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

MicroRNAs01:22

MicroRNAs

3.8K
MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
3.8K
MicroRNAs01:22

MicroRNAs

23.9K
MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
23.9K
RNA Interference01:23

RNA Interference

27.8K
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...
27.8K
Nucleic Acid Structure01:25

Nucleic Acid Structure

8.4K
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...
8.4K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

14.0K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
14.0K
Small interfering RNAs (siRNA)02:30

Small interfering RNAs (siRNA)

4.2K
4.2K

You might also read

Related Articles

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

Sort by
Same author

Self-Contemplating In-Context Learning Enhances T Cell Receptor Generation for Novel Epitopes.

Proceedings of machine learning research·2026
Same author

Iterative attack-and-defend framework for improving TCR-epitope binding prediction models.

Bioinformatics (Oxford, England)·2025
Same author

An AI-Guided Framework Reveals Conserved Features Governing microRNA Strand Selection.

bioRxiv : the preprint server for biology·2025
Same author

Self-Contemplating In-Context Learning Enhances T Cell Receptor Generation for Novel Epitopes.

bioRxiv : the preprint server for biology·2025
Same author

A comprehensive analysis of 3'UTRs in Caenorhabditis elegans.

Nucleic acids research·2024
Same author

Encrypted data-sharing for preserving privacy in wastewater-based epidemiology.

The Science of the total environment·2024
Same journal

Correction to 'New origin firing is inhibited by APC/CCdh1 activation in S-phase after severe replication stress'.

Nucleic acids research·2026
Same journal

VeloRM: disentangling pre- and post-splicing RNA modification dynamics at single-cell resolution.

Nucleic acids research·2026
Same journal

Accessibility of telomeric overhangs to stabilizing small-molecule ligands.

Nucleic acids research·2026
Same journal

Multivalent interactions mediate SNAIL transcription factor stimulation of the nucleosome deacetylase activity of the CoREST complex.

Nucleic acids research·2026
Same journal

Genome-wide mapping of DNA G-quadruplexes in Trypanosoma brucei chromatin reveals enrichment in coding regions and transcription start sites.

Nucleic acids research·2026
Same journal

Correction to 'The Gene Ontology knowledgebase in 2026'.

Nucleic acids research·2026
See all related articles

Related Experiment Video

Updated: Jan 17, 2026

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools
09:29

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

7.9K

An AI-guided framework reveals conserved features governing microRNA strand selection.

Dalton Meadows1,2, Hailee Hargis1,2, Amanda Ellis1,2

  • 1The Biodesign Institute at Arizona State University, 1001 S McAllister Ave, Tempe, AZ 85287.United States.

Nucleic Acids Research
|January 14, 2026
PubMed
Summary
This summary is machine-generated.

Scientists decoded microRNA (miRNA) strand selection using AI and experiments. This reveals conserved, context-dependent rules governing gene regulation across species, offering a programmable layer of control.

More Related Videos

Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells
07:19

Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells

Published on: September 28, 2011

36.8K
MicroRNA-based Regulation of Picornavirus Tropism
09:05

MicroRNA-based Regulation of Picornavirus Tropism

Published on: February 6, 2017

8.0K

Related Experiment Videos

Last Updated: Jan 17, 2026

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools
09:29

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

7.9K
Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells
07:19

Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells

Published on: September 28, 2011

36.8K
MicroRNA-based Regulation of Picornavirus Tropism
09:05

MicroRNA-based Regulation of Picornavirus Tropism

Published on: February 6, 2017

8.0K

Area of Science:

  • Molecular Biology
  • Genetics
  • Bioinformatics

Background:

  • MicroRNAs (miRNAs) are key gene expression regulators.
  • The mechanism of miRNA strand selection (5p vs. 3p) during biogenesis is not fully understood.

Purpose of the Study:

  • To develop a framework for understanding miRNA strand selection logic.
  • To build a predictive model for miRNA strand preference across species.

Main Methods:

  • Developed a high-throughput platform for quantifying miRNA strand usage in *Caenorhabditis elegans*.
  • Created an AI-driven machine learning model integrating 77 features to predict strand preference.
  • Validated the model across nematodes and vertebrates, including humans.

Main Results:

  • Identified conserved, context-dependent rules governing miRNA strand selection.
  • Revealed compositional and structural biases in strand preference that are conserved and functionally repurposed.
  • Demonstrated that strand selection is not stochastic but follows predictable patterns.

Conclusions:

  • Established the first unified, generalizable model for miRNA strand selection.
  • Combined large-scale experimentation with AI to uncover a programmable layer of gene regulation.
  • Provided open-access resources for the research community.