Werner Thomas
Genomatix Software GmbH, Bayerstr. 85A, D-80335 München, Germany.
Curr Opin Biotechnol. 2008 Feb;19(1):50-4. doi: 10.1016/j.copbio.2007.11.005. Epub 2008 Jan 22.
Changes in transcript levels are assessed by microarray analysis on an individual basis, essentially resulting in long lists of genes that were found to have significantly changed transcript levels. However, in biology these changes do not occur as independent events as such lists suggest, but in a highly coordinated and interdependent manner. Understanding the biological meaning of the observed changes requires elucidating such biological interdependencies. The most common way to achieve this is to project the gene lists onto distinct biological processes often represented in the form of gene-ontology (GO) categories or metabolic and regulatory pathways as derived from literature analysis. This review focuses on different approaches and tools employed for this task, starting form GO-ranking methods, covering pathway mappings, finally converging on biological network analysis. A brief outlook of the application of such approaches to the newest microarray-based technologies (Chromatin-ImmunoPrecipitation, ChIP-on-chip) concludes the review.
通过对个体进行微阵列分析来评估转录水平的变化，这基本上会产生一长串被发现转录水平有显著变化的基因列表。然而，在生物学中，这些变化并非像此类列表所暗示的那样作为独立事件发生，而是以高度协调和相互依存的方式发生。理解所观察到的变化的生物学意义需要阐明这种生物学上的相互依存关系。实现这一点最常见的方法是将基因列表投射到通常以基因本体（GO）类别或源自文献分析的代谢和调节途径形式呈现的不同生物学过程上。本综述重点关注为此任务所采用的不同方法和工具，从GO排名方法开始，涵盖途径映射，最后汇聚到生物网络分析。本综述最后简要展望了此类方法在最新的基于微阵列的技术（染色质免疫沉淀，芯片上的染色质免疫沉淀）中的应用。
