MACE: model based analysis of ChIP-exo.
MACE: model based analysis of ChIP-exo.
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DOI：
10.1093/nar/gku846
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发表时间：
2014-11-10
影响因子：
14.9
通讯作者：
Li W
中科院分区：
文献类型：
--
作者：
Wang L;Chen J;Wang C;Uusküla-Reimand L;Chen K;Medina-Rivera A;Young EJ;Zimmermann MT;Yan H;Sun Z;Zhang Y;Wu ST;Huang H;Wilson MD;Kocher JP;Li W
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.
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影响因子：
9.8
作者：
ENCODE Project Consortium
通讯作者：
ENCODE Project Consortium
DOI：
10.1093/bioinformatics/btr064
发表时间：
2011-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Grant CE;Bailey TL;Noble WS
通讯作者：
Noble WS
影响因子：
14.9
作者：
Hansen KD;Brenner SE;Dudoit S
通讯作者：
Dudoit S
影响因子：
9.8
作者：
Funnell, Alister P. W.;Wilson, Michael D.;Crossley, Merlin
通讯作者：
Crossley, Merlin
影响因子：
46.9
作者：
Ji, Hongkai;Jiang, Hui;Ma, Wenxiu;Johnson, David S.;Myers, Richard M.;Wong, Wing H.
通讯作者：
Wong, Wing H.