PhaBOX: a web server for identifying and characterizing phage contigs in metagenomic data

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.

Characterizing the gut phageome and phage-borne antimicrobial resistance genes in pigs

“…However, detecting the phage contigs from metagenomes accurately is a challenge due to the limited reference genomes and high diversity of communities [ 3 ]. Recently, the PhaBOX was developed for phage contig identification, lifestyle prediction, taxonomic classification, and host prediction from the bulk metagenomes conveniently [ 27 ]. The PhaBOX strategy comprises four tools, including PhaMer for phage identification [ 34 ], PhaTYP for lifestyle prediction [ 35 ], PhaGCN for taxonomy classification [ 36 ], and CHERRY for host prediction [ 37 ].…”

“…The PhaBOX strategy comprises four tools, including PhaMer for phage identification [ 34 ], PhaTYP for lifestyle prediction [ 35 ], PhaGCN for taxonomy classification [ 36 ], and CHERRY for host prediction [ 37 ]. These tools combine the strength of alignment-based strategies and deep learning models to learn different sequence-based features [ 27 ] and have been used in recent studies [ 38 – 44 ]. In our study, a total of 174,897 non-redundant gut phage genomes were assembled and a total of 33,487 gut phage genomes were classified from 112 pig gut metagenomes using the PhaBOX strategy.…”

“…In this study, we used a toolbox for phage analysis (named PhaBOX) [ 27 ] to identify and characterize the gut phages in 112 pigs from seven pig breeds. The data indicated that a total of 174,897 non-redundant gut phage genomes were assembled and a total of 33,487 gut phage genomes were classified.…”

Developmental Dynamics of the Gut Virome in Tibetan Pigs at High Altitude: A Metagenomic Perspective across Age Groups

“…To verify the novelty of the vOTUs, the vOTUs sequences were compared with the IMG/VR virus database through blastn v2.0.6 software [ 34 ]. Following this, the species identification was established by geNomad software v1.8.0 [ 35 ], and PhaBOX ( ) [ 36 ] was employed to predict the host information of the phage. The taxonomic level of the sample was determined using the geNomad results, including phylum, class, order, and family.…”

Valp1, a Newly Identified Temperate Phage Facilitating Coexistence of Lysogenic and Non-Lysogenic Populations of Vibrio anguillarum

“…Genome annotation was performed using the Pharokka annotation tool [ 26 ], following a manual curation. The visualization of the phage genome was done with the online tool Proksee [ 27 ], and the phylogenetic analysis was performed with the online tool VipTree [ 28 ] and PhaBOX [ 29 ]. The GeneBank accession number for the Valp1 phage is OR500391.…”