Parameter landscape analysis for common motif discovery programs

Natalia Polouliakh, Michiko Konno, Paul Horton, Kenta Nakai

研究成果: Conference article同行評審

5 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)79-87
期刊Lecture Notes in Bioinformatics (Subseries of Lecture Notes in Computer Science)
出版狀態Published - 2005 一月 1
事件RECOMB 2004 International Workshop, RRG 2004 - Regulatory Genomics - San Diego, CA, United States
持續時間: 2004 三月 262004 三月 27

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 電腦科學(全部)


深入研究「Parameter landscape analysis for common motif discovery programs」主題。共同形成了獨特的指紋。