2C-ChIP: measuring chromatin immunoprecipitation signal from defined genomic regions with deep sequencing
Bibliographic record
Abstract
BACKGROUND: Understanding how transcription occurs requires the integration of genome-wide and locus-specific information gleaned from robust technologies. Chromatin immunoprecipitation (ChIP) is a staple in gene expression studies, and while genome-wide methods are available, high-throughput approaches to analyze defined regions are lacking. RESULTS: Here, we present carbon copy-ChIP (2C-ChIP), a versatile, inexpensive, and high-throughput technique to quantitatively measure the abundance of DNA sequences in ChIP samples. This method combines ChIP with ligation-mediated amplification (LMA) and deep sequencing to probe large genomic regions of interest. 2C-ChIP recapitulates results from benchmark ChIP approaches. We applied 2C-ChIP to the HOXA cluster to find that a region where H3K27me3 and SUZ12 linger encodes HOXA-AS2, a long non-coding RNA that enhances gene expression during cellular differentiation. CONCLUSIONS: 2C-ChIP fills the need for a robust molecular biology tool designed to probe dedicated genomic regions in a high-throughput setting. The flexible nature of the 2C-ChIP approach allows rapid changes in experimental design at relatively low cost, making it a highly efficient method for chromatin analysis.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".