Online Predictor Using Machine Learning to Predict Novel Coronavirus and Other Pathogenic Viruses

The problem of virus classification is always a subject of concern for virology or epidemiology over the decades. In this regard, a machine learning technique can be used to predict the novel coronavirus by considering its sequence. Thus, we are proposing a machine learning-based novel coronavirus prediction technique, called COVID-Predictor, where 1000 sequences of SARS-CoV-1, MERS-CoV, SARS-CoV-2, and other viruses are used to train a Naive Bayes classifier so that it can predict any unknown sequences of these viruses. The model has been validated using 10-fold cross-validation in comparison with other machine learning techniques. The results show the superiority of our predictor by achieving an average 99.7% accuracy on an unseen validation set of viruses. The same pre-trained model has been used to design a web-based application where sequences of unknown viruses can be uploaded to predict the novel coronavirus.

Whole genome analysis of more than 10 000 SARS-CoV-2 virus unveils global genetic diversity and target region of NSP6.

Whole genome analysis of SARS-CoV-2 is important to identify its genetic diversity. Moreover, accurate detection of SARS-CoV-2 is required for its correct diagnosis. To address these, first we have analysed publicly available 10 664 complete or near-complete SARS-CoV-2 genomes of 73 countries globally to find mutation points in the coding regions as substitution, deletion, insertion and single nucleotide polymorphism (SNP) globally and country wise. In this regard, multiple sequence alignment is performed in the presence of reference sequence from NCBI. Once the alignment is done, a consensus sequence is build to analyse each genomic sequence to identify the unique mutation points as substitutions, deletions, insertions and SNPs globally, thereby resulting in 7209, 11700, 119 and 53 such mutation points respectively. Second, in such categories, unique mutations for individual countries are determined with respect to other 72 countries. In case of India, unique 385, 867, 1 and 11 substitutions, deletions, insertions and SNPs are present in 566 SARS-CoV-2 genomes while 458, 1343, 8 and 52 mutation points in such categories are common with other countries. In majority (above 10%) of virus population, the most frequent and common mutation points between global excluding India and India are L37F, P323L, F506L, S507G, D614G and Q57H in NSP6, RdRp, Exon, Spike and ORF3a respectively. While for India, the other most frequent mutation points are T1198K, A97V, T315N and P13L in NSP3, RdRp, Spike and ORF8 respectively. These mutations are further visualised in protein structures and phylogenetic analysis has been done to show the diversity in virus genomes. Third, a web application is provided for searching mutation points globally and country wise. Finally, we have identified the potential conserved region as target that belongs to the coding region of ORF1ab, specifically to the NSP6 gene. Subsequently, we have provided the primers and probes using that conserved region so that it can be used for detecting SARS-CoV-2. Contact:indrajit@nitttrkol.ac.inSupplementary information: Supplementary data are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-Mutation-10K.

COVID-DeepPredictor: Recurrent Neural Network to Predict SARS-CoV-2 and Other Pathogenic Viruses.

The COVID-19 disease for Novel coronavirus (SARS-CoV-2) has turned out to be a global pandemic. The high transmission rate of this pathogenic virus demands an early prediction and proper identification for the subsequent treatment. However, polymorphic nature of this virus allows it to adapt and sustain in different kinds of environment which makes it difficult to predict. On the other hand, there are other pathogens like SARS-CoV-1, MERS-CoV, Ebola, Dengue, and Influenza as well, so that a predictor is highly required to distinguish them with the use of their genomic information. To mitigate this problem, in this work COVID-DeepPredictor is proposed on the framework of deep learning to identify an unknown sequence of these pathogens. COVID-DeepPredictor uses Long Short Term Memory as Recurrent Neural Network for the underlying prediction with an alignment-free technique. In this regard, k-mer technique is applied to create Bag-of-Descriptors (BoDs) in order to generate Bag-of-Unique-Descriptors (BoUDs) as vocabulary and subsequently embedded representation is prepared for the given virus sequences. This predictor is not only validated for the dataset using K -fold cross-validation but also for unseen test datasets of SARS-CoV-2 sequences and sequences from other viruses as well. To verify the efficacy of COVID-DeepPredictor, it has been compared with other state-of-the-art prediction techniques based on Linear Discriminant Analysis, Random Forests, and Gradient Boosting Method. COVID-DeepPredictor achieves 100% prediction accuracy on validation dataset while on test datasets, the accuracy ranges from 99.51 to 99.94%. It shows superior results over other prediction techniques as well. In addition to this, accuracy and runtime of COVID-DeepPredictor are considered simultaneously to determine the value of k in k-mer, a comparative study among k values in k-mer, Bag-of-Descriptors (BoDs), and Bag-of-Unique-Descriptors (BoUDs) and a comparison between COVID-DeepPredictor and Nucleotide BLAST have also been performed. The code, training, and test datasets used for COVID-DeepPredictor are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-DeepPredictor/.

