This seminar and workshop will explain how we analyse gene regulation at genome scale, both at the lab bench and once we have our data. It will highlight some key data types and how these are practically generated. You will work with a software package called Dynamic Regulatory Events Miner (DREM) that allows us to integrate large-scale time-series gene expression data with transcription factor-DNA binding data. DREM uses a Hidden Markov Model-based approach to produce models that can determine when transcription factors (TFs) activate genes and what genes they regulate . DREM outputs an annotated dynamic regulatory map that highlights bifurcation events in the time-series data. In other words, it identifies places in the time series where sets of genes, which previously had roughly similar expression levels, diverge. Often these bifurcation events can be explained by TFs selectively regulating a certain subset of genes. DREM annotates these events with TFs potentially responsible for them .