Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/171955
Title: | Interoperable and scalable data analysis with microservices: applications in metabolomics |
Author: | Emami Khoonsari, Payam Moreno, Pablo Bergmann, Sven Burman, Joachim Capuccini, Marco Carone, Matteo Cascante i Serratosa, Marta Atauri Carulla, Ramón de Foguet Coll, Carles Gonzalez-Beltran, Alejandra N. Hankemeier, Thomas Haug, Kenneth He, Sijin Herman, Stephanie Johnson, David Kale, Namrata Larsson, Anders Neumann, Steffen Peters, Kristian Pireddu, Luca Rocca-Serra, Philippe Roger, Pierrick Rueedi, Rico Ruttkies, Cristoph Sadawi, Noureddin Salek, Reza M. Sansone, Susanna-Assunta Schober, Daniel Selivanov, Vitaly Thevenot, Etienne A. van Vliet, Michael Zanetti, Gianluigi Steinbeck, Christoph Kultima, Kim Spjuth, Ola |
Keywords: | Espectrometria de masses Interoperabilitat en xarxes d'ordinadors Programari Mass spectrometry Internetworking (Telecommunication) Computer software |
Issue Date: | 9-Mar-2019 |
Publisher: | Oxford University Press |
Abstract: | Motivation: 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. Results: 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 massspectrometry-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 (https://por tal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. |
Note: | Reproducció del document publicat a: https://doi.org/10.1093/bioinformatics/btz160 |
It is part of: | Bioinformatics, 2019, vol. 35, num. 19, p. 3752-3760 |
URI: | http://hdl.handle.net/2445/171955 |
Related resource: | https://doi.org/10.1093/bioinformatics/btz160 |
ISSN: | 1367-4803 |
Appears in Collections: | Articles publicats en revistes (Bioquímica i Biomedicina Molecular) Publicacions de projectes de recerca finançats per la UE |
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