A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites
Identifieur interne : 000F63 ( Pmc/Curation ); précédent : 000F62; suivant : 000F64A 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.
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
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DOI: 10.1093/nar/gkl585
PubMed: 17041233
PubMed Central: 1635261
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<front><journal-meta><journal-id journal-id-type="nlm-ta">Nucleic Acids Res</journal-id><journal-id journal-id-type="publisher-id">Nucleic Acids Research</journal-id><journal-title>Nucleic Acids Research</journal-title><issn pub-type="ppub">0305-1048</issn><issn pub-type="epub">1362-4962</issn><publisher><publisher-name>Oxford University Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">17041233</article-id><article-id pub-id-type="pmc">1635261</article-id><article-id pub-id-type="doi">10.1093/nar/gkl585</article-id><article-categories><subj-group subj-group-type="heading"><subject>Computational Biology</subject></subj-group></article-categories><title-group><article-title>A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Naughton</surname><given-names>Brian T.</given-names></name><xref ref-type="corresp" rid="cor1">*</xref></contrib><contrib contrib-type="author"><name><surname>Fratkin</surname><given-names>Eugene</given-names></name><xref rid="au1" ref-type="aff">1</xref><xref ref-type="corresp" rid="cor1">*</xref></contrib><contrib contrib-type="author"><name><surname>Batzoglou</surname><given-names>Serafim</given-names></name><xref rid="au1" ref-type="aff">1</xref></contrib><contrib contrib-type="author"><name><surname>Brutlag</surname><given-names>Douglas L.</given-names></name></contrib><aff><institution>Department of Biochemistry</institution><addr-line>CA 94305, USA</addr-line></aff><aff id="au1"><sup>1</sup><institution>Department of Computer Science, Stanford University</institution><addr-line>CA 94305, USA</addr-line></aff></contrib-group><author-notes><corresp id="cor1"><sup>*</sup>To whom correspondence should be addressed. Tel: 650 723 5976; Fax: 650 723 6783; Email: <email>briannau@stanford.edu</email></corresp><fn><p><sup>*</sup>Correspondence may also be addressed to Eugene Fratkin. Email: <email>fratkin@cs.stanford.edu</email></p></fn></author-notes><pmc-comment>For NAR: both ppub and collection dates generated for PMC processing 1/27/05 beck</pmc-comment>
<pub-date pub-type="collection"><month>11</month><year>2006</year></pub-date><pub-date pub-type="ppub"><month>11</month><year>2006</year></pub-date><pub-date pub-type="epub"><day>13</day><month>11</month><year>2006</year></pub-date><volume>34</volume><issue>20</issue><fpage>5730</fpage><lpage>5739</lpage><history><date date-type="received"><day>08</day><month>6</month><year>2006</year></date><date date-type="rev-recd"><day>25</day><month>7</month><year>2006</year></date><date date-type="accepted"><day>27</day><month>7</month><year>2006</year></date></history><copyright-statement>© 2006 The Author(s)</copyright-statement><copyright-year>2006</copyright-year><license license-type="openaccess"><p>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.</p></license><abstract><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></abstract></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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