title: NucleoFinder: a statistical approach for the detection of nucleosome positions.
abstract: BACKGROUND
  The identification of nucleosomes along the chromatin is key to understanding their role in the regulation of gene expression and other DNA-related processes. However, current experimental methods (MNase-ChIP, MNase-Seq) sample nucleosome positions from a cell population and contain biases, making thus the precise identification of individual nucleosomes not straightforward. Recent works have only focused on the first point, where noise reduction approaches have been developed to identify nucleosome positions.
  
  
  RESULTS
  In this article, we propose a new approach, termed NucleoFinder, that addresses both the positional heterogeneity across cells and experimental biases by seeking nucleosomes consistently positioned in a cell population and showing a significant enrichment relative to a control sample. Despite the absence of validated dataset, we show that our approach (i) detects fewer false positives than two other nucleosome calling methods and (ii) identifies two important features of the nucleosome organization (the nucleosome spacing downstream of active promoters and the enrichment/depletion of GC/AT dinucleotides at the centre of in vitro nucleosomes) with equal or greater ability than the other two methods.
authors.affiliation: Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.
authors.forename: Jeremie
authors.initials: J
authors.lastname: Becker
authors.forename: Christopher
authors.initials: C
authors.lastname: Yau
authors.forename: John M
authors.initials: JM
authors.lastname: Hancock
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Holmes
mesh_terms: D002460:Cell Line; D015894:Genome, Human; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D015233:Models, Statistical; D009707:Nucleosomes / Q000737:chemistry*; D017422:Sequence Analysis, DNA
chemical_list: D009707:Nucleosomes