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Computational tools and databases at the forefront to study geographic and genomic distribution of SARS-CoV-2 variants

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Abstract

In human history, crippling viral pandemics have occurred many times and recently Coronavirus-19 (COVID-19) disease caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged at the end of 2019 in Wuhan, China. The present study aims to use various computational approaches to study the mutational status, mutational frequency in viral genome, phylogenetics, genetic epidemiology, spatiotemporal and mutational dynamics of variants of interest (VOIs), and variants of concern (VOCs). The findings of Coronapp revealed several mutations with the highest number of mutations in OQ118414.1 and OQ118474.1 (SARS-CoV-2/USA) variants. In the present study, the most frequently found events per type, nucleotides, and protein were C>T transition, A18163G, and 3′-UTR 28271 respectively. In the present study, taxonomy-built Cov2Tree evaluated the full diversity of viral genome sequences and displayed 6,652,546 sequence trees of SARS-CoV-2. The findings obtained from ViralVar revealed variations in the dynamics of the SARS-CoV-2 variants. The linear distributions of the Omicron variant were similar across the regions making up most of COVID-19 infections followed by the Delta variant. In the present study, the D614G mutation located in the viral spike protein was the topmost mutated residue demonstrating that this variation facilitates viral transmission. Our study also found a higher concentration of mutations in N protein (average odds ratio = 4.477, q-value = 0), NS8 (average odds ratio = 3.53, q-value = 0) and in the spike protein (average odds ratio = 1.61, q-value = 0) respectively. In the present work, the genetic epidemiology of all the reported SARS-CoV-2 variants was determined via Nextstrain. Thus, computational approaches could offer significant insights into the SARS-CoV-2 and henceforth could facilitate early detection, variant surveillance, and therapeutic interventions. These findings could be very helpful in planning and evaluating the effectiveness of regionally-based actions implemented to stop the spread of SARS-CoV-2.

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Data availability

The data generated during this study has been presented in this manuscript.

Abbreviations

COVID-19:

Coronavirus-19

SARS-CoV-2:

Severe acute respiratory syndrome coronavirus 2

VOI:

Variants of interest

VOC:

Variants of concern

WHO:

World health organization

ACE-2:

Angiotensin-converting enzyme-2

GISAID:

Global initiative on sharing all influenza data

RBD:

Receptor binding domain

NTD:

N-terminal domain

NSP:

Non-structural proteins

UTR:

Untranslated region

SNP:

Single nucleotide polymorphism

References

  • Ahmed-Abakur EH, Ullah MF, Elssaig EH, Alnour TM (2022) In-silico genomic landscape characterization and evolution of SARS-CoV-2 variants isolated in India shows significant drift with high frequency of mutations. Saudi J Biol Sci 29(5):3494–3501

    Article  Google Scholar 

  • Ali A, Rehman MU, Ahmad SM, Mehraj T, Hussain I, Nadeem A et al (2022) In silico tools for analysis of single-nucleotide polymorphisms in the bovine transferrin gene. Animals 12(6):693

    Article  Google Scholar 

  • Andrews RJ, Peterson JM, Haniff HS, Chen J, Williams C, Grefe M et al (2020) An in silico map of the SARS-CoV-2 RNA structurome. BioRxiv

  • Bojkova D, Widera M, Ciesek S, Wass MN, Michaelis M, Cinatl J Jr (2022) Reduced interferon antagonism but similar drug sensitivity in Omicron variant compared to Delta variant of SARS-CoV-2 isolates. Cell Res 32(3):319–321

    Article  Google Scholar 

  • Callaway E (2021) Heavily mutated Omicron variant puts scientists on alert. Nature 600(7887):21

    Article  Google Scholar 

  • Ceraolo C, Giorgi FM (2020) Genomic variance of the 2019-nCoV coronavirus. J Med Virol 92(5):522–528

    Article  Google Scholar 

  • Chiara M, Horner DS, Gissi C, Pesole G (2020) Comparative genomics suggests limited variability and similar evolutionary patterns between major clades of SARS-Cov-2. BioRxiv 2020-03

  • De Maio N, Kalaghatgi P, Turakhia Y, Corbett-Detig R, Minh BQ, Goldman N (2023) Maximum likelihood pandemic-scale phylogenetics. Nat Genet 55(5):746–752

    Article  Google Scholar 

  • Du P, Ding N, Li J, Zhang F, Wang Q, Chen Z et al (2020) Genomic surveillance of COVID-19 cases in Beijing. Nat Commun 11(1):5503

    Article  Google Scholar 

  • Fauver JR, Petrone ME, Hodcroft EB, Shioda K, Ehrlich HY, Watts AG et al (2020) Coast-to-coast spread of SARS-CoV-2 during the early epidemic in the United States. Cell 181(5):990–996

    Article  Google Scholar 

  • Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX et al (2020) Clinical characteristics of coronavirus disease 2019 in China. New Engl J Med 382(18):1708–1720

    Article  Google Scholar 

  • Gudbjartsson DF, Helgason A, Jonsson H, Magnusson OT, Melsted P, Norddahl GL et al (2020) Spread of SARS-CoV-2 in the Icelandic population. New Engl J Med 382(24):2302–2315

