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

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GTfold: enabling parallel RNA secondary structure prediction on multi-core desktops.

M Shel Swenson¹, Joshua Anderson, Andrew Ash

¹School of Mathematics, Georgia Institute of Technology, Atlanta, GA, USA.

BMC Research Notes

|July 4, 2012

Summary

GTfold offers faster RNA secondary structure prediction on multi-core computers. This computational biology tool improves efficiency for predicting RNA structures, especially for long sequences like viral genomes.

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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Published on: May 28, 2021

Area of Science:

Computational molecular biology
Bioinformatics
Structural biology

Background:

Accurate RNA secondary structure prediction is a key challenge in computational molecular biology.
Previous parallelized prediction tools were often inaccessible on high-performance computers.
Modern multi-core processors necessitate new parallel prediction program designs.

Purpose of the Study:

To develop and present the first RNA secondary structure prediction program optimized for modern multi-core computers.
To leverage thermodynamic optimization for enhanced prediction capabilities.

Main Methods:

Implementation of a novel parallelized RNA secondary structure prediction algorithm.
Utilizing thermodynamic optimization principles.
Testing on multi-core computer architectures.

Main Results:

GTfold achieves faster RNA secondary structure prediction compared to UNAfold and RNAfold on machines with four or more cores.
GTfold maintains prediction accuracy while reducing computational time.
The program demonstrates efficient performance on contemporary multi-core hardware.

Conclusions:

GTfold advances RNA structural biology by significantly reducing prediction timescales.
The speed improvements are particularly beneficial for analyzing lengthy RNA sequences, including viral genomes.
GTfold enhances accessibility of efficient RNA structure prediction for researchers.