Background: MicrO is an ontology of microbiological terms, including prokaryotic qualities and processes, material entities (such as cell components), chemical entities (such as microbiological culture media and medium ingredients), and assays. The ontology was built to support the ongoing development of a natural language processing algorithm, MicroPIE (or, Microbial Phenomics Information Extractor). During the MicroPIE design process, we realized there was a need for a prokaryotic ontology which would capture the evolutionary diversity of phenotypes and metabolic processes across the tree of life, capture the diversity of synonyms and information contained in the taxonomic literature, and relate microbiological entities and processes to terms in a large number of other ontologies, most particularly the Gene Ontology (GO), the Phenotypic Quality Ontology (PATO), and the Chemical Entities of Biological Interest (ChEBI). We thus constructed MicrO to be rich in logical axioms and synonyms gathered from the taxonomic literature. Results: MicrO currently has similar to 14550 classes (similar to 2550 of which are new, the remainder being microbiologically-relevant classes imported from other ontologies), connected by similar to 24,130 logical axioms (5,446 of which are new), and is available at (http://purl.obolibrary.org/obo/MicrO.owl) and on the project website at https://github.com/carrineblank/MicrO. MicrO has been integrated into the OBO Foundry Library (http://www.obofoundry.org/ontology/micro.html), so that other ontologies can borrow and re-use classes. Term requests and user feedback can be made using MicrO's Issue Tracker in GitHub. We designed MicrO such that it can support the ongoing and future development of algorithms that can leverage the controlled vocabulary and logical inference power provided by the ontology. Conclusions: By connecting microbial classes with large numbers of chemical entities, material entities, biological processes, molecular functions, and qualities using a dense array of logical axioms, we intend MicrO to be a powerful new tool to increase the computing power of bioinformatics tools such as the automated text mining of prokaryotic taxonomic descriptions using natural language processing. We also intend MicrO to support the development of new bioinformatics tools that aim to develop new connections between microbial phenotypes and genotypes (i.e., the gene content in genomes). Future ontology development will include incorporation of pathogenic phenotypes and prokaryotic habitats.

Cada vez mais, RNAs longos não codificadores (lncRNAs) surgem como importantes reguladores da biologia celular, principalmente em processos de diferenciação durante o desenvolvimento. O interesse no estudo das funções e mecanismos de atuação dessa classe de transcritos durante esses processos é crescente, e mostra-se bastante relevante no processo de diferenciação neural, pelo qual são gerados neurônios e células da glia. A linhagem celular P19, uma célula pluripotente advinda de um tipo de carcinoma embrionário murino, é bem consolidada como modelo in vitro de diferenciação neural. Após tratamento com ácido retinóico, ela é capaz de se diferenciar em neurônios e células da glia (astrócitos e oligodendrócitos). Em busca de evidências que indiquem a atuação de lncRNAs durante o processo de diferenciação neural, nosso grupo realizou experimentos utilizando microarranjos para averiguar os níveis de expressão gênica de lncRNAs e genes codificadores de proteínas (mRNAs) durante a diferenciação de células P19 em neurônios (predominância após 10 dias de diferenciação) e glia (predominância em 14 dias de diferenciação). Em um primeiro momento foi realizada a reanotação das sondas referentes a esses lncRNAs da plataforma de microarranjo, visto que as informações presentes nos arquivos de anotação da mesma eram muito escassas e desatualizadas. Registros de lncRNAs e mRNAs foram obtidos a partir de bancos de dados públicos para esse fim, e ao final dessa etapa aproximadamente 25,0% das sondas que não tinham uma anotação foram reanotadas com identificadores advindos desses bancos de dados. A partir dos dados de expressão, foram identificados todos os lncRNAs e mRNAs que apresentaram expressão diferencial entre as diferentes condições estudadas. As informações dos mRNAs diferencialmente expressos foram então utilizadas para a realização de análises de enriquecimento de categorias gênicas do Gene Ontology, nas ontologias de processo biológico e função molecular. A partir das sondas reanotadas, foram realizadas análises de coexpressão entre lncRNAs e mRNAs. A partir do cruzamento das informações obtidas, foram selecionados lncRNAs que através dos princípios de guilt by association se mostraram propensos a desempenharem um papel regulatório na diferenciação neural. Assim, as informações geradas nesse trabalho servirão como base para estudos futuros de validação funcional desses lncRNAs. / Increasingly, long noncoding RNAs (lncRNAs) emerge as important regulators of cell biology, especially in differentiation processes during development. The interest in the study of functions and mechanisms of action of this class of transcripts during these processes is growing, and shows quite relevant in the neural differentiation process by which neurons and glia are generated. The P19 cell line, pluripotent cells arising from a type of murine embryonal carcinoma, is well established as an in vitro model of neural differentiation. After treatment with retinoic acid, it is capable of differentiating into neurons and glial cells (astrocytes and oligodendrocytes). In search of evidence that indicate the action of lncRNAs during the neural differentiation process, our group conducted experiments using microarrays to assess gene expression levels of lncRNAs and protein coding genes (mRNAs) during differentiation of P19 cells into neurons (mainly after 10 days of differentiation) and glial cells (mainly after 14 days of differentiation). At first was performed the reannotation of the probes relating to these microarrays lncRNAs, as the information provided in the annotation files were very scarce or outdated. LncRNAs and mRNAs records were obtained from public databases for this purpose, and at the end of this stage approximately 25.0% of the probes without annotation were reannotated with identifiers arising from these databases. From the expression data, we identified all lncRNAs and mRNAs that showed differential expression between the different studied conditions. The information of differentially expressed mRNAs were then used to perform Gene Ontology enrichment, in the ontologies biological process and molecular function. From the reannotated probes, coexpression analyses were performed for lncRNAs and mRNAs. From the crosscheck of information obtained, we selected those lncRNAs that by the principles of guilt by association proved likely to play a regulatory role in neural differentiation. Thus, the information generated in this study will serve as a basis for future studies of functional validation of these lncRNAs.

