DOI: 10.25549/usctheses-c40-381101
title: Modeling and analysis of complex genomics data

description: The impressive progress of next-generation sequencing (NGS) technology has enabled genome sequencing to be obtained in a cheap and reliable large-scale manner. Various technologies combined with NGS are developed by researchers to address an increasingly diverse range of biological problems extensively for genomics and epigenetics. Methods for modeling and analyzing these data to infer the biological insights and applications are needed. The overall goal of my dissertation is to develop computational methods which fully utilize complex genomics data to explore the problems and applications in two fields: 3-dimentional (3D) structural organization of genomes and liquid biopsies. This dissertation presents three studies. ❧ First, we address one of the principal challenges of 3D genome reconstructions, namely to systematically integrate data from different experimental technologies to determine 3D structural maps of the nucleome. Here, we develop a probabilistic approach for data integration to determine a population of complete 3D genome structures that are statistically consistent with data from both genome-wide chromosome conformation capture (Hi-C) and lamina-DamID experiments. As a case study, our 3D genome structures of Drosophila melanogaster embryonic cells have shown strong predictive power for structural features, and they also reveal a number of new insights about the genome organization and its functional relevance. ❧ Second, we address a major challenge of cell-free DNA (cfDNA)-based cancer diagnostics that how to identify the tiny amount of tumor DNAs out of total cell-free DNAs in blood. We propose an ultrasensitive cancer detection method, termed “CancerDetector”, using the DNA methylation profiles of cell-free DNAs. The key of our method is to probabilistically model the joint methylation patterns of multiple adjacent CpG sites on an individual sequencing read, in order to exploit the pervasive nature of DNA methylation for signal amplification. We evaluated CancerDetector on the simulated data and showed a high concordance of the predicted and true tumor burden. Testing CancerDetector on real plasma data demonstrated its high sensitivity and specificity in detecting tumor DNAs. In addition, the predicted tumor burden showed great consistency with tumor size and survival outcome. Note that all of the tests were performed on sequencing data at low to medium coverage (1X to 10X). Therefore, CancerDetector holds a great potential to detect cancer early and     cost-effectively. ❧ Third, in addition to sequence variants and DNA methylation aberration, detecting differences in gene expression from a liquid biopsy can significantly improve our understanding and diagnosis of many diseases. We show that deciphering small cfDNA fragments which represent the transcription factor binding regions can help with gene expression prediction in normal plasma. Integrating the read coverage and methylation information from cfDNA bisulfite sequencing data has the best performance for gene expression prediction in plasma. We also develop a platform to infer tumor gene expression based on the DNA methylation read patterns of cfDNAs, and validate on both simulated and real plasma data. The approach can predict around 100 tumor genes’ expression for an individual sample, even though there is only a minor trace of tumor cfDNAs.