Genomic Surveillance of SARS-CoV-2 in Erie County, New York

  1. Natalie A. Lamb
  2. Jonathan E. Bard
  3. Alyssa Pohlman
  4. Amanda Boccolucci
  5. Donald A. Yergeau
  6. Brandon J. Marzullo
  7. Carleen Pope
  8. Gale Burstein
  9. John Tomaszewski
  10. Norma J. Nowak
  11. Jennifer A. Surtees

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Abstract

Early in the SAR-CoV-2 pandemic, we established a whole genome sequencing pipeline to assess lineages circulating in Western New York. Initial sequences revealed entry into the region via Europe, similar to observations in New York City. However, as the pandemic progressed and variants of concern emerged, we observed distinct patterns in lineages relative to NYC. Notably, B.1.427 became dominant in Western New York, before it was displaced by B.1.1.7. Our hierarchical cluster analysis of B.1.1.7 lineages, which by May 2021 made up ∼ 80% of all cases, indicated both multiple introductions and community spread. Our work highlights the importance of widespread, regional surveillance of SARS-CoV-2 across the United States.

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  1. ScreenIT

    SciScore for 10.1101/2021.07.01.21259869: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This study was reviewed by the University at Buffalo Institutional Review Board and determined to be “Not Human Research” (IRB ID: STUDY00004515).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Denatured libraries were diluted to a final concentration in Illumina HT1 buffer(12.5 pM for MiSeq and 1.5 pM for NextSeq).
    MiSeq
    suggested: (A5-miseq, RRID:SCR_012148)
    Upon completion of the sequencing run, data were transferred to the high-performance computing facility (Center for Computational Research) located in the Center of Excellence building at the University at Buffalo. UB GBC SARS-COV-2 Bioinformatics Analysis: The GBC SARS-COV-2 analysis pipeline (https://github.com/UBGBC/fastq-to-consensus) is modelled off of the recommendations provided by the CDC SARS-COV-2 spheres working group (https://github.com/CDCgov/SARS-CoV-2_Sequencing), and is written in the python pipeline framework Snakemake.
    python
    suggested: (IPython, RRID:SCR_001658)
    Then, reads are checked for initial quality using fastqc, fastq_screen, and multiqc, prior to adapter removal analysis via the tool Cutadapt.
    Cutadapt
    suggested: (cutadapt, RRID:SCR_011841)
    To detect inter-lineage variation, we compared each sample’s spread of variants utilizing the Bedtools jaccard function, which generates a Jaccard index score between every sample.
    Bedtools
    suggested: (BEDTools, RRID:SCR_006646)
    The resulting similarity matrix was then used as input for hierarchical cluster analysis in RStudio.
    RStudio
    suggested: (RStudio, RRID:SCR_000432)
    14 The resulting alignment was then used as input into the FastTree algorithm [price, 2009; price, 2010), inferring maximum-likelihood phylogeny using the jukes-cantor distance model of nucleotide evolution, generating a newick formatted phylogenetic tree.
    FastTree
    suggested: (FastTree, RRID:SCR_015501)

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.07.01.21259869v1 on medRxiv