DeepSARS: simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2

  1. Alexander Yermanos
  2. Kai-Lin Hong
  3. Andreas Agrafiotis
  4. Jiami Han
  5. Sarah Nadeau
  6. Cecilia Valenzuela
  7. Asli Azizoglu
  8. Roy Ehling
  9. Beichen Gao
  10. Michael Spahr
  11. Daniel Neumeier
  12. Ching-Hsiang Chang
  13. Andreas Dounas
  14. Ezequiel Petrillo
  15. Ina Nissen
  16. Elodie Burcklen
  17. Mirjam Feldkamp
  18. Christian Beisel
  19. Annette Oxenius
  20. Miodrag Savic
  21. Tanja Stadler
  22. Fabian Rudolf
  23. Sai T. Reddy

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Abstract

Background

The continued spread of SARS-CoV-2 and emergence of new variants with higher transmission rates and/or partial resistance to vaccines has further highlighted the need for large-scale testing and genomic surveillance. However, current diagnostic testing (e.g., PCR) and genomic surveillance methods (e.g., whole genome sequencing) are performed separately, thus limiting the detection and tracing of SARS-CoV-2 and emerging variants.

Results

Here, we developed DeepSARS, a high-throughput platform for simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2 by the integration of molecular barcoding, targeted deep sequencing, and computational phylogenetics. DeepSARS enables highly sensitive viral detection, while also capturing genomic diversity and viral evolution. We show that DeepSARS can be rapidly adapted for identification of emerging variants, such as alpha, beta, gamma, and delta strains, and profile mutational changes at the population level.

Conclusions

DeepSARS sets the foundation for quantitative diagnostics that capture viral evolution and diversity.

DeepSARS uses molecular barcodes (BCs) and multiplexed targeted deep sequencing (NGS) to enable simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2. Image was created using Biorender.com .

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  1. Version published to 10.1186/s12864-022-08403-0
  2. ScreenIT

    SciScore for 10.1101/2021.08.16.21262126: (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

    Experimental Models: Cell Lines
    SentencesResources
    In the case that human RNA was added to synthetic controls, RNA was extracted from HEK293 cell lines and supplied 50 ng.
    HEK293
    suggested: None
    Software and Algorithms
    SentencesResources
    Heatmaps displaying barcode distance were performed using the pheatmap R package (Kolde and Kolde 2015).
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    Maximum likelihood trees were inferred using the Randomized Axelerated Maximum Likelihood (RAxML) tree construction tool in Geneious (v2020.03) by supplying either the full-length viral genome or just the sites recovered by the three primer sets validated by DeepSARS as input.
    RAxML
    suggested: (RAxML, RRID:SCR_006086)
    The sequences are aligned with MAFFT under default parameters (Katoh and Standley 2013).
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Bioinformatic analysis and data visualization: A reference genome was created by appending human GAPDH (NM_002046), human RNAP (AL590622), and the full-length reference SARS-CoV-2 genome (MN908947) using Geneious (version 10.1.3).
    Geneious
    suggested: (Geneious, RRID:SCR_010519)
    Barcode-containing reads were aligned to the reference genome in R using the buildindex function in the Rsubread package (Liao, Smyth, and Shi 2019).
    Rsubread
    suggested: (Rsubread, RRID:SCR_016945)
    Graphical abstract was created using BioRender.com.
    BioRender
    suggested: (Biorender, RRID:SCR_018361)

    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.

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

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  3. Version published to 10.1101/2021.08.16.21262126v1 on medRxiv