Genome-wide identification of GRF transcription factors in soybean and expression analysis of GmGRF family under shade stress
Bibliographic record
Abstract
BACKGROUND: The Growth-regulating factor (GRF) family encodes plant-specific transcription factors which contain two conserved domains, QLQ and WRC. Members of this family play vital roles in plant development and stress response processes. Although GRFs have been identified in various plant species, we still know little about the GRF family in soybean (Glycine max). RESULTS: In the present study, 22 GmGRFs distributed on 14 chromosomes and one scaffold were identified by searching soybean genome database and were clustered into five subgroups according to their phylogenetic relationships. GmGRFs belonging to the same subgroup shared a similar motif composition and gene structure. Synteny analysis revealed that large-scale duplications played key roles in the expansion of the GmGRF family. Tissue-specific expression data showed that GmGRFs were strongly expressed in growing tissues, including the shoot apical meristems, developing seeds and flowers, indicating that GmGRFs play critical roles in plant growth and development. On the basis of expression analysis of GmGRFs under shade conditions, we found that all GmGRFs responded to shade stress. Most GmGRFs were down-regulated in soybean leaves after shade treatment. CONCLUSIONS: Taken together, this research systematically analyzed the characterization of the GmGRF family and its primary roles in soybean development and shade stress response. Further studies of the function of the GmGRFs in the growth, development and stress tolerance of soybean, especially under shade stress, will be valuable.
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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 | 0.001 |
| 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".