Nucleic Acids Research Advance Access originally published online on September 12, 2008
Nucleic Acids Research 2009 37(Database issue):D820-D823; doi:10.1093/nar/gkn593
Nucleic Acids Research, 2009, Vol. 37, Database issue D820-D823
© 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 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.
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ABSTRACT
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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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INTRODUCTION
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In the past few years, it has become increasingly evident that
ovarian cancer (OC) is a biologically complex and multigenic
disease (
1). Most of the genes implicated in OC (OC genes) have
not been thoroughly investigated in the context of OC, especially
at the gene regulation level. Understanding the regulatory mechanisms
and functional operation context of the key OC genes will be
useful for deciphering the impact of various molecules to the
functionality of these genes, as well as effects of these genes,
and thus will help to better understand different aspects of
OC-gene functionality. Since information about OC genes is scattered
across various resources, its integration into one resource
will provide simplified way of exploring functional context
of operation of OC gene, e.g. regulatory mechanisms or modes
of operations. To the best of our knowledge, no database dedicated
to OC genes has been published. Currently, only one database,
Ovarian Kaleidoscope Database (Okdb) (
2), partially addresses
the needs of OC research community and is restricted to the
genes expressed in the ovary of multiple species, making it
more beneficial for general ovarian tissue-based research. For
example, Okdb (
http://ovary.stanford.edu/) originally contained
information about 450 genes expressing in ovary from different
species such as human, mouse, rat and bovine. In the current
version, this list has now been expanded to 2788 genes. The
database search using keyword ‘cancer’ retrieved
only 235 genes for human and rat species combined and there
is no explicit information available regarding the involvement
of these genes in OC. It should be noted that there are two
initiatives aimed at coordinating activities in producing resources
related to cancer research, such as the International Cancer
Genome Consortium—ICGC (
http://www.icgc.org/) and caBIG
(cancer Biomedical Informatics Grid
TM,
http://cabig.cancer.gov/).
These two intend to promote specific data formats and other
conditions that should enable easier integration of cancer-related
resources.
We present here the first database (Dragon Database for Exploration of Ovarian Cancer Genes, DDOC) of genes experimentally linked with OC that possess a comprehensive set of features, which allows users to explore different aspects of functionality of the OC genes indexed in DDOC and provides important information required for in-depth analysis of these genes at pathway and ontology levels. DDOC is unique for its utility to provide an opportunity to explore various gene properties that are not obtainable without complex additional analyses, such as the promoter properties and association of OC genes with other human genes, nuclear proteins, pathways and enzymes. Precompiled results of these analyses, based on promoter content and text mining, have been integrated into DDOC. We have provided ‘Batch query’ option in the search menu and user can select different types of information to be extracted from DDOC. This facility allows for downloading various selection of information from DDOC. 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 OC genetics, helping them get deeper insights into information about OC genes and their molecular operation modes. This information indeed will be useful for functional genomics research. DDOC is freely available at http://apps.sanbi.ac.za/ddoc/ for academic and non-profit users.
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GENE SEARCH AND SELECTION CRITERIA
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The gene-related information provided in the database was compiled
from various repositories. Initially, a list of 900 genes was
collected from sources like Cancer Gene Census (
3) (
http://www.sanger.ac.uk/genetics/CGP/Census/),
GeneCards (
4) (
http://www.genecards.org/index.shtml), SymAtlas
(
5), OMIM (
6) (online Mendelian inheritance in man, 2007) (
http://www.ncbi.nlm.nih.gov/),
Ovarian Kaleidoscope Database (
2) (
http://ovary.stanford.edu/),
Entrez Gene (
7) (
http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene)
and GenAtlas (
8) (
http://www.genatlas.org/). Out of these 900
genes, after a thorough literature search, we compiled a final
list of 379 genes that was used to populate the database. For
inclusion into the database, the gene must have experimental
confirmation of the differential expression in OC tissue using
techniques such as: RT–PCR, immunohistochemistry, western
blotting or FISH (fluorescent
in situ hybridization), to name
a few. Genes documented as having OC-linked SNP were also included
in the database. The genes shown to have differential expression
only based on microarray experiments were not included in the
database.
