Interoperable and scalable data analysis with microservices: applications in metabolomics
| Autor: | Namrata Kale, Sven Bergmann, Philippe Rocca-Serra, Kenneth Haug, Kristian Peters, Gianluigi Zanetti, Ola Spjuth, Kim Kultima, Christoph Ruttkies, Etienne A. Thévenot, David Johnson, Marco Capuccini, Carles Foguet, Payam Emami Khoonsari, Rico Rueedi, Anders Larsson, Pedro de Atauri, Vitaly A. Selivanov, Pierrick Roger, Pablo Moreno, Luca Pireddu, Noureddin Sadawi, Christoph Steinbeck, Sijin He, Marta Cascante, Stephanie Herman, Susanna-Assunta Sansone, Michael van Vliet, Daniel Schober, Thomas Hankemeier, Matteo Carone, Joachim Burman, Steffen Neumann, Reza M. Salek, Alejandra Gonzalez-Beltran |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Data Analysis
Statistics and Probability Source code Programari Bioinformatics Computer science media_common.quotation_subject Distributed computing Interoperability Microservices kubernetes Biochemistry Internetworking (Telecommunication) Field (computer science) Workflow 03 medical and health sciences microservices 0302 clinical medicine Software Interoperabilitat en xarxes d'ordinadors Metabolomics Computer software Molecular Biology e-infrastructure media_common 030304 developmental biology Bioinformatics (Computational Biology) 0303 health sciences Docker Mass spectrometry business.industry Systems Biology Computational Biology container Original Papers metabolomics Computer Science Applications Computational Mathematics Espectrometria de masses Computational Theory and Mathematics Scalability Container (abstract data type) Bioinformatik (beräkningsbiologi) Software engineering business 030217 neurology & neurosurgery |
| Zdroj: | Bioinformatics, vol. 35, no. 19, pp. 3752-3760 Dipòsit Digital de la UB Universidad de Barcelona Bioinformatics, 35(19), 3752-3760 Bioinformatics |
| Popis: | 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 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 (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 information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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