Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.
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

Organelle proteomics reveals novel metabolic vulnerabilities in FLT3-ITD cells.

Leukemia·2026

Same author

Kinase inhibition rewires the HLA-I immunopeptidome in chronic myeloid leukemia.

iScience·2026

Same author

MultiOmicsXplorer, a tool to browse, access and analyse multi-omics data.

BMC bioinformatics·2026

Same author

α-ketoglutarate/succinate ratio imbalance impairs thymine DNA glycosylase function and base excision repair process increasing susceptibility to pancreatic cancer.

Cell death & disease·2026

Same author

TFEB coordinates autophagosome biogenesis and ribophagy during starvation via SQSTM1.

Science advances·2026

Same author

SIGNOR 4.0: the 2025 update with focus on phosphorylation data.

Nucleic acids research·2025

Search research articles

Related Experiment Video

Updated: Jun 15, 2025

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

SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks.

Veronica Venafra^1,2, Francesca Sacco³, Livia Perfetto⁴

¹Ph.D. Program in Cellular and Molecular Biology, Department of Biology, University of Rome 'Tor Vergata', Rome, Italy.

NPJ Systems Biology and Applications

|August 23, 2024

Summary

SignalingProfiler 2.0 is a computational tool that generates mechanistic hypotheses from multi-omics data to understand cellular signaling changes. This pipeline aids in identifying disease mechanisms and potential drug targets by creating interpretable signaling networks.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Related Experiment Videos

Last Updated: Jun 15, 2025

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

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Understanding cellular signaling remodeling is key to disease mechanism research and drug target identification.
Computational tools are essential for generating mechanistic hypotheses from complex multi-omics data.

Purpose of the Study:

To introduce SignalingProfiler 2.0, an updated multi-step pipeline for generating mechanistic hypotheses on signaling events impacting cellular phenotypes.
To provide a flexible and accessible tool for integrating and interpreting multi-omics data.

Main Methods:

SignalingProfiler 2.0 integrates proteogenomic data with prior knowledge-causal networks to derive context-specific signaling networks.
The pipeline employs statistical, footprint-based, and graph algorithms for data integration and interpretation.
Benchmarking was performed on three proof-of-concept studies.

Main Results:

SignalingProfiler 2.0 successfully generated hierarchical mechanistic networks.
The tool recapitulated known and identified novel perturbed signaling pathways and phenotypic outcomes.
The pipeline demonstrated efficacy in both human and mouse contexts.

Conclusions:

SignalingProfiler 2.0 effectively addresses the need for deriving biologically relevant insights from complex multi-omics data.
The tool's ability to extract interpretable networks facilitates a deeper understanding of cellular signaling.
This updated pipeline supports advancements in disease research and therapeutic target discovery.