End-targeting proteomics of isolated chromatin segments of a mammalian ribosomal RNA gene promoter
Bibliographic record
Abstract
The unbiased identification of proteins associated with specific loci is crucial for understanding chromatin-based processes. The proteomics of isolated chromatin fragment (PICh) method has previously been developed to purify telomeres and identify associated proteins. This approach is based on the affinity capture of endogenous chromatin segments by hybridization with oligonucleotide containing locked nucleic acids. However, PICh is only efficient with highly abundant genomic targets, limiting its applicability. Here we develop an approach for identifying factors bound to the promoter region of the ribosomal RNA genes that we call end-targeting PICh (ePICh). Using ePICh, we could specifically enrich the RNA polymerase I pre-initiation complex, including the selectivity factor 1. The high purity of the ePICh material allowed the identification of ZFP106, a novel factor regulating transcription initiation by targeting RNA polymerase I to the promoter. Our results demonstrate that ePICh can uncover novel proteins controlling endogenous regulatory elements in mammals.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".