The distribution and evolution of Arabidopsis thaliana cis natural antisense transcripts
Bibliographic record
Abstract
BACKGROUND: Natural antisense transcripts (NATs) are regulatory RNAs that contain sequence complementary to other RNAs, these other RNAs usually being messenger RNAs. In eukaryotic genomes, cis-NATs overlap the gene they complement. RESULTS: Here, our goal is to analyze the distribution and evolutionary conservation of cis-NATs for a variety of available data sets for Arabidopsis thaliana, to gain insights into cis-NAT functional mechanisms and their significance. Cis-NATs derived from traditional sequencing are largely validated by other data sets, although different cis-NAT data sets have different prevalent cis-NAT topologies with respect to overlapping protein-coding genes. A. thaliana cis-NATs have substantial conservation (28-35% in the three substantive data sets analyzed) of expression in A. lyrata. We examined evolutionary sequence conservation at cis-NAT loci in Arabidopsis thaliana across nine sequenced Brassicaceae species (picked for optimal discernment of purifying selection), focussing on the parts of their sequences not overlapping protein-coding transcripts (dubbed 'NOLPs'). We found significant NOLP sequence conservation for 28-34% NATs across different cis-NAT sets. This NAT NOLP sequence conservation versus A. lyrata is generally significantly correlated with conservation of expression. We discover a significant enrichment of transcription factor binding sites (as evidenced by CHIP-seq data) in NOLPs compared to randomly sampled near-gene NOLP-like DNA , that is linked to significant sequence conservation. Conversely, there is no such evidence for a general significant link between NOLPs and formation of small interfering RNAs (siRNAs), with the substantial majority of unique siRNAs arising from the overlapping portions of the cis-NATs. CONCLUSIONS: In aggregate, our results suggest that many cis-NAT NOLPs function in the regulation of conserved promoter/regulatory elements that they 'over-hang'.
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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".