Studying Genetic Diversity and Evolutionary Pattern in Human Immunodeficiency Virus: Utilizing Sequencing Data and Machine learning

Abstract

The Acquired Immunodeficiency Syndrome (AIDS) pandemic has affected millions of people worldwide and posed a threat to global health. Since the discovery of the Human Immunodeficiency Virus (HIV) as the cause of the AIDS pandemic, numerous studies have been conducted on this virus, and many attempts have been made to develop an effective treatment or vaccine. HIV mutates very often, and it has many subtypes and variants, which makes developing an effective treatment challenging. Therefore, it is important to identify mutations that can lead to drug resistance as well as to identify the subtypes. Studying the evolutionary patterns of HIV is also crucial to understand where this pathogen comes from and what we can expect from it in the future. To identify Drug Resistant Mutations (DRMs), various subtypes, and conduct phylogenetic analysis of sequencing data, various bioinformatic tools and machine learning methods were employed. A pipeline was constructed by combining different bioinformatic software, which was capable of identifying low-frequency DRMs. For identifying different HIV subtypes and studying phylogenetic and evolutionary patterns, both bioinformatic tools and supervised machine learning methods were employed. Each of the two approaches applied succeeded in identifying subtypes and studying phylogenetic relationships, but the feature selection techniques in machine learning used for discovering evolutionary patterns had some limitations. The abundance of sequencing data enables the use of various approaches, such as machine learning, for studying viral genomes. This approach allows for a better understanding of the pathogen and can suggest appropriate solutions for combating it.