Classification of HIV-1 Using Coalescent Theory

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The aim of the project is to develop a method for automatically classifying HIV-1. The main part of this method consists in a scoring algorithm which rates a given classification. This algorithm reconstructs ancestral recombination graphs (ARG) of given HIV-1 sequences under restrictions determined by the given classification and finds an ARG with maximal probability by means of Markov Chain Monte Carlo methods. The probability of the most probable ARG is interpreted as a score for the classification. Afterwards, the classification is iteratively improved with respect to the score. In a preparing step, we will improve the jpHMM algorithm, a tool developed by our group to localize recombination in viral genomes, with respect to runtime and precision. Finally, we will systematically apply our method to re-annotate the HIV-1 genome database at Los Alamos National Laboratory (LANL). Development, evaluation and application of classification algorithm will be carried out in close collaboration with Dr. B. Korber and Dr. T. Leitner at LANL, two leading experts on HIV sequence analysis and evolution, and with Prof. D. Brown, University of Waterloo, Canada, a well-known expert on Hidden Markov Models.