title: An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.
abstract: Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions. To reliably characterize such condition-specific regulatory binding we introduce MultiGPS, an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments. MultiGPS is based on a generalized Expectation Maximization framework that shares information across multiple experiments for binding event discovery. We demonstrate that our framework enables the simultaneous modeling of sparse condition-specific binding changes, sequence dependence, and replicate-specific noise sources. MultiGPS encourages consistency in reported binding event locations across multiple-condition ChIP-seq datasets and provides accurate estimation of ChIP enrichment levels at each event. MultiGPS's multi-experiment modeling approach thus provides a reliable platform for detecting differential binding enrichment across experimental conditions. We demonstrate the advantages of MultiGPS with an analysis of Cdx2 binding in three distinct developmental contexts. By accurately characterizing condition-specific Cdx2 binding, MultiGPS enables novel insight into the mechanistic basis of Cdx2 site selectivity. Specifically, the condition-specific Cdx2 sites characterized by MultiGPS are highly associated with pre-existing genomic context, suggesting that such sites are pre-determined by cell-specific regulatory architecture. However, MultiGPS-defined condition-independent sites are not predicted by pre-existing regulatory signals, suggesting that Cdx2 can bind to a subset of locations regardless of genomic environment. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2-5.
authors.affiliation: Center for Eukaryotic Gene Regulation, Department of Biochemistry & Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
authors.forename: Shaun
authors.initials: S
authors.lastname: Mahony
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
authors.forename: Matthew D
authors.initials: MD
authors.lastname: Edwards
authors.affiliation: Department of Biology, New York University, New York, New York, United States of America.
authors.forename: Esteban O
authors.initials: EO
authors.lastname: Mazzoni
authors.affiliation: Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Richard I
authors.initials: RI
authors.lastname: Sherwood
authors.affiliation: Center for Eukaryotic Gene Regulation, Department of Biochemistry & Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
authors.forename: Akshay
authors.initials: A
authors.lastname: Kakumanu
authors.affiliation: Departments of Pathology and Cell Biology, Neurology, and Neuroscience, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Medical Center, New York, New York, United States of America.
authors.forename: Carolyn A
authors.initials: CA
authors.lastname: Morrison
authors.affiliation: Departments of Pathology and Cell Biology, Neurology, and Neuroscience, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Medical Center, New York, New York, United States of America.
authors.forename: Hynek
authors.initials: H
authors.lastname: Wichterle
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
authors.forename: David K
authors.initials: DK
authors.lastname: Gifford
mesh_terms: D020816:Amino Acid Motifs; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001665:Binding Sites; D000071616:CDX2 Transcription Factor; D002460:Cell Line; D047369:Chromatin Immunoprecipitation; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D053595:Embryonic Stem Cells / Q000166:cytology; D016678:Genome; D018398:Homeodomain Proteins / Q000737:chemistry* / Q000378:metabolism; D051379:Mice; D011485:Protein Binding; D017422:Sequence Analysis, DNA
chemical_list: D000071616:CDX2 Transcription Factor; C491270:CDX2 protein, human; D018398:Homeodomain Proteins