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Related Experiment Video

Updated: Aug 10, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens.

Vasileios L Zogopoulos^1,2, Apostolos Malatras³, Konstantinos Kyriakidis^1,4

¹Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece.

|February 11, 2023

Summary

Human Gene Coexpression Analysis 2.0 (HGCA2.0) maps gene coexpression patterns using 3500 human samples. This tool identifies gene partners and biological processes, aiding research discovery.

Keywords:

RNA-Seq bioinformatics co-expression gene coexpression analysis gene coexpression network transcriptomics webtool

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Area of Science:

Genomics
Bioinformatics
Systems Biology

Background:

Gene coexpression analysis identifies genes with similar expression patterns, suggesting functional relationships.
Understanding coexpression is crucial for deciphering complex biological processes and gene regulatory networks.

Purpose of the Study:

To introduce Human Gene Coexpression Analysis 2.0 (HGCA2.0), a webtool for global human gene coexpression analysis.
To provide researchers with a platform for identifying coexpressed gene sets, enriched functional annotations, and potential regulatory factors.

Main Methods:

Hierarchical clustering of 55,431 Homo sapiens genes based on coexpression analysis of 3500 GTEx bulk RNA-Seq samples.
Utilized diverse tissue-specific samples to capture a comprehensive coexpression landscape.
Integrated gene term enrichment analyses (GO, pathways, diseases) and transcription factor identification.

Main Results:

HGCA2.0 successfully identifies both ubiquitous and tissue-specific coexpressed gene modules.
Benchmarking via STRING analysis positions HGCA2.0 among top-performing coexpression webtools.
The tool reveals enriched transcription factors driving observed coexpression patterns.

Conclusions:

HGCA2.0 is a valuable resource for generating experimentally testable hypotheses regarding gene function and biological pathways.
The webtool offers an intuitive interface and API for broad accessibility in gene coexpression research.
HGCA2.0 advances the study of gene relationships and functional genomics.