Ioannis Michalopoulos and colleagues recently published a study in Cells

Genes with similar expression patterns in a set of diverse samples may be considered "coexpressed". Such genes tend to participate in similar biological processes or common metabolic pathways. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes through the construction of a gene coexpression tree which resembles phylogenetic trees. In this coexpression tree, genes are represented as leaves and neighbouring leaves contain coexpressed gene partners. HGCA2.0 identifies the subclade of coexpressed genes to a gene of interest, and offers a large variety of built-in gene term enrichment analyses, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 creates working hypotheses for the discovery of gene partners or biological processes that can be experimentally validated. It offers a simple and user-friendly web interface, as well as an API endpoint for programmatic data access. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes, as well as genes related to ALS and LGMD neuromuscular conditions. It has also exhibited top performance compared to other coexpression webtools. HGCA2.0 is freely available at: https://www.michalopoulos.net/hgca2.0/

Pubmed

HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens
Vasileios L. Zogopoulos, 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
Cells 2023, 12(3), 388;