ArticleEnvironmental microbiome2025
Creating unity: linking 16S rRNA gene sequence information to the core taxonomy of genomes.
Article in Environmental microbiome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Disruption of the spinal cord-gut axis alters microbial dynamics and carbohydrate cross-feeding in the gut.Communications biology · 2026Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Genome Taxonomy Database (GTDB) initiative aims to modernize prokaryotic taxonomy by aligning it with the great amounts of full-length genomes available today. Unfortunately, there is a poor link between the GTDB and the historically widely used 16S rRNA gene-based taxonomy. The current study explores the within and between divergence of the 16S rRNA gene sequences under GTDB taxonomy, refining our understanding of the 16S gene's resolution under this new taxonomic system. The analysis focuses on the divergence of 16S sequences collected from the GTDB genomes to identify optimal clustering thresholds for taxonomic resolution. Generalized linear mixed models (GLMMs) were fitted to estimate divergences within taxonomic ranks, correcting for the variable quality of the GTDB genomes.
resultsTo achieve GTDB species-level resolution, 16S sequences need clustering at a divergence threshold of around 0.01 (99% identity), while genus-level resolution requires thresholds of 0.04-0.08 (92-96% identity), optimal thresholds vary significantly across branches, highlighting the limitations of using a fixed divergence threshold.
conclusionsThe results suggest a more adaptive approach to taxonomic assignment from 16S data is needed, tailored to the diversity and complexity of the samples. These findings are fundamental for an improved taxonomic classification of environmental 16S data.
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Registered trials
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