{
  "abstract": "MOTIVATION: Network analysis has become a central strategy for dissecting complex biological and environmental systems, particularly as modern omics technologies generate increasingly large and heterogeneous datasets. However, current tools often lack the scalability, flexibility, and native multi-omics support required for high-dimensional data analysis. We developed MetaNet, a high-performance R package that unifies network construction, visualization, and analysis across diverse omics layers. RESULTS: MetaNet enables fast and scalable correlation-based network construction for datasets with more than 10 000 features, providing over 40 layout algorithms, rich annotation utilities, and visualization options compatible with both static and interactive platforms. It further offers comprehensive topological and stability metrics for in-depth network characterization. Benchmarking shows that MetaNet delivers up to a 100-fold improvement in computation time and a 50-fold reduction in memory usage compared to existing R packages. We demonstrate its utility through two representative applications: (1) longitudinal microbial co-occurrence networks revealing airborne microbiome dynamics, and (2) an integrative exposome-transcriptome network of over 40 000 features, uncovering distinct regulatory impacts of biological and chemical exposures. By offering a robust, reproducible, and biologically informed framework, MetaNet advances multi-omics network analysis across biological, ecological, and environmental domains. AVAILABILITY: MetaNet package is freely available at https://github.com/Asa12138/MetaNet.",
  "abstract_shown": true,
  "affiliations": [
    "MOE Key Laboratory of Biosystems Homeostasis & Protection, and Zhejiang Key Laboratory of Molecular Cancer Biology, Life Sciences Institute, Zhejiang University , Hangzhou, Zhejiang 310058,",
    "State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou, Zhejiang 310009,",
    "School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University , Singapore, 637371,",
    "Singapore Phenome Center, Lee Kong Chian School of Medicine, Nanyang Technological University , Singapore, 636921,",
    "Department of Environmental Health and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health , Boston, MA 02115,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Peng Chen",
      "orcid": "0000-0001-7522-3813"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Liuyiqi Jiang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zinuo Huang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Xin Wei",
      "orcid": "0000-0002-7401-4005"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoping Zhu",
      "orcid": "0000-0001-7236-731X"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zhen Liu",
      "orcid": "0000-0001-6816-8342"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Qiong Chen",
      "orcid": "0009-0004-4369-5620"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Xiaotao Shen",
      "orcid": "0000-0002-9608-9964"
    },
    {
      "affs": [
        4
      ],
      "name": "Peng Gao",
      "orcid": "0000-0002-4311-584X"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Chao Jiang",
      "orcid": "0000-0003-0260-7271"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9242kw50k5kcm.bib",
  "cited_by": 10,
  "doi": "10.1093/bioinformatics/btag321",
  "fulltext_url": null,
  "id": "9242kw50k5kcm",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://doi.org/10.1093/bioinformatics/btag321",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Bioinformatics and Genomic Networks"
    ]
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  "openalex": "W4411842395",
  "pdf_url": null,
  "pmcid": "PMC13224960",
  "pmid": "42162964",
  "published": "2026-05-16",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/9242kw50k5kcm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:48Z",
      "native_id": "W4411842395",
      "source": "openalex",
      "url": "https://doi.org/10.1093/bioinformatics/btag321"
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  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Bioinformatics and Genomic Networks",
      "subfield": "molecular-biology"
    }
  ],
  "title": "MetaNet: a scalable and integrated tool for reproducible omics network analysis",
  "title_text": "MetaNet: a scalable and integrated tool for reproducible omics network analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:07Z",
  "url": "https://research.draconitic.com/paper/9242kw50k5kcm",
  "venue": "Bioinformatics",
  "versions": []
}