Evidence map›Paper›PMID 33758855›Full record

ArticlebioRxiv : the preprint server for biology2021

DNA spike-ins enable confident interpretation of SARS-CoV-2 genomic data from amplicon-based sequencing.

Kim A Lagerborg, Erica Normandin, Matthew R Bauer, Gordon Adams, Katherine Figueroa, Christine Loreth, Adrianne Gladden-Young, Bennett Shaw, Leah Pearlman, Erica S Shenoy and 9 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Kim A LagerborgBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Erica NormandinBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Matthew R BauerBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Gordon AdamsBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Katherine FigueroaBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Christine LorethBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Adrianne Gladden-YoungBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Bennett ShawBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Leah PearlmanBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Erica S ShenoyDivision of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA.
David HooperDivision of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA.
Virginia M PierceDepartment of Pathology, Massachusetts General Hospital, Boston, MA, USA.
Kimon C ZacharyDivision of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA.
Daniel J ParkBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Bronwyn L MacInnisBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Jacob E LemieuxBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Pardis C SabetiBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Steven K ReillyBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
Katherine J SiddleBroad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.

Funding

Viral Genomics: evolution, spread, and host interactionsU19AI110818 · NIAID · BROAD INSTITUTE, INC. · PI NEAFSEY, DANIEL E · 2014 to 2024
$66.5M
Comprehensive Characterization of Adaptive Regulatory Variation Linked to Human DiseaseK99HG010669 · NHGRI · BROAD INSTITUTE, INC. · PI REILLY, STEVEN K. · 2019 to 2020
$250k
NHGRI NIH HHS K99 HG010669NIAID NIH HHS U19 AI110818
6 · The paper itself

Abstract

The rapid global spread and continued evolution of SARS-CoV-2 has highlighted an unprecedented need for viral genomic surveillance and clinical viral sequencing. Amplicon-based sequencing methods provide a sensitive, low-cost and rapid approach but suffer a high potential for contamination, which can undermine lab processes and results. This challenge will only increase with expanding global production of sequences by diverse research groups for epidemiological and clinical interpretation. We present an approach which uses synthetic DNA spike-ins (SDSIs) to track samples and detect inter-sample contamination through a sequencing workflow. Applying this approach to the ARTIC Consortium's amplicon design, we define a series of best practices for Illumina-based sequencing and provide a detailed characterization of approaches to increase sensitivity for low-viral load samples incorporating the SDSIs. We demonstrate the utility and efficiency of the SDSI method amidst a real-time investigation of a suspected hospital cluster of SARS-CoV-2 cases.

Identifiers

PMID33758855
PMCPMC7987014

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.