ChIP-Seq: A Powerful Tool for Studying Protein-DNA Interactions in Plants
Bibliographic record
Abstract
DNA-binding proteins, including transcription factors, epigenetic and chromatin modifiers, control gene expressions in plants. To pinpoint the binding sits of DNA-binding proteins in genome is crucial for decoding gene regulatory networks. Chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-Seq) is a widely used approach to identify the DNA regions bound by a specific protein in vivo. The information generated from ChIP-Seq has tremendously advanced our understanding on the mechanism of transcription factors, cofactors and histone modifications in regulating gene expression. In this review, we reviewed the recent research advance of ChIP-Seq in plants, including description of the ChIP-Seq workflow and its various applications in plants, and in addition, provided perspective of the potential advances of ChIP-Seq.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".