Detection and Analysis of Electrophoresis Gels Using YOLO.

Authors

Clenivaldo Pires da Silva, Mateus Filipe T Carvalho, Yandre M G Costa, Franklin Cesar Flores, Julio Cesar Polonio, Claudete Aparecida Mangolim

Published in

IEEE transactions on computational biology and bioinformatics. Volume PP. Sep 03, 2025. Epub Sep 03, 2025.

Abstract

Electrophoresis is essential in molecular biology, providing critical data for genetic research. However, manual interpretation of DNA band patterns in electrophoresis images, particularly for dominant molecular markers, remains challenging and prone to errors. This study applied YOLO (You Only Look Once), an advanced object detection model, to automate electrophoresis gel analysis. Experiments were conducted using a dataset originally composed of 246 manually labeled electrophoresis images, later expanded to 1,200 images through augmentation. The dataset will also be made available as a contribution of this work. Several YOLO models (v5 to v12) were trained over 2,000 epochs. Processing times ranged from 18 to 24 hours, and average precision (mAP) scores across versions were 92.2%, 94.6%, 94.3%, 94.8%, 94.6%, 93.8%, 95.0 and 90,9%, respectively, with YOLO v11 achieving the highest mAP at 95.0%. Additionally, we developed the 'Gel Analysis APP', a cross-platform tool compatible with Windows, Linux, and MacOS, offering an intuitive interface. This software automates electrophoresis gel reading, generating similarity matrices based on molecular marker presence or absence, simplifying a commonly manual task in genetics labs.

PMID:
40902059
Bibliographic data and abstract were imported from PubMed on 04 Sep 2025.

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