HiCBin: Binning metagenomic contigs and recovering metagenome-assembled genomes using Hi-C contact maps

Recovering high-quality metagenome-assembled genomes (MAGs) from complex microbial ecosystems remains challenging. Conventional shotgun-based binning approaches may encounter barriers when multiple samples are scarce. Recently, high-throughput chromosome conformation capture (Hi-C) has been applied to simultaneously study multiple genomes in natural microbial communities. Several Hi-C-based binning pipelines have been put forward and yielded state-of-the-art results using a single sample. We conclude that normalization and clustering are two vital steps in the Hi-C-based binning analyses, and develop HiCBin, a novel open-source pipeline, to resolve high-quality MAGs utilizing Hi-C contact maps. HiCBin employs the HiCzin normalization method and the Leiden community detection algorithm based on the Potts spin-glass model and includes the spurious contact detection into binning pipelines for the first time. Using the metagenomic yeast sample with a perfect ground truth of contigs’ species identity, we comprehensively evaluate the impacts on the binning performance of different normalization methods and clustering algorithms from the HiCBin and other available metagenomic Hi-C analysis pipelines, demonstrate that the HiCzin and the Leiden algorithm achieve the best binning accuracy, and show that the spurious contact detection can improve the retrieval performance. We also validate our method and compare the capability to recover high-quality MAGs of HiCBin against other state-of-the-art Hi-C-based binning tools including ProxiMeta, bin3C, and MetaTOR, and one popular shotgun-based binning software MetaBAT2 on a human gut sample and a wastewater sample. HiCBin provides the best performance and applicability in resolving MAGs and is available at https://github.com/dyxstat/HiCBin .