Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels.
Identifieur interne : 001720 ( PubMed/Curation ); précédent : 001719; suivant : 001721Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels.
Auteurs : Xiaolei Wang ; Hiroyuki Kuwahara ; Xin GaoSource :
- BMC systems biology [ 1752-0509 ] ; 2014.
Descripteurs français
- KwdFr :
- ADN (), ADN (ultrastructure), Algorithmes, Analyse de séquence d'ADN (), Cartographie d'interactions entre protéines (), Données de séquences moléculaires, Facteurs de transcription (), Facteurs de transcription (ultrastructure), Liaison aux protéines, Machine à vecteur de support, Modèles chimiques, Modèles moléculaires, Simulation numérique, Sites de fixation, Séquence d'acides aminés, Séquence nucléotidique.
- MESH :
- ultrastructure : ADN, Facteurs de transcription.
- ADN, Algorithmes, Analyse de séquence d'ADN, Cartographie d'interactions entre protéines, Données de séquences moléculaires, Facteurs de transcription, Liaison aux protéines, Machine à vecteur de support, Modèles chimiques, Modèles moléculaires, Simulation numérique, Sites de fixation, Séquence d'acides aminés, Séquence nucléotidique.
English descriptors
- KwdEn :
- Algorithms, Amino Acid Sequence, Base Sequence, Binding Sites, Computer Simulation, DNA (chemistry), DNA (ultrastructure), Models, Chemical, Models, Molecular, Molecular Sequence Data, Protein Binding, Protein Interaction Mapping (methods), Sequence Analysis, DNA (methods), Support Vector Machine, Transcription Factors (chemistry), Transcription Factors (ultrastructure).
- MESH :
- chemical , chemistry : DNA, Transcription Factors.
- chemical , ultrastructure : DNA, Transcription Factors.
- methods : Protein Interaction Mapping, Sequence Analysis, DNA.
- Algorithms, Amino Acid Sequence, Base Sequence, Binding Sites, Computer Simulation, Models, Chemical, Models, Molecular, Molecular Sequence Data, Protein Binding, Support Vector Machine.
Abstract
A quantitative understanding of interactions between transcription factors (TFs) and their DNA binding sites is key to the rational design of gene regulatory networks. Recent advances in high-throughput technologies have enabled high-resolution measurements of protein-DNA binding affinity. Importantly, such experiments revealed the complex nature of TF-DNA interactions, whereby the effects of nucleotide changes on the binding affinity were observed to be context dependent. A systematic method to give high-quality estimates of such complex affinity landscapes is, thus, essential to the control of gene expression and the advance of synthetic biology.
DOI: 10.1186/1752-0509-8-S5-S5
PubMed: 25605483
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels.</title><author><name sortKey="Wang, Xiaolei" sort="Wang, Xiaolei" uniqKey="Wang X" first="Xiaolei" last="Wang">Xiaolei Wang</name></author><author><name sortKey="Kuwahara, Hiroyuki" sort="Kuwahara, Hiroyuki" uniqKey="Kuwahara H" first="Hiroyuki" last="Kuwahara">Hiroyuki Kuwahara</name></author><author><name sortKey="Gao, Xin" sort="Gao, Xin" uniqKey="Gao X" first="Xin" last="Gao">Xin Gao</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="RBID">pubmed:25605483</idno><idno type="pmid">25605483</idno><idno type="doi">10.1186/1752-0509-8-S5-S5</idno><idno type="wicri:Area/PubMed/Corpus">001720</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001720</idno><idno type="wicri:Area/PubMed/Curation">001720</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001720</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels.</title><author><name sortKey="Wang, Xiaolei" sort="Wang, Xiaolei" uniqKey="Wang X" first="Xiaolei" last="Wang">Xiaolei Wang</name></author><author><name sortKey="Kuwahara, Hiroyuki" sort="Kuwahara, Hiroyuki" uniqKey="Kuwahara H" first="Hiroyuki" last="Kuwahara">Hiroyuki Kuwahara</name></author><author><name sortKey="Gao, Xin" sort="Gao, Xin" uniqKey="Gao X" first="Xin" last="Gao">Xin Gao</name></author></analytic><series><title level="j">BMC systems biology</title><idno type="eISSN">1752-0509</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Amino Acid Sequence</term><term>Base Sequence</term><term>Binding Sites</term><term>Computer Simulation</term><term>DNA (chemistry)</term><term>DNA (ultrastructure)</term><term>Models, Chemical</term><term>Models, Molecular</term><term>Molecular Sequence Data</term><term>Protein Binding</term><term>Protein Interaction Mapping (methods)</term><term>Sequence Analysis, DNA (methods)</term><term>Support Vector Machine</term><term>Transcription Factors (chemistry)</term><term>Transcription Factors (ultrastructure)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ADN ()</term><term>ADN (ultrastructure)</term><term>Algorithmes</term><term>Analyse de séquence d'ADN ()</term><term>Cartographie d'interactions entre protéines ()</term><term>Données de séquences moléculaires</term><term>Facteurs de transcription ()</term><term>Facteurs de transcription (ultrastructure)</term><term>Liaison aux protéines</term><term>Machine à vecteur de support</term><term>Modèles chimiques</term><term>Modèles moléculaires</term><term>Simulation numérique</term><term>Sites de fixation</term><term>Séquence d'acides aminés</term><term>Séquence nucléotidique</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>DNA</term><term>Transcription Factors</term></keywords><keywords scheme="MESH" type="chemical" qualifier="ultrastructure" xml:lang="en"><term>DNA</term><term>Transcription Factors</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Protein Interaction Mapping</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" qualifier="ultrastructure" xml:lang="fr"><term>ADN</term><term>Facteurs de transcription</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Amino Acid Sequence</term><term>Base Sequence</term><term>Binding Sites</term><term>Computer Simulation</term><term>Models, Chemical</term><term>Models, Molecular</term><term>Molecular Sequence Data</term><term>Protein Binding</term><term>Support Vector Machine</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>ADN</term><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Cartographie d'interactions entre protéines</term><term>Données