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

ABI Innovation：用于分析果蝇基因表达的新型自动化数据集成、注释和交互网络推理系统

• 批准号: 1836866
• 负责人: Heng Huang
• 金额: $ 11.26万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1836866&HistoricalAwards=false
• 关键词: ABI; Innovation; New; Automated; Data

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

大规模的原位杂交（ISH）筛选提供了大量数据，显示了基因表达的时空模式，这些模式对于理解基因调节的机制很有价值。从果蝇表达模式的分析中获得的知识非常重要，因为在人类和其他物种中通常发现了大量参与果蝇发育的基因。因此，对果蝇基因表达图像的空间和时间特征的研究工作一直是对不同物种发展的基本原理的科学研究的领先地位。果蝇基因表达模式图像可以将空间表达模式与其他基因组数据集的整合，这些数据集将调节剂与其下游靶标连接起来。该项目通过利用新的生物信息学软件系统来解决分析果蝇基因表达模式的计算挑战。它着重于设计有原则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果蝇胚胎的发育阶段识别和解剖学本体学术语注释的基因表达的多模式空间模式，并将推断基因相互作用网络，以产生基因的更全面的基因图像。项目活动产生的生物信息学方法广泛适用于生物医学科学和工程，系统生物学，临床病理学，肿瘤学和药物等各种领域。计划扩大科学的参与，以增强各种学生人群的课程和研究经验。该项目调查了三个具有挑战性的问题，用于通过创新的生物信息学算法研究果蝇胚胎图像：1）稀疏的多维特征学习方法稀疏的多模式空间基因表达模式，用于整合多模式的空间基因表达模式，用于使用drosophila ish图像的开发，2）相关的多阶段，2）杂物序列，2）解剖本体术语，3）嵌入式稀疏表示算法推断基因相互作用网络。将结构化的稀疏学习，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，并对动物发展的基本原理进行科学研究，这是创新的。作为这项研究结果，算法和工具有望帮助知识发现具有大量高维和异质数据集的更广泛的科学和生物领域的应用。该项目促进了新型教育工具的开发，以增强德克萨斯大学阿灵顿分校的几门课程。 PI与少数族裔学生和服务不足的人群与研究活动相关，以提供接触尖端科学研究的机会。有关更多信息