A brief review and comparative analysis of RNA secondary structure prediction tools

Ribonucleic acid (RNA) lies at the heart of the central dogma. It spans the breadth of biological functions, from information storage to gene regulation and catalysis. RNA molecules must attain specific structures to perform these functions, and their structures depend on their sequences. Predicting...

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Bibliographic Details
Published in:Journal of bioinformatics and computational biology Vol. 23; no. 3; p. 2530001
Main Authors: Ballaney, Pranav, Saha, Gourav, Kulshrestha, Vaibhav, Thaker, Poojan Hasmukhray, Hasija, Prakhar, Talukdar, Indrani, Aduri, Raviprasad
Format: Journal Article
Language:English
Published: Singapore 01.06.2025
Subjects:
Algorithms
Computational Biology - methods
Neural Networks, Computer
Nucleic Acid Conformation
RNA - chemistry
Sequence Analysis, RNA - methods
Software
Thermodynamics
pseudoknots
structure prediction
computational structural biology
RNA structure
software evaluation
ISSN:1757-6334, 1757-6334
Online Access:Get more information
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Summary:Ribonucleic acid (RNA) lies at the heart of the central dogma. It spans the breadth of biological functions, from information storage to gene regulation and catalysis. RNA molecules must attain specific structures to perform these functions, and their structures depend on their sequences. Predicting the structure of RNA has been a central problem in computational biology. Various methods have been developed for this purpose - while some consider the thermodynamics of folding, others abstract away the details behind neural networks (NN). This paper presents a brief overview of the existing tools for predicting RNA secondary structures from a given single RNA sequence. Furthermore, a comparative analysis of the different prediction software packages is also presented. Performance is analyzed by running each of the available software packages on a novel dataset developed using 3D crystal structures of RNA. Software packages considered include those that can predict pseudoknots along with those that cannot. Variation in software performance based on the length and type of RNA is described.
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content type line 23
ISSN:1757-6334
1757-6334
DOI:10.1142/S0219720025300011