EvryRNA : DivideFold

RNA secondary structure prediction


  Accurately predicting the secondary structure of RNA, particularly for long non-coding 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 of long non-coding RNAs.


RNANet pipeline schema


Git repository


DivideFold : A Python repository to predict the secondary structure of long non-coding RNAs.

DivideFold requires installing the following repository :
  • MXfold2: a python repository based on deep learning that predicts secondary structure for RNAs up to 1000 nucleotides.
How to cite DivideFold:
  • 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. [ADD REF WHEN PUBLISHED]
For any questions, comments or suggestions about DivideFold, please feel free to contact: fariza.tahi@ibisc.univ-evry.fr