Abdelkarim, Basma2020-01-082020-01-082020-01-08http://hdl.handle.net/10393/40032http://dx.doi.org/10.20381/ruor-24271Recently, the term super-enhancer (SE) has been gaining more attention since its characterization in 2013 ‎as a subset of enhancers that form large regulatory domains that regulate cell identity processes and ‎coordinate cell development. Since then, SEs have been characterized in over 100 cell types, including ‎diseased and tumor cells. In an attempt to standardize the method for identifying SEs, the ROSE algorithm ‎was developed. This algorithm uses peak files for the transcription or epigenetic factor of interest and the ‎sequence alignment of the ranking factor to identify SEs according to the signal density of the ranking ‎factor. More recently, there has been interest in studying the dynamics of these SEs throughout ‎development. In this study I introduce a novel algorithm, DYSE, that builds onto the functionality of the ‎ROSE algorithm where it determines key changes in SEs as the cell transitions from state to state, using ‎comparative analysis. Here I explain the features of this algorithm and I present my results from testing it ‎using multiple transcription factors for a three-stage analysis of myogenesis. I characterized SEs that are ‎lost, maintained or gained as the cell transitions from myoblast to early myotubes to late myotube stages. ‎Through gene ontology (GO) enrichment analysis, I found that genes associated with SEs that are ‎maintained between stages show more enrichment for myogenic processes than stage-specific ones.‎enSuper enhancersThesis