Spatial genome organization: contrasting views from chromosome conformation capture and fluorescence in situ hybridization
Bibliographic record
Abstract
Although important for gene regulation, most studies of genome organization use either fluorescence in situ hybridization (FISH) or chromosome conformation capture (3C) methods. FISH directly visualizes the spatial relationship of sequences but is usually applied to a few loci at a time. The frequency at which sequences are ligated together by formaldehyde cross-linking can be measured genome-wide by 3C methods, with higher frequencies thought to reflect shorter distances. FISH and 3C should therefore give the same views of genome organization, but this has not been tested extensively. We investigated the murine HoxD locus with 3C carbon copy (5C) and FISH in different developmental and activity states and in the presence or absence of epigenetic regulators. We identified situations in which the two data sets are concordant but found other conditions under which chromatin topographies extrapolated from 5C or FISH data are not compatible. We suggest that products captured by 3C do not always reflect spatial proximity, with ligation occurring between sequences located hundreds of nanometers apart, influenced by nuclear environment and chromatin composition. We conclude that results obtained at high resolution with either 3C methods or FISH alone must be interpreted with caution and that views about genome organization should be validated by independent methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".