EnhancerMatcher: comparing cell-type-specific enhancer activity of DNA sequences using deep convolutional neural networks and explainable AI.

Transcriptional enhancers-unlike promoters-are challenging to identify computationally due to their variable distance and orientation relative to target genes. The scarcity of experimentally confirmed enhancers often limits the training of robust machine-learning models for enhancer prediction. We present EnhancerMatcher, a convolutional neural network-based tool that identifies cell-type-specific enhancers using only two confirmed enhancers as references. Trained on putative enhancers from the CATlas Project and control sequences from the human genome, EnhancerMatcher classifies sequences in triplets: two known enhancers from a common cell type and a third sequence evaluated for enhancer activity. Unlike existing methods, EnhancerMatcher enables classification across all cell types while preserving specificity through two reference enhancers. It achieved 90% accuracy, 92% recall, and 87% specificity on human test data. Furthermore, EnhancerMatcher demonstrated strong cross-species generalization, effectively recognizing mouse enhancers using its human-trained model, and exhibited consistent performance across diverse cell types regardless of their data representation size. EnhancerMatcher extracts features directly from raw sequences and provides interpretability through class activation maps, making it a powerful, versatile, and generalizable tool for enhancer discovery and regulatory sequence analysis.