Bibliographic record
Abstract
Protein-coding and non-coding RNA transcripts perform a wide variety of cellular functions in diverse organisms. Several of their functional roles are expressed and modulated via RNA structure. A given transcript, however, can have more than a single functional RNA structure throughout its life, a fact which has been previously overlooked. Transient RNA structures, for example, are only present during specific time intervals and cellular conditions. We here introduce four RNA families with transient RNA structures that play distinct and diverse functional roles. Moreover, we show that these transient RNA structures are structurally well-defined and evolutionarily conserved. Since Rfam annotates one structure for each family, there is either no annotation for these transient structures or no such family. Thus, our alignments either significantly update and extend the existing Rfam families or introduce a new RNA family to Rfam. For each of the four RNA families, we compile a multiple-sequence alignment based on experimentally verified transient and dominant (dominant in terms of either the thermodynamic stability and/or attention received so far) RNA secondary structures using a combination of automated search via covariance model and manual curation. The first alignment is the Trp operon leader which regulates the operon transcription in response to tryptophan abundance through alternative structures. The second alignment is the HDV ribozyme which we extend to the 5' flanking sequence. This flanking sequence is involved in the regulation of the transcript's self-cleavage activity. The third alignment is the 5' UTR of the maturation protein from Levivirus which contains a transient structure that temporarily postpones the formation of the final inhibitory structure to allow translation of maturation protein. The fourth and last alignment is the SAM riboswitch which regulates the downstream gene expression by assuming alternative structures upon binding of SAM. All transient and dominant structures are mapped to our new alignments introduced here.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".