SysMet: A Tool for Integrative Systems Metabolomics

• Published in: Journal of biomolecular techniques Vol. 30; no. Suppl; p. S39
• Main Authors: Nezami Ranjbar, Mohammad R, Fan, Ziling, Gao, Yan, Ressom, Habtom
• Format: Journal Article
• Language: English
• Published: United States Association of Biomolecular Resource Facilities 01.12.2019
• Subjects: Poster Abstracts
• ISSN: 1524-0215; 1943-4731

Metabolomics plays an indispensable role in the growing systems biology approaches to identify biomarkers for complex diseases such as cancer. Liquid chromatography coupled to mass spectrometry (LC-MS) and gas chromatography coupled to mass spectrometry (GC-MS) have been extensively used for high-throughput comparison of the levels of thousands of metabolites among biological samples. However, the potential values of many disease-associated analytes discovered by these platforms have been inadequately explored in systems biology research due to lack of computational tools. Partly due to these limitations, poor reproducibility of previously identified metabolite biomarker candidates has been observed, especially when they are evaluated through independent platforms and validation sets. Our goal is to provide metabolomics core facilities and research scientists with bioinformatics platforms and expertise that enable them to search for disease-associated metabolites at the systems level through integrative systems metabolomics. To this end, we developed a new browser friendly cloud-based tool (SysMet) to help uncover the relationship of diseases and metabolites by investigating the rewiring and conserved interactions among metabolites and through integrative analysis of multi-omic data. Developed via a modular design and a user-friendly graphical user interface (GUI), SysMet allows users to: (1) import preprocessed metabolomic data for differential analysis of metabolite profiles using a network-based method; (2) import other preprocessed omic data for selection of disease-associated metabolites based on network-based integrative analysis; and (3) visually evaluate the outcome of network-based differential analysis and multi-omic data integration through high-quality figures. We believe SysMet will contribute to improving the ability of researchers to discover disease-associated metabolites by enhancing the role of metabolomics in systems biology research.