Bayesian recombination detection modeling and application

As a key evolutionary process, recombination shapes the genetic structure of virus populations. The increased availability of virus sequences provides a chance to study virus recombination through molecular data. Many statistical methods have been developed, and a lot of the methods are phylogenetic-based. My research focuses on recombination modeling and data analysis. I first apply an existing phylogenetic-base method, Bayesian dual change-point model (DMCP), to investigate the role of representative data types for recombination study. We conclude that consensus sequences are an all-around robust representative of virus genotypes. Using consensus data we study recombination of all full-length hepatitis B virus (HBV) sequences, and setting up a system for using the DMCP model for large scale sequence analysis. We discover that HBV has an extremely high recombination rate. For the first time, we report circulating recombination forms of hepatitis B virus, and identify one potential recombination hotspot. One goal of studying recombination is to find potential recombination hotspots, which could ultimately reveal information about the molecular mechanism of recombination. Finding hotspots requires unambiguous identification of all unique recombination events, a non-trivial task when recombination sequences have similar mosaic structures. Extending the DMCP model, I develop a method to identify the number of recombination events producing multiple recombinants. I apply this method to several HBV recombinants with identical mosaic structure and find at least two recombinant events.