RT Journal
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
JF Genome Research 
JO Genome Research 
YR 2012 
FD September 01 
VO 22 
IS 9 
SP 1658 
OP 1667 
DO 10.1101/gr.136838.111 
UL http://genome.cshlp.org/content/22/9/1658.abstract 
AB 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.