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Inferring Gene Networks from High-Throughput Transcriptomes.

David Navarro-Payá¹, Luis Orduña¹, José D Fernández^2,3

¹Institute for Integrative Systems Biology (I2SysBio), Universitat de València-CSIC, Paterna, Valencia, Spain.

Methods in Molecular Biology (Clifton, N.J.)

|January 10, 2026

Summary

This summary is machine-generated.

This study presents two methods for building gene networks from plant transcriptomic data. These gene regulatory networks (GRNs) and aggregated gene co-expression networks (aggGCNs) aid in understanding gene interactions and transcriptional regulation.

Keywords:

AUROC Gene co-expression networks Gene regulatory networks Genie3 Machine learning Plant transcriptomic regulation

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Area of Science:

Systems biology
Bioinformatics
Genomics

Background:

Systems biology utilizes network theory to understand complex genome-wide gene interactions.
Gene networks, including gene co-expression networks (GCNs) and gene regulatory networks (GRNs), are crucial for predicting gene function and modeling transcriptional regulation in plants.

Purpose of the Study:

To present two distinct strategies for constructing gene networks using high-throughput transcriptomic data.
To provide adaptable workflows for generating aggregated gene co-expression networks (aggGCNs) and inferred gene regulatory networks (GRNs).

Main Methods:

Development of a custom in-house pipeline for building aggGCNs.
Inference of GRNs utilizing the GENIE3 algorithm.
Application of workflows to plant species like grapevine and tomato.

Main Results:

Successful generation of aggGCNs and GRNs from transcriptomic data.
Demonstration of adaptable computational workflows for plant gene network construction.
Public availability of all code and associated repositories.

Conclusions:

The presented strategies and workflows facilitate the construction of plant gene networks.
These methods can be adapted for use in any plant species or eukaryotic organism.
The generated networks enhance the understanding of gene-to-gene interactions and transcriptional regulation.