TRTech-PGR: Increasing the nation's capacity to measure plant phenotypes by image analysis

TRTech-PGR：提高国家通过图像分析测量植物表型的能力

• 批准号: 1940115
• 负责人: Edgar Spalding
• 金额: $ 186.91万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-15 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940115&HistoricalAwards=false
• 关键词: TRTech; PGR; Increasing; nation; capacity

If scientists better understood how the many thousands of genes in a plant affect growth, development, and physiology, it would be easier to improve crops. Scientists would also better understand how non-crop plants fundamentally support life on this planet. It is relatively straightforward to characterize a plant’s genes (the genotype) these days but the growth, development, and physiology (comprising the phenotype) are much harder to measure. Yet, understanding how the genotype produces the phenotype requires good measurements of both. Digital imaging has emerged as a methodology for efficiently measuring many important phenotypes. This project will address three general deficiencies that prevent more phenotypes from being measured efficiently by image analysis. Pipelines: not enough computational pipelines capable of measuring broadly useful phenotypes have been constructed. People: not enough people interested in plant biology research are trained in the computer science and data science methods to make more pipelines. Process: that art and science of combining pieces of technology to form a pipeline that a biologist can use as a tool is an interdisciplinary process that is currently difficult to replicate everywhere it is needed. If 20 years ago the public generally understood how computers and imaging could be applied in plant biology research, this project may not have been necessary. Therefore, a built-in robust outreach component will present concepts and engaging exercises to youth outside of a university setting. Collectively, the project will advance the field of measuring plant phenotypes.Many phenotypes including those related to crop health and productivity can be measured from images using computers and applied math techniques. This approach to measuring phenotypes is presently limited by too few automated and high-throughput image analysis pipelines, too few people in the community capable of creating them, and no systematic process to facilitate new pipeline creators. This project addresses these deficiencies by creating effective high-throughput image analysis pipelines, training postdocs and students in the necessary technical domains and soft skills, and by making the pipeline creation process as formulaic and modular as possible. With input from community surveys and an Advisory Board, a prioritized list of needed measurements will be created. Pipelines designed to meet those measurement needs will be deployed as Web services on public cyberinfrastructure such as CyVerse and the Open Science Grid. Postdocs and graduate students will be trained in image analysis techniques, machine learning, virtual machines and containers, file transfer and storage methods, and high-throughput computing. They will learn to abstract biological imaging problems and adopt the perspective of the user to produce new robust measurement tools. In cases where a good solution exists but is not readily accessible, the trainees will deploy the existing tool on public cyber infrastructure. The overall result will be more high-throughput image analysis tools to meet the community’s phenotype measurement needs and an increase in the community’s capacity to create more. This award was co-funded by the Plant Genome Research Program in the Division of Integrative Organismal Systems, and the Infrastructure Capacity for Biology Program in the Division of Biological Infrastructure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

如果科学家们更好地了解植物中成千上万的基因如何影响生长、发育和生理，那么改良作物就更容易了。科学家们还将更好地了解非作物植物如何从根本上支持地球上的生命。目前，描述植物的基因（基因型）相对简单，但生长，发育和生理（包括表型）却很难测量。然而，理解基因型如何产生表型需要对两者进行良好的测量。数字成像已经成为一种有效测量许多重要表型的方法。这个项目将解决三个一般性的缺陷，防止更多的表型被有效地测量图像分析。管道：没有足够的计算管道能够测量广泛有用的表型已经建成。人员：没有足够多的对植物生物学研究感兴趣的人接受过计算机科学和数据科学方法的培训，以建立更多的管道。工艺流程：将各种技术结合起来形成生物学家可以用作工具的管道的艺术和科学是一个跨学科的过程，目前很难在任何需要的地方复制。如果20年前公众普遍了解计算机和成像技术如何应用于植物生物学研究，这个项目可能就没有必要了。因此，一个内置的强有力的外联部分将向大学环境之外的青年介绍概念和参与练习。总的来说，该项目将推进植物表型测量领域。许多表型，包括与作物健康和生产力相关的表型，都可以使用计算机和应用数学技术从图像中测量。这种测量表型的方法目前受到自动化和高通量图像分析管道太少，社区中能够创建它们的人太少，并且没有系统的过程来促进新的管道创建者的限制。该项目通过创建有效的高通量图像分析管道，在必要的技术领域和软技能方面培训博士后和学生，并使管道创建过程尽可能公式化和模块化来解决这些缺陷。根据社区调查和咨询委员会的意见，将编制一份按优先次序排列的所需衡量标准清单。为满足这些测量需求而设计的管道将作为Web服务部署在公共网络基础设施上，如CyVerse和开放科学网格。博士后和研究生将接受图像分析技术、机器学习、虚拟机和容器、文件传输和存储方法以及高吞吐量计算方面的培训。他们将学习抽象生物成像问题，并采用用户的角度来产生新的强大的测量工具。如果有一个好的解决方案，但不容易获得，学员将部署在公共网络基础设施的现有工具。总体结果将是更多的高通量图像分析工具，以满足社区的表型测量需求，并增加社区的能力，创造更多。该奖项由综合有机系统部的植物基因组研究计划和生物基础设施部的生物学基础设施能力计划共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。