机器学习方法在CRISPR/Cas9系统中的应用

基于CRISPR/Cas9系统介导的第三代基因组定点编辑技术,已被广泛应用于基因编辑和基因表达调控等研究领域。如何提高该技术对基因组编辑的效率与特异性、最大限度降低脱靶风险一直是该领域的难点。近年来,机器学习为解决CRISPR/Cas9系统所面临的问题提供了新思路,基于机器学习的CRISPR/Cas9系统已逐渐成为研究热点。本文阐述了CRISPR/Cas9的作用机理,总结了现阶段该技术面临的基因组编辑效率低、存在潜在的脱靶效应、前间区序列邻近基序(PAM)限制识别序列等问题,最后对机器学习应用于优化设计高效向导RNA (sgRNA)序列、预测sgRNA的活性、脱靶效应评估、基因敲除、高通量功能基因筛选等领域的研究现状与发展前景进行了展望,以期为基因组编辑领域的研究提供参考。

Application of machine learning in the CRISPR/Cas9 system

The third generation of the CRISPR/Cas9-mediated genome fixed-point editing technology has been widely used in the field of gene editing and gene expression regulation. How to improve the on-target efficiency and specificity of this system, as well as reduce its off-target effects are always the bottleneck in its development. Machine learning provides novel methods to the problems of the CRISPR/Cas9 system, and CRISPR/Cas9-based machine learning has recently become a very hot research topic. In this review, we firstly outline the mechanism of the CRISPR/Cas9 system. Subsequently, we elaborate the current issues of CRISPR/Cas9, including low efficiency and potential off-target effects, and sequence-recognizing limitation from protospacer adjacent motif (PAM). Finally, we summarize the applications of methods within the machine learning framework for optimizing the CRISPR/Cas9 system, such as optimized single-guide RNA (sgRNA) design, CRISPR/Cas9 cleavage efficiency prediction, off-target effects evaluation, gene knock-out as well as high-throughput functional genetic screening and prospects for development.