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 Structure01:23

RNA Structure

29.8K
29.8K
RNA Structure01:23

RNA Structure

80.8K
Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. 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): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
80.8K
RNA Structure01:19

RNA Structure

8.3K
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...
8.3K
RNA Stability01:53

RNA Stability

36.2K
Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
36.2K
RNA Stability01:53

RNA Stability

12.1K
12.1K
Nucleic Acid Structure01:25

Nucleic Acid Structure

10.3K
The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA...
10.3K

You might also read

Related Articles

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

Sort by
Same author

Probabilistic RNA designability via interpretable ensemble approximation and dynamic decomposition.

Bioinformatics (Oxford, England)·2026
Same author

Authorship asymmetry at the Women in Thoracic Surgery Inaugural Annual Conference: Gender pairings and senior status disparity.

JTCVS open·2026
Same author

Computational Resources for Molecular Biology 2026.

Journal of molecular biology·2026
Same author

Reparameterization of the Amber RNA Force Field Non-Bonded Terms.

bioRxiv : the preprint server for biology·2026
Same author

Nearest Neighbor Parameters for Estimating the Folding Stability of RNA Including Pseudouridine.

bioRxiv : the preprint server for biology·2026
Same author

Exploring salary negotiation and disparities in thoracic surgery.

JTCVS open·2026

Related Experiment Video

Updated: Mar 29, 2026

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen
11:32

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

Published on: May 24, 2017

12.8K

AccessFold: predicting RNA-RNA interactions with consideration for competing self-structure.

Laura DiChiacchio1, Michael F Sloma1, David H Mathews2

  • 1Department of Biochemistry and Biophysics and Center for RNA Biology and.

Bioinformatics (Oxford, England)
|November 22, 2015
PubMed
Summary

New algorithms improve RNA-RNA interaction prediction by accounting for competing structures. Pseudo-energy minimization significantly enhances base pair prediction accuracy for bimolecular RNA structures.

More Related Videos

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

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.5K
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

5.5K

Related Experiment Videos

Last Updated: Mar 29, 2026

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen
11:32

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

Published on: May 24, 2017

12.8K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.5K
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

5.5K

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • RNA-RNA interactions are crucial for gene regulation, including translation, post-transcriptional modification, and splicing.
  • Predicting RNA-RNA base pairing is challenging due to competition between unimolecular folding and bimolecular complex formation.

Purpose of the Study:

  • To develop and evaluate algorithms for improved prediction of bimolecular RNA structure.
  • To address the competition between self-structure and intermolecular structure in RNA-RNA interactions.

Main Methods:

  • Developed two algorithms incorporating novel accessibility evaluation approaches: free energy density minimization and pseudo-energy minimization.
  • Pseudo-energy minimization (AccessFold) penalizes bimolecular pairing of inaccessible nucleotides using unimolecular pairing probabilities.
  • Assessed prediction accuracy using a benchmark set of 17 bimolecular RNA structures.

Main Results:

  • Pseudo-energy minimization (AccessFold) demonstrated a statistically significant improvement in sensitivity for base pair prediction.
  • Mean sensitivity increased from 36.8% to 57.8% compared to previous state-of-the-art methods.
  • The pseudo-energy approach successfully predicted binding sites separated by unimolecular structures.

Conclusions:

  • Pseudo-energy minimization offers a superior method for predicting bimolecular RNA structures.
  • AccessFold provides enhanced accuracy in identifying RNA-RNA interaction sites.
  • The developed algorithms advance the field of RNA structure prediction and analysis.