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-seq03:21

RNA-seq

10.5K
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...
10.5K
RNA Structure01:19

RNA Structure

5.5K
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...
5.5K
RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

3.3K
3.3K

You might also read

Related Articles

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

Sort by
Same author

Investigation Into the Effectiveness of Community Water Fluoridation and Dental Caries Experience in 4-Year-Old Aotearoa New Zealand Children: A National Repeated Cross-Sectional Study From 2010 to 2022.

Community dentistry and oral epidemiology·2026
Same author

Associations between the Plasmodium falciparum genome and sickle haemoglobin identified in mild malaria cases from Ghana.

Malaria journal·2026
Same author

MolQuery: Prediction of Lipid Synthesizability Using Active Learning.

ACS omega·2026
Same author

What's so hard about RNA-targeting drug discovery?

Nature computational science·2025
Same author

An expanded method for malaria parasite genetic surveillance using targeted nanopore sequencing.

Gates open research·2025
Same author

InDeepNet: a web platform for predicting functional binding sites in proteins using InDeep.

Nucleic acids research·2025
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Oct 10, 2025

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA
13:00

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

Published on: December 2, 2009

11.9K

RNAglib: a python package for RNA 2.5 D graphs.

Vincent Mallet1,2, Carlos Oliver3,4, Jonathan Broadbent3

  • 1Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, CNRS UMR3528, C3BI, USR3756, Paris 75724, France.

Bioinformatics (Oxford, England)
|December 15, 2021
PubMed
Summary
This summary is machine-generated.

RNAglib simplifies RNA 3D structure analysis using graph representations and machine learning. This library provides tools for modeling RNA with 2.5 D graphs, aiding in the study of base pair interactions.

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

31.7K
Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
07:35

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

835

Related Experiment Videos

Last Updated: Oct 10, 2025

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA
13:00

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

Published on: December 2, 2009

11.9K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

31.7K
Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
07:35

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

835

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • RNA 3D structures are stabilized by complex networks of base pair interactions.
  • These interactions can be represented as multi-relational graphs.
  • Graph theory and machine learning offer powerful tools for analyzing these structures.

Purpose of the Study:

  • To introduce RNAglib, a library for representing and analyzing RNA 3D structures.
  • To facilitate the use of graph-based deep learning models for RNA analysis.
  • To provide utilities for RNA modeling and comparison.

Main Methods:

  • Encoding RNA 3D architectures as multi-relational graphs.
  • Developing a Python library (RNAglib) for graph-based RNA analysis.
  • Implementing graph-based deep learning models and utilities for RNA modeling.

Main Results:

  • RNAglib provides clean data and methods for machine learning pipelines.
  • The library supports 2.5 D graph modeling of RNA.
  • Includes drawing tools, comparison functions, and baseline performance metrics.

Conclusions:

  • RNAglib eases the application of graph theoretical approaches and machine learning to RNA 3D structures.
  • The library enhances the study of RNA base pair interactions and structural analysis.
  • RNAglib is available as a pip package with accessible source code and data.