ScEnhancer: a single-cell enhancer resource with annotation across hundreds of tissue/cell types in three species. Issue Volume 50:Issue D1(2022) (11th November 2021)

• Record Type: Journal Article
• Authors: Gao, Tianshun; Zheng, Zilong; Pan, Yihang; Zhu, Chengming; Wei, Fuxin; Yuan, Jinqiu; Sun, Rui; Fang, Shuo; Wang, Nan; Zhou, Yang; Qian, Jiang
• Is Part Of: Nucleic acids research. Volume 50:Issue D1(2022)
• Page Start: D371
• Page End: D379
• Publication Date: 2021-11-11
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkab1032
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Previous studies on enhancers and their target genes were largely based on bulk samples that represent 'average' regulatory activities from a large population of millions of cells, masking the heterogeneity and important effects from the sub-populations. In recent years, single-cell sequencing technology has enabled the profiling of open chromatin accessibility at the single-cell level (scATAC-seq), which can be used to annotate the enhancers and promoters in specific cell types. A comprehensive resource is highly desirable for exploring how the enhancers regulate the target genes at the single-cell level. Hence, we designed a single-cell database scEnhancer (http://enhanceratlas.net/scenhancer/ ), covering 14 527 776 enhancers and 63 658 600 enhancer-gene interactions from 1 196 906 single cells across 775 tissue/cell types in three species. An unsupervised learning method was employed to sort and combine tens or hundreds of single cells in each tissue/cell type to obtain the consensus enhancers. In addition, we utilized a cis-regulatory network algorithm to identify the enhancer-gene connections. Finally, we provided a user-friendly platform with seven useful modules to search, visualize, and browse the enhancers/genes. This database will facilitate the research community towards a functional analysis of enhancers at the single-cell level.