RT Journal Article SR Electronic T1 Interoperable and scalable metabolomics data analysis with microservices JF bioRxiv FD Cold Spring Harbor Laboratory SP 213603 DO 10.1101/213603 A1 Payam Emami Khoonsari A1 Pablo Moreno A1 Sven Bergmann A1 Joachim Burman A1 Marco Capuccini A1 Matteo Carone A1 Marta Cascante A1 Pedro de Atauri A1 Carles Foguet A1 Alejandra Gonzalez-Beltran A1 Thomas Hankemeier A1 Kenneth Haug A1 Sijin He A1 Stephanie Herman A1 David Johnson A1 Namrata Kale A1 Anders Larsson A1 Steffen Neumann A1 Kristian Peters A1 Luca Pireddu A1 Philippe Rocca-Serra A1 Pierrick Roger A1 Rico Rueedi A1 Christoph Ruttkies A1 Noureddin Sadawi A1 Reza M Salek A1 Susanna-Assunta Sansone A1 Daniel Schober A1 Vitaly Selivanov A1 Etienne A. Thévenot A1 Michael van Vliet A1 Gianluigi Zanetti A1 Christoph Steinbeck A1 Kim Kultima A1 Ola Spjuth YR 2017 UL http://biorxiv.org/content/early/2017/11/24/213603.abstract AB 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 in parallel using the Kubernetes container orchestrator. The access point is a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.