使用关系数据库管理系统生成GO Slim以支持蛋白质组学分析

Getiria Onsongo, Hongwei Xie, T. Griffin, J. Carlis
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引用次数: 6

摘要

基因本体联盟建立了基因本体数据库(GO),以解决命名基因和基因产物的通用标准的需求。对相同的概念使用不同的名称,以及对具有相同名称的不同概念使用不同的名称,使得人类和计算机都无法有效地分析不同生物体之间的生物过程。该联盟通过定义对基因和基因产物进行分类的术语来解决这一需求。GO中的一个约定是,每个基因或基因产物在GO数据库中被注释为最具体的GO术语。然而,对于研究人员来说,能够将基因或基因产物分组到广泛的生物学类别中也很有用,这样在分析实验结果时可以从更高层次上了解它们的功能。GO Slim是GO本体的一个子集,它提供了更高级的函数视图。现有的GO Slim生成工具有两个重要的局限性:编程语言依赖性,以及无法在分析时动态生成GO Slim。我们已经扩展了关系数据库引擎来动态地生成GO Slim,从而克服了这一限制。使用此扩展,我们开发了一个工具(动态GOSlim),动态生成GOSlim并使用生成的GOSlim对基因或基因产品进行分类。该工具正用于正在进行的蛋白质组学项目,旨在识别唾液中可能的口腔癌生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generating GO Slim Using Relational Database Management Systems to Support Proteomics Analysis
The Gene Ontology Consortium built the Gene Ontology database (GO) to address the need for a common standard in naming genes and gene products. Using different names for the same concepts and different concepts with the same name makes it effectively impossible for humans and computers alike to analyze biological processes across different organisms. The consortium addresses this need by defining terms for categorizing genes and gene products. A convention in GO is that each gene or gene product is annotated to the most specific GO term in the GO database. It is, however, also useful for researchers to be able to group genesor gene products into broad biological categories that give a higher-level view of their function when analyzing results of an experiment. A GO Slim is a subset of the GO ontology that provides such a higher-level view of functions. Existing GO Slim generation tools have two important limitations: programming language dependence, and an inability to dynamically generate a GO Slim while analyzing. We have extended the relational database engine to dynamically generate a GO Slim overcoming this limitations. Using this extension, we have developed a tool (Dynamic GOSlim) that dynamically generates a GO Slim and uses the generated GO Slim to categorize genes or gene products. This tool is being used in an ongoing proteomics project aimed at identifying possible oral cancer biomarkers in saliva.
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