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.

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Git repository

 

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

Submitted:
 
  • Omnes, L., Angel, E., Bartet, P., Radvanyi, F., & Tahi, F. (2024). A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: focus on pseudoknots identification.
Based on preliminary work:
  • Omnes, L., Angel, E., Bartet, P., Radvanyi, F., & Tahi, F. (2023). Prediction of Secondary Structure for Long Non-Coding RNAs using a Recursive Cutting Method based on Deep Learning. BIBE 2023. https://ieeexplore.ieee.org/document/10431864
For any questions, comments or suggestions about DivideFold, please feel free to contact: fariza.tahi@ibisc.univ-evry.fr