Deployable high-fidelity metagenome binning at scale with QuickBin

Reconstructing genomes from metagenomic assemblies is foundational to microbiome research, yet binning faces a persistent trade-off between fidelity and throughput. Many high-accuracy methods rely on GPU-intensive workflows, marker-gene postprocessing, or heavy computational resources, limiting reproducible use at scale. Here, we present QuickBin, a CPU-native, marker-free binning algorithm designed to recover near-complete, ultra-low-contamination metagenome-assembled genomes (MAGs) efficiently. QuickBin pairs a GC-coverage spatial index (BinMap) with an early-exit Oracle cascade of similarity tests (scalar composition/coverage filters and SIMD-accelerated k-mer comparisons), reserving a compact neural network exclusively for ambiguous merges. Across synthetic communities, evaluated by marker-based and contig-origin ground truth, QuickBin maximizes high-fidelity sequence recovery. In benchmarking 297 diverse real metagenomes, QuickBin completed all runs, recovering more high-quality MAGs (≥95% completeness, ≤1% contamination) than resource-intensive alternatives that frequently failed. QuickBin provides a practical path to reproducible, genome-resolved metagenomics at scale for downstream comparative analyses. Open-source at: https://github.com/bbushnell/BBTools . We develop and benchmark QuickBin, a fast, marker-free method for accurate and scalable metagenome binning. QuickBin recovers near-complete, low-contamination genomes while completing all real metagenome runs with short runtimes and low memory use.