Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection

DNaseI footprinting is an established assay for identi-fying transcription factor (TF)–DNA interactions with single base pair resolution. High-throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. Multiple com-putational approaches have been developed to iden-tify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substan-tial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individ-ual binding sites, we performed DNase-seq on de-proteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive per-formance of these models demonstrated that pre-dicted footprints corresponded to high-confidence TF–DNA interactions. DNase-seq footprints were ab-sent under a fraction of ChIP-seq peaks, which we show to be indicative of weaker binding, indirect TF–DNA interactions or possible ChIP artifacts. The modeling approach was also able to detect variation in the consensus motifs that TFs bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies