bin3C: exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes

Abstract

Abstract Most microbes cannot be easily cultured, and metagenomics provides a means to study them. Current techniques aim to resolve individual genomes from metagenomes, so-called metagenome-assembled genomes (MAGs). Leading approaches depend upon time series or transect studies, the efficacy of which is a function of community complexity, target abundance, and sequencing depth. We describe an unsupervised method that exploits the hierarchical nature of Hi-C interaction rates to resolve MAGs using a single time point. We validate the method and directly compare against a recently announced proprietary service, ProxiMeta. bin3C is an open-source pipeline and makes use of the Infomap clustering algorithm (https://github.com/cerebis/bin3C).

Keywords

• Metagenomics
• Hi-C
• Next-generation sequencing
• Metagenome-assembled genome
• Community detection
• Clustering