Mining, analyzing, and integrating viral signals from metagenomic data

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Viruses, particularly phages, significantly influence microbial community structure and function.
• Existing tools for phage identification from metagenomic data are limited.
• Accurate phage analysis is essential for understanding host-microbe interactions and ecosystem dynamics.

Purpose of the Study:

• To develop VirMiner, a sensitive and comprehensive web-based tool for phage contig prediction and analysis from metagenomic data.
• To improve the identification of high-abundance phage contigs, which are often overlooked by current methods.
• To provide a user-friendly platform for analyzing phage communities in various environments, including the human gut.

Main Methods:

• Development of VirMiner using the random forest algorithm.
• Training and validation with paired metagenomic and phagenomic sequencing data from human gut flora.
• Integration of metagenomic raw reads processing, functional annotation, phage contig identification, and phage-host relationship prediction (CRISPR-spacer recognition).

Main Results:

• VirMiner demonstrated high sensitivity (41.06% ± 17.51%) and specificity (81.91% ± 4.04%) in phage contig prediction.
• VirMiner significantly outperformed existing tools like VirFinder and VirSorter in detecting high-abundance phage contigs (65.23% ± 16.94% sensitivity).
• Analysis of human gut metagenomes revealed significant differential abundance of phage-related genes (CRISPR, multidrug resistance) after antibiotic treatment.

Conclusions:

• VirMiner offers a comprehensive solution for phage prediction and analysis in metagenomic samples.
• The tool effectively captures high-abundance phage contigs, providing deeper insights into their ecological roles.
• VirMiner enhances our ability to study phage populations and their impact on bacterial communities.