Analyzing hCov Genome Sequences: Predicting Virulence and Mutation

Abstract

BackgroundCovid-19 pandemic, caused by the SARS-CoV-2 genome sequence of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analyzed. MethodsWe present here an analysis pipeline comprising a classification exercise to identify the virulence of the genome sequences and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques. ResultsWe have classified the SARS-CoV-2 genome sequences with high accuracy and predicted the mutations in the sites of Interest. ConclusionsIn a nutshell, we have prepared an analysis pipeline for hCov genome sequences leveraging the power of machine intelligence and uncovered what remained apparently shrouded by raw data.