PROJECT SUMMARY The central objective of this renewal R01 project is to elucidate the variation and regulation of alternative splicing (AS) in human transcriptomes. Eukaryotic cells generate astonishing regulatory diversity and complex phenotypes from a finite set of genes. AS of precursor mRNA is a mechanism essential for generating this regulatory diversity. Almost all multi-exon human genes are alternatively spliced. Widespread changes in AS occur between species, within human populations, in response to developmental signals and environmental perturbations, and in disease pathogenesis. Despite the importance of AS in gene regulation and disease, as well as extensive interest and research activities in this field, there remain many open questions and significant knowledge gaps regarding the landscape, regulation, and functional consequence of AS variation in human transcriptomes. Powerful sequencing technologies for characterizing transcriptome complexity (RNA-seq) and protein-RNA interaction (CLIP-seq), as well as the vast amounts of data continuously deposited into the public domain, create exciting and unprecedented opportunities for studies of AS. This proposal integrates data- driven research leveraging big transcriptome data with hypothesis-driven research using molecular and genomic tools. In three aims, we will investigate the evolution of AS in primates (Aim 1), the genetic variation and phenotypic association of AS in human populations (Aim 2), and epigenetic regulation of AS across human tissues and cell states (Aim 3). We will also develop and disseminate innovative computational and statistical methods for analyzing AS using large, heterogeneous sequencing datasets. As in our previous funding cycle, we will tap into our extensive network of expert collaborators to amplify the impact of our work and pursue new research opportunities within and beyond the scope of the proposed project. Collectively, our research will generate significant novel insights into the variation, regulation, and function of AS, and create broadly applicable computational tools for studying AS variation and mRNA isoform complexity in diverse biomedical disciplines.