Understanding transcriptional regulation by integrative analysis of transcription factor binding data
Authors
Cheng, ChaoAlexander, 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
Gerstein, Mark B.
UMass Chan Affiliations
Program in Bioinformatics and Integrative BiologyDepartment of Biochemistry and Molecular Pharmacology
Document Type
Journal ArticlePublication Date
2012-09-01Keywords
Base CompositionBinding 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
Metadata
Show full item recordAbstract
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.Source
Genome Res. 2012 Sep;22(9):1658-67. doi: 10.1101/gr.136838.111. Link to article on publisher's siteDOI
10.1101/gr.136838.111Permanent Link to this Item
http://hdl.handle.net/20.500.14038/25808PubMed ID
22955978Related Resources
Link to Article in PubMedRights
© 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/.ae974a485f413a2113503eed53cd6c53
10.1101/gr.136838.111
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