A highly sensitive in vivo footprinting technique for condition-dependent identification of cis elements
Bibliographic record
Abstract
Knowing which regions of a gene are targeted by transcription factors during induction or repression is essential for understanding the mechanisms responsible for regulation. Therefore, we re-designed the traditional in vivo footprinting method to obtain a highly sensitive technique, which allows identification of the cis elements involved in condition-dependent gene regulation. Data obtained through DMS methylation, HCl DNA cleavage and optimized ligation-mediated PCR using fluorescent labelling followed by capillary gel electrophoresis are analysed by ivFAST. In this work we have developed this command line-based program, which is designed to ensure automated and fast data processing and visualization. The new method facilitates a quantitative, high-throughput approach because it enables the comparison of any number of in vivo footprinting results from different conditions (e.g. inducing, repressing, de-repressing) to one another by employing an internal standard. For validation of the method the well-studied upstream regulatory region of the Trichoderma reesei xyn1 (endoxylanase 1) gene was used. Applying the new method we could identify the motives involved in condition-dependent regulation of the cbh2 (cellobiohydrolase 2) and xyn2 (endoxylanase 2) genes.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".