High-throughput SARS-CoV-2 and host genome sequencing from single nasopharyngeal swabs

  1. J. E. Gorzynski
  2. H. N. De Jong
  3. D. Amar
  4. C. Hughes
  5. A. Ioannidis
  6. R. Bierman
  7. D. Liu
  8. Y. Tanigawa
  9. A. L. Kistler
  10. J. Kamm
  11. J. Kim
  12. L. Cappello
  13. N. F. Neff
  14. S. Rubinacci
  15. O. Delaneau
  16. M. J. Shoura
  17. K. Seo
  18. A. Kirillova
  19. A. Raja
  20. S. Sutton
  21. C. Huang
  22. M. K. Sahoo
  23. K. C. Mallempati
  24. G. Montero-Martin
  25. K. Osoegawa
  26. N. Watson
  27. N. Hammond
  28. R. Joshi
  29. M. A. Fernández-Viña
  30. J. W. Christle
  31. M.T. Wheeler
  32. P. Febbo
  33. K. Farh
  34. G. P. Schroth
  35. F. DeSouza
  36. J. Palacios
  37. J. Salzman
  38. B. A. Pinsky
  39. M. A. Rivas
  40. C.D. Bustamante
  41. E. A. Ashley
  42. V. N. Parikh

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Abstract

During COVID19 and other viral pandemics, rapid generation of host and pathogen genomic data is critical to tracking infection and informing therapies. There is an urgent need for efficient approaches to this data generation at scale. We have developed a scalable, high throughput approach to generate high fidelity low pass whole genome and HLA sequencing, viral genomes, and representation of human transcriptome from single nasopharyngeal swabs of COVID19 patients.

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  1. Version published to 10.1101/2020.07.27.20163147v3 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.07.27.20163147: (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
    Sample Collection and diagnostics: Residual VTM from SARS-CoV-2 positive nasopharyngeal swabs collected during clinical assessment of asymptomatic and symptomatic patients at Stanford Healthcare were
    Stanford Healthcare
    suggested: None
    Non-SARS-CoV-2 reads were filtered out with Kraken220, using an index of human and viral genomes in RefSeq (index downloaded from https://genexa.ch/sars2-bioinformatics-resources/).
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    Reads per COVID gene were collected from the ReadsPerGene STAR output file, and the total mappable reads were collected from the Log.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    500 μl of 1.3 pM DNA sequencing library was loaded into a MiniSeq Mid Output Kit (300-cycles) (FC-420-1004), and sequenced using MiniSeq DNA sequencer (Illumina Inc., San Diego, CA).
    MiniSeq
    suggested: None
    27,28 Host Sequence Alignment: Low-coverage FASTQ sequences underwent quality control assessment via FastQC v0.11.8 before alt-aware alignment to GRCh38.p12 using BWA-MEM v0.7.17-r1188.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    BWA-MEM
    suggested: (Sniffles, RRID:SCR_017619)
    After duplicate marking, base quality score recalibration was performed with Picard Tools’ BaseRecalibrator and high-confidence variant call sets from dbSNP and the 1000 Genomes Project.
    Picard
    suggested: (Picard, RRID:SCR_006525)
    dbSNP
    suggested: (dbSNP, RRID:SCR_002338)
    1000 Genomes Project
    suggested: (1000 Genomes Project and AWS, RRID:SCR_008801)
    Quality control metrics, including coverage, were generated with Qualimap BAMQC v2.2.1, Samtools v1.10, and Mosdepth v0.2.9.
    Qualimap
    suggested: (QualiMap, RRID:SCR_001209)
    Samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Finally, quality control reports for each sample were aggregated using MultiQC v1.9
    MultiQC
    suggested: (MultiQC, RRID:SCR_014982)
    ) Variant Calling, Imputation, PCA, Kinship: BAM files were used for an initial calling with bcftools v1.9 mpileup29.
    bcftools
    suggested: (SAMtools/BCFtools, RRID:SCR_005227)

