Bioinformatics Advance Access originally published online on June 20, 2006
Bioinformatics 2006 22(16):2053-2054; doi:10.1093/bioinformatics/btl331
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HCNet: a database of heart and calcium functional network
1 Department of Life Science, Gwangju Institute of Science and Technology 1 Oryong-dong, Buk-gu, Gwangju 500-712, Korea
2 Laboratory of Human Genomics, Korea Research Institute of Bioscience and Biotechnology Deajeon, Korea
*To whom correspondence should be addressed.
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ABSTRACT |
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 TOP ABSTRACT 1 INTRODUCTION2 DATABASE CONTENT3 IMPLEMENTATION AND UPDATEREFERENCES |
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Summary: The Heart and Calcium functional Network (HCNet) database is a collection of functional gene modules calculated from the microarray data compendium available from the GEO database. It is a specialized database designed to assist experimentalists for cardiac calcium signaling research by providing the pre-calculated gene clusters and their potential correlation network in heart.
In the current release of HCNet, 57 functional modules from 786 target genes obtained by a bi-clustering analysis of 381 microarray datasets are available. Detailed information of the clusters such as expression profiles, network diagrams is provided in two categories, heart-specific genes and heart-specific genes along with calcium toolkit genes. Overrepresented gene ontological categories and transcription factors in each cluster are also provided to infer the biological implications of the detected functional modules.
Availability: HCNet is available at http://sbrg2.gist.ac.kr/hcnet
Contact: dhkim{at}gist.ac.kr
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1 INTRODUCTION |
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TOPABSTRACT1 INTRODUCTION2 DATABASE CONTENT3 IMPLEMENTATION AND UPDATEREFERENCES |
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Gene expression data from the high-throughput DNA microarray technique are now being produced in an unprecedented pace. Statistical methods to identify meaningful patterns in them also have been developed rapidly (Segal et al., 2003; Tanay et al., 2005; Zhou et al., 2005). However, there is a lag for full utilization of the methods by wet-lab scientists who are not familiar with them. Heart and Calcium functional Network (HCNet), a database of HCNet, aims at relieving the burden of analysis job for individual researchers by providing the functional modules pre-calculated from the microarray datasets in the GEO database (Barrett et al., 2005).
Detecting clusters across many heterogeneous microarray experiments under diverse physiological and genetic conditions is not trivial. Especially, the low coverage of a gene over the entire conditions can be a problem. Limiting the target boundary to a subset of whole genome with some expected functional relationships would be one solution to this. HCNet focuses on the heart transcriptome combined with the Ca2+ signaling toolkit genes (Berridge et al., 2003) to understand the roles of Ca2+ in the heart function.
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2 DATABASE CONTENT |
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TOPABSTRACT1 INTRODUCTION2 DATABASE CONTENT3 IMPLEMENTATION AND UPDATEREFERENCES |
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Currently, HCNet contains the information on 57 functional modules calculated from 381 mouse GEO entries including 19 heart-related datasets.
2.1 Selection of target genes
We have classified the target mouse genes into two categories; (1) genes that are significantly expressed in heart and (2) the heart and the Ca2+ signaling toolkit genes. The second category was built as the union of heart significant genes and the Ca2+ signaling toolkit genes, since most of the Ca2+ signaling toolkit genes are not heart specific. This is because Ca2+ plays versatile roles in many other tissues as well as in heart. Initially, 516 UniGene clusters significantly expressed in heart (P < 10–5) identified by a 
2 analysis were merged with 104 genes detected from three GEO datasets, GDS592, GDS182 and GDS868, using the Significance Analysis of Microarray (SAM) method (Tusher et al., 2001). As a result, we classified 559 genes into the heart-specific genes category. The other category was built by joining this with the 238 Ca2+ signaling toolkit genes. Since 11 Ca2+ toolkit genes were also significantly expressed in heart, 786 mouse genes in total were selected for the heart and calcium genes category. After the clustering analysis, 118 of them participated in at least one functional module.
