MetaboliticsDB: A Database of Metabolomics Analyses

Abstract

Web-based metabolomics databases store relative metabolite abundance datasets measured under different physiological conditions. However, their pathway-level analysis capabilities are mostly limited to superimposing the measurements onto the pathways of the measured metabolites. Besides, none of the existing metabolomics databases offer tools to store, manage, compare, and search metabolomics analysis results. In this paper, we present MetaboliticsDB, which features a database of metabolomics analyses and a set of associated analytics tools. It enables users to store and compare their metabolomics analysis results against others to study, for instance, the progression of a disease. Moreover, MetaboliticsDB implements a genome-scale metabolic network-based analysis tool (i.e., Metabolitics) that performs network-based flux analysis. Besides, MetaboliticsDB features an advanced querying interface offering flexible criteria, such as listing all analyses where a certain pathway experiences a major increase in activity, to help researchers identify conditions sharing a similar mechanism. Finally, MetaboliticsDB employs AI-based models to associate the studied metabolomics data with diseases. Currently, the database contains analysis results for 2,174 individuals and 40 diseases. We demonstrate MetaboliticsDB's usage with a case study on Hepatocellular Carcinoma. Our experimental evaluation shows that MetaboliticsDB provides biologically relevant metabolic network-level analysis results, disease association with high accuracy, and a scalable architecture.