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Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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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,...

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Related Experiment Video

Updated: Oct 10, 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

Interpretation of network-based integration from multi-omics longitudinal data.

Antoine Bodein¹, Marie-Pier Scott-Boyer¹, Olivier Perin²

¹Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada.

Nucleic Acids Research

|December 9, 2021

Summary

This summary is machine-generated.

This study introduces a novel approach for interpreting complex multi-omics data, revealing hidden biological mechanisms and interactions. The method uses hybrid networks and R package netOmics for analyzing longitudinal data and predicting functional modules.

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Area of Science:

Systems Biology
Bioinformatics
Genomics

Background:

Multi-omics integration is crucial for understanding complex biological systems.
Longitudinal multi-omics data can reveal dynamic relationships but existing integration methods lack interpretability.
A significant challenge lies in unlocking and interpreting the vast knowledge within multi-omics datasets.

Purpose of the Study:

To develop a generic approach for interpreting multi-omics longitudinal data.
To address the challenge of interpretability in multi-omics integration.
To identify key players and dynamic interactions in biological systems.

Main Methods:

Building and exploring hybrid multi-omics networks incorporating known and inferred relationships.
Utilizing smart node labeling and propagation analysis for mechanism prediction.
Applying the approach to diverse multi-omics longitudinal datasets.

Main Results:

Identification of novel multi-layer interactions and regulatory mechanisms.
Discovery of multi-omics functional modules not detectable by single-omics analysis.
Highlighting kinetic interplay for novel biological mechanism insights.

Conclusions:

The proposed generic approach enhances the interpretation of multi-omics longitudinal data.
This method facilitates the discovery of complex biological mechanisms and interactions.
The R package netOmics provides a readily applicable tool for multi-omics data analysis.