RibonanzaNet is a deep neural network for predicting chemical reactivity of RNA and RNA foundation model trained on millions of diverse RNA sequences collected through Eterna and other crowdsourced initiatives, informed by top submissions to the Kaggle Ribonanza challenge. Additional fine-tuned models are available for predicting secondary structure, degredation, and experimental dropout.

The source code can be downloaded from https://github.com/DasLab/rnet-inference.

For technical support and bug reports, please contact ribonanza@stanford.edu

If you use RibonanzaNet in your research, please cite: He, S.; Huang, R.; Townley, J.; Kretsch, R. C.; Karagianes, T. G.; Cox, D. B. T.; Blair, H.; Penzar, D.; Vyaltsev, V.; Aristova, E.; Zinkevich, A.; Bakulin, A.; Sohn, H.; Krstevski, D.; Fukui, T.; Tatematsu, F.; Uchida, Y.; Jang, D.; Lee, J. S.; Shieh, R.; Ma, T.; Martynov, E.; Shugaev, M. V.; Bukhari, H. S. T.; Fujikawa, K.; Onodera, K.; Henkel, C.; Ron, S.; Romano, J.; Nicol, J. J.; Nye, G. P.; Wu, Y.; Choe, C.; Reade, W.; Das, R. Ribonanza: deep learning of RNA structure through dual crowdsourcing. bioRxiv (Cold Spring Harbor Laboratory) 2024. https://doi.org/10.1101/2024.02.24.581671.

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