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Nucleic Acids Research Advance Access published online on September 12, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn593
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Database Issue

Database for exploration of functional context of genes implicated in ovarian cancer

Mandeep Kaur1, Aleksandar Radovanovic1, Magbubah Essack1, Ulf Schaefer1, Monique Maqungo1, Tracey Kibler1, Sebastian Schmeier1, Alan Christoffels1, Kothandaraman Narasimhan2, Mahesh Choolani2 and Vladimir B. Bajic1,*

1South African National Bioinformatics Institute, University of the Western Cape, Private Bag- X17, Modderdam Road, Bellville, Cape Town, South Africa and 2Department of Obstetrics & Gynaecology, National University Health System, Singapore

To whom correspondence should be addressed. Tel: +27 21 959 2360; Fax: +27 21 959 2512; Email: vlad{at}sanbi.ac.za

Received June 21, 2008. Revised September 2, 2008. Accepted September 3, 2008.

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. Access is free for academic and non-profit users and database can be accessed at http://apps.sanbi.ac.za/ddoc/.


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