Comprehensive benchmarking and ensemble approaches for metagenomic classifiers

TLDR

Metagenomics must identify microorganisms in clinical and environmental samples, yet many computational tools exist and comprehensive comparisons are scarce. This study evaluates the performance of 11 metagenomic classifiers using the largest set of laboratory-generated and simulated controls across 846 species. We assessed each tool’s ability to identify taxa at genus, species, and strain levels, quantify relative abundances, and classify reads, and examined strategies such as abundance filtering, ensemble approaches, and tool intersection to mitigate misclassification. The classifiers varied by over three orders of magnitude in species identified, and although filtering and ensembles reduced false positives, they did not fully eliminate them, especially for medically relevant species, and pairing tools with different classification strategies and careful experimental design improved species resolution and interpretation.

Abstract

One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited. In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages. This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.