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Related Concept Videos

CRISPR/Cas9 Genome Editing01:28

CRISPR/Cas9 Genome Editing

178
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...
178

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Efficient Production and Identification of CRISPR/Cas9-generated Gene Knockouts in the Model System Danio rerio
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TransCrispr: Transformer Based Hybrid Model for Predicting CRISPR/Cas9 Single Guide RNA Cleavage Efficiency.

Yunqi Wan, Zhenran Jiang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |August 25, 2022
    PubMed
    Summary
    This summary is machine-generated.

    TransCrispr, a novel computational tool, accurately predicts single guide RNA (sgRNA) knockout efficacy by integrating Transformer and CNN architectures. This approach improves genome editing precision by considering sequence and biological features for sgRNA design.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Molecular Biology

    Background:

    • CRISPR/Cas9 is a powerful genome editing technology for targeted DNA modification.
    • Accurate prediction of single guide RNA (sgRNA) on- and off-target effects remains a challenge.
    • Computational methods are crucial for optimizing sgRNA design with high cell-specific sensitivity and specificity.

    Purpose of the Study:

    • To develop a novel computational approach for predicting sgRNA knockout efficacy.
    • To improve the accuracy and reliability of sgRNA design for CRISPR/Cas9 applications.
    • To investigate the influence of biological features on sgRNA on-target activity.

    Main Methods:

    • Introduced TransCrispr, a hybrid architecture combining Transformer and Convolutional Neural Network (CNN).
    • Encoded sgRNA sequence data, positional information, and biological features as network input.
    • Utilized CNN for feature representation learning and Transformer for self-attention mechanisms.

    Main Results:

    • TransCrispr demonstrated superior prediction accuracy compared to existing state-of-the-art methods.
    • The model showed enhanced generalization ability across diverse datasets.
    • Experimental validation on seven public datasets confirmed the model's performance.

    Conclusions:

    • TransCrispr offers a significant advancement in predicting sgRNA knockout efficiency.
    • The integration of sequence and biological features improves prediction performance.
    • This tool facilitates more precise and effective sgRNA design for genome editing.