Comparative analysis of the small RNA transcriptomes of <i>Pinus contorta</i> and <i>Oryza sativa</i>
Bibliographic record
Abstract
The diversity of microRNAs and small-interfering RNAs has been extensively explored within angiosperms by focusing on a few key organisms such as Oryza sativa and Arabidopsis thaliana. A deeper division of the plants is defined by the radiation of the angiosperms and gymnosperms, with the latter comprising the commercially important conifers. The conifers are expected to provide important information regarding the evolution of highly conserved small regulatory RNAs. Deep sequencing provides the means to characterize and quantitatively profile small RNAs in understudied organisms such as these. Pyrosequencing of small RNAs from O. sativa revealed, as expected, approximately 21- and approximately 24-nt RNAs. The former contained known microRNAs, and the latter largely comprised intergenic-derived sequences likely representing heterochromatin siRNAs. In contrast, sequences from Pinus contorta were dominated by 21-nt small RNAs. Using a novel sequence-based clustering algorithm, we identified sequences belonging to 18 highly conserved microRNA families in P. contorta as well as numerous clusters of conserved small RNAs of unknown function. Using multiple methods, including expressed sequence folding and machine learning algorithms, we found a further 53 candidate novel microRNA families, 51 appearing specific to the P. contorta library. In addition, alignment of small RNA sequences to the O. sativa genome revealed six perfectly conserved classes of small RNA that included chloroplast transcripts and specific types of genomic repeats. The conservation of microRNAs and other small RNAs between the conifers and the angiosperms indicates that important RNA silencing processes were highly developed in the earliest spermatophytes. Genomic mapping of all sequences to the O. sativa genome can be viewed at http://microrna.bcgsc.ca/cgi-bin/gbrowse/rice_build_3/.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".