Advancing Complex Phenotype Analyses through Machine Vision and Computation

PI: Edgar P. Spalding（威斯康星大学）合作PI: Tessa L. Durham Brooks （Doane学院），Nicola J. Ferrier（威斯康星大学），Nathan D. Miller（威斯康星大学）和a . Mark Settles（佛罗里达大学）合作者：Paul Armstrong （USDA-ARS）和Gokhan Hacisalihoglu（佛罗里达农业和机械大学）了解特定基因对生物体的作用的一种方法是确定当基因被消除或突变时发生了什么。在遗传术语中，这种突变的生物学效应是一种表型。这个项目的中心论点是发现和理解表现型的技术远不如那些编目和操纵基因的技术先进。通过整合工程和计算机科学方法，该项目将通过增加测量的类型、精度、自动化程度和吞吐量，对植物表型检测和定量问题产生特殊影响。这将增加表型信息的数量和质量，这些信息可以从迅速增长的突变体和自然发生的遗传变异的系统结构群体中提取出来。具体来说，种子的化学成分、种子形态以及发芽后出现的根的生长和行为将通过光谱和/或数字图像分析进行分析。结果将进行定量建模，以寻找具有预测能力的关系。基于初步的成功，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，通过延时图像分析，从反射的红外光谱中推断出玉米种子中的油量可以预测发芽后根系发育的某些行为。像这样的结果将是重要的，因为1)它们提供了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开发具有所需性状的品种提供了替代方法。例如，在培育玉米新品种时，选择特定的根系生长模式可能比直接测量含油量更容易、更快捷。成功的大规模表型分析需要自动数据采集和计算。因此，本项目强调硬件和软件的双重开发。因为推动科学进步的是人，而不是机器，所以该项目包括让地理位置分散的专家作为一个网络虚拟组织发挥作用的计划。该项目的一个显著特点是工程、计算机科学、生物学和不同类型的机构的必要整合，包括两所主要的研究型大学，一所长期为代表性不足的群体服务的机构，以及一所主要的本科机构。参与该项目的本科生充分参与数据分析、获取和设计。只有这种整合成功，科学目标才能实现，植物功能基因组学的网络工作方式才能得到检验。该项目的结果，包括方法、设备和软件，将在项目网站（http://phytomorph.wisc.edu）和iPlant协作网站上公布。

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.