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Updated: Mar 26, 2026
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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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Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis
Adam G Clooney1,2,3, Fiona Fouhy4,3, Roy D Sleator2,3
1School of Microbiology, University College Cork, Cork, Ireland.
Plos One
|February 6, 2016
Summary
Methodology significantly impacts microbiome study results, often more than individual differences. Choosing the right sequencing and analysis methods is crucial for reproducible and comparable microbiota data.
Area of Science:
- Microbiology
- Genomics
- Bioinformatics
Background:
- Next-generation sequencing (NGS) offers microbiome research opportunities.
- Numerous methodological variables can affect study outcomes.
- Reproducibility and comparability are threatened by methodological variability.
Purpose of the Study:
- Compare amplicon and shotgun sequencing across leading NGS technologies.
- Evaluate methodological biases in microbiome studies.
- Identify key factors influencing microbiota composition variance.
Main Methods:
- Applied amplicon and shotgun sequencing to six human stool samples.
- Utilized Illumina HiSeq, MiSeq, and Ion PGM platforms.
- Sequenced 16S rRNA gene regions and performed shotgun sequencing.
Main Results:
- Methodology was the primary driver of microbiota variance, exceeding inter-individual differences.
- Amplicon sequencing, especially 16S rRNA V1-V2 on MiSeq, showed significant bias.
- Taxonomic binning software had a greater impact than sequencing technology.
- Illumina HiSeq required 10 million reads for optimal shotgun assembly; MiSeq and PGM were less sufficient.
- Longer read lengths on MiSeq and PGM aided functional gene categorization.
Conclusions:
- Methodological choices introduce significant bias in microbiome studies.
- Comparing data from different sequencing strategies carries substantial risks.
- Laboratories should optimize protocols for specific microbial detection.
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