Telomeric noncoding <scp>RNA</scp>: telomeric repeat‐containing RNA in telomere biology
Bibliographic record
Abstract
Telomeres are nucleoprotein structures that cap the ends of eukaryotic chromosomes, protecting them from degradation and activation of DNA damage response. For this reason, functional telomeres are vital to genome stability. For years, telomeres were assumed to be transcriptionally silent, because of their heterochromatic state. It was only recently shown that, in several organisms, telomeres are transcribed, giving rise to a long noncoding RNA (lncRNA) called telomeric repeat-containing RNA (TERRA). Several lines of evidence now indicate that TERRA molecules play crucial roles in telomere homeostasis and telomere functions. Recent studies have shown that the expression and regulation of TERRA are dynamically controlled by each chromosome end. TERRA has been involved in the regulation of telomere length, telomerase activity, and heterochromatin formation at telomeres. The correct regulation of the telomeric transcripts may be essential to genome stability, and altered TERRA levels associate with tumorigenesis and cellular senescence. Thus, the study of the molecular mechanisms of TERRA biogenesis and function may advance the understanding of telomere-related diseases, including cancer and aging.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".