Short Segment Frequency Equalization: A Simple and Effective Alternative Treatment of Background Models in Motif Discovery

  • Kazuhito Shida
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)

Abstract

One of the most important pattern recognition problems in bioinformatics is the de novo motif discovery. In particular, there is a large room of improvement in motif discovery from eukaryotic genome, where the sequences have complicated background noise. The short segment frequency equalization (SSFE) is a novel treatment method to incorporate Markov background models into de novo motif discovery algorithms, namely Gibbs sampling. Despite its apparent simplicity, SSFE shows a large performance improvement over the current method (Q/P scheme) when tested on artificial DNA datasets with Markov background of human and mouse. Furthermore, SSFE shows a better performance than other methods including much more complicated and sophisticated method, Weeder 1.3, when tested with several biological datasets from human promoters.

Keywords

Motif discovery Markov background model Eukaryotic promoters Stochastic method Gibbs sampling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kazuhito Shida
    • 1
  1. 1.Institute for Material ResearchSendaiJapan

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