresí nebo klinických dat. Navrhované postupy přitom nejsou výpočetně náročné.  Testování je provedeno nejprve se simulovanými datovými sadami v různých nastaveních a následně s~reálnými srovnávacími daty. Také se zde zabýváme určením přídavné hodnoty microarray dat. Dizertační práce obsahuje porovnání příznaků vybraných pomocí klasifikátoru genových expresí na pěti různých sadách dat týkajících se rakoviny prsu. Navrhujeme také postup výběru příznaků, který kombinuje data genových expresí a znalosti z genových ontologií.

Colorectal cancer is the third most common cancer and the leading cause of cancer deaths in Western industrialised countries. Despite recent advances in the screening, diagnosis, and treatment of colorectal cancer, an estimated 608,000 people die every year due to colon cancer. Our current knowledge of colorectal carcinogenesis indicates a multifactorial and multi-step process that involves various genetic alterations and several biological pathways. The identification of molecular markers with early diagnostic and precise clinical outcome in colon cancer is a challenging task because of tumour heterogeneity. This Ph.D.-thesis presents the molecular and cellular mechanisms leading to colorectal cancer. A systematical review of the literature is conducted on Microarray Gene expression profiling, gene ontology enrichment analysis, microRNA and system Biology and various bioinformatics tools. We aimed this study to stratify a colon tumour into molecular distinct subtypes, identification of novel diagnostic targets and prediction of reliable prognostic signatures for clinical practice using microarray expression datasets. We performed an integrated analysis of gene expression data based on genetic, epigenetic and extensive clinical information using unsupervised learning, correlation and functional network analysis. As results, we identified 267-gene and 124-gene signatures that can distinguish normal, primary and metastatic tissues, and also involved in important regulatory functions such as immune-response, lipid metabolism and peroxisome proliferator-activated receptors (PPARs) signalling pathways. For the first time, we also identify miRNAs that can differentiate between primary colon from metastatic and a prognostic signature of grade and stage levels, which can be a major contributor to complex transcriptional phenotypes in a colon tumour.

Determination of cell signalling behaviour is crucial for understanding the physiological response to a specific stimulus or drug treatment. Current approaches for large-scale data analysis do not effectively incorporate critical topological information provided by the signalling network. We herein describe a novel model- and data-driven hybrid approach, or signal transduction score flow algorithm, which allows quantitative visualization of cyclic cell signalling pathways that lead to ultimate cell responses such as survival, migration or death. This score flow algorithm translates signalling pathways as a directed graph and maps experimental data, including negative and positive feedbacks, onto gene nodes as scores, which then computationally traverse the signalling pathway until a pre-defined biological target response is attained. Initially, experimental data-driven enrichment scores of the genes were computed in a pathway, then a heuristic approach was applied using the gene score partition as a solution for protein node stoichiometry during dynamic scoring of the pathway of interest. Incorporation of a score partition during the signal flow and cyclic feedback loops in the signalling pathway significantly improves the usefulness of this model, as compared to other approaches. Evaluation of the score flow algorithm using both transcriptome and ChIP-seq data-generated signalling pathways showed good correlation with expected cellular behaviour on both KEGG and manually generated pathways. Implementation of the algorithm as a Cytoscape plug-in allows interactive visualization and analysis of KEGG pathways as well as user-generated and curated Cytoscape pathways. Moreover, the algorithm accurately predicts gene-level and global impacts of single or multiple in silico gene knockouts
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich

Determination of cell signalling behaviour is crucial for understanding the physiological response to a specific stimulus or drug treatment. Current approaches for large-scale data analysis do not effectively incorporate critical topological information provided by the signalling network. We herein describe a novel model- and data-driven hybrid approach, or signal transduction score flow algorithm, which allows quantitative visualization of cyclic cell signalling pathways that lead to ultimate cell responses such as survival, migration or death. This score flow algorithm translates signalling pathways as a directed graph and maps experimental data, including negative and positive feedbacks, onto gene nodes as scores, which then computationally traverse the signalling pathway until a pre-defined biological target response is attained. Initially, experimental data-driven enrichment scores of the genes were computed in a pathway, then a heuristic approach was applied using the gene score partition as a solution for protein node stoichiometry during dynamic scoring of the pathway of interest. Incorporation of a score partition during the signal flow and cyclic feedback loops in the signalling pathway significantly improves the usefulness of this model, as compared to other approaches. Evaluation of the score flow algorithm using both transcriptome and ChIP-seq data-generated signalling pathways showed good correlation with expected cellular behaviour on both KEGG and manually generated pathways. Implementation of the algorithm as a Cytoscape plug-in allows interactive visualization and analysis of KEGG pathways as well as user-generated and curated Cytoscape pathways. Moreover, the algorithm accurately predicts gene-level and global impacts of single or multiple in silico gene knockouts.
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

