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Nucleic Acids Res. 2014 Oct 29;42(19):11865-78. doi: 10.1093/nar/gku810. Epub 2014 Oct 7.

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

Author information

1
Computational Biology and Bioinformatics Program, Duke University, Durham, NC 27708, USA Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA.
2
Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA Department of Molecular Genetics and Microbiology, Duke University, Durham, NC 27708, USA.
3
Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, NC 27708, USA greg.crawford@duke.edu.
4
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA Max Delbruck Center for Molecular Medicine, 13125 Berlin, Germany greg.crawford@duke.edu.

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.

PMID:
25294828
PMCID:
PMC4231734
DOI:
10.1093/nar/gku810
[Indexed for MEDLINE]
Free PMC Article

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