Accuracy of microbial community diversity estimated by closed- and open-reference OTUs

1. Human land uses, such as agriculture, can leave long-lasting legacies as ecosystems recover. As a consequence, active restoration may be necessary to overcome landuse legacies; however, few studies have evaluated the joint effects of agricultural history and restoration on ecological communities. Those that have studied this joint effect have largely focused on plants and ignored other communities, such as soil microbes. 2. We conducted a large-scale experiment to understand how agricultural history and restoration tree thinning affect soil bacterial and fungal communities within longleaf pine savannas of the southern United States. This experiment contained 64 pairs of remnant (no history of tillage agriculture) and post-agricultural (reforested following abandonment from tillage agriculture >60 years prior) longleaf pine savanna plots. Plots were each 1 ha and arranged into 27 blocks to minimize land-use decision-making biases. We experimentally restored half of the remnant and post-agricultural plots by thinning trees to reinstate open-canopy savanna conditions and collected soils from all plots five growing seasons after tree thinning. We then evaluated soil bacterial and fungal communities using metabarcoding. 3. Agricultural history increased bacterial diversity but decreased fungal diversity, while restoration increased both bacterial and fungal diversity. Both bacterial and fungal richness were correlated with a range of environmental variables including above-ground variables like leaf litter and plant diversity, and below-ground variables such as soil nutrients, pH and organic matter, many of which were also impacted by agricultural history and restoration. 4. Fungal and bacterial community compositions were shaped by restoration and agricultural history resulting in four distinct communities across the four treatment combinations. 5. Synthesis and applications. Past agricultural land use has left persistent legacies on soil microbial biodiversity, even over half a century after agricultural abandonment and after intensive restoration activities. The impacts of these changes on soil microbe biodiversity could influence native plant establishment, plant productivity and other aspects of ecosystem functioning following agricultural abandonment and during restoration.

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“…ting due to open reference OTU clustering (Edgar, 2017), we ran the same PERMANOVA model on the bacterial Unifrac distance matrix.…”

Section: Discussionmentioning

confidence: 99%

Agricultural land‐use history and restoration impact soil microbial biodiversity

Turley

Bell‐Dereske

Evans

et al. 2020

Journal of Applied Ecology

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“…Metabarcoding has become the most popular method to "scale up" ecological studies related to biodiversity (Porter & Hajibabaei, 2018), especially for microscopic organisms (Bucklin et al, 2016;Yu et al, 2012). The clustering strategy for the generation of OTU, a critical technical point in metabarcoding analysis, has been highly discussed and updated (Amir et al, 2017;Callahan et al, 2016;Edgar, 2016Edgar, , 2017). Here, we tested effects of different clustering scenarios on inferring community-environment interactions in multiple aspects.…”

Section: Discussionmentioning

confidence: 99%

Testing clustering strategies for metabarcoding‐based investigation of community–environment interactions

Xiong

Zhan

2018

Molecular Ecology Resources

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The degradation of freshwater ecosystems has become a common ecological and environmental problem globally. Owing to the complexity of biological communities, there remain tremendous technical challenges for investigating influence of environmental stressors (e.g., chemical pollution) on biological communities. High-throughput sequencing-based metabarcoding provides a powerful tool to reveal complex interactions between environments and biological communities. Among many technical issues, the clustering strategies for operational taxonomic units (OTUs) which are crucial for assessing biodiversity of communities, may affect final conclusions. Here, we used zooplankton communities along an environmental pollution gradient in the Chaobai River in Northern China to test different clustering strategies, including nonclustering and clustering with varied thresholds. Our results showed that though the number of OTUs estimated by nonclustering strategies and clustering strategies with divergence thresholds of 99%-97% largely varied, they were able to identify the same set of significant environmental and spatial variables responsible for geographical distributions of zooplankton communities. In addition, the ecological conclusions obtained by clustering thresholds of 99%-97% were consistent with nonclustering strategies, where for all eight clustering scenarios we detected that species sorting predicted by environmental variables overrode dispersal as the dominant factor in structuring zooplankton communities. However, clustering with the divergence thresholds of <95% affected the environmental and spatial variables identified. We conclude that both newly developed nonclustering methods and traditional clustering methods with divergence thresholds ≥97% were reliable to reveal mechanisms of complex community-environment interactions, although different clustering strategies could lead to largely varied biodiversity estimates such as those for α-diversity.

