Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom.
Gigascience. 2019 Feb 1;8(2). doi: 10.1093/gigascience/giy149.
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.
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.
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.
代谢组学是对大量小分子进行全面研究，以深入了解生物体的新陈代谢。该研究领域具有动态性和扩展性，应用于生物医学、生物技术和许多其他应用生物学领域。其计算密集型性质要求使用开放数据格式、数据库和数据分析工具。然而，快速发展导致了一系列独立的、有时是不兼容的分析方法，这些方法很难连接成一个有用的和完整的数据分析解决方案。
PhenoMeNal（表型和代谢组分析）是一种高级的、完整的基础设施即服务（IaaS）解决方案，它将面向工作流程的、互操作的代谢组学数据分析平台引入云端。PhenoMeNal 通过项目的持续集成过程无缝集成了广泛的现有开源工具，并将其测试和打包为 Docker 容器，并基于 Kubernetes 编排框架进行部署。它还在 Galaxy、Jupyter、Luigi 和 Pachyderm 用户界面中提供了许多标准化的、自动化的和已发布的分析工作流程。
PhenoMeNal 是代谢组学云基础设施中的一个关键解决方案。PhenoMeNal 是一种独特的、完整的解决方案，通过易于使用的 Web 界面为设置云基础设施提供了便利，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，以及通过可用于科学工作流程的即用型用户界面，PhenoMeNal 成功地为科学家提供了基于工作流程的、可重复的、可共享的代谢组学数据分析平台，这些平台通过标准的数据格式、代表性的数据集、版本化进行接口，并已针对可重复性和互操作性进行了测试。PhenoMeNal 的弹性实现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。
