Genomic Characterization of a Novel SARS-CoV-2 Lineage from Rio de Janeiro, Brazil

  1. Carolina M. Voloch
  2. Ronaldo da Silva Francisco
  3. Luiz G. P. de Almeida
  4. Cynthia C. Cardoso
  5. Otavio J. Brustolini
  6. Alexandra L. Gerber
  7. Ana Paula de C. Guimarães
  8. Diana Mariani
  9. Raissa Mirella da Costa
  10. Orlando C. Ferreira
  11. Covid19-UFRJ Workgroup
  12. LNCC Workgroup
  13. Adriana Cony Cavalcanti
  14. Thiago Silva Frauches
  15. Claudia Maria Braga de Mello
  16. Isabela de Carvalho Leitão
  17. Rafael Mello Galliez
  18. Débora Souza Faffe
  19. Terezinha M. P. P. Castiñeiras
  20. Amilcar Tanuri
  21. Ana Tereza R. de Vasconcelos

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Abstract

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  1. Version published to 10.1128/jvi.00119-21
  2. ScreenIT

    SciScore for 10.1101/2020.12.23.20248598: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The present study was approved by the National Committee of Research Ethics and by the Ethics Committee from Hospital Universitário
    RandomizationWe attributed each genome’s latitude and longitude to a point randomly sampled within the patient’s municipality of residence (Appendix 1 Table 1).
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableSample collection: A total of 180 participants, 82 males and 98 females, from 19 municipalities located at Rio de Janeiro State, in Brazilian Southeast region, were enrolled in this cross-sectional study.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Quality assessment of sequencing data was performed using FastQC (v0.11.4).
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Next, we filtered low-quality reads (<25) with trimmomatic v0.39 (2).
    trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    The sequences were mapped to the reference genome NC_045512.2 using the BWA 0.7.17 software.
    BWA
    suggested: (BWA, RRID:SCR_010910)
    We then generated the consensus genome sequence using bcftools v1.10.2 and bedtools v2.29.2 packages (3–5)
    bcftools
    suggested: (SAMtools/BCFtools, RRID:SCR_005227)
    bedtools
    suggested: (BEDTools, RRID:SCR_006646)
    Single-nucleotide variants (SNVs) were detected using the GATK v4.1.7.0 (6).
    GATK
    suggested: (GATK, RRID:SCR_001876)
    We screened our datasets for recombination using the Phi-test approach in SplitsTree (11,12) and found no recombination evidence.
    SplitsTree
    suggested: (SplitsTree, RRID:SCR_014734)
    We assessed the temporal signal of each Clade dataset using TempEst 1.5.3 (13) and removed the outlier sequences.
    TempEst
    suggested: (TempEst, RRID:SCR_017304)
    We used a relaxed random walk diffusion model (18) available in BEAST 1.10 (14) with a Cauchy distribution to estimate trees whose internal nodes are associated with geographic coordinates.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    MCMC chains were run for 10 million generations and logged every 1000th step, with convergence assessed using Tracer v1.7.1 (17).
    Tracer
    suggested: (Tracer, RRID:SCR_019121)

    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.

  3. Version published to 10.1101/2020.12.23.20248598v1 on medRxiv