A rapid, cost-effective tailed amplicon method for sequencing SARS-CoV-2

  1. Daryl M. Gohl
  2. John Garbe
  3. Patrick Grady
  4. Jerry Daniel
  5. Ray H. B. Watson
  6. Benjamin Auch
  7. Andrew Nelson
  8. Sophia Yohe
  9. Kenneth B. Beckman

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Abstract

Background

The global COVID-19 pandemic has led to an urgent need for scalable methods for clinical diagnostics and viral tracking. Next generation sequencing technologies have enabled large-scale genomic surveillance of SARS-CoV-2 as thousands of isolates are being sequenced around the world and deposited in public data repositories. A number of methods using both short- and long-read technologies are currently being applied for SARS-CoV-2 sequencing, including amplicon approaches, metagenomic methods, and sequence capture or enrichment methods. Given the small genome size, the ability to sequence SARS-CoV-2 at scale is limited by the cost and labor associated with making sequencing libraries.

Results

Here we describe a low-cost, streamlined, all amplicon-based method for sequencing SARS-CoV-2, which bypasses costly and time-consuming library preparation steps. We benchmark this tailed amplicon method against both the ARTIC amplicon protocol and sequence capture approaches and show that an optimized tailed amplicon approach achieves comparable amplicon balance, coverage metrics, and variant calls to the ARTIC v3 approach.

Conclusions

The tailed amplicon method we describe represents a cost-effective and highly scalable method for SARS-CoV-2 sequencing.

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  1. Version published to 10.1186/s12864-020-07283-6
  2. ScreenIT

    SciScore for 10.1101/2020.05.11.088724: (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
    Ct values were exported and analyzed in Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    To confirm the expected library size of approximately 550 bp, pooled libraries were run on either an Agilent Bioanalyzer or TapeStation (Agilent, Santa Clara, CA).
    Agilent Bioanalyzer
    suggested: None
    Analysis: The analysis method for amplicon libraries is as follows: Sample quality was assessed with FastQC (18).
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Reads that did not align to the host genome were aligned to the reference Wuhan-Hu-1 (5) SARS-CoV-2 genome (MN908947.3) using BWA (20).
    BWA
    suggested: (BWA, RRID:SCR_010910)
    Amplicon read depths were determined by counting the number of aligned reads covering the base at the center of each amplicon region.
    Amplicon
    suggested: (Amplicon, RRID:SCR_003294)
    The iVar software package was used to trim primer sequences from the aligned reads, and iVar and Samtools mpileup were used to call variants and generate consensus sequences (3).
    Samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.05.11.088724v1 on bioRxiv