Evidence map›Paper›PMID 32907806›Full record

ReviewClinical microbiology reviews2020

Performance and Application of 16S rRNA Gene Cycle Sequencing for Routine Identification of Bacteria in the Clinical Microbiology Laboratory.

Deirdre L Church, Lorenzo Cerutti, Antoine Gürtler, Thomas Griener, Adrian Zelazny, Stefan Emler

Open access · greenAbstract readReview
In one paragraph

Review in Clinical microbiology reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 177 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
177citing papers in PubMed, 2 pooled it
16.7field-weighted citation impact, top 1% 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

177 citing papers in PubMed, 2 syntheses or guidelines pooled it, 312 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Trial
  5. Observational
  6. Review
  7. Article
  8. Article
  9. Detection ofASM case reports · 2026
    Article
  10. Article
  11. Review
  12. Characterization ofAntibiotics (Basel, Switzerland) · 2026
    Article
  13. First Report ofMicroorganisms · 2026
    Article
  14. Article
  15. Screening ofMicroorganisms · 2026
    Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

117 more citing papers are in PubMed but not listed here.

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

6 authors at 3 institutions in 3 countries.

Deirdre L ChurchDepartments of Pathology and Laboratory Medicine, University of Calgary, Calgary, Alberta, Canada dchurch@ucalgary.ca.ORCID 0000-0002-8306-8099
Lorenzo CeruttiHealth 2030 Genome Center, Foundation Campus Biotech Geneva, Geneva, Switzerland.
Antoine GürtlerSmartGene Services, Lausanne, Switzerland.
Thomas GrienerDepartments of Pathology and Laboratory Medicine, University of Calgary, Calgary, Alberta, Canada.
Adrian ZelaznyNational Institutes of Health, Bethesda, Maryland, USA.
Stefan EmlerSmartGene Services, Lausanne, Switzerland.
University of Calgary · CAUniversity of Lausanne · CHNational Institutes of Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review provides a state-of-the-art description of the performance of Sanger cycle sequencing of the 16S rRNA gene for routine identification of bacteria in the clinical microbiology laboratory. A detailed description of the technology and current methodology is outlined with a major focus on proper data analyses and interpretation of sequences. The remainder of the article is focused on a comprehensive evaluation of the application of this method for identification of bacterial pathogens based on analyses of 16S multialignment sequences. In particular, the existing limitations of similarity within 16S for genus- and species-level differentiation of clinically relevant pathogens and the lack of sequence data currently available in public databases is highlighted. A multiyear experience is described of a large regional clinical microbiology service with direct 16S broad-range PCR followed by cycle sequencing for direct detection of pathogens in appropriate clinical samples. The ability of proteomics (matrix-assisted desorption ionization-time of flight) versus 16S sequencing for bacterial identification and genotyping is compared. Finally, the potential for whole-genome analysis by next-generation sequencing (NGS) to replace 16S sequencing for routine diagnostic use is presented for several applications, including the barriers that must be overcome to fully implement newer genomic methods in clinical microbiology. A future challenge for large clinical, reference, and research laboratories, as well as for industry, will be the translation of vast amounts of accrued NGS microbial data into convenient algorithm testing schemes for various applications (i.e., microbial identification, genotyping, and metagenomics and microbiome analyses) so that clinically relevant information can be reported to physicians in a format that is understood and actionable. These challenges will not be faced by clinical microbiologists alone but by every scientist involved in a domain where natural diversity of genes and gene sequences plays a critical role in disease, health, pathogenicity, epidemiology, and other aspects of life-forms. Overcoming these challenges will require global multidisciplinary efforts across fields that do not normally interact with the clinical arena to make vast amounts of sequencing data clinically interpretable and actionable at the bedside.

Indexed as

BacteriaBacterial InfectionsBacterial Typing TechniquesClinical Laboratory TechniquesHumansRNA, Ribosomal, 16SRNA, Ribosomal, 16S16S rRNAbacteriacycle sequencingidentification

Identifiers

PMID32907806
PMCPMC7484979
OpenAlexW3084291069

What OpenQuestion holds

Textmetadata
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.