Massive mining of publicly available RNA-seq data from human and mouse

• Published in: Nature communications Vol. 9; no. 1; pp. 1366 - 10
• Main Authors: Lachmann, Alexander, Torre, Denis, Keenan, Alexandra B., Jagodnik, Kathleen M., Lee, Hoyjin J., Wang, Lily, Silverstein, Moshe C., Ma’ayan, Avi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10-04-2018; Nature Publishing Group; Nature Portfolio
• Subjects: 38; 45/91; 631/114/1314; 631/114/2401; 631/553/117; Animals; Cell lines; Data mining; Data Mining - methods; Data processing; Databases, Genetic; Gene expression; Gene Ontology; Gene Regulatory Networks; Gene sequencing; Genome; Genomes; High-Throughput Nucleotide Sequencing; Humanities and Social Sciences; Humans; Internet; Mice; Molecular Sequence Annotation; multidisciplinary; Protein interaction; Protein Interaction Mapping; Ribonucleic acid; RNA; Science; Science (multidisciplinary); Software; Transcription; Transcriptome
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-018-03751-6

RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses. ARCHS4 is a web resource that makes the majority of published RNA-seq data from human and mouse available at the gene and transcript levels. For developing ARCHS4, available FASTQ files from RNA-seq experiments from the Gene Expression Omnibus (GEO) were aligned using a cloud-based infrastructure. In total 187,946 samples are accessible through ARCHS4 with 103,083 mouse and 84,863 human. Additionally, the ARCHS4 web interface provides intuitive exploration of the processed data through querying tools, interactive visualization, and gene pages that provide average expression across cell lines and tissues, top co-expressed genes for each gene, and predicted biological functions and protein–protein interactions for each gene based on prior knowledge combined with co-expression.
Publicly available RNA-seq data is provided mostly in raw form, resulting in a barrier for integrative analyses. Here, Lachmann et al. develop a high-throughput processing infrastructure and search database (ARCHS4) that provides processed RNA-seq data for 187,946 publicly available mouse and human samples to support exploration and reuse.