Background: The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation.Results: In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF - target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes.Conclusion: CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.

The tomato microarray TOM1 offers the possibility to monitor the levels of several thousand transcripts in parallel. The microelements represented on this tomato microarray have been putatively assigned to unigenes, and organised in functional classes using the MapMan ontology (Thimm et al., 2004. Plant J. 37: 914-939). This ontology was initially developed for use with the Arabidopsis ATH1 array, has a low level of redundancy, and can be combined with the MapMan software to provide a biologically structured overview of changes of transcripts, metabolites and enzyme activities. Use of this application is illustrated using three case studies with published or novel TOM1 array data sets for Solanaceous species. Comparison of previously reported data on transcript levels in potato leaves in the middle of the day and the middle of the night identified coordinated changes in the levels of transcripts of genes involved in various metabolic pathways and cellular events. Comparison with diurnal changes of gene expression in Arabidopsis revealed common features, illustrating how MapMan can be used to compare responses in different organisms. Comparison of transcript levels in new experiments performed on the leaves of the cultivated tomato S. lycopersicum and the wild relative S. pennellii revealed a general decrease of levels of transcripts of genes involved in terpene and, phenylpropanoid metabolism as well as chorismate biosynthesis in the crop compared to the wild relative. This matches the recently reported decrease of the levels of secondary metabolites in the latter. In the third case study, new expression array data for two genotypes deficient in TCA cycle enzymes is analysed to show that these genotypes have elevated levels of transcripts associated with photosynthesis. This in part explains the previously documented enhanced rates of photosynthesis in these genotypes. Since the Solanaceous MapMan is intended to be a community resource it will be regularly updated on improvements in tomato gene annotation and transcript profiling resources.

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.

UNLABELLED:A data managing system for editing and visualization of biological pathways is presented. The main component of PaVESy (Pathway Visualization and Editing System) is a relational SQL database system. The database design allows storage of biological objects, such as metabolites, proteins, genes and respective relations, which are required to assemble metabolic and regulatory biological interactions. The database model accommodates highly flexible annotation of biological objects by user-defined attributes. In addition, specific roles of objects are derived from these attributes in the context of user-defined interactions, e.g. in the course of pathway generation or during editing of the database content. Furthermore, the user may organize and arrange the database content within a folder structure and is free to group and annotate database objects of interest within customizable subsets. Thus, we allow an individualized view on the database content and facilitate user customization. A JAVA-based class library was developed, which serves as the database programming interface to PaVESy. This API provides classes, which implement the concepts of object persistence in SQL databases, such as entries, interactions, annotations, folders and subsets. We created editing and visualization tools for navigation in and visualization of the database content. User approved pathway assemblies are stored and may be retrieved for continued modification, annotation and export. Data export is interfaced with a range of network visualization programs, such as Pajek or other software allowing import of SBML or GML data format.AVAILABILITY:http://pavsey.mpimp-golm.mpg.de