RealFeedApp Neural network ChAI-Net deciphers regulatory code for chromatin co-accessibility
Researchers trained the model to predict locus-specific chromatin co-accessibility maps from DNA sequences. ChAI-Net identified unique sequence motif combinations regulating enhancers, promoters, and CTCF-binding sites.
- Field
- genetics and genomes
- What they did
- Researchers developed a neural network called ChAI-Net that predicts how DNA regions are simultaneously accessible to proteins by analyzing genome sequences.
- Why it matters
- This allows understanding how combinations of sequence motifs regulate chromatin accessibility within regulatory elements, which is important for determining cell function and identity.
#chromatin#deep learning#single-molecule footprinting#cis-regulatory elements#mouse embryonic stem cells
