Improving interpretability of transcription factor binding models with DNA shape features

Deep learning models in genomics that predict molecular phenotypes from DNA sequence traditionally focus on one-hot encoded nucleotide representations. Here, we develop a novel model that extends this approach by incorporating DNA structural attributes indicative of local DNA 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 information can improve the identification of regulatory elements to provide a more nuanced understanding of genomic function and regulation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/646034v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@b648b6org.highwire.dtl.DTLVardef@15fa931org.highwire.dtl.DTLVardef@15d0d31org.highwire.dtl.DTLVardef@d212e_HPS_FORMAT_FIGEXP M_FIG C_FIG Schematic overview of the DeepShape model. One-hot encoded sequence, and five DNA shape attributes--minor groove width (MGW), helical twist (HelT), propeller twist (ProT), roll, and electrostatic potential (EP)--are input separately to sequence and shape branches of the model, each processed through two convolutional layers and subsequently concatenated for further processing through additional convolutional layers.