Globaltest and GOEAST: two different approaches for Gene Ontology analysis.
Globaltest and GOEAST: two different approaches for Gene Ontology analysis.
复制标题
DOI：
10.1186/1753-6561-3-s4-s10
复制
发表时间：
2009-07-16
期刊：
影响因子：
--
通讯作者：
Smits MA
中科院分区：
文献类型：
--
作者：
Hulsegge I;Kommadath A;Smits MA
基因集分析是一种常用的分析微阵列数据的方法，通过考虑功能相关的基因组而不是单个基因。在这里，我们提出了两种基因集分析方法的使用：Globaltest和GOEAST。Globaltest是一种用于测试基因组是否与感兴趣的变量显著相关的方法。GOEAST是一个可免费访问的基于网络的工具，用于测试给定基因集内的GO术语富集。将这两种方法应用于分析从微阵列实验中的三个不同对比获得的基因列表，以研究艾美耳球虫感染后肉鸡的宿主反应。Globaltest在微阵列实验中进行的三个对比中的一个中鉴定了显著相关的基因集，而使用GOEAST的差异表达基因的功能分析揭示了在所有三个对比中富集的GO术语。Globaltest和GOEAST给出了不同的结果，可能是由于不同的算法和用于评估GO项的显著性的不同标准。
Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms.