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

CRISPR and crRNAs02:53

CRISPR and crRNAs

17.1K
Bacteria and archaea are susceptible to viral infections just like eukaryotes; therefore, they have developed a unique adaptive immune system to protect themselves. Clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins (CRISPR-Cas) are present in more than 45% of known bacteria and 90% of known archaea.
The CRISPR-Cas system stores a copy of foreign DNA in the host genome and uses it to identify the foreign DNA upon reinfection. CRISPR-Cas has three different...
17.1K
CRISPR/Cas9 Genome Editing01:28

CRISPR/Cas9 Genome Editing

97
The CRISPR-Cas system serves as a bacterial defense mechanism against invading genetic elements such as viruses and plasmids, forming the foundation for its adaptation as a powerful genome-editing tool. Originally discovered in prokaryotes, this system has been repurposed to revolutionize genetic engineering across a wide range of organisms, including plants, animals, and humans. The core component, Cas9, is an endonuclease derived from Streptococcus pyogenes, capable of introducing...
97
CRISPR01:59

CRISPR

52.6K
Genome editing technologies allow scientists to modify an organism’s DNA via the addition, removal, or rearrangement of genetic material at specific genomic locations. These types of techniques could potentially be used to cure genetic disorders such as hemophilia and sickle cell anemia. One popular and widely used DNA-editing research tool that could lead to safe and effective cures for genetic disorders is the CRISPR-Cas9 system. CRISPR-Cas9 stands for Clustered Regularly Interspaced...
52.6K

You might also read

Related Articles

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

Sort by
Same author

Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra.

ACS omega·2026
Same author

Integrative multi-omic and phenotypic analysis of open raceway pond production of Monoraphidium minutum 26B-AM reveals distinct stress signatures for scale-up and infection.

Biotechnology for biofuels and bioproducts·2026
Same author

How quantum computing can enhance biomarker discovery.

Patterns (New York, N.Y.)·2025
Same author

Poplar: a phylogenomics pipeline.

Bioinformatics advances·2025
Same author

Membrane-localized neoantigens predict the efficacy of cancer immunotherapy.

Cell reports. Medicine·2023
Same author

Tunable Intervalence Charge Transfer in Ruthenium Prussian Blue Analog Enables Stable and Efficient Biocompatible Artificial Synapses.

Advanced materials (Deerfield Beach, Fla.)·2022

Related Experiment Video

Updated: Aug 7, 2025

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines
10:46

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines

Published on: June 2, 2018

9.4K

High-Density Guide RNA Tiling and Machine Learning for Designing CRISPR Interference in Synechococcus sp. PCC 7002.

Tessa Dallo1, Raga Krishnakumar2, Stephanie D Kolker1

  • 1Molecular and Microbiology, Sandia National Laboratories, P.O. Box 5800, MS 1413, Albuquerque, New Mexico 87185, United States.

ACS Synthetic Biology
|March 9, 2023
PubMed
Summary
This summary is machine-generated.

This study identifies key factors for effective CRISPRi guide RNA (gRNA) design in Synechococcus sp. PCC 7002. Machine learning accurately predicts gRNA efficiency for gene expression tuning.

Keywords:
CRISPRiSynechococcusSynechococcus sp. PCC 7002cyanobacteriagRNA designmachine learning

More Related Videos

Investigation of Genetic Dependencies Using CRISPR-Cas9-based Competition Assays
11:05

Investigation of Genetic Dependencies Using CRISPR-Cas9-based Competition Assays

Published on: January 7, 2019

9.6K
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

2.5K

Related Experiment Videos

Last Updated: Aug 7, 2025

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines
10:46

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines

Published on: June 2, 2018

9.4K
Investigation of Genetic Dependencies Using CRISPR-Cas9-based Competition Assays
11:05

Investigation of Genetic Dependencies Using CRISPR-Cas9-based Competition Assays

Published on: January 7, 2019

9.6K
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

2.5K

Area of Science:

  • Microbiology
  • Molecular Biology
  • Synthetic Biology

Background:

  • CRISPR interference (CRISPRi) is established in Synechococcus sp. PCC 7002 for gene regulation.
  • However, specific design principles for optimizing guide RNA (gRNA) effectiveness are not well understood.

Purpose of the Study:

  • To systematically evaluate features influencing gRNA efficiency in Synechococcus sp. PCC 7002.
  • To develop predictive models for gRNA design to tune gene expression.

Main Methods:

  • Constructed 76 Synechococcus sp. PCC 7002 strains with various gRNAs targeting reporter systems.
  • Performed correlation analysis to identify significant gRNA design features.
  • Applied machine learning algorithms, including Random Forest, to predict gRNA effectiveness.

Main Results:

  • Identified critical gRNA design features: position relative to start codon, GC content, PAM site, minimum free energy, and DNA strand.
  • Observed unexpected gene activation with promoter-targeting gRNAs and enhanced repression with terminator-targeting gRNAs.
  • Random Forest model demonstrated the highest accuracy in predicting gRNA effectiveness.

Conclusions:

  • High-density gRNA data combined with machine learning significantly improves the prediction of gRNA efficiency.
  • These findings provide actionable insights for designing effective gRNAs to precisely control gene expression in Synechococcus sp. PCC 7002.