In-line probing of RNA G-quadruplexes
Bibliographic record
Abstract
Although the majority of the initial G-quadruplex studies were performed on DNA molecules, there currently exists a rapidly growing interest in the investigation of those formed in RNA molecules that possess high potential of acting as gene expression regulatory elements. Indeed, G-quadruplexes found in the 5'-untranslated regions of mRNAs have been reported to be widespread within the human transcriptome and to act as general translational repressors. In addition to translation regulation, several other mRNA maturation steps and events, including mRNA splicing, polyadenylation and localization, have been shown to be influenced by the presence of these RNA G-quadruplexes. Bioinformatic approaches have identified thousands of potential RNA G-quadruplex sequences in the human transcriptome. Clearly there is a need for the development of rapid, simple and informative techniques and methodologies with which the ability of these sequences, and of any potential new regulatory elements, to fold into G-quadruplexes in vitro can be examined. This report describes an integrated methodology for monitoring RNA G-quadruplexes formation that combines bioinformatic algorithms, secondary structure prediction, in-line probing with semi-quantification analysis and structural representation software. The power of this approach is illustrated, step-by-step, with the determination of the structure adopted by a potential G-quadruplex sequence found in the 5'-untranslated region of the cAMP responsive element modulator (CREM) mRNA. The results unambiguously show that the CREM sequence folds into a G-quadruplex structure in the presence of a physiological concentration of potassium ions. This in-line probing-based method is easy to use, robust, reproducible and informative in the study of RNA G-quadruplex formation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".