The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies
Identifieur interne : 000694 ( Pmc/Checkpoint ); précédent : 000693; suivant : 000695The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies
Auteurs : Abdulkadir Elmas [États-Unis] ; Xiaodong Wang [États-Unis] ; Jacqueline M. Dresch [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2017.
Abstract
Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism’s development, disease, and evolution. The building blocks of this complex molecular machinery are an organism’s genomic DNA sequence and transcription factor proteins. Despite the vast amount of sequence data now available for many model organisms, predicting where transcription factors bind, often referred to as ‘motif detection’ is still incredibly challenging. In this study, we develop a novel bioinformatic approach to binding site prediction. We do this by extending pre-existing SVM approaches in an unbiased way to include all possible gapped
Url:
DOI: 10.1371/journal.pone.0185570
PubMed: 28982128
PubMed Central: 5628859
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">The folded <italic>k</italic>-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies</title><author><name sortKey="Elmas, Abdulkadir" sort="Elmas, Abdulkadir" uniqKey="Elmas A" first="Abdulkadir" last="Elmas">Abdulkadir Elmas</name><affiliation wicri:level="4"><nlm:aff id="aff001"><addr-line>Department of Electrical Engineering, Columbia University, New York, NY, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering, Columbia University, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region><settlement type="city">New York</settlement></placeName><orgName type="university">Université Columbia</orgName></affiliation></author><author><name sortKey="Wang, Xiaodong" sort="Wang, Xiaodong" uniqKey="Wang X" first="Xiaodong" last="Wang">Xiaodong Wang</name><affiliation wicri:level="4"><nlm:aff id="aff001"><addr-line>Department of Electrical Engineering, Columbia University, New York, NY, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering, Columbia University, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region><settlement type="city">New York</settlement></placeName><orgName type="university">Université Columbia</orgName></affiliation></author><author><name sortKey="Dresch, Jacqueline M" sort="Dresch, Jacqueline M" uniqKey="Dresch J" first="Jacqueline M." last="Dresch">Jacqueline M. Dresch</name><affiliation wicri:level="2"><nlm:aff id="aff002"><addr-line>Department of Mathematics and Computer Science, Clark University, Worcester, MA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mathematics and Computer Science, Clark University, Worcester, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">28982128</idno><idno type="pmc">5628859</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5628859</idno><idno type="RBID">PMC:5628859</idno><idno type="doi">10.1371/journal.pone.0185570</idno><date when="2017">2017</date><idno type="wicri:Area/Pmc/Corpus">001035</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001035</idno><idno type="wicri:Area/Pmc/Curation">001035</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">001035</idno><idno type="wicri:Area/Pmc/Checkpoint">000694</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000694</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">The folded <italic>k</italic>-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies</title><author><name sortKey="Elmas, Abdulkadir" sort="Elmas, Abdulkadir" uniqKey="Elmas A" first="Abdulkadir" last="Elmas">Abdulkadir Elmas</name><affiliation wicri:level="4"><nlm:aff id="aff001"><addr-line>Department of Electrical Engineering, Columbia University, New York, NY, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering, Columbia University, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region><settlement type="city">New York</settlement></placeName><orgName type="university">Université Columbia</orgName></affiliation></author><author><name sortKey="Wang, Xiaodong" sort="Wang, Xiaodong" uniqKey="Wang X" first="Xiaodong" last="Wang">Xiaodong Wang</name><affiliation wicri:level="4"><nlm:aff id="aff001"><addr-line>Department of Electrical Engineering, Columbia University, New York, NY, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering, Columbia University, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region><settlement type="city">New York</settlement></placeName><orgName type="university">Université Columbia</orgName></affiliation></author><author><name sortKey="Dresch, Jacqueline M" sort="Dresch, Jacqueline M" uniqKey="Dresch J" first="Jacqueline M." last="Dresch">Jacqueline M. Dresch</name><affiliation wicri:level="2"><nlm:aff id="aff002"><addr-line>Department of Mathematics and Computer Science, Clark University, Worcester, MA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mathematics and Computer Science, Clark University, Worcester, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism’s development, disease, and evolution. The building blocks of this complex molecular machinery are an organism’s genomic DNA sequence and transcription factor proteins. Despite the vast amount of sequence data now available for many model organisms, predicting where transcription factors bind, often referred to as ‘motif detection’ is still incredibly challenging. In this study, we develop a novel bioinformatic approach to binding site prediction. We do this by extending pre-existing SVM approaches in an unbiased way to include all possible gapped <italic>k</italic>-mers, representing different combinations of complex nucleotide dependencies within binding sites. We show the advantages of this new approach when compared to existing SVM approaches, through a rigorous set of cross-validation experiments. We also demonstrate the effectiveness of our new approach by reporting on its improved performance on a set of 127 genomic regions known to regulate gene expression along the anterio-posterior axis in early <italic>Drosophila</italic> embryos.