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MAGE: An Open-Source Tool for Meta-Analysis of Gene Expression Studies
 
 
Review

Approaches in Gene Coexpression Analysis in Eukaryotes

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
Department of Nutrition and Dietetics, Harokopio University, 17671 Athens, Greece
4
Biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, 2029 Nicosia, Cyprus
*
Author to whom correspondence should be addressed.
Academic Editor: Thomas Ulas
Biology 2022, 11(7), 1019; https://doi.org/10.3390/biology11071019
Received: 6 June 2022 / Revised: 28 June 2022 / Accepted: 4 July 2022 / Published: 6 July 2022
(This article belongs to the Special Issue Differential Gene Expression and Coexpression)
Genes whose expression levels rise and fall similarly in a large set of samples, may be considered coexpressed. Gene coexpression analysis refers to the en masse discovery of coexpressed genes from a large variety of transcriptomic experiments. The type of biological networks that studies gene coexpression, known as Gene Coexpression Networks, consist of an undirected graph depicting genes and their coexpression relationships. Coexpressed genes are clustered in smaller subnetworks, the predominant biological roles of which can be determined through enrichment analysis. By studying well-annotated gene partners, the attribution of new roles to genes of unknown function or assumption for participation in common metabolic pathways can be achieved, through a guilt-by-association approach. In this review, we present key issues in gene coexpression analysis, as well as the most popular tools that perform it.
Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs. View Full-Text
Keywords: gene coexpression networks; transcriptomics; RNA-Seq; microarrays; systems biology; webtool gene coexpression networks; transcriptomics; RNA-Seq; microarrays; systems biology; webtool
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MDPI and ACS Style

Zogopoulos, V.L.; Saxami, G.; Malatras, A.; Papadopoulos, K.; Tsotra, I.; Iconomidou, V.A.; Michalopoulos, I. Approaches in Gene Coexpression Analysis in Eukaryotes. Biology 2022, 11, 1019. https://doi.org/10.3390/biology11071019

AMA Style

Zogopoulos VL, Saxami G, Malatras A, Papadopoulos K, Tsotra I, Iconomidou VA, Michalopoulos I. Approaches in Gene Coexpression Analysis in Eukaryotes. Biology. 2022; 11(7):1019. https://doi.org/10.3390/biology11071019

Chicago/Turabian Style

Zogopoulos, Vasileios L., Georgia Saxami, Apostolos Malatras, Konstantinos Papadopoulos, Ioanna Tsotra, Vassiliki A. Iconomidou, and Ioannis Michalopoulos. 2022. "Approaches in Gene Coexpression Analysis in Eukaryotes" Biology 11, no. 7: 1019. https://doi.org/10.3390/biology11071019

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