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Updated: May 7, 2026
11:22
Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
Published on: October 15, 2019
Comparison of microbial diversity determined with the same variable tag sequence extracted from two different PCR
Yan He1, Ben-Jie Zhou, Guan-Hua Deng
1Department of Environmental Health, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China. biodegradation@gmail.com.
BMC Microbiology
|September 17, 2013
Summary
Meta-analysis of microbial ecology data is feasible for beta-diversity and Shannon diversity, but community structure and biomarkers vary. This study compares different 16S rRNA gene sequencing methods for robust microbiome meta-analysis.
Area of Science:
- Microbial Ecology
- Bioinformatics
- Genomics
Background:
- 16S rRNA gene sequencing is standard for microbial ecology.
- Meta-analysis of diverse sequencing datasets is challenging but desirable.
- Experimental validation of meta-analysis approaches is needed.
Purpose of the Study:
- To evaluate the reliability of meta-analysis for microbiome data generated using different 16S rRNA gene targets and sequencing methods.
- To compare the consistency of alpha-diversity, beta-diversity, and community structure metrics across datasets.
Main Methods:
- Fecal samples were amplified using V4F-V6R and V6F-V6R primer sets.
- V6 fragments were sequenced using Illumina, and data compared for diversity and structure.
- Statistical analyses included Principal Component Analysis (PCA) and Procrustes analysis.
Main Results:
- Beta-diversity and Shannon's diversity index showed high concordance across datasets and in meta-analysis.
- Richness estimators (OTU, Chao) varied significantly, and their meta-analysis was biased.
- Community structures differed between datasets, impacting biomarker identification (LEfSe).
Conclusions:
- Beta-diversity and Shannon's diversity are reliable metrics for microbiome meta-analysis.
- Community structure and identified biomarkers are less consistent across different sequencing data.
- Findings guide future meta-analyses of microbiome data from varied sources.
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