Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data Across 27 Tissue Types

There is intense interest in mapping the tissue-specific binding sites of transcription factors in the human genome to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting provides a means to predict the genome-wide binding sites for hundreds of transcription factors (TFs) simultaneously. However, despite the public availability of DNase-seq data for hundreds of samples, there is neither a unified analytical workflow nor a publicly accessible database providing the locations of footprints across all available samples. Here, we describe the implementation of a workflow for uniform processing of footprints using two state-of-the-art footprinting algorithms: Wellington and HINT. Our workflow then scans footprints for 1,530 sequence motifs to predict binding sites for 1,515 human transcription factors. We tested our workflow using 21 DNase-seq experiments of lymphoblastoid cell lines, generated by the ENCODE project. We trained a machine learning model to predict TF binding sites, integrating footprints with additional biologically-related features. This model achieved a maximum MCC of 0.423 and an AUC of 0.943 compared to ENCODE ChIP-seq data for 62 TFs in the same cell type. We applied our workflow to detect footprints in 206 DNase-seq experiments from ENCODE, spanning 27 human tissues. These footprints describe an expansive landscape of TF occupancy in the human genome. Across all tissues, we detected high-quality footprints spanning 9.8% of all nucleotides in the human genome with scores found to enrich for true positives. The highest tissue-specific coverage was observed for samples in the brain (4.4%), followed by extra-embryonic structure (2.6%) and skin (2.4%). In addition, we report a more lenient footprinting call set, providing some evidence of TF occupancy in at least one tissue for 34% of all genomic positions. Our cloud-based workflow and a database with all footprints and TF binding site predictions are available at www.trena.org.