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DATABASE STATISTICS
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Currently, DDOC contains a set of 379 human genes experimentally
verified as involved in OC. The gene symbols, gene names and
EntrezGene IDs are provided for all the genes. HGNC IDs are
available for 374 genes, while Ensembl IDs are available for
370 genes. GO (
9) annotations are available for 367 genes and
353 genes are indexed in eVOC (
10,
11). Pathways in KEGG (
12)
and REACTOME (
13) were mapped to 211 and 50 genes, respectively.
OC genes were associated with 1446 promoters. Analysis of these
promoters shows that the transcription factor binding sites
(TFBSs) have been predicted for 1449 TFs (Transfac IDs). Text-mining
analysis involved 588 727 PubMed abstracts. The summary of statistics
is provided at
http://apps.sanbi.ac.za/ddoc/DDOC.pdf. DDOC will
be regularly updated twice a year.
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UNIQUE FEATURES OF DDOC
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The identification of putative TFBSs on the promoters of OC
genes could provide insights into possible regulatory characteristics
of the genes. This type of information is not freely available
to biologists and requires separate computational analysis.
To generate these reports, we extracted 1446 promoters covering
regions [–1000, +200] relative to the transcription start
sites (TSS) for 371 of the 379 OC genes using the FANTOM 3 promoter
set based on CAGE libraries (
14,
15). TFBSs were mapped to the
both strands of promoter sequences using all mammalian matrix
models of TFBSs contained in the TRANSFAC Professional database
v.11.4 (
16,
17). For this purpose, we used the Match
TM program
with
minFP profile for thresholds of the matrix models in order
to minimize false positive predictions in the predicted TFBS
set (
18). In a subsequent step, we extracted all mammalian TFs
that are associated with the Transfac position weight matrices.
In that manner, we derived an overview of hypothetical transcriptional
control of the OC genes, that is, insight into TFs that are
predicted to bind in the promoter regions of the genes.
One of the unique features of this database is that we have incorporated the pre-compiled results of text mining of the available literature to give an expanded view of the nuclear proteins, pathways, enzymes and mammalian genes potentially associated with each of the OC genes. As a source, we used the National Center for Biotechnology Information (NCBI) PubMed database (http://www.ncbi.nlm.nih.gov). For querying the PubMed, we created a simple tool based on the NCBI ‘Entrez Programming Utilities’. With this tool the PubMed database was queried for each gene using the following keywords:
(‘Gene Symbol’ OR ‘Gene Alias’ OR ‘Gene Alias’, etc.) AND mammal AND cancer.
Such queries produced list of 588 727 abstracts that were analyzed by the licensed Dragon Exploration System (DES) from OrionCell (http://www.orioncell.org), that has an integrated Biomedical Text-Miner, a redeveloped tool based on the concepts from Dragon Plant Biology Explorer (19) and Dragon TF Association Miner (20). By this tool, we indexed the text document by vocabularies for nuclear proteins, pathways, enzymes and mammalian genes, and produced a database of associations that is integrated into DDOC. This integration allowed for presentation of text-mining results as lists of tables and as a graphic system of interactive networks. Figure 1 shows an example of such a network. Color-coded vocabulary entries are interconnected with weighted links representing frequency of appearance of a term and its neighbors in our abstracts list. By clicking on a node, users get relevant abstracts containing the selected term and terms representing its first neighbors (surrounding nodes in the network).
View larger version (32K):
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[Download PowerPoint slide]
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Figure 1. A color-coded network generated from text-mining results for ABCB1 gene. Networks represents that MRP1 (a nuclear protein) appeared 95 times in the abstracts (as both have been shown to transport same substrates) with ABCB1 (P-gp), which in turn has been reviewed 470 times with medium-chain dehydrogenase/reductase (MDR) enzyme. This represents a small network of different biological entities, which have been reviewed with ABCB1 gene in the literature. This gives the user a view and understanding of varied biological interactions a gene might have in the studies published so far.
