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Record W2120624230 · doi:10.1093/nar/gku1311

Small antisense oligonucleotides against G-quadruplexes: specific mRNA translational switches

2014· article· en· W2120624230 on OpenAlexafffund
Samuel G. Rouleau, Jean-Denis Beaudoin, Martin Bisaillon, Jean‐Pierre Perreault

Bibliographic record

VenueNucleic Acids Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Nucleic Acid Chemistry
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchFonds Québécois de la Recherche sur la Nature et les TechnologiesUniversité de Sherbrooke
KeywordsBiologyOligonucleotideRNAComputational biologyTranslation (biology)Untranslated regionG-quadruplexTranscriptomeGeneDNANucleic acid structureMessenger RNAGene expressionCell biologyGenetics

Abstract

fetched live from OpenAlex

G-quadruplexes (G4) are intricate RNA structures found throughout the transcriptome. Because they are associated with a variety of biological cellular mechanisms, these fascinating structural motifs are seen as potential therapeutic targets against many diseases. While screening of chemical compounds specific to G4 motifs has yielded interesting results, no single compound successfully discriminates between G4 motifs based on nucleotide sequences alone. This level of specificity is best attained using antisense oligonucleotides (ASO). Indeed, oligonucleotide-based strategies are already used to modulate DNA G4 folding in vitro. Here, we report that, in human cells, the use of short ASO to promote and inhibit RNA G4 folding affects the translation of specific mRNAs, including one from the 5'UTR of the H2AFY gene, a histone variant associated with cellular differentiation and cancer. These results suggest that the relatively high specificity of ASO-based strategies holds significant potential for applications aimed at modulating G4-motif folding.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.043
GPT teacher head0.296
Teacher spread0.253 · 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.

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

Citations38
Published2014
Admission routes2
Has abstractyes

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