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...
RNA Structure01:23

RNA Structure

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...
RNA Structure01:23

RNA Structure

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...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
RNA Stability01:53

RNA Stability

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...
RNA Stability01:53

RNA Stability

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...

You might also read

Related Articles

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

Sort by
Same author

Postoperative Celecoxib Associated with Improved Early Continence and Flow-Rates After Holmium Laser Enucleation of the Prostate.

Urology practice·2026
Same author

A <i>Peniophora lycii</i> Isolate Simultaneously Parasitizes <i>Vitis vinefera</i> Host and Associated Fungi, and Possibly Contributes to Grapevine Trunk Disease Development.

Journal of fungi (Basel, Switzerland)·2026
Same author

Small RNA sequencing identifies serum tDR-1:34-Gly-GCC tiRNA levels as a biomarker for survival in amyotrophic lateral sclerosis.

iScience·2026
Same author

The history and function of a circular RNA.

Nature communications·2026
Same author

Optimizing C14120-based LNPs for <i>in vitro</i> and <i>in vivo</i> mRNA delivery.

Molecular therapy. Nucleic acids·2026
Same author

Scalable Production of DNA-Probe-Functionalized Heteromeric MspA Nanopores for Biosensing.

ACS applied materials & interfaces·2026

Related Experiment Video

Updated: May 14, 2026

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

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Characterising RNA secondary structure space using information entropy.

Zsuzsanna Sükösd1, Bjarne Knudsen, James W J Anderson

  • 1Bioinformatics Research Center, Aarhus University, Aarhus, Denmark. zs@birc.au.dk

BMC Bioinformatics
|February 2, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a method to calculate the information entropy of RNA secondary structures predicted using evolutionary data. This helps better understand the probability distribution and improve prediction reliability scores.

More Related Videos

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

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

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

Published on: May 24, 2017

Related Experiment Videos

Last Updated: May 14, 2026

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

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

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

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

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

Published on: May 24, 2017

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Comparative RNA structure prediction leverages evolutionary information from RNA alignments to enhance accuracy.
  • Stochastic context-free grammars (SCFGs) model this process, generating probability distributions over secondary structures.
  • Current methods often output a single structure, limiting insight into the full probability space.

Purpose of the Study:

  • To develop an efficient method for computing the information entropy of RNA secondary structure probability distributions derived from RNA alignments.
  • To implement this entropy computation for the PPfold model, a tool utilizing phylogenetic SCFGs (phylo-SCFGs).
  • To explore the interpretations and applications of information entropy in understanding RNA secondary structure prediction reliability.

Main Methods:

  • The study focuses on calculating information entropy for probability distributions over RNA secondary structures.
  • The method is implemented within the framework of phylogenetic stochastic context-free grammars (phylo-SCFGs).
  • The computational approach is specifically applied to the PPfold RNA structure prediction model.

Main Results:

  • An efficient algorithm for computing information entropy of RNA secondary structure probabilities from alignments was developed.
  • The implementation for the PPfold model allows for a more detailed characterization of the predicted secondary structure space.
  • The computed information entropy can elucidate the reasons behind low prediction reliability scores.

Conclusions:

  • Information entropy provides a valuable metric for characterizing the probability distribution of RNA secondary structures.
  • This approach enhances the interpretability of comparative RNA structure prediction, particularly for models like PPfold.
  • Understanding entropy aids in assessing and improving the reliability of RNA secondary structure predictions.