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Article

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

1
Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
2
Section of Cell Biology and Biophysics, Department of Biology, National and Kapodistrian University of Athens, 15701 Athens, Greece
3
Biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, 2029 Nicosia, Cyprus
4
School of Pharmacy, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
5
Centre of Basic Research, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
6
University Research Institute of Maternal and Child Health and Precision Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
7
Personalised Medicine Centre, School of Medicine, Ulster University, Derry-Londonderry BT47 6SB, UK
8
Engineering Design and Computing Laboratory, ETH Zurich, 8092 Zurich, Switzerland
*
Author to whom correspondence should be addressed.
Cells 2023, 12(3), 388; https://doi.org/10.3390/cells12030388
Received: 22 November 2022 / Revised: 9 January 2023 / Accepted: 19 January 2023 / Published: 21 January 2023
(This article belongs to the Special Issue Bioinformatics and Cells)

Abstract

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.
Keywords: gene coexpression analysis; gene coexpression network; co-expression; RNA-Seq; transcriptomics; bioinformatics; webtool gene coexpression analysis; gene coexpression network; co-expression; RNA-Seq; transcriptomics; bioinformatics; webtool

Share and Cite

MDPI and ACS Style

Zogopoulos, V.L.; Malatras, A.; Kyriakidis, K.; Charalampous, C.; Makrygianni, E.A.; Duguez, S.; Koutsi, M.A.; Pouliou, M.; Vasileiou, C.; Duddy, W.J.; Agelopoulos, M.; Chrousos, G.P.; Iconomidou, V.A.; Michalopoulos, I. HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens. Cells 2023, 12, 388. https://doi.org/10.3390/cells12030388

AMA Style

Zogopoulos VL, Malatras A, Kyriakidis K, Charalampous C, Makrygianni EA, Duguez S, Koutsi MA, Pouliou M, Vasileiou C, Duddy WJ, Agelopoulos M, Chrousos GP, Iconomidou VA, Michalopoulos I. HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens. Cells. 2023; 12(3):388. https://doi.org/10.3390/cells12030388

Chicago/Turabian Style

Zogopoulos, Vasileios L., 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, and Ioannis Michalopoulos. 2023. "HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens" Cells 12, no. 3: 388. https://doi.org/10.3390/cells12030388

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