Browsing by Author "Schmeier, Sebastian"
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Item Computational analyses on transcriptional regulation in mammals(University of the Western Cape, 2009) Schmeier, Sebastian; Bajic, VladimirThe genomes of various organisms have been sequenced and their transcriptome elucidated. With the information about genes and gene products readily available it has become of the utmost importance to decipher the underlying biological mechanisms that are involved in the transcriptional control of these genes. Transcription initiation is a fundamental process in living cells. It involves the interaction of transcription factors with DNA to regulate the transcription of a gene. Despite significant research during the last few decades into transcription factors and their role in gene regulation we are still far from understanding the complete transcriptional machinery that acts within biological systems. In this dissertation two computational approaches are presented to contribute to a better understanding of the transcriptional control of genes in mammals. The first addresses the transcriptional regulation of microRNA genes and its influence on the microRNA gene expression during monocytic differentiation. This is the first large-scale approach to decipher how microRNA genes are regulated by transcription factors during monocytic differentiation. The second approach relates to combinatorial gene regulation and the physical interaction of transcription factors. Here, a computational approach is used together with a novel form of numerical representation of transcription factors to predict their interactions. In this setup, the information necessary to predict the transcription factor interactions is kept at the lowest level to minimize the data acquisition overhead that often occurs in computational prediction tasks. Both approaches enhance our insights into transcriptional control and have an impact on the further study of gene regulation.Item Database for exploration of functional context of genes implicated in ovarian cancer(Oxford Journals, 2009) Kaur, Mandeer; Radovanovic, Aleksander; Essack, Magbubah; Schaefer, Ulf; Maqungo, Monique; Kibler, Tracey; Schmeier, Sebastian; Christoffels, Alan; Narasimhan, Kothandaraman; Choolani, Mahesh; Bajic, Vladimir B.Ovarian cancer (OC) is becoming the most common gynecological cancer in developed countries and the most lethal gynecological malignancy. It is also the fifth leading cause of all cancer-related deaths in women. The identification of diagnostic biomarkers and development of early detection techniques for OC largely depends on the understanding of the complex functionality and regulation of genes involved in this disease. Unfortunately, information about these OC genes is scattered throughout the literature and various databases making extraction of relevant functional information a complex task. To reduce this problem, we have developed a database dedicated to OC genes to support exploration of functional characterization and analysis of biological processes related to OC. The database contains general information about OC genes, enriched with the results of transcription regulation sequence analysis and with relevant text mining to provide insights into associations of the OC genes with other genes, metabolites, pathways and nuclear proteins. Overall, it enables exploration of relevant information for OC genes from multiple angles, making it a unique resource for OC and will serve as a useful complement to the existing public resources for those interested in OC genetics.Item DDEC: Dragon databaseof genes implicated in esophageal cancer(BioMed Central, 2009) Essack, Magbubah; Radovanovic, Aleksander; Schaefer, Ulf; Schmeier, Sebastian; Seshadri, Sundararajan V.; Christoffels, Alan; Kaur, Mandeep; Bajic, Vladimir B.Esophageal cancer ranks eighth in order of cancer occurrence. Its lethality primarily stems from inability to detect the disease during the early organ-confined stage and the lack of effective therapies for advanced-stage disease. Moreover, the understanding of molecular processes involved in esophageal cancer is not complete, hampering the development of efficient diagnostics and therapy. Efforts made by the scientific community to improve the survival rate of esophageal cancer have resulted in a wealth of scattered information that is difficult to find and not easily amendable to data-mining. To reduce this gap and to complement available cancer related bioinformatic resources, we have developed a comprehensive database (Dragon Database of Genes Implicated in Esophageal Cancer) with esophageal cancer related information, as an integrated knowledge database aimed at representing a gateway to esophageal cancer related data. Manually curated 529 genes differentially expressed in EC are contained in the database. We extracted and analyzed the promoter regions of these genes and complemented gene-related information with transcription factors that potentially control them. We further, precompiled text-mined and data-mined reports about each of these genes to allow for easy exploration of information about associations of EC-implicated genes with other human genes and proteins, metabolites and enzymes, toxins, chemicals with pharmacological effects, disease concepts and human anatomy. The resulting database, DDEC, has a useful feature to display potential associations that are rarely reported and thus difficult to identify. Moreover, DDEC enables inspection of potentially new 'association hypotheses' generated based on the precompiled reports. We hope that this resource will serve as a useful complement to the existing public resources and as a good starting point for researchers and physicians interested in EC genetics.Item DDPC: Dragon database of genes associated with prostate cancer(Oxford Journals, 2011) Maqungo, Monique; Kaur, Mandeep; Kwofie, Samuel K.; Radovanovic, Aleksander; Schaefer, Ulf; Schmeier, Sebastian; Oppon, Ekow; Christoffels, Alan; Bajic, Vladimir B.Prostate cancer (PC) is one of the most commonly diagnosed cancers in men. PC is relatively difficult to diagnose due to a lack of clear early symptoms. Extensive research of PC has led to the availability of a large amount of data on PC. Several hundred genes are implicated in different stages of PC, which may help in developing diagnostic methods or even cures. In spite of this accumulated information, effective diagnostics and treatments remain evasive. We have developed Dragon Database of Genes associated with Prostate Cancer (DDPC) as an integrated knowledgebase of genes experimentally verified as implicated in PC. DDPC is distinctive from other databases in that (i) it provides pre-compiled biomedical text-mining information on PC, which otherwise require tedious computational analyses, (ii) it integrates data on molecular interactions, pathways, gene ontologies, gene regulation at molecular level, predicted transcription factor binding sites on promoters of PC implicated genes and transcription factors that correspond to these binding sites and (iii) it contains DrugBank data on drugs associated with PC. We believe this resource will serve as a source of useful information for research on PC.