Technologies for mapping genome wide chromatin structure are generating tremendous amounts of data on the 3D organization of genomes. The size of these datasets and the unique data structure necessitate development of new data analysis tools. Major challenges include how to use the static information contained in these datasets to infer dynamic 3D chromatin organization in vivo, identify chromatin interactions, and gain an understanding of spatiotemporal chromatin organization. Here, we propose to develop a variety of novel analytical methodologies for processing various chromatin topology datasets, extracting biophysical properties of chromatin fibers, and gain an in-depth understanding of the chromatin architecture.
In aim 1, we will development a new statistical method using hidden Markov random fields to identify chromatin contacts from the genome-wide chromatin interaction maps.
In aim 2, we will develop analytical methods for analyzing data from Genome Architecture Mapping (GAM) experiment, a novel experimental methodology that we will develop and refine as a part of the mapping component. We will further develop statistical framework to reconstruct 3D chromatin structural models from both chromatin contacts and GAM datasets.
In aim 3, we will develop predictive models from non-equilibrium statistical mechanics and polymer physics that will link chromatin dynamics in live cells to the static molecular interactions maps. Together, these analytical methods will provide comprehensive view of chromatin structural organization and dynamic properties.
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