UFold: fast and accurate RNA secondary structure prediction with deep learning

By Laiyi Fu, Yingxin Cao, Jie Wu, Qinke Peng, Qing Nie, Xiaohui S. Xie. 2020

220 citations recorded by the source corpus.

For many RNA molecules, the secondary structure is essential for the correction 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. Here we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data without any thermodynamic assumptions. UFold improves substantially upon previous models, with approximately 31% improvement over traditional thermodynamic models and 24.5% improvement over other learning-based methods. It achieves an F1 score of 0.96 on base pair prediction accuracy. An online web server running UFold is publicly available at http://ufold.ics.uci.edu.

Subfields: Molecular Biology

How researchers see this paper

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Zhihang Hu
Liang Hong
Jiayang Chen
Licheng Zong
Yixuan Wang
Qingxiong Tan
Yu Li
Qinze Yu

Citations in the corpus

Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022, neutral): “We benchmarked the performance of RhoFold+ on newly determined PDB structures (the ‘new PDB set’) and the ArchiveII dataset 41 , which includes secondary structure information for diverse RNAs.”
Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions (2022, methodology): “For example, SPOT-RNA [28], E2Efold [30], MXfold2 [31], and UFold [32] are shown to be able to improve the prediction accuracy significantly on different datasets.”
Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction (2022, unknown): “Previous studies 11 – 13 have shown success in learning RNA secondary structure directly from sequence.”