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Approaches for containerized scientific workflows in cloud environments with applications in life science

, , , , , , , , , , , ,
1 Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
2 Department of Information Technology, Uppsala Universitet, Uppsala, Sweden
3 Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
4 Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
5 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom
6 Department of Medical Sciences, Uppsala Universitet, Uppsala, Sweden
DOI
10.7287/peerj.preprints.27141v1
Published
Accepted
Subject Areas
Bioinformatics, Computational Biology
Keywords
containers, scientific workflow, cloud computing
Copyright
© 2018 Spjuth et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Spjuth O, Capuccini M, Carone M, Larsson A, Schaal W, Novella J, Di Tommaso P, Notredame C, Moreno P, Khoonsari PE, Herman S, Kultima K, Lampa S. 2018. Approaches for containerized scientific workflows in cloud environments with applications in life science. PeerJ Preprints 6:e27141v1

Abstract

Containers are gaining popularity in life science research as they encompass all dependencies of provisioned tools and simplifies software installations for end users, as well as offering a form of isolation between processes. Scientific workflows are ideal to chain containers into data analysis pipelines to sustain reproducible science. In this manuscript we review the different approaches to use containers inside the workflow tools Nextflow, Galaxy, Pachyderm, Luigi, and SciPipe when deployed in cloud environments. A particular focus is placed on the workflow tool’s interaction with the Kubernetes container orchestration framework.

Author Comment

This is a preprint submission to PeerJ Preprints.

Additional Information

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Ola Spjuth analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Marco Capuccini analyzed the data, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.

Matteo Carone analyzed the data, contributed reagents/materials/analysis tools, approved the final draft.

Anders Larsson analyzed the data, contributed reagents/materials/analysis tools, approved the final draft.

Wesley Schaal analyzed the data, contributed reagents/materials/analysis tools, approved the final draft.

Jon Novella analyzed the data, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.

Paolo Di Tommaso analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Cedric Notredame analyzed the data, approved the final draft.

Pablo Moreno analyzed the data, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.

Payam E Khoonsari analyzed the data, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.

Stephanie Herman analyzed the data, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.

Kim Kultima analyzed the data, authored or reviewed drafts of the paper, approved the final draft.

Samuel Lampa analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Data Deposition

The following information was supplied regarding data availability:

The research in this article did not generate any data or code but relied on previously collected knowledge.

Funding

This research was supported by The European Commission’s Horizon 2020 programme funded under grant agreement number 654241 (PhenoMeNal) and grant agreement number 731075 (OpenRiskNet), the Swedish Foundation for Strategic Research, the Swedish Research Council FORMAS, the Swedish e-Science Research Centre (SeRC) and Åke Wiberg Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Approaches for containerized scientific workflows in cloud environments with applications in life science
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