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Computational Genomic Sequencing for Covid-19

Author

Listed:
  • Jyoti Sarwan

    (University Institute of Biotechnology, Chandigarh University Gharuan 140413, India)

  • Heenu Sharma

    (University Institute of Biotechnology, Chandigarh University Gharuan 140413, India)

  • Anubha Sharma

    (University Institute of Biotechnology, Chandigarh University Gharuan 140413, India)

  • Eleena Barik

    (University Institute of Biotechnology, Chandigarh University Gharuan 140413, India)

  • Jagadeesh Chandra Bose K

    (University Institute of Biotechnology, Chandigarh University Gharuan 140413, India)

  • Junaid Ahmad Malik

    (Department of Zoology, Government Degree College, Bijbehara, Kashmir 192124, India)

Abstract

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.

Suggested Citation

  • Jyoti Sarwan & Heenu Sharma & Anubha Sharma & Eleena Barik & Jagadeesh Chandra Bose K & Junaid Ahmad Malik, 2022. "Computational Genomic Sequencing for Covid-19," Inventum Biologicum: An International Journal of Biological Research, World BIOLOGICA, vol. 2(1), pages 13-23.
    • Handle: RePEc:adg:ibijbr:v:2:y:2022:i:1:p:13-23
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    References listed on IDEAS

    as
    1. Johnathan Watkins & Wahyu Wulaningsih, 2020. "Three further ways that the COVID-19 pandemic will affect health outcomes," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 65(5), pages 519-520, June.
    2. Erik M Volz & Igor Siveroni, 2018. "Bayesian phylodynamic inference with complex models," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-15, November.
    3. Stephen Solis-Reyes & Mariano Avino & Art Poon & Lila Kari, 2018. "An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-21, November.
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