RegEvol: detection of directional selection in regulatory sequences through phenotypic predictions and phenotype-to-fitness functions

Regulatory DNA controls when and where genes are expressed, making it a key driver of phenotypic evolution. Yet detecting selection in non-coding regions remains difficult, as most approaches rely on sequence conservation or changes in substitution rate rather than molecular effects. RegEvol bridges this gap by linking machine learning-based predictions of transcription factor binding to explicit evolutionary models. It uses the distribution of predicted mutational effects to infer fitness functions under different evolutionary scenarios (random drift, stabilising selection, or directional selection). Through maximum-likelihood estimation, it identifies the regime that best explains observed changes along a lineage from an ancestral sequence. RegEvol corrects biases that affected previous tests based on machine learning of transcription factor binding, while remaining conservative across different levels of divergence. Applied to over 3 million Drosophila melanogaster regulatory regions, we identify 5.1% of them under directional selection, enriched near reproductive and immune genes. The framework is readily applicable to experimentally detected regulatory elements with alignable ancestral sequences and is flexible to future advances in understanding regulatory function, providing a powerful basis for investigating adaptation in non-coding regions.