Volume 2 (Issue 1)

pp. 13-23

Open Access

Review paper

Computational Genomic Sequencing for Covid-19

Jyoti Sarwan, Heenu Sharma, Anubha Sharma, Eleena Barik, Jagadeesh Chandra Bose K*, Junaid Ahmad Malik

JS, HS, AS, EB, JCBK: University Institute of Biotechnology, Chandigarh University Gharuan 140413, India

JAM: Department of Zoology, Govt. Degree College Bijbehara, Kashmir, J&K 192124, India

*Corresponding author: Jagadeesh Chandra Bose K; Email: jcboseuibtlab@gmail.com



22 January 2022


15 March 2022

Cite as: Sarwan, J., Sharma, H., Barik, E., Bose, J. C. K., & Malik, J. A. (2022). Computational Genomic Sequencing for Covid-19. Inventum Biologicum, 2(1), 13-23. https://doi.org/10.5281/zenodo.5805008


SARS-CoV-2, a new virus belonging to the Coronaviridae family, has made itself worldwide attention seeker in the last two years owing to its exclusive infection and millions of deaths. Coronavirus has single –stranded RNA with 30 kb nucleotides with a positive sense in its genetic material. Although many years have been invested to study coronavirus still more research is pending. Therefore several tools have been invented called bioinformatics tools, specially designed to monitor and diagnose SARS-CoV-2 for fast detection and rapid reaction to treatment and understanding in its early stages. Following previous studies coronavirus RNA has enzyme furin that is found in organs like the small intestine, lungs, and liver of humans and is responsible for activating spike like proteins. However, coronavirus and associated enzymes can directly attack multiple organs and lead to organ failure in a small period. Therefore In silico studies can help to screen early stages of Covid-19 infections. In silico can provide data related to evolution, lineage, and drug resistance for COVID -19. In nutshell, the genomic sequencing tool is helpful to describe advanced research that is specifically for SARS-CoV-2 for its genomics, proteomics, early detections, rapid reactions, and drug discovery.


SARS-CoV-2, Bioinformatics, Drug design, Virus, Corona virus


  1. Abrams, S., Wambua, J., Santermans, E., Willem, L., Kuylen, E., Coletti, P., Libin, P., Faes, C., Petrof, O., Herzog, S. A., Beutels, P., & Hens, N. (2021). Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories. Epidemics, 35, 100449. https://doi.org/10.1016/j.epidem.2021.100449

  2. Barzkar, N., Sohail, M., Tamadoni Jahromi, S., Gozari, M., Poormozaffar, S., Nahavandi, R., & Hafezieh, M. (2021). Marine bacterial esterases: Emerging biocatalysts for industrial applications. Applied Biochemistry and Biotechnology, 193(4), 1187–1214. https://doi.org/10.1007/s12010-020-03483-8

  3. Bouckaert, R., Vaughan, T. G., Barido-Sottani, J., Duchêne, S., Fourment, M., Gavryushkina, A., Heled, J., Jones, G., Kühnert, D., De Maio, N., Matschiner, M., Mendes, F. K., Müller, N. F., Ogilvie, H. A., du Plessis, L., Popinga, A., Rambaut, A., Rasmussen, D., Siveroni, I., . . . Drummond, A. J. (2019). BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Computational Biology, 15(4), e1006650. https://doi.org/10.1371/journal.pcbi.1006650

  4. Corman, V. M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D. K., Bleicker, T., Brünink, S., Schneider, J., Schmidt, M. L., Mulders, D. G., Haagmans, B. L., van der Veer, B., van den Brink, S., Wijsman, L., Goderski, G., Romette, J. L., Ellis, J., Zambon, M., . . . and Drosten, C. (2020). Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance, 25(3). PubMed: 2000045

