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 as well as for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms existing 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 five widely used binning algorithms, binny 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 from a real-world benchmark comprised of metagenomes from various environments. binny is implemented as Snakemake workflow and available from https://github.com/a-h-b/binny.

Multi-omics analysis of mouse fecal microbiome reveals supplier-dependent functional differences and novel metagenome-assembled genomes

“…Thereafter, an iterative co-assembly of both the metagenomic and metatranscriptomic reads using MEGAHIT v1.2.9 69 is performed. Concurrently, MetaBAT2 v2.12.1, 70 MaxBin2 v2.2.7, 71 and binny 72 were used for binning the assembly, for reconstructing metagenome-assembled genomes (MAGs). Upon completion of binning, we used DASTool 73 to select a non-redundant set of MAGs using a recommended threshold score of 0.7.…”