A rapid simple approach to quantify chromosome conformation capture
Bibliographic record
Abstract
Chromosome conformation capture (3C) is a powerful tool to study DNA looping. The procedure generates chimeric DNA templates after ligation of restriction enzyme fragments juxtaposed in vivo by looping. These unique ligation products (ULPs) are typically quantified by gel-based methods, which are practically inefficient. Taqman probes may be used, but are expensive. Cycle threshold (Ct) determined using SYBR Green, an inexpensive alternative, is hampered by non-specific products and/or background fluorescence, both due to high template/ULP ratio. SYBR Green melting curve analysis (MCA) is a well-known qualitative tool for assessing PCR specificity. Here we present for the first time MCA as a quantitative tool (qMCA) to compare template concentrations across different samples and apply it to 3C to assess looping among remote elements identified by STAT1 and IRF1 ChIP-chip at the interferon-gamma responsive CIITA and SOCS1 loci. This rapid, inexpensive approach provided highly reproducible identification and quantification of ULPs over a significant linear range. Therefore, qMCA is a robust method to assess chromatin looping in vivo, and overcomes several drawbacks associated with other approaches. Our data suggest that basal and induced looping is a involving remote enhancers is a common mechanism at IFNgamma-regulated targets.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".