Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types
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Author
Funk, Cory C.Casella, Alex M.
Jung, Segun
Richards, Matthew A.
Rodriguez, Alex
Shannon, Paul
Donovan-Maiye, Rory
Heavner, Ben
Chard, Kyle
Xiao, Yukai
Glusman, Gustavo
Ertekin-Taner, Nilufer
Golde, Todd E.
Toga, Arthur
Hood, Leroy
Van Horn, John D.
Kesselman, Carl
Foster, Ian
Madduri, Ravi
Price, Nathan D.
Ament, Seth A.
Date
2020-08-18Journal
Cell ReportsPublisher
Elsevier B.V.Type
Article
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Show full item recordAbstract
DNase-seq footprinting provides a means to predict genome-wide binding sites for hundreds of transcription factors (TFs) simultaneously. Funk et al. analyze data from the ENCODE consortium to create a resource of footprints in 27 human tissues, demonstrating associations of tissue-specific TF occupancy with gene regulation and disease risk. © 2020 The AuthorsCharacterizing the tissue-specific binding sites of transcription factors (TFs) is essential to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting enables the prediction of genome-wide binding sites for hundreds of TFs simultaneously. Despite the public availability of high-quality DNase-seq data from hundreds of samples, a comprehensive, up-to-date resource for the locations of genomic footprints is lacking. Here, we develop a scalable footprinting workflow using two state-of-the-art algorithms: Wellington and HINT. We apply our workflow to detect footprints in 192 ENCODE DNase-seq experiments and predict the genomic occupancy of 1,515 human TFs in 27 human tissues. We validate that these footprints overlap true-positive TF binding sites from ChIP-seq. We demonstrate that the locations, depth, and tissue specificity of footprints predict effects of genetic variants on gene expression and capture a substantial proportion of genetic risk for complex traits. © 2020 The AuthorsSponsors
National Human Genome Research InstituteIdentifier to cite or link to this item
http://hdl.handle.net/10713/13681ae974a485f413a2113503eed53cd6c53
10.1016/j.celrep.2020.108029