SOMA

Abstract

Developing analyses for environmental applications requires integrating evidence across biological scales, from molecular perturbations and cellular responses to animal observations, clinical measurements, and adverse health outcomes. Although existing resources capture portions of this information, linking outcomes, measurements, assays, and supporting evidence in a consistent, computable manner remains challenging.
To address this need, we developed the Schema for Outcomes, Measurements & Assays (SOMA), a flexible framework for representing relationships among biological outcomes, measurements, assays, and evidence. SOMA supports integration across diverse toxicological resources while remaining independent of individual biological frameworks or ontologies. By incorporating multiple community standards, SOMA facilitates interoperability across platforms and datasets through an open, computable representation.
SOMA was initially developed to support evidence integration along the source-to-outcome continuum for chronic PM2.5 exposure and lung dysfunction, where evidence spanned clinical endpoints, animal studies, in vitro systems, and mechanistic research. This use case surfaced recurring challenges: inconsistent terminology, variable biological granularity, incomplete assay descriptions, ambiguous measurement definitions, and difficulty connecting observations across experimental systems. Structured representation, guided by domain experts, improved the ability to trace evidence and context across biological scales. The resulting framework supports the reuse of alternative and emerging methods across domains while preserving the contextual information needed to compare results.
SOMA is open-source and publicly available, enabling anyone to inspect, reuse, adapt, or extend the framework. Here, we describe SOMA’s key design principles and demonstrate how structured, openly available representations can improve transparency, interoperability, and reuse of toxicological knowledge providing a foundation for evidence-based decision-making.