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Researchers at University of Tsukuba have comprehensively analyzed genomic binding data of transcription factors (proteins that regulate gene expression) in humans, revealing a substantial amount of unmeasured data. Their findings emphasize the urgent need for strategic prioritization in future data collection efforts to overcome research biases and deepen our understanding of disease mechanisms. To support the global research community, the results are now publicly available as an open-access database.

Tsukuba, Japan—The human genome contains approximately 1,600 types of transcription factors responsible for regulating gene activity across more than 400 tissue and cell types. Chromatin immunoprecipitation sequencing (ChIP-seq) is a key approach for mapping how these factors interact with DNA to control gene expression. However, practical limitations such as the limited availability of suitable antibodies and the limited availability of suitable antibodies have hindered efforts to comprehensively characterize transcription-factor binding, leaving many biologically important contexts uncharted.

In this study, researchers systematically analyzed large-scale publicly available human ChIP-seq data to identify highly expressed transcription factor-tissue/cell type pairs whose activity remains unmeasured. They found that although blood cells have been extensively studied far more than other tissues, over 80% of transcription factor-tissue/cell type combinations in organs such as the pancreas, muscle, and placenta has never been measured. This highlights significant gaps in current knowledge, suggesting that essential regulatory mechanisms may have been overlooked.

Furthermore, integrated analysis with complementary datasets demonstrated that even unmeasured transcription factors substantially impact gene expression. This indicates that current genomic resources alone are insufficient to capture the entire complexity of human gene regulation. Moreover, simulation studies demonstrated that strategically prioritizing measurement targets, particularly by diversifying transcription factors early in the data collection process, can help researchers better interpret genetic variants linked to disease.

This study is the first to clearly show how missing data on transcription factors distorts our understanding of gene regulation. The proposed framework offers a data-driven strategy for optimizing future measurement efforts and more effectively connecting genomic variation with human disease.

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This work was supported by JSPS KAKENHI (grant number JP22K17992 to H.O.) and JST-Mirai Program (grant number JPMJMI20G7 to H.O.).

Original Paper

Title of original paper:

Unmeasured human transcription factor ChIP-seq data shape functional genomics and demand strategic prioritization

Journal:

Briefings in Functional Genomics

DOI:

10.1093/bfgp/elaf016

Correspondence

Visiting Associate Professor OZAKI Haruka
Institute of Medicine, University of Tsukuba
Team Director, Laboratory for AI Biology, RIKEN Center for Biosystems Dynamics Research

Medical Resident
TAHARA Saeko
University of Tsukuba Hospital

Related Link

Institute of Medicine