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Genes with similar transcriptional activation kinetics can display verydifferent temporal mRNA profiles due to differences in transcription time,degradation rate and RNA processing kinetics. Recent studies have shown that asplicing-associated RNA production delay can be significant. We introduce ajoint model of transcriptional activation and mRNA accumulation which can beused for inference of transcription rate, RNA production delay and degradationrate given genome-wide data from high-throughput sequencing time courseexperiments. We combine a mechanistic differential equation model with anon-parametric statistical modelling approach allowing us to capture a broadrange of activation kinetics, and use Bayesian parameter estimation to quantifythe uncertainty in the estimates of the kinetic parameters. We apply the modelto data from estrogen receptor (ER-{\alpha}) activation in the MCF-7 breastcancer cell line. We use RNA polymerase II (pol-II) ChIP-Seq time course datato characterise transcriptional activation and mRNA-Seq time course data toquantify mature transcripts. We find that 11% of genes with a good signal inthe data display a delay of more than 20 minutes between completingtranscription and mature mRNA production. The genes displaying these longdelays are significantly more likely to be short. We also find a statisticalassociation between high delay and late intron retention in pre-mRNA data,indicating significant splicing-associated production delays in many genes.

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