SARS-CoV-2 genomic surveillance in Costa Rica: Evidence of a divergent population and an increased detection of a spike T1117I mutation

  1. Jose Arturo Molina-Mora
  2. Estela Cordero-Laurent
  3. Adriana Godínez
  4. Melany Calderón-Osorno
  5. Hebleen Brenes
  6. Claudio Soto-Garita
  7. Cristian Pérez-Corrales
  8. Jan Felix Drexler
  9. Andres Moreira-Soto
  10. Eugenia Corrales-Aguilar
  11. Francisco Duarte-Martínez

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Abstract

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  1. Version published to 10.1016/j.meegid.2021.104872
  2. ScreenIT

    SciScore for 10.1101/2020.12.21.423850: (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 variableGender distribution of the samples was 112 male and 71 female patients (two cases without gender information), and the age ranged from 4 to 92 years old.

    Table 2: Resources

    No key resources detected.

    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: We detected the following sentences addressing limitations in the study:
    Finally, limitations of our study include a low number of samples, the non-randomized sampling used for the selection of the samples (sampling bias), incomplete clinical and epidemiological data such as travel records and contact tracing for all sequenced samples, and lack of sequenced genome information from key neighboring countries with a strong immigration practice to Costa Rica. Nevertheless this work adds to the efforts of the global scientific community to produce and publicly share SARS-CoV-2 genome sequences. The analyses reported here contribute to the monitoring of the spread of SARS-CoV-2 as part of the surveillance programs during the pandemic, and suggest that surveillance should be continued and (financially-) supported to keep track of important changes in the SARS-CoV-2 genome.

    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 10.1101/2020.12.21.423850v3 on bioRxiv
  4. Version published to 10.1101/2020.12.21.423850v2 on bioRxiv
  5. ScreenIT

    SciScore for 10.1101/2020.12.21.423850: (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 variableGender distribution of the samples was 88 male and 47 female patients (three cases without gender information), and the patients’s age ranged from 4 to 89 years old.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Raw sequencing reads were evaluated using FastQC v0.11.7 (Andrews, 2010) and MultiQC (Ewels, Magnusson, Lundin, & Käller, 2016) before and after trimming.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    MultiQC
    suggested: (MultiQC, RRID:SCR_014982)
    Removal of adapters and trimming of low-quality sequences (Q<30) was done Trimmomatic v0.38 (Bolger, Lohse, & Usadel, 2014).
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    QUAST (Gurevich, Saveliev, Vyahhi, & Tesler, 2013), Qualimap (Okonechnikov, Conesa, & García-Alcalde, 2016), and BRIG (Alikhan, Petty, Ben Zakour, & Beatson, 2011) results were used for the analysis.
    Qualimap
    suggested: (QualiMap, RRID:SCR_001209)
    Clade assignment: PANGOLIN Lineages and GISAID clades were assigned based on genomes by using Bionumerics software v7.6.3 (https://www.applied-maths.com/) or the Coronavirus typing tool v1.13 (https://www.genomedetective.com/app/typingtool/cov/) after their upload into the GISAID database (Global Initiative on Sharing All Influenza Data, www.gisaid.org).
    Bionumerics
    suggested: None
    Variant calling analysis: BAM file from reads mapping was used to remove duplicates using Picard Markduplicates (http://broadinstitute.github.io/picard).
    Picard
    suggested: (Picard, RRID:SCR_006525)
    Freebayes v1.3.1 (Garrison & Marth, 2012) was used as a variant caller with the parameters: -p 1 -q 20 -m 60 --min-coverage 10 –V.
    Freebayes
    suggested: (FreeBayes, RRID:SCR_010761)
    Variant annotation was achieved using SNPeff (Cingolani et al., 2012) with the annotation file of the reference SARS-CoV-2 genome NC_045512.2.
    SNPeff
    suggested: (SnpEff, RRID:SCR_005191)
    Phylogenetic analysis: MAFFT v7.471 (Katoh, Misawa, Kuma, & Miyata, 2002) was used to align all genome sequences.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Construction of the phylogenetic tree was done using IQ-TREE v1.6.12 (Minh et al., 2020), including ModelFinder (Kalyaanamoorthy, Minh, Wong, Von Haeseler, & Jermiin, 2017) to select the best nucleotide substitution model.
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)

    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: We detected the following sentences addressing limitations in the study:

    Limitations of our study include a low number of samples, the non-randomized sampling used for the selection of the samples (sampling bias), incomplete clinical and epidemiological data such as travel records and contact tracing for all sequenced samples, and lack of sequenced genome information from key neighboring countries with a strong immigration story to our country. Nevertheless this work adds to the efforts of the global scientific community to produce and publicly share SARS-CoV-2 genome sequences, which to November 30th, 2020 reached >235 000 genomes in GISAID. The analyses reported here contribute to the monitoring of the spread of SARS-CoV-2 as part of the surveillance programs during the pandemic, and suggest that surveillance should be continued and (financially-) supported to keep track of important changes in the SARS-CoV-2 genome.

    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.

    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.

  6. Version published to 10.1101/2020.12.21.423850v1 on bioRxiv