基因组数据分析是监测病原体进化和传染病爆发的基本系统。基于生物信息学和深度学习,本研究旨在确定全球SARS-CoV-2的基因组变异性,并预测即将发生的突变率.对259044个SARS-CoV-2分离株的分析鉴定出3334545个突变,每个分离株平均有14.01个突变。全球范围内,单核苷酸多态性(SNP)是最普遍的突变事件。C>T(52.67%)的患病率在世界范围内被发现是主要的改变,其次是G>T(14.59%)和A>G(11.13%)。来自印度的菌株显示出最高数量的突变(48),其次是苏格兰,美国,荷兰,挪威,和法国有多达36个突变。D416G,F106F,P314L,UTR:C241T,L93L,A222V,A199A,V30L,发现A220V突变是最常见的突变。D1118H,S194L,R262H,M809L,P314L,A8D,S220G,A890D,G1433C,T1456I,R233C,F263S,L111K,A54T,A74V,L183A,A316T,V212F,L46C,V48G,Q57H,W131R,G172V,Q185H,发现Y206S错义突变大大降低了相应蛋白质的结构稳定性。相反,D3L,L5F,和S97I被发现在很大程度上增加相应蛋白质的结构稳定性。多核苷酸突变GGG>AAC,CC>TT,TG>CA,在我们的分析中,AT>TA出现在前20个突变队列中。未来突变率分析预测17%,7%,和3%的C>T增量,A>G,A>T,分别在未来。相反,7%,7%,估计T>C的减量为6%,G>A,和G>T突变,分别。T>G\\A,C>G\\A,和A>T\C在未来是不可能的。由于SARS-CoV-2不断变异,我们的发现将有助于追踪突变,并有助于绘制全球COVID-19强度的进展图.
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.