ITR: Development of Novel Computational Methods for Genome-Wide Discovery of Gene Function and Networks

ITR：开发用于全基因组基因功能和网络发现的新型计算方法

• 批准号: 0219061
• 负责人: Edward Marcotte
• 金额: $ 49.99万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-10-01 至 2006-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0219061&HistoricalAwards=false
• 关键词: ITR; Development; Novel; Computational; Methods

EIA-0219061Marcotte, Edward MUniversity of TexasITR: Development of novel computational methods for genome-wide discovery of gene function and networksOf the roughly 40,000 genes encoded by the human genome, as with every other genome sequenced to date, about half are completely uncharacterized and of unknown function. There is a broad need for methods to discover the functions of these thousands of uncharacterized genes, as well as how the gene products participate in networks, pathways and systems in the cell. This project's goal is to develop novel computational methods for discovering the functions of genes on a genome-wide scale, and to discover how the genes are organized into systems and pathways. Several novel methods are proposed capable of providing such information. The essence of these methods, called "non-homology" methods, is that they analyze contextual properties of genes-such as which organisms the genes appear in, which genes can be found fused together, and other properties-in addition to the sequences of the genes. Such contextual properties turn out to provide a tremendous increase in the ability of computational methods to discover gene function and gene networks. At the core of this proposal is a novel graph-based method for associating genes together that operate in the same cellular pathway, essentially resulting in a "functional map" of the genes. This method is combined with a new computational method for finding specific physical interaction partners for proteins in large sequence families, and with methods to visualize the resultant complex gene networks. Application of these methods should improve our understanding of the functions of the thousands of uncharacterized genes in each sequenced genome.

EIA-0219061 Marcotte，Edward M德克萨斯大学ITR：全基因组发现基因功能和网络的新计算方法的开发在人类基因组编码的大约40，000个基因中，与迄今为止测序的其他基因组一样，大约一半是完全未表征的，功能未知。人们对发现这数千个未表征基因的功能以及基因产物如何参与细胞中的网络、途径和系统的方法有着广泛的需求。该项目的目标是开发新的计算方法，用于在全基因组范围内发现基因的功能，并发现基因如何组织成系统和途径。提出了几种能够提供这种信息的新方法。这些被称为“非同源”方法的方法的本质是，它们分析基因的背景属性，例如基因出现在哪些生物体中，哪些基因可以被发现融合在一起，以及其他属性，除了基因的序列。这样的上下文属性提供了一个巨大的增长能力的计算方法来发现基因功能和基因网络。该提案的核心是一种新的基于图形的方法，用于将在同一细胞途径中运行的基因关联在一起，基本上导致基因的“功能图”。 该方法结合了一种新的计算方法，用于为大序列家族中的蛋白质找到特定的物理相互作用伙伴，并与可视化所得到的复杂基因网络的方法相结合。 这些方法的应用将提高我们对每个测序基因组中数千个未表征基因的功能的理解。