A critical perspective on interpreting amplicon sequencing data in soil ecological research

Alteio, Lauren; Séneca, Joana; Canarini, Alberto; Angel, Roey; Jansa, Jan; Guseva, Ksenia; Kaiser, Christina; Richter, Andreas; Schmidt, Hannes

doi:10.1016/j.soilbio.2021.108357

Cited by 52 publications

(30 citation statements)

References 155 publications

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“…Two main characteristics of amplicon datasets are: (i) they are compositional, which means that abundances of taxa can only be interpreted in relative terms ( Gloor et al, 2016 ; Morton et al, 2019 ), and, especially for soil microbial communities, (ii) taxa are sparsely distributed, which means that datasets contain a high amount ASVs or OTUs that only occur in a fraction of the samples ( Alteio et al, 2021 ). Both of these characteristics pose a major challenge to network construction approaches, as correlations carried out on relative abundance and sparsely distributed datasets can lead to spurious results ( Morton et al, 2019 ; Alteio et al, 2021 ). In the next subsections we discuss how to reduce the potential bias introduced by these features of the dataset, by applying appropriate data filtering and transforming steps before the actual network construction ( Fig.…”

Section: Network Constructionmentioning

confidence: 99%

“…3.1 ). In addition, one has to be aware that the amplified DNA may also belong to relic (exogenous) DNA, which persist in soil for several years ( Alteio et al, 2021 ).…”

Section: Challenges and Way Forwardmentioning

confidence: 99%

“…We would however argue against such an approach, as combining correlations within and across compositional datasets in the same network may lead to a spurious connection pattern. Possible alternatives propose the use of centered log ratios individually on each dataset before merging them together ( Tipton et al, 2018 ), transform the relative abundance data into a presence/absence dataset (which implies a loss of information) or normalize using qPCR data ( Alteio et al, 2021 ). In addition, one can, depending on the research question, consider constructing bipartite networks, which only contain edges between the kingdoms ( Montesinos-Navarro et al, 2012 ; Toju et al, 2015 ), or multilayer networks, which contain different types of edges ( Pilosof et al, 2017 ).…”

Section: Challenges and Way Forwardmentioning

confidence: 99%

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From diversity to complexity: Microbial networks in soils

Guseva

Darcy

Simon

et al. 2022

Soil Biology and Biochemistry

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Section: Network Constructionmentioning

confidence: 99%

“…3.1 ). In addition, one has to be aware that the amplified DNA may also belong to relic (exogenous) DNA, which persist in soil for several years ( Alteio et al, 2021 ).…”

Section: Challenges and Way Forwardmentioning

confidence: 99%

Section: Challenges and Way Forwardmentioning

confidence: 99%

See 1 more Smart Citation

From diversity to complexity: Microbial networks in soils

Guseva

Darcy

Simon

et al. 2022

Soil Biology and Biochemistry

Self Cite

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“…Indeed, metabarcoding studies do not differ from traditional ecological studies, in which the number and distribution of study sites must be defined appropriately depending on the initial question (Dickie et al, 2018). Additionally, metabarcoding studies require an optimal number of local, biological replicates that can be determined based on the variance reported in previous studies (Alteio et al, 2021) or pilot experiments. Intuitively, more replicates will be required when any expected ecological differences are relatively small or when the studied location exhibits strong spatial or environmental heterogeneity.…”

Section: Planning a Metabarcoding Studymentioning

confidence: 99%

Best practices in metabarcoding of fungi: From experimental design to results

et al. 2022

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The development of high‐throughput sequencing (HTS) technologies has greatly improved our capacity to identify fungi and unveil their ecological roles across a variety of ecosystems. Here we provide an overview of current best practices in metabarcoding analysis of fungal communities, from experimental design through molecular and computational analyses. By reanalysing published data sets, we demonstrate that operational taxonomic units (OTUs) outperform amplified sequence variants (ASVs) in recovering fungal diversity, a finding that is particularly evident for long markers. Additionally, analysis of the full‐length ITS region allows more accurate taxonomic placement of fungi and other eukaryotes compared to the ITS2 subregion. Finally, we show that specific methods for compositional data analyses provide more reliable estimates of shifts in community structure. We conclude that metabarcoding analyses of fungi are especially promising for integrating fungi into the full microbiome and broader ecosystem functioning context, recovery of novel fungal lineages and ancient organisms as well as barcoding of old specimens including type material.

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“…In their recent study, Alteio et al (2021) discussed the possible technical challenges and limitations of amplicon sequencing and how compositionality may influence the integration of relative abundance data in soil microbiome research ( Alteio et al, 2021 ). Different approaches have been proposed to link with amplicon sequencing to quantitively evaluate microbiomes such as qPCR ( Zhang et al, 2017 ; Jian et al, 2020 ), flow cytometry that would allow counting microbial cells ( Vandeputte et al, 2017 ), and the application of an internal standard ( Tourlousse et al, 2016 ; Palmer et al, 2018 ; Tkacz et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Relative and Quantitative Rhizosphere Microbiome Profiling Results in Distinct Abundance Patterns

et al. 2022

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Next-generation sequencing is one of the most popular and cost-effective ways of characterizing microbiome in multiple samples. However, most of the currently available amplicon sequencing approaches are limited, as they result in relative abundance profiles of microbial taxa, which does not represent actual abundance in the environment. Here, we combined amplicon sequencing (16S rRNA gene for bacteria and ITS region for fungi) with real-time quantitative PCR (qPCR) to characterize the rhizosphere microbiome of wheat. We show that changes in the relative abundance of major microbial phyla do not necessarily follow the same pattern as the estimated quantitative abundance. Most of the bacterial phyla linked with the rhizosphere of plants grown in soil with no history of water stress showed enrichment patterns in their estimated absolute abundance, which was in contradiction with the trends observed in the relative abundance data. However, in the case of the fungal groups (except for Basidiomycota), such an enrichment pattern was not observed and the abundance of fungi remained relatively unchanged under different soil water stress history when estimated absolute abundance was considered. Comparing relative and estimated absolute abundances of dominant bacterial and fungal phyla, as well as their correlation with the functional processes in the rhizosphere, our results suggest that the estimated absolute abundance approach gives a different and more realistic perspective than the relative abundance approach. Such a quantification approach provides complementary information that helps to better understand the rhizosphere microbiomes and their associated ecological functional processes.

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A critical perspective on interpreting amplicon sequencing data in soil ecological research

Cited by 52 publications

References 155 publications

From diversity to complexity: Microbial networks in soils

From diversity to complexity: Microbial networks in soils

Best practices in metabarcoding of fungi: From experimental design to results

Relative and Quantitative Rhizosphere Microbiome Profiling Results in Distinct Abundance Patterns

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