The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
UFold: fast and accurate RNA secondary structure prediction with deep learning
Open accessAbstract
For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization, which imposes strong prior assumptions and is slow to run. Here, we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data and base-pairing rules. UFold proposes a novel image-like representation of RNA sequences, which can be efficiently processed by Fully Convolutional Networks (FCNs). We benchmark the performance of UFold on both within- and cross-family RNA datasets. It significantly outperforms previous methods on within-family datasets, while achieving a similar performance as the traditional methods when trained and tested on distinct RNA families. UFold is also able to predict pseudoknots accurately. Its prediction is fast with an inference time of about 160 ms per sequence up to 1500 bp in length. An online web server running UFold is available at https://ufold.ics.uci.edu. Code is available at https://github.com/uci-cbcl/UFold.
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Cited by
Chen, J., Hu, Z., Sun, S., Tan, Q., Wang, Y., Yu, Q., Zong, L., Hong, L., Xiao, J., Shen, T., et al. (2022) · doi:10.1101/2022.08.06.503062 · Cited by 247
Multiple sequence alignment-based RNA language model and its application to structural inference
Zhang, Y., Lang, M., Jiang, J., Gao, Z., Xu, F., Litfin, T., Chen, K., Singh, J., Huang, X., Song, G., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1031 · Cited by 112
ERNIE-RNA: an RNA language model with structure-enhanced representations
Yin, W., Zhang, Z., Zhang, S., He, L., Zhang, R., Jiang, R., Liu, G., Wang, J., Zhang, X., Qin, T., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64972-0 · Cited by 55
Predicting RNA structures and functions by artificial intelligence.
Zhang, J., Lang, M., Zhou, Y., & Zhang, Y. (2024) · Trends in Genetics · doi:10.1016/j.tig.2023.10.001 · Cited by 48
State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction
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Metadata
- Authors
- Fu, L., Cao, Y., Wu, J., Peng, Q., Nie, Q., & Xie, X.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-11-18
- Volume
- 50
- Issue
- 3
- Pages
- e14-e14
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W3212533323
- Cited by
- 266
- References
- 60
- FWCI
- 10.53
- Type
- journal article
- Language
- en
- Open access
- gold
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