2.2 Clustering analysis
After the standardization of the expression values, the genes and the microarray conditions with low coverage values were removed. Bi-clustering was, then, applied to identify the functional modules defined by a group of genes and conditions from multiple datasets using EXPANDER (Sharan et al., 2003). To reduce false-positives, some genes in the obtained clusters were filtered out by the secondary screening step of ‘correlation of correlation profiles’ between the cluster members (Zhou et al., 2005). Finally, 57 clusters having a confidence level of P < 0.001 from 2 analysis with the random bi-clusters of the same dimension were survived.
2.3 Browsing clusters
HCNet provides two types of list pages; the gene list page and the cluster list page. The gene list page contains a list of target genes bearing hyperlinks to further details such as the gene pages providing a summary of clusters and basic cross references to external databases such as GenBank, Ensembl, MGD, SWISS-PROT, InterPro, iHOP, STRING, TRANSFAC, etc. For each gene, the evidence of heart-specific expression is given as a P-value from the UniGene analysis or a tissue expression graph from the GEO analysis.
The cluster list page offers a list of detected clusters sorted by their confidence level and links to the cluster pages providing the summary of cluster member genes, conditions and some graphical information (Fig. 1). To visualize the closeness between member genes of a cluster, a network was built by connecting the gene pairs with the high expression correlation coefficient (>0.7) using the Medusa program (Hooper and Bork, 2005). A table of overrepresented gene ontological categories and transcription factors calculated using a 2 analysis is also shown to examine the quality of modules and appreciate the context of each module.
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3 IMPLEMENTATION AND UPDATE |
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TOPABSTRACT1 INTRODUCTION2 DATABASE CONTENT3 IMPLEMENTATION AND UPDATEREFERENCES |
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HCNet is built on an Apache web server and the MySQL4 database system running on a Linux platform. HCNet offers an html form-type menu and a search function for browsing the genes and the functional modules in each category. A detailed description of the analysis procedure and the statistics on the detected functional modules as well as the target gene list are available on the website.
The contents of the database will be updated quarterly as new GEO datasets are available. New functionalities such as the comparative analysis of the detection algorithms for modules and the data mining of the functional relationships between the identified clusters in the biological context will be pursued.
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Acknowledgments |
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The development of this database was supported by a grant from Korean Systems Biology Research grant, M10503010001-06N0301-00110, from Korea Ministry of Science and Technology.
Conflict of Interest: none declared.
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FOOTNOTES |
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The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. 
Received on April 12, 2006; revised on June 7, 2006; accepted on June 9, 2006
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REFERENCES |
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TOPABSTRACT1 INTRODUCTION2 DATABASE CONTENT3 IMPLEMENTATION AND UPDATEREFERENCES |
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Barrett, T., et al. (2005) NCBI GEO: mining millions of expression profiles—database and tools. Nucleic Acids Res, . 33, D562–D566
Berridge, M.J., Bootman, M.D., Roderick, H.L. (2003) Calcium signalling: dynamics, homeostasis and remodelling. Nat. Rev. Mol. Cell Biol, . 4, 517–529[CrossRef][Web of Science][Medline].
Hooper, S.D. and Bork, P. (2005) Medusa: a simple tool for interaction graph analysis. Bioinformatics, 21, 4432–4433
Segal, E., et al. (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet, . 34, 166–176[CrossRef][Web of Science][Medline].
Sharan, R., et al. (2003) CLICK and EXPANDER: a system for clustering and visualizing gene expression data. Bioinformatics, 19, 1787–1799
Tanay, A., et al. (2005) Integrative analysis of genome-wide experiments in the context of a large high-throughput data compendium. Mol. Syst. Biol, . 1, 2005.0002.
Tusher, V.G., et al. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA, 98, 5116–5121
Zhou, X.J., et al. (2005) Functional annotation and network reconstruction through cross-platform integration of microarray data. Nat. Biotechnol, . 23, 238–243[CrossRef][Web of Science][Medline].
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