Omics data discovery agents: Agent-supported retrieval, reanalysis, and synthesis of published omics data

The biomedical literature contains a vast collection of omics studies, yet most published data remain functionally inaccessible for computational reuse. When raw data are deposited in public repositories, essential information for reproducing reported results is dispersed across main text, supplementary files, and code repositories, and in the rarer cases where intermediate data (e.g., protein abundance files) are shared, their location is irregular. Here we present an agentic framework for the agent-supported retrieval, reanalysis, and synthesis of published omics data. The system employs large language model (LLM) agents with access to tools for fetching omics studies, extracting article metadata, identifying and downloading published data, executing containerized quantification pipelines, and synthesizing results across studies. Applied at corpus scale, the pipeline catalogued dataset references across thousands of PubMed Central articles; we report these as descriptive system outputs rather than as a validated measure of extraction accuracy. Using model context protocol (MCP) servers to expose containerized analysis tools, the agents retrieved and re-quantified data in five end-to-end reanalyses spanning data-dependent and data-independent proteomics and bulk RNA-seq. All five reanalyses completed, each with documented human guidance and workflow accommodations, and reproduced the authors’ deposited abundances with high per-sample correlation (0.85–0.997) and strongly concordant differentially expressed features (fold-change Spearman 0.88–0.91), with no direction reversals among features called differentially expressed in both analyses; residual differences in significant-feature lists were attributable to threshold placement, tool-version, and preprocessing differences rather than to the underlying quantities. We further demonstrate that agents can identify semantically similar studies, judge data compatibility, and synthesize findings across studies, including a random-effects meta-analysis that recovered consistent protein regulation in liver fibrosis. Rather than a validated benchmark of literature-wide performance, this work is a feasibility demonstration together with an auditable, reusable toolset, establishing a foundation for prospective evaluation of automated omics-data reuse.Author summary: Every year, tens of thousands of biological studies measure the abundance of thousands of proteins or genes at once, so-called omics experiments, and deposit the resulting data in public archives. In principle these data can be reused to answer new questions or to check whether published findings hold up. In practice, reuse is rare: the information needed to make sense of a dataset, such as which samples are which, how the data were processed, and with which software, is scattered across the article, its supplementary files, and code repositories, and the raw data often must be reprocessed with specialized tools before it can be compared. We built ODDA, a system in which artificial-intelligence language-model agents read the scientific literature, locate the associated data, and reprocess it using the same kinds of containerized tools the original authors used, all through a controlled, auditable interface. Across five studies spanning proteomics and RNA sequencing, ODDA reproduced the authors’ published measurements closely and combined results across studies. We share the software so that others can build on, and rigorously test, automated data reuse.