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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Genome-wide <italic>in silico</italic> prediction of gene expression</title><author><name sortKey="Mcleay, Robert C" sort="Mcleay, Robert C" uniqKey="Mcleay R" first="Robert C." last="Mcleay">Robert C. Mcleay</name><affiliation><nlm:aff wicri:cut=" and" id="bts529-AFF1">Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia</nlm:aff></affiliation></author><author><name sortKey="Lesluyes, Tom" sort="Lesluyes, Tom" uniqKey="Lesluyes T" first="Tom" last="Lesluyes">Tom Lesluyes</name><affiliation><nlm:aff wicri:cut=" and" id="bts529-AFF1">Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="bts529-AFF1">Département d'Informatique, Université Bordeaux 1 Sciences et Technologies, 33405 Talence, France</nlm:aff></affiliation></author><author><name sortKey="Cuellar Partida, Gabriel" sort="Cuellar Partida, Gabriel" uniqKey="Cuellar Partida G" first="Gabriel" last="Cuellar Partida">Gabriel Cuellar Partida</name><affiliation><nlm:aff wicri:cut=" and" id="bts529-AFF1">Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia</nlm:aff></affiliation></author><author><name sortKey="Bailey, Timothy L" sort="Bailey, Timothy L" uniqKey="Bailey T" first="Timothy L." last="Bailey">Timothy L. Bailey</name><affiliation><nlm:aff wicri:cut=" and" id="bts529-AFF1">Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22954627</idno><idno type="pmc">3476338</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3476338</idno><idno type="RBID">PMC:3476338</idno><idno type="doi">10.1093/bioinformatics/bts529</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">001764</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001764</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Genome-wide <italic>in silico</italic> prediction of gene expression</title><author><name sortKey="Mcleay, Robert C" sort="Mcleay, Robert C" uniqKey="Mcleay R" first="Robert C." last="Mcleay">Robert C. Mcleay</name><affiliation><nlm:aff wicri:cut=" and" id="bts529-AFF1">Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia</nlm:aff></affiliation></author><author><name sortKey="Lesluyes, Tom" sort="Lesluyes, Tom" uniqKey="Lesluyes T" first="Tom" last="Lesluyes">Tom Lesluyes</name><affiliation><nlm:aff wicri:cut=" and" id="bts529-AFF1">Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="bts529-AFF1">Département d'Informatique, Université Bordeaux 1 Sciences et Technologies, 33405 Talence, France</nlm:aff></affiliation></author><author><name sortKey="Cuellar Partida, Gabriel" sort="Cuellar Partida, Gabriel" uniqKey="Cuellar Partida G" first="Gabriel" last="Cuellar Partida">Gabriel Cuellar Partida</name><affiliation><nlm:aff wicri:cut=" and" id="bts529-AFF1">Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia</nlm:aff></affiliation></author><author><name sortKey="Bailey, Timothy L" sort="Bailey, Timothy L" uniqKey="Bailey T" first="Timothy L." last="Bailey">Timothy L. Bailey</name><affiliation><nlm:aff wicri:cut=" and" id="bts529-AFF1">Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia</nlm:aff></affiliation></author></analytic><series><title level="j">Bioinformatics</title><idno type="ISSN">1367-4803</idno><idno type="eISSN">1367-4811</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><bold>Motivation:</bold> Modelling the regulation of gene expression can provide insight into the regulatory roles of individual transcription factors (TFs) and histone modifications. Recently, Ouyang <italic>et al.</italic> in 2009 modelled gene expression levels in mouse embryonic stem (mES) cells using <italic>in vivo</italic> ChIP-seq measurements of TF binding. ChIP-seq TF binding data, however, are tissue-specific and relatively difficult to obtain. This limits the applicability of gene expression models that rely on ChIP-seq TF binding data.</p><p><bold>Results:</bold> In this study, we build regression-based models that relate gene expression to the binding of 12 different TFs, 7 histone modifications and chromatin accessibility (DNase I hypersensitivity) in two different tissues. We find that expression models based on computationally predicted TF binding can achieve similar accuracy to those using <italic>in vivo</italic> TF binding data and that including binding at weak sites is critical for accurate prediction of gene expression. We also find that incorporating histone modification and chromatin accessibility data results in additional accuracy. Surprisingly, we find that models that use <italic>no</italic> TF binding data at all, but only histone modification and chromatin accessibility data, can be as (or more) accurate than those based on <italic>in vivo</italic> TF binding data.