BACKGROUND Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.

中文翻译：

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PhenoMeNal：在云端处理和分析代谢组学数据。

背景代谢组学是对多种小分子的综合研究，以深入了解生物体的新陈代谢。该研究领域充满活力，并随着生物医学、生物技术和许多其他应用生物学领域的应用而不断扩展。其计算密集型特性推动了对开放数据格式、数据存储库和数据分析工具的需求。然而，快速的进步导致了各种独立的、有时甚至是不兼容的分析方法的出现，这些方法很难连接成有用且完整的数据分析解决方案。研究发现 PhenoMeNal（表型组和代谢组分析）是一种先进且完整的解决方案，用于建立基础设施即服务 (IaaS)，将面向工作流程、可互操作的代谢组学数据分析平台引入云端。PhenoMeNal 无缝集成了各种现有的开源工具，这些工具通过项目的持续集成过程进行测试并打包为 Docker 容器，并基于 kubernetes 编排框架进行部署。它还在用户界面 Galaxy、Jupyter、Luigi 和 Pachyderm 中提供了许多标准化、自动化和已发布的分析工作流程。结论 PhenoMeNal 构成了可用于代谢组学的云电子基础设施的关键解决方案。PhenoMeNal 是一个独特且完整的解决方案，用于通过易于使用的 Web 界面设置云电子基础设施，并且可以扩展到任何自定义的公共和私有云环境。通过协调和自动化软件安装和配置，以及通过即用型科学工作流程用户界面，PhenoMeNal 成功地为科学家提供了工作流程驱动、可重复和可共享的代谢组学数据分析平台，这些平台通过标准数据格式、代表性数据集、版本化，并经过可重复性和互操作性测试。PhenoMeNal 的弹性实施进一步允许基础设施轻松适应其他应用领域和“组学研究领域”。