ABI Innovation: A computational framework for integrating image informatics with transcriptomics for discovering spatiotemporally resolved regulatory gene networks in plants

从分子到生态系统的所有尺度上的生物过程都通过信息的编码、交换和解释来协调。生物科学的许多最新进展收集了非常大的DNA(基因)和蛋白质序列：要理解它们的性质如何导致生物体的外观、反应和行为(表型)，需要新的计算工具。将基因型与表型联系起来需要理清非常复杂的关系。为大量样本收集大量数据意味着开发用于数据处理和数据建模的新工具。该项目将开发计算工具来解决这两个挑战，同时研究水稻耐盐性的影响。将收集大量的表达数据，以及来自几种成像技术的正在发育的植物的图像。这些图像将被用来产生植物建筑的3D重建，这些重建可以作为数字表型存储。预计这些数字表型将使植物育种者能够识别标准观察方法没有捕捉到的性状，并找到新的基因型-表型关联。教育活动及时，为植物生物学和计算机科学专业的学生提供了培训机会。随着图像衍生数字数据量的激增，图像信息学正在成为一种前沿的发现工具。外联和培训活动包括在内布拉斯加州大学举办植物表型组学夏季讲习班和建立数据可视化社区。项目资源将在CyVerse上发布，以确保广泛的可用性。高通量测序技术驱动的转录组分析是一种常用的方法，用于获得关于有机体对环境变化的反应的分子洞察。这些转录组水平的反应和潜在的调控基因网络驱动对变化的环境的表型反应是高度动态的。随着基于图像的高通量植物表型的出现，现在可以通过提高时间和空间分辨率来捕捉生长动态和其他数字特征，以响应环境或遗传扰动。该项目将使用时间成像来收集具有3D空间敏感性的时间序列转录组数据，以发现动态调控共表达网络。该项目的成果将是开发创新的算法，以整合不同的数据集，并在原型计算框架中实施。该平台将实现具有空间和时间分辨率的动态监管网络的交互式多维可视化。项目资源可通过以下网址访问：http://cropstressgenomics.org/phenomics.php

Biological processes at all scales from molecules to ecosystems are coordinated through the encoding, exchange, and interpretation of information. Many of the recent advances in the biological sciences collect very large sets of DNA (genotype) and protein sequence: making sense of how their properties lead to the appearance, responses and behaviors (phenotype) of organisms requires new computational tools. Linking the genotype to the phenotype requires untangling very complicated relationships. Collecting large amounts of data for large numbers of samples means developing new tools for data processing and data modeling. This project will develop computational tools to address both of these challenges, while investigating the effects of salt tolerance in rice. Large sets of expression data will be collected along with images of the developing plants from several imaging technologies. The images will be used to produce 3D reconstructions of the plant architecture that can be stored as digital phenotypes. It is expected that these digital phenotypes will allow plant breeders to identify traits that were not captured by standard observation methods, and find new genotype-phenotype associations. The educational activities are timely, providing training opportunities for plant biology and computer science students. As the volumes of image-derived digital data surge, image informatics is emerging as a cutting-edge discovery tool. Outreach and training activities include a summer workshop for plant phenomics and establishing a data visualization community at University of Nebraska. Project resources will be released on CyVerse to ensure wide availability. High-throughput sequencing technology- driven transcriptome analyses are a commonly utilized approach for gaining molecular insights on an organism's response to environmental changes. These transcriptome-level responses and the underlying regulatory gene networks that drive phenotypic responses to changing environment are highly dynamic. With the advent of high-throughput image-based plant phenotyping, it is now possible to capture the dynamics of growth and other digital-features responding to environmental or genetic perturbation with increased temporal and spatial resolution. This project will use temporal imaging to inform the collection of time series transcriptome data with 3D-spatial sensitivity for discovering dynamic regulatory co-expression networks. Outcomes of the project will be to develop innovative algorithms to integrate heterogeneous datasets and implement it in a prototype computational framework. The platform will enable interactive, multidimensional visualization of dynamic regulatory networks with spatial and temporal resolution. The project resources can be accessed at: http://cropstressgenomics.org/phenomics.php