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Text-mining assisted regulatory annotation

Identifieur interne : 000239 ( Pmc/Curation ); précédent : 000238; suivant : 000240

Text-mining assisted regulatory annotation

Auteurs : Stein Aerts [Belgique] ; Maximilian Haeussler [France] ; Steven Van Vooren [Belgique] ; Obi L. Griffith [Canada] ; Paco Hulpiau [Belgique] ; Steven Jm Jones [Canada] ; Stephen B. Montgomery [Royaume-Uni] ; Casey M. Bergman [Royaume-Uni]

Source :

RBID : PMC:2374703

Abstract

Text-mining technologies can be integrated with genome annotation systems, increasing the availability of annotated cis-regulatory data.


Url:
DOI: 10.1186/gb-2008-9-2-r31
PubMed: 18271954
PubMed Central: 2374703

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PMC:2374703

Le document en format XML

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<journal-title>Genome Biology</journal-title>
<issn pub-type="ppub">1465-6906</issn>
<issn pub-type="epub">1465-6914</issn>
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<article-id pub-id-type="pmc">2374703</article-id>
<article-id pub-id-type="publisher-id">gb-2008-9-2-r31</article-id>
<article-id pub-id-type="doi">10.1186/gb-2008-9-2-r31</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Text-mining assisted regulatory annotation</article-title>
</title-group>
<contrib-group>
<contrib id="A1" corresp="yes" contrib-type="author">
<name>
<surname>Aerts</surname>
<given-names>Stein</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<xref ref-type="aff" rid="I2">2</xref>
<email>stein.aerts@med.kuleuven.be</email>
</contrib>
<contrib id="A2" contrib-type="author">
<name>
<surname>Haeussler</surname>
<given-names>Maximilian</given-names>
</name>
<xref ref-type="aff" rid="I3">3</xref>
<email>maximilianh@gmail.com</email>
</contrib>
<contrib id="A3" contrib-type="author">
<name>
<surname>van Vooren</surname>
<given-names>Steven</given-names>
</name>
<xref ref-type="aff" rid="I4">4</xref>
<email>Steven.VanVooren@esat.kuleuven.ac.be</email>
</contrib>
<contrib id="A4" contrib-type="author">
<name>
<surname>Griffith</surname>
<given-names>Obi L</given-names>
</name>
<xref ref-type="aff" rid="I5">5</xref>
<email>obig@bcgsc.ca</email>
</contrib>
<contrib id="A5" contrib-type="author">
<name>
<surname>Hulpiau</surname>
<given-names>Paco</given-names>
</name>
<xref ref-type="aff" rid="I6">6</xref>
<email>paco.hulpiau@dmbr.ugent.be</email>
</contrib>
<contrib id="A6" contrib-type="author">
<name>
<surname>Jones</surname>
<given-names>Steven JM</given-names>
</name>
<xref ref-type="aff" rid="I5">5</xref>
<email>sjones@bcgsc.ca</email>
</contrib>
<contrib id="A7" contrib-type="author">
<name>
<surname>Montgomery</surname>
<given-names>Stephen B</given-names>
</name>
<xref ref-type="aff" rid="I7">7</xref>
<email>sm8@sanger.ac.uk</email>
</contrib>
<contrib id="A8" corresp="yes" contrib-type="author">
<name>
<surname>Bergman</surname>
<given-names>Casey M</given-names>
</name>
<xref ref-type="aff" rid="I8">8</xref>
<email>casey.bergman@manchester.ac.uk</email>
</contrib>
<contrib id="A9" contrib-type="author">
<collab>The Open Regulatory Annotation Consortium</collab>
<email>oreganno@noaddress.com</email>
</contrib>
</contrib-group>
<aff id="I1">
<label>1</label>
Laboratory of Neurogenetics, Department of Molecular and Developmental Genetics, VIB, Leuven, B-3000, Belgium</aff>
<aff id="I2">
<label>2</label>
Department of Human Genetics, Katholieke Universiteit Leuven School of Medicine, Herestraat, Leuven, B-3000, Belgium</aff>
<aff id="I3">
<label>3</label>
Institut de Neurosciences A Fessard, Centre National de la Rechere Scientifique, Gif-sur-Yvette, 91 198, France</aff>
<aff id="I4">
<label>4</label>
Department of Electrical Engineering, Katholieke Universiteit Leuven, Heverlee, B-3001, Belgium</aff>
<aff id="I5">
<label>5</label>
Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, V5Z 4E6, Canada</aff>
<aff id="I6">
<label>6</label>
VIB Department for Molecular Biomedical Research, Ghent University, Ghent, 9052, Belgium</aff>
<aff id="I7">
<label>7</label>
Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK</aff>
<aff id="I8">
<label>8</label>
Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, M13 9PT, UK</aff>
<pub-date pub-type="ppub">
<year>2008</year>
</pub-date>
<pub-date pub-type="epub">
<day>13</day>
<month>2</month>
<year>2008</year>
</pub-date>
<volume>9</volume>
<issue>2</issue>
<fpage>R31</fpage>
<lpage>R31</lpage>
<ext-link ext-link-type="uri" xlink:href="http://genomebiology.com/2008/9/2/R31"></ext-link>
<history>
<date date-type="received">
<day>2</day>
<month>10</month>
<year>2007</year>
</date>
<date date-type="rev-recd">
<day>21</day>
<month>12</month>
<year>2007</year>
</date>
<date date-type="accepted">
<day>13</day>
<month>2</month>
<year>2008</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright © 2008 Aerts et al.; licensee BioMed Central Ltd.</copyright-statement>
<copyright-year>2008</copyright-year>
<copyright-holder>Aerts et al.; licensee BioMed Central Ltd.</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0">
<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0"></ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>
<pmc-comment> Aerts Stein stein.aerts@med.kuleuven.be Text-mining assisted regulatory annotation 2008Genome Biology 9(2): R31-. (2008)1465-6906(2008)9:2urn:ISSN:1465-6906</pmc-comment>
</license>
</permissions>
<abstract abstract-type="short">
<p>Text-mining technologies can be integrated with genome annotation systems, increasing the availability of annotated
<italic>cis</italic>
-regulatory data.</p>
</abstract>
<abstract>
<sec>
<title>Background</title>
<p>Decoding transcriptional regulatory networks and the genomic
<italic>cis</italic>
-regulatory logic implemented in their control nodes is a fundamental challenge in genome biology. High-throughput computational and experimental analyses of regulatory networks and sequences rely heavily on positive control data from prior small-scale experiments, but the vast majority of previously discovered regulatory data remains locked in the biomedical literature.</p>
</sec>
<sec>
<title>Results</title>
<p>We develop text-mining strategies to identify relevant publications and extract sequence information to assist the regulatory annotation process. Using a vector space model to identify Medline abstracts from papers likely to have high
<italic>cis</italic>
-regulatory content, we demonstrate that document relevance ranking can assist the curation of transcriptional regulatory networks and estimate that, minimally, 30,000 papers harbor unannotated
<italic>cis</italic>
-regulatory data. In addition, we show that DNA sequences can be extracted from primary text with high
<italic>cis</italic>
-regulatory content and mapped to genome sequences as a means of identifying the location, organism and target gene information that is critical to the
<italic>cis</italic>
-regulatory annotation process.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>Our results demonstrate that text-mining technologies can be successfully integrated with genome annotation systems, thereby increasing the availability of annotated
<italic>cis</italic>
-regulatory data needed to catalyze advances in the field of gene regulation.</p>
</sec>
</abstract>
</article-meta>
</front>
</pmc>
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