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

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:29636450", "kind": "science-record", "revision": 6, "content_hash": "sha256:08da827fbaae718dcdadb06d3a0dcc43581eb6499688fb6db83d311d71148d58", "created_at": "2026-11-01T15:40:33Z", "updated_at": "2026-11-01T21:21:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Massive mining of publicly available RNA-seq data from human and mouse.", "doi": "10.1038/s41467-018-03751-6", "pmid": "29636450", "authors": ["Alexander Lachmann", "Denis Torre", "Alexandra B Keenan", "Kathleen M Jagodnik", "Hoyjin J Lee", "Lily Wang", "Moshe C Silverstein", "Avi Ma'ayan"], "journal": "Nature communications", "year": 2018, "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.", "claims": [{"id": "claim-method", "statement": "The quality of the predictions is almost identical for the two compared data sets with an average area under the curve (AUC) of 0.69 for predictions made by processed data generated by the two separate methods (Fig. 3f).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29636450", "locator": "sections[5] \"STAR vs. Kallisto comparison\" paragraph 2", "quote": "The quality of the predictions is almost identical for the two compared data sets with an average area under the curve (AUC) of 0.69 for predictions made by processed data generated by the two separate methods (Fig. 3f)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The ARCHS4 website is accessible at https://amp.pharm.mssm.edu/archs4. On the site all processed data is available at the Download tab. Source code for the project is provided at https://github.com/MaayanLab/archs4. Source code is available under the Apache Licence 2.0. Provided gene expression files are made available under the Creative Commons Attribution 4.0 International License (Creative Commons License). All data is free to use for non-commercial purposes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29636450", "locator": "sections[16] \"Data availability\" paragraph 2", "quote": "The ARCHS4 website is accessible at https://amp.pharm.mssm.edu/archs4. On the site all processed data is available at the Download tab. Source code for the project is provided at https://github.com/MaayanLab/archs4. Source code is available under the Apache Licence 2.0. Provided gene expression files are made available under the Creative Commons Attribution 4.0 International License (Creative Commons License). All data is free to use for non-commercial purposes."}], "state": "complete"}, {"id": "claim-target-result", "statement": "For ARCHS4 samples processed from GEO/SRA data, Kallisto and STAR produced similar gene-expression profiles, with an average Pearson correlation of 0.77 and nearly identical GO biological-process prediction performance (average AUC 0.69).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:29636450", "locator": "sections[1] \"Introduction\" paragraph 6", "quote": "The ARCHS4 pipeline (Fig. 2) processes RNA-seq data from GEO/SRA to support retrospective data analyses and reuse."}, {"asset": "source:pmid:29636450", "locator": "sections[5] \"STAR vs. Kallisto comparison\" paragraph 2", "quote": "The average Pearson correlation of the z-score transformed samples between the Kallisto and STAR outputs is 0.77."}, {"asset": "source:pmid:29636450", "locator": "sections[5] \"STAR vs. Kallisto comparison\" paragraph 2", "quote": "The quality of the predictions is almost identical for the two compared data sets with an average area under the curve (AUC) of 0.69 for predictions made by processed data generated by the two separate methods (Fig. 3f)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "For ARCHS4 samples processed from GEO/SRA data, Kallisto and STAR produced similar gene-expression profiles, with an average Pearson correlation of 0.77 and nearly identical GO biological-process prediction performance (average AUC 0.69).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:29636450"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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However, publicly available RNA-seq data is currently provided mostly in raw for", "RNA sequencing (RNA-seq) is the leading technology for genome-wide t", "esource that makes the majority of published RNA-seq data from human and mouse available at the g"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["blicly available RNA-seq data from human and mouse.", "ity of published RNA-seq data from human and mouse available at the gene and transcript levels.", "s are accessible through ARCHS4 with 103,083 mouse and 84,863 human. Additionally, the ARCHS4 w", "is used to calculate Ê=DJL×E. The human and mouse matrices are handled separately. For user qu"], "order_basis": "paper_text_order:20", "id": "assay:2"}]}