Infers of metabolic networks based on high resolution metabolomic data.
Metabolomics aims at the identification and quantification of all metabolites that are present in a cell, tissue or biofluid at a given moment and under particular conditions. Various spectrometric technologies are capable of identifying thousands of metabolites. Recently, ultra high-resolution mass spectrometry (FTICR-MS or Orbitrap) has been successfully used in metabolomic studies. Such high-resolution data has also been used to predict _ab initio_ biochemical interactions between metabolites. Moreover, perturbation studies allow the use of correlation analysis to infer/confirm links between metabolites that correlate across various conditions. The combination of these two inference methods generates networks containing hundreds of nodes (metabolites) and hundreds of predicted edges (biochemical reactions and/or high correlations). To analyze, explore and interpret these two kinds of relations, powerful visualization tools are required. There is currently no available software that allows inference and visualization of such high-resolution metabolomic networks directly from raw data. Here we present a new plugin for Cytoscape dedicated to the inference and visualization of high-resolution metabolomic networks. Inference requires a list of potential biochemical transformations. Since the definition of this list is closely related to experimentation (i.e. the organism or perturbation under study), we propose facilities to edit/select putative biochemical transformations. The plugin also allows the extraction of parts of the network that contain a selected subset of reactions. Finally, to enrich the visual exploration, it is possible to visually render local topological properties of the network (e.g. degree or clustering index).
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