Scientists have harnessed the power of AI in a new tool that promises to speed up analysis of data from the widely used gel electrophoresis technique.
Despite the unprecedented advances in image processing in recent years, software methods for the analysis of gel images have remained essentially unchanged for decades. Most, if not all, image analysis approaches involve either a manual or semi-automatic process of digitally carving out lanes and bands from an image before summing the intensity of the pixels in each band. This process is tedious, prone to user error and relies on assumptions that make it difficult to address bands with irregular shapes or curved trajectories.
However, by framing the extraction and analysis of gel bands from an image as an AI task, a machine learning model can automate most of the tedious steps in the analysis process, while also eliminating biases and assumptions inherent to manual approaches.
In this study, the researchers set up a dataset of over 500 human-labeled gel images featuring a range of common experimental scenarios. They used this dataset to train a lightweight neural network to accurately identify bands from images. The result was a highly effective model capable of identifying bands regardless of their quality, background intensity and even the presence of unexpected discontinuities such as torn gel chunks.
Additionally, the approach was able to produce quantitation results that matched or surpassed those generated using conventional tools.
The team also developed GelGenie, an open-source graphical application that allows users to extract bands from gel images on their own devices, with no expert knowledge or experience required. The entire dataset, model weights and scripting framework have also been released to allow others to use or fine-tune the models for more specialized applications or their own custom pipelines.
Information courtesy of University of Edinburgh