Advancing Complex Phenotype Analyses through Machine Vision and Computation

PI：埃德加P. Spalding（威斯康星州大学）达勒姆布鲁克斯（多恩学院），尼古拉J.费里尔（威斯康星州大学），内森D.米勒（威斯康星州大学）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会发生什么。从遗传学的角度来看，突变的这种生物学效应是一种表型。这个项目的中心论点是，发现和理解表型的技术远不如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、自动化程度和通量，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，这些信息可以从突变体的蓬勃发展和自然发生的遗传变异的系统结构群体中提取。 具体而言，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用光谱学和/或数字图像分析进行分析。将对结果进行定量建模，以搜索具有预测能力的关系。基于初步的成功，可以预期的一个结果是，一种表型可以预测另一种。例如，从反射的红外光谱推断出的玉米种子中的油量可以预测发芽后根系发育的行为，如通过延时图像分析所研究的那样。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）它们可以为植物育种者提供替代手段来开发具有所需性状的品种。例如，在开发新的玉米品种时，选择特定的根系生长模式可能比直接测量含油量更容易和更快。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，该项目强调硬件和软件开发。由于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虚拟组织发挥作用的计划。该项目的一个显着特点是工程学、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，一所长期致力于为代表性不足的群体提供服务的机构，一所以本科为主的大学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，才能实现科学目标，并测试植物功能基因组学的网络工作方式。该项目的结果，包括方法、设备和软件，将在项目网站（http：//www.example.com）上和通过iPlant Collaborative网站提供。phytomorph.wisc.edu

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.