MétaCan
Menu
Back to cohort
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 N 6 ‐position (m 6 A), 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 m 6 Am could constitute an extensive novel layer of gene expression control. Given the role of cOMe and m 6 A 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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score0.610

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 studies0.0010.000
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 head0.020
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations10
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueBioEssaysSame topicRNA modifications and cancerFrench-language works237,207