High-Resolution Sequencing and Modeling Identifies Distinct Dynamic RNA Regulatory Strategies
Author(s)Raychowdhury, Raktima; Jovanovic, Marko; Stumpo, Deborah J.; Pauli, Andrea; Hacohen, Nir; Schier, Alexander F.; Blackshear, Perry J.; Friedman, Nir; Amit, Ido; Rabani, Michal; Rooney, Michael Steven; Regev, Aviv; ... Show more Show less
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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.
DepartmentHarvard University--MIT Division of Health Sciences and Technology; Massachusetts Institute of Technology. Department of Biology; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Rabani, Michal et al. “High-Resolution Sequencing and Modeling Identifies Distinct Dynamic RNA Regulatory Strategies.” Cell 159.7 (2014): 1698–1710.
Author's final manuscript