  5. Cuypers, L., Li, G., Neumann-Haefelin, C., Piampongsant, S., Libin, P., Van Laethem, K., Vandamme, A. M., & Theys, K. (2016). Mapping the genomic diversity of HCV subtypes 1a and 1b: Implications of structural and immunological constraints for vaccine and drug development. Virus Evolution, 2(2), vew024. https://doi.org/10.1093/ve/vew024

  6. Di Renzo, L., Gualtieri, P., Pivari, F., Soldati, L., Attinà, A., Cinelli, G., Leggeri, C., Caparello, G., Barrea, L., Scerbo, F., Esposito, E., & De Lorenzo, A. (2020). Eating habits and lifestyle changes during COVID-19 lockdown: An Italian survey. Journal of Translational Medicine, 18(1), 229. https://doi.org/10.1186/s12967-020-02399-5

  7. El-Gebali, S., Mistry, J., Bateman, A., Eddy, S. R., Luciani, A., Potter, S. C., Qureshi, M., Richardson, L. J., Salazar, G. A., Smart, A., Sonnhammer, E. L. L., Hirsh, L., Paladin, L., Piovesan, D., Tosatto, S. C. E., & Finn, R. D. (2019). The Pfam protein families database in 2019. Nucleic Acids Research, 47(D1), D427–D432. https://doi.org/10.1093/nar/gky995

  8. Grenfell, B. T., Pybus, O. G., Gog, J. R., Wood, J. L., Daly, J. M., Mumford, J. A., & Holmes, E. C. (2004). Unifying the epidemiological and evolutionary dynamics of pathogens. Science, 303(5656), 327–332. https://doi.org/10.1126/science.1090727

  9. Hadfield, J., Megill, C., Bell, S. M., Huddleston, J., Potter, B., Callender, C., Sagulenko, P., Bedford, T., & Neher, R. A. (2018). Nextstrain: Real-time tracking of pathogen evolution. Bioinformatics, 34(23), 4121–4123. https://doi.org/10.1093/bioinformatics/bty407

  10. Kalvari, I. K. (2018). Computational approaches for the identification of LIR-motifs in selective autophagy receptor and adaptor proteins [Doctoral Dissertation]. Faculty of Pure and Applied Sciences, University of Cyprus.

  11. Katoh, K., & Standley, D. M. (2013). MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Molecular Biology Evolution, 30(4), 772–780. https://doi.org/10.1093/molbev/mst010

  12. Kieliszek, M., Pobiega, K., Piwowarek, K., & Kot, A. M. (2021). Characteristics of the proteolytic enzymes produced by lactic acid bacteria. Molecules, 26(7). https://doi.org/10.3390/molecules26071858

  13. Klenk, H. D., & Garten, W. (1994). Host cell proteases controlling virus pathogenicity. Trends in Microbiology, 2(2), 39–43. https://doi.org/10.1016/0966-842x(94)90123-6

  14. Libin, P., Beheydt, G., Deforche, K., Imbrechts, S., Ferreira, F., Van Laethem, K., Theys, K., Carvalho, A. P., Cavaco-Silva, J., Lapadula, G., Torti, C., Assel, M., Wesner, S., Snoeck, J., Ruelle, J., De Bel, A., Lacor, P., De Munter, P., Van Wijngaerden, E., . . . and Vandamme, A. M. (2013). RegaDB: Community-driven data management and analysis for infectious diseases. Bioinformatics, 29(11), 1477–1480. https://doi.org/10.1093/bioinformatics/btt162

  15. Libin, P., Vanden Eynden, E., Incardona, F., Nowé, A., Bezenchek, A., Group, E. S., Sönnerborg, A., Vandamme, A. M., Theys, K., & Baele, G. (2017). PhyloGeoTool: Interactively exploring large phylogenies in an epidemiological context. Bioinformatics, 33(24), 3993–3995. https://doi.org/10.1093/bioinformatics/btx535

