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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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9913097 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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