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 disease...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 of40,000 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.展开更多
基金We thank Dr.Yongkun Wang from the Network and Information Center at Shanghai Jiao Tong University(SJTU)for his support in high-performance computing.We thank Ph.D.Candidate Wei Liu from Yale University for her support in the acquisition of physiological trait-related genes.HL is supported by the National Key R&D Program of China(Grant No.2018YFC0910500)JG and JD are supported by the SJTU-Yale Collaborative Research Seed Fund and Neil Shen’s SJTU Medical Research Fund,China.JG and HL are partially supported by the Shanghai Municipal Commission of Health and Family Planning,China(Grant No.2018ZHYL0223)the Science and Technology Commission of Shanghai Municipality(STCSM),China(Grant No.17DZ2251200).
文摘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 of40,000 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.
