MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities

Binning is an essential procedure during metagenomic data analysis. However, the available individual binning methods usually do not simultaneously fully use different features or biological information. Furthermore, it is challenging to integrate multiple binning results efficiently and effectively. Therefore, we developed an ensemble binner, MetaBinner, which generates component results with multiple types of features and utilizes single-copy gene (SCG) information for k-means initialization. It then utilizes a two-step ensemble strategy based on SCGs to integrate the component results. Extensive experimental results over three large-scale simulated datasets and one real-world dataset demonstrate that MetaBinner outperforms other state-of-the-art individual binners and ensemble binners. MetaBinner is freely available at https://github.com/ziyewang/MetaBinner.