metabolites can span several pathways and thus are diﬃ- cult to detect. Finally, the list of metabolic pathways supposed to be present in the metabolic databases is often predicted in an automatic way and can contain many errors and omissions (10). Furthermore, novel, organism-speciﬁc pathways cannot be integrated into such networks since construction is dependent upon com- parison to reference metabolic pathways. To restore realistic connectivity between metabolites and to overcome the problems of metabolic pathway iden- tiﬁcation, the metabolism of an organism is best inter- preted as a network gathering all pathways into a single structure. The MetExplore web server allows the mapping of data from metabolomic experiments onto genome-scale metabolic networks. Beyond the mapping functionalities, the network can also be analysed using graph-based methods. A graph is a mathematical object made of nodes and edges connecting them. In MetExplore, graphs are used to oﬀer several advanced mining features including choke point analysis (11), computation of biosynthetic capacity (12) and potential precursor de- termination for any set of identiﬁed metabolites. MetExplore provides novel ﬁlters to restrict metabolism to a particular set of pathways and to discard cellular macromolecules such as proteins, RNA and DNA. Filters were also created to remove ubiquitous compounds or generic reactions. Finally, ﬁltered metabolic networks can be exported into SBML ﬁles and metabolic graph ﬁles. MetExplore provides a unique framework to link metabolomics experiments, metabolic network visualiza- tion and modelling. The fact that many metabolites contribute to multiple pathways means that a network approach can oﬀer ad- vantages to interpretation of metabolomic experiments. A network approach can, for example, be used to show how two metabolites that may each be transformed directly to, or from, a common metabolite, but as members of separate classical pathways, are separated by only a single chemical species within the metabolic network. Nucleic Acids Research, 2010, Vol. 38, Web Server issue W133 OVERVIEW MetExplore follows the workﬂow given in Figure 1. It consists in building tailor-made ﬁltered networks in order to place experimental data into the context of the known, or predicted, metabolism of a selected organism. For each functionality, various outputs are proposed. The ﬁrst building block of MetExplore is a relational database (freely available on request) containing the meta- bolic networks of about 50 organisms. Metabolic informa- tion currently comes from BioCyc-like databases (5,13–17). The list of organisms and their source is avail- able in the online documentation of MetExplore. The latter also contains two databases containing metabolic information from multiple organisms: PlantCyc that de- scribes metabolic pathways present in 250 plant species (http://www.plantcyc.org) and MetaCyc that contains 1400 pathways from more than 1800 organisms (5). Analysing the networks as stored in the databases can lead to misinterpretations. For instance, ubiquitous com- pounds like water or ATP contribute to many reactions and can overload networks in spite of their not represent- ing formal transitional steps in classical biochemical pathways (18). To overcome this limitation, MetExplore proposes various ﬁlters that can be applied to the stored networks. Once a relevant network is built for a given organism, two kinds of function can be used to perform metabolite analysis. First, MetExplore can map to the genome- inferred metabolic networks the list of metabolites generated from metabolomics experiments (using metab- olite masses or metabolite identiﬁers). Second, MetExplore provides computational functions that allow investigation of the network features of a set of metabol- ites, allowing, for example, searches for potential drug targets. Each MetExplore function returns a web browsable table and allows visualization of the results through the graph visualization tool, Cytoscape (19). Each ﬁltered Figure 1. MetExplore work ﬂow.