The logic of domains has become a key organizing principle for contemporary computing projects and in broader science policy. The logic parses collectives of expertise into 'domains' that are to be studied or engaged in order to inform computational advancements and/or interventions on the domains themselves. The concept of a domain is set against a proposition that there is a more general, domain independent or agnostic technique that can serve to intermediate the domains. This article contrasts instances of this discourse, organizing and techne, drawing from cases in artificial intelligence, software engineering, and science policy to illustrate three ongoing figurations of the logic as i) experimental research, ii) formalization in method and software tools, and iii) a de facto organizing principle for science policy and technology development.

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.