ORION: An agentic reasoning construct for the analysis of complex human immune profiling

Abstract The capacity to generate high-dimensional biological datasets has outpaced the ability to interpret them. Technologies such as phage immunoprecipitation and sequencing (PhIP-seq) enable proteome-scale profiling of antibody repertoires, but interpreting thousands of enriched peptides into mechanistic hypotheses remains a labor-intensive bottleneck requiring expert synthesis of statistics, literature, and domain knowledge. Here we describe ORION (Omics Reasoning & Interpretation Orchestrator), a multi-agent framework that uses reasoning-capable large language models to perform end-to-end analysis of complex immune profiling data. ORION integrates statistical analysis, machine learning, and automated literature review into a single structured workflow, producing results that are reproducible and fully traceable. Applied to a published PhIP-seq dataset from autoimmune polyendocrine syndrome type 1 (APS-1), ORION recovered the canonical autoantibody signature in approximately two hours, closely recapitulating an analysis that originally required one to two months of manual effort. To test hypothesis-generation capacity on previously unseen data, we applied ORION to a novel PhIP-seq dataset from individuals with Down syndrome, for which no proteome-wide autoantibody reference exists. ORION distinguished disease from control samples with high accuracy, prioritized candidate autoantibody targets, and organized them into biologically coherent groups spanning immune, gut, and neuronal programs, generating testable hypotheses for experimental follow-up. These results demonstrate that agentic AI systems can compress the analysis of complex immune profiling data from weeks to hours, allowing scientists to redirect their time toward the fundamental biology.