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

79.6K
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...
79.6K
RNA Structure01:19

RNA Structure

7.9K
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...
7.9K
RNA-seq03:21

RNA-seq

12.3K
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...
12.3K
Nucleic Acid Structure01:25

Nucleic Acid Structure

9.7K
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...
9.7K
RNA Stability01:53

RNA Stability

35.9K
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...
35.9K
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

12.0K
The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
12.0K

You might also read

Related Articles

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

Sort by
Same author

SHREC 2025: Protein surface shape retrieval including electrostatic potential.

Computers & graphics·2026
Same author

DAQplugin: Deep Learning based Real-time Model Evaluation Plugin for ChimeraX.

bioRxiv : the preprint server for biology·2026
Same author

Direct Detection and Atomic Modeling of Ligands in Cryo-EM Maps Using Deep Learning.

bioRxiv : the preprint server for biology·2026
Same author

Evolutionary dynamics of the polyphenol oxidase gene family across plant lineages from algae to angiosperms.

Horticulture research·2026
Same author

A direct black-hole mass measurement in a little red dot at high redshift.

Nature·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Mar 3, 2026

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

5.3K

Computational approaches for RNA structure prediction and design.

Yuki Kagaya1, Boyuan Liu2, Daisuke Kihara1,2

  • 1Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.

Cell Reports. Physical Science
|March 2, 2026
PubMed
Summary
This summary is machine-generated.

Deep learning revolutionizes RNA structure prediction, significantly improving accuracy over traditional methods. This advancement accelerates RNA design and opens new frontiers in computational RNA biology.

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

32.3K
Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen
11:32

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

Published on: May 24, 2017

12.7K

Related Experiment Videos

Last Updated: Mar 3, 2026

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

5.3K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.3K
Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen
11:32

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

Published on: May 24, 2017

12.7K

Area of Science:

  • Computational biology
  • Structural biology
  • Bioinformatics

Background:

  • Determining RNA's 3D structure is vital for understanding its biological roles.
  • Conventional computational RNA structure prediction methods include homology- and de novo modeling.
  • Deep learning has recently transformed RNA structure prediction, enhancing accuracy.

Purpose of the Study:

  • To overview advancements in computational RNA structure prediction.
  • To detail both conventional and deep-learning-based approaches.
  • To discuss the impact of these methods on RNA design and future directions.

Main Methods:

  • Summarizing foundational conventional RNA structure prediction principles.
  • Detailing state-of-the-art deep-learning-based approaches.
  • Categorizing deep learning methods: MSA-dependent, MSA-free, and generalist models.

Main Results:

  • Deep learning significantly outperforms conventional methods in RNA structure prediction accuracy.
  • New deep learning strategies include leveraging multiple sequence alignments (MSAs), single sequences, and predicting biomolecular complexes.
  • Predictive breakthroughs are accelerating RNA design.

Conclusions:

  • Deep learning represents a paradigm shift in computational RNA structure prediction.
  • Future directions include addressing current challenges in RNA structural biology.
  • Advancements promise to further unlock RNA's functional and design potential.