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“…To account for these factors, a typical first step of microbiome analysis is to resolve the data into Operational Taxonomic Units (OTUs), or clusters of sequences with 97% or greater similarity. There are many methods for identifying OTUs [2], roughly classifiable into closed-reference methods, which use a reference database of known organisms, or de novo methods.However, when applied to mock communities, it is widely found that both types of methods cannot accurately identify true OTUs in a sample [3,4,5,6,7,8].OTUs are problematic entities, lacking both biological and physical interpretability. They only roughly correspond to biological species, genera or higher taxonomic entities, and they do not correspond to true, error-free sequences in the sample.…”

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confidence: 99%

“…However, when applied to mock communities, it is widely found that both types of methods cannot accurately identify true OTUs in a sample [3,4,5,6,7,8].…”

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confidence: 99%

AmpliCI: A High-resolution Model-Based Approach for Denoising Illumina Amplicon Data

Peng

Dorman

2020

Preprint

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Motivation: Next-generation amplicon sequencing is a powerful tool for investigating microbial communities. One main challenge is to distinguish true biological variants from errors caused by PCR and sequencing. In the traditional analysis pipeline, such errors are eliminated by clustering reads within a sequence similarity threshold, usually 97%, and constructing operational taxonomic units, but the arbitrary threshold leads to low resolution and high false positive rates. Recently developed "denoising" methods have proven able to resolve single-nucleotide amplicon variants, but they still miss low frequency sequences, especially those near abundant variants, because they ignore the sequencing quality information.Results: We introduce AmpliCI, a reference-free, model-based method for rapidly resolving the number, abundance and identity of error-free sequences in massive Illumina amplicon datasets. AmpliCI takes into account quality information and allows the data, not an arbitrary threshold or an external database, to drive conclusions. AmpliCI estimates a finite mixture model, using a greedy strategy to gradually select error-free sequences and approximately maximize the likelihood. We show that AmpliCI is superior to three popular denoising methods, with acceptable computation time and memory usage.Availability: Source code available at https://github.com/DormanLab/AmpliCIThe utility of biomarkers is degraded by sequencing errors, PCR amplification errors, and intrastrain/species-specific variability [1]. To account for these factors, a typical first step of microbiome analysis is to resolve the data into Operational Taxonomic Units (OTUs), or clusters of sequences with 97% or greater similarity. There are many methods for identifying OTUs [2], roughly classifiable into closed-reference methods, which use a reference database of known organisms, or de novo methods.However, when applied to mock communities, it is widely found that both types of methods cannot accurately identify true OTUs in a sample [3,4,5,6,7,8].OTUs are problematic entities, lacking both biological and physical interpretability. They only roughly correspond to biological species, genera or higher taxonomic entities, and they do not correspond to true, error-free sequences in the sample. Thus, OTU-based methods are prone to both false positives and negatives, reporting error sequences as OTUs and missing subtle and real biological sequence variation, such as SNPs. The 97% threshold, motivated by empirical studies [9, 10], fails to reliably achieve genus or species level resolution [11,12]. There are distinct species with 97% or more similar 16S rRNA [13,14], and strains whose 16S rRNA locally differ by more than 3% [15].Amplicon sequencing data from current Illumina platforms support de novo single-nucleotide resolution [16]. Modern methods attempt to identify all the unique sequences in the sample [17,18,19,20,16,21,22,23]. Such denoising methods make no biological judgment on taxonomic entities, but simply remove or correct sequences produc...

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Accuracy of microbial community diversity estimated by closed- and open-reference OTUs

Cited by 144 publications

References 31 publications

Agricultural land‐use history and restoration impact soil microbial biodiversity

Agricultural land‐use history and restoration impact soil microbial biodiversity

Testing clustering strategies for metabarcoding‐based investigation of community–environment interactions

AmpliCI: A High-resolution Model-Based Approach for Denoising Illumina Amplicon Data

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