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.

Decoding the genomes from organisms that encompass all taxonomies provides the foundation for extensive, large scale studies of biological molecules such as RNA, protein and carbohydrates. The high-throughput studies facilitated by the existence of these genome sequences necessitate the development of new analytic methods for the interpretation of large sets of results. The work herein focuses on the development of a novel clustering method for the analysis of protein array results and examines its utilization in the analysis of integrated interaction data sets. Sets of proteins that interact with a molecule of interest were clustered according to their functional similarity. The simUI distance metric in the statistical analysis package BioConductor was applied to measure the similarity of two proteins utilizing the assembly of their Gene Ontology annotation. Clusters were identified by partitioning around medoids and interpreted using the summary label provided by the Gene Ontology annotation of the medoid. The utility of the method was tested on two published yeast protein array data sets and shown to allow interpretation of the data to yield novel biological hypotheses. We performed a protein array screen using the E3 ubiquitin ligase and PDZ domain-containing protein LNX1. We combined these results with other published LNX1 interactors to produce a set of 220 proteins that was clustered according to Gene Ontology annotation. From the clustering results, 14 proteins were selected for subsequent examination by co-immunoprecipitation, of which 8 proteins were confirmed as LNX1 interactors. Recognition of 6 proteins by specific LNX1 PDZ domains was confirmed by fusion protein pull-downs. This work supports the role of LNX1 as a signalling scaffold. The interpretation of protein array results using our novel clustering method facilitated the identification of candidate molecules for subsequent experimental analysis. Thus our analytical method facilitates identification of biologically relevant molecules within a large data set, making this method an essential component of complex, high-throughput experimentation.

Decoding the genomes from organisms that encompass all taxonomies provides the foundation for extensive, large scale studies of biological molecules such as RNA, protein and carbohydrates. The high-throughput studies facilitated by the existence of these genome sequences necessitate the development of new analytic methods for the interpretation of large sets of results. The work herein focuses on the development of a novel clustering method for the analysis of protein array results and examines its utilization in the analysis of integrated interaction data sets. Sets of proteins that interact with a molecule of interest were clustered according to their functional similarity. The simUI distance metric in the statistical analysis package BioConductor was applied to measure the similarity of two proteins utilizing the assembly of their Gene Ontology annotation. Clusters were identified by partitioning around medoids and interpreted using the summary label provided by the Gene Ontology annotation of the medoid. The utility of the method was tested on two published yeast protein array data sets and shown to allow interpretation of the data to yield novel biological hypotheses. We performed a protein array screen using the E3 ubiquitin ligase and PDZ domain-containing protein LNX1. We combined these results with other published LNX1 interactors to produce a set of 220 proteins that was clustered according to Gene Ontology annotation. From the clustering results, 14 proteins were selected for subsequent examination by co-immunoprecipitation, of which 8 proteins were confirmed as LNX1 interactors. Recognition of 6 proteins by specific LNX1 PDZ domains was confirmed by fusion protein pull-downs. This work supports the role of LNX1 as a signalling scaffold. The interpretation of protein array results using our novel clustering method facilitated the identification of candidate molecules for subsequent experimental analysis. Thus our analytical method facilitates identification of biologically relevant molecules within a large data set, making this method an essential component of complex, high-throughput experimentation.

