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

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 .