Integrating Diverse Datasets Improves Developmental Enhancer Prediction
Identifieur interne : 000F31 ( Pmc/Checkpoint ); précédent : 000F30; suivant : 000F32Integrating Diverse Datasets Improves Developmental Enhancer Prediction
Auteurs : Genevieve D. Erwin [États-Unis] ; Nir Oksenberg [États-Unis] ; Rebecca M. Truty [États-Unis] ; Dennis Kostka [États-Unis] ; Karl K. Murphy [États-Unis] ; Nadav Ahituv [États-Unis] ; Katherine S. Pollard [États-Unis] ; John A. Capra [États-Unis]Source :
- PLoS Computational Biology [ 1553-734X ] ; 2014.
Abstract
Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through
Url:
DOI: 10.1371/journal.pcbi.1003677
PubMed: 24967590
PubMed Central: 4072507
Affiliations:
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PMC:4072507Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through <italic>in vivo</italic>
validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology.</p>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS Comput Biol</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS Comput. Biol</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">ploscomp</journal-id>
<journal-title-group><journal-title>PLoS Computational Biology</journal-title>
</journal-title-group>
<issn pub-type="ppub">1553-734X</issn>
<issn pub-type="epub">1553-7358</issn>
<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">24967590</article-id>
<article-id pub-id-type="pmc">4072507</article-id>
<article-id pub-id-type="publisher-id">PCOMPBIOL-D-13-01447</article-id>
<article-id pub-id-type="doi">10.1371/journal.pcbi.1003677</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="Discipline-v2"><subject>Biology and Life Sciences</subject>
<subj-group><subject>Computational Biology</subject>
<subj-group><subject>Comparative Genomics</subject>
<subject>Genome Analysis</subject>
</subj-group>
</subj-group>
<subj-group><subject>Developmental Biology</subject>
<subj-group><subject>Organism Development</subject>
</subj-group>
</subj-group>
<subj-group><subject>Genetics</subject>
<subj-group><subject>Genomics</subject>
<subj-group><subject>Functional Genomics</subject>
</subj-group>
</subj-group>
<subj-group><subject>Gene Expression</subject>
<subject>Molecular Genetics</subject>
</subj-group>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>Integrating Diverse Datasets Improves Developmental Enhancer Prediction</article-title>
<alt-title alt-title-type="running-head">Integrative Developmental Enhancer Prediction</alt-title>
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<given-names>Genevieve D.</given-names>
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<xref ref-type="aff" rid="aff1"><sup>1</sup>
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<xref ref-type="aff" rid="aff2"><sup>2</sup>
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<xref ref-type="aff" rid="aff2"><sup>2</sup>
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<xref ref-type="aff" rid="aff3"><sup>3</sup>
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<xref ref-type="aff" rid="aff1"><sup>1</sup>
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</contrib>
<contrib contrib-type="author"><name><surname>Kostka</surname>
<given-names>Dennis</given-names>
</name>
<xref ref-type="aff" rid="aff4"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Murphy</surname>
<given-names>Karl K.</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ahituv</surname>
<given-names>Nadav</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Pollard</surname>
<given-names>Katherine S.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff5"><sup>5</sup>
</xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Capra</surname>
<given-names>John A.</given-names>
</name>
<xref ref-type="aff" rid="aff6"><sup>6</sup>
</xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
<addr-line>Gladstone Institute of Cardiovascular Disease, San Francisco, California, United States of America</addr-line>
</aff>
<aff id="aff2"><label>2</label>
<addr-line>Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America</addr-line>
</aff>
<aff id="aff3"><label>3</label>
<addr-line>Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America</addr-line>
</aff>
<aff id="aff4"><label>4</label>
<addr-line>Department of Developmental Biology and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America</addr-line>
</aff>
<aff id="aff5"><label>5</label>
<addr-line>Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America</addr-line>
</aff>
<aff id="aff6"><label>6</label>
<addr-line>Center for Human Genetics Research and Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States of America</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Ohler</surname>
<given-names>Uwe</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>Duke University, United States of America</addr-line>
</aff>
<author-notes><corresp id="cor1">* E-mail: <email>kpollard@gladstone.ucsf.edu</email>
(KSP); <email>tony.capra@vanderbilt.edu</email>
(JAC)</corresp>
<fn fn-type="conflict"><p>The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="con"><p>Conceived and designed the experiments: GDE RMT DK KSP JAC. Performed the experiments: GDE DK JAC. Analyzed the data: GDE DK KSP JAC. Contributed reagents/materials/analysis tools: NO KKM NA. Wrote the paper: GDE KSP JAC.</p>
</fn>
</author-notes>
<pub-date pub-type="collection"><month>6</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="epub"><day>26</day>
