Bibliographic record
Abstract
DNase I footprinting was developed by Galas and Schmitz in 1978 as a method to study the sequence-specific binding of proteins to DNA ( 1 ). In this technique a suitable uniquely end-labeled DNA fragment is allowed to interact with a given DNA-binding protein and then the complex is partially digested with DNase 1. The bound protein protects the region of the DNA with which it interacts from attack by the DNase. Subsequent molecular weight analysis of the degraded DNA by electrophoresis and autoradiography identifies the region of protection as a gap in the otherwise continuous background of digestion products (for examples, see Fig. 1 ). The technique can be used to determine the site of interaction of most sequence-specific DNAbinding proteins but has been most extensively applied to the study of transcription factors. Since the DNase I molecule is relatively large compared to other footprinting agents ( see Chapters 3 and Chapters 7 in this volume), its attack on the DNA is more readily prevented by steric hindrance. Thus DNase I footprinting is the most likely of all the footprinting techniques to detect a specific DNA-protein interaction. This is clearly demonstrated by our studies on the transcription factor xUBF ( see Fig. 1B ). The xUBF interaction with the xenopus ribosomal DNA enhancer can be easily detected by DNase I footprinting but has not yet been detected by other footprinting techniques. Examples of DNase I footprints. A. Footprint (open box) of a chicken erythrocyte DNA binding factor on the promoter of the H5 gene ( 2 ) (figure kindly donated by A. Ruiz-Carrillo). B. Interaction of the RNA polymerase I transcription factor xUBF with the tandemly repeated 60 and 81 bp. Xenopus ribosomal gene enhancers. Both A and B used 5′ end-labeled fragments. (−) and (+) refer to naked and complexed DNA fragments and (G + A) to the chemical sequence ladder.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.028 |
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".