Advancing Complex Phenotype Analyses through Machine Vision and Computation

通过机器视觉和计算推进复杂表型分析

• 批准号: 1031416
• 负责人: Edgar Spalding
• 金额: $ 406.49万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1031416&HistoricalAwards=false
• 关键词: Advancing; Complex; Phenotype; Analyses; through

PI: Edgar P. Spalding (University of Wisconsin)Co-PIs: Tessa L. Durham Brooks (Doane College), Nicola J. Ferrier (University of Wisconsin), Nathan D. Miller (University of Wisconsin) and A. Mark Settles (University of Florida)Collaborators: Paul Armstrong (USDA-ARS) and Gokhan Hacisalihoglu (Florida Agricultural and Mechanical University) One way to learn what a particular gene does for an organism is to determine what happens when the gene is eliminated, or mutated. In genetic terms, such a biological effect of a mutation is a phenotype. The central thesis of this project is that techniques for finding and understanding phenotypes are far less advanced than those for cataloging and manipulating the genes. By integrating engineering and computer sciences methodologies, this project will have special impact on the problem of detecting and quantifying plant phenotypes by increasing the types, precision, degree of automation, and throughput of measurements. This will increase the amount and quality of phenotype information that can be extracted from the burgeoning collections of mutants and systematically structured populations of naturally-occurring genetic variants. Specifically, the chemical composition of seeds, seed morphology, and the growth and behavior of the root that emerges after germination will be analyzed with spectroscopy and/or digital image analysis. The results will be subjected to quantitative modeling to search for relationships that have predictive power. One result to be expected, based on preliminary success, is that one phenotype can predict another. For example, the amount of oil in a corn seed inferred from reflected infrared spectra can predict something about the behavior of the root that develops after germination, as studied by time-lapse image analysis. Results like these will be important because 1) they give information about how individual genes in an organism interact to produce the myriad functions and behaviors, and 2) they may provide alternative means for plant breeders to develop varieties that have desired traits. It may be easier and faster to select for a particular root growth pattern when developing a new corn variety than directly measuring oil content, for example. Successful large scale phenotyping requires automated data acquisition and computation. Therefore, this project emphasizes both hardware and software development. Because people, not machines, drive science forward, the project includes plans for geographically separated experts to function as a cyber-enabled virtual organization.A distinguishing feature of this project is the necessary integration of engineering, computer sciences, biology, and different types of institutions, including two major research universities, an institution with a longstanding role in serving underrepresented groups, and a primarily undergraduate institution. Undergraduate students who participate in the project contribute fully to data analysis, acquisition and design. Only if this integration is successful will the scientific goals be achieved and the cyber-enabled ways of working in plant functional genomics tested. The results of the project including methods, equipment and software will be accessible at the project website (http://phytomorph.wisc.edu) and through the iPlant Collaborative.

圆周率：埃德加·P·斯伯丁(威斯康星大学)：Tessa L.Durham Brooks(道恩学院)，Nicola J.Ferrier(威斯康星大学)，Nathan D.Miller(威斯康星大学)和A.Mark Settle(佛罗里达大学)合作者：Paul Armstrong(美国农业部-ARS)和Gokhan Hacisalihoglu(佛罗里达农业和机械大学)要了解特定基因对有机体有什么作用，一种方法是确定当基因被消除或突变时会发生什么。用遗传学的术语来说，这种突变的生物效应是一种表型。这个项目的中心论点是，寻找和理解表型的技术远远不如编目和操纵基因的技术先进。通过将工程学和计算机科学方法相结合，该项目将通过提高测量的类型、精度、自动化程度和吞吐量，对检测和量化植物表型的问题产生特殊影响。这将增加表型信息的数量和质量，可以从迅速增长的突变体集合和自然发生的遗传变异的系统结构群体中提取表型信息。具体地说，将使用光谱分析和/或数字图像分析来分析种子的化学成分、种子形态以及萌发后出现的根的生长和行为。结果将接受定量建模，以寻找具有预测能力的关系。根据初步的成功，一个可以预期的结果是，一种表型可以预测另一种表型。例如，从反射红外光谱中推断出的玉米种子中的含油量可以预测发芽后根的行为，这是通过延时图像分析进行的。这样的结果将是重要的，因为1)它们提供了关于生物体中单个基因如何相互作用以产生无数功能和行为的信息，2)它们可能为植物育种者提供替代手段，以开发具有所需特征的品种。例如，在开发新的玉米品种时，为特定的根生长模式进行选择可能比直接测量含油量更容易、更快。成功的大规模表型分析需要自动化的数据采集和计算。因此，本项目既注重硬件开发，又注重软件开发。因为推动科学发展的是人，而不是机器，该项目包括让地理上分散的专家作为一个网络使能的虚拟组织发挥作用的计划。该项目的一个显著特点是必要地整合了工程学、计算机科学、生物学和不同类型的机构，包括两所主要的研究型大学，一个长期服务于代表性不足群体的机构，以及一个以本科生为主的机构。参与该项目的本科生对数据分析、采集和设计做出了充分的贡献。只有这种整合成功，科学目标才能实现，植物功能基因组学中的网络工作方式才能得到测试。项目成果，包括方法、设备和软件，可在项目网站(http://phytomorph.wisc.edu))上查阅，也可通过iPLANT协作组查阅。