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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
Published on: October 15, 2019
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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
Summary
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.
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.
