Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection.
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DNaseI footprinting is an established assay for identifying 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 computational approaches have been developed to identify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substantial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individual binding sites, we performed DNase-seq on deproteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive performance of these models demonstrated that predicted footprints corresponded to high-confidence TF-DNA interactions. DNase-seq footprints were absent 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.
Sequence Analysis, DNA
Published Version (Please cite this version)10.1093/nar/gku810
Publication InfoCrawford, Gregory E; Frank, CL; Ohler, Uwe; & Yardımcı, GG (2014). Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection. Nucleic Acids Res, 42(19). pp. 11865-11878. 10.1093/nar/gku810. Retrieved from https://hdl.handle.net/10161/10682.
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Associate Professor in Pediatrics
My research involves identifying gene regulatory elements across the genome to help us understand how chromatin structure dictates cell function and fate. For the last 30 years, mapping DNase I hypersensitive sites has been the gold standard method to identify the location of active regulatory elements, including promoters, enhancers, silencers, and locus control regions. I have developed technologies that can identify most DNase I hypersensitive sites from potentially any cell type from an
Adjunct Associate Professor in the Department of Biostatistics and Bioinformatics
Computational Biology of Gene Regulation Sequence Analysis Image Expression Analysis Applied Machine Learning
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