Demystifying HPC: Empowering life scientists in HPC use for BioImage analysis
Abstract High‐performance computing (HPC) has become an essential tool for BioImage analysis as recent advances in microscopy are creating increasingly large and complex datasets, the analysis of which is beyond the capabilities of personal computers. Although there is a need for wet lab scientists to utilise HPC resources, HPC training is commonly designed for scientists with prior computational experience and who are comfortable with computers, as opposed to those with limited computational experience, as is often the case with wet lab scientists. We held a 1‐day workshop bringing together wet lab scientists, BioImage analysts, Bioinformaticians, and HPC engineers to help determine what motivated the wet lab scientists to learn HPC and what skills in HPC they would need in order to do their own research. Based on this workshop, we created an 8‐h curriculum to teach basic HPC skills to wet lab scientists, so that they can do their own analysis without being overwhelmed with specialised technical details. This curriculum was trialled at The Francis Crick Institute to positive feedback. The curriculum framework also includes three software tracks, CellProfiler, Fiji and Python, which use familiar BioImage analysis workflows to reinforce HPC concepts. We present this curriculum here as an adaptable framework for other institutions to design a course to empower wet lab scientists to use HPC resources.
Authors
- Stefania Marcotti (ORCID: https://orcid.org/0000-0002-2877-0133)
- Todd L. Fallesen (ORCID: https://orcid.org/0000-0002-8700-3700)
- Courtney Hopf
- Kimberly Meechan (ORCID: https://orcid.org/0000-0003-4939-4170)
- Camille Charoy (ORCID: https://orcid.org/0000-0003-1904-4612)
- Ruaridh M. Gollifer (ORCID: https://orcid.org/0000-0001-9319-936X)
- Camilla Harris
- John Roche
Institutions
- The Francis Crick Institute (GB)
- University College London (GB)
Publication Details
- Journal
- Journal of Microscopy
- Published
- 2026-09-24
- DOI
- https://doi.org/10.1111/jmi.70178
- Primary Topic
- Genetics, Bioinformatics, and Biomedical Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00