Unlocking Reliable 16S rRNA Analysis: A Benchmarking Gold-Standard Ground Truth!

A validated mock community (235 strains, 197 species) offers a gold-standard ground truth for testing OTU/ASV methods. Unlike real data with unknown compositions, this resource ensures accurate pipeline evaluation. Dive into our open dataset (PRJNA975486) & analysis: https://rdcu.be/elVJj
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BioMed Central
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The unresolved struggle of 16S rRNA amplicon sequencing: a benchmarking analysis of clustering and denoising methods - Environmental Microbiome

Background Although 16S rRNA gene amplicon sequencing has become an indispensable method for microbiome studies, this analysis is not error-free, and remains prone to several biases and errors. Numerous algorithms have been developed to eliminate these errors and consolidate the output into distance-based Operational Taxonomic Units (OTUs) or denoising-based Amplicon Sequence Variants (ASVs). An objective comparison between them has been obscured by various experimental setups and parameters. In the present study, we conducted a comprehensive benchmarking analysis of the error rates, microbial composition, over-merging/over-splitting of reference sequences, and diversity analyses using the most complex mock community, comprising 227 bacterial strains and the Mockrobiota database. Using unified preprocessing steps, we were able to compare DADA2, Deblur, MED, UNOISE3, UPARSE, DGC (Distance-based Greedy Clustering), AN (Average Neighborhood), and Opticlust objectively. Results ASV algorithms—led by DADA2— resulted in having a consistent output, yet suffered from over-splitting, while OTU algorithms—led by UPARSE—achieved clusters with lower errors, yet with more over-merging. Notably, UPARSE and DADA2 showed the closest resemblance to the intended microbial community, especially when considering measures for alpha and beta diversity. Conclusion Our unbiased comparative evaluation examined the performance of eight algorithms dedicated to the analysis of 16S rRNA amplicon sequences with a wide range of mock datasets. Our analysis shed light on the pros and cons of each algorithm and the accuracy of the produced OTUs or ASVs. The utilization of the most complex mock community and the benchmarking comparison presented here offer a framework for the comparison between OTU/ASV algorithms and an objective method for the assessment of new tools and algorithms.

The analysis of 16S rRNA gene sequencing data involves several critical steps, including preprocessing, dereplication, chimera removal, and ultimately, clustering or denoising to infer biological sequences. To accurately assess the performance of each of these steps and ensure reliable results, the use of a complex mock community with a validated ground truth is essential for proper benchmarking. While large volumes of publicly available data exist and offer the advantage of being derived from real samples—unlike simulated data, which relies on prior assumptions—these datasets present a significant limitation: the true composition of the microbial communities is often unknown. This lack of a definitive ground truth poses a major challenge for comparative analyses, as it hampers our ability to rigorously evaluate the accuracy and effectiveness of clustering and denoising algorithms.

The mock community presented in this study comprises 235 bacterial strains representing 197 distinct species, providing a valuable and rigorous resource for the bioinformatics community. It offers an ideal framework for developers aiming to optimize their algorithms, as well as for analysts seeking to critically assess and benchmark existing 16S rRNA analysis pipelines. Notably, this same mock community has also been previously characterized at the shotgun metagenomic level by Gleb Goussarov, facilitating accurate metagenomic binning (see publication: https://link.springer.com/article/10.1186/s40793-022-00403-7). This dual availability at both amplicon and shotgun levels further enhances its utility as a comprehensive benchmarking standard for diverse microbial analysis workflow

In this study, we leveraged the complex mock community to conduct a head-to-head comparison of clustering and denoising approaches—specifically, Operational Taxonomic Units (OTUs) and Amplicon Sequence Variants (ASVs). This direct comparison allowed us to systematically highlight the strengths and limitations of each method. We believe that the robust design of our benchmarking framework, combined with the utilization of this complex mock community, provides a solid foundation for evaluating 16S rRNA analysis algorithms. Moreover, this framework offers a scalable model that could be extended to encompass entire pipeline comparisons in future studies.

Our comprehensive benchmarking framework, along with all datasets, detailed analyses, and key insights, is freely accessible https://environmentalmicrobiome.biomedcentral.com/articles/10.1186/s40793-025-00705-6.Additionally, the mock community dataset—available under accession number PRJNA975486—serves as a valuable resource for both bioinformatics algorithm development and rigorous performance evaluation.

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