SummaryMAPMAN is a user-driven tool that displays large data sets onto diagrams of metabolic pathways or other processes. SCAVENGER modules assign the measured parameters to hierarchical categories (formed`BINs', subBINs'). A ®rst build of TRANSCRIPTSCAVENGER groups genes on the Arabidopsis Affymetrix 22K array into >200 hierarchical categories, providing a breakdown of central metabolism (for several pathways, down to the single enzyme level), and an overview of secondary metabolism and cellular processes. METABOLITE-SCAVENGER groups hundreds of metabolites into pathways or groups of structurally related compounds. An IMAGEANNOTATOR module uses these groupings to organise and display experimental data sets onto diagrams of the users' choice. A modular structure allows users to edit existing categories, add new categories and develop SCAVENGER modules for other sorts of data. MAPMAN is used to analyse two sets of 22K Affymetrix arrays that investigate the response of Arabidopsis rosettes to low sugar: one investigates the response to a 6-h extension of the night, and the other compares wild-type Columbia-0 (Col-0) and the starchless pgm mutant (plastid phosphoglucomutase) at the end of the night. There were qualitatively similar responses in both treatments. Many genes involved in photosynthesis, nutrient acquisition, amino acid, nucleotide, lipid and cell wall synthesis, cell wall modi®cation, and RNA and protein synthesis were repressed. Many genes assigned to amino acid, nucleotide, lipid and cell wall breakdown were induced. Changed expression of genes for trehalose metabolism point to a role for trehalose-6-phosphate (Tre6P) as a starvation signal. Widespread changes in the expression of genes encoding receptor kinases, transcription factors, components of signalling pathways, proteins involved in post-translational modi®cation and turnover, and proteins involved in the synthesis and sensing of cytokinins, abscisic acid (ABA) and ethylene revealing large-scale rewiring of the regulatory network is an early response to sugar depletion.

The wide application of high-throughput transcriptomics using microarrays has generated a plethora of technical platforms, data repositories, and sophisticated statistical analysis methods, leaving the individual scientist with the problem of choosing the appropriate approach to address a biological question. Several software applications that provide a rich environment for microarray analysis and data storage are available (e.g. GeneSpring, EMMA2), but these are mostly commercial or require an advanced informatics infrastructure. There is a need for a noncommercial, easy-to-use graphical application that aids the lab researcher to find the proper method to analyze microarray data, without this requiring expert understanding of the complex underlying statistics, or programming skills. We have developed Robin, a Java-based graphical wizard application that harnesses the advanced statistical analysis functions of the R/BioConductor project. Robin implements streamlined workflows that guide the user through all steps of two-color, single-color, or Affymetrix microarray analysis. It provides functions for thorough quality assessment of the data and automatically generates warnings to notify the user of potential outliers, lowquality chips, or low statistical power. The results are generated in a standard format that allows ready use with both specialized analysis tools like MapMan and PageMan and generic spreadsheet applications. To further improve user friendliness, Robin includes both integrated help and comprehensive external documentation. To demonstrate the statistical power and ease of use of the workflows in Robin, we present a case study in which we apply Robin to analyze a two-color microarray experiment comparing gene expression in tomato (Solanum lycopersicum) leaves, flowers, and roots.

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