Deep transcript profiling by RNA-seq has revolutionised our understanding of gene regulation. Tens of thousands of RNA-seq datasets have been made publicly available on NCBI’s Gene Expression Omnibus, but variations in formats, software and genome annotation sets hinders meta-analyses of these data. Moreover, raw RNA-seq data analyses from these databases requires sophisticated Linux pipelines and extensive computational resources. Hence, there is a clear need for an easy-to-use web-based central resource of uniformly processed public RNA-seq derived gene expression data. To address this, we developed Digital Expression Explorer (DEE), a web-based repository of RNA-seq data in the form of gene expression counts. DEE contains over 100,000 processed RNA-seq data sets from several species including yeast, Arabidopsis, worm, fruit fly, zebrafish, rat, mouse and human. Base-space and color-space sequence data downloaded from NCBI Sequence Read Archive underwent quality analysis, filtering and trimming prior to genome alignment and read counting using open-source tools. Uniform reference genome and data processing methods ensures consistency across experiments, facilitating fast and reproducible meta-analyses. Keyword and accession number searches enable users to quickly identify data sets of interest. Data is downloaded as a matrix of gene expression counts with gene names and Ensembl accession numbers that can be immediately analyzed by statistical packages (ie. edgeR, DESeq, Degust). Data can be accessed by command line from within the R environment. Digital Expression Explorer will be a valuable resource for biologists wishing to investigate vast numbers of publicly RNA-seq gene expression data sets in a user-friendly environment.