首页|Comprehensive Analysis of Ubiquitously Expressed Genes in Humans from A Data-driven Perspective

Comprehensive Analysis of Ubiquitously Expressed Genes in Humans from A Data-driven Perspective

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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 transcrip-tomes 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 tran-scriptomes 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 pre-viously predicted disallowed genes in islet beta cells and found that seven of these genes showed rel-atively more varied expression patterns,suggesting that the repression of these genes may not be unique to islet beta cells.

Ubiquitous expressionHousekeeping geneDisallowed geneExpression specificityExpression variability

Jianlei Gu、Jiawei Dai、Hui Lu、Hongyu Zhao

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SJTU-Yale Joint Center for Biostatistics and Data Science,Department of Bioinformatics and Biostatistics,School of Life Sciences and Biotechnology,Shanghai Jiao Tong University,Shanghai 200240,China

Center for Biomedical Informatics,Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine,Shanghai Children's Hospital,Shanghai 200040,China

Department of Biostatistics,Yale University,New Haven,CT 06511,USA

National Key R&D Program of ChinaSJTU-Yale Collaborative Research Seed FundNeil Shen's SJTU Medical Research Fund,ChinaShanghai Municipal Commission of Health and Family Planning,ChinaScience and Technology Commission of Shanghai Municipality(STCSM),China

2018YFC09105002018ZHYL022317DZ2251200

2023

10.1016/j.gpb.2021.08.017

基因组蛋白质组与生物信息学报(英文版)
中国科学院北京基因组研究所

基因组蛋白质组与生物信息学报(英文版)

CSTPCDCSCD
影响因子：0.495
ISSN：1672-0229
年,卷(期)：2023.21(1)
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