Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers
By Leighton J. Core, André L. Martins, Charles G. Danko, C. T. Waters, Adam C. Siepel, J. Lis. Nature Genetics, 2014
653 citations recorded by the source corpus.
Despite the conventional distinction between them, promoters and enhancers share many features in mammals, including divergent transcription and similar modes of transcription factor binding. Here we examine the architecture of transcription initiation through comprehensive mapping of transcription start sites (TSSs) in human lymphoblastoid B cell (GM12878) and chronic myelogenous leukemic (K562) ENCODE Tier 1 cell lines. Using a nuclear run-on protocol called GRO-cap, which captures TSSs for both stable and unstable transcripts, we conduct detailed comparisons of thousands of promoters and enhancers in human cells. These analyses identify a common architecture of initiation, including tightly spaced (110 bp apart) divergent initiation, similar frequencies of core promoter sequence elements, highly positioned flanking nucleosomes and two modes of transcription factor binding. Post-initiation transcript stability provides a more fundamental distinction between promoters and enhancers than patterns of histone modification and association of transcription factors or co-activators. These results support a unified model of transcription initiation at promoters and enhancers.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/4a6b2f1ab743f7f877d1c1882d9180c6165382fd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Manolis Kellis
- Jason Ernst
- Wenqiang Shi
- Yifeng Li
- Wyeth W. Wasserman
- Christina S. Leslie
- Anthony Mathelier
- William Stafford Noble
Citations in the corpus
- Chromatin-state discovery and genome annotation with ChromHMM (2017, methodology): “In the context of gene regulation, ChromHMM has been used for understanding long-range chromatin interactions 21 , nascent transcripts 22 , cellular reprogramming 14,23 , topologically associated domains 24 , transcription-factor binding preferences 1,25 , and regulatory motif disruptions in…”
- Genome-wide prediction of cis-regulatory regions using supervised deep learning methods (2016, background): “Recent studies confirmed that sequence properties can be important for the recognition of promoters and enhancers [3, 5, 24].”