Abstract
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 high-quality 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, nonlinear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared with seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete ($\gt 95\%$ pure, $\gt 90\%$ complete) and high-quality ($\gt 90\%$ pure, $\gt 70\%$ complete) genomes from simulated datasets from the Critical Assessment of Metagenome Interpretation initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method.
Funder
National Research Fund
European Research Council
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Cited by
6 articles.
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