Prenatal alcohol exposure alters gene expression in the rat brain: Experimental design and bioinformatic analysis of microarray data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:26217797", "kind": "science-record", "revision": 6, "content_hash": "sha256:974c82fedcf3d6e29b77a43747b1124150bb5453448880b9aa7dc295284a87e1", "created_at": "2026-11-01T15:40:09Z", "updated_at": "2026-11-01T21:20:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prenatal alcohol exposure alters gene expression in the rat brain: Experimental design and bioinformatic analysis of microarray data.", "doi": "10.1016/j.dib.2015.05.007", "pmid": "26217797", "authors": ["Alexandre A Lussier", "Katarzyna A Stepien", "Joanne Weinberg", "Michael S Kobor"], "journal": "Data in brief", "year": 2015, "abstract": "We previously identified gene expression changes in the prefrontal cortex and hippocampus of rats prenatally exposed to alcohol under both steady-state and challenge conditions (Lussier et al., 2015, Alcohol.: Clin. Exp. Res., 39, 251-261). In this study, adult female rats from three prenatal treatment groups (ad libitum-fed control, pair-fed, and ethanol-fed) were injected with physiological saline solution or complete Freund׳s adjuvant (CFA) to induce arthritis (adjuvant-induced arthritis, AA). The prefrontal cortex and hippocampus were collected 16 days (peak of arthritis) or 39 days (during recovery) following injection, and whole genome gene expression was assayed using Illumina׳s RatRef-12 expression microarray. Here, we provide additional metadata, detailed explanations of data pre-processing steps and quality control, as well as a basic framework for the bioinformatic analyses performed. 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As described, spatial artifacts were masked from the dataset and data were log 2-transformed prior to submission of raw and quantile-normalized data. Moreover, due to GEO submission requirements, the datasets were filtered to include only probes associated with a gene. As such, the current GEO files do not contain any control probes, although these additional data are available upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:26217797", "locator": "sections[16] \"GEO submission\" paragraph 2", "quote": "Probe-level data for this study were deposited on the GEO (GSE63561), along with metadata describing experimental treatment groups. As described, spatial artifacts were masked from the dataset and data were log 2-transformed prior to submission of raw and quantile-normalized data. Moreover, due to GEO submission requirements, the datasets were filtered to include only probes associated with a gene. As such, the current GEO files do not contain any control probes, although these additional data are available upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used surrogate variable analysis to generate covariates for limma linear modeling, controlled the false-discovery rate at 25% with Benjamini–Hochberg correction, and deposited raw and quantile-normalized probe-level data in GEO under accession GSE63561.", "kind": "quantitative", "target": {"value": 25.0, "unit": "per cent", "tolerance": null, "source": "As the effects of experimental treatments were subtle, the false-discovery rate (FDR) was controlled at 25% (q-value <0.25)."}, "evidence": [{"asset": "source:pmid:26217797", "locator": "sections[14] \"Differential expression analysis\" paragraph 2", "quote": "This method identifies eigenvectors of variation not associated with primary variables, which can then be incorporated as covariates during linear modeling to remove unwanted sources of heterogeneity."}, {"asset": "source:pmid:26217797", "locator": "sections[14] \"Differential expression analysis\" paragraph 4", "quote": "Surrogate variables generated by sva were included as covariates in linear modeling of gene expression using the limma package in the statistical program R [11]."}, {"asset": "source:pmid:26217797", "locator": "sections[14] \"Differential expression analysis\" paragraph 4", "quote": "As the effects of experimental treatments were subtle, the false-discovery rate (FDR) was controlled at 25% (q-value <0.25)."}, {"asset": "source:pmid:26217797", "locator": "sections[16] \"GEO submission\" paragraph 2", "quote": "Probe-level data for this study were deposited on the GEO (GSE63561), along with metadata describing experimental treatment groups."}, {"asset": "source:pmid:26217797", "locator": "sections[16] \"GEO submission\" paragraph 2", "quote": "As described, spatial artifacts were masked from the dataset and data were log 2-transformed prior to submission of raw and quantile-normalized data."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study used surrogate variable analysis to generate covariates for limma linear modeling, controlled the false-discovery rate at 25% with Benjamini–Hochberg correction, and deposited raw and quantile-normalized probe-level data in GEO under accession GSE63561.", "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:26217797"], "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": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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