GelGenie: an AI-powered framework for gel electrophoresis image analysis

Matthew Aquilina (), Nathan J. W. Wu, Kiros Kwan, Filip Bušić, James Dodd, Laura Nicolás-Sáenz, Alan O’Callaghan, Peter Bankhead and Katherine E. Dunn ()
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Matthew Aquilina: School of Engineering, University of Edinburgh
Nathan J. W. Wu: School of Engineering, University of Edinburgh
Kiros Kwan: School of Engineering, University of Edinburgh
Filip Bušić: School of Engineering, University of Edinburgh
James Dodd: School of Engineering, University of Edinburgh
Laura Nicolás-Sáenz: University of Edinburgh
Alan O’Callaghan: University of Edinburgh
Peter Bankhead: University of Edinburgh
Katherine E. Dunn: School of Engineering, University of Edinburgh

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-59189-0

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