![]() ![]() The development and increased accessibility of high-throughput sequencing technologies, has supported the advancement of large-scale assessments of microbial diversity over the past decade. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.Įvaluating the microbial diversity of various environments, from host-associated microbiomes to free-living communities in water, soil, and air, is essential for understanding biodiversity and improving human health and agriculture. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Bacterial sequences for this paper are available in the NCBI Sequence Read Archive database ( ), under the BioSample numbers SAMN19838587-SAMN19838803.įunding: Funding was provided from National Science Foundation ( ) award DEB 1831531 to C.R.J. ![]() Received: OctoAccepted: FebruPublished: February 24, 2022Ĭopyright: © 2022 Chiarello et al. PLoS ONE 17(2):Įditor: Gabriel Moreno-Hagelsieb, Wilfrid Laurier University, CANADA ASVs in 16S rRNA amplicon data analysis has stronger effects on diversity measures than rarefaction and OTU identity threshold. Compared to the pipeline’s effect, OTU threshold and rarefaction had a minimal impact on all measurements.Ĭitation: Chiarello M, McCauley M, Villéger S, Jackson CR (2022) Ranking the biases: The choice of OTUs vs. The identification of major classes and genera also revealed significant discrepancies across pipelines. Interestingly, the discrepancy between OTU and ASV-based diversity metrics could be attenuated by the use of rarefaction. 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. We ranked the respective effects of each methodological choice on alpha and beta diversity, and taxonomic composition. We used a dataset comprising freshwater invertebrate (three Unionidae species) gut and environmental (sediment, seston) communities sampled in six rivers in the southeastern USA. 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. We compared the respective influences of two widely used methods, namely DADA2 (a denoising method) vs. rarefaction) and computing diversity indices. 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. 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). ![]()
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