Cluster Analysis of COVID-19 Through Genome Sequences Using Python Bioinformatics Library

Introduction and Importance of Study:During the COVID-19 pandemic, mortality rates varied across different regions of the world. To better understand the virus's behavior, it's important to gain in-depth knowledge of the nucleotide records of the COVID-19 genomic sequence.Novelty Statement:In the study, researchers analyzed clusters of highly affected countries to find similar codons in countries with a similar effect of the virus.Material And Method:Nucleotide records are extracted from the NCBI database in FASTA format. Further Python Bioinformatics library is used to form the clusters of each country using the K-means clustering technique.Result and Discussion:The study focuses on finding the similarities between the codons of amino acids in different countries that are affected in a similar way during COVID-19. For instance, China and the EU have a lower mortality rate and have Leucine, Methionine, Isoleucine, and Valine amino acids in common. On the other hand, countries like Pakistan and India have Leucine, Isoleucine, Valine, and Threonine in common and an average death rate. Moreover, Brazil and the US have a higher mortality rate and share similar codons such as Leucine, Glutamine, and Amber.Concluding Remarks:The study shows that countries affected by COVID-19 in a similar way share some common amino acids and their respective codons.