Using gene ontology on genome-scale studies to find significant associations of biologically relevant terms to groups of genes

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI:10.1109/NNSP.2003.1318003

F. Al-Shahrour, Javier Herrero, Á. Mateos, J. Santoyo, R. Díaz-Uriarte, J. Dopazo

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引用次数: 5

Abstract

The analysis of genome-scale data from different high throughput techniques usually involves the grouping of genes based on experimental criteria. These groups are a consequence of the biological roles the genes are playing within the cell. Establishing which of these groups are functionally important is essential. Gene ontology terms provide a specialised vocabulary to describe the relevant biological properties of genes. We used a simple procedure to extract terms that are significantly over or under-represented in sets of genes within the context of a genome-scale experiment. Said procedure, which takes the multiple-testing nature of the statistical contrast into account, has been implemented as a Web application, FatiGO, allowing for easy and interactive querying. Several examples demonstrate its application and the type of information that can be extracted. Although a number of genes still lack gene ontology annotations, the results were informative enough to characterise the biological processes in the systems analysed.

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在基因组规模的研究中使用基因本体来发现与基因组相关的生物学术语的显著关联

来自不同高通量技术的基因组规模数据的分析通常涉及基于实验标准的基因分组。这些群体是基因在细胞内发挥生物学作用的结果。确定哪些组在功能上是重要的是至关重要的。基因本体术语提供了一个专门的词汇来描述基因的相关生物学特性。我们使用了一个简单的程序来提取在基因组规模实验背景下基因组中显着过度或不足的术语。上述过程考虑了统计对比的多重测试性质，已作为Web应用程序FatiGO实现，允许进行简单的交互式查询。几个例子演示了它的应用和可以提取的信息类型。尽管许多基因仍然缺乏基因本体注释，但结果足以说明所分析系统中的生物过程。

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2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)

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