Jiayu Shang, Cheng Peng, Jiaojiao Guan, Dehan Cai, Donglin Wang, Yanni Sun
Metagenomic sequencing has transformed virus discovery; however, downstream bioinformatic analyses for viral identification, classification, and host prediction remain fragmented across multiple tools. Here, we present PhaBOX2, a major upgrade that extends the platform from a specialized bacteriophage identification tool to a comprehensive and integrated suite for viral sequence analysis. PhaBOX2 broadens its detection, taxonomic, and host prediction scope beyond phages to enable the characterization of archaeal and eukaryotic viruses. The updated workflow incorporates rigorous quality control and quantitative analyses, automatically removes host contamination, clusters sequences into viral operational taxonomic units, and performs phylogenetic analysis based on marker genes. In contrast to traditional "black-box" deep learning approaches, PhaBOX2 combines alignment-based strategies with machine-learning models under a "glass-box" design philosophy, providing interpretable intermediate evidence alongside final predictions to improve transparency and biological interpretability. Powered by a dedicated high-performance computing infrastructure, the server delivers a fully automated, end-to-end workflow, while achieving an ~80% reduction in processing time. PhaBOX2 thus provides a robust and user-friendly ecosystem for viral metagenomic analysis and is freely available at https://phage.ee.cityu.edu.hk/.