Abstract
BackgroundRNA sequencing (RNA-seq) is an indispensable tool in the study of gene regulation. While the technology has brought with it better transcript coverage and quantification, there remain considerable barriers to entry for the computational biologist to analyse large data sets. There is a real need for a repository of uniformly processed RNA-seq data that is easy to use.FindingsTo address these obstacles, we developed Digital Expression Explorer 2 (DEE2), a web-based repository of RNA-seq data in the form of gene-level and transcript-level expression counts. DEE2 contains >5.3 trillion assigned reads from 580,000 RNA-seq data sets including species Escherichia coli, yeast, Arabidopsis, worm, fruit fly, zebrafish, rat, mouse, and human. Base-space sequence data downloaded from the National Center for Biotechnology Information Sequence Read Archive underwent quality control prior to transcriptome and genome mapping using open-source tools. Uniform data processing methods ensure consistency across experiments, facilitating fast and reproducible meta-analyses.ConclusionsThe web interface allows users to quickly identify data sets of interest using accession number and keyword searches. The data can also be accessed programmatically using a specifically designed R package. We demonstrate that DEE2 data are compatible with statistical packages such as edgeR or DESeq. Bulk data are also available for download. DEE2 can be found at http://dee2.io.
Highlights
RNA sequencing (RNA-seq) is an indispensable tool in the study of gene regulation
In an effort to boost reuse of public transcriptome data, we developed Digital Expression Explorer 2 (DEE2), an open-access web-based repository of uniformly processed RNA sequencing (RNAseq) digital gene-level and transcript-level expression data for several major organisms that is compatible with many types of downstream analyses
We can conclude that highly correlated data sets are standard RNA-seq/mRNA-seq and data sets with low correlation are mostly due to the use of nonstandard library construction protocols, and some data sets derived from less characterized biological states
Summary
Since its first description 10 years ago, RNA sequencing (RNAseq) has become a powerful method in transcriptomics, allowing highly accurate gene expression quantification [1]. As the cost of sequencing decreases, RNA-seq data are becoming more ubiquitous in the scientific literature. It is standard practice in the field and a compulsory requirement for journals to deposit these data to Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) [2,3] in the form of raw and processed files, with the aim of fostering greater reuse and transparency.
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