de séquences moléculaires</term><term>Facteurs de transcription</term><term>Liaison aux protéines</term><term>Machine à vecteur de support</term><term>Modèles chimiques</term><term>Modèles moléculaires</term><term>Simulation numérique</term><term>Sites de fixation</term><term>Séquence d'acides aminés</term><term>Séquence nucléotidique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A quantitative understanding of interactions between transcription factors (TFs) and their DNA binding sites is key to the rational design of gene regulatory networks. Recent advances in high-throughput technologies have enabled high-resolution measurements of protein-DNA binding affinity. Importantly, such experiments revealed the complex nature of TF-DNA interactions, whereby the effects of nucleotide changes on the binding affinity were observed to be context dependent. A systematic method to give high-quality estimates of such complex affinity landscapes is, thus, essential to the control of gene expression and the advance of synthetic biology.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25605483</PMID><DateCompleted><Year>2015</Year><Month>09</Month><Day>21</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1752-0509</ISSN><JournalIssue CitedMedium="Internet"><Volume>8 Suppl 5</Volume><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>BMC systems biology</Title><ISOAbbreviation>BMC Syst Biol</ISOAbbreviation></Journal><ArticleTitle>Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels.</ArticleTitle><Pagination><MedlinePgn>S5</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1752-0509-8-S5-S5</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">A quantitative understanding of interactions between transcription factors (TFs) and their DNA binding sites is key to the rational design of gene regulatory networks. Recent advances in high-throughput technologies have enabled high-resolution measurements of protein-DNA binding affinity. Importantly, such experiments revealed the complex nature of TF-DNA interactions, whereby the effects of nucleotide changes on the binding affinity were observed to be context dependent. A systematic method to give high-quality estimates of such complex affinity landscapes is, thus, essential to the control of gene expression and the advance of synthetic biology.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Here, we propose a two-round prediction method that is based on support vector regression (SVR) with weighted degree (WD) kernels. In the first round, a WD kernel with shifts and mismatches is used with SVR to detect the importance of subsequences with different lengths at different positions. The subsequences identified as important in the first round are then fed into a second WD kernel to fit the experimentally measured affinities. To our knowledge, this is the first attempt to increase the accuracy of the affinity prediction by applying two rounds of string kernels and by identifying a small number of crucial k-mers. The proposed method was tested by predicting the binding affinity landscape of Gcn4p in Saccharomyces cerevisiae using datasets from HiTS-FLIP. Our method explicitly identified important subsequences and showed significant performance improvements when compared with other state-of-the-art methods. Based on the identified important subsequences, we discovered two surprisingly stable 10-mers and one sensitive 10-mer which were not reported before. Further test on four other TFs in S. cerevisiae demonstrated the generality of our method.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">We proposed in this paper a two-round method to quantitatively model the DNA binding affinity landscape. Since the ability to modify genetic parts to fine-tune gene expression rates is crucial to the design of biological systems, such a tool may play an important role in the success of synthetic biology going forward.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Xiaolei</ForeName><Initials>X</Initials></Author><Author ValidYN="Y"><LastName>Kuwahara</LastName><ForeName>Hiroyuki</ForeName><Initials>H</Initials></Author><Author ValidYN="Y"><LastName>Gao</LastName><ForeName>Xin</ForeName><Initials>X</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><ArticleDate DateType="Electronic"><Year>2014</Year><Month>12</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Syst Biol</MedlineTA><NlmUniqueID>101301827</NlmUniqueID><ISSNLinking>1752-0509</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="D000595" MajorTopicYN="N">Amino Acid Sequence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001483" MajorTopicYN="N">Base Sequence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001665" MajorTopicYN="N">Binding Sites</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004247" MajorTopicYN="N">DNA</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName><QualifierName UI="Q000648" MajorTopicYN="N">ultrastructure</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008956" MajorTopicYN="Y">Models, Chemical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008958" MajorTopicYN="Y">Models, Molecular</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008969" MajorTopicYN="N">Molecular Sequence Data</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011485" MajorTopicYN="N">Protein Binding</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D025941" MajorTopicYN="N">Protein Interaction Mapping</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017422" MajorTopicYN="N">Sequence Analysis, DNA</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D060388" MajorTopicYN="Y">Support Vector Machine</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014157" MajorTopicYN="N">Transcription Factors</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName><QualifierName UI="Q000648" MajorTopicYN="N">ultrastructure</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>1</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>1</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>9</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25605483</ArticleId><ArticleId 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