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Protein Networks

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

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

Published on: October 19, 2021

PhenoMultiOmics: an enzymatic reaction inferred multi-omics network visualization web server.

Yuying Shi^1,2,3, Botao Xu⁴, Zhe Wang⁵

¹Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.

Bioinformatics (Oxford, England)

|October 17, 2024

Summary

PhenoMultiOmics visualizes enzymatic reaction networks in cancer, integrating multi-omics data to reveal gene, protein, and metabolite interactions. This tool aids in understanding disease mechanisms and discovering biomarkers.

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Area of Science:

Biochemistry
Systems Biology
Bioinformatics

Background:

Enzymatic reactions are crucial for cellular processes and their disruption in disease necessitates visualization of multi-omics networks.
Understanding these perturbed networks aids in comprehending biological system functionality and regulation.

Purpose of the Study:

To design PhenoMultiOmics, a web server for exploring enzymatic reaction-based multi-omics networks in various cancer types.
To enable visualization of gene, protein, and metabolite interplay influenced by cancer-related enzymatic reactions.

Main Methods:

Curated a database of cancer-gene-protein-metabolite relationships linked by enzymatic reactions.
Developed a multi-omics network visualization module and a biomarker discovery module.
Applied the server to analyze transcriptomics data of gastric cancer and metabolomics data of lung cancer.

Main Results:

PhenoMultiOmics provides a platform for exploring cancer multi-omics networks based on enzymatic reactions.
The tool facilitates functional analysis via differential omic feature expression and pathway enrichment.
Case studies on gastric and lung cancer yielded mechanistic insights into disrupted enzymatic reactions.

Conclusions:

PhenoMultiOmics offers a valuable resource for researchers studying enzymatic reactions in cancer.
The web server aids in understanding disease mechanisms and identifying potential biomarkers through multi-omics data integration.