通过深度学习来预测基础编辑器的目标
Chengdong Zhang1,2,3, Yuan Yang1,2, Tao Qi1
1Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center; State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200438, China.
Nature communications
|September 2, 2023
概括
基编辑器可能会导致由于指导RNA (gRNA) 不匹配而导致意外突变. 新的深度学习模型,ABEdeepoff和CBEdeepoff,准确地预测这些非目标突变,有助于更安全的基础编辑应用程序.
科学领域:
- 遗传学和基因组学 在
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 基因编辑器是精确基因组编辑的强大工具.
- 由指导RNA (gRNA) 序列不匹配引起的非目标突变限制了基编辑的安全性和有效性.
- 预测和最小化这些非目标效应对于临床应用至关重要.
研究的目的:
- 开发由腺基编辑器 (ABE) 和细胞基编辑器 (CBE) 诱导的Cas9依赖的目标外突变的预测模型.
- 创建一个用户友好的Web服务器,用于预测目标以外的网站的编辑基础.
主要方法:
- 设计和集成的gRNA-off-target对为ABE和CBE进入人体细胞.
- 产生了非目标突变效率的大规模数据集 (ABE为54,663,CBE为55,727).
- 使用生成的数据集训练深度学习模型 (ABEdeepoff 和 CBEdeepoff).
主要成果:
- 开发了ABEdeepoff和CBEdeepoff模型,能够高精度地预测目标外的地点.
- 实现了内源基位的高预测性能,斯皮尔曼相关性值范围从0.710到0.859.
- 将预测模型集成到一个可访问的在线网络服务器 (http://www.deephf.com/#/bedeep/bedeepoff).
结论:
- 开发的深度学习模型和Web服务器有效地预测了基础编辑器的目标外突变.
- 这些工具可以显著帮助研究人员最大限度地减少非目标效应,提高基础编辑技术的安全性.
- 促进了基础编辑在研究和潜在的治疗环境中的更广泛应用.
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