EvryRNA : DivideFold

DivideFold

Long RNA secondary structure prediction including pseudoknots

 

 

DivideFold framework
DivideFold framework. The sequence is partitioned into several fragments and the different predicted secondary structures are then recombined to the original positions of the fragments in the sequence.
 

  Accurately predicting the secondary structure of RNA including pseudoknots, particularly for long RNA, has direct implications in healthcare. However, many approaches are too costly in terms of computation budget to cope with the increasing complexity of long RNAs.

  We propose DivideFold, an approach combining recursive cutting and machine learning techniques for predicting the secondary structures including pseudoknots of long RNAs.

Downloads


Git repository


Dataset

 

DivideFold : A Python repository to predict the secondary structure including pseudoknots of long RNAs.

bpRNA-NF-15.0 : An RNA secondary structure dataset for family-wise evaluation.
The Train, Validation and Test datasets used in our study are also provided.

References

Submitted:
  • Omnes, L., Angel, E., Bartet, P., & Tahi, F. (2024). A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: focus on pseudoknots identification. https://www.biorxiv.org/content/10.1101/2024.11.19.624426v1
For any questions, comments or suggestions about DivideFold, please feel free to contact: fariza.tahi@ibisc.univ-evry.fr