Browsing by Author "Bajic, Vladimir B."

Now showing 1 - 7 of 7
  • Thumbnail Image
    Item
    DAMPD: a manually curated antimicrobial peptide database
    (Oxford University Press, 2012) Sundararajan, Vijayaraghava S.; Gabere, Musa N.; Pretorius, Ashley; Adam, Saleem; Christoffels, Alan; Minna, Lehvaslaiho; Archer, John A.C.; Bajic, Vladimir B.
    The demand for antimicrobial peptides (AMPs) is rising because of the increased occurrence of pathogens that are tolerant or resistant to conventional antibiotics. Since naturally occurring AMPs could serve as templates for the development of new anti-infectious agents to which pathogens are not resistant, a resource that contains relevant information on AMP is of great interest. To that extent, we developed the Dragon Antimicrobial Peptide Database (DAMPD, http://apps.sanbi.ac.za/dampd) that contains 1232 manually curated AMPs. DAMPD is an update and a replacement of the ANTIMIC database. In DAMPD an integrated interface allows in a simple fashion querying based on taxonomy, species, AMP family, citation, keywords and a combination of search terms and fields (Advanced Search). A number of tools such as Blast, ClustalW, HMMER, Hydrocalculator, SignalP, AMP predictor, as well as a number of other resources that provide additional information about the results are also provided and integrated into DAMPD to augment biological analysis of AMPs.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    Item
    DDESC: Dragon database for exploration of sodium channels in human
    (BMC Cancer, 2008) Sagar, Sunil; Kaur, Mandeep; Dawe, Adam; Seshadri, Sundararajan V.; Christoffels, Alan; Schaefer, Ulf; Radovanovic, Aleksander; Bajic, Vladimir B.
    Sodium channels are heteromultimeric, integral membrane proteins that belong to a superfamily of ion channels. The mutations in genes encoding for sodium channel proteins have been linked with several inherited genetic disorders such as febrile epilepsy, Brugada syndrome, ventricular fibrillation, long QT syndrome, or channelopathy associated insensitivity to pain. In spite of these significant effects that sodium channel proteins/genes could have on human health, there is no publicly available resource focused on sodium channels that would support exploration of the sodium channel related information. We report here Dragon Database for Exploration of Sodium Channels in Human (DDESC), which provides comprehensive information related to sodium channels regarding different entities, such as "genes and proteins", "metabolites and enzymes", "toxins", "chemicals with pharmacological effects", "disease concepts", "human anatomy", "pathways and pathway reactions" and their potential links. DDESC is compiled based on text- and data-mining. It allows users to explore potential associations between different entities related to sodium channels in human, as well as to automatically generate novel hypotheses. DDESC is first publicly available resource where the information related to sodium channels in human can be explored at different levels.
  • Thumbnail Image
    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.
  • Thumbnail Image
    Item
    Mice and men: Their promoter properties
    (PLoS Genetics, 2006) Bajic, Vladimir B.; Tan, Sin lam; Christoffels, Alan; Schonbach, Christian; Lipovich, Leonard; Yang, Liang; Hofmann, Oliver; Kruger, Adele; Hide, Winston; Kai, Chikatoshi; Kawai, Jun; Hume, David, A.; Carninci, Piero; Hayashizaki, Yoshihide
    Using the two largest collections of Mus musculus and Homo sapiens transcription start sites (TSSs) determined based on CAGE tags, ditags, full-length cDNAs, and other transcript data, we describe the compositional landscape surrounding TSSs with the aim of gaining better insight into the properties of mammalian promoters. We classified TSSs into four types based on compositional properties of regions immediately surrounding them. These properties highlighted distinctive features in the extended core promoters that helped us delineate boundaries of the transcription initiation domain space for both species. The TSS types were analyzed for associations with initiating dinucleotides, CpG islands, TATA boxes, and an extensive collection of statistically significant cis-elements in mouse and human. We found that different TSS types show preferences for different sets of initiating dinucleotides and ciselements. Through Gene Ontology and eVOC categories and tissue expression libraries we linked TSS characteristics to expression. Moreover, we show a link of TSS characteristics to very specific genomic organization in an example of immune-response-related genes (GO:0006955). Our results shed light on the global properties of the two transcriptomes not revealed before and therefore provide the framework for better understanding of the transcriptional mechanisms in the two species, as well as a framework for development of new and more efficient promoter- and gene-finding tools.
  • Thumbnail Image
    Item
    Towards a chereme based dynamic South African sign language gesture recognition system
    (University of the Western Cape, 2007) Machanja, Addmore; Bajic, Vladimir B.
    Hand gestures are a natural and intuitive way of human to human communication. Motivated by the achievements made towards automatic speech recognition, and by the ease with which people sign, many researchers started working on sign language recognition systems. Besides, technologies used to build gesture recognition systems pose as an alternative to the cumbersome and the failure prone mechanical devices that are currently used as human-machine interface devices. Most of the available gesture recognition systems represent each sign language gesture with an individual gesture model. Such systems can only recognize a limited number of dynamic sign language gestures. It is cumbersome to build and maintain a gesture recognition system that uses thousands and thousands of individual gesture models. Sign language linguists argue that all sign language gestures are derived from small sets of reusable components, the cheremes.