UCSF

The spatial organization of chromosomes within the cell nucleus facilitates critical genomic processes including transcription, replication, and repair. Understanding how DNA sequence informs genome folding and how chromatin conformation instructs transcription remains a central challenge. This dissertation presents computational strategies to advance our understanding of the principles governing three-dimensional chromatin structure and their implications for gene regulation. In Chapter 2, I perform large-scale in silico mutagenesis using a deep learning model to systematically uncover DNA sequences that encode folding patterns. Chapter 3 introduces new methods to quantify differences between chromatin interaction maps, revealing that integrating simple, map-informed and feature-based strategies provides the most complete perspective on functionally relevant organizational changes. Chapter 4 introduces and applies a non-negative matrix factorization method to decompose single-cell heterogeneity in chromatin structure, linking patterns in cell subpopulations to average folding principles and transcriptional consequences discerned in bulk. Together, these computational methods revealed new biological findings: repetitive elements, sometimes lacking CTCF motifs, provide sequence grammar governing chromatin interactions and the chromatin folding in only a small minority of cells often drives population-wide signals. The work broadly highlights the potential of computational approaches, especially machine learning, to accelerate discovery in genomics. This work provides templates for future studies relating sequence, spatial dynamics, and gene regulation amidst widespread variability in the folded genome.