Application of Spatial Concepts to Genome Data

空间概念在基因组数据中的应用

• 批准号: 9723873
• 负责人: Mary-Kate Beard-Tisdale
• 金额: $ 33.4万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-08-01 至 2000-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9723873&HistoricalAwards=false
• 关键词: Application; Spatial; Concepts; Genome; Data

This project will investigate the application of geographic information science concepts and methods to the modeling and analysis of genome data. The primary objective of the research is to develop a data model for genomes that supports the graphical exploration of the higher order spatial arrangement of genome features through spatial queries and spatial data analysis tools. The spatial genome model formalizes topological and order relationships among genome features (before, after, overlap), uses metric properties to refine spatial topologies, and includes representations of features that have uncertain metric properties. The genome spatial model enhances the integrative and comparative potential of genome data by providing the foundation for more powerful spatial reasoning and inferences than can be achieved by data models that incorporate only a small subset of possible temporal-spatial relationships among genome features (e.g. order and distance). The research represents a logical extension from current "feature by feature" analytical approaches of genome studies to one that allows biologists to ask questions about the contextual and organizational significance of the spatial arrangement of genome features. These functional capabilities should, in turn, aid in the automation of repetitive analytical tasks associated with the mapping of genome features and drive the discovery of biologically significant aspects of genome organization and function.

本计画将探讨地理资讯科学的概念与方法在基因组资料的模拟与分析上的应用。该研究的主要目标是开发一个基因组数据模型，通过空间查询和空间数据分析工具支持基因组特征的高阶空间排列的图形化探索。空间基因组模型形式化基因组特征之间的拓扑和顺序关系（之前，之后，重叠），使用度量属性来细化空间拓扑，并包括具有不确定度量属性的特征的表示。基因组空间模型增强了基因组数据的整合和比较潜力，为更强大的空间推理和推断提供了基础，而这些推理和推断是通过仅包含基因组特征（例如顺序和距离）之间可能的时空关系的一小部分的数据模型实现的。这项研究代表了一个逻辑的延伸，从目前的“功能的功能”分析方法的基因组研究，让生物学家提出问题的上下文和组织意义的空间排列的基因组功能。反过来，这些功能应该有助于与基因组特征映射相关的重复性分析任务的自动化，并推动基因组组织和功能的生物学重要方面的发现。