COVID-Predictor: RNA Sequence based Prediction of Coronavirus

The problem of virus classification is always a subject of concern for virology or epidemiology over the decades. Moreover, the detection of highly divergent or yet unknown viruses is a major challenge despite of its clinical importance. In this situation, the outbreak of novel coronavirus (SARS-CoV-2) and its susceptibility in different epidemic condition around the world clearly suggest that the virus is mutating to create divergent variants and making the task of virus prediction more challenging. On the other hand, despite of novel coronavirus, two more coronaviruses such as MERS and SARS-CoV-1 are already present. Therefore, the use of machine learning technique is highly required at this moment to predict the coronaviruses by considering their divergent genetic functional characteristics. Thus, we are proposing machine learning based coronavirus prediction technique, called COVID-Predictor, where 1000 of RNA sequences of SARS-CoV-1, MERS, SARS-CoV-2 and other virus are used to train a Naïve Bayes classifier so that it can predict any unknown sequence of these viruses. In order to develop the COVID-Predictor, the feature vector is constructed by the motifs of the sequence generated by k-mer and n-gram techniques. The model has been validated using 10 fold cross validation in comparison with other classification techniques. The results show the superiority of our predictor by achieving average 97\% accuracy on unseen validation set. The same pre-trained model has been used to design a web based application where RNA sequences of unknown viruses can be uploaded to predict class of coronavirus.The predictor, code and datasets are available here: http://www.nitttrkol.ac.in/indrajit/projects/COVID-Predictor/