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Updated: Sep 20, 2025

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

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

Published on: December 9, 2022

Deep Learning in RNA Structure Studies.

Haopeng Yu¹, Yiman Qi¹, Yiliang Ding¹

¹Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, United Kingdom.

Frontiers in Molecular Biosciences

|June 9, 2022

Summary

This summary is machine-generated.

Deep learning, a machine learning method, deciphers complex RNA structures and functions from vast data. This review highlights its successful applications in RNA structure prediction and other biological problems.

Keywords:

RNA G-quadruplex RNA secondary structure RNA structure prediction RNA tertiary structure RNA-protein interaction deep learning

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RNA Secondary Structure Prediction Using High-throughput SHAPE

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Published on: May 31, 2013

Area of Science:

Structural biology
Computational biology
Bioinformatics

Background:

RNA molecules fold into intricate structures via hydrogen bonds, crucial for biological functions.
Experimental methods resolve RNA structures genome-wide, but computational approaches are advancing.
Deep learning (DL), a machine learning subset, excels at identifying patterns in large datasets.

Purpose of the Study:

To review successful applications of deep learning in solving RNA structure and function problems.
To provide a guide for utilizing deep learning in RNA structure research.

Main Methods:

Review of existing literature on deep learning applications in RNA biology.
Discussion of specific case studies involving deep learning for RNA structure prediction.
Explanation of deep learning's role in predicting RNA-protein interactions and RNA switches.

Main Results:

Deep learning has demonstrated success in predicting RNA secondary and tertiary structures.
Applications include predicting non-canonical G-quadruplex structures.
Deep learning aids in understanding RNA-protein interactions and identifying RNA switches.

Conclusions:

Deep learning offers powerful tools for advancing RNA structural biology.
Its application extends beyond structure prediction to functional element identification.
This review serves as a foundational guide for researchers entering the field.