Literature DB >> 34907347

Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows.

Kim A Lagerborg1,2, Erica Normandin1,3, Matthew R Bauer1,2, Gordon Adams1, Katherine Figueroa1, Christine Loreth1, Adrianne Gladden-Young1, Bennett M Shaw1,4, Leah R Pearlman1, Daniel Berenzy5, Hannah B 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 Sabeti1,3,11,12,15, Steven K Reilly16,17, Katherine J Siddle1,3.   

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 increase with the expanding global production of sequences across a variety of laboratories for epidemiological and clinical interpretation, as well as for genomic surveillance of emerging diseases in future outbreaks. We present SDSI + AmpSeq, an approach that uses 96 synthetic DNA spike-ins (SDSIs) to track samples and detect inter-sample contamination throughout the sequencing workflow. We apply SDSIs to the ARTIC Consortium's amplicon design, demonstrate their utility and efficiency in a real-time investigation of a suspected hospital cluster of SARS-CoV-2 cases and validate them across 6,676 diagnostic samples at multiple laboratories. We establish that SDSI + AmpSeq provides increased confidence in genomic data by detecting and correcting for relatively common, yet previously unobserved modes of error, including spillover and sample swaps, without impacting genome recovery.
© 2021. The Author(s), under exclusive licence to Springer Nature Limited.

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Year:  2021        PMID: 34907347      PMCID: PMC8923058          DOI: 10.1038/s41564-021-01019-2

Source DB:  PubMed          Journal:  Nat Microbiol        ISSN: 2058-5276            Impact factor:   30.964


  20 in total

Review 1.  Mass spectrometry-based targeted quantitative proteomics: achieving sensitive and reproducible detection of proteins.

Authors:  Emily S Boja; Henry Rodriguez
Journal:  Proteomics       Date:  2012-04       Impact factor: 3.984

2.  Synthetic spike-in standards for RNA-seq experiments.

Authors:  Lichun Jiang; Felix Schlesinger; Carrie A Davis; Yu Zhang; Renhua Li; Marc Salit; Thomas R Gingeras; Brian Oliver
Journal:  Genome Res       Date:  2011-08-04       Impact factor: 9.043

3.  High-Throughput Measure of Bioactive Lipids Using Non-targeted Mass Spectrometry.

Authors:  Kim A Lagerborg; Jeramie D Watrous; Susan Cheng; Mohit Jain
Journal:  Methods Mol Biol       Date:  2019

4.  SARS-CoV-2 Variants of Concern in the United States-Challenges and Opportunities.

Authors:  Rochelle P Walensky; Henry T Walke; Anthony S Fauci
Journal:  JAMA       Date:  2021-03-16       Impact factor: 157.335

5.  Antibody resistance of SARS-CoV-2 variants B.1.351 and B.1.1.7.

Authors:  Pengfei Wang; Manoj S Nair; Lihong Liu; Sho Iketani; Yang Luo; Yicheng Guo; Maple Wang; Jian Yu; Baoshan Zhang; Peter D Kwong; Barney S Graham; John R Mascola; Jennifer Y Chang; Michael T Yin; Magdalena Sobieszczyk; Christos A Kyratsous; Lawrence Shapiro; Zizhang Sheng; Yaoxing Huang; David D Ho
Journal:  Nature       Date:  2021-03-08       Impact factor: 69.504

6.  Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China.

Authors:  Akira Endo; Sam Abbott; Adam J Kucharski; Sebastian Funk
Journal:  Wellcome Open Res       Date:  2020-07-10

7.  Evaluation of NGS-based approaches for SARS-CoV-2 whole genome characterisation.

Authors:  Caroline Charre; Christophe Ginevra; Marina Sabatier; Hadrien Regue; Grégory Destras; Solenne Brun; Gwendolyne Burfin; Caroline Scholtes; Florence Morfin; Martine Valette; Bruno Lina; Antonin Bal; Laurence Josset
Journal:  Virus Evol       Date:  2020-10-05

8.  Increased resistance of SARS-CoV-2 variant P.1 to antibody neutralization.

Authors:  Pengfei Wang; Ryan G Casner; Manoj S Nair; Maple Wang; Jian Yu; Gabriele Cerutti; Lihong Liu; Peter D Kwong; Yaoxing Huang; Lawrence Shapiro; David D Ho
Journal:  Cell Host Microbe       Date:  2021-04-18       Impact factor: 21.023

9.  Emergence and rapid transmission of SARS-CoV-2 B.1.1.7 in the United States.

Authors:  Nicole L Washington; Karthik Gangavarapu; Mark Zeller; Alexandre Bolze; Elizabeth T Cirulli; Kelly M Schiabor Barrett; Brendan B Larsen; Catelyn Anderson; Simon White; Tyler Cassens; Sharoni Jacobs; Geraint Levan; Jason Nguyen; Jimmy M Ramirez; Charlotte Rivera-Garcia; Efren Sandoval; Xueqing Wang; David Wong; Emily Spencer; Refugio Robles-Sikisaka; Ezra Kurzban; Laura D Hughes; Xianding Deng; Candace Wang; Venice Servellita; Holly Valentine; Peter De Hoff; Phoebe Seaver; Shashank Sathe; Kimberly Gietzen; Brad Sickler; Jay Antico; Kelly Hoon; Jingtao Liu; Aaron Harding; Omid Bakhtar; Tracy Basler; Brett Austin; Duncan MacCannell; Magnus Isaksson; Phillip G Febbo; David Becker; Marc Laurent; Eric McDonald; Gene W Yeo; Rob Knight; Louise C Laurent; Eileen de Feo; Michael Worobey; Charles Y Chiu; Marc A Suchard; James T Lu; William Lee; Kristian G Andersen
Journal:  Cell       Date:  2021-03-30       Impact factor: 41.582

10.  Low genetic diversity may be an Achilles heel of SARS-CoV-2.

Authors:  Jason W Rausch; Adam A Capoferri; Mary Grace Katusiime; Sean C Patro; Mary F Kearney
Journal:  Proc Natl Acad Sci U S A       Date:  2020-09-21       Impact factor: 11.205

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  1 in total

1.  Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences.

Authors:  Bahrad A Sokhansanj; Gail L Rosen
Journal:  mSystems       Date:  2022-03-21       Impact factor: 7.324

  1 in total

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