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

Experimental RNAi02:15

Experimental RNAi

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...
siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

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 ATP-dependent...
Nucleic Acid Structure01:25

Nucleic Acid Structure

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 has a double-helix structure. The...
MicroRNAs01:22

MicroRNAs

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...

You might also read

Related Articles

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

Sort by
Same author

Distribution of prophage-encoded Pas sRNAs across pathogenic <i>Escherichia coli</i>.

bioRxiv : the preprint server for biology·2026
Same author

Taxonomic-Level Protein Quantification in Metaproteomics Using a Biomass-Constrained Expectation-Maximization Approach.

Journal of the American Society for Mass Spectrometry·2026
Same author

De novo origin of numerous microproteins in enterobacteria.

Nucleic acids research·2025
Same author

Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm.

Journal of proteome research·2025
Same author

Biological Function Assignment Across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm.

bioRxiv : the preprint server for biology·2025
Same author

MARLOWE: Taxonomic Characterization of Unknown Samples for Forensics Using <i>De Novo</i> Peptide Identification.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: May 18, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

Optimized models for design of efficient miR30-based shRNAs.

Olga V Matveeva1, Nafisa N Nazipova, Aleksey Y Ogurtsov

  • 1Department of Human Genetics, University of Utah Salt Lake City, UT, USA.

Frontiers in Genetics
|September 7, 2012
PubMed
Summary
This summary is machine-generated.

Designing effective small hairpin RNAs (shRNAs) is crucial for functional genomics. This study optimized shRNA design using thermodynamic and nucleotide features, developing the predictive miR_Scan software for enhanced gene silencing efficiency.

Keywords:
computational modelsmiR30-based shRNAshRNA designthermodynamic parameters

More Related Videos

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
10:40

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

Published on: April 25, 2022

A Simple Alternative to Stereotactic Injection for Brain Specific Knockdown of miRNA
06:53

A Simple Alternative to Stereotactic Injection for Brain Specific Knockdown of miRNA

Published on: December 26, 2015

Related Experiment Videos

Last Updated: May 18, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
10:40

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

Published on: April 25, 2022

A Simple Alternative to Stereotactic Injection for Brain Specific Knockdown of miRNA
06:53

A Simple Alternative to Stereotactic Injection for Brain Specific Knockdown of miRNA

Published on: December 26, 2015

Area of Science:

  • Molecular Biology
  • Functional Genomics
  • Bioinformatics

Background:

  • Small hairpin RNAs (shRNAs) are vital research tools for gene silencing.
  • Efficient shRNA design is critical for large-scale functional genomics studies.
  • Existing design methods require optimization for improved efficiency.

Purpose of the Study:

  • To identify key features correlating with shRNA silencing efficiency.
  • To develop an optimized predictive model for designing efficient shRNAs.
  • To create user-friendly software based on the developed model.

Main Methods:

  • Comparative, thermodynamic, and correlation analyses of ~18,000 miR30-based shRNAs.
  • Multiple regression analysis utilizing training and cross-validation datasets.
  • Development of the 'miR_Scan' software based on optimized predictive models.

Main Results:

  • Nucleotide preferences and thermodynamic features significantly correlate with shRNA efficiency (R=0.53).
  • The developed miR_Scan algorithm outperforms existing siRNA design approaches.
  • mRNA target secondary structure stability showed correlation but did not improve the predictive model.

Conclusions:

  • Optimized models incorporating nucleotide and thermodynamic features enhance shRNA design predictability.
  • The miR_Scan software provides a reliable tool for designing efficient shRNAs.
  • This approach offers a superior method for identifying effective shRNA molecules compared to previous strategies.