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使用深度学习预测CRISPR-Cas13d指导RNA的目标和非目标活性

Hans-Hermann Wessels^1,2, Andrew Stirn^1,3, Alejandro Méndez-Mancilla^1,2

¹New York Genome Center, New York City, NY, USA.

Nature biotechnology

|July 3, 2023

概括

研究人员开发了TIGER,这是一种新的AI工具,可以使用RNA向CRISPR技术精确控制基因表达. 虎精确预测指导RNA的有效性,使得活细胞中更好的基因剂量控制.

科学领域:

分子生物学分子生物学
遗传学遗传学是一个
生物信息学是一种生物信息学.

背景情况:

针对RNA的CRISPR系统,如RfxCas13d,对于转录组工程至关重要.
准确预测目标活动和目标外影响对于它们的应用至关重要.
目前的预测模型需要改进,以实现精确的基因调制.

研究的目的:

系统地分析指导RNA修饰对RfxCas13d活动的影响.
开发一种用于RNA向CRISPR疗效的预测模型.
建立一个用于精确控制基因剂量的框架,使用RNA向CRISPRs.

主要方法:

设计和测试了大约20万个RfxCas13d指导RNA,在人体细胞中具有各种不匹配和内置.
收集了关于指导RNA活性的大规模数据,包括目标和非目标效应.
使用此数据集,训练了一个卷积神经网络,TIGER (通过gRNA设计向抑制基因表达).

主要成果:

导向RNA中的不匹配和印值对Cas13d活性表现出位置和上下文依赖的影响.
在不匹配的G-U摇摆配对比其他单基不匹配更好地容忍.
与现有方法相比,TIGER模型在预测导向RNA疗效方面表现优异.

结论:

泰格为预测和优化向RNA的CRISPR导向RNA提供了一个强大的框架.
开发的框架允许精确调节转录表达和基因剂量控制.
这一进步促进了RNA向CRISPR在生物研究和治疗中的更广泛应用.

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