Benchmarking metagenomic binning tools on real datasets across sequencing platforms and binning modes

Area of Science:

• Genomics
• Bioinformatics
• Microbial Ecology

Background:

• Metagenomic binning is crucial for assembling genomes from complex microbial communities without cultivation.
• Existing tools lack comprehensive benchmarking across diverse data types and binning strategies.
• Evaluating binning performance is essential for accurate genome recovery and functional gene identification.

Purpose of the Study:

• To benchmark 13 metagenomic binning tools using short-read, long-read, and hybrid data.
• To evaluate performance across co-assembly, single-sample, and multi-sample binning modes.
• To identify optimal binning strategies for genome recovery and functional gene discovery.

Main Methods:

• Benchmarking 13 metagenomic binning tools.
• Utilizing short-read, long-read, and hybrid sequencing data.
• Comparing co-assembly, single-sample, and multi-sample binning approaches.

Main Results:

• Multi-sample binning demonstrated superior performance across all tested data types (short-read, long-read, hybrid).
• Multi-sample binning effectively identified antibiotic resistance gene hosts and strains with biosynthetic gene clusters.
• Three universally efficient binners and several high-performance binners for specific combinations were identified.

Conclusions:

• Multi-sample binning is the recommended strategy for maximizing genome recovery and functional insights from metagenomic data.
• The study provides valuable recommendations for selecting appropriate binning tools and strategies based on data type and research goals.
• This benchmark advances the field of metagenomics by offering a standardized evaluation of binning tool performance.