title: Multiscale DNA partitioning: statistical evidence for segments.
abstract: BACKGROUND
  DNA segmentation, i.e. the partitioning of DNA in compositionally homogeneous segments, is a basic task in bioinformatics. Different algorithms have been proposed for various partitioning criteria such as Guanine/Cytosine (GC) content, local ancestry in population genetics or copy number variation. A critical component of any such method is the choice of an appropriate number of segments. Some methods use model selection criteria and do not provide a suitable error control. Other methods that are based on simulating a statistic under a null model provide suitable error control only if the correct null model is chosen.
  
  
  RESULTS
  Here, we focus on partitioning with respect to GC content and propose a new approach that provides statistical error control: as in statistical hypothesis testing, it guarantees with a user-specified probability [Formula: see text] that the number of identified segments does not exceed the number of actually present segments. The method is based on a statistical multiscale criterion, rendering this as a segmentation method that searches segments of any length (on all scales) simultaneously. It is also accurate in localizing segments: under benchmark scenarios, our approach leads to a segmentation that is more accurate than the approaches discussed in the comparative review of Elhaik et al. In our real data examples, we find segments that often correspond well to features taken from standard University of California at Santa Cruz (UCSC) genome annotation tracks.
  
  
  METHODS
  Our method is implemented in function smuceR of the R-package stepR available at http://www.stochastik.math.uni-goettingen.de/smuce.
authors.affiliation: Department of Applied Statistics, JK University Linz, A-4040 Linz, Austria, Institute of Mathematics, Technische Universität Ilmenau, D-98693 Ilmenau, Germany, Institute for Mathematical Stochastics and Felix Bernstein Institute for Mathematical Statistics in Biosciences, Georgia Augusta University of Goettingen and Max Planck Institute for Biophysical Chemistry, D-37077 Goettingen, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Futschik
authors.affiliation: Department of Applied Statistics, JK University Linz, A-4040 Linz, Austria, Institute of Mathematics, Technische Universität Ilmenau, D-98693 Ilmenau, Germany, Institute for Mathematical Stochastics and Felix Bernstein Institute for Mathematical Statistics in Biosciences, Georgia Augusta University of Goettingen and Max Planck Institute for Biophysical Chemistry, D-37077 Goettingen, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Hotz
authors.affiliation: Department of Applied Statistics, JK University Linz, A-4040 Linz, Austria, Institute of Mathematics, Technische Universität Ilmenau, D-98693 Ilmenau, Germany, Institute for Mathematical Stochastics and Felix Bernstein Institute for Mathematical Statistics in Biosciences, Georgia Augusta University of Goettingen and Max Planck Institute for Biophysical Chemistry, D-37077 Goettingen, GermanyDepartment of Applied Statistics, JK University Linz, A-4040 Linz, Austria, Institute of Mathematics, Technische Universität Ilmenau, D-98693 Ilmenau, Germany, Institute for Mathematical Stochastics and Felix Bernstein Institute for Mathematical Statistics in Biosciences, Georgia Augusta University of Goettingen and Max Planck Institute for Biophysical Chemistry, D-37077 Goettingen, Germany.
authors.forename: Axel
authors.initials: A
authors.lastname: Munk
authors.affiliation: Department of Applied Statistics, JK University Linz, A-4040 Linz, Austria, Institute of Mathematics, Technische Universität Ilmenau, D-98693 Ilmenau, Germany, Institute for Mathematical Stochastics and Felix Bernstein Institute for Mathematical Statistics in Biosciences, Georgia Augusta University of Goettingen and Max Planck Institute for Biophysical Chemistry, D-37077 Goettingen, Germany.
authors.forename: Hannes
authors.initials: H
authors.lastname: Sieling
mesh_terms: D000465:Algorithms; D010582:Bacteriophage lambda / Q000235:genetics; D001482:Base Composition; D004247:DNA / Q000737:chemistry*; D003627:Data Interpretation, Statistical; D015894:Genome, Human; D006801:Humans; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004247:DNA