Detecting seeded motifs in DNA sequences.

@article{citeulike:313671, abstract = {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.}, address = {Department of Information Engineering, University of Padova, Padova, Italy.}, author = {Pizzi, C. and Bortoluzzi, S. and Bisognin, A. and Coppe, A. and Danieli, G. A. }, citeulike-article-id = {313671}, comment = {MOST}, issn = {1362-4962}, journal = {Nucleic Acids Res}, keywords = {motifpaper}, number = {15}, posted-at = {2006-10-02 17:30:25}, priority = {2}, title = {Detecting seeded motifs in DNA sequences.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16141193}, volume = {33}, year = {2005} }

TY - JOUR ID - citeulike:313671 L3 - citeulike-article-id:313671 TI - Detecting seeded motifs in DNA sequences. JF - Nucleic Acids Res VL - 33 IS - 15 AD - Department of Information Engineering, University of Padova, Padova, Italy. SN - 1362-4962 N2 - 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. KW - motifpaper N1 - MOST AU - Pizzi, C AU - Bortoluzzi, S AU - Bisognin, A AU - Coppe, A AU - Danieli, GA PY - 2005/// UR - http://view.ncbi.nlm.nih.gov/pubmed/16141193 ER -