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AlignmentFold and AlignmentPartition: Improving the align-then-fold approach for RNA secondary structure prediction.

Abhinav Mittal1, David H Mathews1,2

  • 1Department of Biochemistry and Biophysics and Center for RNA Biology, University of Rochester Medical Center, Rochester, New York, 14642, USA.

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|August 6, 2025
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Summary
This summary is machine-generated.

This study introduces AlignmentFold and AlignmentPartition for RNA secondary structure prediction. These tools improve accuracy by incorporating new thermodynamic parameters and assessing alignment impacts, achieving comparable results to existing methods.

Keywords:
RNA secondary structure predictionalign-then-foldconsensus structurecovariationfree energy minimizationpartition function

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Predicting conserved RNA secondary structure from homologous sequences is crucial.
  • Existing methods often combine free energy minimization with sequence covariation analysis.
  • Fixed alignments are commonly used, but introduce challenges like gaps and non-canonical base pairs.

Purpose of the Study:

  • To develop novel computational tools, AlignmentFold and AlignmentPartition, for enhanced RNA secondary structure prediction.
  • To refine thermodynamic models by including parameters for gaps and non-canonical base pairs.
  • To evaluate the impact of alignment quality and covariation analysis on prediction accuracy.

Main Methods:

  • Development of AlignmentFold for consensus minimum free energy structure prediction.
  • Development of AlignmentPartition for base pairing probability prediction.
  • Determination of new nearest neighbor thermodynamic parameters for gaps and non-canonical base pairs.
  • Assessment of prediction accuracy based on alignment methods, size, and inclusion of covariation analysis.

Main Results:

  • AlignmentFold and AlignmentPartition predict RNA secondary structures and base pairing probabilities.
  • New thermodynamic parameters were determined for gaps and non-canonical base pairs.
  • Prediction accuracy was evaluated against various alignment strategies and covariation analyses.
  • Excluding covariation from structure quality scoring did not reduce, and potentially improved, prediction accuracy compared to other tools.

Conclusions:

  • AlignmentFold and AlignmentPartition offer accurate RNA secondary structure prediction.
  • The developed thermodynamic parameters enhance predictions for alignments with gaps and non-canonical pairs.
  • These tools are available within the RNAstructure software package, facilitating broader research applications.