Axel Nagel

MAPMAN 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 first 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. METABOLITESCAVENGER 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 modification, 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 modification 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.

Recent rapid advances in next generation RNA sequencing (RNA-Seq)-based provide researchers with unprecedentedly large data sets and open new perspectives in transcriptomics. Furthermore, RNA-Seq-based transcript profiling can be applied to non-model and newly discovered organisms because it does not require a predefined measuring platform (like e.g. microarrays). However, these novel technologies pose new challenges: the raw data need to be rigorously quality checked and filtered prior to analysis, and proper statistical methods have to be applied to extract biologically relevant information. Given the sheer volume of data, this is no trivial task and requires a combination of considerable technical resources along with bioinformatics expertise. To aid the individual researcher, we have developed RobiNA as an integrated solution that consolidates all steps of RNA-Seq-based differential gene-expression analysis in one user-friendly cross-platform application featuring a rich graphical user interface. RobiNA accepts raw FastQ files, SAM/BAM alignment files and counts tables as input. It supports quality checking, flexible filtering and statistical analysis of differential gene expression based on state-of-the art biostatistical methods developed in the R/Bioconductor projects. In-line help and a step-by-step manual guide users through the analysis. Installer packages for Mac OS X, Windows and Linux are available under the LGPL licence from http://mapman.gabipd.org/web/guest/robin.

MapMan is a user-driven tool that displays large genomics datasets onto diagrams of metabolic pathways or other processes. Here, we present new developments, including improvements of the gene assignments and the user interface, a strategy to visualize multilayered datasets, the incorporation of statistics packages, and extensions of the software to incorporate more biological information including visualization of corresponding genes and horizontal searches for similar global responses across large numbers of arrays.

Next-generation technologies generate an overwhelming amount of gene sequence data. Efficient annotation tools are required to make these data amenable to functional genomics analyses. The Mercator pipeline automatically assigns functional terms to protein or nucleotide sequences. It uses the MapMan 'BIN' ontology, which is tailored for functional annotation of plant 'omics' data. The classification procedure performs parallel sequence searches against reference databases, compiles the results and computes the most likely MapMan BINs for each query. In the current version, the pipeline relies on manually curated reference classifications originating from the three reference organisms (Arabidopsis, Chlamydomonas, rice), various other plant species that have a reviewed SwissProt annotation, and more than 2000 protein domain and family profiles at InterPro, CDD and KOG. Functional annotations predicted by Mercator achieve accuracies above 90% when benchmarked against manual annotation. In addition to mapping files for direct use in the visualization software MapMan, Mercator provides graphical overview charts, detailed annotation information in a convenient web browser interface and a MapMan-to-GO translation table to export results as GO terms. Mercator is available free of charge via http://mapman.gabipd.org/web/guest/app/Mercator.

MapMan is a software tool that supports the visualization of profiling data sets in the context of existing knowledge. Scavenger modules generate hierarchical and essentially non-redundant gene ontologies ('mapping files'). An ImageAnnotator module visualizes the data on a gene-by-gene basis on schematic diagrams ('maps') of biological processes. The PageMan module uses the same ontologies to statistically evaluate responses at the pathway or processes level. The generic structure of MapMan also allows it to be used for transcripts, proteins, enzymes and metabolites. MapMan was developed for use with Arabidopsis, but has already been extended for use with several other species. These tools are available as downloadable and web-based versions. After providing an introduction to the scope and use of MapMan, we present a case study where MapMan is used to analyse the transcriptional response of the crop plant maize to diurnal changes and an extension of the night. We then explain how MapMan can be customized to visually and systematically compare responses in maize and Arabidopsis. These analyses illustrate how MapMan can be used to analyse and compare global transcriptional responses between phylogenetically distant species, and show that analyses at the level of functional categories are especially useful in cross-species comparisons.