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

Transcription factor (TF) chromatin immunoprecipitation followed by sequencing (ChIP-seq) is essential for identifying genome-wide TF-binding sites (TFBSs), and the collected datasets offer a variety of opportunities for downstream analyses such as inference of gene regulatory network and prediction for effects of single-nucleotide polymorphisms (SNPs) on TFBSs. Although TF ChIP-seq data continue to accumulate in public databases, comprehensive coverage of biologically relevant TF-sample pairs (i.e. combination of targeted TF and cell type) remains elusive. This is due to the need for TF-specific antibodies and large cell numbers, limiting feasible TF-cell type combinations. Moreover, ChIP-seq is measurable when the TF is expressed in the target cell type. Thus, defining the full space of biologically relevant TF-sample pairs-including both measured and unmeasured-is essential to assess and improve dataset comprehensiveness. Here, we investigated publicly available human TF ChIP-seq datasets and introduced the concept of unmeasured TF-sample pairs, defined as biologically relevant TF-sample combinations for which ChIP-seq experiments have not yet been performed. Notably, many expressed TFs in specific cell types remain unmeasured by ChIP-seq, affecting the coverage of regulatory regions revealed by TF ChIP-seq and genome-wide association study-SNP analyses. Furthermore, we propose practical strategies to efficiently supplement currently unmeasured data and discuss how these approaches can significantly enhance data-driven research. The database of unmeasured human TF-sample pairs is publicly accessible at https://moccs-db.shinyapps.io/Unmeasured_shiny_v1/, facilitating the systematic expansion of TF ChIP-seq datasets and thereby enhancing our comprehension of gene regulatory mechanisms.