The Sheep and the Goats: Distinguishing transcriptional enhancers in a complex chromatin landscape
Bibliographic record
Abstract
ABSTRACT Predicting regulatory function of non-coding DNA using genomic information remains a major goal in genomics, and an important step in interpreting the cis-regulatory code. Regulatory capacity can be partially inferred from transcription factor occupancy, histone modifications, motif enrichment, and evolutionary conservation. However, combinations of these features in well-studied systems such as Drosophila have limited predictive accuracy. Here we examine the current limits of computational enhancer prediction by applying machine-learning methods to an extensive set of genomic features, validating predictions with the Fly Enhancer Resource, which characterized the transcriptional activity of approximately fifteen percent of the genome. Supervised machine learning trained on a range of genomic features identify active elements with a high degree of accuracy, but are less successful at distinguishing tissue-specific expression patterns. Consistent with previous observations of their widespread genomic interactions, many transcription factors were associated with enhancers not known to be direct functional targets. Interestingly, no single factor was necessary for enhancer identification, although binding by the ′pioneer′ transcription factor Zelda was the most predictive feature for enhancer activity. Using an increasing number of predictive features improved classification with diminishing returns. Thus, additional single-timepoint ChIP data may have only marginal utility for discerning true regulatory regions. On the other hand, spatially- and temporally-differentiated genomic features may provide more power for this type of computational enhancer identification. Inclusion of new types of information distinct from current chromatin-immunoprecipitation data may enable more precise identification of enhancers, and further insight into the features that distinguish their biological functions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".