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Bioinformatics. 2019 Mar 9. pii: btz160. doi: 10.1093/bioinformatics/btz160. [Epub ahead of print]

Interoperable and scalable data analysis with microservices: Applications in Metabolomics.

Author information

1
Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden.
2
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom.
3
Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
4
Swiss Institute of Bioinformatics, Lausanne, Switzerland.
5
Department of Neuroscience, Uppsala University, Uppsala, Sweden.
6
Department of Information Technology, Uppsala, Sweden.
7
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
8
Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain.
9
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and metabolomics node at INB-Bioinfarmatics Platform, Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
10
Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
11
Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
12
National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden.
13
Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.
14
German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany.
15
CRS4: Center for Advanced Studies, Research and Development in Sardinia, Pula, Italy.
16
CEA, LIST, Laboratory for Data Analysis and Systems' Intelligence, MetaboHUB, Gif-sur-Yvette, France.
17
Faculty of Medicine, Department of Surgery & Cancer, Imperial College London, London, United Kingdom.
18
International Agency for Research on Cancer 150 cours Albert Thomas 69372 Lyon CEDEX 08, France.
19
Friedrich-Schiller-University, Jena, Germany.

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 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.

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