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Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization
Published on: April 14, 2016
Benchmarking a 16S rRNA sequencing protocol for microbiome analysis in low-moisture grain environments
Shivaprasad Doddabematti Prakash1, Suhan Bheemaiah Balyatanda1, Jack Sytsma2
1Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas 66506, United States of America.
Food Research International (Ottawa, Ont.)
|August 6, 2026
Summary
Choosing the right bioinformatics pipeline is crucial for accurate 16S rRNA gene sequencing analysis in food research. This study reveals that genus-level diversity metrics are sensitive to workflow choice, impacting low-moisture food safety assessments.
Area of Science:
- Microbiology
- Bioinformatics
- Food Science
Background:
- Microbial amplicon sequencing, particularly 16S rRNA gene surveys, is vital for food and biomedical research.
- Accurate taxonomic profiling from raw sequencing data requires specialized bioinformatics pipelines.
- Lack of consensus on best practices for processing low-moisture food (LMF) samples hinders reliable interpretation.
Purpose of the Study:
- To systematically assess the impact of different bioinformatics workflows and taxonomic databases on 16S rRNA gene data analysis.
- To identify optimal pipeline-database combinations for characterizing microbial communities in low-moisture food environments.
- To evaluate the influence of workflow choices on taxonomic composition and diversity metrics.
Main Methods:
- Evaluated 32 combinations of eight 16S rRNA pipelines and four taxonomic databases.
- Applied workflows to 16S rRNA gene sequences from 160 wheat milling environment samples.
- Utilized weighted composite scores to rank workflow performance and selected top 10 combinations.
Main Results:
- Taxonomic assignments were largely consistent at the family level but showed variability at the genus level.
- Genus-level alpha and beta diversity metrics were more sensitive to workflow choice than family-level metrics.
- Amplicon Sequence Variants (ASVs) workflows retained more low-abundance features than Operational Taxonomic Units (OTUs) workflows, particularly at the genus level.
Conclusions:
- Workflow selection significantly impacts genus-level microbial diversity analysis in 16S rRNA gene studies.
- ASV-based approaches offer advantages for characterizing microbial communities in low-moisture food environments due to their ability to resolve single-nucleotide variations.
- Standardized bioinformatics pipelines are needed for reliable characterization of microbial communities in sparse, low-density, and uneven samples from low-moisture environments.