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Borok, M" uniqKey="Borok M">M Borok</name></author><author><name sortKey="Tran, D" uniqKey="Tran D">D Tran</name></author><author><name sortKey="Ho, M" uniqKey="Ho M">M Ho</name></author><author><name sortKey="Ra, D" uniqKey="Ra D">D RA</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Starr, M" uniqKey="Starr M">M Starr</name></author><author><name sortKey="Ho, M" uniqKey="Ho M">M Ho</name></author><author><name sortKey="Gunther, E" uniqKey="Gunther E">E Gunther</name></author><author><name sortKey="Tu, Y" uniqKey="Tu Y">Y Tu</name></author><author><name sortKey="Shur, A" uniqKey="Shur A">A 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, CA USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">28982128</article-id><article-id pub-id-type="pmc">5628859</article-id><article-id pub-id-type="publisher-id">PONE-D-17-26105</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0185570</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Database and Informatics Methods</subject><subj-group><subject>Bioinformatics</subject><subj-group><subject>Sequence Analysis</subject><subj-group><subject>Sequence Motif Analysis</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Database and Informatics Methods</subject><subj-group><subject>Biological Databases</subject><subj-group><subject>Sequence Databases</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Database and Informatics Methods</subject><subj-group><subject>Bioinformatics</subject><subj-group><subject>Sequence Analysis</subject><subj-group><subject>Sequence Databases</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Computer and Information Sciences</subject><subj-group><subject>Artificial Intelligence</subject><subj-group><subject>Machine Learning</subject><subj-group><subject>Support Vector Machines</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Molecular Biology</subject><subj-group><subject>Molecular Biology Techniques</subject><subj-group><subject>Sequencing Techniques</subject><subj-group><subject>Nucleotide Sequencing</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Molecular Biology Techniques</subject><subj-group><subject>Sequencing Techniques</subject><subj-group><subject>Nucleotide Sequencing</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Database and Informatics Methods</subject><subj-group><subject>Bioinformatics</subject><subj-group><subject>Sequence Analysis</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Genetics</subject><subj-group><subject>Gene Expression</subject><subj-group><subject>Gene Regulation</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Genetics</subject><subj-group><subject>Gene Expression</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and life sciences</subject><subj-group><subject>Biochemistry</subject><subj-group><subject>Proteins</subject><subj-group><subject>DNA-binding proteins</subject><subj-group><subject>Transcription Factors</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Genetics</subject><subj-group><subject>Gene Expression</subject><subj-group><subject>Gene Regulation</subject><subj-group><subject>Transcription Factors</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Biochemistry</subject><subj-group><subject>Proteins</subject><subj-group><subject>Regulatory Proteins</subject><subj-group><subject>Transcription Factors</subject></subj-group></subj-group></subj-group></subj-group></subj-group></article-categories><title-group><article-title>The folded <italic>k</italic>-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies</article-title><alt-title alt-title-type="running-head">The folded <italic>k</italic>-spectrum kernel for detecting transcription factor binding sites</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Elmas</surname><given-names>Abdulkadir</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Data curation</role><role content-type="http://credit.casrai.org/">Formal analysis</role><role content-type="http://credit.casrai.org/">Methodology</role><role content-type="http://credit.casrai.org/">Resources</role><role content-type="http://credit.casrai.org/">Software</role><role content-type="http://credit.casrai.org/">Validation</role><role content-type="http://credit.casrai.org/">Visualization</role><role content-type="http://credit.casrai.org/">Writing – original draft</role><role content-type="http://credit.casrai.org/">Writing – review & editing</role><xref ref-type="aff" rid="aff001"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Wang</surname><given-names>Xiaodong</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Project administration</role><role content-type="http://credit.casrai.org/">Supervision</role><role content-type="http://credit.casrai.org/">Validation</role><role content-type="http://credit.casrai.org/">Writing – original draft</role><role content-type="http://credit.casrai.org/">Writing – review & editing</role><xref ref-type="aff" rid="aff001"><sup>1</sup></xref></contrib><contrib contrib-type="author"><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0001-7626-4959</contrib-id><name><surname>Dresch</surname><given-names>Jacqueline M.