Cada vez mais, RNAs longos não codificadores (lncRNAs) surgem como importantes reguladores da biologia celular, principalmente em processos de diferenciação durante o desenvolvimento. O interesse no estudo das funções e mecanismos de atuação dessa classe de transcritos durante esses processos é crescente, e mostra-se bastante relevante no processo de diferenciação neural, pelo qual são gerados neurônios e células da glia. A linhagem celular P19, uma célula pluripotente advinda de um tipo de carcinoma embrionário murino, é bem consolidada como modelo in vitro de diferenciação neural. Após tratamento com ácido retinóico, ela é capaz de se diferenciar em neurônios e células da glia (astrócitos e oligodendrócitos). Em busca de evidências que indiquem a atuação de lncRNAs durante o processo de diferenciação neural, nosso grupo realizou experimentos utilizando microarranjos para averiguar os níveis de expressão gênica de lncRNAs e genes codificadores de proteínas (mRNAs) durante a diferenciação de células P19 em neurônios (predominância após 10 dias de diferenciação) e glia (predominância em 14 dias de diferenciação). Em um primeiro momento foi realizada a reanotação das sondas referentes a esses lncRNAs da plataforma de microarranjo, visto que as informações presentes nos arquivos de anotação da mesma eram muito escassas e desatualizadas. Registros de lncRNAs e mRNAs foram obtidos a partir de bancos de dados públicos para esse fim, e ao final dessa etapa aproximadamente 25,0% das sondas que não tinham uma anotação foram reanotadas com identificadores advindos desses bancos de dados. A partir dos dados de expressão, foram identificados todos os lncRNAs e mRNAs que apresentaram expressão diferencial entre as diferentes condições estudadas. As informações dos mRNAs diferencialmente expressos foram então utilizadas para a realização de análises de enriquecimento de categorias gênicas do Gene Ontology, nas ontologias de processo biológico e função molecular. A partir das sondas reanotadas, foram realizadas análises de coexpressão entre lncRNAs e mRNAs. A partir do cruzamento das informações obtidas, foram selecionados lncRNAs que através dos princípios de guilt by association se mostraram propensos a desempenharem um papel regulatório na diferenciação neural. Assim, as informações geradas nesse trabalho servirão como base para estudos futuros de validação funcional desses lncRNAs. / Increasingly, long noncoding RNAs (lncRNAs) emerge as important regulators of cell biology, especially in differentiation processes during development. The interest in the study of functions and mechanisms of action of this class of transcripts during these processes is growing, and shows quite relevant in the neural differentiation process by which neurons and glia are generated. The P19 cell line, pluripotent cells arising from a type of murine embryonal carcinoma, is well established as an in vitro model of neural differentiation. After treatment with retinoic acid, it is capable of differentiating into neurons and glial cells (astrocytes and oligodendrocytes). In search of evidence that indicate the action of lncRNAs during the neural differentiation process, our group conducted experiments using microarrays to assess gene expression levels of lncRNAs and protein coding genes (mRNAs) during differentiation of P19 cells into neurons (mainly after 10 days of differentiation) and glial cells (mainly after 14 days of differentiation). At first was performed the reannotation of the probes relating to these microarrays lncRNAs, as the information provided in the annotation files were very scarce or outdated. LncRNAs and mRNAs records were obtained from public databases for this purpose, and at the end of this stage approximately 25.0% of the probes without annotation were reannotated with identifiers arising from these databases. From the expression data, we identified all lncRNAs and mRNAs that showed differential expression between the different studied conditions. The information of differentially expressed mRNAs were then used to perform Gene Ontology enrichment, in the ontologies biological process and molecular function. From the reannotated probes, coexpression analyses were performed for lncRNAs and mRNAs. From the crosscheck of information obtained, we selected those lncRNAs that by the principles of guilt by association proved likely to play a regulatory role in neural differentiation. Thus, the information generated in this study will serve as a basis for future studies of functional validation of these lncRNAs.

The aim of this master's thesis is the design and implementation of tool trackAnalysis for statistical analysis of DNA structure elements. The positions of individual elements in genome are obtained in the form of the track, and with these positions the tool performs a set of analyzes, including randomness test of track, test examining distances between track and genes, detection of clusters and overlaps. The indivudual tests results can be linked together. The results will be displayed in the form of a list, a graph or a new annotation track. An important part of this thesis is also testing the resulting tool on a set of real data.

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.

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

This data article contains data related to the research article entitled, "in vitro characterization of bone marrow stromal cells from osteoarthritic donors" [1]. Osteoarthritis (OA) represents the main indication for total joint arthroplasty and is one of the most frequent degenerative joint disorders. However, the exact etiology of OA remains unknown. Bone marrow stromal cells (BMSCs) can be easily isolated from bone marrow aspirates and provide an excellent source of progenitor cells. The data shows the identification of pivotal genes and pathways involved in osteoarthritis by comparing gene expression patterns of BMSCs from osteoarthritic versus healthy donors using an array-based approach.