GMW: a hybrid graph-based approach for post-assembly metagenome analysis and decontamination.

Accurate genome assembly from metagenomic sequencing data remains challenging, particularly in mixed infections involving multiple pathogens, due to data complexity and contaminant sequences. Here, we present GMW (Genomic Microbe-Wise), a novel computational tool that improves pathogen genome assembly accuracy and enhances contaminant removal capabilities by simplifying the post-assembly graph. GMW leverages community detection algorithms, sequence similarity analysis, and coverage patterns to resolve strain mixtures and improve assembly accuracy. Using datasets of influenza A virus subtypes, we demonstrate GMW's ability to disentangle mixed infections and reconstruct complete viral genomes with high precision. Additionally, GMW outperforms traditional sequence similarity methods in classifying target contigs from contaminants. This tool also provides interactive visualization modules to streamline the inspection of assembly outputs, including simplified representations of complex assembly graphs. By enhancing assembly quality and contamination filtering, GMW emerges as a versatile solution for applications in clinical diagnostics, microbial ecology, and pathogen surveillance.