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Evaluation of miniaturized Illumina DNA preparation protocols for SARS-CoV-2 whole genome sequencing.

Sureshnee PillayJames Emmanuel SanDerek TshiabuilaYeshnee NaidooYusasha PillayAkhil MaharajUgochukwu J AnyanejiEduan WilkinsonHouriiyah TegallyRichard J LessellsCheryl BaxterTulio de OliveiraJennifer Giandhari
Published in: PloS one (2023)
The global pandemic caused by SARS-CoV-2 has increased the demand for scalable sequencing and diagnostic methods, especially for genomic surveillance. Although next-generation sequencing has enabled large-scale genomic surveillance, the ability to sequence SARS-CoV-2 in some settings has been limited by the cost of sequencing kits and the time-consuming preparations of sequencing libraries. We compared the sequencing outcomes, cost and turn-around times obtained using the standard Illumina DNA Prep kit protocol to three modified protocols with fewer clean-up steps and different reagent volumes (full volume, half volume, one-tenth volume). We processed a single run of 47 samples under each protocol and compared the yield and mean sequence coverage. The sequencing success rate and quality for the four different reactions were as follows: the full reaction was 98.2%, the one-tenth reaction was 98.0%, the full rapid reaction was 97.5% and the half-reaction, was 97.1%. As a result, uniformity of sequence quality indicated that libraries were not affected by the change in protocol. The cost of sequencing was reduced approximately seven-fold and the time taken to prepare the library was reduced from 6.5 hours to 3 hours. The sequencing results obtained using the miniaturised volumes showed comparability to the results obtained using full volumes as described by the manufacturer. The adaptation of the protocol represents a lower-cost, streamlined approach for SARS-CoV-2 sequencing, which can be used to produce genomic data quickly and more affordably, especially in resource-constrained settings.
Keyphrases
  • sars cov
  • single cell
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  • respiratory syndrome coronavirus
  • copy number
  • machine learning
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  • deep learning
  • data analysis
  • molecularly imprinted