Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning. (2021)

• Record Type: Journal Article
• Authors: Hossain, Md Shahadat; Pathan, A.Q.M. Sala Uddin; Islam, Md Nur; Tonmoy, Mahafujul Islam Quadery; Rakib, Mahmudul Islam; Munim, Md Adnan; Saha, Otun; Fariha, Atqiya; Reza, Hasan Al; Roy, Maitreyee; Bahadur, Newaz Mohammed; Rahaman, Md Mizanur
• Is Part Of: Informatics in medicine unlocked. Volume 27(2022)
• Publication Date: 2021
• Subjects: Genomic data -- SARS-CoV-2 -- Mutation -- Mutation rate -- COVID-19; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2021.100798
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identified 3334545 mutations with an average of 14.01 mutations per isolate. Globally, single nucleotide polymorphism (SNP) is the most prevalent mutational event. The prevalence of C > T (52.67%) was noticed as a major alteration across the world followed by the G > T (14.59%) and A > G (11.13%). Strains from India showed the highest number of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. D416G, F106F, P314L, UTR:C241T, L93L, A222V, A199A, V30L, and A220V mutations were found as the most frequent mutations. D1118H, S194L, R262H, M809L, P314L, A8D, S220G, A890D, G1433C, T1456I, R233C, F263S, L111K, A54T, A74V, L183A, A316T, V212F, L46C, V48G, Q57H, W131R, G172V, Q185H, and Y206S missense mutations were found to largely decrease the structural stability of the corresponding proteins. Conversely, D3L, L5F, and S97I were found to largely increase the structural stability of the corresponding proteins. Multi-nucleotide mutations GGG > AAC, CC > TT, TG > CA, and AT > TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C > T, A > G, and A > T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T > C, G > A, and G > T mutations, respectively. T > G\A, C > G\A, and A > T\C are not anticipated in the future. Since SARS-CoV-2 is mutating continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide. Highlights: Total 3334545 mutations were identified from 259044 SARS-CoV-2 isolates (14.01 mutations per isolate). D416G, F106F, P314L, UTR:C241T, L93L, A222V, A199A, V30L, and A220V were found as the most frequent mutations. 25 missense mutations were identified which have an effect on the structural stability of the viral proteins. 17%, 7%, and 3% increment is respectively predicted for C > T, A > G, and A > T mutations in SARS-CoV-2 genome in the future. 7%, 7%, and 6% decrement is respectively estimated for T > C, G > A, and G > T mutations in SARS-CoV-2 genome in the future.