Li Mengwei, Ang Kok Siong, Teo Brian, Rom Uddamvathanak, Nguyen Minh N, Maurer-Stroh Sebastian, Chen Jinmiao
Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore.
Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore.
Nucleic Acids Res. 2025 Jan 6;53(D1):D932-D938. doi: 10.1093/nar/gkae1108.
Single-cell RNA sequencing (scRNA-seq) has emerged as the key technique for studying transcriptomics at the single-cell level. In our previous work, we presented the DISCO database (https://www.immunesinglecell.org/) that integrates publicly available human scRNA-seq data. We now introduce an enhanced version of DISCO, which has expanded fourfold to include >100 million cells from >17 thousand samples. It provides uniformly realigned read count tables, curated metadata, integrated tissue and phenotype specific atlases, and harmonized cell type annotations. It also hosts a single-cell enhanced knowledgebase of cell type ontology and gene signatures relating to cell types and phenotypes. Lastly, it offers a suite of tools for data retrieval, integration, annotation, and mapping, allowing users to construct customized atlases and perform integrated analysis with their own data. These tools are also available in a standalone R package for offline analysis.
单细胞RNA测序(scRNA-seq)已成为在单细胞水平研究转录组学的关键技术。在我们之前的工作中,我们推出了DISCO数据库(https://www.immunesinglecell.org/),该数据库整合了公开可用的人类scRNA-seq数据。我们现在介绍DISCO的增强版,其规模扩大了四倍,包含来自超过1.7万个样本的1亿多个细胞。它提供统一重新对齐的读取计数表、精心策划的元数据、整合的组织和表型特异性图谱,以及统一的细胞类型注释。它还拥有一个关于细胞类型本体和与细胞类型及表型相关的基因特征的单细胞增强知识库。最后,它提供了一套用于数据检索、整合、注释和映射的工具,允许用户构建定制图谱并使用自己的数据进行综合分析。这些工具也可在一个独立的R包中获取,用于离线分析。