An algorithm for detecting homologues of known structured rnas in genomes
Bibliographic record
Abstract
Distinct RNA structures are frequently involved in a wide-range of functions in various biological mechanisms. The three dimensional RNA structures solved by X-ray crystallography and various well-established RNA phylogenetic structures indicate that functional RNAs have characteristic RNA structural motifs represented by specific combinations of base pairings and conserved nucleotides in the loop region. Discovery of well-ordered RNA structures and their homologues in genome-wide searches will enhance our ability to detect the RNA structural motifs and help us to highlight their association with functional and regulatory RNA elements. We present here a novel computer algorithm, HomoStRscan, that takes a single RNA sequence with its secondary structure to search for homologous-RNAs in complete genomes. This novel algorithm completely differs from other currently used search algorithms of homologous structures or structural motifs. For an arbitrary segment (or window) given in the target sequence, that has similar size to the query sequence, HomoStRscan finds the most similar structure to the input query structure and computes the maximal similarity score (MSS) between the two structures. The homologousRNA structures are then statistically inferred from the MSS distribution computed in the target genome. The method provides a flexible, robust and fine search tool for any homologous structural RNAs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".