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

Loading...
Thumbnail Image

Date

2014-10-29

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

201
views
190
downloads

Citation Stats

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.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1093/nar/gku810

Publication Info

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.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Crawford

Gregory E. Crawford

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 chromatin accessible 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 any species with a sequenced genome. We are combining this data with other wet-lab and computational data types to better understand how these regulatory regions control global gene expression in a set of diverse tissues (normal and diseased) representative of the human body.

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


Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.