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Record W2485654968 · doi:10.1095/biolreprod.116.140319

Conservation of DNA Methylation Programming Between Mouse and Human Gametes and Preimplantation Embryos

2016· review· en· W2485654968 on OpenAlexaff
Craig R. White, William A. MacDonald, Mellissa R.W. Mann

Bibliographic record

VenueBiology of Reproduction · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsWestern UniversityChildren’s Health Research Institute
Fundersnot available
KeywordsBiologyDNA methylationGenomic imprintingGameteEpigeneticsReproductive technologyGeneticsMethylationPreimplantation genetic diagnosisImprinting (psychology)ReprogrammingZygoteEmbryoGeneSpermEmbryogenesisGene expression

Abstract

fetched live from OpenAlex

In mice, assisted reproductive technologies (ARTs) applied during gametogenesis and preimplantation development can result in disruption of genomic imprinting. In humans, these technologies and/or subfertility have been linked to perturbations in genomic imprinting. To understand how ARTs and infertility affect DNA methylation, it is important to understand DNA methylation dynamics and the role of regulatory factors at these critical stages. Recent genome studies performed using mouse and human gametes and preimplantation embryos have shed light onto these processes. Here, we comprehensively review the current state of knowledge regarding global and imprinted DNA methylation programming in the mouse and human. Available data highlight striking similarities in mouse and human DNA methylation dynamics during gamete and preimplantation development. Just as fascinating, these studies have revealed sex-, gene-, and allele-specific differences in DNA methylation programming, warranting future investigation to untangle the complex regulation of DNA methylation dynamics during gamete and preimplantation development.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.865
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.050
GPT teacher head0.350
Teacher spread0.300 · 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 designOther design
Domainnot available
GenreReview

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

Citations25
Published2016
Admission routes1
Has abstractyes

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