ABI Innovation: A New Automated Data Integration, Annotations, and Interaction Network Inference System for Analyzing Drosophila Gene Expression

大规模原位杂交(ISH)筛查提供了丰富的数据，显示了基因表达的时空模式，这对于理解基因调控机制是有价值的。从果蝇表达模式分析中获得的知识非常重要，因为大量参与果蝇发育的基因通常在人类和其他物种中发现。因此，对果蝇基因表达图像的空间和时间特征的研究一直处于不同物种发育基本原理的科学调查的前沿。果蝇基因表达模式图像使空间表达模式与其他基因组数据集整合在一起，这些基因组数据集将调节子与其下游靶标联系在一起。该项目利用一种新的生物信息学软件系统，解决了分析果蝇基因表达模式的计算挑战。它侧重于设计原则性的生物信息学和计算生物学算法和工具，这些算法和工具将整合多模式基因表达的空间模式，用于果蝇胚胎的发育阶段识别和解剖本体术语标注，并将推断基因相互作用网络，以生成更全面的基因功能和相互作用图。项目活动产生的生物信息学方法广泛应用于生物医学科学与工程、系统生物学、临床病理学、肿瘤学和制药学等领域。计划采用新的工具，为不同群体的学生改进课程和研究经验，以扩大对科学的参与。本课题通过创新的生物信息学算法研究了果蝇胚胎图像的三个具有挑战性的问题：1)稀疏多维特征学习方法，用于集成多模式空间基因表达模式来标注果蝇图像；2)基于高阶关系图的异构型多任务学习模型，用于联合识别发育阶段和标注解剖本体术语；3)嵌入式稀疏表示算法，用于推断基因相互作用网络。将结构化稀疏学习、多任务学习和高阶关系图模型应用于果蝇基因表达模式分析是一种创新，为动物发育基本原理的科学研究提供了广阔的前景。作为本研究的成果，算法和工具有望帮助知识发现在具有海量高维和异质数据集的更广泛的科学和生物领域中的应用。该项目促进了开发新的教育工具，以加强德克萨斯大学阿灵顿分校目前的几门课程。私人投资机构让少数族裔学生和未得到充分服务的人群参与研究活动，以提供接触尖端科学研究的机会。欲了解更多信息，请访问网站：http://ranger.uta.edu/~heng/NSF-DBI-1356628.html。

Large-scale in situ hybridization (ISH) screens are providing an abundance of data showing spatio-temporal patterns of gene expression that are valuable for understanding the mechanisms of gene regulation. Knowledge gained from analysis of Drosophila expression patterns is widely important, because a large number of genes involved in fruit fly development are commonly found in humans and other species. Thus, research efforts into the spatial and temporal characteristics of Drosophila gene expression images have been at the leading-edge of scientific investigations into the fundamental principles of different species development. Drosophila gene expression pattern images enable the integration of spatial expression patterns with other genomic datasets that link regulator with their downstream targets. This project addresses the computational challenges in analyzing Drosophila gene expression patterns by leveraging a new bioinformatics software system. It focuses on designing principled bioinformatics and computational biology algorithms and tools that will integrate multi-modal spatial patterns of gene expression for Drosophila embryos' developmental stage recognition and anatomical ontology term annotation, and will infer gene interaction networks to generate a more comprehensive picture of gene function and interaction. The bioinformatics methods resulting from the project activities are broadly applicable to a variety of fields such as biomedical science and engineering, systems biology, clinical pathology, oncology, and pharmaceutics. Novel tools to enhance courses and research experiences for diverse populations of students are planned to broaden participation in science. This project investigates three challenging problems for studying the Drosophila embryo ISH Images via innovative bioinformatics algorithms: 1) the sparse multi-dimensional feature learning method to integrate the multimodal spatial gene expression patterns for annotating Drosophila ISH images, 2) the heterogeneous multi-task learning models using the high-order relational graph to jointly recognize the developmental stages and annotate anatomical ontology terms, 3) the embedded sparse representation algorithm to infer the gene interaction network. It is innovative to apply structured sparse learning, multi-task learning, and high-order relational graph models to Drosophila gene expression patterns analysis and holds great promise for scientific investigations into the fundamental principles of animal development. The algorithms and tools as outcomes of this research are expected to help knowledge discovery for applications in broader scientific and biological domains with massive high-dimensional and heterogeneous data sets. This project facilitates the development of novel educational tools to enhance several current courses at University of Texas at Arlington. The PIs engage minority students and under-served populations in research activities to provide opportunities for exposure to cutting-edge scientific research. For further information see the web site at: http://ranger.uta.edu/~heng/NSF-DBI-1356628.html