iEnhancer-Flow: Integrating Transformer-Based Sequence Learning with DNA Shape Insights for Robust Enhancer Prediction.

Abstract

Purpose: Enhancers are critical non-coding regulatory elements, but their prediction remains challenging due to their variability and the absence of clear sequence motifs. This study aims to promote enhancer classification through a novel framework integrating DNA sequence and shape features, addressing the limitations of sequence-only models and improving prediction performance across diverse genomic contexts.

Methods: We propose iEnhancer-Flow, a dual-branch model that integrates DNABERT-2 for extracting robust sequence representations and a hybrid convolutional network-based branch for DNA shape information. Drawing inspiration from central-difference techniques in image processing, the shape branch utilizes similar methods to capture local structural variations. The extracted sequence and shape features are fused via a flow attention mechanism to facilitate dynamic interaction between these complementary feature sets. The combined features are further enhanced with a weighted residual connection and attention pooling before being passed to an MLP classifier for final enhancer prediction.

Results: iEnhancer-Flow consistently outperformed competing methods, achieving significant improvements in balanced accuracy (Bacc), Matthews correlation coefficient (MCC), and other key metrics across six of the eight cell lines tested. For the remaining two cell lines, the model achieved comparable performance across several key metrics, suggesting its stability and robustness in diverse biological contexts.

Conclusion: The integration of sequence and DNA shape information in iEnhancer-Flow marks a significant advancement in enhancer prediction by capturing complementary regulatory signals beyond traditional sequence features. These findings suggest that understanding genomic regulation requires a comprehensive view, incorporating both sequence and structural contexts.