  Hepatitis C virus (HCV) is a widely spread disease all over the world. HCV
has very high mutation rate that makes it resistant to antibodies. Modeling HCV
to identify the virus mutation process is essential to its detection and
predicting its evolution. This paper presents a model based framework for
estimating mutation rate of HCV in two steps. Firstly profile hidden Markov
model (PHMM) architecture was builder to select the sequences which represents
sequence per year. Secondly mutation rate was calculated by using pair-wise
distance method between sequences. A pilot study is conducted on NS5B zone of
HCV dataset of genotype 4 subtype a (HCV4a) in Egypt.
