This BioConductor package provides a pipeline for the analysis of GRO- seq data. Among the more advanced features, r-bioc-grohmm predicts the boundaries of transcriptional activity across the genome de novo using a two-state hidden Markov model (HMM).

The used model essentially divides the genome into transcribed and non- transcribed regions in a strand specific manner. HMMs are used to identify the leading edge of Pol II at genes activated by a stimulus in GRO-seq time course data. This approach allows the genome-wide interrogation of transcription rates in cells.