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Updated: Mar 5, 2026

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RNA Secondary Structure Prediction Using High-throughput SHAPE
Published on: May 31, 2013
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In Silico Prediction of RNA Secondary Structure
Fariza Tahi1,2, Van Du T Tran3, Anouar Boucheham4,5
1IBISC, UEVE/Genopole, 23 bv. de France, 91000, Evry, France. fariza.tahi@ibisc.univ-evry.fr.
Methods in Molecular Biology (Clifton, N.J.)
|March 29, 2017
Summary
This chapter reviews RNA secondary structure prediction tools and noncoding RNA identification methods. It highlights microRNA identification and their role in gene regulation and human diseases.
Area of Science:
- Molecular Biology
- Bioinformatics
Background:
- RNA secondary structure dictates molecular function.
- Noncoding RNAs, including microRNAs, are crucial regulators of gene expression.
- Dysregulation of microRNAs is implicated in various human diseases.
Purpose of the Study:
- To provide an overview of RNA secondary structure prediction tools.
- To discuss methods for identifying noncoding RNAs from genomic and RNA sequencing data.
- To focus on the identification and significance of microRNAs.
Main Methods:
- Review of computational approaches for RNA secondary structure prediction.
- Exploration of algorithms for noncoding RNA identification.
- Discussion of microRNA identification strategies.
Main Results:
- Established computational tools exist for predicting RNA secondary structures.
- Various methods are available for detecting noncoding RNAs in genomic data.
- MicroRNA identification is a key area with significant biological implications.
Conclusions:
- Accurate RNA secondary structure prediction is fundamental to understanding RNA function.
- Effective identification of noncoding RNAs is essential for biological research.
- MicroRNAs are vital regulators with critical roles in health and disease.
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