PT  - JOURNAL ARTICLE
AU  - Kristian Peters
AU  - James Bradbury
AU  - Sven Bergmann
AU  - Marco Capuccini
AU  - Marta Cascante
AU  - Pedro de Atauri
AU  - Timothy M D Ebbels
AU  - Carles Foguet
AU  - Robert Glen
AU  - Alejandra Gonzalez-Beltran
AU  - Ulrich Guenther
AU  - Evangelos Handakas
AU  - Thomas Hankemeier
AU  - Kenneth Haug
AU  - Stephanie Herman
AU  - Petr Holub
AU  - Massimiliano Izzo
AU  - Daniel Jacob
AU  - David Johnson
AU  - Fabien Jourdan
AU  - Namrata Kale
AU  - Ibrahim Karaman
AU  - Bita Khalili
AU  - Payam Emami Khonsari
AU  - Kim Kultima
AU  - Samuel Lampa
AU  - Anders Larsson
AU  - Christian Ludwig
AU  - Pablo Moreno
AU  - Steffen Neumann
AU  - Jon Ander Novella
AU  - Claire O’Donovan
AU  - Jake TM Pearce
AU  - Alina Peluso
AU  - Luca Pireddu
AU  - Marco Enrico Piras
AU  - Michelle AC Reed
AU  - Philippe Rocca-Serra
AU  - Pierrick Roger
AU  - Antonio Rosato
AU  - Rico Rueedi
AU  - Christoph Ruttkies
AU  - Noureddin Sadawi
AU  - Reza M Salek
AU  - Susanna-Assunta Sansone
AU  - Vitaly Selivanov
AU  - Ola Spjuth
AU  - Daniel Schober
AU  - Etienne A. Thévenot
AU  - Mattia Tomasoni
AU  - Merlijn van Rijswijk
AU  - Michael van Vliet
AU  - Mark R Viant
AU  - Ralf J. M. Weber
AU  - Gianluigi Zanetti
AU  - Christoph Steinbeck
TI  - PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud
AID  - 10.1101/409151
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 409151
4099  - http://biorxiv.org/content/early/2018/09/13/409151.short
4100  - http://biorxiv.org/content/early/2018/09/13/409151.full
AB  - 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 The PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which 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 infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and ‘omics research domains.