Identifying clusters of <i>cis</i>-regulatory elements underpinning TAD structures and lineage-specific regulatory networks
Bibliographic record
Abstract
Cellular identity relies on cell-type–specific gene expression controlled at the transcriptional level by cis-regulatory elements (CREs). CREs are unevenly distributed across the genome, giving rise to individual CREs and clusters of CREs (COREs). Technical and biological features hinder CORE identification. We addressed these issues by developing an unsupervised machine learning approach termed clustering of genomic regions analysis method (CREAM). CREAM automates CORE detection from chromatin accessibility profiles that are enriched in CREs strongly bound by master transcription regulators, proximal to highly expressed and essential genes, and discriminating cell identity. Although COREs share similarities with super-enhancers, we highlight differences in terms of the genomic distribution and structure of these cis-regulatory units. We further show the enhanced value of COREs over super-enhancers to identify master transcription regulators, highly expressed and essential genes defining cell identity. COREs enrich at topologically associated domain (TAD) boundaries. They are also preferentially bound by the chromatin looping factors CTCF and cohesin, in contrast to super-enhancers, forming clusters of CTCF and cohesin binding regions and defining homotypic clusters of transcription regulator binding regions (HCTs). Finally, we show the clinical utility of CREAM to identify COREs across chromatin accessibility profiles to stratify more than 400 tumor samples according to their cancer type and to delineate cancer type–specific active biological pathways. Collectively, our results support the utility of CREAM to delineate COREs underlying, with greater accuracy than individual CREs or super-enhancers, the cell-type–specific biological underpinning across a wide range of normal and cancer cell types.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".