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CistromeMeta: a large language model powered tool for automated ChIP-seq metadata extraction.

Nicholas Piccaro¹, Myles Brown^2,3, Clifford Meyer^1,4

¹Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, United States.

Bioinformatics (Oxford, England)

|June 13, 2026

Summary

This summary is machine-generated.

CistromeMeta, a new tool, uses large language models to automatically extract and standardize ChIP-seq metadata from public repositories. This enhances the reuse of valuable gene expression data.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Public repositories like NCBI's Gene Expression Omnibus (GEO) host numerous ChIP-seq experiments.
Heterogeneous free-text metadata limits the reuse of this valuable ChIP-seq data.
Standardized metadata is crucial for scalable data curation and analysis.

Purpose of the Study:

To introduce CistromeMeta, a novel Python-based command-line tool.
To automate the extraction and standardization of ChIP-seq metadata from GEO XML records.
To improve the discoverability and reusability of ChIP-seq datasets.

Main Methods:

Leveraging large language models (LLMs) in a few-shot setting for metadata extraction.
Utilizing CistromeMeta to process GEO XML records without custom model training.
Validating extracted terms against authoritative biological databases (NCBI Gene, Harmonizome, etc.).

Main Results:

Automatic extraction and standardization of ChIP-seq metadata.
Production of standardized outputs with official gene symbols and ontology identifiers.
Enhanced data curation for scalable metadata management.

Conclusions:

CistromeMeta effectively addresses the limitations of heterogeneous metadata in public ChIP-seq repositories.
The tool facilitates the reuse of ChIP-seq data by providing standardized and validated metadata.
This advancement supports more efficient genomic data analysis and discovery.