[Paper Review] PhaBOX: A web server for identifying and characterizing phage contigs in metagenomic data

Jiayu Shang, Cheng Peng+3 more|arXiv (Cornell University)|Mar 28, 2023

Bacteriophages and microbial interactionsEnvironmental Science3 citations

TL;DR

PhaBOX is a web server that integrates phage contig identification, lifestyle prediction, taxonomic classification, and host prediction in metagenomic data using optimized, user-friendly tools. It accelerates end-to-end phage analysis by 40% compared to running individual tools, with visualizations of key features like sequence homology and protein organization for transparent, interpretable results.

ABSTRACT

Motivation: There is accumulating evidence showing the important roles of bacteriophages (phages) in regulating the structure and functions of the microbiome. However, lacking an easy-to-use and integrated phage analysis software hampers microbiome-related research from incorporating phages in the analysis. Results: In this work, we developed a web server, PhaBOX, which can comprehensively identify and analyze phage contigs in metagenomic data. It supports integrated phage analysis, including phage contig identification from the metagenomic assembly, lifestyle prediction, taxonomic classification, and host prediction. Instead of treating the algorithms as a black box, PhaBOX also supports visualization of the essential features for making predictions. The web server is designed with a user-friendly graphical interface that enables both informatics-trained and non-specialist users to analyze phages in microbiome data with ease. Availability: The web server of PhaBOX is available via: https://phage.ee.cityu.edu.hk. The source code of PhaBOX is available at: https://github.com/KennthShang/PhaBOX Contact: [e-mail]

Motivation & Objective

• To address the lack of an integrated, user-friendly platform for phage analysis in metagenomic data.
• To reduce the computational and technical burden of installing and running multiple standalone phage analysis tools.
• To provide transparent, visual explanations of prediction features such as sequence homology and protein organization.
• To enable both bioinformatics experts and non-specialists to perform comprehensive phage characterization efficiently.
• To improve analysis speed and accuracy through optimized integration of state-of-the-art methods.

Proposed method

• PhaBOX uses a modular web architecture with a Flask-based backend and a JavaScript/CSS/Bootstrap-based frontend for user interaction.
• It integrates four core modules: phage identification (PhaMer), lifestyle prediction (PhaTYP), taxonomic classification (PhaGCN), and host prediction (CHERRY).
• Each module combines alignment-based methods with deep learning, including Transformers for sequence pattern learning and graph convolutional networks (GCNs) for knowledge graph-based feature extraction.
• Sequence similarity is visualized using BlasterJS, protein features with pViz, and topological structures with Plotly in R.
• The system supports both full pipeline and selective module execution, with optimized database queries and multi-threading for performance.
• All predictions and intermediate results are stored and made available for download, with results accessible via a user-friendly interface.

Experimental results

Research questions

• RQ1 Can an integrated web server streamline phage contig identification and characterization in metagenomic data more efficiently than standalone tools?
• RQ2 To what extent does PhaBOX improve processing speed compared to running individual tools in sequence?
• RQ3 How transparent and interpretable are the prediction results, particularly in terms of visualizing key biological features like protein homology and sequence similarity?
• RQ4 Can PhaBOX accurately predict phage lifestyle, taxonomy, and host range on diverse datasets, including short and highly divergent contigs?
• RQ5 How does the modular design of PhaBOX support flexible use by researchers with varying levels of bioinformatics expertise?

Key findings

• PhaBOX reduced the total analysis time for 129,138 contigs across 145 samples from 328 minutes (individual tools) to 216 minutes, achieving a 40% speedup.
• The platform identified 4,851 phage contigs across 18 families, infecting 211 distinct host species, demonstrating broad detection capability.
• PhaBOX achieved high accuracy in phage identification, lifestyle prediction, taxonomy, and host prediction, outperforming existing tools on benchmark datasets including RefSeq, low-similarity, and metagenomic data.
• The integration of visualization tools (e.g., BlasterJS, pViz, Plotly) enables users to inspect key features such as protein homology and sequence similarity, enhancing interpretability.
• The system supports both full analysis and selective module execution, increasing flexibility for diverse user needs.
• The web server is publicly available at https://phage.ee.cityu.edu.hk, with source code hosted on GitHub, ensuring accessibility and reproducibility.