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

Abstract The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present binny , a binning tool that produces complete and pure metagenome-assembled genomes (MAG) from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms or is highly competitive with commonly-used and state- of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, non-linear dimension reduction of genomic signatures, as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared to seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete (>95% pure, >90% complete) and high-quality (>90% pure, >70% complete) genomes from simulated data sets from the Critical Assessment of Metagenome Interpretation (CAMI) initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome (MIMAG) standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method.