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Nucleic Acids Research 2005 33(15):e135; doi:10.1093/nar/gni131
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Published online 1 September 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
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Methods Online

Detecting seeded motifs in DNA sequences

Cinzia Pizzi, Stefania Bortoluzzi1, Andrea Bisognin1, Alessandro Coppe1 and Gian Antonio Danieli1,*

Department of Information Engineering, University of Padova Padova, Italy 1Department of Biology, University of Padova Padova, Italy

*To whom correspondence should be addressed. Tel: +39 049 8276215; Fax: +39 049 8276209; Email: danieli{at}bio.unipd.it

Received May 3, 2005. Revised July 21, 2005. Accepted August 7, 2005.

The problem of detecting DNA motifs with functional relevance in real biological sequences is difficult due to a number of biological, statistical and computational issues and also because of the lack of knowledge about the structure of searched patterns. Many algorithms are implemented in fully automated processes, which are often based upon a guess of input parameters from the user at the very first step. In this paper, we present a novel method for the detection of seeded DNA motifs, composed by regions with a different extent of variability. The method is based on a multi-step approach, which was implemented in a motif searching web tool (MOST). Overrepresented exact patterns are extracted from input sequences and clustered to produce motifs core regions, which are then extended and scored to generate seeded motifs. The combination of automated pattern discovery algorithms and different display tools for the evaluation and selection of results at several analysis steps can potentially lead to much more meaningful results than complete automation can produce. Experimental results on different yeast and human real datasets proved the methodology to be a promising solution for finding seeded motifs. MOST web tool is freely available at http://telethon.bio.unipd.it/bioinfo/MOST.


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