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Updated: May 14, 2025
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Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
Published on: August 25, 2018
12.7K
Development of reference-based model for improved analysis of bacterial community
Changwoo Park1, Jinyoung Park2, Dongho Chang3
1Biometrology Group, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea.
Food Research International (Ottawa, Ont.)
|May 13, 2025
Summary
A new model corrects sequencing biases in microbiome analysis of probiotic products. This method improves accuracy across different platforms and regions, even with partial reference data, enhancing gut health research.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Probiotic bacteria are crucial for gut health and are common in commercial products.
- Next-generation sequencing (NGS) using 16S rRNA is standard for analyzing these products but suffers from biases.
- These biases stem from amplification regions, sequencing platforms, and library kits, affecting data accuracy.
Purpose of the Study:
- To develop and validate a reference-based model for correcting sequencing biases in 16S rRNA amplicon data.
- To improve the accuracy of microbiome analysis in probiotic products and broader metagenomic research.
Main Methods:
- Developed a reference-based bias correction model using PCR efficiencies from mock communities.
- Validated the model with eight mock communities and 12 commercial probiotic products across multiple NGS platforms and 16S rRNA regions.
- Utilized droplet digital PCR (ddPCR) with validated primer-probe assays for accurate bacterial quantification and establishing initial community ratios.
Main Results:
- Identified consistent platform- and region-specific biases in mock communities, leading to over- or under-representation of species.
- Observed biased results in commercial product analyses due to varying sequencing protocols.
- Demonstrated that the correction model successfully adjusted biased ratios across regions and platforms, aligning closely with ddPCR-predicted proportions.
- Showed that partial reference datasets (approx. 40% species) yielded comparable correction results to complete references.
- Confirmed effective correction of polymerase-induced biases.
Conclusions:
- The developed reference-based model significantly improves the accuracy of microbiome analysis by correcting sequencing biases.
- This approach enhances the reliability of analyzing probiotic products and has potential applications in broader metagenomic studies.
- Partial reference datasets are sufficient for effective bias correction, offering a more practical approach for microbiome research.
