2026-04-13T11:44:56Zhttps://recercat.cat/oai/requestoai:recercat.cat:2445/1719552025-11-19T20:40:51Zcom_2072_1057col_2072_478781col_2072_478917
urn:hdl:2445/171955
Interoperable and scalable data analysis with microservices: applications in metabolomics
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
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
Espectrometria de masses
Interoperabilitat en xarxes d'ordinadors
Programari
Mass spectrometry
Internetworking (Telecommunication)
Computer software
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.
2020-11-11T11:23:28Z
2020-11-11T11:23:28Z
2019-03-09
2020-11-11T11:23:28Z
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Reproducció del document publicat a: https://doi.org/10.1093/bioinformatics/btz160
Bioinformatics, 2019, vol. 35, num. 19, p. 3752-3760
https://doi.org/10.1093/bioinformatics/btz160
info:eu-repo/grantAgreement/EC/H2020/654241/EU//PhenoMeNal
cc-by (c) Emami Khoonsari, Payam et al., 2019
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
Oxford University Press
Articles publicats en revistes (Bioquímica i Biomedicina Molecular)