HiCBin

HiCBin performs Hi-C-based metagenomic binning to recover high-quality metagenome-assembled genomes (MAGs) from complex microbial ecosystems.

Key Features:

• Hi-C integration: Leverages high-throughput chromosome conformation capture (Hi-C) contact data to link contigs originating from the same genome across mixed microbial communities.
• Normalization (HiCzin): Applies the HiCzin normalization method to process Hi-C contact maps and mitigate biases in raw Hi-C data.
• Community detection (Leiden): Uses the Leiden community detection algorithm based on the Potts spin-glass model for clustering contigs into genomic bins.
• Spurious contact detection: Identifies and removes erroneous Hi-C contacts to improve accuracy of MAG recovery.

Scientific Applications:

• MAG recovery from complex microbial ecosystems: Recovers high-quality MAGs from mixed samples using Hi-C contact information.
• Benchmarking with ground-truth yeast metagenome: Evaluated on a metagenomic yeast sample with known contig species identities to assess binning accuracy.
• Comparative evaluation on human gut and wastewater metagenomes: Compared against ProxiMeta, bin3C, MetaTOR, and the shotgun-based MetaBAT2 on human gut and wastewater datasets, demonstrating improved binning performance.

Methodology:

Processes Hi-C contact maps with HiCzin normalization, applies spurious contact detection, and clusters contigs using the Leiden community detection algorithm based on the Potts spin-glass model.