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Related Concept Videos

CRISPR01:59

CRISPR

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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...
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CRISPR/Cas9 Genome Editing01:28

CRISPR/Cas9 Genome Editing

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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...
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CRISPR and crRNAs02:53

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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...
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Related Experiment Video

Updated: Aug 10, 2025

CIRCLE-Seq for Interrogation of Off-Target Gene Editing
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Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches.

Xiaolong Cheng1,2, Zexu Li3, Ruocheng Shan1,4

  • 1Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, 20010, USA.

Nature Communications
|February 10, 2023
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We developed DeepCas13, a deep learning model to predict CRISPR-Cas13d guide efficiency for RNA targeting. This tool improves accuracy in predicting on-target and off-target effects, aiding gene function studies.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • CRISPR-Cas13d systems offer powerful RNA manipulation tools.
  • Accurate prediction of guide RNA on-target and off-target effects remains a significant challenge.

Purpose of the Study:

  • To develop a deep learning model, DeepCas13, for predicting CRISPR-Cas13d on-target activity.
  • To enhance the prediction of guide efficiency for targeting both protein-coding and non-coding RNAs.

Main Methods:

  • CRISPR-Cas13d proliferation screens were conducted.
  • A deep learning model, DeepCas13, was designed using guide sequences and RNA secondary structures.
  • Model performance was validated using secondary screens and quantitative RT-PCR.

Main Results:

  • DeepCas13 demonstrated superior performance over existing methods in predicting guide efficiency.
  • Off-target viability effects were observed for guides targeting non-essential genes, correlating with on-target efficiency.
  • lncRNAs impacting cell viability and proliferation were identified using DeepCas13.

Conclusions:

  • DeepCas13 accurately predicts CRISPR-Cas13d guide activity, improving RNA targeting predictions.
  • Proper normalization strategies using negative controls are crucial for mitigating false positives in proliferation screens.
  • The study identifies novel lncRNAs involved in cell viability and proliferation.