Hybrid Gibbs-sampling algorithm for challenging motif discovery: GibbsDST.

Kazuhito Shida

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


The difficulties of computational discovery of transcription factor binding sites (TFBS) are well represented by (l, d) planted motif challenge problems. Large d problems are difficult, particularly for profile-based motif discovery algorithms. Their local search in the profile space is apparently incompatible with subtle motifs and large mutational distances between the motif occurrences. Herein, an improved profile-based method called GibbsDST is described and tested on (15,4), (12,3), and (18,6) challenging problems. For the first time for a profile-based method, its performance in motif challenge problems is comparable to that of Random Projection. It is noteworthy that GibbsDST outperforms a pattern-based algorithm, WINNOWER, in some cases. Effectiveness of GibbsDST using a biological dataset as an example and its possible extension to more realistic evolution models are also introduced.

Original languageEnglish
Pages (from-to)3-13
Number of pages11
JournalGenome informatics. International Conference on Genome Informatics
Issue number2
Publication statusPublished - 2006 Jan 1

ASJC Scopus subject areas

  • Medicine(all)


Dive into the research topics of 'Hybrid Gibbs-sampling algorithm for challenging motif discovery: GibbsDST.'. Together they form a unique fingerprint.

Cite this