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Quantitative Amplicon Sequencing Is Necessary to Identify Differential Taxa and Correlated Taxa Where Population Sizes Differ

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Abstract

High-throughput, multiplexed-amplicon sequencing has become a core tool for understanding environmental microbiomes. As researchers have widely adopted sequencing, many open-source analysis pipelines have been developed to compare microbiomes using compositional analysis frameworks. However, there is increasing evidence that compositional analyses do not provide the information necessary to accurately interpret many community assembly processes. This is especially true when there are large gradients that drive distinct community assembly processes. Recently, sequencing has been combined with Q-PCR (among other sources of total quantitation) to generate “Quantitative Sequencing” (QSeq) data. QSeq more accurately estimates the true abundance of taxa, is a more reliable basis for inferring correlation, and, ultimately, can be more reliably related to environmental data to infer community assembly processes. In this paper, we use a combination of published data sets, synthesis, and empirical modeling to offer guidance for which contexts QSeq is advantageous. As little as 5% variation in total abundance among experimental groups resulted in more accurate inference by QSeq than compositional methods. Compositional methods for differential abundance and correlation unreliably detected patterns in abundance and covariance when there was greater than 20% variation in total abundance among experimental groups. Whether QSeq performs better for beta diversity analysis depends on the question being asked, and the analytic strategy (e.g., what distance metric is being used); for many questions and methods, QSeq and compositional analysis are equivalent for beta diversity analysis. QSeq is especially useful for taxon-specific analysis; QSeq transformation and analysis should be the default for answering taxon-specific questions of amplicon sequence data. Publicly available bioinformatics pipelines should incorporate support for QSeq transformation and analysis.

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Data Availability

The datasets supporting the conclusions of this article are available at https://github.com/djeppschmidt/QSeq_Model along with scripts used to generate the results.

Abbreviations

CLR :

Centered log-ratio

QPCR :

Quantitative polymerase chain reaction

QSeq :

Quantitative sequencing

ASV :

Amplicon sequence variant

TFW :

Tidal freshwater wetlands

FSP :

Farming Systems Project

GLUSEEN :

Global Urban Soil Ecology and Education Network

USDA :

United States Department of Agriculture

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Acknowledgements

We thank Dr. Mihai Pop for feedback that substantially improved the manuscript. The Long-Term Agroecosystem Research (LTAR) network is supported by the United States Department of Agriculture.

Funding

Dietrich Epp Schmidt was supported by NRT-INFEWS: UMD Global STEWARDS (STEM Training at the Nexus of Energy, WAter Reuseand FooD Systems) that was awarded to the University of Maryland School of Public Health by the National Science Foundation National Research Traineeship Program, Grant number 1828910.

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Authors and Affiliations

  1. University of Maryland College Park, College Park, MD, USA

    Dietrich Epp Schmidt & Stephanie A. Yarwood

  2. United States Department of Agriculture, Agricultural Research Service, Beltsville, MD, USA

    Jude E. Maul

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  1. Dietrich Epp Schmidt
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DES, SAY, and JEM conceptualized and revised the manuscript. DES ran the analyses.

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Correspondence to Dietrich Epp Schmidt.

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Epp Schmidt, D., Maul, J.E. & Yarwood, S. . Quantitative Amplicon Sequencing Is Necessary to Identify Differential Taxa and Correlated Taxa Where Population Sizes Differ. Microb Ecol 86, 2790–2801 (2023). https://doi.org/10.1007/s00248-023-02273-z

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