Tese de mestrado em Bioinformática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2007
One of the current challenges in the Life Sciences is to extract the knowledge contained in the vast amount of data that the genomic and post-genomic techniques are producing. One of the major efforts in this area was the development of the Gene Ontology (GO), a BioOntology that contains terms that describe gene products, organized in a graph structure. Gene products annotated with ontology terms can be compared according to them. This process is called semantic similarity and it is based on the structure of the BioOntology and the relations between its terms, focusing either on a structural comparison or more frequently on the semantic similarity between the terms themselves. In this work, I developed two novel hybrid measures of semantic similarity for proteins based on the Gene Ontology: simGIC (Graph-Information Content similarity) and simGED (Graph-Edit-Distance similarity). These measures were designed to take into account both graph attributes and the terms' information content, thus capturing more information than the previously existing measures which focused mostly on a single aspect (graph structure or term similarity). These two novel measures were evaluated against several previously proposed measures, using two strategies: relationship with sequence similarity and correlation with family similarity. The evaluation metric in the sequence similarity studies was the resolution of the measures, i.e. the range of semantic similarity values they cover, since most measures showed the same behaviour and similar correlation values. Overall simGIC was shown to be the best performer, with both the highest resolutions in the sequence similarity evaluation and highest correlation to family similarity, while simGED obtained above average results. The in uence of electronic annotations was also investigated but I found no conclusive evidence to support the general view that these are unreliable to use in semantic similarity studies. Keywords: Semantic Similarity, BioOntologies, Gene Ontology, Genome Annotation.Abstract One of the current challenges in the Life Sciences is to extract the knowledge contained in the vast amount of data that the genomic and post-genomic techniques are producing. One of the major efforts in this area was the development of the Gene Ontology (GO), a BioOntology that contains terms that describe gene products, organized in a graph structure. Gene products annotated with ontology terms can be compared according to them. This process is called semantic similarity and it is based on the structure of the BioOntology and the relations between its terms, focusing either on a structural comparison or more frequently on the semantic similarity between the terms themselves. In this work, I developed two novel hybrid measures of semantic similarity for proteins based on the Gene Ontology: simGIC (Graph-Information Content similarity) and simGED (Graph-Edit-Distance similarity). These measures were designed to take into account both graph attributes and the terms' information content, thus capturing more information than the previously existing measures which focused mostly on a single aspect (graph structure or term similarity). These two novel measures were evaluated against several previously proposed measures, using two strategies: relationship with sequence similarity and correlation with family similarity. The evaluation metric in the sequence similarity studies was the resolution of the measures, i.e. the range of semantic similarity values they cover, since most measures showed the same behaviour and similar correlation values. Overall simGIC was shown to be the best performer, with both the highest resolutions in the sequence similarity evaluation and highest correlation to family similarity, while simGED obtained above average results. The influence of electronic annotations was also investigated but I found no conclusive evidence to support the general view that these are unreliable to use in semantic similarity studies.

Tese de mestrado em Bioinformática e Biologia Computacional (Bioinformática), apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2014
Com o aparecimento dos novos métodos de sequenciação de alto desempenho, tem-se verificado uma diminuição de custos na sequenciação em larga escala de genomas através da tecnologia denominada de “Next Generation Sequencing” (NGS), resultado numa cada vez maior produção de informação genómica. Um dos campos onde a aplicação destas novas tecnologias de sequenciação, têm provas dadas de sucesso é em Epidemiologia Molecular, cujo objectivo é detectar e seguir surtos bacterianos. Casos recentes e mediáticos de surtos de estirpes bacterianas perigosas para a saúde pública, como o surto de cólera em 2010 no Haiti e E.coli O104:H4 na Alemanha em 2011, têm revelado as competências das tecnologias NGS relativamente às tecnicas de tipagem até então utilizadas. No entanto para os dados serem comparáveis e reprodutíveis, todo o processo desde a extração do DNA até há analise final de resultados necessitam de ser documentados. Até ao momento, os principais serviços de pesquisa e inserção de dados de NGS, como o Sequence Read Archive (SRA) e European Nucleotide Archive (ENA), apresentam algumas limitações, nomeadamente no que se refere à anotação dos processos laboratoriais e em processos de analise in silico dos dados. Neste trabalho foi desenvolvida uma ontologia relacionado com a sequenciação de nova geração, a NGSOnto. Esta ontologia foi construida de forma a descrever o fluxo de trabalho de um processo de sequenciação por NGS, sendo que esta ontologia reutiliza conceitos da Ontology for Biomedical Investigation (OBI) entre outras. Para construir a ontologia foi utilizada a Web Ontology Language (OWL), utilizando a estrutura da Basic Formal Ontology (BFO) 1.1 e guardando a informação através da Resource Description Framework (RDF). Foi também criada, como prova de conceito de aplicação da ontologia, uma interface programática REST de modo a possibilitar a inserção e consulta de dados num formato que sejam possíveis de ser lidos por máquinas , e uma interface web de fácil utilização para clientes com menos conhecimentos programáticos, que utiliza a REST API desenvolvida. Com a anotação dos dados usando a NGSOnto, a captura do fluxo de trabalho de todos os processos envolvidos permite assegurar a reprodutibilidade de todo o processo através da utilização um vocabulário controlado e especifico para o campo, com benefícios óbvios para investigação em diversas áreas que usam NGS e para validação e certificação de resultados em aplicações clinicas.
With the appearance of new high throughput sequencing tecnologies, there has been a significant decrease in large scale sequencing costs through technologies known as "Next Generation Sequencing"(NGS), resulting in an increasing genomic information production. One of the successful application fields of this new sequencing technologies has been the Molecular Epidemology, where the main aim is at detecting and following bacterian outbreaks. Recent and known cases of such outbreaks where NGS technologies have proven their capacity, comparing with previous typing methods, are the cholera outbreak in 2010 at Haiti and the E.coli O104:H4 at Germany in 2011. However, to be able to compare and reproduce this data, it's necessary to keep the information of all processes, starting at the DNA Extraction process until the last final results analysis. At this moment, the main NGS data search and insertion services, such as o Sequence Read Archive (SRA) and European Nucleotide Archive (ENA), present some limitations, namely at the annotation of performed laboratorial processes and consequent in silico data analysis processes. Considering the previous facts, an ontology about the new sequencing generation was developed, the NGSOnto. This ontology was developed in order to describe the full workflow of a NGS sequencing process, reusing concepts of the Ontology for Biomedical Investigation (OBI) and others. The Web Ontology Language (OWL) was used to develop the NGSOnto, using the Basic Formal Ontology (BFO) 1.1 high level structure and saving the data trough the Resource Description Framework (RDF). In order to perform a concept proof of case of the NGSOnto, a REST Application Programming Interface (API) was developed, providing a mean to insert and access the data in a machine readable format, and a web interface, that uses the REST API developed, for users with less programatic skills. Using NGSOnto, the capture of all the workflow process information provides a mean to ensure the reproducibility of the NGS process, through a controled and domain specific vocabulary, providing obvious benefits for scientific investigation areas using NGS technologies and certification of clinical results.

