GelGenie: an AI-powered framework for gel electrophoresis image analysis
Gel electrophoresis is a ubiquitous laboratory method for the separation and semi-quantitative analysis of biomolecules. However, gel image analysis principles have barely advanced for decades, in stark contrast to other fields where AI has revolutionised data processing. Here, we show that an AI-based system can automatically identify gel bands in seconds for a wide range of experimental conditions, surpassing the capabilities of current software in both ease-of-use and versatility. We use a dataset containing 500+ images of manually-labelled gels to train various U-Nets to accurately identify bands through segmentation, i.e. classifying pixels as 'band' or 'background'. When applied to gel electrophoresis data from other laboratories, our system generates results that quantitatively match those of the original authors. We have publicly released our models through GelGenie, an open-source application that allows users to extract bands from gel images on their own devices, with no expert knowledge or experience required.
Authors
- Matthew Aquilina (ORCID: https://orcid.org/0000-0002-4039-1398)
- Laura Nicolás-Sáenz (ORCID: https://orcid.org/0000-0003-4731-8262)
- Alan O’Callaghan (ORCID: https://orcid.org/0000-0003-4817-6171)
- Peter Bankhead (ORCID: https://orcid.org/0000-0003-4851-8813)
- N. Wu (ORCID: https://orcid.org/0000-0003-2870-5419)
- Katherine E. Dunn (ORCID: https://orcid.org/0000-0002-5068-4354)
- Filip Bušić
- Jonathan A. Dodd
- Kiros Kwan
Institutions
- Harvard University (US)
- Edinburgh Cancer Research (GB)
- Heriot-Watt University (GB)
- Dana-Farber Cancer Institute (US)
- Dana-Farber/Harvard Cancer Center (US)
- University of Edinburgh (GB)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-05-05
- DOI
- https://doi.org/10.1038/s41467-025-59189-0
- Citations
- 6
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 9.80
Funders
- Bristol-Myers Squibb
- Harvard University
- State University of New York
- Wellcome Trust
- University of Edinburgh
- Tsinghua University
- Technische Universität München
- University at Albany
- Hansjörg Wyss Institute for Biologically Inspired Engineering, Harvard University
- Medical Research Council
- School of Informatics, University of Edinburgh
- Institute of Genetics