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The nf-core framework for community-curated bioinformatics pipelines

Nature Biotechnology volume 38, pages 276–278 (2020)Cite this article

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Being able to reproduce scientific results is the central tenet of the scientific method. However, moving toward FAIR (findable, accessible, interoperable and reusable) research methods1 in data-driven science is complex2,3. Central repositories, such as bio.tools4, omictools5 and the Galaxy toolshed6, make it possible to find existing pipelines and their associated tools. However, it is still notoriously challenging to develop analysis pipelines that are fully reproducible and interoperable across multiple systems and institutions — primarily because of differences in hardware, operating systems and software versions.

Although the recommended guidelines for some analysis pipelines have become standardized (for example, GATK best practices7), the actual implementations are usually developed on a case-by-case basis. As such, there is often little incentive to test, document and implement pipelines in a way that permits their reuse by other researchers. This can hamper sustainable sharing of data and tools, and results in a proliferation of heterogeneous analysis pipelines, making it difficult for newcomers to find what they need to address a specific analysis question.

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Fig. 1: Main concepts of nf-core.

Data availability

No datasets were generated or analyzed during this study.

Code availability

The source code for the nf-core framework and all nf-core pipelines is publicly available at https://github.com/nf-core/. Where applicable, Zenodo DOIs are available on the respective pipeline repositories.

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Acknowledgements

The authors would also like to thank the following people for their contributions: F. Bonath, O. Contreras-López, S. Haglund, R. Hammarén, A. Jemt, R. A. Olsen, S. Paneerselvam, M. Proks, J. Wan, C. Wang, J. Westholm and D. Yuen (Science for Life Laboratory, Stockholm, Sweden); C. C. Shih (A*STAR Genome Institute of Singapore); M. Hoeppner (Institut für Klinische Molekularbiologie, Kiel, Germany); V. Malladi (University of Texas Southwestern Medical Center, Dallas); A. Duncan (Ontario Institute for Cancer Research); H. Gourlé (Swedish University of Agricultural Sciences, Uppsala); G. Gabernet, S. Heumos, T. Koch, C. Mohr and D. Straub (Quantitative Biology Centre, Tübingen, Germany); G. Kelly (Francis Crick Institute London); Q. Zhao (Sun Yat-sen University Cancer Center, Guangzhou, China); and E. Floden (Comparative Bioinformatics Laboratory Centre for Genomic Regulation (CRG), Barcelona, Spain). A.P., S.F. and S.N. acknowledge funding from the Deutsche Forschungsgemeinschaft (German Research Foundation, DFG; core facilities initiative, KO-2313/6-1 and KO- 2313-2, Institutional Strategy of the University of Tübingen, ZUK 63). A.P. and S.N. acknowledge funding by the Sonderforschungsbereich SFB/TR 209 “Liver cancer” of the DFG. S.N. acknowledges funding from the DFG Project ID 398967434 – TRR 261 and the DFG im Rahmen der Exzellenzstrategie des Bundes und der Länder EXC 2180 – 390900677 and EXC 2124 – 390838134. P.D. acknowledges the support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme / Generalitat de Catalunya. P.D.’s work is supported by the European Union’s Horizon 2020 research and innovation program under grant agreement 815668 (BovReg), the Spanish Ministry of Economy, Industry and Competitiveness (BFU2017-88264-P) and the Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (2017 SGR 447).

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Author notes

  1. These authors contributed equally: Philip A. Ewels, Alexander Peltzer.

Authors and Affiliations

  1. Science for Life Laboratory (SciLifeLab), Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden

    Philip A. Ewels

  2. Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany

    Alexander Peltzer, Sven Fillinger & Sven Nahnsen

  3. Bioinformatics and Biostatistics, The Francis Crick Institute, London, UK

    Harshil Patel

  4. Science for Life Laboratory (SciLifeLab), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Royal Institute of Technology, Stockholm, Sweden

    Johannes Alneberg

  5. Computational & Systems Biology, Genome Institute of Singapore, Singapore, Singapore

    Andreas Wilm

  6. Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden

    Maxime Ulysse Garcia

  7. Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain

    Paolo Di Tommaso

  8. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    Paolo Di Tommaso

Authors
  1. Philip A. Ewels
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  4. Harshil Patel
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  8. Paolo Di Tommaso
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  9. Sven Nahnsen
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Corresponding author

Correspondence to Sven Nahnsen.

Supplementary information

Supplementary Information

Supplementary Note, Supplementary Figs. 1–4 and Supplementary Tables 1 and 2

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Ewels, P.A., Peltzer, A., Fillinger, S. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol 38, 276–278 (2020). https://doi.org/10.1038/s41587-020-0439-x

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