摘要
Hepatitis C virus（HCV） treatment is on the cutting edge of medicine. Due to the high rate of mutations and low fidelity of HCV replication, resistant strains quickly become dominant in a viral population under the selection pressure of a drug. In this paper, we examined the drug resistance mechanism in the NS5 A region of genotype1 a HCV virus by comparing the sequence data from interferon-ribavirin treated and untreated patients. To find the drug resistance difference, we used innovative Bayesian probability models to detect mutation combinations and inferred detailed interaction structures of these mutations. We aim to provide reference to drug design and mutation mechanism understanding through our work.
Hepatitis C virus（HCV） treatment is on the cutting edge of medicine. Due to the high rate of mutations and low fidelity of HCV replication, resistant strains quickly become dominant in a viral population under the selection pressure of a drug. In this paper, we examined the drug resistance mechanism in the NS5 A region of genotype1 a HCV virus by comparing the sequence data from interferon-ribavirin treated and untreated patients. To find the drug resistance difference, we used innovative Bayesian probability models to detect mutation combinations and inferred detailed interaction structures of these mutations. We aim to provide reference to drug design and mutation mechanism understanding through our work.
基金
supported by start-up funding and Sesseel Award from Yale University
supported by the NIH grant RR19895
