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Related Concept Videos

CRISPR01:59

CRISPR

51.7K
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 and crRNAs02:53

CRISPR and crRNAs

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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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Homologous Recombination02:31

Homologous Recombination

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The basic reaction of homologous recombination (HR) involves two chromatids that contain DNA sequences sharing a significant stretch of identity. One of these sequences uses a strand from another as a template to synthesize DNA in an enzyme-catalyzed reaction. The final product is a novel amalgamation of the two substrates. To ensure an accurate recombination of sequences, HR is restricted to the S and G2 phases of the cell cycle. At these stages, the DNA has been replicated already and the...
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Related Experiment Video

Updated: Jul 9, 2025

CIRCLE-Seq for Interrogation of Off-Target Gene Editing
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CIRCLE-Seq for Interrogation of Off-Target Gene Editing

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piCRISPR: Physically informed deep learning models for CRISPR/Cas9 off-target cleavage prediction.

Florian Störtz1, Jeffrey K Mak1, Peter Minary1

  • 1Department of Computer Science, University of Oxford, Parks Road, Oxford OX1 3QD, UK.

Artificial Intelligence in the Life Sciences
|December 4, 2023
PubMed
Summary

This study introduces piCRISPR, a new deep learning model for predicting CRISPR/Cas gene editing off-target effects. It utilizes physically informed features to improve prediction accuracy, crucial for safe in vivo gene therapies.

Keywords:
CRISPRCas9Cleavage predictionDeep learningNucleosome organisation

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Gene Editing Technologies

Background:

  • CRISPR/Cas systems are powerful gene editing tools with therapeutic potential.
  • Off-target edits pose a significant safety concern for in vivo gene editing applications.
  • Existing prediction algorithms often underutilize physically informed features.

Purpose of the Study:

  • To develop an advanced off-target prediction model incorporating physically informed features.
  • To enhance the accuracy of predicting CRISPR/Cas nuclease cleavage activity.
  • To improve the safety and efficacy of gene editing for therapeutic applications.

Main Methods:

  • Implementation of state-of-the-art deep learning algorithms.
  • Utilization of physically informed features capturing the biological environment of cleavage sites.
  • Training and evaluation on the comprehensive crisprSQL off-target cleavage dataset.

Main Results:

  • The piCRISPR model demonstrates high performance in off-target prediction.
  • Physically informed features, including sequence context and chromatin accessibility, are critical for accurate predictions.
  • Novel features significantly improve prediction accuracy for sequence-identical locus pairs.

Conclusions:

  • piCRISPR offers a more accurate method for predicting CRISPR/Cas off-target effects.
  • The model's environmentally sensitive features are vital for clinical guide design.
  • This approach advances the safety of in vivo gene editing therapies.