title: Probabilistic partitioning methods to find significant patterns in ChIP-Seq data.
abstract: BACKGROUND
  We have witnessed an enormous increase in ChIP-Seq data for histone modifications in the past few years. Discovering significant patterns in these data is an important problem for understanding biological mechanisms.
  
  
  RESULTS
  We propose probabilistic partitioning methods to discover significant patterns in ChIP-Seq data. Our methods take into account signal magnitude, shape, strand orientation and shifts. We compare our methods with some current methods and demonstrate significant improvements, especially with sparse data. Besides pattern discovery and classification, probabilistic partitioning can serve other purposes in ChIP-Seq data analysis. Specifically, we exemplify its merits in the context of peak finding and partitioning of nucleosome positioning patterns in human promoters.
  
  
  METHODS
  The software and code are available in the supplementary material.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne and Swiss Institute for Bioinformatics, 1015 Lausanne, Switzerland.
authors.forename: Nishanth Ulhas
authors.initials: NU
authors.lastname: Nair
authors.affiliation: Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne and Swiss Institute for Bioinformatics, 1015 Lausanne, Switzerland.
authors.forename: Sunil
authors.initials: S
authors.lastname: Kumar
authors.affiliation: Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne and Swiss Institute for Bioinformatics, 1015 Lausanne, Switzerland Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne and Swiss Institute for Bioinformatics, 1015 Lausanne, Switzerland.
authors.forename: Bernard M E
authors.initials: BM
authors.lastname: Moret
authors.affiliation: Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne and Swiss Institute for Bioinformatics, 1015 Lausanne, Switzerland Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne and Swiss Institute for Bioinformatics, 1015 Lausanne, Switzerland.
authors.forename: Philipp
authors.initials: P
authors.lastname: Bucher
mesh_terms: D000465:Algorithms; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006657:Histones / Q000378:metabolism*; D006801:Humans; D009707:Nucleosomes / Q000378:metabolism; D011336:Probability; D011401:Promoter Regions, Genetic; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
chemical_list: D006657:Histones; D009707:Nucleosomes