Bioinformatics analysis of the auxin response factor gene family in <i>Prunus persica</i>
Bibliographic record
Abstract
Auxin plays an important role in various aspects of plant growth and development. Auxin response factors (ARFs) are plant-specific transcription factors that regulate the expression of auxin-responsive genes by binding with auxin response elements (AuxinREs) in the promoter region of such genes. In this study, a genome-wide analysis of the ARF genes in Prunus persica was carried out using the latest updated genomics data of this plant. A total of 17 ARF genes were identified and were named PpARF1 to PpARF17. A comprehensive overview of these PpARFs was undertaken, including a phylogenetic analysis and analysis of gene structures, conserved motifs and domains, chromosome location, cis-elements in the promoter region, and gene expression patterns. The 17 PpARF genes were distributed over eight chromosomes. All identified PpARF proteins had an ARF domain and a typical B3-DNA-binding domain that consisted of two α-helixes and seven β-sheets. Some of the PpARF proteins also had an Aux/IAA domain. Phylogenetic analysis categorized PpARF proteins into four groups. PpARF genes had many elements related to stress responses in the promotor region and showed distinct expression levels in leaves and roots. The present study provides basic information about the ARF gene family in Prunus persica and enables further verification of candidate genes related to plant growth.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.002 |
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".