Evidence map›Paper›PMID 25915756›Full record

ArticlePloS one2015

Estimating bacterial diversity for ecological studies: methods, metrics, and assumptions.

Julia Birtel, Jean-Claude Walser, Samuel Pichon, Helmut Bürgmann, Blake Matthews

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
–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

30 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Microbiota in Periodontitis: Advances in the Omic Era.Advances in experimental medicine and biology · 2022
    Review
  6. Article
  7. Evaluating tannery wastewater treatment performance based on physicochemical and microbiological characteristics: An Ethiopian case study.Water environment research : a research publication of the Water Environment Federation · 2021
    Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults.Proceedings of the National Academy of Sciences of the United States of America · 2018
    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

5 authors.

Julia BirtelEawag, Department of Aquatic Ecology, Kastanienbaum, Switzerland; Department of Environmental Systems Sciences (D-USYS), Swiss Federal Insitute of Technology (ETH), Zürich, Switzerland.
Jean-Claude WalserGenetic Diversity Centre (GDC), Department of Environmental System Sciences (D-USYS), Swiss Federal Institute of Technology (ETH), Zürich, Switzerland.
Samuel PichonDepartment of Environmental Sciences, Zoology and Evolution, Universität Basel, Basel, Switzerland.
Helmut BürgmannEawag, Department of Surface Waters, Kastanienbaum, Switzerland.
Blake MatthewsEawag, Department of Aquatic Ecology, Kastanienbaum, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Methods to estimate microbial diversity have developed rapidly in an effort to understand the distribution and diversity of microorganisms in natural environments. For bacterial communities, the 16S rRNA gene is the phylogenetic marker gene of choice, but most studies select only a specific region of the 16S rRNA to estimate bacterial diversity. Whereas biases derived from from DNA extraction, primer choice and PCR amplification are well documented, we here address how the choice of variable region can influence a wide range of standard ecological metrics, such as species richness, phylogenetic diversity, β-diversity and rank-abundance distributions. We have used Illumina paired-end sequencing to estimate the bacterial diversity of 20 natural lakes across Switzerland derived from three trimmed variable 16S rRNA regions (V3, V4, V5). Species richness, phylogenetic diversity, community composition, β-diversity, and rank-abundance distributions differed significantly between 16S rRNA regions. Overall, patterns of diversity quantified by the V3 and V5 regions were more similar to one another than those assessed by the V4 region. Similar results were obtained when analyzing the datasets with different sequence similarity thresholds used during sequences clustering and when the same analysis was used on a reference dataset of sequences from the Greengenes database. In addition we also measured species richness from the same lake samples using ARISA Fingerprinting, but did not find a strong relationship between species richness estimated by Illumina and ARISA. We conclude that the selection of 16S rRNA region significantly influences the estimation of bacterial diversity and species distributions and that caution is warranted when comparing data from different variable regions as well as when using different sequencing techniques.

Indexed as

BacteriaBiodiversityMicrobiotaEcologyLakesRNA, Ribosomal, 16SRNA, Ribosomal, 16S

Identifiers

PMID25915756
PMCPMC4411174

What OpenQuestion holds

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Registered trials

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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.