Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

RNA Structure01:19

RNA Structure

The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA) involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

RNAmountAlign: Efficient software for local, global, semiglobal pairwise and multiple RNA sequence/structure alignment.

PloS one·2020
Same author

Small-World Networks and RNA Secondary Structures.

Journal of computational biology : a journal of computational molecular cell biology·2018
Same author

RNA folding kinetics using Monte Carlo and Gillespie algorithms.

Journal of mathematical biology·2017
Same author

New tools to analyze overlapping coding regions.

BMC bioinformatics·2016
Same author

RNAdualPF: software to compute the dual partition function with sample applications in molecular evolution theory.

BMC bioinformatics·2016
Same author

RNAiFold2T: Constraint Programming design of thermo-IRES switches.

Bioinformatics (Oxford, England)·2016
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jun 4, 2026

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Computing the partition function for kinetically trapped RNA secondary structures.

William A Lorenz1, Peter Clote

  • 1Department of Mathematics and Computer Science, Denison University, Granville, Ohio, United States of America.

Plos One
|February 8, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces RNAlocopt, an efficient algorithm for analyzing RNA secondary structures. It accurately predicts native RNA structures by focusing on locally optimal structures, which act as kinetic traps.

More Related Videos

Practical Aspects of Sample Preparation and Setup of 1H R1ρ Relaxation Dispersion Experiments of RNA
08:17

Practical Aspects of Sample Preparation and Setup of 1H R1ρ Relaxation Dispersion Experiments of RNA

Published on: July 9, 2021

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

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

Published on: December 9, 2022

Related Experiment Videos

Last Updated: Jun 4, 2026

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Practical Aspects of Sample Preparation and Setup of 1H R1ρ Relaxation Dispersion Experiments of RNA
08:17

Practical Aspects of Sample Preparation and Setup of 1H R1ρ Relaxation Dispersion Experiments of RNA

Published on: July 9, 2021

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

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

Published on: December 9, 2022

Area of Science:

  • Computational Biology
  • Biophysics
  • Bioinformatics

Background:

  • RNA secondary structures are crucial for biological function.
  • Locally optimal structures act as kinetic traps, influencing RNA folding pathways.
  • Current methods struggle with exhaustive enumeration of these structures for long sequences.

Purpose of the Study:

  • To develop an efficient algorithm for computing the partition function over locally optimal RNA secondary structures.
  • To analyze the properties of locally optimal structures and their subensemble.
  • To improve RNA secondary structure prediction accuracy.

Main Methods:

  • Developed a novel algorithm (RNAlocopt) with O(n3) time and O(n2) space complexity.
  • Computed the partition function over locally optimal secondary structures.
  • Sampled structures from the Boltzmann subensemble of locally optimal structures.
  • Applied the algorithm to analyze the number and diversity of locally optimal structures.

Main Results:

  • The number of locally optimal structures is significantly smaller than the total number of structures (approx. sqrt(N)).
  • Structural diversity of locally optimal structures can differ from the entire Boltzmann ensemble.
  • The modified maximum expected accuracy structure, using locally optimal structure frequencies, improves native structure prediction.

Conclusions:

  • RNAlocopt provides a breakthrough in studying RNA folding landscapes by enabling rapid generation of locally optimal structures.
  • This approach leads to state-of-the-art secondary structure prediction, outperforming existing thermodynamics-based methods.
  • The software is available for broader scientific use.