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Gene expression profiling studies are usually performed on pooled samples grown under tightly controlled experimental conditions to suppress variability among individuals and increase experimental reproducibility. In addition, to mask unwanted residual effects, the samples are often subjected to relatively harsh treatments that are unrealistic in a natural context. Here, we show that expression variations among individual wild-type Arabidopsis thaliana plants grown under the same macroscopic growth conditions contain as much information on the underlying gene network structure as expression profiles of pooled plant samples under controlled experimental perturbations. We advocate the use of subtle uncontrolled variations in gene expression between individuals to uncover functional links between genes and unravel regulatory influences. As a case study, we use this approach to identify ILL6 as a new regulatory component of the jasmonate response pathway. [ABSTRACT FROM AUTHOR]

As in other eukaryotes, cell division in plants is highly conserved and regulated by cyclin-dependent kinases (CDKs) that are themselves predominantly regulated at the posttranscriptional level by their association with proteins such as cyclins. Although over the last years the knowledge of the plant cell cycle has considerably increased, little is known on the assembly and regulation of the different CDK complexes. To map protein–protein interactions between core cell cycle proteins of Arabidopsis thaliana , a binary protein–protein interactome network was generated using two complementary high-throughput interaction assays, yeast two-hybrid and bimolecular fluorescence complementation. Pairwise interactions among 58 core cell cycle proteins were tested, resulting in 357 interactions, of which 293 have not been reported before. Integration of the binary interaction results with cell cycle phase-dependent expression information and localization data allowed the construction of a dynamic interaction network. The obtained interaction map constitutes a framework for further in-depth analysis of the cell cycle machinery. [ABSTRACT FROM AUTHOR]

As an overwhelming amount of functional genomics data have been generated, the retrieval, integration, and interpretation of these data need to be facilitated to enable the advance of (systems) biological research. For example, gathering and processing microarray data that are related to a particular biological process is not straightforward, nor is the compilation of protein- protein interactions from numerous partially overlapping databases identified through diverse approaches. However, these tasks are inevitable to address the following questions. Does a group of differentially expressed genes show similar expression in diverse microarray experiments? Was an identified protein-protein interaction previously detected by other approaches? Are the interacting proteins encoded by genes with similar expression profiles and localization? We developed CORNET (for CORrelation NETworks) as an access point to transcriptome, protein interactome, and localization data and functional information on Arabidopsis (Arabidopsis thaliana). It consists of two flexible and versatile tools, namely the coexpression tool and the protein-protein interaction tool. The ability to browse and search microarray experiments using ontology terms and the incorporation of personal microarray data are distinctive features of the microarray repository. The coexpression tool enables either the alternate or simultaneous use of diverse expression compendia, whereas the protein-protein interaction tool searches experimentally and computationally identified protein-protein interactions. Different search options are implemented to enable the construction of coexpression and/or protein-protein interaction networks centered around multiple input genes or proteins. Moreover, networks and associated evidence are visualized in Cytoscape. Localization is visualized in pie charts, thereby allowing multiple localizations per protein. CORNET is available at http://bioinformatics.psb.ugent.be/cornet. [ABSTRACT FROM AUTHOR]

Summary: Many large ‘omics’ datasets have been published and many more are expected in the near future. New analysis methods are needed for best exploitation. We have developed a graphical user interface (GUI) for easy data analysis. Our discovery of all significant substructures (DASS) approach elucidates the underlying modularity, a typical feature of complex biological data. It is related to biclustering and other data mining approaches. Importantly, DASS-GUI also allows handling of multi-sets and calculation of statistical significances. DASS-GUI contains tools for further analysis of the identified patterns: analysis of the pattern hierarchy, enrichment analysis, module validation, analysis of additional numerical data, easy handling of synonymous names, clustering, filtering and merging. Different export options allow easy usage of additional tools such as Cytoscape. [ABSTRACT FROM PUBLISHER]