ArticleBMC bioinformatics2025
TaxaCal: enhancing species-level profiling accuracy of 16S amplicon data.
Article in BMC bioinformatics, 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.
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Authors and funding
5 authors.
Funding
Abstract
background16S rRNA amplicon sequencing is a widely used method for microbiome composition analysis due to its cost-effectiveness and lower data requirements compared to metagenomic whole-genome sequencing (WGS). However, inherent limitations in 16S-based approach often lead to profiling discrepancies, particularly at the species level, compromising the accuracy and reliability of findings.
resultsTo address this issue, we present TaxaCal (Taxonomic Calibrator), a machine learning algorithm designed to calibrate species-level taxonomy profiles in 16S amplicon data using a two-tier correction strategy. Validation on in-house produced and public datasets shows that TaxaCal effectively reduces biases in amplicon sequencing, mitigating discrepancies between microbial profiles derived from 16S and WGS. Moreover, TaxaCal enables seamless cross-platform comparisons between these two sequencing approaches, significantly improving disease detection in 16S-based microbiome data.
conclusionsTherefore, TaxaCal offers a cost-effective solution for generating high-resolution microbiome species profiles that closely align with WGS results, enhancing the utility of 16S-based profiling in microbiome research. As microbiome-based diagnostics continue to evolve, TaxaCal has the potential to be a crucial tool in advancing the utility of 16S sequencing in clinical and research settings.
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