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

Informatics in Medicine Unlocked

Mahmudul Islam Rakib¹  Maitreyee Roy¹  Otun Saha¹  A.Q.M. Sala Uddin Pathan¹  Hasan Al Reza²  Md Shahadat Hossain²  Md Adnan Munim²  Mahafujul Islam Quadery Tonmoy²  Md Mizanur Rahaman²  Atqiya Fariha³  Md Nur Islam³  Newaz Mohammed Bahadur⁴ 

[1] Genetic Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh;;Department of Biotechnology &Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh;Department of Microbiology, University of Dhaka, Dhaka, Bangladesh;

关键词: Genomic data;    SARS-CoV-2;    Mutation;    Mutation rate;    COVID-19;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown