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

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2014-10-29

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Abstract

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.

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10.1093/nar/gku810

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Yardımcı, Galip Gürkan, Christopher L Frank, Gregory E Crawford and Uwe Ohler (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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Scholars@Duke

Crawford

Gregory E. Crawford

Professor in Pediatrics

My primary research interest is understanding how the genome is regulated.  The human genome contains approximately 25,000 genes, which are encoded in ~2% of the genome. The overarching goal of my research program is to identify and characterize how these genes are turned on and off in different cell types, tissues, development states, environmental responses, diseases, and individuals. By understanding where all gene regulatory elements are located, how they work to regulate gene expression, and how non-coding variants within these regions affect function, my research program can address a number of important basic and clinical questions.

Ohler

Uwe Ohler

Adjunct Associate Professor in the Department of Biostatistics & Bioinformatics

Computational Biology of Gene Regulation
Sequence Analysis
Image Expression Analysis
Applied Machine Learning


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