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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

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Probabilistic RNA designability via interpretable ensemble approximation and dynamic decomposition.

Tianshuo Zhou¹, David H Mathews^2,3,4, Liang Huang^1,5

¹School of EECS, Oregon State University, Corvallis, OR 97330, USA.

Bioinformatics (Oxford, England)

|July 7, 2026

Summary

This summary is machine-generated.

This study introduces a new method for RNA design, moving beyond binary designability to a probabilistic approach. It quantifies how likely RNA designs are to fold into target structures, improving RNA inverse folding predictions.

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RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Area of Science:

Computational Biology
Bioinformatics
Molecular Biology

Background:

RNA design, or inverse folding, seeks RNA sequences for specific secondary structures.
Previous methods identified structures as either designable or not based on minimum free energy (MFE).
This binary approach overlooks nuanced designability and the likelihood of folding.

Purpose of the Study:

To develop a probabilistic framework for RNA designability beyond MFE.
To quantify the likelihood of RNA sequences folding into target structures.
To introduce a method for analyzing and improving RNA inverse folding.

Main Methods:

Developed a theory of ensemble approximation for bounding RNA folding probabilities.
Introduced a probability decomposition framework for explainable analysis of RNA structures and motifs.
Created a linear-time dynamic programming algorithm for efficient search of optimal motif decompositions.

Main Results:

The LinearDecompose method achieved tighter probability bounds on RNA structures compared to existing baselines.
Demonstrated the method's effectiveness on both native and artificial RNA structures from ArchiveII and Eterna100 datasets.
Provided tools for analyzing RNA structure design difficulty at the motif level.

Conclusions:

The probabilistic framework offers a more refined measure of RNA designability.
The developed algorithm efficiently identifies optimal RNA sequence designs.
This work enhances understanding and capability in RNA inverse folding and design.