Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers.
Identifieur interne : 001D06 ( PubMed/Corpus ); précédent : 001D05; suivant : 001D07Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers.
Auteurs : Bo Jiang ; Jun S. Liu ; Martha L. BulykSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2013.
English descriptors
- KwdEn :
- Analysis of Variance, Artifacts, Bayes Theorem, Binding Sites, Chromatin Immunoprecipitation, DNA-Binding Proteins (classification), DNA-Binding Proteins (metabolism), Oligonucleotide Array Sequence Analysis (methods), Regulatory Elements, Transcriptional, Software, Transcription Factors (classification), Transcription Factors (metabolism).
- MESH :
- chemical , classification : DNA-Binding Proteins, Transcription Factors.
- chemical , metabolism : DNA-Binding Proteins, Transcription Factors.
- methods : Oligonucleotide Array Sequence Analysis.
- Analysis of Variance, Artifacts, Bayes Theorem, Binding Sites, Chromatin Immunoprecipitation, Regulatory Elements, Transcriptional, Software.
Abstract
Sequence-specific transcription factors (TFs) regulate the expression of their target genes through interactions with specific DNA-binding sites in the genome. Data on TF-DNA binding specificities are essential for understanding how regulatory specificity is achieved.
DOI: 10.1093/bioinformatics/btt152
PubMed: 23559638
Links to Exploration step
pubmed:23559638Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers.</title><author><name sortKey="Jiang, Bo" sort="Jiang, Bo" uniqKey="Jiang B" first="Bo" last="Jiang">Bo Jiang</name><affiliation><nlm:affiliation>Department of Statistics, Harvard University, Cambridge, MA 02138, USA. bojiang83@gmail.com</nlm:affiliation></affiliation></author><author><name sortKey="Liu, Jun S" sort="Liu, Jun S" uniqKey="Liu J" first="Jun S" last="Liu">Jun S. Liu</name></author><author><name sortKey="Bulyk, Martha L" sort="Bulyk, Martha L" uniqKey="Bulyk M" first="Martha L" last="Bulyk">Martha L. Bulyk</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="RBID">pubmed:23559638</idno><idno type="pmid">23559638</idno><idno type="doi">10.1093/bioinformatics/btt152</idno><idno type="wicri:Area/PubMed/Corpus">001D06</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001D06</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers.</title><author><name sortKey="Jiang, Bo" sort="Jiang, Bo" uniqKey="Jiang B" first="Bo" last="Jiang">Bo Jiang</name><affiliation><nlm:affiliation>Department of Statistics, Harvard University, Cambridge, MA 02138, USA. bojiang83@gmail.com</nlm:affiliation></affiliation></author><author><name sortKey="Liu, Jun S" sort="Liu, Jun S" uniqKey="Liu J" first="Jun S" last="Liu">Jun S. Liu</name></author><author><name sortKey="Bulyk, Martha L" sort="Bulyk, Martha L" uniqKey="Bulyk M" first="Martha L" last="Bulyk">Martha L. Bulyk</name></author></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="eISSN">1367-4811</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Analysis of Variance</term><term>Artifacts</term><term>Bayes Theorem</term><term>Binding Sites</term><term>Chromatin Immunoprecipitation</term><term>DNA-Binding Proteins (classification)</term><term>DNA-Binding Proteins (metabolism)</term><term>Oligonucleotide Array Sequence Analysis (methods)</term><term>Regulatory Elements, Transcriptional</term><term>Software</term><term>Transcription Factors (classification)</term><term>Transcription Factors (metabolism)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="classification" xml:lang="en"><term>DNA-Binding Proteins</term><term>Transcription Factors</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>DNA-Binding Proteins</term><term>Transcription Factors</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Oligonucleotide Array Sequence Analysis</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Analysis of Variance</term><term>Artifacts</term><term>Bayes Theorem</term><term>Binding Sites</term><term>Chromatin Immunoprecipitation</term><term>Regulatory Elements, Transcriptional</term><term>Software</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Sequence-specific transcription factors (TFs) regulate the expression of their target genes through interactions with specific DNA-binding sites in the genome. Data on TF-DNA binding specificities are essential for understanding how regulatory specificity is achieved.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">23559638</PMID><DateCompleted><Year>2013</Year><Month>11</Month><Day>05</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN><JournalIssue CitedMedium="Internet"><Volume>29</Volume><Issue>11</Issue><PubDate><Year>2013</Year><Month>Jun</Month><Day>01</Day></PubDate></JournalIssue><Title>Bioinformatics (Oxford, England)</Title><ISOAbbreviation>Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers.</ArticleTitle><Pagination><MedlinePgn>1390-8</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bioinformatics/btt152</ELocationID><Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">Sequence-specific transcription factors (TFs) regulate the expression of their target genes through interactions with specific DNA-binding sites in the genome. Data on TF-DNA binding specificities are essential for understanding how regulatory specificity is achieved.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Numerous studies have used universal protein-binding microarray (PBM) technology to determine the in vitro binding specificities of hundreds of TFs for all possible 8 bp sequences (8mers). We have developed a Bayesian analysis of variance (ANOVA) model that decomposes these 8mer data into background noise, TF familywise effects and effects due to the particular TF. Adjusting for background noise improves PBM data quality and concordance with in vivo TF binding data. Moreover, our model provides simultaneous identification of TF subclasses and their shared sequence preferences, and also of 8mers bound preferentially by individual members of TF subclasses. Such results may aid in deciphering cis-regulatory codes and determinants of protein-DNA binding specificity.</AbstractText><AbstractText Label="AVAILABILITY AND IMPLEMENTATION" NlmCategory="METHODS">Source code, compiled code and R and Python scripts are available from http://thebrain.bwh.harvard.edu/hierarchicalANOVA.</AbstractText><AbstractText Label="SUPPLEMENTARY INFORMATION" NlmCategory="BACKGROUND">Supplementary data are available at Bioinformatics online.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Jiang</LastName><ForeName>Bo</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Department of Statistics, Harvard University, Cambridge, MA 02138, USA. bojiang83@gmail.com</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Jun S</ForeName><Initials>JS</Initials></Author><Author ValidYN="Y"><LastName>Bulyk</LastName><ForeName>Martha L</ForeName><Initials>ML</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 HG003985</GrantID><Acronym>HG</Acronym><Agency>NHGRI 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></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>04</Month><Day>04</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="D004268">DNA-Binding Proteins</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D014157">Transcription 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