深度学习在作物基因编辑技术的应用与研究进展

doi:10.19788/j.issn.2096-6369.000120

农业大数据学报 ›› 2026, Vol. 8 ›› Issue (1): 24-35.doi: 10.19788/j.issn.2096-6369.000120

深度学习在作物基因编辑技术的应用与研究进展

赵晓燕¹^,²^,⁴(), 周焕斌²^,³, 周国民²^,⁴^,⁵^,⁶^,^*(), 张建华¹^,²^,⁴^,^*()

¹ 中国农业科学院农业信息研究所/农业农村部农业大数据重点实验室，北京 100081
² 中国农业科学院国家南繁研究院，海南三亚 572024
³ 中国农业科学院植物保护研究所，北京 100193
⁴ 国家农业科学数据中心，北京 100081
⁵ 农业农村部南京农业机械化研究所，南京 210014
⁶ 中国农业科学院西部农业研究中心，新疆昌吉 831100

收稿日期:2025-06-18 接受日期:2025-09-28 出版日期:2026-03-26 发布日期:2026-04-01
通讯作者: 张建华，E-mail：zhangjianhua@caas.cn；
周国民，E-mail：zhouguomin@caas.cn。
作者简介:赵晓燕，E-mail：2896216851@qq.com。
基金资助:
国家重点研发计划(2022YFF0711805);国家重点研发计划(2022YFF0711801);海南省自然科学基金(325MS155);三亚崖州湾科技城科技专项资助(SCKJ-JYRC-2023-45);三亚中国农业科学院国家南繁研究院南繁专项(YBXM2409);三亚中国农业科学院国家南繁研究院南繁专项(YBXM2410);三亚中国农业科学院国家南繁研究院南繁专项(YBXM2508);三亚中国农业科学院国家南繁研究院南繁专项(YBXM2509);中央级公益性科研院所基本科研业务费专项(JBYW-AII-2024-05);中央级公益性科研院所基本科研业务费专项(JBYW-AII-2025-05);中央级公益性科研院所基本科研业务费专项(Y2025YC90);中国农业科学院科技创新工程(CAAS-ASTIP-2024-AII)

Application of Deep Learning in Crop Gene Editing Technology and Research Progress

ZHAO XiaoYan¹^,²^,⁴(), ZHOU HuanBin²^,³, ZHOU GuoMin²^,⁴^,⁵^,⁶^,^*(), ZHANG JianHua¹^,²^,⁴^,^*()

¹ Agricultural Information Institute of CAAS/Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
² Nation Nanfan Research Institute of CAAS, Sanya 572024, Hainan, China
³ Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
⁴ National Agricultural Science Data Center, Beijing 100081, China
⁵ Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
⁶ Institute of Western Agriculture, Chinese Academy of Agricultural Sciences, Changji 831100, Xinjiang, China

Received:2025-06-18 Accepted:2025-09-28 Published:2026-03-26 Online:2026-04-01

摘要/Abstract

摘要：

近年来，基因编辑技术发展迅猛，已成为基础基因功能研究与生物育种的核心工具。并且随着计算机和大数据的增长推动了深度学习在基因编辑中的应用，深度学习技术在优化基因编辑过程，特别是在提升作物改良效率方面，正发挥着日益显著的作用。本文综述了深度学习在基因编辑优化方面的研究进展，重点介绍了深度学习在基因编辑效率和特异性增强方面的应用。此外，文章深入探讨了深度学习与基因编辑深度融合所面临的技术挑战，并展望了其未来发展前景。通过将先进的基因编辑技术与深度学习相结合，未来将进一步加快作物育种的进展。

关键词: 深度学习, 语言模型, 基因编辑

Abstract:

In recent years, gene editing technology has developed rapidly and has become a core tool for basic gene function research and biological breeding. And with the growth of computers and big data driving the application of deep learning in gene editing, deep learning technology is playing an increasingly significant role in optimizing the gene editing process, especially in enhancing the efficiency of crop improvement. This article reviews the research progress of deep learning in gene editing optimization, focusing on the application of deep learning in gene editing efficiency and specificity enhancement. In addition, the article delves into the technical challenges facing the deep integration of deep learning and gene editing, and looks forward to its future development prospects. By combining advanced gene editing technology with deep learning, the progress of crop breeding will be further accelerated in the future.

