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

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Metagenomic sequencing presents challenges in accurate genome assembly, especially with mixed infections and contaminant sequences.
• Existing methods struggle with data complexity, impacting pathogen identification and analysis.

Purpose of the Study:

• To introduce Genomic Microbe-Wise (GMW), a novel computational tool designed to enhance pathogen genome assembly accuracy.
• To improve contaminant removal from metagenomic data.
• To provide simplified visualization of complex assembly graphs.

Main Methods:

• GMW utilizes community detection algorithms, sequence similarity analysis, and coverage patterns.
• The tool simplifies post-assembly graphs to resolve strain mixtures.
• It was tested using influenza A virus subtype datasets.

Main Results:

• GMW successfully disentangled mixed infections and reconstructed complete viral genomes with high precision.
• The tool demonstrated superior performance in classifying target contigs over traditional methods.
• Interactive visualization modules were developed for streamlined output inspection.

Conclusions:

• GMW significantly improves pathogen genome assembly accuracy and contaminant filtering in metagenomic data.
• This versatile tool supports applications in clinical diagnostics, microbial ecology, and pathogen surveillance.
• GMW offers a robust solution for analyzing complex microbial communities.