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Record W1975202253 · doi:10.4161/rna.9.1.18047

Potential G-quadruplexes in the human long non-coding transcriptome

2012· article· en· W1975202253 on OpenAlexaff
Gopal Gunanathan Jayaraj, Satyaprakash Pandey, Vinod Scaria, Souvik Maiti

Bibliographic record

VenueRNA Biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Nucleic Acid Chemistry
Canadian institutionsCanadian Society of Intestinal Research
FundersCouncil of Scientific and Industrial Research, India
KeywordsBiologyG-quadruplexNon-coding RNATranscriptomeUntranslated regionComputational biologyRNAGenemicroRNAGeneticsTranscription (linguistics)Coding regionGene expressionDNA

Abstract

fetched live from OpenAlex

DNA G-quadruplexes are known as modulators of transcription. More recently G-quadruplexes, located in the untranslated regions of the mRNA of protein coding genes, have been described to negatively regulate gene expression at the post transcriptional/ translational levels. Here we describe the possibility of the existence of G-quadruplexes in non-coding RNA (ncRNA) and discuss their potential biological roles. Using an in house prediction tool (Quadfinder) we observe a significant occurrence and distribution of G-quadruplexes in ncRNA of various sizes. We also observe that most of non-coding RNAs harboring these potential quadruplex motifs peak at the sizes ranging from 200-300 bases. More importantly we report enrichment for single and dinucleotide loops indicating a degree of high stability of these G-quadruplexes and their potential functions in vivo. Subsequent in vitro analyses of a subset of these sequences were performed which support our predictions.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.001

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 head0.010
GPT teacher head0.268
Teacher spread0.258 · 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 designSimulation or modeling
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

Citations68
Published2012
Admission routes1
Has abstractyes

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