DivideFold+: an AI-based tool for RNA secondary structure prediction with subdomains identification and visualization and data augmentation

Predicting the secondary structure of RNAs, particularly long RNAs, remains a challenging problem despite its importance in identifying the structural roles of RNAs. Deep-learning-based methods face a lack of data and cannot provide very accurate predictions for long RNAs. To overcome this difficulty, we presented a method called DivideFold in a previous work, which divides long RNAs into structurally independent, shorter fragments. This approach enables the overall secondary structure of the RNA to be inferred by predicting the secondary structure of each fragment, a much easier task. We present here an enhanced version called DivideFold + that improves upon several aspects. Since our method is a deep-learning-based one, we introduce a new data augmentation strategy specifically designed for RNA secondary structure predictions, which is more elaborate than the traditional ones used in the literature. The computational results we obtain show the benefit of such a strategy. Besides the secondary structure prediction we obtain, DivideFold + provides a segmentation of the secondary structure into subdomains, each subdomain corresponding to a fragment. These subdomains can serve, in a similar way to proteins, as potential candidates for functional domains in RNAs. Finally, We provide a user-friendly web server that allows visualization of the predicted secondary structure, as well as the different subdomains. DivideFold+, along with all the datasets used for this study, is publicly accessible on the EvryRNA platform at https://evryrna.ibisc.univ-evry.fr/.