人工智能加速作物表观遗传设计育种

doi:10.13304/j.nykjdb.2022.1031

摘要/Abstract

摘要：

全球人口持续增长、极端天气频发等因素给全球粮食安全带来新的挑战，需要开辟新的育种途径提升作物育种性状。近年来，生物技术（biotechnology，BT）与人工智能、大数据等信息技术（information technology，IT）的深度融合推进了智能设计育种技术的快速发展。智能设计育种技术加速多维组学全景图谱绘制、基因调控网络解析和全基因组优异等位变异的挖掘，并通过基因组设计改良育种性状。作物育种性状受到遗传、表观遗传和环境因素协同调控，表观遗传与二者的互作起着关键作用。智能设计技术的发展使得基于表观遗传的设计育种成为可能，成为提升作物育种性状的新途径。表观遗传学与人工智能的深度融合极大地促进了表观遗传全基因组修饰位点的鉴定和调控网络解析，创新了基于表观遗传修饰位点的回路设计，实现定向创制综合性状优良的新种质和培育新品种。

关键词: 表观遗传学, 人工智能, 智能设计育种, 修饰位点, 调控回路

Abstract:

The rapid growth of the global population and tremendous climatic changes are new challenges for food security and compel breeders to develop new avenues of crop breeding. In recent years， the deep integration of biotechnology （BT） with artificial intelligence （AI）， big data， and other information technology （IT） has promoted the rapid development of intelligent design breeding technology. AI-based breeding technology accelerates multi-omics mapping and functional elucidation and the identification of excellent allele variations around the genome， and breeds new varieties through genome design breeding traits. The crucial agricultural breeding traits of crops are synergistically regulated by genetic， epigenetic， and environmental factors that epigenetic marks bridge other factors playing a pivotal role in the regulation of traits. The development of intelligent design technology makes it possible for accurate design breeding based on epigenetic inheritance， which is becoming a new way to improve crop breeding traits. The deep integration of epigenetics and AI has greatly facilitated the identification of genome-wide epigenetic modification sites and the elucidation of regulatory mechanisms. The design of regulatory circuits based on these epigenetic sites has been innovated to achieve the directional creation of new germplasm with excellent comprehensive traits and the cultivation of major varieties.

Key words: epigenetics, artificial intelligence, smart design, modification sites, regulatory circuits

中图分类号:

S336

郭位军, 李东维, 谢上, 杨立文, 李聪, 田健, 普莉, 谷晓峰. 人工智能加速作物表观遗传设计育种[J]. 中国农业科技导报, 2022, 24(12): 90-100.

Weijun GUO, Dongwei LI, Shang XIE, Liwen YANG, Cong LI, Jian TIAN, Li PU, Xiaofeng GU. Artificial Intelligence Accelerates Epigenetics and Plant Breeding[J]. Journal of Agricultural Science and Technology, 2022, 24(12): 90-100.

图/表 3

图1 表观基因组构成

Fig. 1 Component of epigenome

图2 表观遗传修饰检测方法BS-seq：亚硫酸氢盐测序；MeDIP-seq： DNA甲基化免疫共沉淀测序；MeRIP-seq： RNA甲基化免疫共沉淀测序；ChIP-seq：染色质免疫共沉淀测序；SMRT-seq和Nanopore-seq：三代单分子测序；ATAC-seq：转座酶染色质可及性测序；HiC：染色质构象捕捉技术

Fig. 2 Technologies relevant to epigenomeBS-seq：Bisulfite sequencing； MeDIP-seq： Methylated DNA immunoprecipitation sequencing； MeRIP-seq： Methylated RNA immunoprecipitation sequencing； ChIP-seq： Chromatin immunoprecipitation sequencing； SMRT-seq： Single-molecule real-time sequencing；Nanopore-seq： Nanopore sequencing； ATAC-seq： Assay for transposase-accessible chromatin with high-throughput sequencing （HTS）； HiC： High-throughput chromosome conformation capture technology

图3 表观遗传智能设计创制优异种质

Fig. 3 Smart epigenetic design to culture elite germplasm

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