SGER: Connecting the Transcriptome and Metabolome with Natural Genetic Variation.

SGER：将转录组和代谢组与自然遗传变异联系起来。

• 批准号: 0642481
• 负责人: Daniel Kliebenstein
• 金额: --
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0642481&HistoricalAwards=false
• 关键词: SGER; Connecting; Transcriptome; Metabolome; Natural

PI: Daniel J. Kliebenstein (University of California, Davis)Intellectual Merit:Recent technological advancements have enabled massively parallel analysis of gene expression, metabolite accumulation and protein accumulation. The ability of these "-omics" approaches to generate biological inference faces two limitations. The first is how to combine the different -omics datasets into a single analysis. Secondly, the high number of unknown genes and metabolites limit the potential inference from any given dataset. The primary goal of this project is to test whether natural genetic variation can be used to integrate Metabolomics and Transcriptomics datasets and identify linkages between unknown metabolites and enzymes. Specifically metabolite QTL analysis will be performed using the Arabidopsis Bay-0 X Sha recombinant inbred line population as well as wildtype accessions to develop and test methods for integrating Metabolomics and Transcriptomics analysis using genetic co-variance, rather than metabolite and transcript co-variance, to link these omics-level datasets. All computational approaches will first be developed with known metabolites and enzyme encoding genes and then tested using the unknown metabolites and enzyme encoding genes.In addition to facilitating the development of approaches towards integrating different omics-level datasets, the use of natural genetic variation will also allow for the testing of the link between genetic variation and phenotypic variation by combining transcriptomics and metabolomics for QTL analysis. Expected outcomes include the generation of techniques that will be directly applicable to any species in which transcriptomics and metabolomics are feasible and a database of plant metabolic variation that will be of general use to all plant biologists and can potentially enhance the rate of QTL identification and validation within a model plant system. Broader Impacts: Because the approaches used are based on the ability to detect and measure metabolites and transcripts, all statistical methodology and theoretical underpinnings developed in this project will be useful in any biological system where metabolites and transcripts can be measured. In addition, the project has potential broader impact for the biological sciences in general through the study of the link between genetic variation and phenotypic variation, of particular importance in Agriculture, where plant and animal improvement relies upon genetic and phenotypic variation, and Medicine, where numerous diseases are dependent upon genetic variation that alters metabolism. Finally the project will enable the training of a graduate student in the experimental design, technical details and analysis required to obtain and interrogate large metabolomic datasets. These skills will be integral for the success of post-genomic researchers attempting to generate and test hypotheses through the use of large-scale genomic datasets.

PI：Daniel J. Kliebenstein（加利福尼亚大学戴维斯分校）知识分子：最新的技术进步使得对基因表达，代谢产物积累和蛋白质积累的大规模平行分析。 这些“ - 组”方法产生生物学推断的能力面临两个局限性。 首先是如何将不同的 - 组数据集组合到单个分析中。 其次，大量未知基因和代谢产物限制了任何给定数据集的潜在推断。 该项目的主要目标是测试自然遗传变异是否可以用于整合代谢组学和转录组学数据集并确定未知代谢物和酶之间的联系。 特定代谢物QTL分析将使用拟南芥Bay-0 X SHA重组近交系数以及野生型材料加入，以开发和测试使用遗传学共同变量的代谢组学和转录组分析的方法，而不是代谢物和转录物，而不是代谢物和成绩单共同差异，链接这些OMICS级数据集。 所有计算方法将首先使用已知的代谢产物和编码基因的酶开发，然后使用未知代谢物和编码基因的酶进行测试。在促进促进不同OMICS级数据集的方法的开发外，自然遗传变异的使用还将使用通过结合转录组学和代谢组学以进行QTL分析，允许测试遗传变异与表型变异之间的联系。 预期的结果包括将直接适用于转录组和代谢组学是可行的任何物种的生成，以及植物代谢变异的数据库，这些数据库将普遍用于所有植物生物学家，并有可能提高QTL识别和验证速率在模型工厂系统中。 更广泛的影响：由于所使用的方法基于检测和测量代谢物和成绩单的能力，因此在该项目中开发的所有统计方法和理论基础都将在任何可以测量代谢物和转录本的生物系统中都有用。 此外，通过研究遗传变异与表型变异之间的联系，对生物科学的一般生物科学具有更广泛的影响，在农业中尤为重要，在农业中，植物和动物的改善依赖于遗传和表型变异，以及医学，那里的遗传和动物改善许多疾病取决于改变代谢的遗传变异。最后，该项目将使研究生在实验设计，技术细节和分析中培训并询问大型代谢组合数据集。 这些技能将是试图通过使用大规模基因组数据集生成和检验假设的后基因组研究人员成功的重要组成部分。