A systematic review and benchmarking of modern metagenomic tools for taxonomic classification

Advancements in metagenomics have been driven by the continuous development of bioinformatic tools, particularly taxonomic classification software, which are central to the accurate characterization of microbial communities. However, establishing direct comparisons between these tools remains challenging due to variations in evaluation metrics, reference databases, and input data types. In this study, we present a systematic review of recently developed metagenomic taxonomic classification tools. Of the 31 identified tools, nine satisfied all functional and methodological criteria for the benchmark analysis. We evaluated their accuracy and computational performance using a standardized dataset derived from the NCBI RefSeq database. Our analysis revealed that most of these tools are domain-specific, each excelling in particular areas. Tools like TAMA, CAMITAX and PhyloFlash achieved higher accuracy for prokaryotic organisms, while ViWrap and PhaBOX achieved higher accuracy for viral classifications. SqueezeMeta achieved high F1 scores across most domains, though its assembly-based architecture limits effectiveness on highly diverse samples. MegaPath-Nano was least affected by increased mutation rates. The execution time varied widely among the tools, with domain-specific and machine learning-based tools generally being faster, while tools like BASTA had longer runtimes and lower accuracy. This review synthesizes performance results for current tools, providing an overview of their strengths and computational methodologies. • We reviewed and benchmarked nine modern metagenomic classification tools published since 2019 on NCBI RefSeq. • TAMA and CAMITAX excel for prokaryotes; MegaPath-Nano handles high mutation; PhaBOX and ViWrap lead for viruses. • SqueezeMeta scores well broadly but struggles with diverse low-coverage samples; BASTA is slow with lower accuracy. • No single tool excels universally; selection should consider target organisms, sample size, mutation rate, and resources.