Genome Sequence Analysis of Severe Acute Respiratory Syndrome Using GenoAnalytica Model

Abstract

We proposed a GenoAnalytica model for examining the SARS’s genomics sequences. The technologies make proper data extraction from genomics sequences of viruses. We use the GenoAnalytica model, i.e. GenoCompute, and IGMiner Algorithm; to classify the range of genomics sequences, including recognizing the sequence variation from the datasets. The projected algorithm computes the nucleotide patterns and represents the nucleotide genome sequence of SARS (airborne virus) by IGMiner technique and works out on the GenoCompute to calculate computation time with minimum count in second. Along with this, we proposed a UMRA algorithm to compute the mutation rate of the genome sequence with minimum count in seconds as compared to traditional method. Furthermore, we work out the different datasets (China and Algeria datasets) and determine the whole variation at the index level inside the all genome sequence. This learning also signifies the performance evaluation on altering minsup using IGMiner and Aprori-based SPM. Also, we calculate the mutation rate of the genome sequence of airborne virus using Unique Mutation Rate Analysis algorithm. The severe acute respiratory syndrome coronavirus 2 has been responsible for the deadly COVID-19 pandemic. It has ruined limitless individuals all over the globe, and along with this, it continues to harm well-being and people’s health. Healthcare specialists and Researchers can obtain insight into COVID-19’s inherited variation or SAR-CoV-2 through cutting-edge Artificial Intelligence and genome sequence analysis tools.