If you have any feedback on GelGenie or any requests/ideas for new features please let us know - we’re excited to see how GelGenie will fit into your workflow! We’re really grateful for all those who’ve already provided feedback or supported the project throughout the early stages!

GelGenie has already been downloaded 100+ times since the preprint was released and we’ve seen lots of enthusiasm from researchers at our institutions (including winning the ‘Most Innovative Project’ prize at DFCI’s annual Cancer Biology retreat!) and at #FNANO 2025.

- Export directly to csv or to an auto-generated bar graph
- Use scripting to analyse multiple images in one go
- Make freeform manual edits if necessary
- and much more!

Just as a reminder, with GelGenie you can:

- One-click extract bands (even wacky ones!) from gels in various imaging conditions, resolutions, contrasts, etc.
- Collate your data, apply background correction and normalize the results automatically

GitHub - mattaq31/GelGenie: Open-Source package for segmentation and analysis of gel electrophoresis images using machine learning. Both a python framework for training/running models and a QuPath GU... Open-Source package for segmentation and analysis of gel electrophoresis images using machine learning. Both a python framework for training/running models and a QuPath GUI extension are available...

Download it now (available for Mac, Windows or Linux) from here: github.com/mattaq31/Gel...

A model usable within ImageJ is now also available on BioImage.IO (docs here: github.com/mattaq31/Gel...)

#machinelearning #AI #segmentation #imageanalysis #gelelectrophoresis #gelquantification #qupath

GelGenie has finally been published in Nature Communications (DOI: doi.org/10.1038/s414...)!

GelGenie is a free, open-source app for the one-click extraction of band data from gel images using AI, all bundled within a cross-platform GUI that can be integrated in your workflow immediately. 🧬

Would love to be added too, thanks!