  16. Madsen, E. E., Krustrup, P., Larsen, C. H., Elbe, A. M., Wikman, J. M., Ivarsson, A., & Lautenbach, F. (2021). Resilience as a protective factor for well-being and emotional stability in elite-level football players during the first wave of the COVID-19 pandemic. Science and Medicine in Football, 5(sup1)(Suppl. 1), 62–69. https://doi.org/10.1080/24733938.2021.1959047

  17. Mitchell, A. L., Almeida, A., Beracochea, M., Boland, M., Burgin, J., Cochrane, G., Crusoe, M. R., Kale, V., Potter, S. C., Richardson, L. J., Sakharova, E., Scheremetjew, M., Korobeynikov, A., Shlemov, A., Kunyavskaya, O., Lapidus, A., & Finn, R. D. (2020). MGnify: The microbiome analysis resource in 2020. Nucleic Acids Research, 48(D1), D570–D578. https://doi.org/10.1093/nar/gkz1035

  18. Naveed, M., Nadeem, F., Mehmood, T., Bilal, M., Anwar, Z., & Amjad, F. (2021). Protease—A versatile and ecofriendly biocatalyst with multi-industrial applications: An updated review. Catalysis Letters, 151(2), 307–323. https://doi.org/10.1007/s10562-020-03316-7

  19. Nawrocki, E. P., & Eddy, S. R. (2013). Infernal 1.1: 100-Fold faster RNA homology searches. Bioinformatics, 29(22), 2933–2935. https://doi.org/10.1093/bioinformatics/btt509

  20. Ngcapu, S., Theys, K., Libin, P., Marconi, V. C., Sunpath, H., Ndung’u, T., & Gordon, M. L. (2017). Characterization of nucleoside reverse transcriptase inhibitor-associated mutations in the RNase H region of HIV-1 subtype C infected individuals. Viruses, 9(11), 330. https://doi.org/10.3390/v9110330

  21. Rambaut, A., Holmes, E. C., O’Toole, Á., Hill, V., McCrone, J. T., Ruis, C., du Plessis, L., & Pybus, O. G. (2020). A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nature Microbiology, 5(11), 1403–1407. https://doi.org/10.1038/s41564-020-0770-5

  22. Reiling, N., Hölscher, C., Fehrenbach, A., Kröger, S., Kirschning, C. J., Goyert, S., & Ehlers, S. (2002). Cutting edge: Toll-like receptor (TLR) 2-and TLR4-mediated pathogen recognition in resistance to airborne infection with Mycobacterium tuberculosis. Journal of Immunology, 169(7), 3480–3484. https://doi.org/10.4049/jimmunol.169.7.3480

  23. Reimering, S., Muñoz, S., & McHardy, A. C. (2020). Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic. PLOS Computational Biology, 16(2), e1007101. https://doi.org/10.1371/journal.pcbi.1007101

  24. Solis-Reyes, S., Avino, M., Poon, A., & Kari, L. (2018). An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes. PLOS ONE, 13(11), e0206409. https://doi.org/10.1371/journal.pone.0206409

  25. Spiteri, G., Fielding, J., Diercke, M., Campese, C., Enouf, V., Gaymard, A., Bella, A., Sognamiglio, P., Sierra Moros, M. J., Riutort, A. N., Demina, Y. V., Mahieu, R., Broas, M., Bengnér, M., Buda, S., Schilling, J., Filleul, L., Lepoutre, A., Saura, C., . . . and Ciancio, B. C. (2020). First cases of coronavirus disease 2019 (COVID-19) in the WHO European Region, 24 January to 21 February 2020. Eurosurveillance, 25(9). PubMed: 2000178

  26. Tcherepanov, V., Ehlers, A., & Upton, C. (2006). Genome Annotation Transfer Utility (GATU): Rapid annotation of viral genomes using a closely related reference genome. BMC Genomics, 7(1), 150. https://doi.org/10.1186/1471-2164-7-150

