Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays

Significance Gene transcription is a highly regulated dynamic process. Delays in transcription have important consequences on dynamics of gene expression and consequently on downstream biological function. We model temporal dynamics of transcription using genome-wide time course data measuring transcriptional activity and mRNA concentration. We find a significant number of genes exhibit a long RNA processing delay between transcription termination and mRNA production. These long processing delays are more common for short genes, which would otherwise be expected to transcribe most rapidly. The distribution of intronic reads suggests that these delays are required for splicing to be completed. Understanding such delays is essential for understanding how a rapid cellular response is regulated. Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.