Benchmarking metagenomic binning tools on real datasets across sequencing platforms and binning modes
Haitao Han, Ziye Wang, Shanfeng Zhu
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. Metagenomic binning is the process of grouping metagenomic sequences by their organism of origin. Here the authors benchmark 13 metagenomic binning tools using seven data-binning combinations on five real-world datasets.