LAUSR

The human genome is dynamic in structure, complicating researcher's attempts at fully understanding it. Time series "Fluorescent in situ Hybridization" (FISH) imaging has increased our ability to observe genome structure, but due to cell type and experimental variability this data is often noisy and difficult to analyze. Furthermore, computational analysis techniques are needed for homolog discrimination and canonical framework detection, in the case of time-series images. In this paper we introduce novel ideas for nucleus imaging analysis, present findings extracted using dynamic genome imaging, and propose an objective algorithm for high-throughput, time-series FISH imaging. While a canonical framework could not be detected beyond statistical significance in the analyzed dataset, a mathematical framework for detection has been outlined with extension to 3D image analysis.