Kirov Stefan A, Zhang Bing, Snoddy Jay R
Oak Ridge National Laboratory, University of Tennessee, USA.
Methods Mol Biol. 2007;408:19-33. doi: 10.1007/978-1-59745-547-3_2.
High-throughput experiments in biology often produce sets of genes of potential interests. Some of those gene sets might be of considerable size. Therefore, computer-assisted analysis is necessary for the biological interpretation of the gene sets, and for creating working hypotheses, which can be tested experimentally. One obvious way to analyze gene set data is to associate the genes with a particular biological feature, for example, a given pathway. Statistical analysis could be used to evaluate if a gene set is truly associated with a feature. Over the past few years many tools that perform such analysis have been created. In this chapter, using WebGestalt as an example, it will be explained in detail how to associate gene sets with functional annotations, pathways, publication records, and protein domains.
生物学中的高通量实验常常会产生一系列潜在感兴趣的基因。其中一些基因集可能规模相当大。因此，对于基因集的生物学解读以及形成可通过实验验证的工作假设而言，计算机辅助分析是必要的。分析基因集数据的一种明显方法是将基因与特定的生物学特征相关联，例如给定的通路。统计分析可用于评估一个基因集是否真的与某一特征相关。在过去几年中，已经创建了许多执行此类分析的工具。在本章中，将以WebGestalt为例，详细解释如何将基因集与功能注释、通路、出版物记录和蛋白质结构域相关联。
