A benchmarking study of SARS-CoV-2 whole-genome sequencing protocols using COVID-19 patient samples

  1. Tiantian Liu
  2. Zhong Chen
  3. Wanqiu Chen
  4. Xin Chen
  5. Maryam Hosseini
  6. Zhaowei Yang
  7. Jing Li
  8. Diana Ho
  9. David Turay
  10. Ciprian P. Gheorghe
  11. Wendell Jones
  12. Charles Wang

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Abstract

No abstract available

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  1. Version published to 10.1016/j.isci.2021.102892
  2. ScreenIT

    SciScore for 10.1101/2020.11.10.375022: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethics statement: The study was approved by the Institutional Review Board (IRB number 5200127) and the Institutional Biosafety Committee (IBC) of the Loma Linda University (LLU).
    IACUC: Ethics statement: The study was approved by the Institutional Review Board (IRB number 5200127) and the Institutional Biosafety Committee (IBC) of the Loma Linda University (LLU).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    SARS-CoV-2 WGS library construction using QIAseq
    WGS
    suggested: None
    QIAseq
    suggested: None
    SARS-CoV-2 WGS library construction using Tecan Trio RNA-Seq library protocol (Protocol 4): Eight RNA samples isolated from fresh and frozen specimens were used for Tecan Trio RNA-seq library construction (NuGEN/Tecan), following the NuGEN protocol with integrated DNase treatment.
    SARS-CoV-2
    suggested: (Active Motif Cat# 91351, RRID:AB_2847848)
    SARS-CoV-2 WGS library construction using QIAseq
    WGS
    suggested: None
    QIAseq
    suggested: None
    A Kraken244 database was built based on the complete genomes in the NCBI RefSeq database for archaea, bacteria, protozoa, fungi, human and viruses (SARS-COV-2 genome included).
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    To summarize the read mapping percentages to multiple taxa, the trimmed reads were classified into human, SARS-CoV-2, bacterial, and remaining reads (e.g., unclassified, archaeal, viral, fungi, protozoa) by using the Kraken2 database.
    Kraken2
    suggested: None
    SARS-Co-V-2 SNV variant calling and generation of consensus SNV variants: Variants were called on the bam files by VarScan 2 (v2.4.4) and BCFTools (v1.9).
    VarScan
    suggested: (VARSCAN, RRID:SCR_006849)
    To accurately identify SNVs, we used samtools mpileup (parameters: -A -d 20000 -Q 0) and varscan2 (v2.4.4) (parameters: --p-value 0.99 –variants).
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    The variant fasta files generated from the same clinical sample but different protocols were piled up using Jalview (v2.11.1.0)47 alignment tool, from which one consensus fasta file was compiled for each clinical sample.
    Jalview
    suggested: (Jalview, RRID:SCR_006459)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Although the primer-panel based target amplicon sequencing has been shown as a cost-effective approach for sequencing the clinical COVID-19 samples to discover the individual genetic diversity29, we found there were some limitations for the ARTIC V3 amplicon-based target whole-amplification protocols. First, by design, the current ARTIC V3 amplicons only covered genome regions from positions 30 to 29836, which would make it impossible for the ARTIC V3 amplicon-based protocols to detect a SNV outside of the PCR amplified regions. This scenario actually occurred in our benchmarking study and we found that a consensus variant, g.29868 G>A in sample NP29, was consistently detected by protocols P2, P3, and P4, but was missed by P1 and P7 (Fig. 5a, c&d). Second, a single-base mismatch between the primer and template may produce a PCR error such as chimeric PCR amplification30, which might lead to a false SNV call. For example, we found that P7, at low viral input, called a unique “false” SNV (g.28321 G>T) with almost 100% allele frequency and >1,000X coverage (Suppl. Fig. 13). However, this putative SNV was not detected in the same clinical sample prepared using either P7 at high viral input (1M) or P1, P2, P3 and P4 (Fig. 5c, Suppl. Fig. 13) at any input. Third, PCR amplified primer-originated “contaminated” sequences associated with the Qiagen protocols P1 and P7 may lead to an error in SNV calling. Coincidently, we had a consensus SNV (g.6543 C>T) which was within the overlappin...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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 10.1101/2020.11.10.375022v1 on bioRxiv