Predicting the binding preference of transcription factors to individual DNA k-mers
Identifieur interne : 000658 ( Ncbi/Curation ); précédent : 000657; suivant : 000659Predicting the binding preference of transcription factors to individual DNA k-mers
Auteurs : Trevis M. Alleyne ; Lourdes Pe A-Castillo [Canada] ; Gwenael Badis [Canada] ; Shaheynoor Talukder ; Michael F. Berger [États-Unis] ; Andrew R. Gehrke [États-Unis] ; Anthony A. Philippakis [États-Unis] ; Martha L. Bulyk [États-Unis] ; Quaid D. Morris [Canada] ; Timothy R. Hughes [Canada]Source :
- Bioinformatics [ 1367-4803 ] ; 2008.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : DNA, Transcription Factors.
- chemical , metabolism : DNA, Transcription Factors.
- methods : Computational Biology, Sequence Analysis, DNA.
- Binding Sites.
Abstract
Url:
DOI: 10.1093/bioinformatics/btn645
PubMed: 19088121
PubMed Central: 2666811
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PMC:2666811Le document en format XML
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-mers</title>
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<author><name sortKey="Morris, Quaid D" sort="Morris, Quaid D" uniqKey="Morris Q" first="Quaid D." last="Morris">Quaid D. Morris</name>
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<region type="state">Ontario</region>
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<author><name sortKey="Hughes, Timothy R" sort="Hughes, Timothy R" uniqKey="Hughes T" first="Timothy R." last="Hughes">Timothy R. Hughes</name>
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<series><title level="j">Bioinformatics</title>
<idno type="ISSN">1367-4803</idno>
<idno type="eISSN">1460-2059</idno>
<imprint><date when="2008">2008</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Binding Sites</term>
<term>Computational Biology (methods)</term>
<term>DNA (chemistry)</term>
<term>DNA (metabolism)</term>
<term>Sequence Analysis, DNA (methods)</term>
<term>Transcription Factors (chemistry)</term>
<term>Transcription Factors (metabolism)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>ADN ()</term>
<term>ADN (métabolisme)</term>
<term>Analyse de séquence d'ADN ()</term>
<term>Biologie informatique ()</term>
<term>Facteurs de transcription ()</term>
<term>Facteurs de transcription (métabolisme)</term>
<term>Sites de fixation</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>DNA</term>
<term>Transcription Factors</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>DNA</term>
<term>Transcription Factors</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term>
<term>Sequence Analysis, DNA</term>
</keywords>
<keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>ADN</term>
<term>Facteurs de transcription</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Binding Sites</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>ADN</term>
<term>Analyse de séquence d'ADN</term>
<term>Biologie informatique</term>
<term>Facteurs de transcription</term>
<term>Sites de fixation</term>
</keywords>
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<front><div type="abstract" xml:lang="en"><p><bold>Motivation:</bold>
Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity. New techniques that estimate the affinity of TFs to all possible <italic>k</italic>
-mers provide a new opportunity to study DNA–protein interaction mechanisms, and may facilitate inference of binding preferences for members of a given TF family when such information is available for other family members.</p>
<p><bold>Results:</bold>
We employed a new dataset consisting of the relative preferences of mouse homeodomains for all eight-base DNA sequences in order to ask how well we can predict the binding profiles of homeodomains when only their protein sequences are given. We evaluated a panel of standard statistical inference techniques, as well as variations of the protein features considered. Nearest neighbour among functionally important residues emerged among the most effective methods. Our results underscore the complexity of TF–DNA recognition, and suggest a rational approach for future analyses of TF families.</p>
<p><bold>Contact:</bold>
<email>t.hughes@utorotno.ca</email>
</p>
<p><bold>Supplementary information:</bold>
<ext-link ext-link-type="uri" xlink:href="http://bioinformatics.oxfordjournals.org/cgi/content/full/btn645/DC1">Supplementary data</ext-link>
are available at <italic>Bioinformatics</italic>
online.</p>
</div>
</front>
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</TEI>
</record>
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Pour générer des pages wiki
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