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Record W4413421494 · doi:10.1016/j.dib.2025.111984

RNA-seq dataset of the estrogen-dependent regulation of the transcriptome in mouse mammary gland organoids

2025· article· en· W4413421494 on OpenAlexafffund
Aurélie Lacouture, Mame Sokhna Sylla, Lucas Germain, Étienne Audet‐Walsh

Bibliographic record

VenueData in Brief · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsFondation CHU de QuébecUniversité Laval
KeywordsTranscriptomeOrganoidRNA-SeqBiologyComputational biologyRNAMessenger RNAmicroRNABioinformaticsCell biologyGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

The mammary gland development in utero and during life is strongly regulated by hormones. To study the genes regulated specifically by the estrogen signalling pathway in the epithelial compartment, we treated mouse mammary epithelial organoids with estradiol, the most potent endogenous estrogen. At maturity, after 11 days of treatment, organoids were collected, and RNA was purified for next-generation sequencing. The bulk mRNA-seq data obtained were verified for raw quality, and reads were pseudo-aligned on the murine reference transcriptome (Gencode vM25). Differentially expressed genes were identified using DESeq2 to gain a better understanding of the impact of estrogens on the mammary epithelial cell transcriptome ex vivo . These data can be reanalyzed and combined with recent single-cell RNA-seq data to study the estrogen-dependent transcriptome at the cellular level and better understand the functional impact on the mammary gland in physiopathological conditions, such as during lactation, following endocrine-disrupting chemical exposure, or through the course of carcinogenesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.282
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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