Genome-wide analysis of cis-regulatory element structure and discovery of motif-driven gene co-expression networks in grapevine
Bibliographic record
Abstract
Coordinated transcriptional and metabolic reprogramming ensures a plant's continued growth and survival under adverse environmental conditions. Transcription factors (TFs) act to modulate gene expression through complex cis-regulatory element (CRE) interactions. Genome-wide analysis of known plant CREs was performed for all currently predicted protein-coding gene promoters in grapevine (Vitis vinifera L.). Many CREs such as abscisic acid (ABA)-responsive, drought-responsive, auxin-responsive, and evening elements, exhibit bona fide CRE properties such as strong position bias towards the transcription start site (TSS) and over-representation when compared with random promoters. Genes containing these CREs are enriched in a large repertoire of plant biological pathways. Large-scale transcriptome analyses also show that these CREs are highly implicated in grapevine development and stress response. Numerous CRE-driven modules in condition-specific gene co-expression networks (GCNs) were identified and many of these modules were highly enriched for plant biological functions. Several modules corroborate known roles of CREs in drought response, pathogen defense, cell wall metabolism, and fruit ripening, whereas others reveal novel functions in plants. Comparisons with Arabidopsis suggest a general conservation in promoter architecture, gene expression dynamics, and GCN structure across species. Systems analyses of CREs provide insights into the grapevine cis-regulatory code and establish a foundation for future genomic studies in grapevine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".