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

By Alexander Lachmann, Denis Torre, A. Keenan, Kathleen M. Jagodnik, Hoyjin J Lee, Lily Wang, Moshe C. Silverstein, Avi Ma’ayan. Nature Communications, 2017

666 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a3636512a48321baab95c94052de2a0a88460602

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Citations in the corpus

Discovery of Human Signaling Systems: Pairing Peptides to G Protein-Coupled Receptors (2019, methodology): “Generation of expression datasets Gene expression for all 21 assayed orphan receptors was quantified through RNA sequencing (RNA-seq) data obtained from ARCHS4 RestAPI (Lachmann et al., 2018), which aggregates the majority of published RNA-seq data from human and mouse available at the gene and tran”
STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets (2018, methodology): “In addition to NCBI Geo, for a sub-set of species, gene count data was downloaded from the ARCHS4 and ARCHS4 zoo collections (48).”
Gene Set Knowledge Discovery with Enrichr (2021, methodology): “Gene-gene similarity matrices include GeneRIF co-occurrence, Enrichr co-occurrence, Tagger co-occurrence, and ARCHS4 co-expression.”