Evidence map›Paper›PMID 35202411›Full record

ArticlePloS one2022

Ranking the biases: The choice of OTUs vs. ASVs in 16S rRNA amplicon data analysis has stronger effects on diversity measures than rarefaction and OTU identity threshold.

Marlène Chiarello, Mark McCauley, Sébastien Villéger, Colin R Jackson

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
106citing papers in PubMed, 7 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

106 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  6. Effect of bovine respiratory disease on the respiratory microbiome: a meta-analysis.Frontiers in cellular and infection microbiology · 2023
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46 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

4 authors.

Marlène ChiarelloDepartment of Biology, University of Mississippi, University, MS, United States of America.ORCID 0000-0001-6588-7077
Mark McCauleyDepartment of Biology, University of Mississippi, University, MS, United States of America.
Sébastien VillégerMARBEC, University of Montpellier, CNRS, Ifremer, IRD, Montpellier, France.
Colin R JacksonDepartment of Biology, University of Mississippi, University, MS, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advances in the analysis of amplicon sequence datasets have introduced a methodological shift in how research teams investigate microbial biodiversity, away from sequence identity-based clustering (producing Operational Taxonomic Units, OTUs) to denoising methods (producing amplicon sequence variants, ASVs). While denoising methods have several inherent properties that make them desirable compared to clustering-based methods, questions remain as to the influence that these pipelines have on the ecological patterns being assessed, especially when compared to other methodological choices made when processing data (e.g. rarefaction) and computing diversity indices. We compared the respective influences of two widely used methods, namely DADA2 (a denoising method) vs. Mothur (a clustering method) on 16S rRNA gene amplicon datasets (hypervariable region v4), and compared such effects to the rarefaction of the community table and OTU identity threshold (97% vs. 99%) on the ecological signals detected. We used a dataset comprising freshwater invertebrate (three Unionidae species) gut and environmental (sediment, seston) communities sampled in six rivers in the southeastern USA. We ranked the respective effects of each methodological choice on alpha and beta diversity, and taxonomic composition. The choice of the pipeline significantly influenced alpha and beta diversities and changed the ecological signal detected, especially on presence/absence indices such as the richness index and unweighted Unifrac. Interestingly, the discrepancy between OTU and ASV-based diversity metrics could be attenuated by the use of rarefaction. The identification of major classes and genera also revealed significant discrepancies across pipelines. Compared to the pipeline's effect, OTU threshold and rarefaction had a minimal impact on all measurements.

Indexed as

BiodiversityData AnalysisGastrointestinal MicrobiomeGenetic VariationAnimalsBiasBivalviaCluster AnalysisDatasets as TopicDNA, BacterialInvertebratesRiversRNA, Ribosomal, 16SSequence Analysis, DNADNA, BacterialRNA, Ribosomal, 16S

Identifiers

PMID35202411
PMCPMC8870492

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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.