A Genome based Detection and Classification of Coronavirus Infection

Abstract

The Coronavirus (COVID-19) infection has become a global threat in recent time. Many researchers have been dedicated to control COVID-19 pandemic. In this paper, an effective method is presented for detection and classification of COVID-19 infection based on genome sequences. First, the COVID-19 infection is detected based on the induction of changes in the DNA microarray gene expression pattern of the host during and after infection and comparing it with DNA sequences of Coronavirus (SARS-CoV-2). In order to analyse DNA microarray gene expression data, a bi-directional string matching algorithm is used and the analytical result is represented in terms of eight-directional chain code sequence. At the end of the work, an approach for categorization of Coronavirus infection is provided based on the distribution probabilities of eight-directional chain code sequences correspond to DNA microarray gene expression data of different Corona viruses by taking random samples from the GenBank. The categorization of Coronavirus infection will be helpful for forecasting rate of mortality, rate of infection, severity of the infection and other issues related to COVID-19.