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The potential uses and advantages of the database are described
in the documentation section (
http://apps.sanbi.ac.za/ddoc/DDOC.pdf),
where various aspects of the database creation and usage have
been thoroughly discussed. An example of analysis (
http://apps.sanbi.ac.za/ddoc/DDOC.pdf)
has been shown which should help users to understand and use
different functions implemented in this database to maximize
information extracted.
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DISCUSSION
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To date, there is no resource available, which could provide
detailed information about the various aspects of the functionality
and regulation of the genes known to be associated with OC.
We have compiled the first database where information taken
from other resources and several new analyses have been integrated
into a new database resource along with details about the experimental
methods used to identify the gene as a component of OC genetics.
A number of components of this database are manually curated,
though some parts, such as promoter analysis and text-mining
components are not. The purpose of creating this database is
to provide an integrated knowledgebase that allows researchers,
students and clinicians to get an overview of and explore efficiently
the biology of the genes involved in OC. The database provides
detailed information about the homologs, regulatory mechanisms,
pathways, as well as text-mining results where association of
genes with other biological entities and pathways (described
in literature) has been deciphered and presented as association
networks rendering the incorporated information more understandable
and useful. Text mining is a convenient and efficient method
to summarize information from and explore the huge amount of
documents in short period of time and to visualize important
potential associations between different concepts in an easy
to follow graphical representations. In addition to the various
data-querying possibilities that our database enables, we have
provided all standard facilities for users of bioinformatics
databases that allow them, for example, to download data, make
batch queries or take the MySQL database dump.
We hope that this database would serve as a useful resource for researchers and medical professionals who are involved in OC at any level. DDOC is aimed to serve as one-stop shop for OC research community and is created to save time and effort in order to facilitate the biological discovery process. By time, the database will be enriched by addition of new OC genes and other functionalities based on users comments.
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FUTURE DIRECTIONS
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DDOC reflects the information available for genes involved in
OC at the period of its creation and will continue to grow both
in content and functionality as more data is made available
in literature. We plan to include similar information for genes
differentially expressing in OC cell lines and tissue as identified
by microarray and other experiments. Another line of expansion
would be to incorporate the information about the drugs interacting
with these genes/gene products, which will make it more useful
and attractive for medical researchers and will serve a broader
scientific community. Additional features that may enhance search
and retrieval of DDOC information will be added in due course,
as well as incorporation into ICGC and caBIG.
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FUNDING
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National Bioinformatics Network grants (to A.R., U.S., M.M.,
S.S. and V.B.B.) partially; National Research Foundation (61070
to M.M. and V.B.B.) partially; DST/NRF Research Chair (64751
to V.B.B.) and National Research Foundation (62302 to V.B.B.)
partially. Funding for open access charge: National Bioinformatics
Network grants.
Conflict of interest statement. V.B.B. and A.R. are partners in the OrionCell company whose product, Dragon Exploration System (DES), has been used in creation of DDOC precompiled reports. Other authors declare no conflict of interest.
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ACKNOWLEDGEMENTS
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M.K. has been supported by the postdoctoral fellowship from
the Claude Leon Foundation, South Africa.
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REFERENCES
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|---|
- Edlich RF, Winters KL, Lin KY. Breast cancer and ovarian cancer genetics. J. Long Term Eff. Med. Implants (2005) 15:533–545.[CrossRef][Medline]
- Leo CP, Vitt UA, Hsueh AJ. The Ovarian Kaleidoscope database: an online resource for the ovarian research community. Endocrinology (2000) 141:3052–3054.[Abstract/Free Full Text]
- Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, Rahman N, Stratton MR. A census of human cancer genes. Nat. Rev. Cancer (2004) 4:177–183.[CrossRef][Web of Science][Medline]
- Safran M, Solomon I, Shmueli O, Lapidot M, Shen-Orr S, Adato A, Ben Dor U, Esterman N, Rosen N, Peter I, et al. GeneCards 2002: towards a complete, object-oriented, human gene compendium. Bioinformatics (2002) 18:1542–1543.[Abstract/Free Full Text]
- Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, Zhang J, Soden R, Hayakawa M, Kreiman G, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl Acad. Sci. USA (2004) 101:6062–6067.[Abstract/Free Full Text]
- Baxevanis AD. Searching online Mendelian inheritance in man (OMIM) for information for genetic loci involved in human disease. Curr. Protoc. Hum. Genet (2003) Chapter 9, Unit9, 13.
