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

79.1K
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...
79.1K
RNA Structure01:19

RNA Structure

7.5K
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...
7.5K
Chromatin Structure and RNA Splicing02:41

Chromatin Structure and RNA Splicing

3.4K
3.4K
Secondary Active Transport01:55

Secondary Active Transport

137.9K
One example of how cells use the energy contained in electrochemical gradients is demonstrated by glucose transport into cells. The ion vital to this process is sodium (Na+), which is typically present in higher concentrations extracellularly than in the cytosol. Such a concentration difference is due, in part, to the action of an enzyme “pump” embedded in the cellular membrane that actively expels Na+ from a cell. Importantly, as this pump contributes to the high concentration of...
137.9K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Secondary Healthcare System01:11

Secondary Healthcare System

2.1K
Secondary healthcare is offered by a specialist, generally in hospitals or clinics for patients referred by primary healthcare providers. It occurs when a person has an illness or injury that requires specific medical care. Secondary care is often referred to as acute care. Secondary care can range from uncomplicated care to repair a minor laceration or treat a strep throat infection to more complicated emergent care, such as treating a head injury sustained in an automobile accident. Whatever...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Undesignable motifs in structural RNAs and combinatorial consequences.

Journal of mathematical biology·2026
Same author

Designing molecular RNA switches with Restricted Boltzmann machines.

Nature communications·2025
Same author

RNA triplet repeats: improved algorithms for structure prediction and interactions.

Algorithms for molecular biology : AMB·2025
Same author

RNA inverse folding can be solved in linear time for structures without isolated stacks or base pairs.

Algorithms for molecular biology : AMB·2025
Same author

CREMSA: compressed indexing of (ultra) large multiple sequence alignments.

Bioinformatics (Oxford, England)·2025
Same author

IPANEMAP Suite: a pipeline for probing-informed RNA structure modeling.

NAR genomics and bioinformatics·2025
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

Related Experiment Video

Updated: Feb 3, 2026

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

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.2K

Small-World Networks and RNA Secondary Structures.

Defne Surujon1, Yann Ponty2, Peter Clote1

  • 11 Department of Biology, Boston College, Chestnut Hill, Massachusetts.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|November 2, 2018
PubMed
Summary
This summary is machine-generated.

This study analyzes RNA secondary structure networks, revealing their average degree grows linearly with length (O(n)) and clustering coefficient decreases inversely with length (O(1/n)). These findings demonstrate that RNA secondary structure networks are not small-world networks.

Keywords:
RNA secondary structurealgebraic combinatoricssmall-world network

More Related Videos

A FACS-based Protocol to Isolate RNA from the Secondary Cells of Drosophila Male Accessory Glands
06:54

A FACS-based Protocol to Isolate RNA from the Secondary Cells of Drosophila Male Accessory Glands

Published on: September 5, 2019

7.5K
RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
09:36

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

26.3K

Related Experiment Videos

Last Updated: Feb 3, 2026

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

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.2K
A FACS-based Protocol to Isolate RNA from the Secondary Cells of Drosophila Male Accessory Glands
06:54

A FACS-based Protocol to Isolate RNA from the Secondary Cells of Drosophila Male Accessory Glands

Published on: September 5, 2019

7.5K
RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
09:36

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

26.3K

Area of Science:

  • Computational Biology
  • Network Science
  • Bioinformatics

Background:

  • RNA secondary structures are crucial for gene regulation and function.
  • Understanding the network properties of RNA secondary structures provides insights into their biological roles.
  • Previous studies have explored various aspects of RNA structure but network-level analysis is less common.

Purpose of the Study:

  • To characterize the network properties of RNA secondary structures.
  • To determine if RNA secondary structure networks exhibit small-world properties.
  • To analyze the asymptotic behavior of these networks with increasing RNA length.

Main Methods:

  • Utilized context-free grammars to model RNA secondary structures.
  • Employed generating functions for analytical calculations.
  • Applied complex analysis techniques to derive asymptotic properties.
  • Defined network edges based on single base pair modifications (addition, removal, shift).

Main Results:

  • The asymptotic average degree of RNA secondary structure networks is O(n).
  • The asymptotic clustering coefficient is O(1/n).
  • These results indicate that the networks are not small-world.

Conclusions:

  • RNA secondary structure networks do not exhibit small-world characteristics.
  • The network topology changes significantly with increasing RNA length.
  • This research contributes to a deeper understanding of the structural organization of RNA molecules.