Molecular Traits of Long Non-protein Coding RNAs from Diverse Plant Species Show Little Evidence of Phylogenetic Relationships
Bibliographic record
Abstract
Abstract Long non-coding RNAs (lncRNAs) represent a diverse class of regulatory loci with roles in development and stress responses throughout all kingdoms of life. LncRNAs, however, remain under-studied in plants compared to animal systems. To address this deficiency, we applied a machine learning prediction tool, Classifying RNA by Ensemble Machine learning Algorithm (CREMA), to analyze RNAseq data from 11 plant species chosen to represent a wide range of evolutionary histories. Transcript sequences of all expressed and/or annotated loci from plants grown in unstressed (control) conditions were assembled and input into CREMA for comparative analyses. On average, 6.4% of the plant transcripts were identified by CREMA as encoding lncRNAs. Gene annotation associated with the transcripts showed that up to 99% of all predicted lncRNAs for Solanum tuberosum and Amborella trichopoda were missing from their reference annotations whereas the reference annotation for the genetic model plant Arabidopsis thaliana contains 96% of all predicted lncRNAs for this species. Thus a reliance on reference annotations for use in lncRNA research in less well-studied plants can be impeded by the near absence of annotations associated with these regulatory transcripts. Moreover, our work using phylogenetic signal analyses suggests that molecular traits of plant lncRNAs display different evolutionary patterns than all other transcripts in plants and have molecular traits that do not follow a classic evolutionary pattern. Specifically, GC content was the only tested trait of lncRNAs with consistently significant and high phylogenetic signal, contrary to high signal in all tested molecular traits for the other transcripts in our tested plant species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".