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Related Experiment Video

Updated: Dec 26, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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A framework for assessing 16S rRNA marker-gene survey data analysis methods using mixtures.

Nathan D Olson1,2,3, M Senthil Kumar4,5, Shan Li6

  • 1Biosystems and Biomaterials Division, National Institute of Standards and Technology, 100 Bureau Dr., Gaithersburg, 20899, MD, USA. nolson@nist.gov.

Microbiome
|March 15, 2020
PubMed
Summary
This summary is machine-generated.

A new framework using sample mixtures helps assess 16S rRNA sequencing analysis methods. DADA2 showed higher false negatives, while Mothur and QIIME had more false positives in this study.

Keywords:
16S rRNA geneAssessmentBioinformatic pipelineDifferential abundanceNormalization

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • 16S rRNA marker-gene surveys are crucial for microbial community analysis.
  • Limited guidance exists for selecting appropriate bioinformatic pipelines and analysis methods.
  • Environmental sample mixtures offer a robust approach for method assessment, previously used for RNAseq but not 16S rRNA sequencing.

Purpose of the Study:

  • To develop and validate a novel framework for assessing 16S rRNA sequencing analysis methods.
  • To evaluate the qualitative and quantitative performance of different bioinformatic pipelines using this framework.
  • To provide a community resource for selecting optimal analysis methods for 16S rRNA marker-gene surveys.

Main Methods:

  • Developed a framework utilizing a two-sample titration mixture dataset.
  • Implemented qualitative assessment metrics evaluating feature presence/absence against random sampling expectations.
  • Implemented quantitative assessment metrics comparing observed and expected relative and differential abundance values.

Main Results:

  • DADA2 exhibited a higher false-negative rate, indicated by features not accounted for by random sampling.
  • Mothur and QIIME demonstrated higher false-positive rates, evidenced by features only present in unmixed samples or titrations.
  • Quantitative assessments showed observed relative and differential abundance values were consistent with expected values across all three pipelines.
  • Count table sparsity analysis supported the false-negative/positive rate findings.

Conclusions:

  • The developed framework effectively assesses 16S rRNA sequencing analysis methods.
  • The study identified distinct error profiles (false-negative vs. false-positive rates) for DADA2, Mothur, and QIIME.
  • This framework serves as a valuable resource for researchers to choose appropriate bioinformatic tools for their marker-gene surveys.