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深度学习和机器学习的融合框架,用于预测sgRNA分裂效率

Yu Liu¹, Rui Fan¹, Jingkun Yi¹

¹Department of Biomedical Informatics, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing, China.

Computers in biology and medicine

|September 11, 2023

概括

这项研究引入了一种新的深度学习和机器学习框架,用于预测CRISPR基因组编辑的单导向RNA (sgRNA) 分裂效率. 新模型显著提高了预测准确性,有助于设计更有效的基因编辑工具.

科学领域:

遗传学和基因组学遗传学和基因组学
生物信息学是一种生物信息学.
分子生物学分子生物学

背景情况:

克里斯普尔/卡斯9系统是基因组编辑的关键技术.
单导向RNA (sgRNA) 设计极大地影响了编辑效率.
目前对sgRNA裂变效率的预测模型缺乏足够的准确性.

研究的目的:

开发一种更准确的方法来预测sgRNA裂变效率.
为CRISPR/Cas9应用改进高效的sgRNAs的设计.
为评估sgRNA有效性提供一个用户友好的工具.

主要方法:

一个融合框架,结合深度学习 (CNN和RNN) 和机器学习 (LGBM).
利用sgRNAs的主要序列和次要结构特征.
使用深度神经网络提取的特征训练了一种机器学习模型.

主要成果:

获得了0.917的斯皮尔曼相关系数,比现有方法改进了>5%.
从7.89 × 10-3的平均平方误差减少到4.75 × 10-3的平均平方误差.
开发了一个在线工具,CRISep,用于sgRNA评估.

结论:

拟议的融合框架显著提高了sgRNA裂变效率的预测准确性.
与以前的工具相比,这种方法为设计有效的sgRNA提供了一种优越的方法.
对于使用CRISPR/Cas9技术的研究人员来说,CRISep工具提供了一个宝贵的资源.

关键词:

这就是CRISPR/Cas9的作用.有效率预测的预测.机器学习机器学习 sgRNARNA 是一个

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