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To obtain rates of mRNA synthesis and decay in yeast, we established dynamic transcriptome analysis (DTA). DTA combines non-perturbing metabolic RNA labeling with dynamic kinetic modeling. DTA reveals that most mRNA synthesis rates are around several transcripts per cell and cell cycle, and most mRNA half-lives range around a median of 11 min. DTA can(More)
During the cell cycle, the levels of hundreds of mRNAs change in a periodic manner, but how this is achieved by alterations in the rates of mRNA synthesis and degradation has not been studied systematically. Here, we used metabolic RNA labeling and comparative dynamic transcriptome analysis (cDTA) to derive mRNA synthesis and degradation rates every 5 min(More)
Development, growth and adult survival are coordinated with available metabolic resources, ascertaining that the organism responds appropriately to environmental conditions. MicroRNAs are short (21-23 nt) regulatory RNAs that confer specificity on the RNA-induced silencing complex (RISC) to inhibit a given set of mRNA targets. We profiled changes in miRNA(More)
Standard transcriptomics measures total cellular RNA levels. Our understanding of gene regulation would be greatly improved if we could measure RNA synthesis and decay rates on a genome-wide level. To that end, the Dynamic Transcriptome Analysis (DTA) method has been developed. DTA combines metabolic RNA labeling with standard transcriptomics to measure RNA(More)
Pervasive transcription of the genome produces both stable and transient RNAs. We developed transient transcriptome sequencing (TT-seq), a protocol that uniformly maps the entire range of RNA-producing units and estimates rates of RNA synthesis and degradation. Application of TT-seq to human K562 cells recovers stable messenger RNAs and long intergenic(More)
Accurate maps of promoters and enhancers are required for understanding transcriptional regulation. Promoters and enhancers are usually mapped by integration of chromatin assays charting histone modifications, DNA accessibility , and transcription factor binding. However, current algorithms are limited by unrealistic data distribution assumptions. Here we(More)
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