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

3.9K
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,...
3.9K
Proteomics01:33

Proteomics

7.2K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.2K

You might also read

Related Articles

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

Sort by
Same author

Correction to "Multi-Laboratory Assessment Reveals Variable Ion Species Profiles in Electrospray Ionization Mass Spectrometer".

Journal of the American Society for Mass Spectrometry·2026
Same author

Multi-Laboratory Assessment Reveals Variable Ion Species Profiles in Electrospray Ionization Mass Spectrometry.

Journal of the American Society for Mass Spectrometry·2026
Same author

Comparison of Extraction Methods for the Quantification of Phytohormones from Tomato Fruits and Leaves by LC-MS/MS.

bioRxiv : the preprint server for biology·2026
Same author

Creating taxonomically-informed metabolome libraries for any species using the pubchem.bio R package.

The Analyst·2025
Same author

mzPeak: Designing a Scalable, Interoperable, and Future-Ready Mass Spectrometry Data Format.

Journal of proteome research·2025
Same author

The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.

Journal of mass spectrometry : JMS·2025

Related Experiment Video

Updated: Jun 9, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K

Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data.

Sean M Colby1, Madelyn R Shapiro2, Andy Lin3

  • 1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.

Journal of Proteome Research
|October 22, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces molecular hypernetworks (MHNs), a novel approach to analyze complex metabolomics data. MHNs offer improved visualization and annotation confidence compared to traditional molecular networks.

Keywords:
feature annotationhypergraphsmass spectrometrymetabolomicsmolecular hypernetworksmolecular networksspectral similarity

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

3.1K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

20.9K

Related Experiment Videos

Last Updated: Jun 9, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K
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

3.1K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

20.9K

Area of Science:

  • Analytical Chemistry
  • Computational Biology
  • Bioinformatics

Background:

  • High-resolution mass spectrometry (HRMS) is crucial for untargeted metabolomics.
  • Orthogonal separations enhance HRMS data analysis for complex samples.
  • Molecular networks (MNs) visualize molecular relationships but have limitations in representing complex data.

Purpose of the Study:

  • To introduce molecular hypernetworks (MHNs) as an advanced model for multiway relationships in metabolomics data.
  • To demonstrate the utility of MHNs for improved exploratory data analysis, visualization, and annotation confidence.
  • To present a method for constructing MHNs from existing MNs.

Main Methods:

  • Development and illustration of molecular hypernetwork (MHN) models.
  • Construction of MHNs from liquid chromatography- and ion mobility spectrometry-separated MS data.
  • MHN construction via "clique reconstructions" from existing molecular networks (MNs).

Main Results:

  • MHNs natively represent multiway relationships among observations, offering a more parsimonious model than traditional MNs.
  • MHNs enhance exploratory data analysis and visualization of complex metabolomics data.
  • MHNs show potential for increasing confidence in molecular annotation propagation.

Conclusions:

  • Molecular hypernetworks provide a powerful framework for analyzing complex, multidimensional metabolomics data.
  • MHNs improve upon existing molecular network strategies for data exploration and annotation.
  • The proposed method facilitates the transition from MNs to MHNs, enabling more robust molecular discovery.