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

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Transcription factor (TF) chromatin immunoprecipitation followed by sequencing (ChIP-seq) identifies genome-wide TF-binding sites (TFBSs).
• Existing TF ChIP-seq datasets in public databases lack comprehensive coverage of biologically relevant TF-sample pairs (TF and cell type combinations).
• Limitations include the need for TF-specific antibodies, large cell numbers, and TF expression in the target cell type.

Purpose of the Study:

• To define the full space of biologically relevant TF-sample pairs, including both measured and unmeasured combinations.
• To assess and improve the comprehensiveness of TF ChIP-seq datasets.
• To identify strategies for supplementing currently unmeasured data.

Main Methods:

• Investigation of publicly available human TF ChIP-seq datasets.
• Introduction of the concept of 'unmeasured TF-sample pairs' for biologically relevant but un-experimented TF-sample combinations.
• Development of practical strategies to supplement unmeasured data.

Main Results:

• Many expressed TFs in specific cell types remain unmeasured by ChIP-seq.
• This data gap affects the coverage of regulatory regions and genome-wide association study-SNP analyses.
• A database of unmeasured human TF-sample pairs was created and is publicly accessible.

Conclusions:

• Systematic expansion of TF ChIP-seq datasets is crucial for enhancing comprehension of gene regulatory mechanisms.
• Addressing unmeasured TF-sample pairs can significantly improve data-driven research.
• The developed strategies and database facilitate the expansion of TF ChIP-seq data.