Interoperable and scalable data analysis with microservices: applications in metabolomics

Published in Bioinformatics, 2019

Recommended citation: Payam Emami Khoonsari, Pablo Moreno, Sven Bergmann, Joachim Burman, Marco Capuccini, Matteo Carone, Marta Cascante, Pedro de Atauri, Carles Foguet, Alejandra N Gonzalez-Beltran, Thomas Hankemeier, Kenneth Haug, Sijin He, Stephanie Herman, David Johnson, Namrata Kale, Anders Larsson, Steffen Neumann, Kristian Peters, Luca Pireddu, Philippe Rocca-Serra, Pierrick Roger, Rico Rueedi, Christoph Ruttkies, Noureddin Sadawi, Reza M Salek, Susanna-Assunta Sansone, Daniel Schober, Vitaly Selivanov, Etienne A Thévenot, Michael van Vliet, Gianluigi Zanetti, Christoph Steinbeck, Kim Kultima, Ola Spjuth, Interoperable and scalable data analysis with microservices: applications in metabolomics, Bioinformatics, , btz160,

This is an open access paper published in the Bioinformatics journal by Oxford University Press.



Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator.


We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.

Availability and implementation

The PhenoMeNal consortium maintains a web portal ( providing a GUI for launching the Virtual Research Environment. The GitHub repository hosts the source code of all projects.