Prediction and identification of Arabidopsis thaliana microRNAs and their mRNA targets
Bibliographic record
Abstract
BACKGROUND: A class of eukaryotic non-coding RNAs termed microRNAs (miRNAs) interact with target mRNAs by sequence complementarity to regulate their expression. The low abundance of some miRNAs and their time- and tissue-specific expression patterns make experimental miRNA identification difficult. We present here a computational method for genome-wide prediction of Arabidopsis thaliana microRNAs and their target mRNAs. This method uses characteristic features of known plant miRNAs as criteria to search for miRNAs conserved between Arabidopsis and Oryza sativa. Extensive sequence complementarity between miRNAs and their target mRNAs is used to predict miRNA-regulated Arabidopsis transcripts. RESULTS: Our prediction covered 63% of known Arabidopsis miRNAs and identified 83 new miRNAs. Evidence for the expression of 25 predicted miRNAs came from northern blots, their presence in the Arabidopsis Small RNA Project database, and massively parallel signature sequencing (MPSS) data. Putative targets functionally conserved between Arabidopsis and O. sativa were identified for most newly identified miRNAs. Independent microarray data showed that the expression levels of some mRNA targets anti-correlated with the accumulation pattern of their corresponding regulatory miRNAs. The cleavage of three target mRNAs by miRNA binding was validated in 5' RACE experiments. CONCLUSIONS: We identified new plant miRNAs conserved between Arabidopsis and O. sativa and report a wide range of transcripts as potential miRNA targets. Because MPSS data are generated from polyadenylated RNA molecules, our results suggest that at least some miRNA precursors are polyadenylated at certain stages. The broad range of putative miRNA targets indicates that miRNAs participate in the regulation of a variety of biological processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
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".