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Nucleic Acids Res. 2014 Nov 10;42(20):e156. doi: 10.1093/nar/gku846. Epub 2014 Sep 23.

MACE: model based analysis of ChIP-exo.

Author information

1
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA Division of Biostatistics, Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA wl1@bcm.edu.
2
School of Life Science and Technology, Tongji University, Shanghai 200092, China.
3
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA.
4
Genetics & Genome Biology Program, SickKids Research Institute, 686 Bay St. Toronto, ON, M5G 0A4, Canada.
5
Division of Biostatistics, Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
6
Department of Biochemistry and Molecular Biology, Mayo Clinic, MN 55905, USA.
7
Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S1A8, Canada.
8
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA wl1@bcm.edu.
9
Division of Biostatistics, Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA wl1@bcm.edu.

Abstract

Understanding the role of a given transcription factor (TF) in regulating gene expression requires precise mapping of its binding sites in the genome. Chromatin immunoprecipitation-exo, an emerging technique using λ exonuclease to digest TF unbound DNA after ChIP, is designed to reveal transcription factor binding site (TFBS) boundaries with near-single nucleotide resolution. Although ChIP-exo promises deeper insights into transcription regulation, no dedicated bioinformatics tool exists to leverage its advantages. Most ChIP-seq and ChIP-chip analytic methods are not tailored for ChIP-exo, and thus cannot take full advantage of high-resolution ChIP-exo data. Here we describe a novel analysis framework, termed MACE (model-based analysis of ChIP-exo) dedicated to ChIP-exo data analysis. The MACE workflow consists of four steps: (i) sequencing data normalization and bias correction; (ii) signal consolidation and noise reduction; (iii) single-nucleotide resolution border peak detection using the Chebyshev Inequality and (iv) border matching using the Gale-Shapley stable matching algorithm. When applied to published human CTCF, yeast Reb1 and our own mouse ONECUT1/HNF6 ChIP-exo data, MACE is able to define TFBSs with high sensitivity, specificity and spatial resolution, as evidenced by multiple criteria including motif enrichment, sequence conservation, direct sequence pileup, nucleosome positioning and open chromatin states. In addition, we show that the fundamental advance of MACE is the identification of two boundaries of a TFBS with high resolution, whereas other methods only report a single location of the same event. The two boundaries help elucidate the in vivo binding structure of a given TF, e.g. whether the TF may bind as dimers or in a complex with other co-factors.

PMID:
25249628
PMCID:
PMC4227761
DOI:
10.1093/nar/gku846
[Indexed for MEDLINE]
Free PMC Article

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