Mini-XT, a miniaturized tagmentation-based protocol for efficient sequencing of SARS-CoV-2

  1. Marc Fuchs
  2. Clara Radulescu
  3. Miao Tang
  4. Arun Mahesh
  5. Deborah Lavin
  6. Syed Umbreen
  7. James McKenna
  8. Mark Smyth
  9. Eilís McColgan
  10. Zoltan Molnar
  11. Chris Baxter
  12. Timofey Skvortsov
  13. Aditi Singh
  14. Fiona Rogan
  15. Julia Miskelly
  16. Stephen Bridgett
  17. Derek Fairley
  18. David A. Simpson

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Abstract

Background

The COVID-19 pandemic has highlighted the importance of whole genome sequencing (WGS) of SARS-CoV-2 to inform public health policy. By enabling definition of lineages it facilitates tracking of the global spread of the virus. The evolution of new variants can be monitored and knowledge of specific mutations provides insights into the mechanisms through which the virus increases transmissibility or evades immunity. To date almost 1 million SARS-CoV-2 genomes have been sequenced by members of the COVID-19 Genomics UK (COG-UK) Consortium. To achieve similar feats in a more cost-effective and sustainable manner in future, improved high throughput virus sequencing protocols are required. We have therefore developed a miniaturized library preparation protocol with drastically reduced consumable use and costs.

Results

We present the ‘Mini-XT’ miniaturized tagmentation-based library preparation protocol available on protocols.io ( 10.17504/protocols.io.bvntn5en ). SARS-CoV-2 RNA was amplified using the ARTIC nCov-2019 multiplex RT-PCR protocol and purified using a conventional liquid handling system. Acoustic liquid transfer (Echo 525) was employed to reduce reaction volumes and the number of tips required for a Nextera XT library preparation. Sequencing was performed on an Illumina MiSeq. The final version of Mini-XT has been used to sequence 4384 SARS-CoV-2 samples from N. Ireland with a COG-UK QC pass rate of 97.4%. Sequencing quality was comparable and lineage calling consistent for replicate samples processed with full volume Nextera DNA Flex (333 samples) or using nanopore technology (20 samples). SNP calling between Mini-XT and these technologies was consistent and sequences from replicate samples paired together in maximum likelihood phylogenetic trees.

Conclusions

The Mini-XT protocol maintains sequence quality while reducing library preparation reagent volumes eightfold and halving overall tip usage from sample to sequence to provide concomitant cost savings relative to standard protocols. This will enable more efficient high-throughput sequencing of SARS-CoV-2 isolates and future pathogen WGS.

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  1. Version published to 10.1186/s12967-022-03307-9
  2. ScreenIT

    SciScore for 10.1101/2021.09.29.21263685: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    The minimap2 [26] read aligner is used.
    minimap2
    suggested: None
    Software and Algorithms
    SentencesResources
    Illumina Sequencing: The Library pool was diluted to 4 nM with 10 mM Tris pH 8.0, denatured with 0.2 N NaOH solution and diluted to 20 pM with Hyb Buffer following Illumina instructions for the Miseq.
    Miseq
    suggested: (A5-miseq, RRID:SCR_012148)
    MinION sequencing was controlled using MinKNOW™ software.
    MinION
    suggested: (MinION, RRID:SCR_017985)
    MinKNOW™
    suggested: None
    Data analysis: For each Illumina sequencing run, the base-calling and demultiplexing of reads by sample-id, was routinely performed by the “MiSeq Reporter” software [18].
    Reporter”
    suggested: None
    The resulting data was then transferred from the Illumina MiSeq computer to the Queen’s University Kelvin2 HPC compute cluster for analysis. (Optionally, if the sample-sheet required modification, the Illumina “bcl2fastq” [19] or “BCL Convert” [20] pipeline was used to base-call and demultiplex the data).
    Convert”
    suggested: None
    Data was collated and summarised using bash and python scripts.
    python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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.

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

    About SciScore

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  3. Version published to 10.1101/2021.09.29.21263685v1 on medRxiv