High-Resolution Mapping of Protein-DNA Interactions in Mouse Stem Cell-Derived Neurons using Chromatin Immunoprecipitation-Exonuclease (ChIP-Exo)
Bibliographic record
Abstract
Identification of specific protein-DNA interactions on the genome is important for understanding gene regulation. Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) is widely used to identify genome-wide binding locations of DNA-binding proteins. However, the ChIP-seq method is limited by its heterogeneity in length of sonicated DNA fragments and non-specific background DNA, resulting in low mapping resolution and uncertainty in DNA-binding sites. To overcome these limitations, the combination of ChIP with exonuclease digestion (ChIP-exo) utilizes 5' to 3' exonuclease digestion to trim the heterogeneously sized immunoprecipitated DNA to the protein-DNA crosslinking site. Exonuclease treatment also eliminates non-specific background DNA. The library-prepared and exonuclease-digested DNA can be sent for high-throughput sequencing. The ChIP-exo method allows for near base-pair mapping resolution with greater detection sensitivity and reduced background signal. An optimized ChIP-exo protocol for mammalian cells and next-generation sequencing is described below.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".