Kwofie, Samuel K.
Bajic, Vladimir B.
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Permanent link to this recordhttp://hdl.handle.net/10754/325450
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AbstractProstate 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. DDPC is freely accessible for academic and non-profit users via http://apps.sanbi.ac.za/ddpc/ and http://cbrc .kaust.edu.sa/ddpc/. The Author(s) 2010.
CitationMaqungo M, Kaur M, Kwofie SK, Radovanovic A, Schaefer U, et al. (2011) DDPC: Dragon Database of Genes associated with Prostate Cancer. Nucleic Acids Research 39: D980-D985. doi:10.1093/nar/gkq849.
PublisherOxford University Press (OUP)
JournalNucleic Acids Research
PubMed Central IDPMC3013759
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Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- DDEC: Dragon database of genes implicated in esophageal cancer.
- Authors: Essack M, Radovanovic A, Schaefer U, Schmeier S, Seshadri SV, Christoffels A, Kaur M, Bajic VB
- Issue date: 2009 Jul 6
- Dragon exploratory system on hepatitis C virus (DESHCV).
- Authors: Kwofie SK, Radovanovic A, Sundararajan VS, Maqungo M, Christoffels A, Bajic VB
- Issue date: 2011 Jun
- Database for exploration of functional context of genes implicated in ovarian cancer.
- Authors: Kaur M, Radovanovic A, Essack M, Schaefer U, Maqungo M, Kibler T, Schmeier S, Christoffels A, Narasimhan K, Choolani M, Bajic VB
- Issue date: 2009 Jan
- dPORE-miRNA: polymorphic regulation of microRNA genes.
- Authors: Schmeier S, Schaefer U, MacPherson CR, Bajic VB
- Issue date: 2011 Feb 4
- HCVpro: hepatitis C virus protein interaction database.
- Authors: Kwofie SK, Schaefer U, Sundararajan VS, Bajic VB, Christoffels A
- Issue date: 2011 Dec
Showing items related by title, author, creator and subject.
DDEC: Dragon database of genes implicated in esophageal cancerEssack, Magbubah; Radovanovic, Aleksandar; Schaefer, Ulf; Schmeier, Sebastian; Seshadri, Sundararajan V; Christoffels, Alan; Kaur, Mandeep; Bajic, Vladimir B. (BMC Cancer, Springer Nature, 2009-07-06) [Article]Background: 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. Description: 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. Conclusion: 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. DDEC is freely accessible to academic and non-profit users at http://apps.sanbi.ac.za/ ddec/. DDEC will be updated twice a year. 2009 Essack et al; licensee BioMed Central Ltd.
dPORE-miRNA: Polymorphic regulation of microRNA genesSchmeier, Sebastian; Schaefer, Ulf; MacPherson, Cameron R.; Bajic, Vladimir B. (PLoS ONE, Public Library of Science (PLoS), 2011-02-04) [Article]Background: MicroRNAs (miRNAs) are short non-coding RNA molecules that act as post-transcriptional regulators and affect the regulation of protein-coding genes. Mostly transcribed by PolII, miRNA genes are regulated at the transcriptional level similarly to protein-coding genes. In this study we focus on human miRNAs. These miRNAs are involved in a variety of pathways and can affect many diseases. Our interest is on possible deregulation of the transcription initiation of the miRNA encoding genes, which is facilitated by variations in the genomic sequence of transcriptional control regions (promoters). Methodology: Our aim is to provide an online resource to facilitate the investigation of the potential effects of single nucleotide polymorphisms (SNPs) on miRNA gene regulation. We analyzed SNPs overlapped with predicted transcription factor binding sites (TFBSs) in promoters of miRNA genes. We also accounted for the creation of novel TFBSs due to polymorphisms not present in the reference genome. The resulting changes in the original TFBSs and potential creation of new TFBSs were incorporated into the Dragon Database of Polymorphic Regulation of miRNA genes (dPORE-miRNA). Conclusions: The dPORE-miRNA database enables researchers to explore potential effects of SNPs on the regulation of miRNAs. dPORE-miRNA can be interrogated with regards to: a/miRNAs (their targets, or involvement in diseases, or biological pathways), b/SNPs, or c/transcription factors. dPORE-miRNA can be accessed at http://cbrc.kaust.edu.sa/dpore and http://apps.sanbi.ac.za/dpore/. Its use is free for academic and non-profit users. © 2011 Schmeier et al.
Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literatureChowdhary, Rajesh; Tan, Sin Lam; Zhang, Jinfeng; Karnik, Shreyas; Bajic, Vladimir B.; Liu, Jun S. (PLoS ONE, Public Library of Science (PLoS), 2012-04-06) [Article]Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.