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 Interference01:23

RNA Interference

26.5K
RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
26.5K
Experimental RNAi02:15

Experimental RNAi

6.3K
RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
6.3K
Types of RNA01:20

Types of RNA

6.5K
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA Performs Diverse...
6.5K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

9.0K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
9.0K
siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

17.0K
Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the...
17.0K
RNA-seq03:21

RNA-seq

10.4K
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...
10.4K

You might also read

Related Articles

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

Sort by
Same author

The EU AI Act: implications and compliance guidance for healthcare facilities.

Frontiers in digital health·2026
Same author

In Reply to Sengul I and Sengul D.

Advances in radiation oncology·2026
Same author

Making Machine Learning Clinically Useful in Thrombosis and Hemostasis: A Roadmap for Diagnostic Translation.

Seminars in thrombosis and hemostasis·2026
Same author

Development, validation, and user-centric evaluation of an interpretable machine learning decision support tool for the preoperative prediction of mild bleeding disorders (MBD-Check): a prospective diagnostic prediction study.

The Lancet. Digital health·2026
Same author

Addition of Intravesical Recombinant Bacillus Calmette-Guérin to Perioperative Chemoimmunotherapy in Muscle-Invasive Bladder Cancer: Primary Analysis of the Single-Arm Phase II Trial SAKK 06/19.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Cracking the (zip)code of dynein-dependent RNA localization.

Nature structural & molecular biology·2026
Same journal

Plucking cellular ribosomes with Ribo-Tweezer.

Nature reviews. Molecular cell biology·2026
Same journal

COPII meets autophagy at the ER membrane.

Nature reviews. Molecular cell biology·2026
Same journal

Diapause presses pause on life's developmental and ageing clock.

Nature reviews. Molecular cell biology·2026
Same journal

Histone acetylation at the dawn of gene regulation.

Nature reviews. Molecular cell biology·2026
Same journal

Regulation and function of specialized membrane protrusions in intercellular communication.

Nature reviews. Molecular cell biology·2026
Same journal

Ancient enzymes, new biotechnology applications.

Nature reviews. Molecular cell biology·2026
See all related articles

Related Experiment Video

Updated: Sep 18, 2025

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
07:24

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Published on: July 9, 2021

2.5K

Decoding the interactions and functions of non-coding RNA with artificial intelligence.

Vincent Jung1,2, Cédric Vincent-Cuaz3, Charlotte Tumescheit4,5

  • 1Idiap Research Institute, Martigny, Switzerland.

Nature Reviews. Molecular Cell Biology
|June 19, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can revolutionize RNA biology by integrating large language models and graph neural networks. This approach will enhance our understanding of messenger RNA (mRNA) functions and interactions.

More Related Videos

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

25.6K
Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells
07:23

Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells

Published on: May 30, 2025

653

Related Experiment Videos

Last Updated: Sep 18, 2025

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
07:24

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Published on: July 9, 2021

2.5K
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

25.6K
Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells
07:23

Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells

Published on: May 30, 2025

653

Area of Science:

  • Molecular Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Messenger RNAs (mRNAs) possess regulatory functions beyond protein-coding.
  • Traditional methods struggle to uncover novel mRNA functions.
  • Non-coding mRNA regions (introns, UTRs) play crucial roles in gene regulation.

Purpose of the Study:

  • To outline a roadmap for applying artificial intelligence (AI) in RNA biology.
  • To explore the potential of large language models (LLMs) for understanding mRNA.
  • To integrate LLMs with graph neural networks (GNNs) for predicting RNA interactions.

Main Methods:

  • Discussing the regulatory roles of non-coding mRNA regions.
  • Leveraging LLMs for biologically meaningful RNA sequence representation.
  • Integrating LLMs with GNNs to analyze public sequencing and knowledge data.

Main Results:

  • AI offers a transformative approach to RNA biology research.
  • LLMs can learn effective RNA sequence representations.
  • The proposed roadmap facilitates prediction of RNA interactions and interactomes.

Conclusions:

  • AI, particularly LLMs and GNNs, can significantly advance RNA biology.
  • This integrated approach will enable prediction of mRNA interactions and context-specific interactomes.
  • Fostering collaboration between RNA biologists and computational scientists is key to innovation.