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<p>Viruses are the most abundant biological entities on Earth, playing essential roles in shaping microbial communities, driving evolution, and maintaining ecosystem functions. Metagenomic sequencing has unveiled a vast landscape of uncharacterized viral “dark matter”, comprising highly divergent sequences that elude traditional taxonomic approaches. Here, we develop PhaGCN_Cluster, a next-generation viral classification tool built upon a graph convolutional neural network (GCN) framework. By integrating protein-level sequence similarity and contig-level genomic features, PhaGCN_Cluster establishes a scalable knowledge graph-based analytical system. The optimized algorithm yields significant gains in computational efficiency, supporting accurate taxonomic assignment of up to 300,000 contigs per run. Compared with existing methods, PhaGCN_Cluster demonstrates superior classification accuracy and F1-scores, particularly under conditions of low sequence similarity, and exhibits strong robustness in detecting evolutionarily distant viruses. Notably, PhaGCN_Cluster incorporates an updated logic for assigning “_like” taxa, which enhances its capacity to accommodate novel viral groups while preserving high precision—though at the cost of a slight reduction in recall. By generating high-fidelity network graphs, PhaGCN_Cluster uncovers previously unrecognized clades and bridges evolutionary gaps between reference viruses and novel sequences, thereby providing critical insights into viral diversity and evolution. PhaGCN_Cluster represents an interpretable, efficient, and scalable solution for automated virus classification. The source code of PhaGCN_Cluster is available via <a href="https://github.com/xiahaolong/PhaGCN_Cluster">https://github.com/xiahaolong/PhaGCN_Cluster</a>.</p> Graphical Abstract <p></p>

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PhaGCN_Cluster: A Scalable and Robust Framework for Automated Classification and Discovery of Viral Dark Matter from Metagenomes

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Hao-Long Xia,
  • Pei-Yu Liang,
  • Wen-Guang Yuan,
  • Xu-Dong Cao,
  • Yanni Sun,
  • Jing-Zhe Jiang,
  • Li-Hong Yuan

摘要

Viruses are the most abundant biological entities on Earth, playing essential roles in shaping microbial communities, driving evolution, and maintaining ecosystem functions. Metagenomic sequencing has unveiled a vast landscape of uncharacterized viral “dark matter”, comprising highly divergent sequences that elude traditional taxonomic approaches. Here, we develop PhaGCN_Cluster, a next-generation viral classification tool built upon a graph convolutional neural network (GCN) framework. By integrating protein-level sequence similarity and contig-level genomic features, PhaGCN_Cluster establishes a scalable knowledge graph-based analytical system. The optimized algorithm yields significant gains in computational efficiency, supporting accurate taxonomic assignment of up to 300,000 contigs per run. Compared with existing methods, PhaGCN_Cluster demonstrates superior classification accuracy and F1-scores, particularly under conditions of low sequence similarity, and exhibits strong robustness in detecting evolutionarily distant viruses. Notably, PhaGCN_Cluster incorporates an updated logic for assigning “_like” taxa, which enhances its capacity to accommodate novel viral groups while preserving high precision—though at the cost of a slight reduction in recall. By generating high-fidelity network graphs, PhaGCN_Cluster uncovers previously unrecognized clades and bridges evolutionary gaps between reference viruses and novel sequences, thereby providing critical insights into viral diversity and evolution. PhaGCN_Cluster represents an interpretable, efficient, and scalable solution for automated virus classification. The source code of PhaGCN_Cluster is available via https://github.com/xiahaolong/PhaGCN_Cluster.

Graphical Abstract