Comprehensive annotations of the mutational spectra of SARS‐CoV‐2 spike protein: a fast and accurate pipeline

  1. Mohammad Shaminur Rahman
  2. Mohammad Rafiul Islam
  3. Mohammad Nazmul Hoque
  4. Abu Sayed Mohammad Rubayet Ul Alam
  5. Masuda Akther
  6. Joynob Akter Puspo
  7. Salma Akter
  8. Azraf Anwar
  9. Munawar Sultana
  10. Mohammad Anwar Hossain

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1111/tbed.13834
  2. ScreenIT

    SciScore for 10.1101/2020.06.29.177238: (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
    We aligned each file through the MAFFT (maximum limit 10,000 sequences
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    2.2 Mutational frequency analysis: MEGA 7 was used to differentiate the spike protein of SARS-CoV-2 from multiple sequence alignment (Kumar et al., 2016)
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)
    Amino-acid mutation analysis was done with bio-python program using pairwise alignment (https://github.com/SShaminur/Mutation-Analysis).
    bio-python
    suggested: None
    Swiss-Model, a structure homology-modelling server (https://swissmodel.expasy.org/) was used to predict the 3D structure (template, PDB ID:6VSB) of the S protein of the reference genome and the structure was visualized in PyMOL (DeLano,
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)
    . Microsoft Excel 2016 was used for all the statistical analyses (David, 2017).
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    Results from OddPub: Thank you for sharing your data.

    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.

  3. Version published to 10.1101/2020.06.29.177238v1 on bioRxiv