ECSFinder: optimized prediction of evolutionarily conserved RNA secondary structures from genome sequences
Bibliographic record
Abstract
Accurate prediction of RNA secondary structures is essential for understanding the evolutionary conservation and functional roles of long noncoding RNAs (lncRNAs) across diverse species. In this study, we benchmarked two leading tools for predicting evolutionarily conserved RNA secondary structures (ECSs), SISSIz and R-scape, using two distinct experimental frameworks: one focusing on well-characterized mitochondrial RNA structures and the other on experimentally validated Rfam structures embedded within simulated genome alignments. While both tools performed similarly overall, each displayed subtle preferences in detecting ECSs. To overcome their individual limitations, we evaluated two interpretable machine learning approaches that integrate the strengths of both methods. By balancing thermodynamic stability features from RNALalifold and SISSIz with robust covariation metrics from R-scape, a random forest model classifier significantly outperformed individual tools in identifying ECSs. This classifier was implemented in ECSFinder, a new tool designed for large-scale comparative genomics applications, that can provide robust, genome-wide identification of conserved RNA structures, and offer valuable insights into the modular elements and evolutionary conservation of lncRNAs.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".