DECOD: fast and accurate discriminative DNA motif finding.
Identifieur interne : 001E60 ( PubMed/Curation ); précédent : 001E59; suivant : 001E61DECOD: 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
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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></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="1"><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></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><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">21752801</PMID><DateCompleted><Year>2012</Year><Month>01</Month><Day>10</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN><JournalIssue CitedMedium="Internet"><Volume>27</Volume><Issue>17</Issue><PubDate><Year>2011</Year><Month>Sep</Month><Day>01</Day></PubDate></JournalIssue><Title>Bioinformatics (Oxford, England)</Title><ISOAbbreviation>Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>DECOD: fast and accurate discriminative DNA motif finding.</ArticleTitle><Pagination><MedlinePgn>2361-7</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bioinformatics/btr412</ELocationID><Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">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.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">To address these issues we developed a method for DECOnvolved Discriminative motif discovery (DECOD). DECOD uses a k-mer count table and so its running time is independent of the size of the input set. By deconvolving the k-mers DECOD considers context information without using the sequences directly. DECOD outperforms previous methods both in speed and in accuracy when using simulated and real biological benchmark data. We performed new binding experiments for p53 mutants and used DECOD to identify p53 co-factors, suggesting new mechanisms for p53 activation.</AbstractText><AbstractText Label="AVAILABILITY" NlmCategory="BACKGROUND">The source code and binaries for DECOD are available at http://www.sb.cs.cmu.edu/DECOD CONTACT: zivbj@cs.cmu.edu</AbstractText><AbstractText Label="SUPPLEMENTARY INFORMATION" NlmCategory="BACKGROUND">Supplementary data are available at Bioinformatics online.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Huggins</LastName><ForeName>Peter</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhong</LastName><ForeName>Shan</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Shiff</LastName><ForeName>Idit</ForeName><Initials>I</Initials></Author><Author ValidYN="Y"><LastName>Beckerman</LastName><ForeName>Rachel</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Laptenko</LastName><ForeName>Oleg</ForeName><Initials>O</Initials></Author><Author ValidYN="Y"><LastName>Prives</LastName><ForeName>Carol</ForeName><Initials>C</Initials></Author><Author ValidYN="Y"><LastName>Schulz</LastName><ForeName>Marcel H</ForeName><Initials>MH</Initials></Author><Author ValidYN="Y"><LastName>Simon</LastName><ForeName>Itamar</ForeName><Initials>I</Initials></Author><Author ValidYN="Y"><LastName>Bar-Joseph</LastName><ForeName>Ziv</ForeName><Initials>Z</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>1R01 GM085022</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2011</Year><Month>07</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Bioinformatics</MedlineTA><NlmUniqueID>9808944</NlmUniqueID><ISSNLinking>1367-4803</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D016159">Tumor Suppressor Protein p53</NameOfSubstance></Chemical><Chemical><RegistryNumber>9007-49-2</RegistryNumber><NameOfSubstance UI="D004247">DNA</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" 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