Computational Analysis of Gene Expression Pattern Images

描述(申请人提供)：模式生物--黑腹果蝇的空间和时间基因表达模式的不断收集极大地促进了将序列信息翻译成基因功能和相互作用。这些模式是基因的初级序列与其对表型的影响之间的联系，因为它们的重叠为功能、遗传或调节相互作用提供了初步线索。然而，今天通过高通量和个人实验室努力获得的各种基因表达模式的海量集合，已经使手动检查图像的标准做法黯然失色。为了大规模地发现基因表达模式中的空间重叠，研究人员需要一个创新的、基于图像的发展生物信息学框架。因此，本项目的目标是建立一个全面的资源，以加速基因表达数据的分析，以发现基因相互作用网络中的新链接。这个框架将包括我们在第一个项目期间成功产生的独特的FlyExpress资源的第二代。我们正在应对一种迫切的需求，即开发最先进的计算方法和统计方法，以使用CTE novo模式找到重叠表达，自动化图像标准化，并基于空间重叠将图像分类。此外，FlyExpress知识库的内容必须通过从已发表的文献和高通量研究中添加大量图像以及通过建立易于使用的数据和信息提交网络工具来发展。这些拟议的发展将使研究人员能够利用所有相关的生物学信息，基于表达模式的重叠有效地生成和评估他们的基因相互作用假说。这个系统将始终可以通过网络免费访问，它将消除现有的跨实验室研究努力的障碍。该项目中开发的计算算法、统计方法和生物信息学技术将为构建类似的框架来组织来自其他物种的表达模式数据提供动力。FlyExpress系统将满足基础和应用研究人员以及在人类健康研究中至关重要的分子生物学许多领域的学生的日常需求，包括计算基因组学、分子遗传学、发育生物学、遗传学和进化论。

DESCRIPTION (provided by applicant): Translating sequence information to gene function and interaction is greatly facilitated by the growing collection of spatial and temporal gene expression patterns in the model organism, Drosophila melanogaster. These patterns are links between a gene's primary sequence and its influence on the phenotype, as their overlaps provide the initial clues to functional, genetic, or regulatory interactions. However, today's vast collection of diverse gene expression patterns, made available by way of high-throughput and individual laboratory efforts, has eclipsed the standard practice of manually inspecting the images. In order to discover spatial overlap in the expression patterns of genes on a large scale, investigators need an innovative, image- based developmental bioinformatics framework. Therefore, the objective of this project is to establish a comprehensive resource to accelerate the analysis of gene expression data in the discovery of novel links in gene interaction networks. This framework will comprise the second generation of the unique FlyExpress resource we successfully engendered in the first project period. We are responding to an urgent need for developing state-of-the-art computational methods and statistical approaches to find overlapping expression using cte novo patterns, automating image standardization, and classifying images into groups based on spatial overlaps. In addition, the content of the FlyExpress knowledge-base must evolve by adding vast numbers of images from published literature and high-throughput studies and by building easy-to-use data and information submission web tools. These proposed developments will enable investigators to effectively generate and evaluate their gene interaction hypotheses based on overlaps in expression patterns by using all relevant biological information. This system will always be freely accessible through the web, and it will remove existing impediments to cross-laboratory research endeavors. The computational algorithms, statistical methods, and bioinformatics technologies developed in this project will provide the impetus for constructing similar frameworks for organizing expression pattern data from other species. The FlyExpress system will fulfill the day-to-day needs of basic and applied researchers as well as students in many areas of molecular biology crucial in human health research, including computational genomics, molecular genetics, developmental biology, genetics, and evolution.