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COMETS 分析:一种用于在大型研究联盟中分析和荟萃代谢组学数据的在线工具。

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

出版信息

Am J Epidemiol. 2022 Jan 1;191(1):147-158. doi: 10.1093/aje/kwab120.

DOI:10.1093/aje/kwab120
PMID:33889934
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8897993/
Abstract

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.

摘要

基于联盟的研究对于产生可靠、高质量的研究结果至关重要,但现有的联盟研究工具在数据保护、易于部署和分析严谨性方面存在重要缺陷。为了解决这些问题,我们开发了 COnsortium of METabolomics Studies (COMETS) Analytics,以支持和简化基于联盟的代谢组学和其他组学数据的分析。该应用程序不需要专门的专业知识,既可以在本地运行以保证数据保护,也可以通过基于 Web 的服务器运行以提高便利性和速度。与其他基于 Web 的工具不同,COMETS Analytics 可以使用算法、可重复的方法来诊断、记录和修复模型问题,从而在所有队列中运行标准化分析。这消除了通过按队列手动定制模型的耗时且潜在容易出错的步骤,有助于加速基于联盟的项目并提高分析的可重复性。我们通过在 45 项队列研究中进行 2 项数据分析来证明该应用程序的可扩展性,这些研究总共包含了多达 134742 名参与者的 4647 种代谢物的测量结果。根据分析师在准备数据输入方面的最小错误和所有尝试的模型的成功执行情况,COMETS Analytics 在这项测试中表现良好。随着代谢组学在生物医学和流行病学研究人员中逐渐普及,COMETS Analytics 可能成为促进大规模基于联盟的研究的有用工具。