Open AccessCOMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia
Author(s) -
Marinella Temprosa,
Steven C. Moore,
Krista A. Zanetti,
Nathan M. Appel,
David Ruggieri,
Kaitlyn M Mazzilli,
Kai-Ling Chen,
Rachel S. Kelly,
Jessica LaskySu,
Erikka Loftfield,
Kathleen M. McClain,
Brian Park,
Laura Trijsburg,
Oana A. Zeleznik,
Ewy A. Mathé
Publication year - 2021
Publication title -
american journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 256
eISSN - 1476-6256
pISSN - 0002-9262
DOI - 10.1093/aje/kwab120
Subject(s) - analytics , computer science , data science , data analysis , metabolomics , web analytics , software deployment , data quality , data mining , world wide web , the internet , bioinformatics , engineering , software engineering , web development , metric (unit) , operations management , web application security , biology
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.