Source-linked AI summary
PhaBOX: A web server for identifying and characterizing phage contigs in metagenomic data
Jiayu Shang, Cheng Peng, Herui Liao, Xubo Tang, Yanni Sun
TL;DR
Phage analysis can require installing several tools and parsing their intermediate files, sometimes with substantial computing resources or informatics training. PhaBOX integrates phage identification, lifestyle, taxonomy, and host prediction with visualizations of prediction features. In its case study, it analyzed 129,138 contigs in 216 minutes, saving 40% of the time needed to run the programs separately.
Problem
Characterizing phages in metagenomic data can require installing multiple tools and parsing intermediate files, and some software demands informatics training and substantial computing resources.
Source: §1 · pp. 1–2Method
PhaBOX integrates phage identification, lifestyle prediction, taxonomic classification, and host prediction, and visualizes features behind its predictions.
Source: §1 · p. 2, §1.1 · p. 3Results
216 minutes analyzed 129,138 contigs from 145 samples, saving 40% of the 328 minutes required to run each program separately.
Source: §2.2 · p. 5Takeaways & Limitations
The case study demonstrates fast and convenient phage characterization in metagenomic data using an integrated platform with detailed visualizations.
Source: §3 · p. 5Some contigs with database alignments receive confidence scores too low to be predicted as phages; possible reasons include assembly errors or regions shared by phages and bacteria.
Source: §2.1 · p. 5
Abstract
from arXiv · showhide
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: yannisun@cityu.edu.hk
1 Introduction
Phage analysis tools are fragmented and can require informatics training, substantial computing resources, and difficult installation. PhaBOX integrates phage identification and characterization with visualizations of prediction features, and offers modular or end-to-end analysis.
1 Introduction
- Separate tools can require users to install multiple programs, parse intermediate files, and have informatics training and substantial computing resources.Some open-source tools lack detailed manuals, making installation tedious and error-prone.Source: §1 · pp. 1–2
- PhaBOX integrates phage identification, lifestyle prediction, taxonomic classification, and host prediction in a web service that visualizes features behind its predictions.It accepts metagenomic or whole-genome assemblies and provides predictions and intermediate results for downstream analysis.Source: §1 · p. 2, Figure 1 · p. 2
1.1 Overview
- PhaBOX combines PhaMer, PhaTYP, PhaGCN, and CHERRY, whose methods combine alignment-based strategies and deep learning to use sequence-based features.Tests on highly diverged phages, short contigs, mock metagenomic data, and real metagenomic data are reported to outperform available programs; CHERRY supports species-level host prediction.Source: §1.1 · p. 2
- The default mode identifies phage contigs with PhaMer before lifestyle, taxonomy, and host analyses, while a modular mode runs selected functions.PhaBOX accepts metagenomic or whole-genome FASTA assemblies, searches each module against NCBI RefSeq, and has a multithreaded local version for large datasets.Source: §1.1 · p. 2
- PhaBOX visualizes sequence-similarity relationships, predicted proteins, and protein homology to show features behind its predictions.The overview presents these visualizations as unavailable in previous works and describes the platform as faster end-to-end analysis.Source: §1.1 · p. 3
2 Case study
In a case study of 145 Chinese individuals, PhaBOX analyzed assembled gut microbiome contigs and produced phage identification, classification, lifestyle, and host outputs. Its result page combines downloadable predictions with visualizations and flags low-confidence contigs for further analysis.
2 Case study
- The case study used contigs from 145 Chinese fecal samples—71 from people with type 2 diabetes and 74 controls—and set a 10 kbp minimum contig length.The assembled contigs came from data provided by the study authors; the length threshold was chosen as suggested in that study.Source: §2 · p. 3
2.1 Example of the result page
- The result page provides prediction downloads, pie or stacked-bar charts, contig-level homology views, and family-classification and host-prediction tables.Downloaded outputs include four CSV files for phage identification, taxonomy, lifestyle, and hosts, plus intermediate homology and network files.Source: §2.1 · pp. 4–5
- Low-confidence results include contigs with database alignments that the model does not confidently predict as phages.Possible reasons include assembly errors or sequence regions shared by phages and bacteria; the page provides results and visualizations for further analysis.Source: §2.1 · p. 5
2.2 Outputs of PhaBOX
- 216 minutes analyzed 129,138 contigs from 145 samples, saving 40% of the 328 minutes required to run each program separately.Both timings were recorded on an Intel Xeon Gold 6258R CPU with 36 threads.Source: §2.2 · p. 5
- 4,851 phage contigs were identified across the 145 samples, spanning 18 families and 211 host species.The summary file provides comprehensive analysis for each contig, with prediction-confidence details available in supplementary files.Source: §2.2 · p. 5
3 Conclusion
PhaBOX integrates phage identification, lifestyle prediction, taxonomy, and host prediction with visualizations, and its case study demonstrates fast, convenient characterization of metagenomic phages. The authors identify novel protein annotation, protein structural analysis, and faster server hardware as future improvements.
3 Conclusion
- PhaBOX integrates phage identification, lifestyle prediction, taxonomic classification, and host prediction, with detailed visualizations for metagenomic assemblies.The case study demonstrates fast and convenient phage characterization in metagenomic data.Source: §3 · p. 5
- The web server is intended to serve users studying phages across ecosystems including soil and marine samples.The authors note that phages occur in many different ecosystems.Source: §3 · p. 5
- Future improvements include novel protein annotation, protein structural analysis, and upgraded server hardware for faster predictions.Source: §3 · p. 5
Method and materials
PhaBOX uses a web interface and server backend to run four phage-analysis modules. Transformer models support identification and lifestyle prediction, while graph convolutional networks use integrated sequence features for taxonomy and host prediction.
Method and materials
- PhaBOX separates task submission and output display in a client interface from input handling and prediction execution in a server backend.The interface uses JavaScript, CSS, jQuery, and Bootstrap, with tools for sequence similarity, protein viewing, and graph display.Source: Method and materials · p. 6
- Transformer language models learn abstract phage-sequence patterns for phage identification and lifestyle prediction.The pipeline also includes taxonomic classification and host-prediction modules.Source: Method and materials · p. 6
- For taxonomy and host prediction, PhaBOX integrates protein- and DNA-based sequence features in a knowledge graph and applies graph convolutional networks to labeled and unlabeled samples.The methods were benchmarked against state-of-the-art tools on multiple datasets, including RefSeq, low-similarity, and metagenomic datasets.Source: Method and materials · p. 6