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Packaging and containerization of computational methods

Nature Protocols (2024)Cite this article

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Abstract

Methods for analyzing the full complement of a biomolecule type, e.g., proteomics or metabolomics, generate large amounts of complex data. The software tools used to analyze omics data have reshaped the landscape of modern biology and become an essential component of biomedical research. These tools are themselves quite complex and often require the installation of other supporting software, libraries and/or databases. A researcher may also be using multiple different tools that require different versions of the same supporting materials. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging and containerization are different approaches to satisfy this need by delivering omics tools already wrapped in additional software that makes the tools easier to install and use. In this systematic review, we describe and compare the features of prominent packaging and containerization platforms. We outline the challenges, advantages and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers and system administrators. We also propose principles to make the distribution of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research.

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Fig. 1: An overview of packaging, virtualization and containerization platforms for addressing challenges of omics software installation.

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Acknowledgements

O.M. and SAFARI Research Group members (M.A., C.F. and N.A.) are supported by funding from Intel, VMware, Semiconductor Research Corporation, the National Institutes of Health and the Eidgenössische Technische Hochschule (ETH) Future Computing Laboratory. S.M. and R.A. are supported by the National Science Foundation grants 2041984 and 2316223 and National Institutes of Health grant R01AI173172. We thank M. Sarahan (Principal Software Engineer, Manager at Anaconda, Inc.) for our useful discussion at the AnacondaCON 2019 conference. We thank Dr. Mosqueiro for the fruitful discussion and feedback.

Author information

Author notes

  1. These authors jointly supervised this work: Malak S. Abedalthagafi, Onur Mutlu, Serghei Mangul.

Authors and Affiliations

  1. Department of Information Technology and Electrical Engineering, ETH Zürich, Zurich, Switzerland

    Mohammed Alser, Nour Almadhoun, Can Firtina & Onur Mutlu

  2. Department of Computer Science, Munster Technological University, Cork, Ireland

    Brendan Lawlor

  3. Department of Biological Sciences, Munster Technological University, Cork, Ireland

    Brendan Lawlor

  4. Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA

    Richard J. Abdill

  5. Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA

    Sharon Waymost, Nathan LaPierre, Varuni Sarwal & Eleazar Eskin

  6. Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA

    Ram Ayyala & Serghei Mangul

  7. Titus Family Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA

    Ram Ayyala, Jaqueline Brito & Serghei Mangul

  8. Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA

    Neha Rajkumar

  9. Department of Human Genetics, University of Chicago, Chicago, IL, USA

    Nathan LaPierre

  10. Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA

    André M. Ribeiro-dos-Santos

  11. Center for Advanced Research Computing, University of Southern California, Los Angeles, CA, USA

    Tomasz Osinski, Derek Strong & Byoung-Do (B.D) Kim

  12. Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Eleazar Eskin

  13. Department of Human Genetics, University of California, Los Angeles, CA, USA

    Eleazar Eskin

  14. Office of Advanced Research Computing, University of California, Los Angeles, CA, USA

    Qiyang Hu

  15. Department of Pathology & Laboratory Medicine, Emory University Hospital, Atlanta, GA, USA

    Malak S. Abedalthagafi

  16. King Salman Center for Disability Research, Riyadh, Saudi Arabia

    Malak S. Abedalthagafi

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  1. Mohammed Alser
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Contributions

M.A. and S.M. led the project. S.M. conceived of the presented idea. M.A., R.A., N.R., S.W., N.A. and V.S. collected data. M.A., S.W. and N.A. produced the figures. M.A., B.L., R.J.A., S.W., R.A., D.S., T.O., BD.K., M.S.A, O.M. and S.M. wrote, reviewed and edited the manuscript. All authors discussed the text and commented on the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Serghei Mangul.

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Nature Protocols thanks Bernard Pope and Devon Ryan for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 General overview of the installation process for omics software tools.

When biomedical researchers need to use omics software tools and reproduce reported results, they first need to locate the version number and web address of each omics tool using the published research paper and supplementary information. They can then download the omics tools, determine each tool’s dependencies using information provided by the tool’s developers, and try to install the tools on their personal computer or HPC cluster. If the tool is successfully installed, the researchers download the relevant omics data and apply the tools as needed. However, even when the hardware resource requirements (CPU type, memory capacity, and storage) of each tool are met, some omics tools are likely to fail when exact reproduction is attempted because of installation challenges.

Extended Data Fig. 2 Development timeline and brief description of popular (a) package managers and (b) containers.

Each tool is described with key information regarding its functionality, purpose, and supported operating system. In addition to the surveyed package managers and containers, the first package manager, PMS, and the first container, FreeBSD Jail, are shown.

Extended Data Fig. 3 Standard workflow for installing software with a package manager.

The user, usually an administrator, asks the package manager to install a specific piece of software. If the software is not already installed, the package manager fetches the appropriate package from a repository. If any of the dependencies are not already installed, the package manager retrieves the dependency’s package from the repository and starts the installation procedure for that package. Once all the dependencies are installed, the initially requested software is installed. The package manager often goes through several iterations of this process, because every dependency can have its own list of dependencies, in which case each of the dependency’s dependencies must be verified through the same process.

Extended Data Fig. 4 Standard workflow for running software with containerization.

The user asks to install a specific container image. If the container image is available locally, then the user can run it directly through the container engine. Potential dependencies are already handled without any intervention from the user. If the software image is not available locally, the appropriate image must be fetched from a repository.

Supplementary information

Supplementary Information

Supplementary Methods for Tables 1–3.

Reporting Summary

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Alser, M., Lawlor, B., Abdill, R.J. et al. Packaging and containerization of computational methods. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-00986-0

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