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A computational solution to automatically map metabolite libraries in the context of genome scale metabolic networks

Abstract : This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.
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Benjamin Merlet, Nils Paulhe, Florence Vinson, Clément Frainay, Maxime Chazalviel, et al.. A computational solution to automatically map metabolite libraries in the context of genome scale metabolic networks. Frontiers in Molecular Biosciences, Frontiers Media, 2016, 3, ⟨10.3389/fmolb.2016.00002⟩. ⟨hal-02631089⟩

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