Expression Atlas update: from tissues to single cells

Irene Papatheodorou(European Bioinformatics Institute), Pablo Moreno(European Bioinformatics Institute), Jonathan Manning(European Bioinformatics Institute), Alfonso Muñoz-Pomer Fuentes(European Bioinformatics Institute), Nancy George(European Bioinformatics Institute), Silvie Fexová(European Bioinformatics Institute), Nuno A. Fonseca(European Bioinformatics Institute), Anja Füllgrabe(European Bioinformatics Institute), Matthew Green(European Bioinformatics Institute), Ni Huang(European Bioinformatics Institute), Laura Huerta(European Bioinformatics Institute), Haider Iqbal(European Bioinformatics Institute), Monica Jianu(European Bioinformatics Institute), Suhaib Mohammed(European Bioinformatics Institute), Lingyun Zhao(European Bioinformatics Institute), Andrew F. Jarnuczak(European Bioinformatics Institute), Simon Jupp(European Bioinformatics Institute), John C. Marioni(European Bioinformatics Institute), Kerstin B. Meyer(Wellcome Sanger Institute), Robert Petryszak(European Bioinformatics Institute), Cesar A. Prada‐Medina(European Bioinformatics Institute), Carlos Talavera‐López(Wellcome Sanger Institute), Sarah A. Teichmann(Wellcome Sanger Institute), Juan Antonio Vizcaíno(European Bioinformatics Institute), Alvis Brāzma(European Bioinformatics Institute)
Nucleic Acids Research
October 16, 2019
10.1093/nar/gkz947
Cited by 560Open Access
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Abstract

Expression Atlas is EMBL-EBI's resource for gene and protein expression. It sources and compiles data on the abundance and localisation of RNA and proteins in various biological systems and contexts and provides open access to this data for the research community. With the increased availability of single cell RNA-Seq datasets in the public archives, we have now extended Expression Atlas with a new added-value service to display gene expression in single cells. Single Cell Expression Atlas was launched in 2018 and currently includes 123 single cell RNA-Seq studies from 12 species. The website can be searched by genes within or across species to reveal experiments, tissues and cell types where this gene is expressed or under which conditions it is a marker gene. Within each study, cells can be visualized using a pre-calculated t-SNE plot and can be coloured by different features or by cell clusters based on gene expression. Within each experiment, there are links to downloadable files, such as RNA quantification matrices, clustering results, reports on protocols and associated metadata, such as assigned cell types.

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