Prediction of RNA secondary structure with pseudoknots using coupled deep neural networks

Noncoding RNAs play important roles in cell and their secondary structures are vital for understanding their tertiary structures and functions. Many prediction methods of RNA secondary structures have been proposed but it is still challenging to reach high accuracy, especially for those with pseudoknots. Here we present a coupled deep learning model, called 2dRNA, to predict RNA secondary structure. It combines two famous neural network architectures bidirectional LSTM and U-net and only needs the sequence of a target RNA as input. Benchmark shows that our method can achieve state-of-the-art performance compared to current methods on a testing dataset. Our analysis also shows that 2dRNA can learn structural information from similar RNA sequences without aligning them.