Genome-wide Identification and Expression Analysis of GRAS Genes in Cucumber
Bibliographic record
Abstract
GRAS transcription factors regulate various biological processes in plant growth, development and stress responses. Cucumber ( Cucumis sativus L.) is an economically important vegetable crop. However, the biological functions of GRAS gene family remain largely unknown in cucumber. In order to explore the potential function of the GRAS gene family in cucumber, the whole genomic identification of CsGRAS family was performed, and the gene structure, protein structure, characteristic, subcellular locations, phylogenetic relationship and tissue expression pattern were analyzed by bioinformatics. Here, a total of 37 GRAS members were identified in cucumber genome, most of the CsGRAS proteins are neutral or acidic protein, which are encoded by a single exon. Phylogenetic analysis divided CsGRAS members into 16 subfamilies, with each having distinct conserved domains and functions. 37 GRAS genes are unevenly distributed on the 7 chromosomes of cucumber and contain 1 pair of tandem repeat genes and 3 pairs of fragment repeat genes. Gene expression analysis in various tissues demonstrated that most CsGRAS genes showed tissue-specific expression, uncovering their potential function in cucumber growth and development. This study provided a comprehensive analysis of the cucumber GRAS gene family and laid a foundation for further studying the roles of GRAS gene family members in cucumber.
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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) | 0.001 | 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".