Metagenomic Hi‑C Protocols for Viral Genome Binning, Taxonomic Annotation, and Interaction Network Visualization

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Metagenomic Hi-C (metaHi-C) is crucial for linking mobile genetic elements to their hosts in microbial communities.
• Post-library generation, challenges lie in bioinformatics for preprocessing, genome binning, taxonomic annotation, and network interpretation.

Purpose of the Study:

• To present a standardized metaHi-C protocol from raw reads to downstream data analysis.
• To provide a reproducible workflow for reconstructing viral and host genomes and their interactions.

Main Methods:

• Quality control of shotgun and Hi-C reads, metagenomic assembly, and Hi-C read mapping.
• Viral genome recovery (vMAGs) and virus-host linkage inference using ViralCC.
• Host genome binning with ImputeCC, taxonomic annotation with GTDB-Tk and Virgo, and network visualization with MetaHiCNet.

Main Results:

• Detailed protocols for preprocessing, assembly, and mapping of metagenomic Hi-C data.
• Successful recovery of viral metagenome-assembled genomes (vMAGs) and inference of virus-host interactions.
• Generation of cross-taxa and cross-bin Hi-C interaction networks for visualizing microbial community structure.

Conclusions:

• The presented metaHi-C protocols offer a reproducible end-to-end workflow for microbiome research.
• Enables accurate reconstruction of viral and host genomes and consistent taxonomic assignments.
• Facilitates visualization of virus-host interaction structures in diverse microbiomes.