SUMMARY: With the development of new high-throughput DNA sequencing technologies and decreasing costs, large gene expression datasets are being generated at an accelerating rate, but can be complex to visualize. New, more interactive and intuitive tools are needed to visualize the spatiotemporal context of expression data and help elucidate gene function. Using tomato fruit as a model, we have developed the Tomato Expression Atlas to facilitate effective data analysis, allowing the simultaneous visualization of groups of genes at a cell/tissue level of resolution within an organ, enhancing hypothesis development and testing in addition to candidate gene identification. This atlas can be adapted to different types of expression data from diverse multicellular species. AVAILABILITY AND IMPLEMENTATION: The Tomato Expression Atlas is available at http://tea.solgenomics.net/ . Source code is available at https://github.com/solgenomics/Tea . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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/.