Algorithms for Viral Population Analysis

摘要: The genetic structure of an intra-host viral population has 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 a heterogeneous, yet highly similar, population; more specifically, the detection rare variants. Admittedly, while depth is less issue modern sequencers, short length generated reads complicates assembly. This task worsened by presence both random systematic errors in huge amounts data. In this dissertation I present completed work reconstructing given next-generation Several algorithms are described solving problem under error-free amplicon (or sliding-window) model. order these methods handle actual real-world data, error-correction method proposed. A formal derivation its likelihood model along with optimization steps EM algorithm presented. Although perform well, they cannot take into account paired-end address this, new detailed that works case maximum a-posteriori estimation parameters. INDEX WORDS: Algorithm, Viral reconstruction, Variant quantification, Assembly, Read overlap graph, Network flows, Integer programming, Quadratic Expectation maximization, Maximum likelihood, ALGORITHMS FOR VIRAL POPULATION ANALYSIS