title: Unrealistic phylogenetic trees may improve phylogenetic footprinting.
abstract: BACKGROUND
  The computational investigation of DNA binding motifs from binding sites is one of the classic tasks in bioinformatics and a prerequisite for understanding gene regulation as a whole. Due to the development of sequencing technologies and the increasing number of available genomes, approaches based on phylogenetic footprinting become increasingly attractive. Phylogenetic footprinting requires phylogenetic trees with attached substitution probabilities for quantifying the evolution of binding sites, but these trees and substitution probabilities are typically not known and cannot be estimated easily.
  
  
  RESULTS
  Here, we investigate the influence of phylogenetic trees with different substitution probabilities on the classification performance of phylogenetic footprinting using synthetic and real data. For synthetic data we find that the classification performance is highest when the substitution probability used for phylogenetic footprinting is similar to that used for data generation. For real data, however, we typically find that the classification performance of phylogenetic footprinting surprisingly increases with increasing substitution probabilities and is often highest for unrealistically high substitution probabilities close to one. This finding suggests that choosing realistic model assumptions might not always yield optimal predictions in general and that choosing unrealistically high substitution probabilities close to one might actually improve the classification performance of phylogenetic footprinting.
  
  
  METHODS
  The proposed PF is implemented in JAVA and can be downloaded from https://github.com/mgledi/PhyFoo.
  
  
  BACKGROUND
  : martin.nettling@informatik.uni-halle.de.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Nettling
authors.affiliation: Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.
authors.forename: Hendrik
authors.initials: H
authors.lastname: Treutler
authors.affiliation: Institut d'Investigació en Intel ligència Artificial, IIIA-CSIC, Campus UAB, Cerdanyola, Spain.
authors.forename: Jesus
authors.initials: J
authors.lastname: Cerquides
authors.affiliation: Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.|German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
authors.forename: Ivo
authors.initials: I
authors.lastname: Grosse
mesh_terms: D000818:Animals; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D004268:DNA-Binding Proteins / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism; D053263:Gene Regulatory Networks; D006801:Humans; D010802:Phylogeny; D017422:Sequence Analysis, DNA / Q000379:methods*; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D004268:DNA-Binding Proteins