植物表型组学大数据及其研究进展

doi:10.19788/j.issn.2096-6369.190201

摘要/Abstract

摘要：

植物表型组学通过集成自动化平台装备和信息化技术手段,获取多尺度、多生境、多源异构植物表型海量数据,形成植物表型组学大数据,从组学高度系统深入地挖掘“基因型-表型-环境型”内在关系、全面揭示特定生物性状的形成机制,将极大地促进功能基因组学、作物分子育种与高效栽培的进程。本文概括了植物表型组学大数据的发展背景、含义、产生过程和特点,系统综述了植物表型组学大数据研究进展,包括植物表型数据获取与解析、植物表型组大数据管理及建库技术、表型性状预测和基于表型组的多重组学分析的进展;从植物表型数据采集标准、多样化表型配套设施和低成本表型设备研发、开放共享植物表型组大数据平台构建、表型大数据融合与挖掘理论方法、植物表型组学协同共享和互作机制五个方面探讨了当前植物表型组学大数据研究与应用中面临的问题和挑战;最后从加强植物表型组技术体系设计与标准研究、植物表型-环境感知机理研究和智能化设备研发、植物表型组大数据建设以及人才队伍和协作网络建设四个方面提出具体建议。

关键词: 植物表型组学, 大数据, 数字植物, 数据挖掘, 数据管理, 数据获取, 性状预测, 植物表型组大数据平台

Abstract:

Plant phenomics is capable of acquiring gigantic multi-dimensional, multi-environment, and multi-source heterogeneous plant phenotyping datasets through integrated automation platforms and information retrieval technologies, based on which the big-data driven plant phenomics research is established. This emerging research domain aims to systematically and thoroughly explore the internal relationship between "gene-phenotype-environment" at the omics level, so that phenomics methods can be utilized to unravel the formation mechanism of specific biological traits in a comprehensive manner. As a result, it is greatly catalyzing the research progress of functional genomics, crop molecular breeding, and efficient cultivation. In this paper, we summarized the background, definition, initiation, and features of the big-data driven plant phenomics, followed by a systemic overview of the progress of this field, including the acquisition and analysis of plant phenotyping data, data management and relevant database construction techniques for administering big data generated, the prediction of phenotypic traits, and its connection with the plant omics research. Furthermore, this paper focuses on discussing present problems and challenges encountered by both plant research and related applications, including (1) the standardization of collecting plant phenotypes, (2) research and development (R&D) of diverse phenotyping devices, supporting facilities, and low-cost phenotyping equipment, (3) the establishment of big data platforms that can openly share phenotyping data and phenotypic traits information, (4) theoretical approaches for fusion algorithms and data mining techniques, and (5) collaborative, sharing and interactive mechanisms for the plant phenomics community to adopt. Finally, the paper puts forward suggestions in four aspects that need to be strengthened: (1) systematic design and standards of plant phenomics research, (2) revealing the mechanism of plant phenotype and environtype to facilitate intelligent equipment R&D, (3) the establishment of big data for plant phenomics, and (4) the formation of collaborations through academic networks and specialized research groups and laboratories.

Key words: plant phenomics, big data, digital plant, data mining, data management, data acquisition, trait prediction, big data platform of plant phenomics

中图分类号:

赵春江. 植物表型组学大数据及其研究进展[J]. 农业大数据学报, 2019, 1(2): 5-14.

Chunjiang Zhao. Big Data of Plant Phenomics and Its Research Progress[J]. Journal of Agricultural Big Data, 2019, 1(2): 5-14.

