Noncoding but nonexpendable: transcriptional regulation by large noncoding RNA in eukaryotesThis paper is one of a selection of papers published in this Special Issue, entitled 28th International West Coast Chromatin and Chromosome Conference, and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
Genome sequencing and annotation has advanced our understanding of genome organization and gene structure but initially only allowed predictions of how many genes might be present. Mechanisms such as alternative splicing reveal that these predictions only scratch the surface of the true nature of the transcriptome. Several thousand expressed partial gene fragments have been cloned but were considered transcriptional noise or cloning artifacts. We now know that genomes are indeed expressed at much higher levels than was previously predicted, and much of the additional transcription maps to intergenic regions, intron sequences, and untranslated regions of mRNAs. These transcripts are expressed from either the sense or the antisense strand and can be confirmed by conventional techniques. In addition to the already established roles for small RNAs in gene regulation, large noncoding RNAs (ncRNAs) are also emerging as potent regulators of gene expression. In this review, we summarize several illustrative examples of gene regulatory mechanisms that involve large ncRNAs. We describe several distinct regulatory mechanisms that involve large ncRNAs, such as transcriptional interference and promoter inactivation, as well as indirect effects on transcription regulatory proteins and in genomic imprinting. These diverse functions for large ncRNAs are likely to be only the first of many novel regulatory mechanisms emerging from this growing field.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".