Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China.
Curr Genomics. 2015 Feb;16(1):3-22. doi: 10.2174/1389202915666141110210634.
Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.
转录调控在许多基本的生物过程中起着至关重要的作用。从高通量转录组数据中反向工程全基因组调控网络，为描述调控因子与其靶基因之间的全局调控关系提供了一种很有前途的方法。在这篇综述中，我们总结和分类了目前可从微阵列基因表达谱数据推断转录调控网络的主要框架和方法。我们分别概述了每种策略，并介绍了代表性方法。还阐明和评论了它们的假设、优点、缺点以及可能的改进和扩展。
