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

被引：0

作者：

Matthew Aquilina ^{[1
]}

Nathan J. W. Wu ^{[2
]}

Kiros Kwan ^{[6
]}

Filip Bušić ^{[7
]}

James Dodd ^{[8
]}

Laura Nicolás-Sáenz ^{[1
]}

Alan O’Callaghan ^{[9
]}

Peter Bankhead ^{[1
]}

Katherine E. Dunn ^{[1
]}

机构：

[1] School of Engineering,Institute for Bioengineering

[2] University of Edinburgh,Deanery of Molecular, Genetic and Population Health Sciences

[3] University of Edinburgh,Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer

[4] University of Edinburgh,Edinburgh Pathology, Institute of Genetics and Cancer

[5] University of Edinburgh,CRUK Scotland Centre, Institute of Genetics and Cancer

[6] University of Edinburgh,Department of Cancer Biology

[7] Dana-Farber Cancer Institute,Wyss Institute for Biological Engineering

[8] Harvard University,Department of Biological Chemistry and Molecular Pharmacology

[9] Harvard Medical School,Institute of Biological Chemistry, Biophysics and Bioengineering, School of Engineering and Physical Sciences

[10] Heriot-Watt University,undefined

来源：

Nature Communications | / 16卷 / 1期

关键词：

D O I：

10.1038/s41467-025-59189-0

中图分类号：

学科分类号：

摘要：

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.

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