Liu Fengkai, Miranda-Saavedra Diego
School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics (BUAA), Building 16, Min'an Street, Dong Cheng District, Beijing 100007, China.
Fibrosis Laboratories, Institute of Cellular Medicine, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom.
Gene. 2014 Aug 10;546(2):417-20. doi: 10.1016/j.gene.2014.06.016. Epub 2014 Jun 11.
Transcription factors (TFs) bind to specific DNA regions, although their binding specificities cannot account for their cell type-specific functions. It has been shown in well-studied systems that TFs combine with co-factors into transcriptional regulatory modules (TRMs), which endow them with cell type-specific functions and additional modes of regulation. Therefore, the prediction of TRMs can provide fundamental mechanistic insights, especially when experimental data are limiting or when no regulatory proteins have been identified. Our method rTRM predicts TRMs by integrating genomic information from TF ChIP-seq data, cell type-specific gene expression and protein-protein interaction data. Here we present a freely available web interface to rTRM (http://www.rTRM.org/) supporting all the options originally described for rTRM while featuring flexible display and network calculation parameters, publication-quality figures as well as annotated information on the list of genes constituting the TRM.
转录因子（TFs）可与特定的DNA区域结合，尽管它们的结合特异性并不能解释其细胞类型特异性功能。在深入研究的系统中已表明，转录因子与辅助因子结合形成转录调节模块（TRMs），这些模块赋予它们细胞类型特异性功能和额外的调节模式。因此，TRM的预测可以提供基本的机制性见解，特别是在实验数据有限或尚未鉴定出调节蛋白的情况下。我们的rTRM方法通过整合来自TF ChIP-seq数据、细胞类型特异性基因表达和蛋白质-蛋白质相互作用数据的基因组信息来预测TRMs。在这里，我们展示了一个可供免费使用的rTRM网络界面（http://www.rTRM.org/），它支持最初为rTRM描述的所有选项，同时具有灵活的显示和网络计算参数、可用于发表的高质量图表以及构成TRM的基因列表的注释信息。
