MotifClick: prediction of cis-regulatory binding sites via merging cliques
Identifieur interne : 002473 ( Main/Merge ); précédent : 002472; suivant : 002474MotifClick: prediction of cis-regulatory binding sites via merging cliques
Auteurs : Shaoqiang Zhang [République populaire de Chine] ; Shan Li [États-Unis] ; Meng Niu [États-Unis] ; Phuc T. Pham [États-Unis] ; Zhengchang Su [États-Unis]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2011.
Descripteurs français
- KwdFr :
- MESH :
- génétique : Drosophila melanogaster, Escherichia coli, Saccharomyces cerevisiae.
- métabolisme : ADN, Facteurs de transcription.
- Algorithmes, Animaux, Sites de fixation, Séquence nucléotidique.
English descriptors
- KwdEn :
- MESH :
- chemical , metabolism : DNA, Transcription Factors.
- genetics : Drosophila melanogaster, Escherichia coli, Saccharomyces cerevisiae.
- Algorithms, Animals, Base Sequence, Binding Sites.
Abstract
Although dozens of algorithms and tools have been developed to find a set of
Here, we present a graph-based polynomial-time algorithm, MotifClick, for the prediction of
When evaluated on both synthetic and real datasets of prokaryotes and eukaryotes, MotifClick outperforms existing leading motif-finding tools for prediction accuracy and balancing the prediction sensitivity and specificity in general. In particular, when the distribution of nucleotides of binding sites is similar to that of their background sequences, MotifClick is more likely to identify the binding sites than the other tools.
Url:
DOI: 10.1186/1471-2105-12-238
PubMed: 21679436
PubMed Central: 3225181
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PMC:3225181Le document en format XML
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">MotifClick: prediction of <italic>cis</italic>
-regulatory binding sites via merging cliques</title>
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<series><title level="j">BMC Bioinformatics</title>
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<term>Animals</term>
<term>Base Sequence</term>
<term>Binding Sites</term>
<term>DNA (metabolism)</term>
<term>Drosophila melanogaster (genetics)</term>
<term>Escherichia coli (genetics)</term>
<term>Saccharomyces cerevisiae (genetics)</term>
<term>Transcription Factors (metabolism)</term>
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<keywords scheme="KwdFr" xml:lang="fr"><term>ADN (métabolisme)</term>
<term>Algorithmes</term>
<term>Animaux</term>
<term>Drosophila melanogaster (génétique)</term>
<term>Escherichia coli (génétique)</term>
<term>Facteurs de transcription (métabolisme)</term>
<term>Saccharomyces cerevisiae (génétique)</term>
<term>Sites de fixation</term>
<term>Séquence nucléotidique</term>
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<keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>DNA</term>
<term>Transcription Factors</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Drosophila melanogaster</term>
<term>Escherichia coli</term>
<term>Saccharomyces cerevisiae</term>
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<term>Escherichia coli</term>
<term>Saccharomyces cerevisiae</term>
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<term>Facteurs de transcription</term>
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<term>Animals</term>
<term>Base Sequence</term>
<term>Binding Sites</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Although dozens of algorithms and tools have been developed to find a set of <italic>cis</italic>
-regulatory binding sites called a motif in a set of intergenic sequences using various approaches, most of these tools focus on identifying binding sites that are significantly different from their background sequences. However, some motifs may have a similar nucleotide distribution to that of their background sequences. Therefore, such binding sites can be missed by these tools.</p>
</sec>
<sec><title>Results</title>
<p>Here, we present a graph-based polynomial-time algorithm, MotifClick, for the prediction of <italic>cis</italic>
-regulatory binding sites, in particular, those that have a similar nucleotide distribution to that of their background sequences. To find binding sites with length <italic>k</italic>
, we construct a graph using some 2(<italic>k</italic>
-1)-mers in the input sequences as the vertices, and connect two vertices by an edge if the maximum number of matches of the local gapless alignments between the two 2(<italic>k</italic>
-1)-mers is greater than a cutoff value. We identify a motif as a set of similar <italic>k</italic>
-mers from a merged group of maximum cliques associated with some vertices.</p>
</sec>
<sec><title>Conclusions</title>
<p>When evaluated on both synthetic and real datasets of prokaryotes and eukaryotes, MotifClick outperforms existing leading motif-finding tools for prediction accuracy and balancing the prediction sensitivity and specificity in general. In particular, when the distribution of nucleotides of binding sites is similar to that of their background sequences, MotifClick is more likely to identify the binding sites than the other tools.</p>
</sec>
</div>
</front>
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</TEI>
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