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Effective binning of metagenomic contigs using contrastive multi-view representation learning

Author

Listed:
  • Ziye Wang

    (Fudan University)

  • Ronghui You

    (Fudan University)

  • Haitao Han

    (Fudan University)

  • Wei Liu

    (Fudan University)

  • Fengzhu Sun

    (University of Southern California)

  • Shanfeng Zhu

    (Fudan University
    Shanghai Qi Zhi Institute
    Ministry of Education
    Fudan University)

Abstract

Contig binning plays a crucial role in metagenomic data analysis by grouping contigs from the same or closely related genomes. However, existing binning methods face challenges in practical applications due to the diversity of data types and the difficulties in efficiently integrating heterogeneous information. Here, we introduce COMEBin, a binning method based on contrastive multi-view representation learning. COMEBin utilizes data augmentation to generate multiple fragments (views) of each contig and obtains high-quality embeddings of heterogeneous features (sequence coverage and k-mer distribution) through contrastive learning. Experimental results on multiple simulated and real datasets demonstrate that COMEBin outperforms state-of-the-art binning methods, particularly in recovering near-complete genomes from real environmental samples. COMEBin outperforms other binning methods remarkably when integrated into metagenomic analysis pipelines, including the recovery of potentially pathogenic antibiotic-resistant bacteria (PARB) and moderate or higher quality bins containing potential biosynthetic gene clusters (BGCs).

Suggested Citation

  • Ziye Wang & Ronghui You & Haitao Han & Wei Liu & Fengzhu Sun & Shanfeng Zhu, 2024. "Effective binning of metagenomic contigs using contrastive multi-view representation learning," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44290-z
    DOI: 10.1038/s41467-023-44290-z
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