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

Genome Biol. 2019 Feb 26;20(1):46. doi: 10.1186/s13059-019-1643-1.

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: Clustering; Community detection; Hi-C; Metagenome-assembled genome; Metagenomics; Next-generation sequencing.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Computer Simulation
  • Feces / microbiology
  • Genome, Bacterial*
  • Humans
  • Metagenomics / methods*
  • Microbiota / genetics*
  • Sequence Analysis, DNA
  • Software*