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

Vasileios L. Zogopoulos, Apostolos Malatras, Konstantinos Kyriakidis, Chrysanthi Charalampous, Evanthia A. Makrygianni, Stéphanie Duguez, Marianna A. Koutsi, Marialena Pouliou, Christos Vasileiou, William J. Duddy, Marios Agelopoulos, George P. Chrousos, Vassiliki A. Iconomidou, Ioannis Michalopoulos

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Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 Homo sapiens genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint.
Original languageEnglish
Article number388
Number of pages1
Issue number3
Early online date21 Jan 2023
Publication statusPublished online - 21 Jan 2023

Bibliographical note

Funding Information:
This work was supported by the project “ELIXIR-GR: Managing and Analysing Life Sciences Data” (MIS: 5002780) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). A.M was supported by the CY-Biobank project, under the European Union’s Horizon 2020 research and innovation program, GA (Grant Agreement) No 857122.

Publisher Copyright:
© 2023 by the authors.


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


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