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Open Access Software

Gepoclu: a software tool for identifying and analyzing gene positional clusters in large-scale gene expression analysis

Tania Dottorini1, Nicola Senin2, Giorgio Mazzoleni13, Kalle Magnusson3 and Andrea Crisanti3*

Author Affiliations

1 University of Perugia, Department of Experimental Medicine, via del Giochetto, Perugia 06100, Italy

2 University of Perugia, Department of Industrial Engineering, via G.Duranti, Perugia 06125, Italy

3 Imperial College London, Biological Sciences, Imperial College Road, London SW7 2AZ, UK

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BMC Bioinformatics 2011, 12:34  doi:10.1186/1471-2105-12-34

Published: 26 January 2011

Abstract

Background

The notion that genes are non-randomly organized within the chromosomes of eukaryotic organisms has recently received strong experimental support. Clusters of co-expressed and co-localized genes have been recognized as playing key roles in a number of functional pathways and adaptive responses including organism development, differentiation, disease states and aging. The identification of genes arranged in close proximity with each other within a particular temporal and spatial transcriptional program is anticipated to unravel possible functional links and reciprocal interactions.

Results

We developed a novel software tool Gepoclu (Gene Positional Clustering) that automatically selects genes based on expression values from multiple sources, including microarray, EST and qRT-PCR, and performs positional clustering. Gepoclu provides expression-based gene selection from multiple experimental sources, position-based gene clustering and cluster visualization functionalities, all as parts of the same fully integrated, and interactive, package. This means rapid iterations while exploring for emergent behavior, and full programmability of the filtering and clustering steps.

Conclusions

Gepoclu is a useful data-mining tool for exploring relationships among transcriptional data deriving form different sources. It provides an easy interactive environment for analyzing positional clustering behavior of co-expressed genes, and at the same time it is fully programmable, so that it can be customized and extended to support specific analysis needs.