  27. Theys, K., Deforche, K., Libin, P., Camacho, R. J., Van Laethem, K., & Vandamme, A. M. (2010). Resistance pathways of human immunodeficiency virus type 1 against the combination of zidovudine and lamivudine. Journal of General Virology, 91(8), 1898–1908. https://doi.org/10.1099/vir.0.022657-0

  28. UniProt Consortium. (2019). UniProt: A worldwide hub of protein knowledge. Nucleic Acids Research, 47(D1), D506–D515. https://doi.org/10.1093/nar/gky1049

  29. Upton, C., Hogg, D., Perrin, D., Boone, M., & Harris, N. L. (2000). Viral genome organizer: A system for analyzing complete viral genomes. Virus Research, 70(1–2), 55–64. https://doi.org/10.1016/s0168-1702(00)00210-0

  30. Varatharaj, A., Thomas, N., Ellul, M. A., Davies, N. W. S., Pollak, T. A., Tenorio, E. L., Sultan, M., Easton, A., Breen, G., Zandi, M., Coles, J. P., Manji, H., Al-Shahi Salman, R., Menon, D. K., Nicholson, T. R., Benjamin, L. A., Carson, A., Smith, C., Turner, M. R., . . . and CoroNerve Study Group. (2020). Neurological and neuropsychiatric complications of COVID-19 in 153 patients: A UK-wide surveillance study. Lancet Psychiatry, 7(10), 875–882. https://doi.org/10.1016/S2215-0366(20)30287-X

  31. Volz, E. M., & Siveroni, I. (2018). Bayesian phylodynamic inference with complex models. PLOS Computational Biology, 14(11), e1006546. https://doi.org/10.1371/journal.pcbi.1006546

  32. Wang, Y., Cui, X., Chen, X., Yang, S., Ling, Y., Song, Q., Zhu, S., Sun, L., Li, C., Li, Y., Deng, X., Delwart, E., & Zhang, W. (2020). A recombinant infectious bronchitis virus from a chicken with a spike gene closely related to that of a turkey coronavirus. Archives of Virology, 165(3), 703–707. https://doi.org/10.1007/s00705-019-04488-3

  33. Watkins, J., & Wulaningsih, W. (2020). Three further ways that the COVID-19 pandemic will affect health outcomes. International Journal of Public Health, 65(5), 519–520. https://doi.org/10.1007/s00038-020-01383-6

  34. Westerhoff, H. V., & Kolodkin, A. N. (2020). Advice from a systems-biology model of the corona epidemics. npj Systems Biology and Applications, 6(1), 18. https://doi.org/10.1038/s41540-020-0138-8

  35. Wu, D., Lu, J., Liu, Y., Zhang, Z., & Luo, L. (2020). Positive effects of COVID-19 control measures on influenza prevention. International Journal of Infectious Diseases, 95, 345–346. https://doi.org/10.1016/j.ijid.2020.04.009

  36. Yang, G. Z., J Nelson, B., Murphy, R. R., Choset, H., Christensen, H., H Collins, S., Dario, P., Goldberg, K., Ikuta, K., Jacobstein, N., Kragic, D., Taylor, R. H., & McNutt, M. (2020). Combating COVID-19—The role of robotics in managing public health and infectious diseases. Science Robotics, 5(40), eabb5589. https://doi.org/10.1126/scirobotics.abb5589

  37. Zhao, S., Lin, Q., Ran, J., Musa, S. S., Yang, G., Wang, W., Lou, Y., Gao, D., Yang, L., He, D., & Wang, M. H. (2020). Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. International Journal of Infectious Diseases, 92, 214–217. https://doi.org/10.1016/j.ijid.2020.01.050

  38. Zsidisin, G. A., Panelli, A., & Upton, R. (2000). Purchasing organization involvement in risk assessments, contingency plans, and risk management: An exploratory study. Supply Chain Management, 5(4), 187–198. https://doi.org/10.1108/13598540010347307

Funding Information

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Conflict

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.