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Record W2132635936 · doi:10.1093/icb/icu034

The Dynamic Nature of DNA Methylation: A Role in Response to Social and Seasonal Variation

2014· review· en· W2132635936 on OpenAlexafffund
Sebastian Alvarado, Russell D. Fernald, Kenneth B. Storey, Moshe Szyf

Bibliographic record

VenueIntegrative and Comparative Biology · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsCarleton UniversityMcGill University
FundersNational Institute of Neurological Disorders and StrokeCanadian Institutes of Health Research
KeywordsDNA methylationEpigeneticsBiologyPhenotypic plasticityRegulation of gene expressionGeneModel organismGeneticsContext (archaeology)EpigenomicsOrganismAdaptive responseFunction (biology)EpigenesisGene expressionEvolutionary biologyComputational biology

Abstract

fetched live from OpenAlex

An organism's ability to adapt to its environment depends on its ability to regulate and maintain tissue specific, temporal patterns of gene transcription in response to specific environmental cues. Epigenetic mechanisms are responsible for many of the intricacies of a gene's regulation that alter expression patterns without affecting the genetic sequence. In particular, DNA methylation has been shown to have an important role in regulating early development and in some human diseases. Within these domains, DNA methylation has been extensively characterized over the past 60 years, but the discovery of its role in regulating behavioral outcomes has led to renewed interest in its potential roles in animal behavior and phenotypic plasticity. The conservation of DNA methylation across the animal kingdom suggests a possible role in the plasticity of genomic responses to environmental cues in natural environments. Here, we review the historical context for the study of DNA methylation, its function and mechanisms, and provide examples of gene/environment interactions in response to social and seasonal cues. Finally, we discuss useful tools to interrogate and dissect the function of DNA methylation in non-model organisms.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.636

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 head0.029
GPT teacher head0.378
Teacher spread0.349 · 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 designNot applicable
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

Citations64
Published2014
Admission routes2
Has abstractyes

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