机器学习方法在CRISPR/Cas9系统中的应用

doi:10.16288/j.yczz.18-135

遗传 ›› 2018, Vol. 40 ›› Issue (9): 704-723.doi: 10.16288/j.yczz.18-135

机器学习方法在CRISPR/Cas9系统中的应用

张桂珊(),杨勇,张灵敏,戴宪华()

中山大学电子与信息工程学院,广州 510006

收稿日期:2018-05-15 修回日期:2018-07-19 出版日期:2018-09-20 发布日期:2018-07-30
作者简介:张桂珊,博士研究生,研究方向：生物信息学。E-mail: zhanggsh7@mail2.sysu.edu.cn
基金资助:
国家自然科学基金项目(61872396)

Application of machine learning in the CRISPR/Cas9 system

Zhang Guishan(),Yang Yong,Zhang Lingmin,Dai Xianhua()

School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China

Received:2018-05-15 Revised:2018-07-19 Online:2018-09-20 Published:2018-07-30
Supported by:
Supported by National Natural Science Foundation of China(61872396)

摘要/Abstract

摘要：

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。

关键词: CRISPR/Cas9, 机器学习, sgRNA, 脱靶效应, 基因敲除

Abstract:

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.

Key words: CRISPR/Cas9, machine learning, sgRNA, off-target effect, gene knock-out

张桂珊, 杨勇, 张灵敏, 戴宪华. 机器学习方法在CRISPR/Cas9系统中的应用[J]. 遗传, 2018, 40(9): 704-723.

Zhang Guishan, Yang Yong, Zhang Lingmin, Dai Xianhua. Application of machine learning in the CRISPR/Cas9 system[J]. Hereditas(Beijing), 2018, 40(9): 704-723.

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