Scholarlink

<p>CRISPR genome-editing tools, including Cas9 and Cas12a nucleases, base editors and prime editors, have revolutionized genome manipulation across various species and cell types. These tools have undergone continuous improvement as new variants or types of editors have been generated to improve their efficiency, specificity and applicability. However, given the vast array of genome editors and the multitude of designable guide RNAs, selecting the optimal combinations for efficient and precise genome editing has become increasingly challenging, especially under variable experimental conditions. To address this issue, several methods for evaluating genome-editing tools in a high-throughput manner have been developed. The resulting large datasets of editing efficiencies or specificities have been used to develop machine learning models that predict efficiency and specificity, greatly facilitating the optimal selection of genome editors and guide RNAs. Here, we review recent developments in high-throughput evaluations and machine learning-based predictions of genome-editing efficiencies and/or off-target effects, together with recent advances in diverse genome-editing tools. We also cover artificial intelligence-based development and evolution of genome-editing tools.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluation and prediction of guide RNA activities in genome-editing tools

  • 期刊论文

  • 发表日期：

    2025年8月29日

查看全文
  • Hui Kwon Kim,
  • Hyongbum Henry Kim

摘要

CRISPR genome-editing tools, including Cas9 and Cas12a nucleases, base editors and prime editors, have revolutionized genome manipulation across various species and cell types. These tools have undergone continuous improvement as new variants or types of editors have been generated to improve their efficiency, specificity and applicability. However, given the vast array of genome editors and the multitude of designable guide RNAs, selecting the optimal combinations for efficient and precise genome editing has become increasingly challenging, especially under variable experimental conditions. To address this issue, several methods for evaluating genome-editing tools in a high-throughput manner have been developed. The resulting large datasets of editing efficiencies or specificities have been used to develop machine learning models that predict efficiency and specificity, greatly facilitating the optimal selection of genome editors and guide RNAs. Here, we review recent developments in high-throughput evaluations and machine learning-based predictions of genome-editing efficiencies and/or off-target effects, together with recent advances in diverse genome-editing tools. We also cover artificial intelligence-based development and evolution of genome-editing tools.