Computational Analysis of Gene Expression Pattern Images

描述（由申请人提供）：拟议项目的总体目标是开发计算方法和实用的生物信息学资源，用于数据驱动的表达图像和序列数据的综合分析，以发现基因和基因组元件之间的功能，遗传和调控相互作用。快速增长的集合的空间和时间的基因表达模式的模式生物，果蝇，提供了前所未有的机会，了解时空调控的表达不仅为果蝇基因，而且人类基因，显示出广泛的进化相似性和功能的保护。这些表达模式是基因的一级序列与其对表型的影响之间的第一个联系，它们的重叠为功能、遗传或调控相互作用提供了最初的线索。因此，我们将大量图像转化为功能性知识的主要框架是发现和分析共表达（以及因此潜在的共调节）基因。到目前为止，我们的努力已经导致开发和建立了一个独特的和创新的基于图像的框架（FlyExpress），以进行这些大型数据集的高通量分析，因为手动检查图像的标准做法不再可行，由于可用图像的数量庞大。我们现在正准备满足日益增长的迫切需求，开发计算工具和数据集成方法，以便有效利用快速增长的图像和序列数据，并促进研究界在构建FlyExpress知识库方面的参与。因此，我们计划（a）开发一种新的软件工具，以实现有效的表达图像分析，同时推进社区合作，（B）通过开发用于图像和序列数据综合分析的新方法，将时空表达重叠的知识转化为调控基序的发现，（c）将FlyExpress发展成为一个全面的知识-胚胎表达图像的基础，以产生更好的预测和跨异质图像源的综合分析。这些发展将使研究人员能够有效地产生和评估他们的基因相互作用的假设的基础上重叠的表达模式，通过使用所有相关的生物信息。软件工具和网络系统，包括源代码，将始终免费提供。在这个项目中开发的计算算法，统计方法和生物信息学技术将是可重构的，适用于构建类似的框架，用于组织来自其他物种和生活史阶段的表达模式数据。FlyExpress系统将满足基础和应用研究人员以及在基础生物医学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。

DESCRIPTION (provided by applicant): The overarching goal of the proposed project is to develop computational methods and practical bioinformatics resources for data-driven, integrative analysis of expression images and sequence data to discover functional, genetic, and regulatory interactions between genes and genomic elements. Fast growing collections of spatial and temporal gene expression patterns in the model organism, Drosophila melanogaster, are providing unprecedented opportunities for understanding the spatiotemporal regulation of expression not only for fruit fly genes, but also human genes that show extensive evolutionary similarity and functional conservation. These expression patterns are the first links between a gene's primary sequence and its influence on the phenotype, and their overlaps provide initial clues to functional, genetic, or regulatory interactions. Therefore, our primary framework for translating large volumes of images into functional knowledge is to discover and analyze co- expressed (and, thus, potentially co-regulated) genes. To date, our efforts have led to the development and establishment of a unique and innovative image-based framework (FlyExpress) to carry out high-throughput analyses of these large datasets, because the standard practice of manually inspecting images is no longer feasible owing to the sheer volume of available images. We are now poised to address a growing and urgent need to develop computational tools and data-integration methods that enable effective harnessing of fast- growing image and sequence data as well as foster enhanced engagement of the research community in building the FlyExpress knowledgebase. Therefore, we plan to (a) develop a new software tool to enable effective expression image analysis while advancing community collaborations, (b) translate knowledge of spatiotemporal expression overlap into the discovery of regulatory motifs by developing novel methods for integrative analysis of image and sequence data, and (c) evolve FlyExpress into a comprehensive knowledge- base of embryonic expression images in order to generate better predictions and integrative analysis across heterogeneous image sources. These 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. The software tool and web system, including the source code, will always be freely available. The computational algorithms, statistical methods, and bioinformatics technologies developed in this project will be reconfigurable and adaptable for application in constructing similar frameworks for organizing expression pattern data from other species and life history stages. 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 basic biomedicine, including computational genomics, molecular genetics, developmental biology, genetics, and evolution.