The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Nucleic Acids Res. 2024 Jan 5;52(D1):D293-D303. doi: 10.1093/nar/gkad885.
Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237 051 cell type-specific GRNs (62 999 692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizing and downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.
基因调控网络（GRNs）是一种可解释的图模型，包含转录因子（TFs）与其下游靶基因之间的调控相互作用。理解 GRNs 的拓扑结构和动态变化对于解释疾病病因机制以及将相应的研究结果转化为新的治疗方法至关重要。单细胞多组学技术的最新进展促使人们能够以前所未有的分辨率从单细胞转录组和表观基因组数据中计算推断 GRNs。在这里，我们展示了 scGRN（https://bio.liclab.net/scGRN/），这是一个全面的人类和小鼠单细胞多组学基因调控网络平台。当前版本的 scGRN 包含了 237051 个细胞类型特异性 GRN（62999692 个 TF-靶基因对），涵盖了 160 种组织/细胞系和 1324 个单细胞样本。scGRN 是第一个记录了从单细胞多组学数据推断出的多种人类和小鼠条件下大规模细胞类型特异性 GRN 信息的资源。我们实现了多个用于有效 GRN 分析的在线工具，包括差异 TF-靶网络分析、TF 富集分析和途径下游分析。我们还提供了有关 TF 与靶基因启动子、超级增强子和典型增强子结合的详细信息。总之，scGRN 是一个用于搜索、浏览、分析、可视化和下载感兴趣的 GRN 的综合性和有用的平台，使人们能够深入了解不同条件下调控机制的差异。
