Improving interpretability of transcription factor binding models with DNA shape features
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Deep learning models in genomics that predict molecular phenotypes from DNA sequence traditionally focus on one-hot encoded representations. Here, we develop a novel model that extends this approach by incorporating DNA structural attributes indicative of local shape alongside canonical sequence inputs. This augmentation provides an additional axis for model interpretability and aids in identifying regulatory patterns not apparent from sequence alone. Applying this approach to prediction of transcription factor binding (ChIP-seq) demonstrates that combining sequence and structural DNA data can improve the identification of regulatory elements to provide a more nuanced understanding of genomic function and regulation.
