dGCR
version 1.0 October 2013
Database of Gene Co-regulation dGCR
dGCR is a web tool to facilitate the analysis of co-regulation patterns of query genes across publically
available gene expression data. Genes whose expression patterns correlate across many experiments tend
to share biological function and consequently this application can serve to elucidate novel biological
connections between genes. In addition to revealing functional connections between individual gene pairs,
extended sets of co-regulated sets of genes are assessed for enrichment of gene ontology classes and
interaction pathways. This latter functionality can provide an insight into the biological function of
the query gene itself.
Introduction
Global gene expression serves as a truly quantitative high content descriptor of biological states.
This together with the vast amount of publically available data provides an ideal basis for comparing
biological perturbations through their associated transcriptional profiles.
To this end
SPIEDw
(
Williams BMC Genomics 2013) was developed
enabling the querying of publically available expression data. The SPIED
(
Williams BMC Genomics 2012)
database incorporates a significant fraction
of available expression data and thereby offers an ideal platform for investigating patterns gene co-regulation.
The dGCR web tool is based on a global matrix of gene expression correlations across over 200,000 separate experiments.
Correlation scores are derived by assigning statistical weights to patterns of up and down regulion across the SPIED database.
Details
The user inputs a gene name and then selects from a menu a gene from a candidate list containing the query name.
The number of co-regulating genes is selected together with other desired species or platform type filters.
Once the chosen gene is selected the output lists the top co-regulated genes together with details of the platforms
where the co-regulation obtains. Simple links enable the user to query literature on the given genes.
The gene list as a whole is usually enriched for informative gene ontology classes and pathways.
With this in mind the output page hosts buttons that link to filtered gene ontology and
pathway classes that can reveal functional aspects of the query gene.
All queries should be sent to:
Gareth Williams
Bioinformatics
Wolfson Centre for Age-Related Diseases
King's College London
London SE1 1UL
gareth.2.williams@kcl.ac.uk
