Abstract
Transcriptional cis-regulation has emerged as the predominant force underlying the evolution of phenotypic diversity, yet our understanding of it is still rudimentary. While empirical comparative genomic approaches have been quite informative, they also suffer from numerous confounders and limited scalability. Here we propose using machine-learning-based methods that predict cis-regulatory activity from DNA sequence to perform in silico 'genome transplants' to predict cis-regulatory features as if the genome from one species had been transplanted into the nuclei of another species. Inference of natural selection from the resulting genome-wide catalogs of cis-regulatory divergence could be far more powerful, efficient, and widely applicable than current empirical approaches, enabling unprecedented insights into the genetic basis of biodiversity across the tree of life.