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Towards Better Prioritization of Epigenetically Modified DNA Regions

  • Ernesto Iacucci
  • Dusan Popovic
  • Georgios A. Pavlopoulos
  • Léon-Charles Tranchevent
  • Marijke Bauters
  • Bart De Moor
  • Yves Moreau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7297)

Abstract

Epigenetic modifications of the genome can cause profound changes in phenotype of an organism. Experimental methods allow us to detect regions of the DNA that have been epigenetically modified; these regions are said to be enriched in a queried state versus a control. Detecting the enriched regions is not a simple matter as making sense of the data involves multiple analytical steps and often results in false calls. In this study, we analyze the utility of using additional features of the data (such as the transcription start site (TSS) and the histone coverage) to detect enrichment. We train a decision tree ensemble using these three features and review how well they identify regions that are truly enriched (as validated by q-PCR). We find that the enrichment score derived directly from ChIP-chip experiment data is less informative than the histone coverage.

Keywords

ChIP-chip data integration protein-DNA machine learning decision trees 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ernesto Iacucci
    • 1
    • 2
  • Dusan Popovic
    • 1
    • 2
  • Georgios A. Pavlopoulos
    • 1
    • 2
  • Léon-Charles Tranchevent
    • 1
    • 2
  • Marijke Bauters
    • 3
    • 2
  • Bart De Moor
    • 1
    • 2
  • Yves Moreau
    • 1
    • 2
  1. 1.ESAT-SCDKatholieke Universiteit LeuvenLeuvenBelgium
  2. 2.IBBT-K.U.Leuven Future Health DepartmentKatholieke Universiteit LeuvenLeuvenBelgium
  3. 3.Department of Human GeneticsKatholieke Universiteit LeuvenLeuvenBelgium

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