A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information.
Identifieur interne : 001E18 ( PubMed/Corpus ); précédent : 001E17; suivant : 001E19A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information.
Auteurs : Xiaotu Ma ; Ashwinikumar Kulkarni ; Zhihua Zhang ; Zhenyu Xuan ; Robert Serfling ; Michael Q. ZhangSource :
- Nucleic acids research [ 1362-4962 ] ; 2012.
English descriptors
- KwdEn :
- Algorithms, Animals, Binding Sites, CCCTC-Binding Factor, Chromatin Immunoprecipitation, Cluster Analysis, Computer Simulation, Drosophila melanogaster (genetics), Embryonic Stem Cells (metabolism), Gene Regulatory Networks, High-Throughput Nucleotide Sequencing, Mice, Nucleotide Motifs, Oligonucleotide Array Sequence Analysis, Regulatory Elements, Transcriptional, Repressor Proteins, Sequence Analysis, DNA, Software, Transcription Factors (metabolism).
- MESH :
- chemical , metabolism : Transcription Factors.
- chemical : CCCTC-Binding Factor, Repressor Proteins.
- genetics : Drosophila melanogaster.
- metabolism : Embryonic Stem Cells.
- Algorithms, Animals, Binding Sites, Chromatin Immunoprecipitation, Cluster Analysis, Computer Simulation, Gene Regulatory Networks, High-Throughput Nucleotide Sequencing, Mice, Nucleotide Motifs, Oligonucleotide Array Sequence Analysis, Regulatory Elements, Transcriptional, Sequence Analysis, DNA, Software.
Abstract
Identification of DNA motifs from ChIP-seq/ChIP-chip [chromatin immunoprecipitation (ChIP)] data is a powerful method for understanding the transcriptional regulatory network. However, most established methods are designed for small sample sizes and are inefficient for ChIP data. Here we propose a new k-mer occurrence model to reflect the fact that functional DNA k-mers often cluster around ChIP peak summits. With this model, we introduced a new measure to discover functional k-mers. Using simulation, we demonstrated that our method is more robust against noises in ChIP data than available methods. A novel word clustering method is also implemented to group similar k-mers into position weight matrices (PWMs). Our method was applied to a diverse set of ChIP experiments to demonstrate its high sensitivity and specificity. Importantly, our method is much faster than several other methods for large sample sizes. Thus, we have developed an efficient and effective motif discovery method for ChIP experiments.
DOI: 10.1093/nar/gkr1135
PubMed: 22228832
Links to Exploration step
pubmed:22228832Le document en format XML
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Campbell Road, Richardson, TX 75080, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Kulkarni, Ashwinikumar" sort="Kulkarni, Ashwinikumar" uniqKey="Kulkarni A" first="Ashwinikumar" last="Kulkarni">Ashwinikumar Kulkarni</name></author><author><name sortKey="Zhang, Zhihua" sort="Zhang, Zhihua" uniqKey="Zhang Z" first="Zhihua" last="Zhang">Zhihua Zhang</name></author><author><name sortKey="Xuan, Zhenyu" sort="Xuan, Zhenyu" uniqKey="Xuan Z" first="Zhenyu" last="Xuan">Zhenyu Xuan</name></author><author><name sortKey="Serfling, Robert" sort="Serfling, Robert" uniqKey="Serfling R" first="Robert" last="Serfling">Robert Serfling</name></author><author><name sortKey="Zhang, Michael Q" sort="Zhang, Michael Q" uniqKey="Zhang M" first="Michael Q" last="Zhang">Michael Q. Zhang</name></author></analytic><series><title level="j">Nucleic acids research</title><idno type="eISSN">1362-4962</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Binding Sites</term><term>CCCTC-Binding Factor</term><term>Chromatin Immunoprecipitation</term><term>Cluster Analysis</term><term>Computer Simulation</term><term>Drosophila melanogaster (genetics)</term><term>Embryonic Stem Cells (metabolism)</term><term>Gene Regulatory Networks</term><term>High-Throughput Nucleotide Sequencing</term><term>Mice</term><term>Nucleotide Motifs</term><term>Oligonucleotide Array Sequence Analysis</term><term>Regulatory Elements, Transcriptional</term><term>Repressor Proteins</term><term>Sequence Analysis, DNA</term><term>Software</term><term>Transcription Factors (metabolism)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Transcription Factors</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>CCCTC-Binding Factor</term><term>Repressor Proteins</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Drosophila melanogaster</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Embryonic Stem Cells</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Binding Sites</term><term>Chromatin Immunoprecipitation</term><term>Cluster Analysis</term><term>Computer Simulation</term><term>Gene Regulatory Networks</term><term>High-Throughput Nucleotide Sequencing</term><term>Mice</term><term>Nucleotide Motifs</term><term>Oligonucleotide Array Sequence Analysis</term><term>Regulatory Elements, Transcriptional</term><term>Sequence Analysis, DNA</term><term>Software</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Identification of DNA motifs from ChIP-seq/ChIP-chip [chromatin immunoprecipitation (ChIP)] data is a powerful method for understanding the transcriptional regulatory network. However, most established methods are designed for small sample sizes and are inefficient for ChIP data. Here we propose a new k-mer occurrence model to reflect the fact that functional DNA k-mers often cluster around ChIP peak summits. With this model, we introduced a new measure to discover functional k-mers. Using simulation, we demonstrated that our method is more robust against noises in ChIP data than available methods. A novel word clustering method is also implemented to group similar k-mers into position weight matrices (PWMs). Our method was applied to a diverse set of ChIP experiments to demonstrate its high sensitivity and specificity. Importantly, our method is much faster than several other methods for large sample sizes. Thus, we have developed an efficient and effective motif discovery method for ChIP experiments.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22228832</PMID><DateCompleted><Year>2012</Year><Month>06</Month><Day>15</Day></DateCompleted><DateRevised><Year>2019</Year><Month>12</Month><Day>10</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1362-4962</ISSN><JournalIssue CitedMedium="Internet"><Volume>40</Volume><Issue>7</Issue><PubDate><Year>2012</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Nucleic acids research</Title><ISOAbbreviation>Nucleic Acids Res.</ISOAbbreviation></Journal><ArticleTitle>A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information.</ArticleTitle><Pagination><MedlinePgn>e50</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/nar/gkr1135</ELocationID><Abstract><AbstractText>Identification of DNA motifs from ChIP-seq/ChIP-chip [chromatin immunoprecipitation (ChIP)] data is a powerful method for understanding the transcriptional regulatory network. However, most established methods are designed for small sample sizes and are inefficient for ChIP data. Here we propose a new k-mer occurrence model to reflect the fact that functional DNA k-mers often cluster around ChIP peak summits. With this model, we introduced a new measure to discover functional k-mers. Using simulation, we demonstrated that our method is more robust against noises in ChIP data than available methods. A novel word clustering method is also implemented to group similar k-mers into position weight matrices (PWMs). Our method was applied to a diverse set of ChIP experiments to demonstrate its high sensitivity and specificity. Importantly, our method is much faster than several other methods for large sample sizes. 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Campbell Road, Richardson, TX 75080, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kulkarni</LastName><ForeName>Ashwinikumar</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Zhihua</ForeName><Initials>Z</Initials></Author><Author ValidYN="Y"><LastName>Xuan</LastName><ForeName>Zhenyu</ForeName><Initials>Z</Initials></Author><Author ValidYN="Y"><LastName>Serfling</LastName><ForeName>Robert</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Michael Q</ForeName><Initials>MQ</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>HG001696</GrantID><Acronym>HG</Acronym><Agency>NHGRI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D023362">Evaluation Study</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType 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