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的生物过程都是通过信息的编码、交换和解释来协调的。生物科学的许多最新进展收集了大量的DNA（基因型）和蛋白质序列：理解它们的特性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。将基因型与表现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，同时调查水稻耐盐性的影响。大量的表达数据集将与来自几种成像技术的发育植物的图像一起收集沿着。这些图像将用于生成植物结构的3D重建，可以存储为数字表型。预计这些数字表型将允许植物育种者识别标准观察方法未捕获的性状，并发现新的基因型-表型关联。教育活动是及时的，为植物生物学和计算机科学的学生提供培训机会。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植物表型组学夏季研讨会和在内布拉斯加大学建立数据可视化社区。项目资源将在CyVerse上发布，以确保广泛可用。高通量测序技术驱动的转录组分析是用于获得关于生物体对环境变化的响应的分子见解的常用方法。这些转录组水平的反应和潜在的调控基因网络，驱动表型反应不断变化的环境是高度动态的。随着高通量基于图像的植物表型分析技术的出现，现在可以以更高的时间和空间分辨率捕获生长动态和响应环境或遗传扰动的其他数字特征。该项目将使用时间成像来通知具有3D空间灵敏度的时间序列转录组数据的收集，以发现动态调控共表达网络。该项目的成果将是开发创新算法，以整合异构数据集，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