Genomic diversity of SARS-CoV-2 during early introduction into the Baltimore–Washington metropolitan area

  1. Peter M. Thielen
  2. Shirlee Wohl
  3. Thomas Mehoke
  4. Srividya Ramakrishnan
  5. Melanie Kirsche
  6. Oluwaseun Falade-Nwulia
  7. Nídia S. Trovão
  8. Amanda Ernlund
  9. Craig Howser
  10. Norah Sadowski
  11. C. Paul Morris
  12. Mark Hopkins
  13. Matthew Schwartz
  14. Yunfan Fan
  15. Victoria Gniazdowski
  16. Justin Lessler
  17. Lauren Sauer
  18. Michael C. Schatz
  19. Jared D. Evans
  20. Stuart C. Ray
  21. Winston Timp
  22. Heba H. Mostafa

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Abstract

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  1. Version published to 10.1172/jci.insight.144350
  2. Version published to 10.1101/2020.08.13.20174136v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.08.13.20174136: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationFor computational efficiency, we downsampled this dataset homogeneously through time and space, by randomly selecting 7 and 34 sequences per month, to obtain global datasets with 1168 (hereafter referred to as Global 1K) and 3113 (hereafter referred to as Global 3K) sequences from around the world, respectively.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Primer binding regions were masked and variant calling was performed with Nanopolish v0.13.2 with a minimum candidate allele frequency of 0.15 36.
    Nanopolish
    suggested: (Nanopolish, RRID:SCR_016157)
    The same normalization process was applied to Illumina reads, and variant calling was performed with FreeBayes v0.9.21 39, iVar v1.0 40, and samtools.
    FreeBayes
    suggested: (FreeBayes, RRID:SCR_010761)
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Final variants were annotated with SnpEff 41.
    SnpEff
    suggested: (SnpEff, RRID:SCR_005191)
    Multiple sequence alignment was performed using MAFFT v7.458 44 using parameters --reorder --anysymbol --nomemsave --adjustdirection.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    This dataset was subjected to multiple iterations of phylogeny reconstruction using IQ-TREE multicore software version v1.6.12 45 with parameters -m GTR+G -nt 50, and exclusion of outlier sequences whose genetic divergence and sampling date were incongruent using TempEst 46, resulting in a dataset with 19,565 sequences.
    TempEst
    suggested: (TempEst, RRID:SCR_017304)
    We conducted the analyses through the Molecular Evolutionary Genetics Analysis software version 10 (MEGA X) 47,48 and applied the maximum composite likelihood mode 49.
    Molecular Evolutionary Genetics Analysis
    suggested: None
    We computed the phylogeny with IQ-TREE v1.6.12 45,51 with parameters -me 0.05 -nt 4 -m GTR -n 4.
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    Trees were rooted on the Wuhan-Hu-1 reference genome in FigTree 52 and visualized using ggtree 53 in R 54.
    FigTree
    suggested: (FigTree, RRID:SCR_008515)

    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.
    • 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.

  4. Version published to 10.1101/2020.08.13.20174136v1 on medRxiv