A systematic review and benchmarking of modern metagenomic tools for taxonomic classification
Clinical Snapshot
PICO Framework
| P — Population | Metagenomic taxonomic classification software tools developed recently |
| I — Intervention | Various bioinformatic tools for taxonomic classification (TAMA, CAMITAX, PhyloFlash, ViWrap, PhaBOX, SqueezeMeta, MegaPath-Nano, BASTA, and one additional tool) |
| C — Comparator | Head-to-head comparison between different tools using standardized evaluation metrics |
| O — Outcomes | Accuracy metrics (F1 scores), computational performance (execution time), domain-specific performance (prokaryotic vs viral classification), and robustness to mutation rates |
Bottom Line
This systematic review evaluated nine metagenomic taxonomic classification tools, revealing that tool selection should be guided by specific research needs rather than seeking a universal solution. For prokaryotic analysis, TAMA, CAMITAX, and PhyloFlash demonstrated superior accuracy, while ViWrap and PhaBOX excelled in viral classification. SqueezeMeta showed consistently high F1 scores across domains but may struggle with highly diverse samples due to its assembly-based approach. MegaPath-Nano proved most robust against sequence mutations. The study highlights significant trade-offs between accuracy and computational efficiency, with domain-specific and machine learning-based tools generally offering faster processing times. While this benchmarking provides valuable guidance for tool selection, the lack of statistical analysis and confidence intervals limits the strength of recommendations. Researchers should consider their specific taxonomic targets, sample diversity, and computational constraints when selecting tools for metagenomic analysis.
Key Findings
P Value: Not reported
Effect Size: Variable F1 scores across domains with SqueezeMeta showing consistently high performance
Primary Outcome: Domain-specific performance with no single tool excelling across all taxonomic domains
Nnt Or Sensitivity: TAMA, CAMITAX, and PhyloFlash showed higher accuracy for prokaryotic classification; ViWrap and PhaBOX for viral classification
Confidence Interval: Not reported
Clinical Application
High feasibility for implementation in research settings with appropriate computational resources and bioinformatics expertise Relevant to Australian research institutions and clinical laboratories conducting microbiome research, particularly in infectious disease diagnostics and environmental health surveillance supported by NHMRC funding priorities Researchers conducting metagenomic studies, clinical microbiologists analyzing complex microbial communities, and bioinformaticians developing analysis pipelines
Abstract
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.
References
- 1.Martins, I. B., Silva, J. M., & Almeida, J. R. (2026). A systematic review and benchmarking of modern metagenomic tools for taxonomic classification. Computers in Biology and Medicine, 111600. https://doi.org/10.1016/j.compbiomed.2026.111600
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