SARS-CoV-2 genomic diversity and the implications for qRT-PCR diagnostics and transmission

  1. Nicolae Sapoval
  2. Medhat Mahmoud
  3. Michael D. Jochum
  4. Yunxi Liu
  5. R.A. Leo Elworth
  6. Qi Wang
  7. Dreycey Albin
  8. Huw A. Ogilvie
  9. Michael D. Lee
  10. Sonia Villapol
  11. Kyle M. Hernandez
  12. Irina Maljkovic Berry
  13. Jonathan Foox
  14. Afshin Beheshti
  15. Krista Ternus
  16. Kjersti M. Aagaard
  17. David Posada
  18. Christopher E. Mason
  19. Fritz J. Sedlazeck
  20. Todd J. Treangen

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Abstract

The COVID-19 pandemic has sparked an urgent need to uncover the underlying biology of this devastating disease. Though RNA viruses mutate more rapidly than DNA viruses, there are a relatively small number of single nucleotide polymorphisms (SNPs) that differentiate the main SARS-CoV-2 lineages that have spread throughout the world. In this study, we investigated 129 RNA-seq data sets and 6928 consensus genomes to contrast the intra-host and inter-host diversity of SARS-CoV-2. Our analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights intra-host single nucleotide variant (iSNV) and SNP similarity, albeit with differences in C > U changes. Second, iSNV and SNP patterns in SARS-CoV-2 are more similar to MERS-CoV than SARS-CoV-1. Third, a significant fraction of insertions and deletions contribute to the genetic diversity of SARS-CoV-2. Altogether, our findings provide insight into SARS-CoV-2 genomic diversity, inform the design of detection tests, and highlight the potential of iSNVs for tracking the transmission of SARS-CoV-2.

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  1. Version published to 10.1101/gr.268961.120
  2. ScreenIT

    SciScore for 10.1101/2020.07.02.184481: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Read QC and mapping: We processed the Illumina paired-end reads using Trimmomatic ver.
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    We aligned the trimmed reads to the reference genome using Burrows-Wheeler Alignment tool (BWA) ver. 0.7.17 (39, 40).
    BWA
    suggested: (BWA, RRID:SCR_010910)
    We used SAMtools ver.
    SAMtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    1.9 to convert the output of BWA from SAM to BAM format, and to sort and generate indices for the BAM files (41)
    SAM
    suggested: (SAM, RRID:SCR_010951)
    SNV calling and annotation: We used LoFreq ver.
    LoFreq
    suggested: (LoFreq, RRID:SCR_013054)
    We annotated the SNVs found in each of the datasets with snpEff ver.
    snpEff
    suggested: (SnpEff, RRID:SCR_005191)
    SV calling: Structural Variations were identified using Manta (version 1.6.0) (45).
    Manta
    suggested: None
    To test the significance of the overlap we used a permutation test where we randomized the TRS sites (using bedtools random) to generate random TRS with length of 5bp, 1000 times and calculated per TRS the number of start/stop breakpoints of the SV catalog.
    bedtools
    suggested: (BEDTools, RRID:SCR_006646)
    The plot was generated using Circos (v 0.69-8) (48).
    Circos
    suggested: (Circos, RRID:SCR_011798)
    We used RAxML (50) to infer a phylogenetic tree from the GISAID alignment.
    RAxML
    suggested: (RAxML, RRID:SCR_006086)
    We mapped probes and primers against the SARS-CoV-2 reference genome (NC_045512) with bowtie2 (56).
    bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    Analysis of the primer and probe mapping regions was performed with a custom Python script and visualizations were done with R-3.6.1.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    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

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  3. Version published to 10.1101/2020.07.02.184481v1 on bioRxiv