A Deconvolution Protocol for ChIP-Seq Reveals Analogous Enhancer Structures on the Mouse and Human Ribosomal RNA Genes
Bibliographic record
Abstract
Abstract The combination of Chromatin Immunoprecipitation and Massively Parallel Sequencing, or ChIP-Seq, has greatly advanced our genome-wide understanding of chromatin and enhancer structures. However, its resolution at any given genetic locus is limited by several factors. In applying ChIP-Seq to the study of the ribosomal RNA genes, we found that a major limitation to resolution was imposed by the underlying variability in sequence coverage that very often dominates the protein–DNA interaction profiles. Here, we describe a simple numerical deconvolution approach that, in large part, corrects for this variability, and significantly improves both the resolution and quantitation of protein–DNA interaction maps deduced from ChIP-Seq data. This approach has allowed us to determine the in vivo organization of the RNA polymerase I preinitiation complexes that form at the promoters and enhancers of the mouse (Mus musculus) and human (Homo sapiens) ribosomal RNA genes, and to reveal a phased binding of the HMG-box factor UBF across the rDNA. The data identify and map a “Spacer Promoter” and associated stalled polymerase in the intergenic spacer of the human ribosomal RNA genes, and reveal a very similar enhancer structure to that found in rodents and lower vertebrates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".