Digital expression explorer 2: a repository of uniformly processed RNA sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• RNA sequencing (RNA-seq) is crucial for gene regulation studies.
• Analyzing large RNA-seq datasets presents computational challenges.
• A need exists for accessible, uniformly processed RNA-seq data repositories.

Purpose of the Study:

• To develop a centralized, easy-to-use repository for RNA-seq data.
• To overcome barriers in analyzing large-scale gene expression datasets.
• To facilitate reproducible meta-analyses of RNA-seq experiments.

Main Methods:

• Developed Digital Expression Explorer 2 (DEE2), a web-based repository.
• Processed >5.3 trillion reads from 580,000 RNA-seq datasets across multiple species.
• Utilized quality control, transcriptome, and genome mapping with open-source tools.

Main Results:

• DEE2 provides gene-level and transcript-level expression counts.
• Data processing ensures consistency across diverse experiments.
• The repository supports multiple species, including human and model organisms.

Conclusions:

• DEE2 simplifies data retrieval via web interface and R package.
• Compatible with standard statistical packages like edgeR and DESeq.
• Facilitates efficient and reproducible RNA-seq data meta-analysis.