W134 Nucleic Acids Research, 2010, Vol. 38, Web Server issue metabolic network can be exported as an SBML ﬁle (20) or as a graph ﬁle to allow further studies using other modelling tools as described. METABOLIC NETWORK FILTERING The ﬁlters available in MetExplore have three functions. First, their implementation can allow investigation of selected subparts of metabolism. Second, they can be used to avoid sources of misinterpretation in metabolic graph analyses and ﬁnally, they can aid in providing clearer visualizations of the network, by restricting analyses to only small molecule metabolites excluding cellular macromolecules (proteins, nucleic acids, glycoconjugates, etc.). A common problem during metabolic network modelling is to deal with the so-called ubiquitous com- pounds. These compounds, involved in many reactions, can cause artefacts when considered in the same way as components of a linear transformation series as metabol- ites of classical biochemical pathways. These molecules, e.g. ATP, which provides phosphate to hundreds of reac- tions, short-circuit the network and confound network analysis (18). Two ways are proposed in MetExplore to ﬁlter out these compounds. First, we can distinguish between main compounds and side compounds in the metabolic pathways stored in the BioCyc-like databases. The main compounds are involved in the backbone of the metabolic pathway, while the side compounds are molecules such as common cofactors that contribute to reactions as chemical donors or recipients without being part of the transform- ation chain of the metabolic pathway. The MetExplore ﬁlter removes metabolites that are annotated as a side compound for given reactions regardless of the metabolic pathway considered. The second MetExplore method to deal with ubiquitous compounds uses a list of 62 cofactor transformations (available in the online documentation). The ﬁlter removes these compounds from each reaction in which they participate together. For instance, ATP, ADP and phosphate are removed in each reaction where the transformation ‘ATP = ADP + Phosphate’ appears. Unlike the previous ﬁlter, this function does not use any pathway information and deals with reactions not classi- ﬁed in any metabolic pathway. Filtering ubiquitous com- pounds also simpliﬁes the visual representation of the metabolic network by removing highly connected nodes. Finally, a MetExplore user can choose to keep or to remove all of the reactions involved in the pathways identiﬁed in the selected organism, but for which no enzymes are assigned [classically called pathway holes (21)]. METABOLOME MAPPING User input After choosing an organism and tuning the network ﬁlters, the required input data for metabolome mapping is a tabulated ﬁle. Depending on the selected mode, the ﬁrst column of the input ﬁle corresponds either to measured masses, database metabolite identiﬁers or me- tabolite names. In the mass mapping mode, an error limit in p.p.m. (parts per million) has to be deﬁned. The iden- tiﬁcation of metabolites by their masses is not always satisfactory since, even with high resolution mass spec- trometry, the identiﬁcation may be ambiguous (22), par- ticularly with respect to isomers that by deﬁnition are of identical mass. For this reason, MetExplore also uses me- tabolite identiﬁers or user-deﬁned names. The names can be described by a regular expression when their syntax is not exactly known. For instance, if the user does not know if the metabolite coenzyme A is stored in the database as co-A, coenzyme-A, or coA, he can use the regular expres- sion co.*a that corresponds to any metabolite name that starts by co, followed by any number of additional char- acters and ends by a (the case is not sensitive). Additional columns of the input ﬁle correspond to numerical values that quantify, for instance, the retention time or the peak intensities. Each mass, name or identiﬁer is compared to the infor- mation stored in the MetExplore relational database for the selected organism. As queries to external databases are not required, the processing is quite fast: a mapping of 380 masses on the complete metabolic network of MetaCyc, for example, takes 6 min. Results MetExplore is capable of mapping experimental data uploaded by the user onto user-selected metabolic networks. The output is a list of identiﬁed metabolites which is enriched by metabolic network informa- tion such as metabolic pathways involving these metabolites. Whatever the selected mode, a result table as described in Figure 2A is displayed. The name of each identiﬁed metabolite is a hyperlink to the source database. The pathways in which the metabolite appears in the ﬁltered metabolic network are also indicated. Each numerical value from the input ﬁle is reported and coloured depend- ing on the quartile computed in the whole column to which it belongs. A glyph corresponding to the local topology around the metabolite in the ﬁltered metabolic network is also displayed. Simple visual inspection is thus suﬃcient to indicate whether the metabolite is a source, an output or a choke point (see deﬁnition below) in the ﬁltered metabolic network. To facilitate interpretation of the results, it is possible to visualize identiﬁed metabolites on the ﬁltered metabolic network by launching Cytoscape directly from MetExplore. The user does not have to install Cytoscape since it is loaded via Java Web Start (the only requirement is that the browser supports Java 1.5 or higher). A MetExplore visual style is automatically applied and highlights the identiﬁed metabolites (Figure 2B). Moreover, metabolic network attributes including Enzyme Commission (EC) numbers, metabolic pathways, masses and chemical formulae are loaded as attributes in Cytoscape. All of these attributes and the SBML ﬁle corresponding to the ﬁltered metabolic network are also downloadable from the MetExplore interface.