    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

    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. ScreenIT

    SciScore for 10.1101/2020.07.27.20163147: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementInstitutional Review Board approval for anonymous sequencing of host and viral genomics was obtained from the Stanford University School of Medicine IRB.Randomizationnot detected.BlindingThis also enabled confirmation of six blindly duplicated samples and 2 pairings of first-degree relatives through kinship analysis (​Figure 2C​).Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The estimated phylogenetic tree is the maximum clade credibility tree obtained with BEAST 2​15​ using a fixed mutation rate of 1.04x10​-3 per base per year, the Coalescent Extended Bayesian Skyline prior​16​, and the HKY substitution model​17​. ​(F)​ RPKMs for individual SARS-CoV-2 genes were averaged over samples with similar CT values.
    BEAST
    suggested: (BEAST, SCR_010228)
    Methods Sample Collection and diagnostics:​ Residual VTM from SARS-CoV-2 positive nasopharyngeal swabs collected during clinical assessment of asymptomatic and symptomatic patients at Stanford Healthcare were used.
    Stanford Healthcare
    suggested: None
    Non-SARS-CoV-2 reads were filtered out with Kraken2​20​, using an index of human and viral genomes in RefSeq (index downloaded from https://genexa.ch/sars2-bioinformatics-resources/​).
    RefSeq
    suggested: (RefSeq, SCR_003496)
    Reads per COVID gene were collected from the ReadsPerGene STAR output file, and the total mappable reads were collected from the Log.
    STAR
    suggested: (STAR, SCR_015899)
    500 µl of 1.3 pM DNA sequencing library was loaded into a MiniSeq Mid Output Kit (300-cycles) (FC-420-1004), and sequenced using MiniSeq DNA sequencer (Illumina Inc., San Diego, CA).
    MiniSeq
    suggested: None
    , KIR ligands (C1 and C2) and imputed HLA haplotypes.​27,28 Host Sequence Alignment: Low-coverage FASTQ sequences underwent quality control assessment via FastQC v0.11.8 before alt-aware alignment to GRCh38.p12 using BWA-MEM v0.7.17-r1188.
    FastQC
    suggested: (FastQC, SCR_014583)
          <div style="margin-bottom:8px">
        <div><b>BWA-MEM</b></div>
        <div>suggested: (Sniffles, <a href="https://scicrunch.org/resources/Any/search?q=SCR_017619">SCR_017619</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After duplicate marking, base quality score recalibration was performed with Picard Tools’ BaseRecalibrator and high-confidence variant call sets from dbSNP and the 1000 Genomes Project.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Picard</b></div>
        <div>suggested: (Picard, <a href="https://scicrunch.org/resources/Any/search?q=SCR_006525">SCR_006525</a>)</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>dbSNP</b></div>
        <div>suggested: (dbSNP, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002338">SCR_002338</a>)</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>1000 Genomes Project</b></div>
        <div>suggested: (1000 Genomes Project and AWS, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008801">SCR_008801</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Quality control metrics, including coverage, were generated with Qualimap BAMQC v2.2.1, Samtools v1.10, and Mosdepth v0.2.9.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Qualimap</b></div>
        <div>suggested: (QualiMap, <a href="https://scicrunch.org/resources/Any/search?q=SCR_001209">SCR_001209</a>)</div>
      </div>
    
      <div style="margin-bottom:8px">
        <div><b>Samtools</b></div>
        <div>suggested: (Samtools, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002105">SCR_002105</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Finally, quality control reports for each sample were aggregated using MultiQC v1.9</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>MultiQC</b></div>
        <div>suggested: (MultiQC, <a href="https://scicrunch.org/resources/Any/search?q=SCR_014982">SCR_014982</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Variant Calling, Imputation, PCA, Kinship: ​BAM files were used for an initial calling with bcftools v1.9 mpileup​29​.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>bcftools</b></div>
        <div>suggested: (SAMtools/BCFtools, <a href="https://scicrunch.org/resources/Any/search?q=SCR_005227">SCR_005227</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Beckman independence fellowship (MJS) and American Heart Association (MJS, KS), the National Heart Lung and Blood Institute (K08HL143185 to VNP), The John Taylor Babbitt Foundation (VNP) and Sarnoff Cardiovascular Research Foundation (VNP)</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>American Heart Association</b></div>
        <div>suggested: (American Heart Association, <a href="https://scicrunch.org/resources/Any/search?q=SCR_007210">SCR_007210</a>)</div>
      </div>
    </td></tr></table>

    Data from additional tools added to each annotation on a weekly basis.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.07.27.20163147v1 on medRxiv
  5. Version published to 10.1101/2020.07.27.20163147v2 on medRxiv