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MetaDecoder: a novel method for clustering metagenomic contigs.

Cong-Cong LiuShan-Shan DongJia-Bin ChenChen WangPan NingYan GuoTie-Lin Yang
Published in: Microbiome (2022)
In conclusion, we developed the GPU-based MetaDecoder for effectively clustering metagenomic contigs and reconstructing microbial communities from microbial data. Applying MetaDecoder on both simulated and real-world datasets demonstrated that it could generate more complete clusters with lower contamination. Using MetaDecoder, we identified novel high-quality genomes and expanded the existing catalog of bacterial genomes. Video Abstract.
Keyphrases
  • rna seq
  • antibiotic resistance genes
  • single cell
  • microbial community
  • risk assessment
  • electronic health record
  • drinking water
  • big data
  • health risk
  • machine learning
  • heavy metals