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基于深度学习的CRISPR/Cas目标外预测模型

Mingming Cao¹, Alexander Brennan², Ciaran M Lee²

¹Department of Bioengineering, Rice University, Houston, TX, 77030, USA.

|June 5, 2025

概括

深度学习模型显示出预测CRISPR/Cas基因组编辑目标外部位 (OTS) 的前景. 整合经过验证的OTS数据可以提高模型性能和稳定性,从而实现更安全的基因编辑应用.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

克里斯普尔/卡斯基因组编辑提供了精确的基因改造,但面临着非目标效应 (OTS) 的挑战.
准确预测OTS对于CRISPR/Cas技术的安全临床应用至关重要.
在 silico 方法,特别是深度学习,正在成为 OTS 预测的强大工具,因为它们能够学习复杂的序列特征.

研究的目的:

审查现有的目标外站 (OTS) 预测工具,重点关注深度学习方法.
描述用于训练和测试OTS预测深度学习模型的数据集.
评估和比较六个突出的深度学习模型的CRISPR/Cas OTS预测的性能.

主要方法:

审查了当前的OTS预测工具,强调深度学习方法.
用于深度学习模型培训和验证的分析数据集.
通过使用六个公共数据集和CRISPRoffT数据库,评估了六个深度学习模型 (CRISPR-Net,CRISPR-IP,R-CRISPR,CRISPR-M,CrisprDNT,Crispr-SGRU).
使用精度,回忆,F1分数,MCC,AUROC和PRAUC等指标评估模型性能.

主要成果:

深度学习模型显示了预测非目标CRISPR/Cas位点的潜力.
将验证的OTS数据集纳入培训中显著提高了模型性能和预测稳定性,特别是在不平衡的数据集中.
在各种评估场景中,CRISPR-Net,R-CRISPR和Crispr-SGRU的整体表现强.
没有一个单一的模型在所有测试条件下始终超过所有其他模型.

结论:

高质量,经过验证的目标外站点数据对于提高基于深度学习的预测的准确性和可靠性至关重要.
先进的深度学习架构,当与适当的数据进行训练时,可以显著提高CRISPR/Cas目标外站点的预测.
整合强大的预测工具对于确保CRISPR/Cas基因组编辑在临床环境中的安全性和有效性至关重要.

关键词:

这就是CRISPR/CasPR.深度学习模型深度学习模型基因编辑基因编辑目标网站之外的网站

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