A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites.

@article{citeulike:2097758, abstract = {Given a set of known binding sites for a specific transcription factor, it is possible to build a model of the transcription factor binding site, usually called a motif model, and use this model to search for other sites that bind the same transcription factor. Typically, this search is performed using a position-specific scoring matrix (PSSM), also known as a position weight matrix. In this paper we analyze a set of eukaryotic transcription factor binding sites and show that there is extensive clustering of similar k-mers in eukaryotic motifs, owing to both functional and evolutionary constraints. The apparent limitations of probabilistic models in representing complex nucleotide dependencies lead us to a graph-based representation of motifs. When deciding whether a candidate k-mer is part of a motif or not, we base our decision not on how well the k-mer conforms to a model of the motif as a whole, but how similar it is to specific, known k-mers in the motif. We elucidate the reasons why we expect graph-based methods to perform well on motif data. Our MotifScan algorithm shows greatly improved performance over the prevalent PSSM-based method for the detection of eukaryotic motifs.}, address = {Department of Biochemistry, Stanford University, CA 94305, USA. briannau@stanford.edu}, author = {Naughton, B. T. and Fratkin, E. and Batzoglou, S. and Brutlag, D. L. }, citeulike-article-id = {2097758}, issn = {1362-4962}, journal = {Nucleic Acids Res}, keywords = {motif}, number = {20}, pages = {5730--5739}, posted-at = {2007-12-12 11:00:03}, priority = {2}, title = {A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/17041233}, volume = {34}, year = {2006} }

TY - JOUR ID - citeulike:2097758 L3 - citeulike-article-id:2097758 TI - A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites. JF - Nucleic Acids Res VL - 34 IS - 20 SP - 5730 EP - 5739 AD - Department of Biochemistry, Stanford University, CA 94305, USA. briannau@stanford.edu SN - 1362-4962 N2 - Given a set of known binding sites for a specific transcription factor, it is possible to build a model of the transcription factor binding site, usually called a motif model, and use this model to search for other sites that bind the same transcription factor. Typically, this search is performed using a position-specific scoring matrix (PSSM), also known as a position weight matrix. In this paper we analyze a set of eukaryotic transcription factor binding sites and show that there is extensive clustering of similar k-mers in eukaryotic motifs, owing to both functional and evolutionary constraints. The apparent limitations of probabilistic models in representing complex nucleotide dependencies lead us to a graph-based representation of motifs. When deciding whether a candidate k-mer is part of a motif or not, we base our decision not on how well the k-mer conforms to a model of the motif as a whole, but how similar it is to specific, known k-mers in the motif. We elucidate the reasons why we expect graph-based methods to perform well on motif data. Our MotifScan algorithm shows greatly improved performance over the prevalent PSSM-based method for the detection of eukaryotic motifs. KW - motif AU - Naughton, BT AU - Fratkin, E AU - Batzoglou, S AU - Brutlag, DL PY - 2006/// UR - http://view.ncbi.nlm.nih.gov/pubmed/17041233 ER -