Transposable elements orchestrate subgenome-convergent and -divergent transcription in common wheat
Bibliographic record
Abstract
The success of common wheat as a global staple crop was largely attributed to its genomic diversity and redundancy due to the merge of different genomes, giving rise to the major question how subgenome-divergent and -convergent transcription is mediated and harmonized in a single cell. Here, we create a catalog of genome-wide transcription factor-binding sites (TFBSs) to assemble a common wheat regulatory network on an unprecedented scale. A significant proportion of subgenome-divergent TFBSs are derived from differential expansions of particular transposable elements (TEs) in diploid progenitors, which contribute to subgenome-divergent transcription. Whereas subgenome-convergent transcription is associated with balanced TF binding at loci derived from TE expansions before diploid divergence. These TFBSs have retained in parallel during evolution of each diploid, despite extensive unbalanced turnover of the flanking TEs. Thus, the differential evolutionary selection of paleo- and neo-TEs contribute to subgenome-convergent and -divergent regulation in common wheat, highlighting the influence of TE repertory plasticity on transcriptional plasticity in polyploid.
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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 | 0.000 | 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) | 0.000 | 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".