表型组学的概念及植物表型组学的发展   总被引：5，自引：1，他引：4  

玉光惠  方宣钧 《分子植物育种》2009,7(4):639-645

表型组(phenome)是指某一生物的全部性状特征;表型组学(phenomics)是一门在基因组水平上系统研究某一生物或细胞在各种不同环境条件下所有表型的学科.1996年,衰老研究中心主任Steven A.Garan在滑铁卢大学的一次应邀演讲上首次提出了Phenomics(表型组学)这一概念.1998年,比利时的CropDesign公司一成立即开始着手研发可大规模开发转基因和植物性状评价的高通量的技术平台.2005年,该公司的Christophe等发表具有里程碑意义的论文,详细阐述了称之为"性状工厂"(TraitMill)的可大规模自动化分析全生育期植物表型的技术设施.2006年,Niculescu及其同事在发表的论文中描述了一种新的用于表型组学分析的实验定量研究方法,并称之为PhenoChipping.2008年,澳洲植物表型组学设施(Australian Plant Phenomics Facility)在澳大利亚阿德雷德大学威特校区建立.2009年4月,第一届国际植物表型组大会在澳大利亚堪培拉成功举办.本文主要就表型组、表型组学相关概念的定义、研究机构、研究设施、相关文献及植物表型组学的进展做了概述.  相似文献   

论植物表型组和植物表型组学的概念与范畴   总被引：13，自引：0，他引：13  

潘映红 《作物学报》2015,41(2):175-186

植物表型分析是理解植物基因功能及环境效应的关键环节,随着植物功能基因组学和作物分子育种研究的深入,传统的表型观测已经成为制约其发展的主要瓶颈,而高通量的植物表型组分析技术和植物表型组学研究是解决这一困境的有效途径。虽然植物表型组分析正在成为国内外研究的热点,相关概念仍然较为模糊,阻碍了这一新兴学科的发展。本文分析了植物表型组和植物表型组学的相关概念和范畴,引入了准表型组、可辨识性状、映射性状、植物表型的遗传和环境包容性等新概念,将植物表型组定义为"受基因组和环境因素决定或影响的,反映植物结构及组成、植物生长发育过程及结果的全部物理、生理、生化特征和性状",将植物表型组学定义为"对植物表型组信息及相关环境参数的综合控制、完整采集和系统分析",并提出了植物表型组学的研究范围、研究方向和顶层设计原则。  相似文献   

A chemometric evaluation of the underlying physical and chemical patterns that support near infrared spectroscopy of barley seeds as a tool for explorative classification of endosperm genes and gene combinations     

S. Jacobsen  I. Sndergaard  B. Mller  T. Desler  L. Munck 《Journal of Cereal Science》2005,42(3):281-299

Near infrared spectroscopic (NIR; 1100–2500 nm), chemical and genetic data were combined to study the pleiotropic secondary effects of mutant genes on milled samples in a barley seed model. NIR and chemical data were both effective in classifying gene and gene combinations by Principal Component Analysis (PCA). Risø mutants R-13, R-29 high (1→3, 1→4)-β-glucan, low starch and R-1508 (high lysine, reduced starch), near isogeneic controls and normal lines and recombinants were studied. Based on proteome analysis results, six anti-microbial proteins were followed during endosperm development revealing pleiotropic gene effects in expression timing that supporting the gene classification. To verify that NIR spectroscopy data represents a physio–chemical fingerprint of the barley seed, physical and chemical spectral components were partially separated by Multiple Scatter Correction and their genetic classification ability verified. Wavelength bands with known water binding and (1→3, 1→4)-β-glucan assignments were successfully predicted by partial least squares regression giving insight into how NIR-data works in classification. Highly reproducible gene-specific, covariate, pleiotropic classification patterns from NIR and chemical data were demonstrated in PCAs and by visual inspection of NIR spectra. Thus PCA classification of NIR-data gives the classical genetic concept, ‘pleiotropy’, a new operational definition as a fingerprint from a spectroscopic representation of the phenome carrying genetic, physical and chemical information. It is concluded that barley seed phenotyping by NIR and chemometrics is a new, reliable tool for characterising the pleiotropic effects of mutant gene combinations and other genotypes in selecting barley for quality in plant breeding.  相似文献