CHANGE-POINT MODEL ON NON-HOMOGENEOUS POISSON PROCESSES WITH APPLICATIONS IN COPY NUMBER PROFILING BY NEXT-GENERATION DNA SEQUENCING By

We propose a flexible change-point model for inhomogeneous Poisson Processes, which arise naturally from next-generation DNA sequencing, and derive score and generalized likelihood statistics for shifts in relative intensity functions. We construct a modified Bayesian information criterion (mBIC) to guide model selection, and point-wise approximate Bayesian confidence intervals for assessing the confidence in the segmentation. The model is applied to DNA Copy Number profiling with sequencing data and evaluated on simulated spike-in and real data sets.