Abstract

MetExplore 2.0: Handling Genome Scale Metabolic Networks Online

Fabien Jourdan, Research Scientist, INRA-MetaboHub

Since 2009 the MetExplore web server has offered access to more than 200 metabolic networks representing a large variety of organisms. A key feature of MetExplore is its provision of a single framework that allows analysis of metabolomics datasets within the context of these networks. We have recently changed the architecture of the server, creating a fully online interface (from data import to network visualization) thus eliminating any need for software installation and file exchanges. Most original functions were kept in version 2.0, including graph modelling, SBML file import and Cytoscape exports. Flux Balance Analysis methods, available in version 1, are also being transferred to the new release. Flux computation on in silico engineered models is possible through computation in a newly developed Java framework (FlexFlux). Many new features have been added to the online release, as we move to integrate metabolomics data within a more polyomics based context, offering organism-specific metabolome alongside genome, transcriptome and proteome centered information. Omics data regarding each of these elements can be imported, from Excel spreadsheets, processed then visualized within an interactive webpage. MetExplore 2.0 is offered as a freely accessible web service to the community. MetExplore is capable of extracting a variety of sub-networks from within the larger network, based on individual pathways, or modules responding to environmental challenge. MetExplore tackles sub network identification in two ways. A first approach filters for selected features e.g. certain metabolites either known to be involved in particular responses, or inferred to be involved through association with others that are. Secondly, application of graph algorithms to the network structure can identify metabolic paths between identified metabolites. The MetExplore 2.0 architecture allows a flexible analysis of integrated polyomics data within the context of metabolic networks. It ha