ArticleBioinformatics (Oxford, England)2026
CistromeMeta: a large language model powered tool for automated ChIP-seq metadata extraction.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
summaryPublic 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 An archived version of the software is available through Zenodo at DOI: 10.5281/zenodo.20244834.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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.