Omics Data Discovery Agents

TL;DR

This paper introduces an agentic framework utilizing large language models to automate the retrieval, extraction, and analysis of omics data from biomedical literature, enabling scalable and reproducible data reuse.

Contribution

It presents a novel LLM-based system that fetches articles, extracts metadata, identifies datasets, and performs analyses, transforming static literature into executable research objects.

Findings

Achieved 80% precision in dataset identification from PubMed articles.

Re-quantified proteomics data with 63% overlap in differentially expressed proteins.

Demonstrated cross-study comparisons revealing consistent protein regulation patterns.

Abstract

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. In rarer instances where intermediate data is made available (e.g. protein abundance files), its location is irregular. In this article, we present an agentic framework that fetches omics-related articles and transforms the unstructured information into searchable research objects. Our 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 running analyses to address novel question. We demonstrate automated metadata extraction from PubMed Central articles, achieving 80% precision for dataset identification from standard data repositories. Using model context protocol (MCP) servers to expose containerized analysis tools, our set of agents were able to identify a set of relevant articles, download the associated datasets, and re-quantify the proteomics data. The results had a 63% overlap in differentially expressed proteins when matching reported preprocessing methods. Furthermore, we show that agents can identify semantically similar studies, determine data compatibility, and perform cross-study comparisons, revealing consistent protein regulation patterns in liver fibrosis. This work establishes a foundation for converting the static biomedical literature into an executable, queryable resource that enables automated data reuse at scale.