In a path breaking development, scientists in India has prepare a web based COVID-19 predictor to predict the sequence of viruses online.
On the basis of machine learning, they analysed 566 Indian SARS-CoV-2 genomes to find the genetic variability in terms of point mutation and Single Nucleotide Polymorphism (SNP), an official release said. Dr IndrajitSaha, Assistant Professor in the Department of Computer Science and Engineering of National Institute of Technical Teachers’ Training and Research, Kolkata led the team of researchers.
They mainly found that 57 out of 64 SNPs are present in 6 coding regions of Indian SARS-CoV-2 genomes, and all are nonsynonymous in nature.
The researchers also extended this research for more than 10,000 sequences around the globe, including India and found 20260, 18997, and 3514 unique mutation points globally, including India, excluding India and only for India, respectively.
The scientists are on the track to identify the genetic variability in SARS-CoV-2 genomes around the globe including India, find the number of virus strains using Single Nucleotide Polymorphism (SNP), spot the potential target proteins of the virus and human host based on Protein-Protein Interactions. They also carried out integrating the knowledge of genetic variability, recognise candidates of synthetic vaccine based on conserved genomic regions that is highly immunogenic and antigenic and detect the virus miRNAs that are also involved in regulating human mRNA.
They have computed the mutation similarity in sequences of different countries. The results show that the USA, England, and India are the top three countries having the geometric mean, 3.27%, 3.59%, and 5.39%, respectively, of mutation similarity score with other 72 countries. The scientists have also developed a web application for searching the mutation points in SARS-CoV-2 genomes globally and country wise. Besides, they are now working more towards protein-protein interactions, epitopes discovery, and virus miRNA prediction.
The study was published in Infection, Genetics, and Evolution journal.