Topological Analysis for Sequence Variability: Case Study on more than 2K SARS-CoV-2 sequences of COVID-19 infected 54 countries in comparison with SARS-CoV-1 and MERS-CoV.

The pandemic due to novel coronavirus, SARS-CoV-2 is a serious global concern now. More than thousand new COVID-19 infections are getting reported daily for this virus across the globe. Thus, the medical research communities are trying to find the remedy to restrict the spreading of this virus, while the vaccine development work is still under research in parallel. In such critical situation, not only the medical research community, but also the scientists in different fields like microbiology, pharmacy, bioinformatics and data science are also sharing effort to accelerate the process of vaccine development, virus prediction, forecasting the transmissible probability and reproduction cases of virus for social awareness. With the similar context, in this article, we have studied sequence variability of the virus primarily focusing on three aspects: (a) sequence variability among SARS-CoV-1, MERS-CoV and SARS-CoV-2 in human host, which are in the same coronavirus family, (b) sequence variability of SARS-CoV-2 in human host for 54 different countries and (c) sequence variability between coronavirus family and country specific SARS-CoV-2 sequences in human host. For this purpose, as a case study, we have performed topological analysis of 2391 global genomic sequences of SARS-CoV-2 in association with SARS-CoV-1 and MERS-CoV using an integrated semi-alignment based computational technique. The results of the semi-alignment based technique are experimentally and statistically found similar to alignment based technique and computationally faster. Moreover, the outcome of this analysis can help to identify the nations with homogeneous SARS-CoV-2 sequences, so that same vaccine can be applied to their heterogeneous human population.

Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a threat to the human population and has created a worldwide pandemic. Daily thousands of people are getting affected by the SARS-CoV-2 virus; India being no exception. In this situation, there is no doubt that vaccine is the primary prevention strategy to contain the wave of COVID-19 pandemic. In this regard, genome-wide analysis of SARS-CoV-2 is important to understand its genetic variability. This has motivated us to analyse 566 Indian SARS-CoV-2 sequences using multiple sequence alignment techniques viz. ClustalW, MUSCLE, ClustalO and MAFFT to align and subsequently identify the lists of mutations as substitution, deletion, insertion and SNP. Thereafter, a consensus of these results, called as Consensus Multiple Sequence Alignment (CMSA), is prepared to have the final list of mutations so that the advantages of all four alignment techniques can be preserved. The analysis shows 767, 2025 and 54 unique substitutions, deletions and SNPs in Indian SARS-CoV-2 genomes. More precisely, out of 54 SNPs, 4 SNPs are present close to the 60% of the virus population. The results of this experiment can be useful for virus classification, designing and defining the dose of vaccine for the Indian population.

Genome-wide analysis of Indian SARS-CoV-2 genomes for the identification of genetic mutation and SNP.

The wave of COVID-19 is a big threat to the human population. Presently, the world is going through different phases of lock down in order to stop this wave of pandemic; India being no exception. We have also started the lock down on 23rd March 2020. In this current situation, apart from social distancing only a vaccine can be the proper solution to serve the population of human being. Thus it is important for all the nations to perform the genome-wide analysis in order to identify the genetic variation in Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) so that proper vaccine can be designed. This fast motivated us to analyze publicly available 566 Indian complete or near complete SARS-CoV-2 genomes to find the mutation points as substitution, deletion and insertion. In this regard, we have performed the multiple sequence alignment in presence of reference sequence from NCBI. After the alignment, a consensus sequence is built to analyze each genome in order to identify the mutation points. As a consequence, we have found 933 substitutions, 2449 deletions and 2 insertions, in total 3384 unique mutation points, in 566 genomes across 29.9 K bp. Further, it has been classified into three groups as 100 clusters of mutations (mostly deletions), 1609 point mutations as substitution, deletion and insertion and 64 SNPs. These outcomes are visualized using BioCircos and bar plots as well as plotting entropy value of each genomic location. Moreover, phylogenetic analysis has also been performed to see the evolution of SARS-CoV-2 virus in India. It also shows the wide variation in tree which indeed vivid in genomic analysis. Finally, these SNPs can be the useful target for virus classification, designing and defining the effective dose of vaccine for the heterogeneous population.