Department of Biochemistry and Molecular Pharmacology; Program in Bioinformatics and Integrative Biology
Base Composition; Binding Sites; Cell Line; Chromatin; Computational Biology; *Gene Expression Regulation; *Genomics; Histones; Humans; Models, Biological; Promoter Regions, Genetic; Protein Binding; Transcription Factors; Transcription Initiation Site; *Transcription, Genetic
Bioinformatics | Biostatistics | Computational Biology | Genetics and Genomics | Systems Biology
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.
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© 2012, Published by Cold Spring Harbor Laboratory Press
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at http://creativecommons.org/licenses/by-nc/3.0/.
DOI of Published Version
Genome Res. 2012 Sep;22(9):1658-67. doi: 10.1101/gr.136838.111. Link to article on publisher's site
Cheng, Chao; Alexander, Roger; Min, Rengqiang; Leng, Jing; Yip, Kevin Y.; Rozowsky, Joel; Yan, Koon-Kiu; Dong, Xianjun; Djebali, Sarah; Ruan, Yijun; Davis, Carrie A.; Carninci, Piero; Lassman, Timo; Gingeras, Thomas R.; Guigo, Roderic; Birney, Ewan; Weng, Zhiping; Snyder, Michael; and Gerstein, Mark B., "Understanding transcriptional regulation by integrative analysis of transcription factor binding data" (2012). Program in Bioinformatics and Integrative Biology Publications and Presentations. 10.