Boosting the Detection of Transposable Elements Using Machine Learning

  • Tiago Loureiro
  • Rui Camacho
  • Jorge Vieira
  • Nuno A. Fonseca
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 222)

Abstract

Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single one achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning classifiers.

Keywords

Transposable Elements Machine Learning Genomics 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Tiago Loureiro
    • 1
  • Rui Camacho
    • 2
  • Jorge Vieira
    • 3
  • Nuno A. Fonseca
    • 4
    • 5
  1. 1.DEI & Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  2. 2.DEI & Faculdade de Engenharia & LIAAD-INESCTECUniversidade do PortoPortoPortugal
  3. 3.IBMC - Instituto de Biologia Molecular e Celular & Universidade do PortoPortoPortugal
  4. 4.EMBL Outstation, European Bioinformatics Institute (EBI)HinxtonUK
  5. 5.CRACS-INESCTECPortoPortugal

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