Comprehensive Analysis of Ubiquitously Expressed Genes in Humans from A Data-driven Perspective - Jianlei Gu; Jiawei Dai; Hui Lu; Hongyu Zhao (Genomics, Proteomics & Bioinformatics, 2023)
Comprehensive characterization of spatial and temporal gene expression patterns in humans is critical for uncovering the regulatory codes of the human genome and understanding the molecular mechanisms of human diseases. Ubiquitously expressed genes (UEGs) refer to the genes expressed across a majority of, if not all, phenotypic and physiological conditions of an organism. It is known that many human genes are broadly expressed across tissues. However, most previous UEG studies have only focused on providing a list of UEGs without capturing their global expression patterns, thus limiting the potential use of UEG information. In this study, we proposed a novel data-driven framework to leverage the extensive collection of ∼ 40,000 human transcriptomes to derive a list of UEGs and their corresponding global expression patterns, which offers a valuable resource to further characterize human transcriptome. Our results suggest that about half (12,234; 49.01%) of the human genes are expressed in at least 80% of human transcriptomes, and the median size of the human transcriptome is 16,342 genes (65.44%). Through gene clustering, we identified a set of UEGs, named LoVarUEGs, which have stable expression across human transcriptomes and can be used as internal reference genes for expression measurement. To further demonstrate the usefulness of this resource, we evaluated the global expression patterns for 16 previously predicted disallowed genes in islet beta cells and found that seven of these genes showed relatively more varied expression patterns, suggesting that the repression of these genes may not be unique to islet beta cells.