title: *omeSOM: a software for clustering and visualization of transcriptional and metabolite data mined from interspecific crosses of crop plants.
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/.
authors.affiliation: Research Center for Signals, Systems and Computational Intelligence, FICH-UNL, CONICET, Ciudad Universitaria UNL, Santa Fe, (3000), Argentina
authors.forename: Diego H
authors.initials: DH
authors.lastname: Milone
authors.forename: Georgina S
authors.initials: GS
authors.lastname: Stegmayer
authors.forename: Laura
authors.initials: L
authors.lastname: Kamenetzky
authors.forename: Mariana
authors.initials: M
authors.lastname: López
authors.forename: Je Min
authors.initials: JM
authors.lastname: Lee
authors.forename: James J
authors.initials: JJ
authors.lastname: Giovannoni
authors.forename: Fernando
authors.initials: F
authors.lastname: Carrari
mesh_terms: D016000:Cluster Analysis; D018556:Crops, Agricultural / Q000235:genetics* / Q000378:metabolism; D003433:Crosses, Genetic; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D018745:Genome, Plant; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D012984:Software; D014158:Transcription, Genetic
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