On the identification of differentially-active transcription factors from ATAC-seq data.

Transcription factors regulate gene expression by binding sites in the genome that often harbor a specific DNA motif. The collective accessibility of these motifs, measured by technologies such as ATAC-seq, can be used to infer the activity of the corresponding transcription factors. Here we use curated datasets of 11 TF-specific perturbations as well as 116 semi-simulated datasets to benchmark various methods for identifying factors that differ in activity between experimental conditions. We investigate important analytic variations and make recommendations pertaining to such analysis. Finally, we illustrate the application of the top methods to characterize the impacts of a novel method for perturbing transcription factors at the protein level.