n the clinical domain, substantial work has been dedicated to the development of computational phenotypes.1 Traditionally, these approaches have largely relied on rule-based methods and large sources of clinical data to identify cohorts of patients with or without a specific disease.2–5 However, they were not developed to enable deep phenotyping of abnormalities, to facilitate computational analysis of interpatient phenotypic similarity, or to support computational decision support. To address this, the Global Alliance for Genomics and Health6 (GA4GH) has developed the Phenopacket schema, which supports exchange of computable longitudinal case-level phenotypic information for diagnosis of and research on all types of disease, including Mendelian and complex genetic diseases, cancer, and infectious diseases. A Phenopacket characterizes an individual person or biosample, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments (Fig 1). The Phenopacket software is available at https://github.com/phenopackets/.