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一个可概括的Cas9/sgRNA预测模型,使用机器转移学习与小型高质量数据集进行预测.

Dalton T Ham¹, Tyler S Browne¹, Pooja N Banglorewala¹

¹Department of Biochemistry, Schulich School of Medicine and Dentistry, London, ON, N6A5C1, Canada.

Nature communications

|September 7, 2023

概括

此摘要是机器生成的。

我们开发了crisprHAL,这是一种机器学习工具,可以准确预测细菌中的CRISPR/Cas9 (SpCas9) 单向导RNA (sgRNA) 活性. 这通过改进sgRNA设计和功能预测,推动了抗微生物开发和基因组工程.

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Published on: February 26, 2019

科学领域:

微生物学微生物学
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:

来自Streptococcus pyogenes (SpCas9) 的CRISPR/Cas9系统是用于抗菌应用和细菌基因组工程的多功能工具.
预测细菌单向导RNA (sgRNA) 活动的现有模型缺乏准确性和通用性,部分原因是训练数据集的局限性,这些数据集将SpCas9/sgRNA活动与细胞毒性结合起来.

研究的目的:

开发一个高质量的数据集,用于训练SpCas9/sgRNA活动预测模型.
创建一个机器学习架构,crisprHAL,用于准确和可概括的细菌sgRNA活动的预测.
改进用于抗微生物和基因组工程应用的sgRNA的设计.

主要方法:

利用双等离子体阳性选择系统生成高可靠性数据,区分SpCas9/sgRNA裂变活性和毒性.
开发了crisprHAL机器学习模型,结合了转移学习能力.
在现有数据集上验证了该模型,并测试了它在不同细菌物种的概括性.

主要成果:

创建了一个新的,高质量的数据集,用于SpCas9/sgRNA活动评估.
crisprHAL模型在sgRNA活动预测准确度方面取得了显著的改进,特别是当使用有限的高质量数据进行微调时.
crisprHAL成功地回顾了已知的SpCas9/sgRNA-目标DNA相互作用,并显示了对各种细菌物种的概括性.

结论:

crisprHAL模型为预测细菌SpCas9/sgRNA活动提供了一个强大的解决方案,克服了以前模型的局限性.
这种工具提高了sgRNA设计的可靠性,无论是针对特定序列的抗微生物药物还是精确的细菌基因组工程.
crisprHAL是迈向一种基于CRISPR的细菌应用程序的通用预测工具的重要一步.