<month>6</month>
<year>2014</year>
</pub-date>
<volume>10</volume>
<issue>6</issue>
<elocation-id>e1003677</elocation-id>
<history><date date-type="received"><day>15</day>
<month>8</month>
<year>2013</year>
</date>
<date date-type="accepted"><day>6</day>
<month>5</month>
<year>2014</year>
</date>
</history>
<permissions><copyright-statement>© 2014 Erwin et al</copyright-statement>
<copyright-year>2014</copyright-year>
<copyright-holder>Erwin 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 Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.</license-p>
</license>
</permissions>
<abstract><p>Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through <italic>in vivo</italic>
validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology.</p>
</abstract>
<abstract abstract-type="summary"><title>Author Summary</title>
<p>The human genome contains an immense amount of non-protein-coding DNA with unknown function. Some of this DNA regulates when, where, and at what levels genes are active during development. Enhancers, one type of regulatory element, are short stretches of DNA that can act as “switches” to turn a gene on or off at specific times in specific cells or tissues. Understanding where in the genome enhancers are located can provide insight into the genetic basis of development and disease. Enhancers are hard to identify, but clues about their locations are found in different types of data including DNA sequence, evolutionary history, and where proteins bind to DNA. Here, we introduce a new tool, called EnhancerFinder, which combines these data to predict the location and activity of enhancers during embryonic development. We trained EnhancerFinder on a large set of functionally validated human enhancers, and it proved to be very accurate. We used EnhancerFinder to predict tens of thousands of enhancers in the human genome and validated several of the predictions near three important developmental genes in mouse or zebrafish. EnhancerFinder's predictions will be useful in understanding functional regions hidden in the vast amounts of human non-coding DNA.</p>
</abstract>
<funding-group><funding-statement>This project was supported by NIH (<ext-link ext-link-type="uri" xlink:href="http://www.nih.gov/">http://www.nih.gov/</ext-link>
) grants from NIGMS (GM082901, GM61390), NHGRI (HG005058, HG006768), NICHD (HD059862), NIDDK (DK090382), NINDS (NS079231), and NHLBI (HL098179), a PhRMA Foundation fellowship (<ext-link ext-link-type="uri" xlink:href="http://www.phrmafoundation.org/">http://www.phrmafoundation.org/</ext-link>
), a University of California Achievement Awards for College Scientists (ARCS) Scholarship (<ext-link ext-link-type="uri" xlink:href="https://www.arcsfoundation.org/">https://www.arcsfoundation.org/</ext-link>
), a gift from the San Simeon Fund (URL unavailable), and institutional funds from the J. David Gladstone Institutes (<ext-link ext-link-type="uri" xlink:href="http://gladstoneinstitutes.org/">http://gladstoneinstitutes.org/</ext-link>
) as well as institutional funds from Vanderbilt University. 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><page-count count="20"></page-count>
</counts>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Californie</li>
<li>Pennsylvanie</li>
<li>Tennessee</li>
</region>
<settlement><li>Pittsburgh</li>
</settlement>
<orgName><li>Université de Pittsburgh</li>
</orgName>
</list>
<tree><country name="États-Unis"><region name="Californie"><name sortKey="Erwin, Genevieve D" sort="Erwin, Genevieve D" uniqKey="Erwin G" first="Genevieve D." last="Erwin">Genevieve D. Erwin</name>
</region>
<name sortKey="Ahituv, Nadav" sort="Ahituv, Nadav" uniqKey="Ahituv N" first="Nadav" last="Ahituv">Nadav Ahituv</name>
<name sortKey="Ahituv, Nadav" sort="Ahituv, Nadav" uniqKey="Ahituv N" first="Nadav" last="Ahituv">Nadav Ahituv</name>
<name sortKey="Capra, John A" sort="Capra, John A" uniqKey="Capra J" first="John A." last="Capra">John A. Capra</name>
<name sortKey="Erwin, Genevieve D" sort="Erwin, Genevieve D" uniqKey="Erwin G" first="Genevieve D." last="Erwin">Genevieve D. Erwin</name>
<name sortKey="Kostka, Dennis" sort="Kostka, Dennis" uniqKey="Kostka D" first="Dennis" last="Kostka">Dennis Kostka</name>
<name sortKey="Murphy, Karl K" sort="Murphy, Karl K" uniqKey="Murphy K" first="Karl K." last="Murphy">Karl K. Murphy</name>
<name sortKey="Murphy, Karl K" sort="Murphy, Karl K" uniqKey="Murphy K" first="Karl K." last="Murphy">Karl K. Murphy</name>
<name sortKey="Oksenberg, Nir" sort="Oksenberg, Nir" uniqKey="Oksenberg N" first="Nir" last="Oksenberg">Nir Oksenberg</name>
<name sortKey="Oksenberg, Nir" sort="Oksenberg, Nir" uniqKey="Oksenberg N" first="Nir" last="Oksenberg">Nir Oksenberg</name>
<name sortKey="Pollard, Katherine S" sort="Pollard, Katherine S" uniqKey="Pollard K" first="Katherine S." last="Pollard">Katherine S. Pollard</name>
<name sortKey="Pollard, Katherine S" sort="Pollard, Katherine S" uniqKey="Pollard K" first="Katherine S." last="Pollard">Katherine S. Pollard</name>
<name sortKey="Pollard, Katherine S" sort="Pollard, Katherine S" uniqKey="Pollard K" first="Katherine S." last="Pollard">Katherine S. Pollard</name>
<name sortKey="Truty, Rebecca M" sort="Truty, Rebecca M" uniqKey="Truty R" first="Rebecca M." last="Truty">Rebecca M. Truty</name>
</country>
</tree>
</affiliations>
</record>
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