Modeling Bias in DNase-seq Data for Improved Chromatin Occupancy Prediction

Whether or not a single gene is transcribed relies on a myriad of stochastic factors which may not be adequately described by the cell’s genome alone. Understanding the connection between the occupancy of a cell’s chromatin and the transcription of its genes would provide insight into the dynamic regulatory dependencies that control its internal transcription state, and so enhanced techniques for modeling chromatin state would be advantageous. In this thesis we consider improved methods of integrating DNase-seq data as input for a statistical inference model which outputs a nucleotide-resolution probability distribution of the genome’s chromatin occupancy profile. In particular, we focus on some initial observations made concerning the probabilistic distribution of permitted cuts as part of the DNase-seq data, as well as extending the multivariate model so that it may account for sequence bias that is exhibited by the DNase I enzyme.