Evidence map›Paper›PMID 28330894›Full record

ArticleJournal of clinical microbiology2017

Targeted Enrichment for Pathogen Detection and Characterization in Three Felid Species.

Justin S Lee, Ryan S Mackie, Thomas Harrison, Basir Shariat, Trey Kind, Timo Kehl, Martin Löchelt, Christina Boucher, Sue VandeWoude

Open access · hybridAbstract readEvaluation Study
In one paragraph

Article in Journal of clinical microbiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
3.2field-weighted citation impact, top 9% 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

23 citing papers in PubMed, 48 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. OLTA: Optimizing bait seLection for TArgeted sequencing.Bioinformatics (Oxford, England) · 2025
    Article
  5. Article
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  7. Phylogenomic reconstruction ofbioRxiv : the preprint server for biology · 2024
    Article
  8. Article
  9. Article
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  11. Non-target RNA depletion strategy to improve sensitivity of next-generation sequencing for the detection of RNA viruses in poultry.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2022
    Article
  12. Syotti: scalable bait design for DNA enrichment.Bioinformatics (Oxford, England) · 2022
    Article
  13. Article
  14. Article
  15. Review
  16. Article
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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

9 authors at 2 institutions in 2 countries.

Justin S LeeDepartment of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA justinlee337@gmail.com.
Ryan S MackieDepartment of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA.
Thomas HarrisonDepartment of Computer Science, Colorado State University, Fort Collins, Colorado, USA.
Basir ShariatDepartment of Computer Science, Colorado State University, Fort Collins, Colorado, USA.
Trey KindDepartment of Computer Science, Colorado State University, Fort Collins, Colorado, USA.
Timo KehlGerman Cancer Research Center, Research Program in Infection, Inflammation and Cancer, Heidelberg, Germany.
Martin LöcheltGerman Cancer Research Center, Research Program in Infection, Inflammation and Cancer, Heidelberg, Germany.
Christina BoucherDepartment of Computer Science, Colorado State University, Fort Collins, Colorado, USA.
Sue VandeWoudeDepartment of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA.ORCID 0000-0001-9227-1622
Colorado State University · USGerman Cancer Research Center · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional diagnostic assays often lack sensitivity and can be difficult to multiplex across many pathogens. Next-generation sequencing (NGS) can overcome some of these problems but has limited application in the detection of low-copy-number pathogens in complex samples. Targeted genome capture (TGC) utilizes oligonucleotide probes to enrich specific nucleic acids in heterogeneous extracts and can therefore increase the proportion of NGS reads for low-abundance targets. While earlier studies have demonstrated the utility of this technology for detection of novel pathogens in human clinical samples, the capacity and practicality of TGC-NGS in a veterinary diagnostic setting have not yet been evaluated. Here we report the use of TGC-NGS assays for the detection and characterization of diverse feline pathogen taxa. We detected 31 pathogens comprising nine pathogen taxa in 28 felid samples analyzed. This included 20 pathogens detected via traditional PCR and 11 additional pathogens that had not been previously detected in the same samples. Most of the pathogens detected were sequenced at sufficient breadth and depth to confidently classify them at the species or subspecies level. Target nucleic acids were enriched from a low of 58-fold to 56 million-fold relative to host nucleic acids. Despite the promising performance of these assays, a number of pathogens detected by conventional PCR or serology were not isolated by TGC-NGS, suggesting that further validation is required before this technology can be used in lieu of quality-controlled standard assays. We conclude that TGC-NGS offers great potential as a broad multiplex pathogen characterization assay in veterinary diagnostic and research settings.

Indexed as

AnimalsBacterial InfectionsCat DiseasesCatsHigh-Throughput Nucleotide SequencingMolecular Diagnostic TechniquesNucleic Acid Amplification TechniquesVirus Diseasesmultiplex diagnostic assaynext-generation sequencingpathogen genomicstargeted enrichment

Identifiers

PMID28330894
PMCPMC5442522
OpenAlexW2603116829

What OpenQuestion holds

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LicenceCC BY
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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.