MACE: model based analysis of ChIP-exo. Nucleic Acids Res 2014 Nov 10;42(20):e156
Date
09/25/2014Pubmed ID
25249628Pubmed Central ID
PMC4227761DOI
10.1093/nar/gku846Scopus ID
2-s2.0-84926099280 (requires institutional sign-in at Scopus site) 67 CitationsAbstract
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.
Author List
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 WAuthor
Michael T. Zimmermann PhD Director, Associate Professor in the Data Science Institute department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsAnimals
Binding Sites
CCCTC-Binding Factor
Chromatin Immunoprecipitation
Computer Simulation
DNA-Binding Proteins
Exodeoxyribonucleases
Genome
Hepatocyte Nuclear Factor 6
Humans
Male
Mice
Mice, Inbred C57BL
Repressor Proteins
Saccharomyces cerevisiae Proteins
Sequence Analysis, DNA
Transcription Factors
