LAUSR

Motivation Gene expression has inherent stochasticity resulting from transcription’s burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions (i.e., exponentially distributed dwell time in ON and OFF states). However, growing evidence suggests that the gene-state dwell times are nonexponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. Results We develop an interpretable and tractable model, the generalized telegraph model (GTM), to carve transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework (BayesGTM). BayesGTM demonstrates efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of BayesGTM to genome-wide data from mouse embryonic fibroblasts reveals that CTM would overestimate burst frequency and underestimate burst size. In conclusion, the GTM and the corresponding BayesGTM are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data.