Publication: Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows
No Thumbnail Available
Open/View Files
Date
2021-12-14
Published Version
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media LLC
The Harvard community has made this article openly available. Please share how this access benefits you.
Citation
Lagerborg, Kim A, Erica Normandin, Matthew Bauer, Gordon Adams, Katherine Figueroa, Christine Loreth, Adrianne Gladden-Young et al. "Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows." Nat Microbiol 7, no. 1 (2021): 108-119. DOI: 10.1038/s41564-021-01019-2
Research Data
Abstract
The global spread and continued evolution of SARS-CoV-2 has driven an unprecedented surge in viral genomic surveillance. Amplicon-based sequencing methods provide a sensitive, low-cost and rapid approach but suffer a high potential for contamination, which can undermine laboratory processes and results. This challenge will only increase with expanding global production of sequences by diverse laboratories for epidemiological and clinical interpretation, as well in genomic surveillance in future outbreaks. We present SDSI+AmpSeq, an approach which uses synthetic DNA spike-ins (SDSIs) to track samples and detect inter-sample contamination through the sequencing workflow. Applying SDSIs to the ARTIC Consortium’s amplicon design, we demonstrate their utility and efficiency in a real-time investigation of a suspected hospital cluster of SARS-CoV-2 cases and across thousands of diagnostic samples at multiple laboratories. We establish that SDSI+AmpSeq provides increased confidence in genomic data by detecting and in some cases correcting for relatively common, yet previously unobserved modes of error without impacting genome recovery.
Description
SDSI+AmpSeq: A DNA spike-in tool to track samples and detect key modes of error in SARS-CoV-2 genomic data
Kim A. Lagerborg☨,1,2, Erica Normandin*,☨,1,3, Matthew R. Bauer☨,1,2, Gordon Adams1, Katherine Figueroa1, Christine Loreth1, Adrianne Gladden-Young1, Bennett Shaw1,4, Leah R. Pearlman1, Daniel Berenzy5, Hannah H Dewey5, Susan Kales5, Sabrina T. Dobbins1, Erica S. Shenoy4, David Hooper4, Virginia M. Pierce6,7,8, Kimon C. Zachary4,9,10, Daniel J. Park1, Bronwyn L. MacInnis1,11,12, Ryan Tewhey5,13,14, Jacob E. Lemieux1,4, Pardis C. Sabeti ⟊,1,3,11,12,15, Steven K Reilly *,⟊,1,3, Katherine J. Siddle⟊,1,3.
☨,⟊ denote equal contribution. *denotes corresponding author.
Other Available Sources
Keywords
Cell Biology, Microbiology (medical), Genetics, Applied Microbiology and Biotechnology, Immunology, Microbiology
Terms of Use
Metadata Only