ALGORITHMS FOR VIRAL POPULATION ANALYSIS by

The genetic structure of an intra-host viral population has an effect on many clinically important phenotypic traits such as escape from vaccine induced immunity, virulence, and response to antiviral therapies. Next-generation sequencing provides read-coverage sufficient for genomic reconstruction of a heterogeneous, yet highly similar, viral population; and more specifically, for the detection of rare variants. Admittedly, while depth is less of an issue for modern sequencers, the short length of generated reads complicates viral population as-sembly. This task is worsened by the presence of both random and systematic sequencing errors in huge amounts of data. In this dissertation I present completed work for recon-structing a viral population given next-generation sequencing data. Several algorithms are described for solving this problem under the error-free amplicon (or sliding-window) model. In order for these methods to handle actual real-world data, an error-correction method is proposed. A formal derivation of its likelihood model along with optimization steps for an EM algorithm are presented. Although these methods perform well, they cannot take into account paired-end sequencing data. In order to address this, a new method is detailed tha