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Cell. 2014 Dec 18;159(7):1698-710. doi: 10.1016/j.cell.2014.11.015. Epub 2014 Dec 11.

High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategies.

Author information

1
The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02140, USA.
2
The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
3
The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard/MIT Division of Health Sciences and Technology, Cambridge, MA 02141, USA.
4
The Laboratory of Signal Transduction, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA.
5
Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.
6
The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA; FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA.
7
The Laboratory of Signal Transduction, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA; Departments of Medicine and Biochemistry, Duke University Medical Center, Durham, NC 27710, USA.
8
School of Computer Science and Institute of Life Sciences, Hebrew University, Jerusalem 91904, Israel.
9
Department of Immunology, Weizmann Institute of Science, Rehovot 76100, Israel.
10
The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biology, Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02140, USA. Electronic address: aregev@broadinstitute.org.

Abstract

Cells control dynamic transitions in transcript levels by regulating transcription, processing, and/or degradation through an integrated regulatory strategy. Here, we combine RNA metabolic labeling, rRNA-depleted RNA-seq, and DRiLL, a novel computational framework, to quantify the level; editing sites; and transcription, processing, and degradation rates of each transcript at a splice junction resolution during the LPS response of mouse dendritic cells. Four key regulatory strategies, dominated by RNA transcription changes, generate most temporal gene expression patterns. Noncanonical strategies that also employ dynamic posttranscriptional regulation control only a minority of genes, but provide unique signal processing features. We validate Tristetraprolin (TTP) as a major regulator of RNA degradation in one noncanonical strategy. Applying DRiLL to the regulation of noncoding RNAs and to zebrafish embryogenesis demonstrates its broad utility. Our study provides a new quantitative approach to discover transcriptional and posttranscriptional events that control dynamic changes in transcript levels using RNA sequencing data.

PMID:
25497548
PMCID:
PMC4272607
DOI:
10.1016/j.cell.2014.11.015
[Indexed for MEDLINE]
Free PMC Article
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