Evidence map›Paper›PMID 27822541›Full record

ArticlemSystems

16S rRNA Amplicon Sequencing for Epidemiological Surveys of Bacteria in Wildlife.

Maxime Galan, Maria Razzauti, Emilie Bard, Maria Bernard, Carine Brouat, Nathalie Charbonnel, Alexandre Dehne-Garcia, Anne Loiseau, Caroline Tatard, Lucie Tamisier and 3 more

Open access · goldAbstract read
In one paragraph

Article in mSystems. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed
18.2field-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

45 citing papers in PubMed, 167 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Grappling with the tick microbiome.Trends in parasitology · 2021
    Review
  18. Article
  19. Article
  20. 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

13 authors at 6 institutions in 1 country.

Maxime GalanINRA, CBGP, Montferrier sur Lez, France.
Maria RazzautiINRA, CBGP, Montferrier sur Lez, France.
Emilie BardINRA, EpiA, Clermont-Ferrand, France.
Maria BernardINRA, Sigenae, France; INRA, GABI, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.
Carine BrouatIRD, CBGP, Montferrier sur Lez, France.
Nathalie CharbonnelINRA, CBGP, Montferrier sur Lez, France.
Alexandre Dehne-GarciaINRA, CBGP, Montferrier sur Lez, France.
Anne LoiseauINRA, CBGP, Montferrier sur Lez, France.
Caroline TatardINRA, CBGP, Montferrier sur Lez, France.
Lucie TamisierINRA, CBGP, Montferrier sur Lez, France.
Muriel Vayssier-TaussatINRA, Bipar, Maisons-Alfort, France.
Helene VignesCIRAD, AGAP, Montpellier, France.
Jean-François CossonINRA, CBGP, Montferrier sur Lez, France; INRA, Bipar, Maisons-Alfort, France.ORCID 0000-0003-0863-5871
Agropolis International · FRAgroParisTech · FRBipar · FRCentre de Biologie pour la Gestion des Populations · FRCentre de Coopération Internationale en Recherche Agronomique pour le Développement · FRInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The human impact on natural habitats is increasing the complexity of human-wildlife interactions and leading to the emergence of infectious diseases worldwide. Highly successful synanthropic wildlife species, such as rodents, will undoubtedly play an increasingly important role in transmitting zoonotic diseases. We investigated the potential for recent developments in 16S rRNA amplicon sequencing to facilitate the multiplexing of the large numbers of samples needed to improve our understanding of the risk of zoonotic disease transmission posed by urban rodents in West Africa. In addition to listing pathogenic bacteria in wild populations, as in other high-throughput sequencing (HTS) studies, our approach can estimate essential parameters for studies of zoonotic risk, such as prevalence and patterns of coinfection within individual hosts. However, the estimation of these parameters requires cleaning of the raw data to mitigate the biases generated by HTS methods. We present here an extensive review of these biases and of their consequences, and we propose a comprehensive trimming strategy for managing these biases. We demonstrated the application of this strategy using 711 commensal rodents, including 208

Indexed as

bacteriaemerging infectious diseaseshigh-throughput sequencingmetabarcodingmolecular epidemiologynext-generation sequencingrodentsWest Africazoonoses

Identifiers

PMID27822541
PMCPMC5069956
OpenAlexW2503886882

What OpenQuestion 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 OpenQuestion graph.