Benchmarking metagenomic binning tools on real datasets across sequencing platforms and binning modes

Haitao Han, Ziye Wang and Shanfeng Zhu ()
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Haitao Han: Fudan University
Ziye Wang: Nankai University
Shanfeng Zhu: Fudan University

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Metagenomic binning is a culture-free approach that facilitates the recovery of metagenome-assembled genomes by grouping genomic fragments. However, there remains a lack of a comprehensive benchmark to evaluate the performance of metagenomic binning tools across various combinations of data types and binning modes. In this study, we benchmark 13 metagenomic binning tools using short-read, long-read, and hybrid data under co-assembly, single-sample, and multi-sample binning, respectively. The benchmark results demonstrate that multi-sample binning exhibits optimal performance across short-read, long-read, and hybrid data. Moreover, multi-sample binning outperforms other binning modes in identifying potential antibiotic resistance gene hosts and near-complete strains containing potential biosynthetic gene clusters across diverse data types. This study also recommends three efficient binners across all data-binning combinations, as well as high-performance binners for each combination.

Date: 2025
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DOI: 10.1038/s41467-025-57957-6

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