Interoperable and scalable data analysis with microservices: applications in metabolomics.

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_2E01B5B0EEB9
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Interoperable and scalable data analysis with microservices: applications in metabolomics.
Journal
Bioinformatics
Author(s)
Emami Khoonsari P., Moreno P., Bergmann S., Burman J., Capuccini M., Carone M., Cascante M., de Atauri P., Foguet C., Gonzalez-Beltran A.N., Hankemeier T., Haug K., He S., Herman S., Johnson D., Kale N., Larsson A., Neumann S., Peters K., Pireddu L., Rocca-Serra P., Roger P., Rueedi R., Ruttkies C., Sadawi N., Salek R.M., Sansone S.A., Schober D., Selivanov V., Thévenot E.A., van Vliet M., Zanetti G., Steinbeck C., Kultima K., Spjuth O.
ISSN
1367-4811 (Electronic)
ISSN-L
1367-4803
Publication state
Published
Issued date
01/10/2019
Peer-reviewed
Oui
Volume
35
Number
19
Pages
3752-3760
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
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.
The PhenoMeNal consortium maintains a web portal (https://portal.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.
Supplementary data are available at Bioinformatics online.
Pubmed
Open Access
Yes
Create date
08/04/2019 16:49
Last modification date
15/01/2021 7:08
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