ABI Innovation: A New Framework to Analyze Plant Energy-related Phenomics Data

为了提高作物产量，必须严格调节光合作用，以有效地捕获光能，避免光损伤。在自然环境中不可预测的波动下，这种调节尤其重要，因为这可能会破坏光输入与吸收反应处理光的能力之间的平衡。David Kramer博士的实验室开发了新的植物光合作用表型平台，使人们能够确定光合作用机制是如何整合到细胞中，并在正确的时间以正确的形式提供适量的能量，而不会损害植物。目前的主要步骤是从大量的植物表型（性能）数据中提取有用的信息，以产生可测试的假设，并发现未知的植物能量相关基因和过程。具体而言，目标是开发新的软件方法，用于处理、建模和可视化植物科学中大量复杂的表型组学数据，使其成为可解释的计算机（将植物分为遗传和性能类别）和人类（通过高级可视化）的形式，从而对植物的功能和植物改良的新目标产生新的见解。大规模表型（表型组学）有望弥合基因组学、基因功能和性状之间的差距。具体来说，为了满足我们对食物和燃料日益增长的需求，新的生物成像方法被开发出来，以实现高通量、详细的植物表型，重点是提高光合作用的效率。Jin Chen博士（PI）和David Kramer博士（co-PI）旨在确定控制光合作用效率的基因和过程，以应对波动的环境条件，这对于理解和改善植物能量储存和提高作物生产力至关重要。为了实现这一目标，研究团队必须解决在频率、持续时间和条件强度的非常宽的动态范围内对环境因素做出反应的各种相互作用因素。最近，David Kramer博士（co-PI）的实验室开发了动态环境表型成象仪（Dynamic Environmental Phenotype Imager， DEPI），这是一个监测植物在动态条件下表型反应的新平台。DEPI的初步数据揭示了以前看不见的影响，这些影响可归因于以前认为没有已知功能的基因。虽然植物表型学的这些进展令人兴奋，但研究人员受到充分分析表型组学数据的工具的限制。消除这一限制是本项目提出的目标。Jin Chen博士（PI）和David Kramer博士（co-PI）将发现、开发和应用植物表型组学数据分析（PPDA）解决方案，将大量表型组学数据转化为知识或可测试的假设，以识别重要基因，以提高动态环境条件下的光合作用效率。PPDA将确保高数据质量，从复杂的植物表型组学数据中识别和可视化重要基因，并将在更广泛的社区中推进知识发现。该项目由四个部分组成：目标1。开发，测试和应用表型组学数据质量控制程序，以识别异常数据，并区分它们是否来自噪音，人工制品或更有趣的改变生物反应的情况。目标2。开发，测试和应用表型组模式发现算法，从光合作用表型组数据中识别重要的能量相关基因。研究团队将开发动态表型网络构建和表型模块发现算法，将复杂的表型组学数据转化为可测试的假设，发现未知基因，并连接生物过程。目标3。利用综合多维可视化方法开发复杂表型组学数据显示数据可视化包，促进能源相关基因在环境条件变化中的科学发现。目标4。通过应用PPDA来测试G蛋白激活状态对光合作用效率的调节，提供实用性证明。研究人员将在不断变化的环境条件下对拟南芥的大量G蛋白突变进行表型分析。然后，他们将应用PPDA来识别在动态环境条件子集下具有紧急功能的基因。他们将解决G信号在波动检测中的作用。该项目的结果可以在http://www.msu.edu/~jinchen/PPDA上找到。

To increase crop productivity, photosynthetic reactions must be tightly regulated to efficiently capture light energy and to avoid photodamage. This regulation is especially critical under unpredictable fluctuations in the natural environment, which could damage the balance between light input and the capacity of assimilatory reaction to process it. New plant photosynthesis phenotyping platforms have been developed in Dr. David Kramer (co-PI)'s lab, allowing one to determine how the photosynthetic machinery is integrated into cells and is delicately balanced to provide the right amount of energy, at the right times, in the correct forms without damaging the plant. The current major step is to extract useful information from massive plant phenotyping (performance) data to generate testable hypotheses and discover unknown plant energy-related genes and processes. Specifically, the objective is to develop new software approaches for processing, modeling and visualizing sophisticated and overwhelming amount of phenomics data in plant science to forms that are interpretable computers (to classify plants into genetic and performance categories) and by humans (through advanced visualization), leading to new insights on how plants function and new targets for plant improvement. Large-scale phenotyping (phenomics) promises to bridge the gap between genomics, gene functions and traits. Specifically, to meet our growing needs for food and fuel, new bio-imaging approaches were developed to allow high-throughput, detailed plant phenotyping, with a focus on improving the efficiency of photosynthesis. Dr. Jin Chen (PI) and Dr. David Kramer (co-PI) aim to identify genes and processes that control photosynthesis efficiency in response to fluctuating environmental conditions, which are critical for understanding and improving plant energy storage and improving crop productivity. To achieve this, the research team must resolve a wide range of interacting factors that respond to environmental factors over very wide dynamic ranges of frequency, duration and intensity of conditions. Recently, the Dynamic Environmental Phenotype Imager (DEPI), a novel platform for monitoring responses of plant phenotypes under dynamic conditions has been developed in Dr. David Kramer (co-PI)'s lab. Initial data from DEPI reveals previously unseen effects attributable to genes formerly thought to have no known function. While these developments on plant phenotyping are exciting, researchers are limited by the tools to analyze fully the phenomics data. Removing that limitation is the proposed goal of this project. Dr. Jin Chen (PI) and Dr. David Kramer (co-PI) will discover, develop, and apply Plant Phenomics Data Analytics (PPDA) solutions, such that massive phenomics data is transformed into knowledge or testable hypotheses to identify important genes to improve photosynthesis efficiency under dynamic environmental conditions. PPDA will ensure high data quality, identify and visualize important genes from complex plant phenomics data, and will advance knowledge discovery in the broader community. The project is comprised of four components: Aim 1. Develop, test and apply phenomics data quality control program to identify abnormal data and distinguish whether they arise from noise, artifacts or more interesting cases of altered biological responses. Aim 2. Develop, test and apply phenomics pattern discovery algorithms to identify important energy-related genes from photosynthesis phenomics data. The research team will develop dynamic phenotype network construction and phenotype module discovery algorithms to turn sophisticated phenomics data to testable hypotheses, to discover unknown genes, and to connect biological processes. Aim 3. Develop a data visualization package for complex phenomics data display using integrative multi-dimensional visualization methods, in order to facilitate scientific discovery on energy-related genes in response to changing environmental conditions. Aim 4. Provide proof of utility by applying PPDA to rationale for testing the G protein activation state regulation on photosynthesis efficiency. The researchers will phenotype Arabidopsis thaliana a large informative set of G protein mutants under changing environmental conditions. Then they will apply PPDA to identify genes with emergent functions under subsets of the dynamic environmental conditions. They will resolve the role of G signaling in fluctuation detection. The results of the project can be found at http://www.msu.edu/~jinchen/PPDA.