TY  - JOUR
A1  - Cheng, Chao
A1  - Alexander, Roger
A1  - Min, Renqiang
A1  - Leng, Jing
A1  - Yip, Kevin Y.
A1  - Rozowsky, Joel
A1  - Yan, Koon-Kiu
A1  - Dong, Xianjun
A1  - Djebali, Sarah
A1  - Ruan, Yijun
A1  - Davis, Carrie A.
A1  - Carninci, Piero
A1  - Lassman, Timo
A1  - Gingeras, Thomas R.
A1  - Guigó, Roderic
A1  - Birney, Ewan
A1  - Weng, Zhiping
A1  - Snyder, Michael
A1  - Gerstein, Mark
T1  - Understanding transcriptional regulation by integrative analysis of transcription factor binding data
Y1  - 2012/09/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1658 
EP  - 1667 
DO  - 10.1101/gr.136838.111 
VL  - 22 
IS  - 9 
UR  - http://genome.cshlp.org/content/22/9/1658.abstract 
N2  - Statistical models have been used to quantify the relationship between gene expression and transcription factor (TF) binding signals. Here we apply the models to the large-scale data generated by the ENCODE project to study transcriptional regulation by TFs. Our results reveal a notable difference in the prediction accuracy of expression levels of transcription start sites (TSSs) captured by different technologies and RNA extraction protocols. In general, the expression levels of TSSs with high CpG content are more predictable than those with low CpG content. For genes with alternative TSSs, the expression levels of downstream TSSs are more predictable than those of the upstream ones. Different TF categories and specific TFs vary substantially in their contributions to predicting expression. Between two cell lines, the differential expression of TSS can be precisely reflected by the difference of TF-binding signals in a quantitative manner, arguing against the conventional on-and-off model of TF binding. Finally, we explore the relationships between TF-binding signals and other chromatin features such as histone modifications and DNase hypersensitivity for determining expression. The models imply that these features regulate transcription in a highly coordinated manner. 
ER  -