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Updated: Sep 21, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE
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ShapeSorter: a fully probabilistic method for detecting conserved RNA structure features supported by SHAPE evidence.

Volodymyr Tsybulskyi1,2, Irmtraud M Meyer1,3,2

  • 1Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Hannoversche Str. 28, 10115 Berlin, Germany.

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Summary

ShapeSorter predicts RNA structures using evolutionary and SHAPE-probing evidence, not thermodynamics. This new method accurately captures complex RNA structures and outperforms existing state-of-the-art approaches.

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Area of Science:

  • Computational biology
  • RNA structure analysis
  • Bioinformatics

Background:

  • High-throughput RNA structure determination in vivo is increasingly feasible using methods like SHAPE protocols.
  • Current computational RNA secondary structure prediction integrates SHAPE data using minimum free energy assumptions, which may overlook evolutionary constraints.
  • Existing methods often assume a single, thermodynamically most stable RNA configuration, neglecting structural diversity and evolutionary conservation.

Purpose of the Study:

  • To introduce ShapeSorter, a novel computational method for predicting RNA structure features.
  • To develop a method that integrates both evolutionary and experimental SHAPE-probing evidence without relying on thermodynamic stability assumptions.
  • To capture complex RNA structural phenomena such as heterogeneity, pseudo-knots, and transient or mutually exclusive structures.

Main Methods:

  • ShapeSorter utilizes a fully probabilistic framework to identify RNA structure features.
  • The method integrates evidence from evolutionary data and SHAPE-probing experiments.
  • It incorporates P-value estimation for predicted RNA structure features to facilitate filtering and ranking.

Main Results:

  • ShapeSorter successfully predicts RNA structure features by combining evolutionary and SHAPE-probing data.
  • The method demonstrates capability in capturing RNA structure heterogeneity, pseudo-knots, and transient/mutually exclusive structures.
  • Performance benchmarking indicates ShapeSorter significantly outperforms existing state-of-the-art methods in base-pair prediction.

Conclusions:

  • ShapeSorter offers a superior approach to RNA secondary structure prediction by integrating evolutionary and experimental data.
  • The method's probabilistic framework and ability to capture structural complexity provide valuable insights into in vivo RNA folding.
  • ShapeSorter's performance advantages suggest it will be a valuable tool for RNA structure research.