Metax: A Coverage-Informed Probabilistic Framework for Accurate Cross-Domain Taxon Profiling

Abstract

Metagenomic taxonomic profiling is essential for characterizing microbial community composition in both environmental and clinical contexts. Existing profilers have greatly advanced community characterization; however, achieving accurate profiling across all domains of life - especially for archaea, fungi, and viruses - and for low-biomass, host-dominated samples, remains challenging. We describe Metax, a cross-domain taxonomic profiler that employs probabilistic modeling of genome coverage to distinguish true community members from artifactual signals arising from reference contamination, local genomic similarity, or reagent-derived DNA fragments. In comprehensive benchmarks across more than 500 samples, Metax demonstrated accurate species-level profiling, with consistent performance for bacteria, archaea, eukaryotes and viruses, and robustness to shallow sequencing. Applied to an oral microbiome cohort, Metax identified differentially abundant viral taxa distinguishing peri-implantitis from healthy sites, while analyses of tumor microbiome data revealed reagent-borne contaminants and potential reference misassemblies. By integrating coverage-informed statistics, Metax delivers accurate, robust, and interpretable cross-domain taxonomic profiles, maintaining stable performance across diverse sequencing depths and sample types.