2D Similarity Map of Multiple Coronavirus Gene Sequences

The outbreak of a novel coronavirus (SARS-CoV-2) in many countries in the world from late 2019 to 2020 resulted in millions of infected people, and caused serious damage to the social environments with significant changes in human power and material resources in the world. The novel coronavirus is an RNA virus. RNA mutation is common in nature. This makes it extremely difficult to develop a virus vaccine in a short period. The evolution of the virus has been in a mutation state, in which a certain sequence changes associated with time and environments in similar distributions. A larger number of genomes were collected in various open source databases for scientists in further explorations. In this paper, a 2D similarity comparison scheme on the A2 module of the MAS is proposed for extracting internal information among a genome undertaken M segment partitions to provide visual results based on probability measures and quantitative statistics. First, a genome is segmented into corresponding numerical transformations, and then four numbers of meta symbols in each segment are counted. Corresponding probability measures are calculated. Second, the probability is transformed into polar coordinates, and the polar coordinates are mapped into a M × M matrix. Then, a 1D genome can be processed into 2D measures with similarity properties in sequence. Through this correlation matrix, relevant similarity results are analyzed.