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Record W4311869637 · doi:10.1002/bies.202200198

The cap epitranscriptome: Early directions to a complex life as mRNA

2022· article· en· W4311869637 on OpenAlexafffund
Ina Anreiter, Yuan Tian, Matthias Soller

Bibliographic record

VenueBioEssays · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaBiotechnology and Biological Sciences Research CouncilDirectorate for Biological SciencesLeverhulme Trust
KeywordsBiologyGeneMethyltransferaseGene expressionMessenger RNATranscription (linguistics)MethylationRegulation of gene expressionTranslation (biology)GeneticsCell biology

Abstract

fetched live from OpenAlex

Abstract Animal, protist and viral messenger RNAs (mRNAs) are most prominently modified at the beginning by methylation of cap‐adjacent nucleotides at the 2′‐O‐position of the ribose (cOMe) by dedicated cap methyltransferases (CMTrs). If the first nucleotide of an mRNA is an adenosine, PCIF1 can methylate at the N6‐position (m6A), while internally the Mettl3/14 writer complex can methylate. These modifications are introduced co‐transcriptionally to affect many aspects of gene expression including localisation to synapses and local translation. Of particular interest, transcription start sites of many genes are heterogeneous leading to sequence diversity at the beginning of mRNAs, which together with cOMe and m6Am could constitute an extensive novel layer of gene expression control. Given the role of cOMe and m6A in local gene expression at synapses and higher brain functions including learning and memory, such code could be implemented at the transcriptional level for lasting memories through local gene expression at synapses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.020
GPT teacher head0.269
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations10
Published2022
Admission routes2
Has abstractyes

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