Partition, Predict and Assemble: Targeting Long RNA Secondary Structure Prediction

Accurately predicting the secondary structure of Non-coding RNAs is crucial for understanding their biological roles. However, current methods face challenges in accuracy and computational efficiency, particularly for long RNA sequences. Here, we present a novel three-step framework for RNA secondary structure prediction, PPA (Partition, Predict, and Assemble). It first partitions the RNA sequence into independent fragments based on exterior loops, then independently predicts the secondary structure of each fragment, and finally assembles them to construct the complete RNA secondary structure. Following the PPA framework, we introduce ELPBert, a model for RNA exterior loop prediction. We partition the RNA sequence at the central nucleotide of the predicted exterior-loop bases. The performance of six state-of-the-art RNA secondary structure prediction methods with and without RNA partition using our ELPBert were compared. Results demonstrate a great improvement of these methods with RNA partition, especially for long sequences. We believe our PPA framework could serve as an universal framework for RNA secondary structure prediction, particularly for long RNA sequences. Data and code are provided at https://github.com/Kali-ym/PPAwithELPBert.