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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

Inconsistent Denoising and Clustering Algorithms for Amplicon Sequence Data.

Kaisa Koskinen¹, Petri Auvinen¹, K Johanna Björkroth²

¹1 Institute of Biotechnology, University of Helsinki , Helsinki, Finland .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|December 20, 2014

Summary

Analyzing microbial communities using 16S rRNA gene sequencing reveals significant differences based on analysis software. Different algorithms impact operational taxonomic unit (OTU) counts and taxonomic affiliations, affecting comparability.

Keywords:

amplicon sequencing clustering denoising operational taxonomic unit taxonomy

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Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

The 16S rRNA gene is a standard marker for studying microbial communities.
Second-generation sequencing has advanced microbial community analysis, enabling detailed characterization and detection of rare taxa.
Despite advancements, biases and lack of comparability persist in amplicon sequence data analysis.

Purpose of the Study:

To evaluate publicly available amplicon sequence data analysis algorithms.
To compare software and parameter impacts on microbial community analysis results.
To assess the reliability and comparability of different analysis pipelines.

Main Methods:

Contrasted amplicon sequence data analysis algorithms using two datasets: a clone-based community and a food spoilage community.
Assessed software and parameter performance against benchmark communities.
Analyzed the impact of denoising and clustering methods on results.

Main Results:

Commonly used denoising and clustering methods produce significantly different outcomes.
Clustering methods greatly affect the number of operational taxonomic units (OTUs), with differences up to 40-fold.
Denoising algorithms primarily influence taxonomic affiliations, with significant differences observed even at the phylum level.
Effective denoising can mitigate differences caused by clustering.

Conclusions:

The choice of bioinformatics pipeline significantly impacts microbial community analysis results.
Differences in OTU numbers and taxonomic assignments are substantial and method-dependent.
Careful selection of denoising and clustering methods is crucial for reliable and comparable microbial community studies.