Metagenomic Profiling of Microbial Communities

Abstract

Metagenomic sequencing captures the collective genomic content of microbial communities directly from environmental or clinical samples, enabling culture-independent profiling of taxonomic composition and functional potential, yet the computational pipelines that transform raw reads into biological insight vary widely in accuracy, speed, and scope across analytical approaches. We evaluated 212 metagenomic profiling programmes across centres in Austria and Estonia between 2017 and 2021, spanning five pipeline categories: read-based taxonomic classification, marker-gene amplicon profiling, whole-metagenome assembly and binning, functional pathway annotation, and strain-level resolution methods. A Metagenomic Profiling Pipeline Index (MPPI) was constructed from five sub-scores -- taxonomic classification accuracy, community diversity estimation fidelity, functional annotation completeness, computational scalability, and cross-sample reproducibility -- with weights from regression against sustained adoption into microbiome research pipelines. MPPI correlated with adoption at r = +0.84 and discriminated adopted from non-adopted pipelines with an AUC of 0.882. Read-based taxonomic classifiers scored highest (mean MPPI 0.824), while strain-level methods trailed at 0.600. Only 35.4 percent exceeded the 0.75 threshold. Classification accuracy carried the largest weight (beta = +0.280), followed by reproducibility (beta = +0.228).