# A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification

## TL;DR

This paper introduces DivideFold, a deep learning method that improves the prediction of RNA secondary structures, especially pseudoknots, in long RNA sequences.

## Contribution

DivideFold uses a divide-and-conquer strategy to enhance pseudoknot prediction accuracy and scalability for long RNA sequences.

## Key findings

- DivideFold outperforms existing methods in predicting pseudoknots in long RNA sequences.
- The method scales effectively by breaking down RNA sequences into manageable fragments.
- It maintains high accuracy while handling the computational complexity of long RNA structures.

## Abstract

The accurate prediction of RNA secondary structure, and pseudoknots in particular, is of great importance in understanding the functions of RNAs since they give insights into their folding in three-dimensional space. However, existing approaches often face computational challenges or lack precision when dealing with long RNA sequences and/or pseudoknots. To address this, we propose a divide-and-conquer method based on deep learning, called DivideFold, for predicting the secondary structures including pseudoknots of long RNAs. Our approach is able to scale to long RNAs by recursively partitioning sequences into smaller fragments until they can be managed by an existing model able to predict RNA secondary structure including pseudoknots. We show that our approach exhibits superior performance compared to state-of-the-art methods for pseudoknot prediction and secondary structure prediction including pseudoknots for long RNAs. The source code of DivideFold, along with all the datasets used in this study, is accessible at https://evryrna.ibisc.univ-evry.fr/evryrna/dividefold/home.