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

Yuxuan DuFengzhu Sun
Published in: Genome biology (2022)
Recovering high-quality metagenome-assembled genomes (MAGs) from complex microbial ecosystems remains challenging. Recently, high-throughput chromosome conformation capture (Hi-C) has been applied to simultaneously study multiple genomes in natural microbial communities. We 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 clustering algorithm and includes the spurious contact detection into binning pipelines for the first time. HiCBin is validated on one synthetic and two real metagenomic samples and is shown to outperform the existing Hi-C-based binning methods. HiCBin is available at https://github.com/dyxstat/HiCBin .
Keyphrases
  • high throughput
  • single cell
  • antibiotic resistance genes
  • machine learning
  • climate change
  • microbial community
  • deep learning
  • rna seq
  • gene expression
  • molecular dynamics simulations
  • label free
  • quantum dots