Datta Debayan, Zhao Hongyu
Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA.
Bioinformatics. 2008 Feb 15;24(4):545-52. doi: 10.1093/bioinformatics/btm523. Epub 2007 Nov 7.
Transcription factors regulate transcription in prokaryotes and eukaryotes by binding to specific DNA sequences in the regulatory regions of the genes. This regulation usually occurs in a coordinated manner involving multiple transcription factors. Genome-wide location data, also called ChIP-chip data, have enabled researchers to infer the binding sites for individual regulatory proteins. However, current methods to infer binding sites, such as simple thresholding based on p-values, are not optimal for a number of study objectives like combinatorial regulation, leading to potential loss of information. Hence, there is a need to develop more efficient statistical methods for analyzing such data.
We propose to use log-linear models to study cooperative binding among transcription factors and have developed an Expectation-Maximization algorithm for statistical inferences. Our method is advantageous over simple thresholding methods both based on simulation and real data studies. We apply our method to infer the cooperative network of 204 regulators in Rich Medium and a subset of them in four different environmental conditions. Our results indicate that the cooperative network is condition specific; for a set of regulators, the network structure changes under different environmental conditions.
Our program is available at http://bioinformatics.med.yale.edu/TFcooperativity.
转录因子通过与基因调控区域中的特定DNA序列结合来调控原核生物和真核生物中的转录。这种调控通常以涉及多个转录因子的协调方式发生。全基因组定位数据，也称为芯片杂交数据（ChIP-chip数据），使研究人员能够推断单个调控蛋白的结合位点。然而，当前推断结合位点的方法，如基于p值的简单阈值法，对于诸如组合调控等许多研究目标并非最优，会导致潜在的信息丢失。因此，需要开发更有效的统计方法来分析此类数据。
我们建议使用对数线性模型来研究转录因子之间的协同结合，并开发了一种期望最大化算法用于统计推断。基于模拟和实际数据研究，我们的方法比简单阈值法更具优势。我们将我们的方法应用于推断丰富培养基中204个调控因子的协同网络以及其中一部分在四种不同环境条件下的协同网络。我们的结果表明，协同网络是条件特异性的；对于一组调控因子，网络结构在不同环境条件下会发生变化。
我们的程序可在http://bioinformatics.med.yale.edu/TFcooperativity获取。
