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生成,建模和评估一组大规模的CRISPR/Cas9异位点与凸起

Ofir Yaish¹, Yaron Orenstein^2,3

¹School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.

Nucleic acids research

|May 30, 2024

概括

这项研究通过创建具有凸起的目标外部位 (OTS) 的综合数据集来增强CRISPR/Cas9基因编辑. 在这些数据上训练的机器学习模型显著提高了这些意外DNA修改的预测准确性.

科学领域:

基因组学就是基因组学.
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:

克里斯普尔/卡斯9基因编辑精确,但可能会导致目标外部位 (OTS).
现有的机器学习模型由于数据有限,难以预测带有凸起的OTS.
之前的高吞吐量测试,如CHANGE-seq生成了大型数据集,但在GUIDE-seq实验中缺乏膨胀数据.

研究的目的:

为了生成最全面的 GUIDE-seq 数据集,包括带有凸起的非目标站点.
训练和评估能够预测有凸起的OTS的先进机器学习模型.
为了提高预测CRISPR/Cas9基因编辑中的意外DNA修改的准确性.

主要方法:

重新处理公共CHANGE-seq数据以生成20个新的GUIDE-seq实验.
创建了数百个目标之外的网站,其中包括来自原始和新GUIDE-seq实验的凸起.
在综合数据集上训练和评估多个最先进的机器学习模型.

主要成果:

实现了机器学习模型在预测OTS时的最先进性能,无论是体外还是细胞内.
在聚焦有凸起的非目标位置时,表现出显著提高的准确性.
成功可视化了OTS与凸起的模型所学到的关键特征.

结论:

开发的机器学习模型可以准确地预测CRISPR/Cas9的目标外部位,包括那些有凸起的地方.
这项工作解决了当前基因编辑安全评估的关键局限性.
这些发现有助于更安全,更有效地应用CRISPR/Cas9技术.

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