Abstract

Hepatitis C Virus: Computational Biology Approach towards the Research - A Review

Chronic Hepatitis C Virus is a common reason for liver disease and it is the common signal for the transplantation of liver in the area of the US, Australia and in European countries. The disease included 3 percent of the total global population caused by the HCV. HCV virus, a common infection caused by blood-borne mostly seen in the US which includes 40% of chronic liver disease. Wide-reaching approx. 171 million of peoples they are infected from chronic Hepatitis C virus and some of those who are chronically infected will form cirrhosis or liver cancer, hepatic failure or hepatocellular carcinoma which leads to thousands of death every year. Despite the study of HCV for the past 15 years, our knowledge towards the infection caused by HCV has been partial by our inability to grow the virus in cell culture. There are some antiviral medicines that can cure some of the HCV infections and it will reduce the effect of death from cancer and cirrhosis but identification and treatment of Hepatitis C infection are very less. The HCV infection rate is relatively high with replication range between 10¹⁰ to 10¹² virions, and a half-life of 2-4 hours. The HCV RNA mutates rapidly because of the lack of error proofreading by viral RNA polymerase. And these increases in genotype mutation and their subtypes make the research to develop HCV vaccine a challenge. As the technology is growing so vast and computational biology are one of the technology that are changing the way and methods to understand the viruses, mostly in the field of genome sequencing, epigenetics, evolution, and transcriptomic analysis, whereas NGS (Next-Generation Sequencing) provides a great platform for the researcher to get better quality and quantity in various fields. Now to get the entire genomic variation data it has been now possible for laboratory-based experiments and to investigate genetic variation and its structure, computation based mechanism and analysis of data on genomic level comes with complexity.

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