Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

[Expression analysis of transcription factor ERF gene family of Panax ginseng].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2020
Same author

Shenhuang granule in the treatment of severe coronavirus disease 2019 (COVID-19): study protocol for an open-label randomized controlled clinical trial.

Trials·2020
Same author

Efficacy of weekly amrubicin for refractory or relapsed non-small cell lung cancer: A protocol of systematic review and meta-analysis.

Medicine·2020
Same author

Effects and safety of Buyang-Huanwu Decoction for the treatment of patients with acute ischemic stroke: A protocol of systematic review and meta-analysis.

Medicine·2020
Same author

Effect and safety of Huangqi-Guizhi-Wuwu Decoction and Erxian Decoction in the treatment of frozen shoulder: A protocol for systematic review and meta-analysis.

Medicine·2020
Same author

A Highly Efficient BODIPY Based Turn-off Fluorescent Probe for Detecting Cu<sup>2</sup>.

Journal of fluorescence·2020

Related Experiment Video

Updated: May 22, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

MetaNet: a scalable and integrated tool for reproducible omics network analysis.

Chen Peng1,2, Liuyiqi Jiang1,2, Zinuo Huang1,2

  • 1MOE Key Laboratory of Biosystems Homeostasis & Protection, and Zhejiang Key Laboratory of Molecular Cancer Biology, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, China.

Bioinformatics (Oxford, England)
|May 21, 2026
PubMed
Summary

MetaNet is a new R package for analyzing large biological and environmental datasets. It offers fast, scalable network construction and analysis for multi-omics data, improving computational efficiency.

More Related Videos

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: May 22, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

  • Computational Biology
  • Systems Ecology
  • Environmental Health

Background:

  • Network analysis is crucial for understanding complex biological and environmental systems.
  • Existing tools struggle with the scale and heterogeneity of modern omics data.
  • There is a need for scalable, flexible, and multi-omics-capable network analysis tools.

Purpose of the Study:

  • To develop a high-performance R package for unified network construction, visualization, and analysis across diverse omics layers.
  • To address the limitations of current tools in handling high-dimensional, multi-omics datasets.
  • To provide a robust framework for biological and environmental network research.

Main Methods:

  • Developed MetaNet, an R package for network analysis.
  • Implemented fast and scalable correlation-based network construction for large datasets (>10,000 features).
  • Integrated over 40 layout algorithms, annotation utilities, and visualization options for static and interactive platforms.
  • Included comprehensive topological and stability metrics for network characterization.

Main Results:

  • MetaNet achieves up to 100x speedup and 50x memory reduction compared to existing R packages.
  • Demonstrated utility in analyzing longitudinal microbial co-occurrence networks (airborne microbiome).
  • Showcased integrative exposome-transcriptome network analysis (>40,000 features) to identify exposure impacts.
  • Validated the package's performance and applicability in diverse biological and environmental contexts.

Conclusions:

  • MetaNet provides a powerful, efficient, and flexible framework for multi-omics network analysis.
  • The package advances the study of complex biological, ecological, and environmental systems.
  • MetaNet enhances reproducibility and biological insight in high-dimensional data analysis.