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dc.contributor.author
Zogopoulos, Vasileios L.
dc.contributor.author
Malatras, Apostolos
dc.contributor.author
Kyriakidis, Konstantinos
dc.contributor.author
Charalampous, Chrysanthi
dc.contributor.author
Makrygianni, Evanthia A.
dc.contributor.author
Duguez, Stéphanie
dc.contributor.author
Koutsi, Marianna A.
dc.contributor.author
Pouliou, Marialena
dc.contributor.author
Vasileiou, Christos
dc.contributor.author
Duddy, William J.
dc.contributor.author
Agelopoulos, Marios
dc.contributor.author
Chrousos, George P.
dc.contributor.author
Iconomidou, Vassiliki A.
dc.contributor.author
Michalopoulos, Ioannis
dc.date.accessioned
2023-02-20T09:14:17Z
dc.date.available
2023-02-18T07:30:19Z
dc.date.available
2023-02-20T09:14:17Z
dc.date.issued
2023-02-01
dc.identifier.issn
2073-4409
dc.identifier.other
10.3390/cells12030388
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/599339
dc.identifier.doi
10.3929/ethz-b-000599339
dc.description.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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
gene coexpression analysis
en_US
dc.subject
gene coexpression network
en_US
dc.subject
co-expression
en_US
dc.subject
RNA-Seq
en_US
dc.subject
transcriptomics
en_US
dc.subject
bioinformatics
en_US
dc.subject
webtool
en_US
dc.title
HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2023-01-21
ethz.journal.title
Cells
ethz.journal.volume
12
en_US
ethz.journal.issue
3
en_US
ethz.pages.start
388
en_US
ethz.size
36 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Basel
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2023-02-18T07:30:20Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2023-02-20T09:14:19Z
ethz.rosetta.lastUpdated
2024-02-02T20:02:04Z
ethz.rosetta.versionExported
true
ethz.COinS
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