In silico expression analysis to identify potentially functional plant cis-regulatory elements
Bibliographic record
Abstract
Plants have developed sophisticated mechanisms to cope with environmental stresses. Alterations in the expression of specific stress-resistance and tolerance proteins are essential for the plant to adapt to a whole array of biotic and abiotic stresses. The expression of these proteins is mainly achieved by transcriptional gene activation, which is in turn largely controlled by the binding of transcription factors to cis-regulatory elements (CREs) in promoter regions. The goal of the present work was the development of bioinformatics methods for the identification of putatively functional stress-associated CREs in plants. A novel tool called in silico expression analysis was developed which correlates genome-wide promoter occurrences of a given sequence with microarray expression data stored in the PathoPlant database. The tool provides statistical values which serve to evaluate the probability of a sequence being associated to a given stress. Novel web tools were developed during the present study. An on-line version of the in silico expression analysis allows the identification of genes containing a user-submitted sequence within promoters. Information about the expression of such genes, together with statistical analysis are given to the user, thus allowing the evaluation of possible sequence functionality. Finally a new program was developed for the prediction of combinatorial CREs. The program searches for motif combinations in the Arabidopsis gene promoters and calculates the expression of genes containing such combinations within the promoters. The statistical significance of the calculated expression was used to identify putatively functional combinatorial elements. The results show that the novel developed bioinformatics tools serve to predict CREs associated to different biotic and abiotic stresses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".