http://csbdb.mpimp-golm.mpg.de/

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 coresponding genes and horizontal searches for similar global responses across large numbers of arrays.

Gas chromatography-mass spectrometry based metabolite profiling of biological samples is rapidly becoming one of the cornerstones of functional genomics and systems biology. Thus, the technology needs to be available to many laboratories and open exchange of information is required such as those achieved for transcript and protein data. The key-step in metabolite profiling is the unambiguous identification of metabolites in highly complex metabolite preparations with composite structure. Collections of mass spectra, which comprise frequently observed identified and non-identified metabolites, represent the most effective means to pool the identification efforts currently performed in many laboratories around the world. Here, we describe a platform for mass spectral and retention time index libraries that will enable this process (MSRI; www.csbdb.mpimpgolm.mpg.de/gmd.html). This resource should ameliorate many of the problems that each laboratory will face both for the initial establishment of metabolome analysis and for its maintenance at a constant sample throughput.

Gene co-expression analysis has emerged in the past 5 years as a powerful tool for gene function prediction. In essence, co-expression analysis asks the question 'what are the genes that are co-expressed, that is, those that show similar expression profiles across many experiments, with my gene of interest?'. Genes that are highly co-expressed may be involved in the biological process or processes of the query gene. This review describes the tools that are available for performing such analyses, how each of these perform, and also discusses statistical issues including how normalization of gene expression data can influence co-expression results, calculation of co-expression scores and P values, and the influence of data sets used for co-expression analysis. Finally, examples from the literature will be presented, wherein co-expression has been used to corroborate and discover various aspects of plant biology.