Characterization of Structure, Divergence and Regulation Patterns of Plant Promoters
Bibliographic record
Abstract
Plant promoters have attracted increasing attention because of their irreplaceable role in modulating the spatio-temporal expression of genes interacting with transcription factors (TFs). Despite their importance, the basic characteristics of plant promoters are not well understood. In order to determine sequence diversity within promoter regions, evolutionary divergence of promoters between plant species, and the general structural characteristics of promoter sequences, we downloaded and analyzed 3922 plant promoter sequences from a wide range of plant species. The average plant promoter GC content was lower in dicotyledons than in monocotyledons, which might suggest different evolutionary pressures for promoter sequences between the two clades. Approximately 3.3% of plant promoters harbored minisatellite sequences, and 15.4% of plant promoters harbored microsatellite sequences (also called simple sequence repeats). Very few transposable elements were detected within the plant promoters. The most common transcription factor binding site (TFBS) motif was AGAGAGAGA, followed by TTAGGGTTT and then GCCGCC. Transcribed gene regions with promoters containing the corresponding TFBSs were predicted to be most commonly involved in metabolic processes, biological regulation, and stimulus response in plants. These results reveal some basic structural characteristics of plant promoters and clarify the evolutionary forces shaping plant promoters. This data might facilitate cloning of plant promoter sequences and aid in our understanding of gene spatio-temporal expression patterns in plants.
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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) | 0.000 | 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".