Evidence map›Paper›PMID 37323571›Full record

ArticleCell reports methods2023

A CRISPR-enhanced metagenomic NGS test to improve pandemic preparedness.

Agnes P Chan, Azeem Siddique, Yvain Desplat, Yongwook Choi, Sridhar Ranganathan, Kumari Sonal Choudhary, M Faizan Khalid, Josh Diaz, Jon Bezney, Dante DeAscanis and 11 more

Open access · goldAbstract read
In one paragraph

Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
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

21 authors at 3 institutions in 1 country.

Agnes P ChanThe Translational Genomics Research Institute (TGen), An Affiliate of the City of Hope National Medical Center, Phoenix, AZ 85004, USA.
Azeem SiddiqueJumpcode Genomics, San Diego, CA 92121, USA.
Yvain DesplatJumpcode Genomics, San Diego, CA 92121, USA.
Yongwook ChoiThe Translational Genomics Research Institute (TGen), An Affiliate of the City of Hope National Medical Center, Phoenix, AZ 85004, USA.
Sridhar RanganathanJumpcode Genomics, San Diego, CA 92121, USA.
Kumari Sonal ChoudharyJumpcode Genomics, San Diego, CA 92121, USA.
M Faizan KhalidJumpcode Genomics, San Diego, CA 92121, USA.
Josh DiazJumpcode Genomics, San Diego, CA 92121, USA.
Jon BezneyJumpcode Genomics, San Diego, CA 92121, USA.
Dante DeAscanisJumpcode Genomics, San Diego, CA 92121, USA.
Zenas GeorgeJumpcode Genomics, San Diego, CA 92121, USA.
Shukmei WongThe Translational Genomics Research Institute (TGen), An Affiliate of the City of Hope National Medical Center, Phoenix, AZ 85004, USA.
William SelleckThe Translational Genomics Research Institute (TGen), An Affiliate of the City of Hope National Medical Center, Phoenix, AZ 85004, USA.
Jolene BowersThe Translational Genomics Research Institute (TGen), An Affiliate of the City of Hope National Medical Center, Phoenix, AZ 85004, USA.
Victoria ZismannThe Translational Genomics Research Institute (TGen), An Affiliate of the City of Hope National Medical Center, Phoenix, AZ 85004, USA.
Lauren ReiningThe Translational Genomics Research Institute (TGen), An Affiliate of the City of Hope National Medical Center, Phoenix, AZ 85004, USA.
Sarah HighlanderThe Translational Genomics Research Institute (TGen), An Affiliate of the City of Hope National Medical Center, Phoenix, AZ 85004, USA.
Keith BrownJumpcode Genomics, San Diego, CA 92121, USA.
Jon R ArmstrongJumpcode Genomics, San Diego, CA 92121, USA.
Yaron HakakJumpcode Genomics, San Diego, CA 92121, USA.
Nicholas J SchorkThe Translational Genomics Research Institute (TGen), An Affiliate of the City of Hope National Medical Center, Phoenix, AZ 85004, USA.
Pathway Genomics (United States) · USTranslational Genomics Research Institute · USScripps Research Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The lack of preparedness for detecting and responding to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogen (i.e., COVID-19) has caused enormous harm to public health and the economy. Testing strategies deployed on a population scale at day zero, i.e., the time of the first reported case, would be of significant value. Next-generation sequencing (NGS) has such capabilities; however, it has limited detection sensitivity for low-copy-number pathogens. Here, we leverage the CRISPR-Cas9 system to effectively remove abundant sequences not contributing to pathogen detection and show that NGS detection sensitivity of SARS-CoV-2 approaches that of RT-qPCR. The resulting sequence data can also be used for variant strain typing, co-infection detection, and individual human host response assessment, all in a single molecular and analysis workflow. This NGS work flow is pathogen agnostic and, therefore, has the potential to transform how large-scale pandemic response and focused clinical infectious disease testing are pursued in the future.

Indexed as

Communicable DiseasesCOVID-19High-Throughput Nucleotide SequencingHumansPandemicsSARS-CoV-2CRISPRinfectious diseasemetatranscriptomicsNGSpathogen detection

Identifiers

PMID37323571
PMCPMC10110940
OpenAlexW4366286156

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.