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Nat Methods. 2016 Apr;13(4):303-9. doi: 10.1038/nmeth.3772. Epub 2016 Feb 22.

Analysis of computational footprinting methods for DNase sequencing experiments.

Author information

1
IZKF Computational Biology Research Group, RWTH Aachen University Medical School, Aachen, Germany.
2
Department of Cell Biology, Institute of Biomedical Engineering, RWTH Aachen University Medical School, Aachen, Germany.
3
Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Aachen, Germany.

Abstract

DNase-seq allows nucleotide-level identification of transcription factor binding sites on the basis of a computational search of footprint-like DNase I cleavage patterns on the DNA. Frequently in high-throughput methods, experimental artifacts such as DNase I cleavage bias affect the computational analysis of DNase-seq experiments. Here we performed a comprehensive and systematic study on the performance of computational footprinting methods. We evaluated ten footprinting methods in a panel of DNase-seq experiments for their ability to recover cell-specific transcription factor binding sites. We show that three methods--HINT, DNase2TF and PIQ--consistently outperformed the other evaluated methods and that correcting the DNase-seq signal for experimental artifacts significantly improved the accuracy of computational footprints. We also propose a score that can be used to detect footprints arising from transcription factors with potentially short residence times.

PMID:
26901649
DOI:
10.1038/nmeth.3772
[Indexed for MEDLINE]

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