Multiple latent clusterisation model for the inference of RNA life-cycle kinetic rates from sequencing data

We propose a hierarchical Bayesian approach to infer the RNA synthesis, processing, and degradation rates from sequencing data. We parametrise kinetic rates with novel functional forms and estimate the parameters through a Dirichlet process defined at a low level of hierarchy. Despite the complexity of this approach, we manage to perform inference, clusterisation and model selection simultaneously. We apply our method to investigate transcriptional and post-transcriptional responses of murine fibroblasts to the activation of proto-oncogene MYC. We uncover a widespread choral regulation of the three rates, which was not previously observed in this biological system.