MetaNet: a scalable and integrated tool for reproducible omics network analysis
5 affiliations
- 1MOE Key Laboratory of Biosystems Homeostasis & Protection, and Zhejiang Key Laboratory of Molecular Cancer Biology, Life Sciences Institute, Zhejiang University , Hangzhou, Zhejiang 310058,
- 2State 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,
- 3School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University , Singapore, 637371,
- 4Singapore Phenome Center, Lee Kong Chian School of Medicine, Nanyang Technological University , Singapore, 636921,
- 5Department of Environmental Health and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health , Boston, MA 02115,
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.
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BibTeX
@article{chen2026metanet,
title = {{MetaNet: a scalable and integrated tool for reproducible omics network analysis}},
author = {Peng Chen and Liuyiqi Jiang and Zinuo Huang and Xin Wei and Xiaoping Zhu and Zhen Liu and Qiong Chen and Xiaotao Shen and Peng Gao and Chao Jiang},
journal = {Bioinformatics},
year = {2026},
month = may,
doi = {10.1093/bioinformatics/btag321},
url = {https://doi.org/10.1093/bioinformatics/btag321},
note = {CC BY},
}
RIS
TY - JOUR
TI - MetaNet: a scalable and integrated tool for reproducible omics network analysis
AU - Peng Chen
AU - Liuyiqi Jiang
AU - Zinuo Huang
AU - Xin Wei
AU - Xiaoping Zhu
AU - Zhen Liu
AU - Qiong Chen
AU - Xiaotao Shen
AU - Peng Gao
AU - Chao Jiang
PY - 2026
DA - 2026/05/16
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag321
UR - https://doi.org/10.1093/bioinformatics/btag321
AB - 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.
SN - 1367-4803
SN - 1367-4811
ER -