WhichTF is functionally important in your open chromatin data?
Creators: name: Tanigawa, Yosuke
name: Dyer, Ethan
name: Bejerano, Gill
Publisher: name: F1000 Research Limited
Year: 2021

description: <b>Preprint</b><br> <br> We have a preprint describing this work: Y. Tanigawa, E. S. Dyer, and G. Bejerano. WhichTF is dominant in your open chromatin data? bioRxiv 730200 (2019) doi:10.1101/730200.<br> <br> <b>Abstract</b><br> Transcription factors (TFs) are the master regulators of development and regulate biological processes in healthy adult tissues. Mutations in both TF genes and their genomic binding sites have been linked to human disease. With the advent of high-throughput experimental profiling of context-specific TF activities, virtually all TF families, if not all ~1500 TFs, have their binding profile characterized in some cellular context. Many computational tools that benefit the reference data were developed to identify functionally important TFs. However, those tools often focus on the sheer abundance of the TFs in their ranking, limiting the ability to identify context-specific TFs of functional importance across diverse cell-types. Here, we develop WhichTF, a computational method to identify functionally dominant TFs from chromatin accessibility profiles. To rank TFs, WhichTF applies an ontology-guided functional approach to compute novel enrichment by integrating accessibility measurements, high-confidence pre-computed conservation-aware TF binding sites, and putative gene-regulatory models. Comparison with prior methods reveals the unique ability of WhichTF to identify context-specific TFs with known functional relevance, including NF-κB family members in lymphocytes, GATA factors in cardiac cells, and SOX2 in brain cells, while the results from other approaches are often not cell-type specific and are dominated by structural elements, such as CTCF. Our scoring framework naturally provides a way to investigate the ontology term-TF pairs contributing to the ranking. To further distinguish the transcriptional regulatory landscape in closely related samples, we devise a differential analysis framework and demonstrate its comparative utility against existing tools in lymphocyte, mesoderm developmental, and disease cells. In lymphocytes, we find ETS family members in B-cells and RUNX family members in T-cells, both of which are well supported by the literature. On top of the confirmatory results, we find many suggestive, under-characterized TFs, such as RUNX3 in mesoderm developmental trajectory and GLI1 in systemic lupus erythematosus. We also find TFs known for stress response in many samples, suggesting routine experimental caveats that warrant careful consideration, as well as ZNF410, a novel candidate stress response factor. Together, we demonstrate the utilities in the ontology-guided functional approach that integrates experimentally characterized chromatin accessibility, high-quality TFBS reference datasets, and ontology-based genome annotation, suggesting that systematic identification of dominant TFs across a large number of samples will be a powerful approach to understand the molecular mechanisms of gene regulation and their influence on cell type differentiation, development, and disease.