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

Alexander Lachmann, Denis Torre, Alexandra B. Keenan, Kathleen M. Jagodnik, Hoyjin J. Lee, Lily Wang, Moshe C. Silverstein and Avi Ma’ayan ()
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Alexander Lachmann: Icahn School of Medicine at Mount Sinai
Denis Torre: Icahn School of Medicine at Mount Sinai
Alexandra B. Keenan: Icahn School of Medicine at Mount Sinai
Kathleen M. Jagodnik: Icahn School of Medicine at Mount Sinai
Hoyjin J. Lee: Icahn School of Medicine at Mount Sinai
Lily Wang: Icahn School of Medicine at Mount Sinai
Moshe C. Silverstein: Icahn School of Medicine at Mount Sinai
Avi Ma’ayan: Icahn School of Medicine at Mount Sinai

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: Abstract 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.

Date: 2018
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DOI: 10.1038/s41467-018-03751-6

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