SPEED: Single-cell Pan-species atlas in the light of Ecology and Evolution for Development and Diseases. Issue Volume 51:Issue D1(2023) (28th October 2022)

• Record Type: Journal Article
• Authors: Chen, Yangfeng; Zhang, Xingliang; Peng, Xi; Jin, Yicheng; Ding, Peiwen; Xiao, Jiedan; Li, Changxiao; Wang, Fei; Chang, Ashley; Yue, Qizhen; Pu, Mingyi; Chen, Peixin; Shen, Jiayi; Li, Mengrou; Jia, Tengfei; Wang, Haoyu; Huang, Li; Guo, Guoji; Zhang, Wensheng; Liu, Hebin; Wang, Xiangdong; Chen, Dongsheng
• Is Part Of: Nucleic acids research. Volume 51:Issue D1(2023)
• Page Start: D1150
• Page End: D1159
• Publication Date: 2022-10-28
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkac930
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: It is a challenge to efficiently integrate and present the tremendous amounts of single-cell data generated from multiple tissues of various species. Here, we create a new database named SPEED for single-cell pan-species atlas in the light of ecology and evolution for development and diseases (freely accessible at http://8.142.154.29 or http://speedatlas.net ). SPEED is an online platform with 4 data modules, 7 function modules and 2 display modules. The 'Pan' module is applied for the interactive analysis of single cell sequencing datasets from 127 species, and the 'Evo', 'Devo', and 'Diz' modules provide comprehensive analysis of single-cell atlases on 18 evolution datasets, 28 development datasets, and 85 disease datasets. The 'C2C', 'G2G' and 'S2S' modules explore intercellular communications, genetic regulatory networks, and cross-species molecular evolution. The 'sSearch', 'sMarker', 'sUp', and 'sDown' modules allow users to retrieve specific data information, obtain common marker genes for cell types, freely upload, and download single-cell datasets, respectively. Two display modules ('HOME' and 'HELP') offer easier access to the SPEED database with informative statistics and detailed guidelines. All in all, SPEED is an integrated platform for single-cell RNA sequencing (scRNA-seq) and single-cell whole-genome sequencing (scWGS) datasets to assist the deep-mining and understanding of heterogeneity among cells, tissues, and species at multi-levels, angles, and orientations, as well as provide new insights into molecular mechanisms of biological development and pathogenesis.