{
  "abstract": "Long-read sequencing advances metagenomics by producing highly contiguous assemblies and more complete metagenome-assembled genomes (MAGs). However, current long-read metagenomic binners fail to incorporate the rich information of long-read assemblies into representation learning and exhibit limited performance on complex datasets. Here, we show that a higher proportion of long-read assembled contigs contain single-copy genes (SCGs) and more SCGs per contig. Therefore, we developed SCGBinner, which leverages SCG-guided contrastive learning to exploit the advantage of long-read data for learning high-quality contig embeddings. SCGBinner consistently outperforms other binning methods across five simulated and seven real-world long-read datasets, especially on real-world high-diversity samples. For a deep agricultural soil metagenome, SCGBinner recovered 71% more high-quality MAGs and 38% more near-complete MAGs than the second-best method. Notably, SCGBinner uniquely recovered 449 novel high-quality species, which shed light on the predicted ecological roles of 65 uncharacterised families and 93 novel genera. Overall, SCGBinner could harness the potential of long-read sequencing to provide unprecedented insights into the microbial dark matter of complex microbial communities.",
  "abstract_shown": true,
  "affiliations": [
    "Fudan University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "H. Han"
    },
    {
      "name": "L. F. Messer"
    },
    {
      "name": "C. Quince"
    },
    {
      "name": "G. D. Bending"
    },
    {
      "name": "S. Raguideau"
    },
    {
      "name": "Z. Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Zhu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8zdye3c1fdbat.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.07.749871",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749871v1.full",
  "id": "8zdye3c1fdbat",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749871v1",
  "language": null,
  "licence": "CC-BY-ND",
  "licence_label": "CC BY-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Genomics and Phylogenetic Studies"
    ]
  ],
  "openalex": "W7212177334",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749871v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8zdye3c1fdbat.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:42Z",
      "native_id": "10.64898/2026.09.07.749871",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749871v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Advancing long-read metagenomic binning via single-copy-gene guided contrastive learning",
  "title_text": "Advancing long-read metagenomic binning via single-copy-gene guided contrastive learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:42Z",
  "url": "https://research.draconitic.com/paper/8zdye3c1fdbat",
  "venue": "bioRxiv",
  "versions": []
}