    Article  Google Scholar 

  • Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C et al (2018) Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34(23):4121–4123

    Article  Google Scholar 

  • Kandeel M, Mohamed ME, Abd El-Lateef HM, Venugopala KN, El-Beltagi HS (2022) Omicron variant genome evolution and phylogenetics. J Med Virol 94(4):1627–1632

    Article  Google Scholar 

  • Kannan SR, Spratt AN, Cohen AR, Naqvi SH, Chand HS, Quinn TP et al (2021) Evolutionary analysis of the Delta and Delta Plus variants of the SARS-CoV-2 viruses. J Autoimmunity 124:102715

    Article  Google Scholar 

  • Kannan SR, Spratt AN, Sharma K, Chand HS, Byrareddy SN, Singh K (2022) Omicron SARS-CoV-2 variant: unique features and their impact on pre-existing antibodies. J Autoimmun 126:102779

    Article  Google Scholar 

  • Khailany RA, Safdar M, Ozaslan M (2020) Genomic characterization of a novel SARS-CoV-2. Gene Rep 19:100682

    Article  Google Scholar 

  • Khan A, Khan T, Ali S, Aftab S, Wang Y, Qiankun W et al (2021) SARS-CoV-2 new variants: characteristic features and impact on the efficacy of different vaccines. Biomed Pharmacotherapy 143:112176

    Article  Google Scholar 

  • Kim D, Lee JY, Yang JS, Kim JW, Kim VN, Chang H (2020) The architecture of SARS-CoV-2 transcriptome. Cell 181(4):914–921

    Article  Google Scholar 

  • Korber B, Fischer WM, Gnanakaran S, Yoon H, Theiler J, Abfalterer W, Montefiori DC (2020) Tracking changes in SARS-CoV-2 spike: evidence that D614G increases infectivity of the COVID-19 virus. Cell 182(4):812–827

    Article  Google Scholar 

  • Koyama T, Platt D, Parida L (2020a) Variant analysis of SARS-CoV-2 genomes. Bull World Health Organ 98(7):495

    Article  Google Scholar 

  • Koyama T, Weeraratne D, Snowdon JL, Parida L (2020b) Emergence of drift variants that may affect COVID-19 vaccine development and antibody treatment. Pathogens 9(5):324

    Article  Google Scholar 

  • Kumar S, Thambiraja TS, Karuppanan K, Subramaniam G (2022) Omicron and Delta variant of SARS-CoV-2: a comparative computational study of spike protein. J Med Virol 94(4):1641–1649

    Article  Google Scholar 

  • McBroome J, Thornlow B, Hinrichs AS, Kramer A, De Maio N, Goldman N et al (2021) A daily-updated database and tools for comprehensive SARS-CoV-2 mutation-annotated trees. Mol Biol Evol 38(12):5819–5824

    Article  Google Scholar 

  • McBroome J, Martin J, de Bernardi Schneider A, Turakhia Y, Corbett-Detig R (2022) Identifying SARS-CoV-2 regional introductions and transmission clusters in real time. Virus Evol 8(1):veac048

    Article  Google Scholar 

  • Mears HV, Young GR, Sanderson T, Harvey R, Crawford M, Snell DM et al (2022) Emergence of new subgenomic mRNAs in SARS-CoV-2. bioRxiv 2022-04

  • Mercatelli D, Giorgi FM (2020a) Geographic and genomic distribution of SARS-CoV-2 mutations. Front Microbiol 2020(11):1800

    Article  Google Scholar 

  • Mercatelli D, Giorgi FM (2020b) Geographic and genomic distribution of SARS-CoV-2 mutations. Front Microbiol 11:1800

    Article  Google Scholar 

  • Mercatelli D, Holding AN, Giorgi FM (2021) Web tools to fight pandemics: the COVID-19 experience. Brief Bioinform 22(2):690–700

    Article  Google Scholar 

  • Obermeyer F, Jankowiak M, Barkas N, Schaffner SF, Pyle JD, Yurkovetskiy L et al (2022) Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness. Science 376(6599):1327–1332

    Article  Google Scholar 

  • Phua J, Weng L, Ling L, Egi M, Lim CM, Divatia JV et al (2020) Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations. Lancet Respir Med 8(5):506–517

    Article  Google Scholar 

  • Plante JA, Liu Y, Liu J, Xia H, Johnson BA, Lokugamage KG et al (2021) Spike mutation D614G alters SARS-CoV-2 fitness. Nature 592(7852):116–121

    Article  Google Scholar 

  • Quarleri J, Galvan V, Delpino MV (2022) Omicron variant of the SARS-CoV-2: a quest to define the consequences of its high mutational load. GeroScience 44:53–56

    Article  Google Scholar 

  • Rangan R, Zheludev IN, Hagey RJ, Pham EA, Wayment-Steele HK, Glenn JS, Das R (2020) RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look. RNA 26(8):937–959

    Article  Google Scholar 

  • Rehman MU, Ali A, Ansar R, Arafah A, Imtiyaz Z, Wani TA et al (2023) In Silico molecular docking and dynamic analysis of natural compounds against major non-structural proteins of SARS-COV-2. J Biomol Struct Dyn 41(18):9072–9088