The non-genealogical transfer of genetic information between prokaryotes is a frequent and omnipresent event. The acquisition of foreign genomic segments may aid organisms in adaptation to novel or extreme habitats with these rapid evolution events phenotypically bene cial to the recipient. These regions of atypical and foreign origin are vernacularly termed islands and are identi ed by their unique local genomic signature or composition which di ers from the global host genomic signature. The SeqWord Genomic Island Sni er program utilizes tetranucleotide frequency patterns and statistics to identify regions of probable horizontal transfer. Optimum parameter values were determined for this compositional-based island identi er to ensure acceptable levels of false negative and false positive occurrence. Post-identi cation island analysis is demonstrated with the aid of the LingvoCom package available from the SeqWord project. This island identi er was furthermore compared with other existing transfer detection packages to indicate relevance and reliability. The continued identi cation of islands in prokaryotic genomes requires a novel and functional repository with the ability to expand as newly sequenced archaeal/bacterial genomes are available. The amalgamation of a robust database, convenient interface and island analysis tools presents a novel avenue in prokaryotic island research. The Predicted Genomic Islands database currently houses 26,744 islands identi ed in 2,407 archaeal/bacterial genomes and is freely available from http://pregi.bi.up.ac.za/. The database serves as an island information hub and collection of analytical tools allowing users the ability to address a myriad of horizontal transfer and island ontology research questions. Inclusion of various novel island information criteria and analytical tools may distinguish this platform from extant island databases and tools. Novel island comparison against the current content enables a rapid yet reliable tool set in the age of brisk and economically e cient genome sequencing. The collection of all island information in a single set allows for various avenues of research and stratigraphy of islands by allowing for the deconstruction and inspection of layers in archaeal/ bacterial island communities and exchange between island hosts. The capability of this island garage was previewed with novel island identi cation results and research directions in an attempt to convey the future potential. This collection of island information and tools may prove a reliable and innovative approach in variable elds of horizontal transfer and island research with numerous applications and associations.
Thesis (PhD)--University of Pretoria, 2016.
tm2016
Biochemistry
PhD
Unrestricted

Lampreys are primitive jawless fishes that diverged over 550 million years ago. As adults, they are either parasitic or non-parasitic. In non-parasitic species, sexual differentiation and oocyte development generally occur earlier than in parasitic species; fecundity is reduced and sexual maturation is accelerated following metamorphosis. The genes controlling ovarian differentiation and maturation in lampreys are poorly understood. This study used RNA-Seq data in the parasitic chestnut lamprey Ichthyomyzon castaneus and non-parasitic northern brook lamprey Ichthyomyzon fossor to identify suites of genes expressed during different stages of ovarian development that show different developmental trajectories with respect to ovarian differentiation and sexual maturation. For this, reference-guided and de novo assembly pipelines were designed for studying a non-model species. To test and explore the relative advantages of the pipelines, expression of insulin superfamily genes was used. This research helps to identify genes involved in lamprey ovarian development and provides insight into evolution of the insulin superfamily in vertebrates.
May 2017