Short segment frequency equalization: A simple and effective alternative treatment of background models in motif discovery

Kazuhito Shida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationPattern Recognition in Bioinformatics - 4th IAPR International Conference, PRIB 2009, Proceedings
Pages355-364
Number of pages10
DOIs
Publication statusPublished - 2009 Oct 16
Event4th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2009 - Sheffield, United Kingdom
Duration: 2009 Sep 72009 Sep 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5780 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2009
CountryUnited Kingdom
CitySheffield
Period09/9/709/9/9

Keywords

  • Eukaryotic promoters
  • Gibbs sampling
  • Markov background model
  • Motif discovery
  • Stochastic method

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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