Evidence map›Paper›PMID 39772804›Full record

ArticlemSystems2025

Targeted sequencing of

Hugh Cottingham, Louise M Judd, Jessica A Wisniewski, Ryan R Wick, Thomas D Stanton, Ben Vezina, Nenad Macesic, Anton Y Peleg, Iruka N Okeke, Kathryn E Holt and 1 more

Abstract read
In one paragraph

Article in mSystems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Article
  10. Article
  11. Transgene Mapping in Animals: What to Choose?International journal of molecular sciences · 2025
    Review
  12. 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

11 authors.

Hugh CottinghamDepartment of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.ORCID 0000-0002-5156-1970
Louise M JuddDepartment of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.
Jessica A WisniewskiDepartment of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.
Ryan R WickDepartment of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.
Thomas D StantonDepartment of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.
Ben VezinaDepartment of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.
Nenad MacesicDepartment of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.
Anton Y PelegDepartment of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.ORCID 0000-0002-2296-2126
Iruka N OkekeDepartment of Pharmaceutical Microbiology, Faculty of Pharmacy, University of Ibadan, Ibadan, Nigeria.
Kathryn E Holt *Department of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.ORCID 0000-0003-3949-2471
Jane Hawkey *Department of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.ORCID 0000-0001-9661-5293

Funding

Bill and Melinda Gates Foundation (GF) OPP1210746Gates Foundation INV-036234
6 · The paper itself

Abstract

Sequencing DNA directly from patient samples enables faster pathogen characterization compared to traditional culture-based approaches, but often yields insufficient sequence data for effective downstream analysis. CRISPR-Cas9 enrichment is designed to improve the yield of low abundance sequences but has not been thoroughly explored with Oxford Nanopore Technologies (ONT) for use in clinical bacterial epidemiology. We designed CRISPR-Cas9 guide RNAs to enrich the human pathogen IMPORTANCE: Understanding bacteria in complex samples can be challenging due to their low abundance, which often results in insufficient data for analysis. To improve the detection of harmful bacteria, we implemented a technique aimed at increasing the amount of data from target pathogens when combined with modern DNA sequencing technologies. Our technique uses CRISPR-Cas9 to target specific gene sequences in the bacterial pathogen

Indexed as

CRISPR-Cas SystemsKlebsiella pneumoniaeNanopore SequencingDrug Resistance, BacterialFecesHigh-Throughput Nucleotide SequencingHumansMultilocus Sequence TypingNanoporesRNA, Guide, CRISPR-Cas SystemsSequence Analysis, DNARNA, Guide, CRISPR-Cas SystemsCRISPR-Cas9 enrichmentEnterobacteralesKlebsiellametagenomicsOxford Nanopore

Identifiers

PMID39772804
PMCPMC11834407

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.