A Comparative Genomic Analysis of Coronavirus Families Using Chaos Game Representation and Fisher-Shannon Complexity

TL;DR

This study uses Chaos Game Representation and Fisher-Shannon Complexity to compare genomes of eight coronaviruses, revealing their similarities and differences through a novel alignment-free, fractal-based analysis method.

Contribution

It introduces a new CGR-FSC approach for genomic comparison, providing a visual and quantitative tool for virus classification and similarity assessment.

Findings

CGR-FSC effectively distinguishes different coronavirus genomes.

The method reveals genetic similarities and differences among the viruses.

It offers a rapid, alignment-free analysis technique for viral genomics.

Abstract

From its first emergence in Wuhan, China in December, 2019 the COVID-19 pandemic has caused unprecedented health crisis throughout the world. The novel coronavirus disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which belongs to the coronaviridae family. In this paper, a comparative genomic analysis of eight coronaviruses namely Human coronavirus OC43 (HCoV-OC43), Human coronavirus HKU1 (HCoV-HKU1), Human coronavirus 229E (HCoV-229E), Human coronavirus NL63 (HCoV-NL63), Severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome-related coronavirus (MERS-CoV), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Bat coronavirus RaTG13 has been carried out using Chaos Game Representation and Fisher-Shannon Complexity (CGR-FSC) measure. Chaos Game Representation (CGR) is a unique alignment-free method to visualize one dimensional DNA sequence in a two-dimensional fractal-like pattern. The two-dimensional CGR pattern is then quantified by Fisher-Shannon Complexity (FSC) measure. The CGR-FSC can effectively identify the viruses uniquely and their similarity/dissimilarity can be revealed in the Fisher-Shannon Information Plane (FSIP).