CistromeMeta: A Large Language Model Powered Tool for Automated ChIP-seq Metadata Extraction

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ID: 317409
2026
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Abstract
SUMMARY: Public repositories such as NCBI's Gene Expression Omnibus (GEO) contain large numbers of ChIP-seq experiments, but their reuse is limited by heterogeneous free-text metadata describing target proteins, histone marks, cell lines, tissues, and disease states. We introduce CistromeMeta, a Python-based command-line tool that leverages large language models (LLMs) in a few-shot setting to automatically extract and standardize ChIP-seq metadata from GEO XML records without custom model training. The tool validates extracted terms against authoritative biological databases, including NCBI Gene, Harmonizome 3.0, AnimalTFDB 4.0, Cellosaurus, Experimental Factor Ontology, and Uberon, producing standardized outputs with official gene symbols and ontology identifiers for scalable metadata curation. AVAILABILITY AND IMPLEMENTATION: The Python source code is freely available at https://github.com/nickpiccaro/CistromeMetaX. An archived version of the software is available through Zenodo at DOI: 10.5281/zenodo.20244834. The tool requires Python 3.6+ and an OpenAI API key. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7164770084 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nicholas Piccaro, Myles Brown, Clifford Meyer
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag380
URL
https://doi.org/10.1093/bioinformatics/btag380
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