    Article  Google Scholar 

  • Roser M, Ritchie H, Ortiz-Ospina E, Hasell J (2020) Coronavirus disease (COVID-19)–statistics and research. Our World Data 4:1–45

    Google Scholar 

  • Rotondo JC, Martini F, Maritati M, Mazziotta C, Di Mauro G, Lanzillotti C, Barp N, Gallerani A, Tognon M, Contini C(2021) SARS-CoV-2 infection: new molecular, phylogenetic, and pathogenetic insights. Efficacy of current vaccines and the potential risk of variants. Viruses 13(9):1687

    Article  Google Scholar 

  • Sruthi CK, Prakash MK (2019) Statistical characteristics of amino acid covariance as possible descriptors of viral genomic complexity. Sci Rep 9(1):1–12

    Article  Google Scholar 

  • Su S, Wong G, Shi W, Liu J, Lai AC, Zhou J et al (2016) Epidemiology, genetic recombination, and pathogenesis of coronaviruses. Trends Microbiol 24(6):490–502

    Article  Google Scholar 

  • Tao K, Tzou PL, Nouhin J, Gupta RK, de Oliveira T, Kosakovsky Pond SL, Fera D, Shafer RW (2021) The biological and clinical significance of emerging SARS-CoV-2 variants. Nat Rev Genet 22:757–773

    Article  Google Scholar 

  • Uelze L, Grützke J, Borowiak M, Hammerl JA, Juraschek K, Deneke C et al (2020) Typing methods based on whole genome sequencing data. One Health Outlook 2:1–19

    Article  Google Scholar 

  • Volz E, Hill V, McCrone JT, Price A, Jorgensen D, O’Toole Á, Allan J (2021) Evaluating the effects of SARS-CoV-2 spike mutation D614G on transmissibility and pathogenicity. Cell 184(1):64–75

    Article  Google Scholar 

  • Wang C, Horby PW, Hayden FG, Gao GF (2020) A novel coronavirus outbreak of global health concern. Lancet 395(10223):470–473

    Article  Google Scholar 

  • Wang R, Chen J, Gao K, Hozumi Y, Yin C, Wei GW (2021) Analysis of SARS-CoV-2 mutations in the United States suggests presence of four substrains and novel variants. Commun Biol 4(1):228

    Article  Google Scholar 

  • Yung-Fang T, Chien CS, Yarmishyn AA, Yi-Ying L, Yung-Hung L, Yi-Tsung L et al (2020) A review of SARS-CoV-2 and the ongoing clinical trials. Int J Mol Sci 21(7):2657

    Article  Google Scholar 

  • Zhang C, Zheng W, Huang X, Bell EW, Zhou X, Zhang Y (2020) Protein structure and sequence reanalysis of 2019-nCoV genome refutes snakes as its intermediate host and the unique similarity between its spike protein insertions and HIV-1. J Proteome Res 19(4):1351–1360

    Article  Google Scholar 

  • Zhang W, Davis BD, Chen SS, Martinez JMS, Plummer JT, Vail E (2021) Emergence of a novel SARS-CoV-2 variant in Southern California. JAMA 325(13):1324–1326

    Article  Google Scholar 

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Acknowledgements

The first author acknowledges the Indian Council of Medical Research (ICMR), New Delhi, India, for providing financial assistance as Senior Research Fellowship (SRF) during this study.

Funding

This study was supported by the Indian Council of Medical Research (ICMR) (Grant number 2021-12265), New Delhi, India.

Author information

Authors and Affiliations

  1. Division of Veterinary Biochemistry, FVSc & AH, SKUAST-K, Shuhama Alusteng, Srinagar, J&K, 190006, India

    Aarif Ali & Sheikh Bilal Ahmad

  2. Department of Biochemistry, School of Biological Sciences, University of Kashmir, Hazratbal, Srinagar, J&K, 190006, India

    Bashir Ahmad Malla

  3. Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Hazratbal, Srinagar, J&K, 190006, India

    Showkat Ahmad Ganie

  4. Department of Clinical Pharmacy, College of Pharmacy, King Saud University, 11451, Riyadh, Saudi Arabia

    Muneeb U. Rehman

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  1. Aarif Ali
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  5. Sheikh Bilal Ahmad
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Contributions

Conceptualization: A.A., Methodology: A.A., Software: A.A., Formal analysis: A.A., Writing original draft: A.A., Writing-review and editing: A.A., M.U.R., B.A.M., S.B.A., S.A.G., Supervision: S.B.A., S.A.G. All authors have read the manuscript and given consent for publishing this article.

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Correspondence to Aarif Ali or Sheikh Bilal Ahmad.

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Ali, A., Malla, B.A., Ganie, S.A. et al. Computational tools and databases at the forefront to study geographic and genomic distribution of SARS-CoV-2 variants. Netw Model Anal Health Inform Bioinforma 13, 27 (2024). https://doi.org/10.1007/s13721-024-00462-5

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  • DOI: https://doi.org/10.1007/s13721-024-00462-5

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