Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data - Luo, Kaixuan; Zhong, Jianling; Safi, Alexias; Hong, Linda K.; Tewari, Alok K.; Song, Lingyun; Reddy, Timothy E.; Ma, Li; Crawford, Gregory E.; Hartemink, Alexander J. (Genome Research, 2022)
Over a thousand different transcription factors (TFs) bind with varying occupancy across the human genome. Chromatin immunoprecipitation (ChIP) can assay occupancy genome-wide, but only one TF at a time, limiting our ability to comprehensively observe the TF occupancy landscape, let alone quantify how it changes across conditions. We developed TF occupancy profiler (TOP), a Bayesian hierarchical regression framework, to profile genome-wide quantitative occupancy of numerous TFs using data from a single chromatin accessibility experiment (DNase- or ATAC-seq). TOP is supervised, and its hierarchical structure allows it to predict the occupancy of any sequence-specific TF, even those never assayed with ChIP. We used TOP to profile the quantitative occupancy of hundreds of sequence-specific TFs at sites throughout the genome and examined how their occupancies changed in multiple contexts: in approximately 200 human cell types, through 12 h of exposure to different hormones, and across the genetic backgrounds of 70 individuals. TOP enables cost-effective exploration of quantitative changes in the landscape of TF binding.
10.1101/gr.272203.120