Capturing <scp>Protein–Nucleic</scp> Acid Interactions by <scp>High‐Intensity Laser‐Induced</scp> Covalent Cross‐Linking<sup>†</sup>
Bibliographic record
Abstract
Interactions of DNA with structural proteins such as histones, regulatory proteins and enzymes play a crucial role in major cellular processes such as transcription, replication and repair. The in vivo mapping and characterization of the binding sites of the involved biomolecules are of primary importance for a better understanding of genomic deployment that is implicated in tissue and developmental stage-specific gene expression regulation. The most powerful and commonly used approach to date is immunoprecipitation of chemically cross-linked chromatin (XChIP) coupled with sequencing analysis (ChIP-seq). While the resolution and the sensitivity of the high-throughput sequencing techniques have been constantly improved, little progress has been achieved in the cross-linking step. Because of its low efficiency, the use of the conventional UVC lamps remains very limited while the formaldehyde method was established as the "gold standard" cross-linking agent. Efficient biphotonic cross-linking of directly interacting nucleic acid-protein complexes by a single short UV laser pulse has been introduced as an innovative technique for overcoming limitations of conventionally used chemical and photochemical approaches. In this survey, the main available methods including the laser approach are critically reviewed for their ability to generate DNA-protein cross-links in vitro model systems and cells.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".