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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 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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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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DeepIndel: An Interpretable Deep Learning Approach for Predicting CRISPR/Cas9-Mediated Editing Outcomes.

Guishan Zhang1, Huanzeng Xie1, Xianhua Dai2

  • 1College of Engineering, Shantou University, Shantou 515063, China.

International Journal of Molecular Sciences
|October 26, 2024
PubMed
Summary
This summary is machine-generated.

DeepIndel, a novel deep learning model, accurately predicts CRISPR/Cas9 genome editing repair outcomes. This method enhances understanding of DNA repair mechanisms and improves prediction accuracy for frameshifts.

Keywords:
BERTCRISPR/Cas9Deep SHAPrepair outcomes

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • CRISPR/Cas9 genome editing is widely used, but predicting repair outcomes post-cleavage remains challenging.
  • Existing deep learning models for predicting DNA repair outcomes have limitations in accuracy and interpretability, especially for frameshifts.

Purpose of the Study:

  • To develop an advanced deep learning model for predicting CRISPR/Cas9 editing outcomes.
  • To improve the accuracy and generalizability of DNA repair outcome predictions.
  • To enhance the interpretability of predictive models for genome editing.

Main Methods:

  • Developed DeepIndel, an end-to-end multi-label regression model utilizing the BERT-base module.
  • Employed Deep SHAP for visualizing nucleotide importance in DNA sequences surrounding the Cas9 cut site.

Main Results:

  • DeepIndel demonstrated superior performance compared to existing methods across various accuracy and generalizability metrics.
  • The model effectively predicted frameshift outcomes.
  • Nucleotide composition, specifically mononucleotides and trinucleotides near the cut site, was identified as crucial for outcome prediction.

Conclusions:

  • DeepIndel offers a significant advancement in predicting CRISPR/Cas9 editing repair outcomes.
  • The model's interpretability provides insights into the sequence determinants of DNA repair.
  • This work contributes to more precise genome engineering applications.