Complex Multi‐Enhancer Contacts Captured By Genome Architecture Mapping, A Novel Ligation‐Free Approach
Bibliographic record
Abstract
The organization of the genome in the nucleus and the interactions of genes with their regulatory elements are key elements of transcriptional control and their disruption causes disease. Technologies based on chromosome conformation capture (3C) have profoundly expanded our understanding of the role of genome architecture in gene regulation. However, 3C‐based techniques have important limitations, many of which arise from their reliance on digestion and ligation of the interacting DNA segments. We present a new genome‐wide method, Genome Architecture Mapping (GAM) for measuring three‐dimensional chromatin topology without ligation. We use this new method to generate a genome‐wide dataset of chromatin interactions in mouse ES cells, and compare to published Hi‐C data. GAM identifies specific chromatin contacts enriched for interactions between active genes and enhancers across very large genomic distances. GAM also reveals abundant three‐way contacts genome‐wide, especially between the enhancers most highly occupied by pluripotency transcription factors and highly transcribed genomic regions. These contacts are most prominent for sequences further away from the nuclear lamina. Our results shed light on a previously inaccessible complexity in genome architecture and a major role for gene‐expression specific contacts in organizing genome architecture of mammalian nuclei. Support or Funding Information The work was supported by the Medical Research Council, UK (AP, RAB, MC, SQX, IdS, LG, ND), by the MRC‐Technology (AP, MC), by the Helmholtz Foundation (AP, MS, MB, DK), by Breast Cancer Campaign (PAWE) and by Cancer Research UK (PAWE). Work by JD and JF was supported by grants from the Canadian Institutes of Health Research (CIHR) [MOP‐86716, CAP‐120350].
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".