MetScape provides a bioinformatics framework for the visualization and interpretation of metabolomic data using Cytoscape. MetScape allows users to build and analyze networks of genes and compounds, identify enriched pathways from expression profiling data, and visualize changes in metabolite data. Gene expression and/or compound concentration data can be loaded from file(s) (in CSV, TSV, or Excel formats), or the user can simply enter individual compounds/genes/pathways (using KEGG, EHMN, or Entrez Gene IDs) to build metabolic networks without loading a file. MetScape uses an internal relational database stored at NCIBI that integrates data from KEGG and EHMN.
Starting with version 3.1, MetScape also provides the ability to build, visualize and explore correlation networks, where nodes are metabolites and edges are the correlations between them. Correlation networks can include both known and unknown metabolites. Correlation thresholds can be defined using sliders, and metabolites can be divided into groups with the capability of filtering on correlations within or across groups.
As part of the correlation workflow, we introduce the [http://metscape.med.umich.edu/calculator.html Correlation Calculator], a standalone Java application providing the ability to import, normalize, filter and determine various correlation measures for MS or similar quantitative data. Correlation results can then be viewed interactively in MetScape as correlation networks.
For more information please visit [http://metscape.med.umich.edu] or contact the MetScape development team: firstname.lastname@example.org
This work developed at the University of Michigan, Department of Computational Medicine and Bioinformatics (DCMB), under National Institutes of Health Grants #U54DA021519, #U24DK097153 and #P30DK089503.