Variant analysis of SARS-CoV-2 strains in Middle Eastern countries

  1. Khalid Mubarak Bindayna
  2. Shane Crinion

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Abstract

Background

SARS-CoV-2 is diverging from the initial Wuhan serotype, and different variants of the virus are reported. Mapping the variant strains and studying their pattern of evolution will provide better insights into the pandemic spread

Methods

Data on different SARS-CoV2 for WHO EMRO countries were obtained from the Chinese National Genomics Data Center (NGDC), Genbank and the Global Initiative on Sharing All Influenza Data (GISAID). Multiple sequence alignments (MSA) was performed to study the evolutionary relationship between the genomes. Variant calling, genome and variant alignment were performed to track the strains in each country. Evolutionary and phylogenetic analysis is used to explore the evolutionary hypothesis.

Findings

Of the total 50 samples, 4 samples did not contain any variants. Variant calling identified 379 variants. Earliest strains are found in Iranian samples. Variant alignment indicates Iran samples have a low variant frequency. Saudi Arabia has formed an outgroup. Saudi Arabia, Qatar and Kuwait were the most evolved genomes and are the countries with the highest number of cases per million.

Interpretation

Iran was exposed to the virus earlier than other countries in the Eastern Mediterranean Region.

Funding

None

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  1. ScreenIT

    SciScore for 10.1101/2020.06.18.156810: (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
    Sample filing: We performed multiple sequence alignments (MSA) using EMBOSS Clustal Omega8 and observed for conserved and consensus sequences to study the evolutionary relationship between the genomes.
    EMBOSS
    suggested: (EMBOSS, RRID:SCR_008493)
    SNPeff, a genetic variant annotation program in the Galaxy server, was used to identify the protein level changes caused by SNPs11.
    SNPeff
    suggested: (SnpEff, RRID:SCR_005191)
    Galaxy
    suggested: (Galaxy, RRID:SCR_006281)
    The genome database for both SARS-CoV-2 NC_045512.2 and SARS NC_004718 were built and compared using SNPeff, and Genbank7.
    SARS-CoV-2
    suggested: (Active Motif Cat# 91351, RRID:AB_2847848)
    (BEAST) v1.10.4, is used to perform Bayesian analysis of molecular sequences using MCMC14.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    The unrooted tree obtained from the models and the phylogenetic tree generated by Clustal Omega is used to predict the associations between samples.
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • 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 10.1101/2020.06.18.156810 on bioRxiv