Author
Listed:
- Michele Simonetti
(Karolinska Institutet
Science for Life Laboratory)
- Ning Zhang
(Karolinska Institutet
Science for Life Laboratory
Qilu hospital of Shandong University)
- Luuk Harbers
(Karolinska Institutet
Science for Life Laboratory)
- Maria Grazia Milia
(Ospedale ‘Amedeo di Savoia’)
- Silvia Brossa
(Instituto di Candiolo FPO—IRCCS, Candiolo)
- Thi Thu Huong Nguyen
(Karolinska Institutet
Science for Life Laboratory)
- Francesco Cerutti
(Ospedale ‘Amedeo di Savoia’)
- Enrico Berrino
(Instituto di Candiolo FPO—IRCCS, Candiolo
University of Turin)
- Anna Sapino
(Instituto di Candiolo FPO—IRCCS, Candiolo
University of Turin)
- Magda Bienko
(Karolinska Institutet
Science for Life Laboratory)
- Antonino Sottile
(Instituto di Candiolo FPO—IRCCS, Candiolo)
- Valeria Ghisetti
(Ospedale ‘Amedeo di Savoia’)
- Nicola Crosetto
(Karolinska Institutet
Science for Life Laboratory)
Abstract
While mass-scale vaccination campaigns are ongoing worldwide, genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical to monitor the emergence and global spread of viral variants of concern (VOC). Here, we present a streamlined workflow—COVseq—which can be used to generate highly multiplexed sequencing libraries compatible with Illumina platforms from hundreds of SARS-CoV-2 samples in parallel, in a rapid and cost-effective manner. We benchmark COVseq against a standard library preparation method (NEBNext) on 29 SARS-CoV-2 positive samples, reaching 95.4% of concordance between single-nucleotide variants detected by both methods. Application of COVseq to 245 additional SARS-CoV-2 positive samples demonstrates the ability of the method to reliably detect emergent VOC as well as its compatibility with downstream phylogenetic analyses. A cost analysis shows that COVseq could be used to sequence thousands of samples at less than 15 USD per sample, including library preparation and sequencing costs. We conclude that COVseq is a versatile and scalable method that is immediately applicable for SARS-CoV-2 genomic surveillance and easily adaptable to other pathogens such as influenza viruses.
Suggested Citation
Michele Simonetti & Ning Zhang & Luuk Harbers & Maria Grazia Milia & Silvia Brossa & Thi Thu Huong Nguyen & Francesco Cerutti & Enrico Berrino & Anna Sapino & Magda Bienko & Antonino Sottile & Valeria, 2021.
"COVseq is a cost-effective workflow for mass-scale SARS-CoV-2 genomic surveillance,"
Nature Communications, Nature, vol. 12(1), pages 1-10, December.
Handle:
RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24078-9
DOI: 10.1038/s41467-021-24078-9
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