4.6 Article

COMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia

Journal

AMERICAN JOURNAL OF EPIDEMIOLOGY
Volume 191, Issue 1, Pages 147-158

Publisher

OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwab120

Keywords

bioinformatics; data science; meta-analysis; metabolomics

Funding

  1. Division of Cancer Control and Population Sciences, National Cancer Institute
  2. Intramural Research Program of the National Institutes of Health (Division of Cancer Epidemiology and Genetics, National Cancer Institute)
  3. Intramural Research Program of the National Institutes of Health (National Center for Advancing Translational Sciences)

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COMETS Analytics is a tool developed to support and streamline consortium-based analyses of metabolomics and other -omics data, providing standardized analyses across all cohorts with algorithmic, reproducible approach. It has been shown to scale well and may be useful for facilitating large-scale consortium-based research in metabolomics.
Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.

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