|Full name:||Single-cell Pan-species atlas in the light of Ecology and Evolution for Development and Diseases|
|Description:||SPEED is the largest pan-species single cell database at present, covering 127 species to date. SPEED also collects various scRNA-seq atlases in evolution, development and disease, providing an ecological and evolutionary perspective for the study of development and disease.|
|University/Institution:||Chinese Academy of Medical Sciences and Peking Union Medical College|
|Address:||100 Chongwen Road, Suzhou Industrial Park, Suzhou, China, 215123|
|Contact name (PI/Team):||Dongsheng Chen|
|Contact email (PI/Helpdesk):||email@example.com|
SPEED: Single-cell Pan-species atlas in the light of Ecology and Evolution for Development and Diseases. [PMID: 36305818]
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://18.104.22.168 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.