What is it about?
Viruses are the most common infectious agents on Earth, infecting living organisms including bacteria, plants, and animals. They play an important role in the balance of various ecosystems by modulating microbial populations. In humans, they are responsible for some common diseases and can cause severe illness. Viral metagenomic studies have become essential and offer the possibility to understand and extend the knowledge of viral diversity and functionality. An essential step for these approaches is the classification of viral sequences. This study evaluates the performance of 11 taxonomic classification tools widely used in viral metagenomics, namely BLAST, Centrifuge, DIAMOND, drVM, FastViromeExplorer, Kraken2, One Codex, SLIMM, Taxonomer, Vipie, and VirusFinder . By assessing their sensitivity, precision, computational efficiency (processing time and memory usage), and ability to classify viral sequences at different taxonomic levels (species and family), we provide a comparative analysis of their strengths and limitations. The study also explores how factors such as viral richness in samples and read length influence classification accuracy.
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Why is it important?
Viral metagenomics is crucial for understanding virus diversity, identifying emerging pathogens, and analyzing their ecological roles in different environments. The accuracy of taxonomic classification tools directly affects the reliability of virus identification in clinical and environmental samples. By systematically comparing these tools, our study provides valuable guidelines for selecting the most suitable approach based on dataset characteristics, ultimately improving the quality and reproducibility of future viral metagenomic analyses.
Perspectives
The integration of artificial intelligence techniques, such as deep learning models, could significantly improve taxonomic classification, particularly at lower taxonomic levels where traditional methods struggle with accuracy. Future research should focus on developing AI-enhanced classifiers that adapt to variations in read length, viral richness, and dataset complexity, further optimizing metagenomic analyses in virology.
Lorena Díaz-González
Universidad Autonoma del Estado de Morelos
Read the Original
This page is a summary of: Evaluation of tools for taxonomic classification of viruses, Briefings in Functional Genomics, November 2022, Oxford University Press (OUP),
DOI: 10.1093/bfgp/elac036.
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