Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 3, 29-35
eCollection

Database of Gene Co-Regulation (dGCR): A Web Tool for Analysing Patterns of Gene Co-regulation Across Publicly Available Expression Data

Affiliations

Database of Gene Co-Regulation (dGCR): A Web Tool for Analysing Patterns of Gene Co-regulation Across Publicly Available Expression Data

Gareth Williams. J Genomics.

Abstract

The database of Gene Co-Regulation (dGCR) is a web tool for the analysis of gene relationships based on correlated patterns of gene expression over publicly available transcriptional data. The motivation behind dGCR is that genes whose expression patterns correlate across many experiments tend to be co-regulated and hence share biological function. In addition to revealing functional connections between individual gene pairs, extended sets of co-regulated genes can also be assessed for enrichment of gene ontology classes and interaction pathways. This functionality provides an insight into the biological function of the query gene itself. The dGCR web tool extends the range of expression data curated by existing co-regulation databases and provides additional insights into gene function through the analysis of pathways, gene ontology classes and co-regulation modules.

Keywords: Global gene expression; connectivity map; microarray..

Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
The dGCR web page screen shot with query 'DAG'. The possible genes are listed with links for further analysis. At left the user selects the number of co-regulating genes (50-500) and may restrict the analysis to a given species. The two genes that will be analysed here are the two isoforms of DAG lipase, DAGLα (DAGLA) and DAGLβ (DAGLB).
Figure 2
Figure 2
The genes most co-regulated with the two isoforms of DAG lipase. The scores are the sums of the fisher log-odds scores for each platform. Each gene entry is hyperlinked to explanatory web material. The correlation values and significance scores (in brackets) are given for the individual platforms in the right column. Moving the mouse over the individual scores reveals the platform where the correlation obtains.
Figure 3
Figure 3
The user can analyse the co-regulated gene set for the enrichment of gene ontology classes, shown in A and C, and pathways, shown in B and D. Both analyses point to a synaptic function for DAGLα and immunological function for DAGLβ.
Figure 4
Figure 4
Screen shot of module #23. The module consists of 107 genes and DAGLα is shown highlighted in blue. The module links the post-synaptic 2-AG synthesising enzyme, DAGLα, to the pre-synaptic 2-AG receptor, the cannabinoid receptor (CB1, CNR1), circled in red.
Figure 5
Figure 5
Pathways, GO sets and interacting proteins were scored for mutual co-regulation. The summed log-odds co-regulation scores across the array platforms for each gene pair in a given pathway and GO set were compiled and the distributions plotted in A. It is clear that for both pathway sets and gene ontology sets the distributions are significantly shifted towards high scores relative to the random gene sets distribution. The co-regulation score averages across the protein interacting pairs from the 12 distinct experimental platforms in the BioGrid database are shown in the table B.

Similar articles

See all similar articles

References

    1. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C. et al. NCBI GEO: mining tens of millions of expression profiles--database and tools update. Nucleic Acids Res. 2007;35:D760–5. - PMC - PubMed
    1. Parkinson H, Sarkans U, Kolesnikov N, Abeygunawardena N, Burdett T, Dylag M. et al. ArrayExpress update--an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res. 2011;39:D1002–4. - PMC - PubMed
    1. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–35. - PubMed
    1. Wei G, Twomey D, Lamb J, Schlis K, Agarwal J, Stam RW. et al. Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. Cancer Cell. 2006;10:331–42. - PubMed
    1. Feng C, Araki M, Kunimoto R, Tamon A, Makiguchi H, Niijima S. et al. GEM-TREND: a web tool for gene expression data mining toward relevant network discovery. BMC Genomics. 2009;10:411. - PMC - PubMed
Feedback