Using alignment-free and pattern mining methods for SARS-CoV-2 genome analysis

Examining the genome sequences of the SARS-CoV-2 virus, that causes the respiratory disease known as coronavirus disease 2019 (COVID-19), play important role in the proper understanding of this virus, its main characteristics and functionalities. This paper investigates the use of alignment-free (AF) sequence analysis and sequential pattern mining (SPM) to analyze SARS-CoV-2 genome sequences and learn interesting information about them respectively. AF methods are used to find (dis)similarity in the genome sequences of SARS-CoV-2 by using various distance measures, to compare the performance of these measures and to construct the phylogenetic trees. SPM algorithms are used to discover frequent amino acid patterns and their relationship with each other and to predict the amino acid(s) by using various sequence-based prediction models. In last, an algorithm is proposed to analyze mutation in genome sequences. The algorithm finds the locations for changed amino acid(s) in the genome sequences and computes the mutation rate. From obtained results, it is found that that both AF and SPM methods can be used to discover interesting information/patterns in SARS-CoV-2 genome sequences for examining the variations and evolution among strains.