图/表 3

图 1

表1

多尺度植物表型数据获取与解析案例"

不同尺度分类	数据类型	表型解析方法	表型参数	植物类别
	激光共聚焦显微图像	随机森林	下胚轴细胞表型	拟南芥^[24]
细胞、组织尺度	Micro-CT图像	随机森林	茎秆维管束表型	玉米^[25]
		人工神经网络（ANN)	幼苗病害识别	蝴蝶兰^[26]
		随机森林	生物量、叶面积指数、叶长等表型	拟南芥^[27]
			根系、叶片、果穗等识别与分类	小麦^[28]
	RGB图像	卷积神经网络（CNN)	豆荚中的种子数目	大豆^[29]
			果实成熟度	苹果^[30]
			叶片识别（叶型胁迫的类型）、分类（低、	大豆^[31]
		深度卷积神经网络(DCNN)		大豆^[31]
器官尺度			中或高胁迫）及量化（胁迫严重度）
	RGB+深度图像	支持向量机（SVM)	叶片病毒侵染识别	西红柿^{[32, 33]}
	荧光成像	支持向量机（SVM)	叶片黄龙病检测	柑橘^[34]
		支持向量机（SVM)	病害早期检测	大麦、西红柿、甜菜^[35]
	高光谱成像	K-均值聚类	叶片黄褐斑病检测	大麦^[36]
	光谱、红外、叶绿素	随机森林	小麦黑斑病检测	小麦^[37]
	荧光成像
	RGB+光谱图像	支持向量机（SVM)	植株动态生长表型	大麦^[38]
植株尺度	点云数据	Faster R-CNN	植株分割、株高检测	玉米^[39]
		简单线性迭代聚类(SLIC)	大田水稻稻穗识别	水稻^[40]
	RGB 图像	+卷积神经网络（CNN)
		卷积神经网络（CNN)	开花表型性状	小麦^[41]
	RGB+高程图	卷积神经网络（CNN)	出苗率、生物量	小麦^[42]
群体尺度	立体相机成像	深度卷积神经网络(DCNN)	茎秆数目、茎宽表型	高粱、甘蔗、谷物、玉米^[43]
			冠层覆盖度、植被指数、开花表型检测	棉花^[44]
	多光谱成像	支持向量机（SVM)	黄龙病检测	柑橘^[45]
	点云数据	人工神经网络（ANN)	绿叶面积指数(GAI)解析	小麦^[46]
	点云数据	浅层卷积神经网络（CNN)	产量性状	莴苣^[47]

表1

表2

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名称	简介	发表时间	URL
CropSight^[51]	针对物联网传感器和表型平台自动化获取数据的开源信息管理系统	2019	https://github.com/Crop-Phenomics-Group/cropsigt/releases
PHIS^[54]	本体驱动的表型混合信息系统一一处理植物表型组学中多源、多尺度信息	2019	http://www.phis.inra.fr/
Planteome^[53]	植物基因组和表型组数据共享平台	2018	http://www.planteome.org
Crop Phenotyping Center^[55]	华中农业大学作物表型中心	2017	http://plantphenomics.hzau.edu.cn/checkiflogin_en.action
PGP repository^[56]	植物表型和基因组学数据发布基础平台	2016	http://edal.ipk-gatersleben.de/repos/pgp/
Seed breeding cloud platform	金种子育种云平台	2016	http://ebreed.ccm.cn/#bz
Phenotyper^[57]	使用移动终端收集表型数据的软件	2015	http://www.bioinformatics.org/groups/7group_id=1210
SensorDB^[52]	用于集成、可视化和分析各种生物传感器数据的虚拟实验室	2015	http://sensordb.csiro.au.
OPTIMAS-DW^[50]	玉米的转录组学、代谢组学、离子组学、蛋白质组学和表型组学综合数据资源库	2012	https://apex.ipk-gatersleben.de/apex/f?p=270 :1::::::
iPlant (又称 CyVerse)^[58]	植物研究数据管理系统	2011	http://www.cyverse.org
BIOGEN BASE- CASSAVA^[59]	木薯表型组和基因组信息资源库	2011	http://www.tnaugenomics.con/biogenbase/casava.php
BreeDB	收录育种所需数量农艺性状	2009	https://www.wur.nl/en/show/BreeDRhtm