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Updated: Nov 16, 2025

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

Primer, Pipelines, Parameters: Issues in 16S rRNA Gene Sequencing.

Isabel Abellan-Schneyder¹, Monica S Matchado², Sandra Reitmeier¹

¹Core Facility Microbiome, ZIEL-Institute for Food & Health, Technische Universität München, Freising, Germany.

|February 25, 2021

Summary

Short-amplicon 16S rRNA gene sequencing protocols significantly impact microbiome study results. Careful selection of variable regions, primers, databases, and bioinformatic settings is crucial for accurate taxonomic profiling and reliable cross-study comparisons.

Keywords:

16S rRNA gene sequencing amplicon sequencing bioinformatic settings clustering databases microbiome mock communities variable regions

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Last Updated: Nov 16, 2025

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

Published on: April 14, 2016

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Area of Science:

Microbiology
Bioinformatics
Genetics

Background:

Short-amplicon 16S rRNA gene sequencing is a standard method for microbiome analysis.
Comparative studies evaluating procedural differences are limited, hindering reproducibility.
Variability in protocols can lead to discrepancies in microbiome composition outcomes.

Purpose of the Study:

To systematically compare the impact of different 16S rRNA gene variable regions (V-regions) and primer pairs on microbiome profiling.
To investigate the influence of clustering methods (OTUs, zOTUs, ASVs), reference databases, and bioinformatic settings on taxonomic assignment.
To provide recommendations for optimizing study design and data processing in microbiome research.

Main Methods:

Sequencing of human stool samples and mock communities with varying complexity.
Testing of multiple short amplicons targeting different V-regions (V1-V2, V1-V3, V3-V4, V4, V4-V5, V6-V8, V7-V9).
Evaluation of various clustering algorithms, databases (GreenGenes, RDP, Silva, NCBI, GTDB), and bioinformatic pipeline parameters.

Main Results:

Primer choice significantly influences microbial composition; results require independent validation across different primer pairs.
Comparing datasets across V-regions or using different databases can be misleading due to nomenclature differences and varying classification precision.
Certain bacterial taxa can be missed or underrepresented depending on primer selection and database choice (e.g., Bacteroidetes, Acetatifactor).

Conclusions:

Inadequate primer combinations, outdated databases, or suboptimal bioinformatic settings can lead to underrepresentation or absence of key bacterial genera.
Amplicon truncation and bioinformatic pipeline settings (quality thresholds, clustering, truncation) critically affect observed microbial profiles.
Robust study design, including the use of complex mock communities and appropriate V-region selection, is essential for reliable microbiome analysis and cross-study validation.