Synthetic Enhancer Design Pipeline
Enhancer design links sequence edits, regulatory-track prediction, cell-type specificity, and experimental caution.
Site connection
The HiC-TAD project sketches a pipeline using AlphaGenome predictions, CNN training, motif insertion, and validation loops.
Visual model
Predicted regulatory-track response
Change the deletion size index to see how regulatory tracks might remain stable or become disrupted.
Interactive
Sequence edits can be compared as predicted regulatory tracks
Design Is a Loop, Not a Single Prediction
A synthetic enhancer pipeline proposes sequence changes, predicts regulatory effects, checks cell-type specificity, filters for safety and off-target structure, then returns to sequence design. The model is a guide; biology still gets the final vote.
What AlphaGenome Adds
AlphaGenome-style models predict many regulatory outputs from long DNA sequence context. That matters because an enhancer's effect is not just local motif grammar; it depends on surrounding sequence and cell type.
For design, the useful object is the delta: what changed between the original and edited sequence across regulatory tracks.
Why Structure Matters
An enhancer that raises a desired signal may still be wrong if it disrupts chromatin boundaries or activates in the wrong cell type.
That is why this project connects enhancer design with Hi-C/TAD analysis: sequence function and 3D genome organization are entangled.
| Signal | Question |
|---|---|
| ATAC | Did accessibility increase? |
| H3K27ac | Does the sequence look enhancer-active? |
| CTCF | Did boundary-associated binding change? |
| RNA | Could expression change downstream? |
| Contact map | Did 3D organization shift? |
Common Pitfalls
- Treating predicted enhancer strength as experimental truth.
- Ignoring cell-type specificity.
- Optimizing one track while damaging another.
- Forgetting that non-coding edits can affect chromatin structure.
- Skipping negative controls.
Quick check
Quiz
What is the main use of model predictions in enhancer design?
- Prioritizing and filtering candidates before experiments
- Proving biology is solved
- Avoiding controls
- Removing cell-type context
Predictions guide candidate selection; experiments remain necessary for validation.