Model Based Framework for Estimating Mutation Rate of Hepatitis C Virus in Egypt

TL;DR

This paper introduces a model-based framework combining profile hidden Markov models and pairwise distance methods to estimate the mutation rate of Hepatitis C Virus in Egypt, aiding in understanding its evolution.

Contribution

It presents a novel two-step approach using PHMM and pairwise distances to estimate HCV mutation rates, specifically applied to Egyptian genotype 4a data.

Findings

Successful construction of PHMM architecture for HCV sequences

Effective estimation of mutation rates using pairwise distances

Application to Egyptian HCV data demonstrates framework's utility

Abstract

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.