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 assembly. 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 reconstructing 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 that works under the error-free paired-end case along with maximum a-posteriori estimation of the model parameters.

This study was a comprehensive evaluation of population reconstruction techniques. Population reconstruction techniques are population estimation methods that calculate a minimum population size based on age-specific harvest data (Downing 1980, Roseberry and Woolf 1991). Population reconstruction techniques share the following characteristics: 1) utilization of catch-at-age data and 2) backward addition of cohorts to estimate a minimum population size. I developed a questionnaire to survey the biologists participating in this survey to determine the most common reconstruction technique used to estimate population sizes of exploited white-tailed deer (Odocoileus virginianus) and black bear (Ursus americanus). Downing reconstruction (Downing 1980) was the most commonly used reconstruction technique among biologists participating in this study. Based on a comprehensive literature review and discussions with state biologists, I decided to evaluate virtual reconstruction (Roseberry and Woolf 1991) and develop a new reconstruction technique: Reverse Order reconstruction. I developed a quantitative population model in Microsoft Visual Basic 6.0 to evaluate the ability of the 3 reconstruction techniques to estimate population sizes given a variety of conditions. I evaluated the effects of stochasticity on reconstruction population estimates by incorporating different levels of environmental stochasticity (i.e. process error) and measurement error in the generated or "known" population. I also evaluated the effects of collapsing age classes and aging biases on population estimates. In all conditions, Downing and virtual reconstruction were underestimates of the actual population size. Reverse Order reconstruction more closely estimated the actual population size, but is also more data-intensive than the other 2 methods. Measurement error introduces more uncertainty in the reconstructed population estimates than does process error. The population simulation model proved that Downing and virtual reconstruction are consistently underestimates and the percent underestimation is due to lack of inclusion of a natural mortality rates in population estimation. I used the results of the questionnaire to characterize the harvest datasets of the states participating in this study. From these results, I chose two harvest datasets to further analyze: a white-tailed deer harvest dataset from North Carolina and a black bear harvest dataset from Pennsylvania. I analyzed these datasets with Downing and virtual reconstruction. I also applied the quantitative population model to these datasets to evaluate the effect of increasing levels of measurement error on the variance of the population estimates. I found that Downing and virtual reconstruction estimated the population sizes very closely to one another, within 5%, for both datasets, and the reconstructed estimates closely tracked the actual harvest numbers. I also found that increasing levels of measurement error increased the variance associated with reconstructed population estimates and may decrease the ability of these techniques to accurately capture population trends. / Master of Science

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This thesis is devoted to designing and applying advanced algorithmical and statistical tools for analysis of NGS data related to cancer and infection diseases. NGS data under investigation are obtained either from host samples or viral variants. Recently, random peptide phage display libraries (RPPDL) were applied to studies of host's antibody response to different diseases. We study human antibody response to breast cancer and mouse antibody response to Lyme disease by sequencing of the whole antibody repertoire profiles which are represented by RPPDL. Alternatively, instead of sequencing immune response NGS can be applied directly to a viral population within an infected host. Specifically, we analyze the following RNA viruses: the human immunodeficiency virus (HIV) and the infectious bronchitis virus (IBV). Sequencing of RNA viruses is challenging because there are many variants inside population due to high mutation rate. Our results show that NGS helps to understand RNA viruses and explore their interaction with infected hosts. NGS also helps to analyze immune response to different diseases, trace changing of immune response at different disease stages.