RegEvol: detection of directional selection in regulatory sequences through phenotypic predictions and phenotype-to-fitness functions
RegEvol is a novel computational framework that detects directional selection in regulatory DNA by integrating machine learning-based predictions of transcription factor binding with evolutionary models to infer fitness functions, successfully identifying adaptive signals in *Drosophila* and human genomes that were previously difficult to detect using sequence conservation alone.