DECOD: fast and accurate discriminative DNA motif finding.
Identifieur interne : 002475 ( Main/Exploration ); précédent : 002474; suivant : 002476DECOD: fast and accurate discriminative DNA motif finding.
Auteurs : Peter Huggins [États-Unis] ; Shan Zhong ; Idit Shiff ; Rachel Beckerman ; Oleg Laptenko ; Carol Prives ; Marcel H. Schulz ; Itamar Simon ; Ziv Bar-JosephSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2011.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : DNA.
- chemical , metabolism : Tumor Suppressor Protein p53.
- Algorithms, Base Sequence, Nucleotide Motifs, Sequence Analysis, DNA.
Abstract
Motif discovery is now routinely used in high-throughput studies including large-scale sequencing and proteomics. These datasets present new challenges. The first is speed. Many motif discovery methods do not scale well to large datasets. Another issue is identifying discriminative rather than generative motifs. Such discriminative motifs are important for identifying co-factors and for explaining changes in behavior between different conditions.
DOI: 10.1093/bioinformatics/btr412
PubMed: 21752801
Affiliations:
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Le document en format XML
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Schulz</name></author><author><name sortKey="Simon, Itamar" sort="Simon, Itamar" uniqKey="Simon I" first="Itamar" last="Simon">Itamar Simon</name></author><author><name sortKey="Bar Joseph, Ziv" sort="Bar Joseph, Ziv" uniqKey="Bar Joseph Z" first="Ziv" last="Bar-Joseph">Ziv Bar-Joseph</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2011">2011</date><idno type="RBID">pubmed:21752801</idno><idno type="pmid">21752801</idno><idno type="doi">10.1093/bioinformatics/btr412</idno><idno type="wicri:Area/PubMed/Corpus">001E60</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001E60</idno><idno type="wicri:Area/PubMed/Curation">001E60</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001E60</idno><idno type="wicri:Area/PubMed/Checkpoint">001D90</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">001D90</idno><idno type="wicri:Area/Ncbi/Merge">000871</idno><idno type="wicri:Area/Ncbi/Curation">000871</idno><idno type="wicri:Area/Ncbi/Checkpoint">000871</idno><idno type="wicri:Area/Main/Merge">002500</idno><idno type="wicri:Area/Main/Curation">002475</idno><idno type="wicri:Area/Main/Exploration">002475</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">DECOD: fast and accurate discriminative DNA motif finding.</title><author><name sortKey="Huggins, Peter" sort="Huggins, Peter" uniqKey="Huggins P" first="Peter" last="Huggins">Peter Huggins</name><affiliation wicri:level="4"><nlm:affiliation>Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université Carnegie-Mellon</orgName></affiliation></author><author><name sortKey="Zhong, Shan" sort="Zhong, Shan" uniqKey="Zhong S" first="Shan" last="Zhong">Shan Zhong</name></author><author><name sortKey="Shiff, Idit" sort="Shiff, Idit" uniqKey="Shiff I" first="Idit" last="Shiff">Idit Shiff</name></author><author><name sortKey="Beckerman, Rachel" sort="Beckerman, Rachel" uniqKey="Beckerman R" first="Rachel" last="Beckerman">Rachel Beckerman</name></author><author><name sortKey="Laptenko, Oleg" sort="Laptenko, Oleg" uniqKey="Laptenko O" first="Oleg" last="Laptenko">Oleg Laptenko</name></author><author><name sortKey="Prives, Carol" sort="Prives, Carol" uniqKey="Prives C" first="Carol" last="Prives">Carol Prives</name></author><author><name sortKey="Schulz, Marcel H" sort="Schulz, Marcel H" uniqKey="Schulz M" first="Marcel H" last="Schulz">Marcel H. Schulz</name></author><author><name sortKey="Simon, Itamar" sort="Simon, Itamar" uniqKey="Simon I" first="Itamar" last="Simon">Itamar Simon</name></author><author><name sortKey="Bar Joseph, Ziv" sort="Bar Joseph, Ziv" uniqKey="Bar Joseph Z" first="Ziv" last="Bar-Joseph">Ziv Bar-Joseph</name></author></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="eISSN">1367-4811</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Base Sequence</term><term>DNA (chemistry)</term><term>Nucleotide Motifs</term><term>Sequence Analysis, DNA</term><term>Tumor Suppressor Protein p53 (metabolism)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ADN ()</term><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Motifs nucléotidiques</term><term>Protéine p53 suppresseur de tumeur (métabolisme)</term><term>Séquence nucléotidique</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>DNA</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Tumor Suppressor Protein p53</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Protéine p53 suppresseur de tumeur</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Base Sequence</term><term>Nucleotide Motifs</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>ADN</term><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Motifs nucléotidiques</term><term>Séquence nucléotidique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Motif discovery is now routinely used in high-throughput studies including large-scale sequencing and proteomics. These datasets present new challenges. The first is speed. Many motif discovery methods do not scale well to large datasets. Another issue is identifying discriminative rather than generative motifs. Such discriminative motifs are important for identifying co-factors and for explaining changes in behavior between different conditions.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Pennsylvanie</li></region><settlement><li>Pittsburgh</li></settlement><orgName><li>Université Carnegie-Mellon</li></orgName></list><tree><noCountry><name sortKey="Bar Joseph, Ziv" sort="Bar Joseph, Ziv" uniqKey="Bar Joseph Z" first="Ziv" last="Bar-Joseph">Ziv Bar-Joseph</name><name sortKey="Beckerman, Rachel" sort="Beckerman, Rachel" uniqKey="Beckerman R" first="Rachel" last="Beckerman">Rachel Beckerman</name><name sortKey="Laptenko, Oleg" sort="Laptenko, Oleg" uniqKey="Laptenko O" first="Oleg" last="Laptenko">Oleg Laptenko</name><name sortKey="Prives, Carol" sort="Prives, Carol" uniqKey="Prives C" first="Carol" last="Prives">Carol Prives</name><name sortKey="Schulz, Marcel H" sort="Schulz, Marcel H" uniqKey="Schulz M" first="Marcel H" last="Schulz">Marcel H. 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