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Bioconda: sustainable and comprehensive software distribution for the life sciences

Nature Methods volume 15, pages 475–476 (2018)Cite this article

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To the Editor: Bioinformatics software comes in a variety of programming languages and requires diverse installation methods. This heterogeneity makes management of a software stack complicated, error-prone, and inordinately time-consuming. Whereas software deployment has traditionally been handled by administrators, ensuring the reproducibility of data analyses1,2,3 requires that the researcher be able to maintain full control of the software environment, rapidly modify it without administrative privileges, and reproduce the same software stack on different machines.

The Conda package manager (https://conda.io) has become an increasingly popular means to overcome these challenges for all major operating systems. Conda normalizes software installations across language ecosystems by describing each software with a human readable ‘recipe’ that defines meta-information and dependencies, as well as a simple ‘build script’ that performs the steps necessary to build and install the software. Conda builds software packages in an isolated environment, transforming them into relocatable binaries. Importantly, it obviates reliance on system-wide administration privileges by allowing users to generate isolated software environments in which they can manage software versions by project, without generating incompatibilities and side-effects (Supplementary Results). These environments support reproducibility, as they can be rapidly exchanged via files that describe their installation state. Conda is tightly integrated into popular solutions for reproducible data analysis such as Galaxy4, bcbio-nextgen (https://github.com/chapmanb/bcbio-nextgen), and Snakemake5. To further enhance reproducibility guarantees, Conda can be combined with container or virtual machine-based approaches and archive facilities such as Zenodo (Supplementary Results). Finally, although Conda provides many commonly used packages by default, it also allows users to optionally include additional, community-managed repositories of packages (termed channels).

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Fig. 1: Package numbers and usage.

References

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Acknowledgements

We thank all contributors, the conda-forge team, and Anaconda Inc. for excellent cooperation. Further, we thank Travis CI (https://travis-ci.com) and Circle CI (https://circleci.com) for providing free Linux and macOS computing capacity. Finally, we thank ELIXIR (https://www.elixir-europe.org) for constant support and donation of staff. This work was supported by the Intramural Program of the National Institute of Diabetes and Digestive and Kidney Diseases, US National Institutes of Health (R.D.), the Netherlands Organisation for Scientific Research (NWO) (VENI grant 016.Veni.173.076 to J.K.), the German Research Foundation (SFB 876 to J.K.), and the NYU Abu Dhabi Research Institute for the NYU Abu Dhabi Center for Genomics and Systems Biology, program number CGSB1 (grant to J.R. and A. Yousif).

Author information

Author notes

  1. These authors contributed equally: Björn Grüning and Ryan Dale.

  2. A full list of authors and affiliations is available as Supplementary Table 1.

Authors and Affiliations

  1. Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany

    Björn Grüning

  2. Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, US National Institutes of Health, Bethesda, MD, USA

    Ryan Dale

  3. Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden

    Andreas Sjödin

  4. Department of Chemistry, Computational Life Science Cluster (CLiC), Umeå University, Umeå, Sweden

    Andreas Sjödin

  5. Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Brad A. Chapman

  6. Center for Genomics and Systems Biology, Genomics Core,, NYU Abu Dhabi,, Abu Dhabi,, United Arab Emirates

    Jillian Rowe

  7. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA

    Christopher H. Tomkins-Tinch

  8. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Christopher H. Tomkins-Tinch

  9. Laboratory of Bioinformatics and Computational Biology, A. C. Camargo Cancer Center, São Paulo, Brazil

    Renan Valieris

  10. Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg–Essen, Essen, Germany

    Johannes Köster

  11. Medical Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Johannes Köster

Authors
  1. Björn Grüning
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  2. Ryan Dale
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  4. Brad A. Chapman
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  5. Jillian Rowe
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  6. Christopher H. Tomkins-Tinch
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  7. Renan Valieris
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  8. Johannes Köster
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Consortia

The Bioconda Team

Contributions

J.K. and R.D. wrote the manuscript and conducted the data analysis. K. Beauchamp, C. Brueffer, B.A.C., F. Eggenhofer, B.G., E. Pruesse, M. Raden, J.R., D. Ryan, I. Shlyakter, A.S., C.H.T.-T., and R.V. (in alphabetical order) contributed to writing of the manuscript. D.A. Søndergaard supervised student programmers on writing Conda package recipes and maintaining the connection with ELIXIR. All other members of the Bioconda Team contributed or maintained recipes (author order was determined by the number of commits in October 2017).

Corresponding author

Correspondence to Johannes Köster.

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The authors declare no competing interests.

Supplementary information

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Supplementary Results

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Supplementary Table 1

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Grüning, B., Dale, R., Sjödin, A. et al. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods 15, 475–476 (2018). https://doi.org/10.1038/s41592-018-0046-7

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