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

为了提高作物生产力，必须严格控制光合作用反应，以有效地捕捉光能，避免光损害。在自然环境不可预测的波动可能破坏光输入与处理光的同化反应能力之间的平衡的情况下，这一调节尤其关键。David Kramer博士(合作者)的S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如何整合到细胞中，并进行微妙的平衡，以在正确的时间以正确的形式提供适量的能量，而不会对植物造成损害。目前的主要步骤是从海量的植物表型(表现)数据中提取有用的信息，以产生可检验的假设，并发现未知的植物能源相关基因和过程。具体地说，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复杂的和压倒性的大量表型组学数据，将其转化为可解释的计算机形式(将植物分类为遗传和表现类别)，并由人类(通过高级可视化)来实现，从而对植物如何发挥作用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、基因功能和特征之间的差距。具体地说，为了满足我们对食物和燃料日益增长的需求，开发了新的生物成像方法，以实现高通量、详细的植物表型鉴定，重点是提高光合作用的效率。金晨博士(Pi)和David Kramer博士(co-Pi)的目标是识别控制光合作用效率的基因和过程，以响应波动的环境条件，这对于了解和改善植物能量储存和提高作物生产力至关重要。为了实现这一点，研究团队必须解决广泛的相互作用的因素，这些因素在非常宽的频率、持续时间和条件强度的动态范围内对环境因素做出反应。最近，动态环境表型成像仪(DEPI)作为一种监测植物表型在动态条件下的响应的新平台，在大卫·克莱默(David Kramer)博士的S实验室得到了发展。DEPI的初步数据揭示了以前未见过的效应，这些效应可归因于以前被认为没有已知功能的基因。虽然植物表型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。消除这一限制是该项目的拟议目标。金晨博士(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，以便将海量的表型组学数据转化为知识或可验证的假设，以识别在动态环境条件下提高光合作用效率的重要基因。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、测试和应用表观组学数据质量控制程序，以识别异常数据，并区分它们是来自噪声、伪影还是更有趣的生物反应改变案例。目的2.开发、测试和应用表观组学模式发现算法，从光合作用表观组学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结构和表型模块发现算法，将复杂的表型数据转化为可验证的假设，发现未知基因，并连接生物过程。目的3.利用综合多维可视化方法开发用于复杂表型组学数据显示的数据可视化软件包，以便于科学发现与能源相关的基因，以响应环境条件的变化。目的4.将PPDA应用于检测G蛋白激活状态对光合作用效率调节的理论基础，为其实用性提供证据。研究人员将在不断变化的环境条件下对拟南芥进行表型分析，这是一组信息量很大的G蛋白突变体。然后，他们将应用PPDA来识别在动态环境条件的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。该项目的结果可在http://www.msu.edu/~jinchen/PPDA.上查阅。

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.