A Comparison Study for DNA Motif Modeling on Protein Binding Microarray.
Identifieur interne : 001184 ( PubMed/Corpus ); précédent : 001183; suivant : 001185A Comparison Study for DNA Motif Modeling on Protein Binding Microarray.
Auteurs : Ka-Chun Wong ; Yue Li ; Chengbin Peng ; Hau-San WongSource :
- IEEE/ACM transactions on computational biology and bioinformatics [ 1557-9964 ]
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : DNA, Transcription Factors.
- chemical , metabolism : DNA, Transcription Factors.
- methods : Computational Biology, Protein Array Analysis.
- Algorithms, Binding Sites, Chromatin Immunoprecipitation, Nucleotide Motifs, Protein Binding.
Abstract
Transcription factor binding sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k = 8∼10). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build TFBS (also known as DNA motif) models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement if choosing di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.
DOI: 10.1109/TCBB.2015.2443782
PubMed: 27045826
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Identifying them is a computationally challenging task. In particular, protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k = 8∼10). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build TFBS (also known as DNA motif) models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement if choosing di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">27045826</PMID><DateCompleted><Year>2017</Year><Month>01</Month><Day>04</Day></DateCompleted><DateRevised><Year>2017</Year><Month>01</Month><Day>05</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1557-9964</ISSN><JournalIssue CitedMedium="Internet"><Volume>13</Volume><Issue>2</Issue><PubDate><MedlineDate>2016 Mar-Apr</MedlineDate></PubDate></JournalIssue><Title>IEEE/ACM transactions on computational biology and bioinformatics</Title><ISOAbbreviation>IEEE/ACM Trans Comput Biol Bioinform</ISOAbbreviation></Journal><ArticleTitle>A Comparison Study for DNA Motif Modeling on Protein Binding Microarray.</ArticleTitle><Pagination><MedlinePgn>261-71</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1109/TCBB.2015.2443782</ELocationID><Abstract><AbstractText>Transcription factor binding sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k = 8∼10). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build TFBS (also known as DNA motif) models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement if choosing di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wong</LastName><ForeName>Ka-Chun</ForeName><Initials>KC</Initials></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Yue</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Peng</LastName><ForeName>Chengbin</ForeName><Initials>C</Initials></Author><Author ValidYN="Y"><LastName>Wong</LastName><ForeName>Hau-San</ForeName><Initials>HS</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>IEEE/ACM Trans Comput Biol Bioinform</MedlineTA><NlmUniqueID>101196755</NlmUniqueID><ISSNLinking>1545-5963</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D014157">Transcription Factors</NameOfSubstance></Chemical><Chemical><RegistryNumber>9007-49-2</RegistryNumber><NameOfSubstance UI="D004247">DNA</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001665" MajorTopicYN="Y">Binding Sites</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D047369" MajorTopicYN="N">Chromatin Immunoprecipitation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004247" MajorTopicYN="N">DNA</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D059372" MajorTopicYN="Y">Nucleotide Motifs</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D040081" MajorTopicYN="N">Protein Array Analysis</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011485" MajorTopicYN="N">Protein Binding</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014157" MajorTopicYN="N">Transcription Factors</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>4</Month><Day>6</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>4</Month><Day>6</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2017</Year><Month>1</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">27045826</ArticleId><ArticleId IdType="doi">10.1109/TCBB.2015.2443782</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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