Frank Alber, PhD
Population-based modeling reveals guiding principles of 3D genome organization
University of Southern California
Molecular and Computational Biology
Knowledge about the 3-D organization of genomes offers great insights into how cells retrieve and process the genetic information. We discuss a probabilistic method to construct and analyze 3D structures of entire genomes and chromatin regions using various data sources from chromosome conformation capture and imaging experiments. Our approach explicitly considers the highly variable nature of genome structures, allowing studying functional states in the genome structure population. We show that our population-based approach is not only able to predict remarkable well structural features of genomes but also discovers interesting principles of genome organization and proposes important driving forces that shape chromosomal positioning in the nucleus. We also discuss methods of how to analyze populations of genome structures to reveal structure-function correlations. We propose a tensor-based method to extract frequent occurring spatial patterns and are able to relate these structural patterns with functional and epigenomic data from ENCODE. We will discuss several examples, including an analysis of human lymphoblastoid cells.