</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Funding acquisition</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Methodology</role><role content-type="http://credit.casrai.org/">Project administration</role><role content-type="http://credit.casrai.org/">Supervision</role><role content-type="http://credit.casrai.org/">Validation</role><role content-type="http://credit.casrai.org/">Writing – original draft</role><role content-type="http://credit.casrai.org/">Writing – review & editing</role><xref ref-type="aff" rid="aff002"><sup>2</sup></xref><xref ref-type="corresp" rid="cor001">*</xref></contrib></contrib-group><aff id="aff001"><label>1</label><addr-line>Department of Electrical Engineering, Columbia University, New York, NY, United States of America</addr-line></aff><aff id="aff002"><label>2</label><addr-line>Department of Mathematics and Computer Science, Clark University, Worcester, MA, United States of America</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Liu</surname><given-names>Bin</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>Harbin Institute of Technology Shenzhen Graduate School, CHINA</addr-line></aff><author-notes><fn fn-type="COI-statement" id="coi001"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><corresp id="cor001">* E-mail: <email>jdresch@clarku.edu</email></corresp></author-notes><pub-date pub-type="collection"><year>2017</year></pub-date><pub-date pub-type="epub"><day>5</day><month>10</month><year>2017</year></pub-date><volume>12</volume><issue>10</issue><elocation-id>e0185570</elocation-id><history><date date-type="received"><day>11</day><month>7</month><year>2017</year></date><date date-type="accepted"><day>14</day><month>9</month><year>2017</year></date></history><permissions><copyright-statement>© 2017 Elmas et al</copyright-statement><copyright-year>2017</copyright-year><copyright-holder>Elmas et al</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="pone.0185570.pdf"></self-uri><abstract><p>Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism’s development, disease, and evolution. The building blocks of this complex molecular machinery are an organism’s genomic DNA sequence and transcription factor proteins. Despite the vast amount of sequence data now available for many model organisms, predicting where transcription factors bind, often referred to as ‘motif detection’ is still incredibly challenging. In this study, we develop a novel bioinformatic approach to binding site prediction. We do this by extending pre-existing SVM approaches in an unbiased way to include all possible gapped <italic>k</italic>-mers, representing different combinations of complex nucleotide dependencies within binding sites. We show the advantages of this new approach when compared to existing SVM approaches, through a rigorous set of cross-validation experiments. We also demonstrate the effectiveness of our new approach by reporting on its improved performance on a set of 127 genomic regions known to regulate gene expression along the anterio-posterior axis in early <italic>Drosophila</italic> embryos.</p></abstract><funding-group><award-group id="award001"><funding-source><institution>National Institutes of Health (US)</institution></funding-source><award-id>GM110571</award-id><principal-award-recipient><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0001-7626-4959</contrib-id><name><surname>Dresch</surname><given-names>Jacqueline M.</given-names></name></principal-award-recipient></award-group><funding-statement>This work has been supported by a National Institutes of Health (GM110571) grant (<ext-link ext-link-type="uri" xlink:href="https://www.nih.gov">https://www.nih.gov</ext-link>) to JMD. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement></funding-group><counts><fig-count count="6"></fig-count><table-count count="0"></table-count><page-count count="22"></page-count></counts><custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name><meta-value>All relevant data are within the paper and its Supporting Information files.</meta-value></custom-meta></custom-meta-group></article-meta><notes><title>Data Availability</title><p>All relevant data are within the paper and its Supporting Information files.</p></notes></front></pmc><affiliations><list><country><li>États-Unis</li></country><region><li>Massachusetts</li><li>État de New York</li></region><settlement><li>New York</li></settlement><orgName><li>Université Columbia</li></orgName></list><tree><country name="États-Unis"><region name="État de New York"><name sortKey="Elmas, Abdulkadir" sort="Elmas, Abdulkadir" uniqKey="Elmas A" first="Abdulkadir" last="Elmas">Abdulkadir Elmas</name></region><name sortKey="Dresch, Jacqueline M" sort="Dresch, Jacqueline M" uniqKey="Dresch J" first="Jacqueline M." last="Dresch">Jacqueline M. Dresch</name><name sortKey="Wang, Xiaodong" sort="Wang, Xiaodong" uniqKey="Wang X" first="Xiaodong" last="Wang">Xiaodong Wang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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