UFold: Fast and Accurate RNA Secondary Structure Prediction with Deep Learning

Fu, Laiyi; Cao, Yingxin; Wu, Jie; Peng, Qinke; Nie, Qing; Xie, Xiaohui

Motivation 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. Results 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 10~30% improvement over traditional thermodynamic models and 14% improvement over other learning-based methods. It achieves an F1 score of 0.91 on base pair prediction accuracy on an RNA structure prediction benchmark dataset. UFold is also fast with an inference time about 160ms per sequence up to 1600bp length. We provide an online web server that implements UFold for RNA structure prediction and is made freely available. Availability An online web server running UFold is available at https://ufold.ics.uci.edu. Code is available at https://github.com/uci-cbcl/UFold. Contact [e-mail]