TaxaCal: enhancing species-level profiling accuracy of 16S amplicon data.

ABSTRACT:

Background

16S 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.

Results

To 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 d

SUBMITTER: Shen Q 

PROVIDER: S-EPMC12107961 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature