Deep learning in CRISPR-Cas systems: a review of recent studies

Frontiers in Bioengineering and Biotechnology

Bioengineering and Biotechnology

Minhyeok Lee¹ 

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关键词: CRISPR-Cas system;    CRISPR-Cas9;    deep learning;    guide RNA;    genome editing;    on-target activity;    off-target activity;    artificial intelligence;   

DOI  :  10.3389/fbioe.2023.1226182

 received in 2023-05-24, accepted in 2023-06-22,  发布年份 2023

来源: Frontiers

【 摘 要 】

In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements mandate regular investigation of the state-of-the-art, particularly given the pace of recent developments. This review focuses on the significant progress achieved during 2019–2023 in the utilization of deep learning for predicting guide RNA (gRNA) activity in the CRISPR-Cas system, a key element determining the effectiveness and specificity of genome editing procedures. In this paper, an analytical overview of contemporary research is provided, with emphasis placed on the amalgamation of artificial intelligence and genetic engineering. The importance of our review is underscored by the necessity to comprehend the rapidly evolving deep learning methodologies and their potential impact on the effectiveness of the CRISPR-Cas system. By analyzing recent literature, this review highlights the achievements and emerging trends in the integration of deep learning with the CRISPR-Cas systems, thus contributing to the future direction of this essential interdisciplinary research area.

【 授权许可】

Unknown   
Copyright © 2023 Lee.