- Maglott D, Ostell J, Pruitt KD, Tatusova T. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res. (2007) 35:D26–D31.[Abstract/Free Full Text]
- Frezal J. Genatlas database, genes and development defects. C. R. Acad. Sci. III (1998) 321:805–817.[Medline]
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. (2000) 25:25–29.[CrossRef][Web of Science][Medline]
- Kelso J, Visagie J, Theiler G, Christoffels A, Bardien S, Smedley D, Otgaar D, Greyling G, Jongeneel CV, McCarthy MI, et al. eVOC: a controlled vocabulary for unifying gene expression data. Genome Res. (2003) 13:1222–1230.[Abstract/Free Full Text]
- Kruger A, Hofmann O, Carninci P, Hayashizaki Y, Hide W. Simplified ontologies allowing comparison of developmental mammalian gene expression. Genome Biol. (2007) 8:R229.[CrossRef][Medline]
- Kanehisa M, Goto S, Kawashima S, Nakaya A. The KEGG databases at GenomeNet. Nucleic Acids Res. (2002) 30:42–46.[Abstract/Free Full Text]
- Vastrik I, D’Eustachio P, Schmidt E, Joshi-Tope G, Gopinath G, Croft D, de Bono B, Gillespie M, Jassal B, Lewis S, et al. Reactome: a knowledge base of biologic pathways and processes. Genome Biol. (2007) 8:R39.[CrossRef][Medline]
- Carninci P, Kasukawa T, Katayama S, Gough J, Frith MC, Maeda N, Oyama R, Ravasi T, Lenhard B, Wells C, et al. The transcriptional landscape of the mammalian genome. Science (2005) 309:1559–1563.[Abstract/Free Full Text]
- Carninci P, Sandelin A, Lenhard B, Katayama S, Shimokawa K, Ponjavic J, Semple CA, Taylor MS, Engstrom PG, Frith MC, et al. Genome-wide analysis of mammalian promoter architecture and evolution. Nat. Genet. (2006) 38:626–635.[CrossRef][Web of Science][Medline]
- Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, et al. TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. (2006) 34:D108–D110.[Abstract/Free Full Text]
- Wingender E, Chen X, Fricke E, Geffers R, Hehl R, Liebich I, Krull M, Matys V, Michael H, Ohnhauser R, et al. The TRANSFAC system on gene expression regulation. Nucleic Acids Res. (2001) 29:281–283.[Abstract/Free Full Text]
- Kel AE, Gossling E, Reuter I, Cheremushkin E, Kel-Margoulis OV, Wingender E. MATCH: a tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res. (2003) 31:3576–3579.[Abstract/Free Full Text]
- Bajic VB, Veronika M, Veladandi PS, Meka A, Heng MW, Rajaraman K, Pan H, Swarup S. Dragon plant biology explorer. A text-mining tool for integrating associations between genetic and biochemical entities with genome annotation and biochemical terms lists. Plant Physiol. (2005) 138:1914–1925.[Abstract/Free Full Text]
- Pan H, Zuo L, Choudhary V, Zhang Z, Leow SH, Chong FT, Huang Y, Ong VW, Mohanty B, Tan SL, et al. Dragon TF association miner: a system for exploring transcription factor associations through text-mining. Nucleic Acids Res. (2004) 32:W230–W234.[Abstract/Free Full Text]
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ABSTRACT
INTRODUCTION