Madhavan, S. Integrative Analysis Workflow for Untargeted Metabolomics in Translational Research. Metabolomics, 2014.


Background: Metabolomics is an emerging ‘omics’ science that has demonstrated its fast gaining importance as a powerful profiling tool for determining an individual’s response to a foreign stimulus such as a drug, toxin, or environmental change; or as an indicator of disease progression. Such small molecule profiles can used as biological markers of disease, and provide an indicator of drug efficacy or toxicity.Several studies have demonstrated that the results of any single ‘omics’ analysis may not be sufficient to decode extremely complex biological mechanisms.

Methods: We have developed a translational research workflow to enable researchers to perform cutting-edge integrative analysis of metabolomics data with transcriptomics (gene expression) data using knowledge-driven networks. This network based view of interconnected functional partners can provide valuable new insight about the underlying biochemical processes and pathways associated with the phenotype of interest. To enhance and simplify metabolomics annotation we built a fully cross-referenced database called MetPlus DB, which integrates data from the three most comprehensive metabolite databases tailored largely towards mammalian metabolomics: HMDB, HUAMNCYC, and LIPID MAPS.Cross-referencing information is provided for linking to several other mainstream cheminformatics/bioinformatics repositories including KEGG, METLIN, ChEBI, FooDB, Pubchem, and Chemspider to provide unambiguous knowledge on clinically and physiologically relevant metabolites. We have made the integrated database available freely to the research community (

Results: To demonstrate the usefulness and strength of our methodology, we have tested it on a multi-omics profiling dataset from NCI-60 breast cancer cell lines to explore the biological dynamics of breast cancer.

Conclusions: Our results demonstrate a streamlined approach for the comprehensive annotation of metabolites using MetPlus DB, and the subsequent integration of metabolomics and transcriptomics data to explore potentially relevant biological interactions and candidate biomarkers associated with disease phenotype.

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