Analysis of Replicating Yeast Chromosomes by DNA Combing
Bibliographic record
Abstract
Molecular combing of DNA fibers is a powerful technique to monitor origin usage and DNA replication fork progression in the budding yeast Saccharomyces cerevisiae. In contrast to traditional flow cytometry, microarray, or sequencing techniques, which provide population-level data, DNA combing provides DNA replication profiles of individual molecules. DNA combing uses yeast strains that express human thymidine kinase, which facilitates the incorporation of thymidine analogs into nascent DNA. First, DNA is isolated and stretched uniformly onto silanized glass coverslips. Following immunodetection with antibodies that recognize the thymidine analog and the DNA, the DNA fibers are imaged using a fluorescence microscope. Finally, the lengths of newly replicated DNA tracks are measured and converted to base pairs, allowing calculations of the speed of the replication fork and of interorigin distances. DNA combing can be applied to monitor replication defects caused by gene mutations or by chemical agents that induce replication stress. Here, we present a methodology for studying replicating yeast chromosomes by molecular DNA combing. We begin with procedures for the preparation of silanized coverslips and for assembly of a DNA combing machine (DCM) and conclude by presenting a detailed protocol for molecular DNA combing in yeast.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".