W136 Nucleic Acids Research, 2010, Vol. 38, Web Server issue given set of metabolites (12). The scope of a set of metab- olites (so-called seeds) is deﬁned as the sum of all metab- olites that the seeds are able to produce using the reactions available in an organism. In contrast to the shortest paths computed in simple graphs, the scope concept takes into account the availability of all of the substrates used in a reaction. The scope is computed in an iterative way [referred to as the expansion process (12)]. A table is generated to display information about the metabolites produced during the process. Precursors Precursors are the set of metabolites from which a deﬁned set of target metabolites can be produced. Precursor sets can be calculated by the inverse of the expansion process described in the scope function, starting with a set of given target metabolites, then moving backwards through the dataset until a metabolite is reached that is not produced by any reaction or metabolite already visited during the process. Two outputs can be generated from this computation: a table containing all the metabolites visited during the process and the precursors or a table containing only the precursors. For instance, these precur- sors might then be considered essential components of any deﬁned culture medium required for growth of a microbial organism or cell type. When Cytoscape is launched via the scope and precur- sor functions, the ﬁltered metabolic network and the sub- network visited during the process are automatically loaded. METABOLIC NETWORK EXPORT While MetExplore, especially used in conjunction with visualization environments such as Cytoscape, oﬀers multiple functions regarding presentation and analysis of metabolomic data sets, clearly there are a multitude of additional software options that may be useful in further analysis of MetExplore ﬁle outputs. SBML is an XML- based format dedicated to the description of systems biology type data sets including metabolic networks (20). Filtered networks from each MetExplore function described above and the corresponding attributes can be exported directly to SBML. Moreover, the extended SBML that we created stores information about masses, EC numbers, links between genes, reactions and pathways not present in a classical SBML ﬁle. This format is described in the online documentation. From the ﬁltered metabolic networks, MetExplore is able to build three kinds of graphs: the compound graph, the reaction graph and the bipartite graph [for def- initions see (3)]. Each one is available in edge-list format and can be visualized in Cytoscape or used to perform other graph analyses. DISCUSSION AND CONCLUSION Here, we describe novel software to help visualize, navigate, mine and draw biological inference from metabolomics experiments set within the context of global metabolic networks. MetExplore gathers a set of original functionalities that can be easily and freely accessed through its web server. The mapping function enables identiﬁcation of metabolites predicted within the metabolism of a given organism, using either mass information, database identiﬁers or their simple names. Since metabolite names are frequently poorly formatted, MetExplore allows the inclusion of regular expressions to describe them. As MetExplore does not require access to other web servers, the processing involved in mapping is very fast as it queries only the MetExplore database. Rather than mapping the identiﬁed metabolites to individ- ual pathways or to a collection of pathways, MetExplore maps them onto a single metabolic network. This yields topological information about the identiﬁed metabolites. Another advantage of MetExplore resides in its ability to highlight identiﬁed metabolites within the metabolic network by directly launching Cytoscape from the MetExplore interface. It is thus possible to visualize output from metabolomic experiments with the attributes of the metabolic network also loaded. To generate a clearer visualization or to study only a subsection of the metabolic network, MetExplore contains various ﬁlters to be applied to networks prior to mapping. Moreover, the GapFiller plug-in installed in the Cytoscape version loaded from MetExplore further facili- tates analysis by computing subnetworks that link identiﬁed metabolites. MetExplore also provides additional high-level func- tions that allow further inference from the mapping results. By computing the choke points in a ﬁltered meta- bolic network, it is possible in MetExplore to detect weak points in the metabolic network, which oﬀer potential drug targets. Furthermore, the scope function imple- mented in MetExplore helps to decipher which metabol- ites and reactions can be aﬀected by the changes in concentration of a set of metabolites. In a converse function, since metabolomics generally measures the outputs of the metabolism, MetExplore also computes which metabolites are necessary to produce those observed experimentally. The results of these functions can also be directly visualized by launching Cytoscape. Finally, the possibility to save the ﬁltered metabolic networks into SBML and several graph formats facilitates the ﬁrst steps of the metabolic network modelling. We are currently working on further improvements for MetExplore. The database currently contains only about 50 organism-speciﬁc data sets. We plan to increase this number, especially by including data sets from other data- bases such as KEGG (4) or HMDB (25). Furthermore, we are working on the possibility of applying the MetExplore functions to a metabolic network uploaded by the user. Other graph analysis tools will be included in future versions of MetExplore. For instance, some topological graph measures including betweeness centrality (26) should facilitate an appreciation of the importance of identiﬁed metabolites in a metabolic network. Inclusion of data from other sources (e.g. proteomics and transcriptomics) is also under consideration for enhanced analysis through MetExplore.