HarteminkLab/TOP: Predict transcription factor occupancy using DNase- or ATAC-seq data

Transcription factor (TF) occupancy profiler (TOP) predicts quantitative TF occupancy at candidate TF motif locations across cell types or conditions. It is a Bayesian hierarchical model trained using quantitative DNase- or ATAC-seq and ChIP-seq data from many TFs and cell types (from ENCODE data). TOP learns both TF- and cell type- specific parameters as well as TF-generic parameters jointly for TFs and cell types from existing DNase-/ATAC-seq and ChIP-seq data. Once trained, it could predict quantitative TF occupancy or TF binding probability for TFs across cell types or conditions using DNase- or ATAC- seq data without requring new ChIP data.

install.packages("remotes")
remotes::install_github("HarteminkLab/TOP")