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Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers

Abstract

The rapid growth of high-throughput technologies has transformed biomedical research. With the increasing amount and complexity of data, scalability and reproducibility have become essential not just for experiments, but also for computational analysis. However, transforming data into information involves running a large number of tools, optimizing parameters, and integrating dynamically changing reference data. Workflow managers were developed in response to such challenges. They simplify pipeline development, optimize resource usage, handle software installation and versions, and run on different compute platforms, enabling workflow portability and sharing. In this Perspective, we highlight key features of workflow managers, compare commonly used approaches for bioinformatics workflows, and provide a guide for computational and noncomputational users. We outline community-curated pipeline initiatives that enable novice and experienced users to perform complex, best-practice analyses without having to manually assemble workflows. In sum, we illustrate how workflow managers contribute to making computational analysis in biomedical research shareable, scalable, and reproducible.

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Fig. 1: Overview of bioinformatics analysis workflows using an example of transcript expression quantification.

Code availability

Minimal example workflows and links to documentation are available under https://github.com/GoekeLab/bioinformatics-workflows.

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Acknowledgements

J.G. is supported by funding from the Agency for Science, Technology, and Research (ASTAR), Singapore, and by the Singapore Ministry of Health’s National Medical Research Council under its Individual Research Grant funding scheme. L.W. was supported by the Singapore International Pre-Graduate Award (SIPGA) from A*STAR and the New Colombo Plan Scholarship from the Australian Department of Foreign Affairs and Trade. We thank B. Grüning for helpful comments and suggestions on this manuscript. We would like to thank R. Patro for contributing a test dataset for the example workflow implementations. We thank M. van den Beek for contributing the Galaxy workflow to the GitHub repository. We thank J. Köster for contributing the Snakemake workflow to the GitHub repository. We thank P. Di Tommaso for contributing the Nextflow workflow to the GitHub repository. We thank S. Lampa for contributing the SciPipe workflow to the GitHub repository. We thank J.H. Gálvez López, P.-O. Quirion, E. Henrion, and M. Bourgey for contributing the GenPipes workflow to the GitHub repository. We thank A. Novak, B. Paten, L. Blauvelt, and L. Koziol for contributing the Toil workflow to the GitHub repository. We thank S. Sadedin for contributing the Bpipe workflow to the GitHub repository. We thank S. Sadedin for contributing the Bpipe workflow to the GitHub repository.

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L.W., A.W., and J.G. planned the manuscript. L.W. and J.G. wrote the first draft. L.W., A.W., and J.G. wrote and revised the final manuscript.

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Correspondence to Jonathan Göke.

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A.W. is an employee of ImmunoScape Pte Ltd. L.W. and J.G. declare no competing interests.

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Peer review information Nature Methods thanks Johannes Köster, Yasset Perez-Riverol, Anton Nekrutenko, and Paolo Di Tommaso for their contribution to the peer review of this work. Lin Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary Table 1

Overview of workflow managers for bioinformatics.

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Wratten, L., Wilm, A. & Göke, J. Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers. Nat Methods 18, 1161–1168 (2021). https://doi.org/10.1038/s41592-021-01254-9

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