Furthermore, this workflow can be extended with other reaction information by users, allows for the integration of transcriptomics or proteomics data, and includes identifier mappings to two popular databases for metabolomics dataset annotations. Our approach aims to overcome the boundaries that individual pathway models and resources encompass, by performing metabolomics network enrichment analysis by connecting individual pathways from three resources into one graph. The developed workflow creates a Directed Small Molecules Network (DSMN) as an unweighted, directed, labeled property bipartite graph, solving the issues described previously. The workflow starts by collecting directed reactions between metabolites from three unique and freely available pathway databases: LIPID MAPS , Reactome , and WikiPathways . The semantic web format Resource Description Framework (RDF) was used as the starting point to retrieve reaction knowledge, with ontologies providing harmonization over these databases and incorporating several metabolite and protein database annotations. The reactions are retrieved from the RDF through the SPARQL-query language, after which the information is stored in the graph database Neo4j (neo4j.com) . Substrate metabolites were connected to their counterpart product through a reaction node creating a bipartite graph, which enabled adding enzymes catalyzing the reactions as individual entities in the model and therefore suitable for downstream data analysis and visualization purposes. Each conversion reaction was considered to contribute in an equal amount to the overall reaction rate (unweighted) omitting specific kinetic information. All reactions were assumed to not be in molecular equilibrium, providing directionality to the model. Interpretation of the biochemical relationships underlying metabolic changes was performed by retrieving the directed paths between individual biomarkers with the shortest path algorithm, calculating the shortest distance between the nodes of the network . The resulting metabolic subnetwork can be connected to (multiple) -omics datasets for data visualization and interpretation by network tools such as Cytoscape, a widely adopted network analysis software tool . The resulting subnetworks contained directed reactions between metabolites including information on the enzyme(s) catalyzing the reaction. Additionally, available meta-data includes associated publications, pathway name(s), identifiers, and related ontologies.