Species-level taxonomy calibration and cross-platform correction for 16S microbiome data

TaxaCal (Taxonomic Calibrator), a machine learning algorithm designed to calibrate species-level taxonomy profiles in 16S amplicon data using a two-tier correction strategy. 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.