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    A framework for assessing 16S rRNA marker-gene survey data analysis methods using mixtures.

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    Author
    Olson, N.D.
    Li, S.
    Stine, O.C.
    Date
    2020
    Journal
    Microbiome
    Publisher
    BioMed Central Ltd.
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1186/s40168-020-00812-1
    Abstract
    Background: There are a variety of bioinformatic pipelines and downstream analysis methods for analyzing 16S rRNA marker-gene surveys. However, appropriate assessment datasets and metrics are needed as there is limited guidance to decide between available analysis methods. Mixtures of environmental samples are useful for assessing analysis methods as one can evaluate methods based on calculated expected values using unmixed sample measurements and the mixture design. Previous studies have used mixtures of environmental samples to assess other sequencing methods such as RNAseq. But no studies have used mixtures of environmental to assess 16S rRNA sequencing. Results: We developed a framework for assessing 16S rRNA sequencing analysis methods which utilizes a novel two-sample titration mixture dataset and metrics to evaluate qualitative and quantitative characteristics of count tables. Our qualitative assessment evaluates feature presence/absence exploiting features only present in unmixed samples or titrations by testing if random sampling can account for their observed relative abundance. Our quantitative assessment evaluates feature relative and differential abundance by comparing observed and expected values. We demonstrated the framework by evaluating count tables generated with three commonly used bioinformatic pipelines: (i) DADA2 a sequence inference method, (ii) Mothur a de novo clustering method, and (iii) QIIME an open-reference clustering method. The qualitative assessment results indicated that the majority of Mothur and QIIME features only present in unmixed samples or titrations were accounted for by random sampling alone, but this was not the case for DADA2 features. Combined with count table sparsity (proportion of zero-valued cells in a count table), these results indicate DADA2 has a higher false-negative rate whereas Mothur and QIIME have higher false-positive rates. The quantitative assessment results indicated the observed relative abundance and differential abundance values were consistent with expected values for all three pipelines. Conclusions: We developed a novel framework for assessing 16S rRNA marker-gene survey methods and demonstrated the framework by evaluating count tables generated with three bioinformatic pipelines. This framework is a valuable community resource for assessing 16S rRNA marker-gene survey bioinformatic methods and will help scientists identify appropriate analysis methods for their marker-gene surveys. Copyright 2020 The Author(s).
    Keyword
    16S rRNA gene
    Assessment
    Bioinformatic pipeline
    Differential abundance
    Normalization
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081694897&doi=10.1186%2fs40168-020-00812-1&partnerID=40&md5=beac36b3b85ba06bbe2eb33be7a4c67d; http://hdl.handle.net/10713/12372
    ae974a485f413a2113503eed53cd6c53
    10.1186/s40168-020-00812-1
    Scopus Count
    Collections
    UMB Open Access Articles 2020

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