MOTIVATION: Metabolomics is a rapidly evolving field that holds promise to provide insights into genotype-phenotype relationships in cancers, diabetes and other complex diseases. One of the major informatics challenges is providing tools that link metabolite data with other types of high-throughput molecular data (e.g. transcriptomics, proteomics), and incorporate prior knowledge of pathways and molecular interactions. RESULTS: We describe a new, substantially redesigned version of our tool Metscape that allows users to enter experimental data for metabolites, genes and pathways and display them in the context of relevant metabolic networks. Metscape 2 uses an internal relational database that integrates data from KEGG and EHMN databases. The new version of the tool allows users to identify enriched pathways from expression profiling data, build and analyze the networks of genes and metabolites, and visualize changes in the gene/metabolite data. We demonstrate the applications of Metscape to annotate molecular pathways for human and mouse metabolites implicated in the pathogenesis of sepsis-induced acute lung injury, for the analysis of gene expression and metabolite data from pancreatic ductal adenocarcinoma, and for identification of the candidate metabolites involved in cancer and inflammation. AVAILABILITY: Metscape is part of the National Institutes of Health-supported National Center for Integrative Biomedical Informatics (NCIBI) suite of tools, freely available at http://metscape.ncibi.org. It can be downloaded from http://cytoscape.org or installed via Cytoscape plugin manager. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The increasing availability of large network datasets along with the progresses in experimental high-throughput technologies have prompted the need for tools allowing easy integration of experimental data with data derived form network computational analysis. In order to enrich experimental data with network topological parameters, we have developed the Cytoscape plug-in CentiScaPe. The plug-in computes several network centrality parameters and allows the user to analyze existing relationships between experimental data provided by the users and node centrality values computed by the plug-in. CentiScaPe allows identifying network nodes that are relevant from both experimental and topological viewpoints. CentiScaPe also provides a Boolean logic-based tool that allows easy characterization of nodes whose topological relevance depends on more than one centrality. Finally, different graphic outputs and the included description of biological significance for each computed centrality facilitate the analysis by the end users not expert in graph theory, thus allowing easy node categorization and experimental prioritization. AVAILABILITY: CentiScaPe can be downloaded via the Cytoscape web site: http://chianti.ucsd.edu/cyto_web/plugins/index.php. Tutorial, centrality descriptions and example data are available at: http://profs.sci.univr.it/ approximately scardoni/centiscape/centiscapepage.php CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.