A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites
Identifieur interne : 000477 ( Ncbi/Merge ); précédent : 000476; suivant : 000478A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites
Auteurs : Brian T. Naughton ; Eugene Fratkin ; Serafim Batzoglou ; Douglas L. BrutlagSource :
- Nucleic Acids Research [ 0305-1048 ] ; 2006.
Descripteurs français
- KwdFr :
- MESH :
- analyse : Nucléotides.
- génétique : Saccharomyces cerevisiae.
- métabolisme : Facteurs de transcription.
- Algorithmes, Analyse de séquence d'ADN, Expression des gènes, Humains, Modèles génétiques, Modèles statistiques, Sites de fixation, Éléments de régulation transcriptionnelle.
English descriptors
- KwdEn :
- MESH :
- chemical , analysis : Nucleotides.
- genetics : Saccharomyces cerevisiae.
- chemical , metabolism : Transcription Factors.
- methods : Sequence Analysis, DNA.
- Algorithms, Binding Sites, Gene Expression, Humans, Models, Genetic, Models, Statistical, Regulatory Elements, Transcriptional.
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
Url:
DOI: 10.1093/nar/gkl585
PubMed: 17041233
PubMed Central: 1635261
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PMC:1635261Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>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 <italic>k</italic>
-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 <italic>k</italic>
-mer is part of a motif or not, we base our decision not on how well the <italic>k</italic>
-mer conforms to a model of the motif as a whole, but how similar it is to specific, known <italic>k</italic>
-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.</p>
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<front><div type="abstract" xml:lang="en">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.</div>
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