About
RNA-FM (RNA Foundation Model) is a state-of-the-art pretrained language model for RNA sequences, serving as the foundation for an integrated RNA research ecosystem. Trained on 23+ million non-coding RNA (ncRNA) sequences via self-supervised learning, RNA-FM extracts comprehensive structural and functional information from RNA sequences without relying on experimental labels. mRNA‑FM is a direct extension of RNA-FM, trained exclusively on 45 million mRNA coding sequences (CDS). It is specifically designed to capture information unique to mRNA and has demonstrated excellent performance in related tasks. Consequently, RNA-FM generates general-purpose RNA embeddings suitable for a broad range of downstream tasks, including but not limited to secondary and tertiary structure prediction, RNA family clustering, and functional RNA analysis.
The full codes are available at GitHub: https://github.com/ml4bio/RNA-FM.
Citation
If you use the model in your research, please cite our paper with the following.
@article{chen2022interpretable,
title={Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions},
author={Chen, Jiayang and Hu, Zhihang and Sun, Siqi and Tan, Qingxiong and Wang, Yixuan and Yu, Qinze and Zong, Licheng and Hong, Liang and Xiao, Jin and Shen, Tao and others},
journal={arXiv preprint arXiv:2204.00300},
year={2022}
}
@article{shen2024accurate,
title={Accurate RNA 3D structure prediction using a language model-based deep learning approach},
author={Shen, Tao and Hu, Zhihang and Sun, Siqi and Liu, Di and Wong, Felix and Wang, Jiuming and Chen, Jiayang and Wang, Yixuan and Hong, Liang and Xiao, Jin and others},
journal={Nature Methods},
pages={1--12},
year={2024},
publisher={Nature Publishing Group US New York}
}
@article{chen2020rna,
title={RNA secondary structure prediction by learning unrolled algorithms},
author={Chen, Xinshi and Li, Yu and Umarov, Ramzan and Gao, Xin and Song, Le},
journal={arXiv preprint arXiv:2002.05810},
year={2020}
}