Healthy vertebrate development critically depends on precise, tightly-regulated expression of genes. Resulting gene-expression patterns derive from a system of molecular and genetic processes that work together in tissue-specific developmental regulatory programs, many of which remain poorly understood. However, with the help of high-throughput *-omics approaches, computational analytics, and, more recently, with single-cell technologies, progress is being made. Researchers continue to better and more comprehensively understand gene expression and its regulation during development and differentiation. Insights and knowledge gained are increasingly relevant from clinical and translational perspectives. For example, these insights and knowledge affect in vitro differentiation protocols that are used for technologies like drug screening and “organ-on-a-chip,” and they enable the meaningful prioritization and interpretation of genetic variants underlying disease. In this context, Kostka and colleagues develop statistical and computational methods for high-throughput genomics data. Kostka will discuss work in comparative genomics to uncover developmental gene-regulatory genomic loci and to characterize the effect of between-species differences on transcription-factor binding. In addition, through several applications, he will describe how new methods rooted in statistical phylogenetics and machine learning, applied to epigenomics and single-cell data, improve the study of tissue and cell-type differentiation.