Key words: deep learning, language models, gene editing

赵晓燕, 周焕斌, 周国民, 张建华. 深度学习在作物基因编辑技术的应用与研究进展[J]. 农业大数据学报, 2026, 8(1): 24-35.

ZHAO XiaoYan, ZHOU HuanBin, ZHOU GuoMin, ZHANG JianHua. Application of Deep Learning in Crop Gene Editing Technology and Research Progress[J]. Journal of Agricultural Big Data, 2026, 8(1): 24-35.

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名称 Tool name	链接 Accession link	主要功能 Main Functions	年份 Year	引用 References
GPP Web Portal	https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design	结合深度学习预测sgRNA切割效率及脱靶效应，支持CRISPRko/i/a设计	2023	/
DeepHF	http://www.deephf.com/	基于LSTM预测sgRNA在人类细胞中的编辑效率	2019	[31]
DeepCRISPR	http://www.deepcrispr.net/	结合CNN预测sgRNA活性与脱靶效应	2018	[32]
CRISPR-Net	https://codeocean.com/ capsule/9553651/tree/v1	基于CNN和注意力机制优化sgRNA效率预测	2020	[33]
SpliceRover	http://bioit2.irc.ugent.be/splicerover/	专为CRISPR剪接调控设计的sgRNA优化工具	2018	[34]
CRISPRO	http://gitlab.com/bauerlab/crispro	结合gRNA结构预测sgRNA结合效率	2020	[35]
DeepPE	https://www.crisprbehive.design/ Source code: https://github.com/	预测不同gRNA序列的编辑效率	2020	[36]
DeepSpCas9	http://deepcrispr.info/DeepSpCas9/	预测不同gRNA的活性	2019	[37]

名称 Tool name	链接 Accession link	主要功能 Main Functions	年份 Year	引用 References
Protein2PAM	https://protein2pam.profluent.bio/login	基于45,000+ CRISPR-Cas进化数据集训练，预测并定制Cas蛋白的PAM识别能力，突破靶向范围限制	2025	[43]
PRO-PRIME	https://github.com/ai4protein/Pro-Prime	引入物种温度标签的语言模型，预测单点突变对蛋白稳定性与活性的影响，在LbCas12a等5种蛋白中实现>30%阳性突变率	2024	[44]
AlphaFold2	https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb	蛋白质结构预测或RNA结构预测	2018	/
DeepChrome	https:// www.deepchrome.org	基于 CNN 的组蛋白修饰数据预测基因表达水平	2020	[45]
DeepHistone	https://github.com/QijinYin/DeepHistone	基于CNN的序列和DNase测序准确预测组蛋白修饰位点	2021	[46]
DeepFIGV	https://deepfigv.mssm.edu/index.html	基于CNN预测对染色质可及性和组蛋白修饰的影响	2020	[47]

名称 Tool name	可标注单位 Annotatable units	链接 Accession link	年份 Year	引用 References
CRISPRcasIdentifier	Cas gene	https://github.com/BackofenLab/CRISPRcasIdentifier	2020	[49]
CASPredict	Cas gene	http://i.uestc.edu.cn/caspredict/cgi-bin/CASPredict.pl	2021	[50]
CRISPRcasStack	Cas gene	https://bioinfor.nefu.edu.cn/CRISPRCasStack/	2022	[51]
CRISPR-Cas-Docker	Cas gene	https://www.crisprcasdocker.org	2020	/
CRISPRloci	Cas gene (based on CRISPRcasIdentifier)	https://rna.informatik.uni-freiburg.de/CRISPRloci/	2021	[52]

深度学习在作物基因编辑技术的应用与研究进展

Application of Deep Learning in Crop Gene Editing Technology and Research Progress

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