binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Genome reconstruction is vital for understanding microbial communities.
• Integrating multi-omic data requires high-quality genomes.
• Existing binning tools have limitations in accuracy and scope.

Purpose of the Study:

• Introduce binny, a novel computational tool for metagenome-assembled genome (MAG) reconstruction.
• Evaluate binny's performance against established and state-of-the-art binning methods.
• Demonstrate binny's capability to identify unique and high-quality genomes.

Main Methods:

• Binny utilizes k-mer composition and metagenomic read coverage for dimension reduction.
• It employs iterative, nonlinear reduction of genomic signatures.
• Automated contig clustering is performed with lineage-specific marker gene assessment.

Main Results:

• Binny significantly outperforms or is competitive with existing binning tools.
• It identifies unique genomes missed by other methods.
• Binny recovers the most near-complete and high-quality genomes from simulated and real-world datasets.

Conclusions:

• Binny is a superior tool for generating high-quality metagenome-assembled genomes.
• Its advanced methodology enables the discovery of previously undetectable genomes.
• Binny advances genome-resolved metagenomics and multi-omic data integration.