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

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 gen...

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Autores principales: Zogopoulos, Vasileios L., Malatras, Apostolos, Kyriakidis, Konstantinos, Charalampous, Chrysanthi, Makrygianni, Evanthia A., Duguez, Stéphanie, Koutsi, Marianna A., Pouliou, Marialena, Vasileiou, Christos, Duddy, William J., Agelopoulos, Marios, Chrousos, George P., Iconomidou, Vassiliki A., Michalopoulos, Ioannis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913097/
https://www.ncbi.nlm.nih.gov/pubmed/36766730
http://dx.doi.org/10.3390/cells12030388
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author Zogopoulos, Vasileios L.
Malatras, Apostolos
Kyriakidis, Konstantinos
Charalampous, Chrysanthi
Makrygianni, Evanthia A.
Duguez, Stéphanie
Koutsi, Marianna A.
Pouliou, Marialena
Vasileiou, Christos
Duddy, William J.
Agelopoulos, Marios
Chrousos, George P.
Iconomidou, Vassiliki A.
Michalopoulos, Ioannis
author_facet Zogopoulos, Vasileios L.
Malatras, Apostolos
Kyriakidis, Konstantinos
Charalampous, Chrysanthi
Makrygianni, Evanthia A.
Duguez, Stéphanie
Koutsi, Marianna A.
Pouliou, Marialena
Vasileiou, Christos
Duddy, William J.
Agelopoulos, Marios
Chrousos, George P.
Iconomidou, Vassiliki A.
Michalopoulos, Ioannis
author_sort Zogopoulos, Vasileios L.
collection PubMed
description 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.
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spelling pubmed-99130972023-02-11 HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens Zogopoulos, Vasileios L. Malatras, Apostolos Kyriakidis, Konstantinos Charalampous, Chrysanthi Makrygianni, Evanthia A. Duguez, Stéphanie Koutsi, Marianna A. Pouliou, Marialena Vasileiou, Christos Duddy, William J. Agelopoulos, Marios Chrousos, George P. Iconomidou, Vassiliki A. Michalopoulos, Ioannis Cells Article 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. MDPI 2023-01-21 /pmc/articles/PMC9913097/ /pubmed/36766730 http://dx.doi.org/10.3390/cells12030388 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zogopoulos, Vasileios L.
Malatras, Apostolos
Kyriakidis, Konstantinos
Charalampous, Chrysanthi
Makrygianni, Evanthia A.
Duguez, Stéphanie
Koutsi, Marianna A.
Pouliou, Marialena
Vasileiou, Christos
Duddy, William J.
Agelopoulos, Marios
Chrousos, George P.
Iconomidou, Vassiliki A.
Michalopoulos, Ioannis
HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens
title HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens
title_full HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens
title_fullStr HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens
title_full_unstemmed HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens
title_short HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens
title_sort hgca2.0: an rna-seq based webtool for gene coexpression analysis in homo sapiens
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913097/
https://www.ncbi.nlm.nih.gov/pubmed/36766730
http://dx.doi.org/10.3390/cells12030388
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