Genome-wide identification and expression analyses of TCP transcription factor genes in Gossypium barbadense
Bibliographic record
Abstract
Sea-island cotton (Gossypium barbadense) has drawn great attention in the textile industry for its comprehensive resistance and superior fiber properties. However, the mechanisms involved in fiber growth and development are unclear. As TCP transcription factors play important roles in plant growth and development, this study investigated the TCP family genes in G. barbadense (GbTCP). We identified 75 GbTCP genes, of which 68 had no introns. Phylogenetic analyses categorized the GbTCP transcription factors into 11 groups. Genomic analyses showed that 66 genes are located on 21 chromosomes. Phylogenetic analyses of G. arboreum, G. raimondii, G. hirsutum, G. barbadense, Theobroma cacao, Arabidopsis thaliana, Oryza sativa, Sorghum bicolor, and Zea mays, Picea abies, Sphagnum fallax and Physcomitrella patens, categorized 373 TCP genes into two classes (Classes I and II). By studying the structures of TCP genes in sea-island cotton, we identified genes from the same evolutionary branches that showed similar motif patterns. qRT-PCR results suggested that the GbTCPs had different expression patterns in fibers at various developmental stages of cotton, with several showing specific expression patterns during development. This report helps lay the foundation for future investigations of TCP functions and molecular mechanisms in sea-island cotton fiber development.
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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".