Analysis of single nucleotide polymorphisms between 2019-nCoV genomes and its impact on codon usage

  1. Suruchi Gupta
  2. Ravail Singh
  3. Prosenjit Paul

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Abstract

The spread of COVID-19 is a global concern that has taken a toll on entire human health. Researchers across the globe has been working in devising the strategies to combat this dreadful disease. Studies focused on genetic variability helps design effective drugs and vaccines. Considering this, the present study entails the information regarding the genome-wide mutations detected in the 108 SARS CoV-2 genomes worldwide. We identified a few hypervariable regions localized in orf1ab, spike, and nucleocapsid gene. These nucleotide polymorphisms demonstrated their effect on both codon usage as well as amino acid usage pattern. Altogether the present study provides valuable information that would be helpful to ongoing research on 2019-nCoV vaccine development.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.05.237404: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    ClustalW software was used for sequence alignment (Guo and Sun, 2000).
    ClustalW
    suggested: (ClustalW, RRID:SCR_017277)
    The aligned sequences were used to create phylogenetic tree using MEGA X software (Kumar et al., 2018).
    MEGA X
    suggested: None
    Sequence handling and data processing were carried out in Bioedit software (Hall et al., 2011)
    Bioedit
    suggested: (BioEdit, RRID:SCR_007361)
    AutoDock 4.2 software was used to dock the protein structures with their potential ligands (Rizvi et al., 2013).
    AutoDock
    suggested: (AutoDock, RRID:SCR_012746)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.08.05.237404 on bioRxiv