Inferring the kinetics of stochastic gene expression fromsingle-cell RNA-sequencing data

We develop a statistical framework for studying the kinetics of stochastic gene expression from single-cell RNA- seq data. By applying our model to a single-cell RNA-seq dataset generated by profiling mouse embryonic stem cells, we find that the inferred kinetic parameters are consistent with RNA polymerase II binding and chromatin modifications. Our results suggest that histone modifications affect transcriptional bursting by modulating both burst size and frequency. Furthermore, we show that our model can be used to identify genes with slow promoter kinetics, which are important for probabilistic differentiation of embryonic stem cells.We conclude that the proposed statistical model provides a flexible and efficient way to investigate the kinetics of transcription.