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*omeSOM: a software for clustering and visualization of transcriptional and metabolite data mined from interspecific crosses of crop plants

Diego H Milone1, Georgina S Stegmayer2*, Laura Kamenetzky3, Mariana López3, Je Min Lee4, James J Giovannoni4 and Fernando Carrari3

Author Affiliations

1 Research Center for Signals, Systems and Computational Intelligence, FICH-UNL, CONICET, Ciudad Universitaria UNL, Santa Fe, (3000), Argentina

2 Centro de Investigación en Ingeniería en Sistemas de Información, CONICET, Lavaise 610, Santa Fe, (3000), Argentina

3 Instituto de Biotecnología, Instituto Nacional de Tecnología Agrícola (IB-INTA), CONICET, PO Box 25, B1712WAA Castelar, Argentina (partner group of the Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany

4 Boyce Thompson Institute for Plant Research, Cornell University Campus, Ithaca, NY 14853, USA

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BMC Bioinformatics 2010, 11:438  doi:10.1186/1471-2105-11-438

Published: 26 August 2010

Abstract

Background

Modern biology uses experimental systems that involve the exploration of phenotypic variation as a result of the recombination of several genomes. Such systems are useful to investigate the functional evolution of metabolic networks. One such approach is the analysis of transcript and metabolite profiles. These kinds of studies generate a large amount of data, which require dedicated computational tools for their analysis.

Results

This paper presents a novel software named *omeSOM (transcript/metabol-ome Self Organizing Map) that implements a neural model for biological data clustering and visualization. It allows the discovery of relationships between changes in transcripts and metabolites of crop plants harboring introgressed exotic alleles and furthermore, its use can be extended to other type of omics data. The software is focused on the easy identification of groups including different molecular entities, independently of the number of clusters formed. The *omeSOM software provides easy-to-visualize interfaces for the identification of coordinated variations in the co-expressed genes and co-accumulated metabolites. Additionally, this information is linked to the most widely used gene annotation and metabolic pathway databases.

Conclusions

*omeSOM is a software designed to give support to the data mining task of metabolic and transcriptional datasets derived from different databases. It provides a user-friendly interface and offers several visualization features, easy to understand by non-expert users. Therefore, *omeSOM provides support for data mining tasks and it is applicable to basic research as well as applied breeding programs. The software and a sample dataset are available free of charge at http://sourcesinc.sourceforge.net/omesom/ webcite.