Parameter landscape analysis for common motif discovery programs

Natalia Polouliakh, Michiko Konno, Paul Horton, Kenta Nakai

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)

Abstract

The identification of regulatory elements as over-represented motifs in the promoters of potentially co-regulated genes is an important and challenging problem in computational biology. Although many motif detection programs have been developed so far, they still seem to be immature practically. In particular the choice of tunable parameters is often critical to success. Thus knowledge regarding which parameter settings are most appropriate for various types of target motifs is invaluable, but unfortunately has been scarce. In this paper, we report our parameter landscape analysis of two widely-used programs (the Gibbs Sampler (GS) and MEME). Our results show that GS is relatively sensitive to the changes of some parameter values while MEME is more stable. We present recommended parameter settings for GS optimized for four different motif lengths. Thus, running GS four times with these settings should significantly decrease the risk of overlooking subtle motifs.

Original languageEnglish
Pages (from-to)79-87
Number of pages9
JournalLecture Notes in Bioinformatics (Subseries of Lecture Notes in Computer Science)
Volume3318
DOIs
Publication statusPublished - 2005 Jan 1
EventRECOMB 2004 International Workshop, RRG 2004 - Regulatory Genomics - San Diego, CA, United States
Duration: 2004 Mar 262004 Mar 27

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Parameter landscape analysis for common motif discovery programs'. Together they form a unique fingerprint.

Cite this