ALBERT

Description: 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/.

Description: Background Many fruits, including watermelon, are proficient in carotenoid accumulation during ripening. While most genes encoding steps in the carotenoid biosynthetic pathway have been cloned, few transcriptional regulators of these genes have been defined to date. Here we describe the identification of a set of putative carotenoid-related transcription factors resulting from fresh watermelon carotenoid and transcriptome analysis during fruit development and ripening. Our goal is to both clarify the expression profiles of carotenoid pathway genes and to identify candidate regulators and molecular targets for crop improvement. Results Total carotenoids progressively increased during fruit ripening up to ~55 μg g-1 fw in red-ripe fruits. Trans-lycopene was the carotenoid that contributed most to this increase. Many of the genes related to carotenoid metabolism displayed changing expression levels during fruit ripening generating a metabolic flux toward carotenoid synthesis. Constitutive low expression of lycopene cyclase genes resulted in lycopene accumulation. RNA-seq expression profiling of watermelon fruit development yielded a set of transcription factors whose expression was correlated with ripening and carotenoid accumulation. Nineteen putative transcription factor genes from watermelon and homologous to tomato carotenoid-associated genes were identified. Among these, six were differentially expressed in the flesh of both species during fruit development and ripening. Conclusions Taken together the data suggest that, while the regulation of a common set of metabolic genes likely influences carotenoid synthesis and accumulation in watermelon and tomato fruits during development and ripening, specific and limiting regulators may differ between climacteric and non-climacteric fruits, possibly related to their differential susceptibility to and use of ethylene during ripening.