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Peer-reviewedCC BYBioinformatics (Oxford, England)

hpci improves reproducibility of computational biology research by integrating Continuous Integration with High Performance Computing clusters.

  • Ignatius Jeppe Menzies1
  • Eric Urng1
  • George Bouras2,3
  • Robert Moss4
  • John Reeves5
  • Leonard D Goldstein1,6
6 affiliations
  1. 1Data Science Platform, Garvan Institute of Medical Research, Darlinghurst, New South Wales 2010, Australia.
  2. 2School of Medicine, College of Health, Adelaide University, Adelaide, South Australia 5011, Australia.
  3. 3Department of Surgery-Otolaryngology Head and Neck Surgery, Central Adelaide Local Health Network, Adelaide, South Australia 5070, Australia.
  4. 4Infectious Disease Dynamics Unit, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria 3010, Australia.
  5. 5Swarbrick Lab, Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, New South Wales 2010, Australia.
  6. 6St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, New South Wales 2010, Australia.
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Abstract

Motivation

The reproducibility crisis in bioinformatics and computational biology can be mitigated by using Continuous Integration (CI), a proven software engineering practice, to automate the testing of analysis pipelines and models. However, the adoption of CI in bioinformatics is limited as existing solutions are typically tightly linked to specific CI platforms, specific HPC job schedulers, or require actions from HPC administrators.Results

We developed hpci, a platform-agnostic, open-source utility that integrates CI platforms (e.g. GitHub Actions, GitLab CI) with common HPC job schedulers (Slurm, PBS). hpci operates as a standard unprivileged user via SSH, and requires no cluster-side installation. It terminates on the CI runner using the exact same exit code generated by the HPC job, providing feedback to researchers and preventing downstream CI steps if there are errors. We demonstrate hpci's utility across five diverse computational biology workflows on HPC, highlighting the value of automated testing and continuous analysis for verifying reproducibility in bioinformatics workflows.Availability

hpci is implemented in Haskell and is available under the GPL-3.0 licence. Source code and binaries can be downloaded from https://github.com/Garvan-Data-Science-Platform/hpci.Source code is archived in Software Heritage via Zenodo (https://zenodo.org/records/19587650).

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Cite

BibTeXRIS
BibTeX
@article{menzies2026hpci,
  title = {{hpci improves reproducibility of computational biology research by integrating Continuous Integration with High Performance Computing clusters.}},
  author = {Ignatius Jeppe Menzies and Eric Urng and George Bouras and Robert Moss and John Reeves and Leonard D Goldstein},
  journal = {Bioinformatics (Oxford, England)},
  year = {2026},
  month = oct,
  doi = {10.1093/bioinformatics/btag720},
  url = {https://europepmc.org/article/MED/42853652},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - hpci improves reproducibility of computational biology research by integrating Continuous Integration with High Performance Computing clusters.
AU  - Ignatius Jeppe Menzies
AU  - Eric Urng
AU  - George Bouras
AU  - Robert Moss
AU  - John Reeves
AU  - Leonard D Goldstein
PY  - 2026
DA  - 2026/10/01
JO  - Bioinformatics (Oxford, England)
DO  - 10.1093/bioinformatics/btag720
UR  - https://europepmc.org/article/MED/42853652
AB  - Motivation  The reproducibility crisis in bioinformatics and computational biology can be mitigated by using Continuous Integration (CI), a proven software engineering practice, to automate the testing of analysis pipelines and models. However, the adoption of CI in bioinformatics is limited as existing solutions are typically tightly linked to specific CI platforms, specific HPC job schedulers, or require actions from HPC administrators.Results  We developed hpci, a platform-agnostic, open-source utility that integrates CI platforms (e.g. GitHub Actions, GitLab CI) with common HPC job schedulers (Slurm, PBS). hpci operates as a standard unprivileged user via SSH, and requires no cluster-side installation. It terminates on the CI runner using the exact same exit code generated by the HPC job, providing feedback to researchers and preventing downstream CI steps if there are errors. We demonstrate hpci's utility across five diverse computational biology workflows on HPC, highlighting the value of automated testing and continuous analysis for verifying reproducibility in bioinformatics workflows.Availability  hpci is implemented in Haskell and is available under the GPL-3.0 licence. Source code and binaries can be downloaded from https://github.com/Garvan-Data-Science-Platform/hpci.Source code is archived in Software Heritage via Zenodo (https://zenodo.org/records/19587650).
SN  - 1367-4803
SN  - 1367-4811
ER  -