Enhancer identification in mouse embryonic stem cells using integrative modeling of chromatin and genomic features
Bibliographic record
Abstract
BACKGROUND: Epigenetic modifications, transcription factor (TF) availability and differences in chromatin folding influence how the genome is interpreted by the transcriptional machinery responsible for gene expression. Enhancers buried in non-coding regions are found to be associated with significant differences in histone marks between different cell types. In contrast, gene promoters show more uniform modifications across cell types. Here we used histone modification and chromatin-associated protein ChIP-Seq data sets in mouse embryonic stem (ES) cells as well as genomic features to identify functional enhancer regions. Using co-bound sites of OCT4, SOX2 and NANOG (co-OSN, validated enhancers) and co-bound sites of MYC and MYCN (limited enhancer activity) as enhancer positive and negative training sets, we performed multinomial logistic regression with LASSO regularization to identify key features. RESULTS: Cross validations reveal that a combination of p300, H3K4me1, MED12 and NIPBL features to be top signatures of co-OSN regions. Using a model from 10 signatures, 83% of top 1277 putative 1 kb enhancer regions (probability greater than or equal to 0.8) overlapped with at least one TF peak from 7 mouse ES cell ChIP-Seq data sets. These putative enhancers are associated with increased gene expression of neighbouring genes and significantly enriched in multiple TF bound loci in agreement with combinatorial models of TF binding. Furthermore, we identified several motifs of known TFs significantly enriched in putative enhancer regions compared to random promoter regions and background. Comparison with an active H3K27ac mark in various cell types confirmed cell type-specificity of these enhancers. CONCLUSIONS: The top enhancer signatures we identified (p300, H3K4me1, MED12 and NIPBL) will allow for the identification of cell type-specific enhancer regions in diverse cell types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".