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Published on: December 22, 2017
CistromeMeta: a large language model powered tool for automated ChIP-seq metadata extraction.
Nicholas Piccaro1, Myles Brown2,3, Clifford Meyer1,4
1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, United States.
Bioinformatics (Oxford, England)
|June 13, 2026
Summary
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.
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.
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