Arabidopsis 2010: Metabolomics: A Functional Genomics Tool for Deciphering Functions of Arabidopsis Genes in the Context of Metabolic and Regulatory Networks
Arabidopsis 2010: Metabolomics: A Functional Genomics Tool for Deciphering Functions of Arabidopsis Genes in the Context of Metabolic and Regulatory Networks
批准号:
0820823
负责人:
Basil Nikolau
金额:
$292.54万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-03-01 至 2014-02-28
中文摘要
拟南芥基因组中超过1/3的已注释蛋白编码基因的功能尚不清楚,而且对更大部分基因组的注释不足以在生化和生理水平上明确地分配功能。该项目将汇集一个多学科合作者联盟,以建立生成代谢组学数据流的管道,并提供对所产生的综合数据集的统计和计算解释。目标是开发代谢物分析能力,这将增强研究界制定关于拟南芥基因功能的可测试假说的能力。该联盟已经开发了代谢组平台,总共可以检测到大约1800种代谢物,其中900种是化学定义的。该项目的目的是应用这些已建立的代谢物分析平台来揭示与多达200个功能未知的拟南芥基因的敲除突变相关的代谢组的变化,并将这些变化与50个已知功能基因的类似突变体进行比较。该联盟将通过现有的多功能新陈代谢组学数据库发布这些数据:www.PLANTEMATIOMICS.org。该数据库以及相关的统计和可视化工具集的增强将使研究人员能够制定可测试的拟南芥代谢网络的计算模型。这些目标的成功完成以及与其他NSF赞助的功能基因组学和网络基础设施开发的整合,将为最终模拟拟南芥的复杂新陈代谢产生变革性的资源。更广泛的影响该项目将为研究界开发新的资源,增强在代谢物水平上全球描述基因组表达的能力。这些代谢物资源与其他NSF资助的资源开发项目合作,将使社区中的研究人员能够制定关于基因功能的可信的、可验证的假说。该项目将通过讲习班、实习和组织国内和国际会议,促进代谢学作为一种功能基因组学工具的发展。该项目还将开展新的活动,通过在联合会实验室和国际生物学会议上为研究人员举办讲习班,加强科学教育和培训在社区中的影响。此外,还将向本科生提供研究实习机会,其中8人将有机会在欧洲基因组实验室体验国际科学培训。这些以研究为基础的实习将向学生展示化学、生物化学、遗传学、生物信息学和计算科学的综合应用所带来的协同作用,以解决复杂的生物学问题。
英文摘要
The functions of over 1/3 of the annotated protein-coding genes of the Arabidopsis genome are still unknown, and the annotation of an even larger portion of the genome is not sufficiently accurate for unambiguous assignment of function at the biochemical and physiological levels. This project will bring together a consortium of multidisciplinary collaborators to establish pipelines for generating metabolomics data-streams and to provide statistical and computational interpretation of the resulting integrated datasets. The goal is to develop metabolite-profiling capabilities that will enhance the research community's ability to formulate testable hypotheses concerning Arabidopsis gene functions. The consortium has developed metabolomic platforms that together detect approximately 1,800 metabolites, of which 900 are chemically defined. The aim of the project is to apply these established metabolite-profiling platforms to reveal changes in the metabolome associated with knockout mutations in up to 200 Arabidopsis genes of unknown function and compare these to similar mutants in 50 genes of known function. The consortium will disseminate these data via the existing multi-functional metabolomics database: www.plantmetabolomics.org. Enhancement of this database and associated statistical and visualization toolsets will enable researchers to formulate testable computational models of the metabolic network of Arabidopsis. The successful completion of these goals and integration with other NSF-sponsored functional genomics and cyber infrastructure developments will generate transformational resources for ultimately modeling the complex metabolism of Arabidopsis. Broader ImpactsThe project will develop new resources for the research community that will enhance the capability to globally profile genome expression at the metabolite level. These metabolite resources, in collaboration with other NSF-funded resource development projects, will enable researchers in the community to formulate credible, testable hypotheses concerning gene function. The project will foster the development of the science of metabolomics as a functional genomics tool through workshops, internships and organization of national and international meetings. The project will also develop new activities to enhance the impact of science education and training in the community, by conducting workshops for researchers at consortium labs and at international biological meetings. In addition, research internships will be offered to undergraduate students, eight of whom will have the opportunity to experience international science training in a European genomics laboratory. These research-based training internships will illustrate to the students the synergy that accompanies the integrated applications of chemistry, biochemistry, genetics, bioinformatics and computational sciences to solving complex biological problems.
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