</p><p><bold>Availability and implementation:</bold> All scripts, motifs and data presented in this article are available online at <ext-link ext-link-type="uri" xlink:href="http://research.imb.uq.edu.au/t.bailey/supplementary_data/McLeay2011a">http://research.imb.uq.edu.au/t.bailey/supplementary_data/McLeay2011a</ext-link>.</p><p><bold>Contact:</bold><email>t.bailey@imb.uq.edu.au</email></p><p><bold>Supplementary information:</bold><ext-link ext-link-type="uri" xlink:href="http://bioinformatics.oxfordjournals.org/cgi/content/full/bts529/DC1">Supplementary data</ext-link> are available at <italic>Bioinformatics</italic> online.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Bioinformatics</journal-id><journal-id journal-id-type="iso-abbrev">Bioinformatics</journal-id><journal-id journal-id-type="publisher-id">bioinformatics</journal-id><journal-id journal-id-type="hwp">bioinfo</journal-id><journal-title-group><journal-title>Bioinformatics</journal-title></journal-title-group><issn pub-type="ppub">1367-4803</issn><issn pub-type="epub">1367-4811</issn><publisher><publisher-name>Oxford University Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22954627</article-id><article-id pub-id-type="pmc">3476338</article-id><article-id pub-id-type="doi">10.1093/bioinformatics/bts529</article-id><article-id pub-id-type="publisher-id">bts529</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Papers</subject><subj-group><subject>Gene Expression</subject></subj-group></subj-group></article-categories><title-group><article-title>Genome-wide <italic>in silico</italic> prediction of gene expression</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>McLeay</surname><given-names>Robert C.</given-names></name><xref ref-type="aff" rid="bts529-AFF1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Lesluyes</surname><given-names>Tom</given-names></name><xref ref-type="aff" rid="bts529-AFF1"><sup>1</sup></xref><xref ref-type="aff" rid="bts529-AFF1"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Cuellar Partida</surname><given-names>Gabriel</given-names></name><xref ref-type="aff" rid="bts529-AFF1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Bailey</surname><given-names>Timothy L.</given-names></name><xref ref-type="aff" rid="bts529-AFF1"><sup>1</sup></xref><xref ref-type="corresp" rid="bts529-COR1">*</xref></contrib></contrib-group><aff id="bts529-AFF1"><sup>1</sup>Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia and<sup>2</sup>Département d'Informatique, Université Bordeaux 1 Sciences et Technologies, 33405 Talence, France</aff><author-notes><corresp id="bts529-COR1">*To whom correspondence should be addressed.</corresp><fn><p>Associate Editor: Janet Kelso</p></fn></author-notes><pub-date pub-type="ppub"><day>1</day><month>11</month><year>2012</year></pub-date><pub-date pub-type="epub"><day>6</day><month>9</month><year>2012</year></pub-date><pub-date pub-type="pmc-release"><day>1</day><month>11</month><year>2013</year></pub-date><pmc-comment> PMC Release delay is 12 months and 0 days and was based on the
<volume>28</volume><issue>21</issue><fpage>2789</fpage><lpage>2796</lpage><history><date date-type="received"><day>19</day><month>12</month><year>2011</year></date><date date-type="rev-recd"><day>7</day><month>7</month><year>2012</year></date><date date-type="accepted"><day>23</day><month>8</month><year>2012</year></date></history><permissions><copyright-statement>© The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com</copyright-statement><copyright-year>2012</copyright-year></permissions><abstract><p><bold>Motivation:</bold> Modelling the regulation of gene expression can provide insight into the regulatory roles of individual transcription factors (TFs) and histone modifications. Recently, Ouyang <italic>et al.</italic> in 2009 modelled gene expression levels in mouse embryonic stem (mES) cells using <italic>in vivo</italic> ChIP-seq measurements of TF binding. ChIP-seq TF binding data, however, are tissue-specific and relatively difficult to obtain. This limits the applicability of gene expression models that rely on ChIP-seq TF binding data.</p><p><bold>Results:</bold> In this study, we build regression-based models that relate gene expression to the binding of 12 different TFs, 7 histone modifications and chromatin accessibility (DNase I hypersensitivity) in two different tissues. We find that expression models based on computationally predicted TF binding can achieve similar accuracy to those using <italic>in vivo</italic> TF binding data and that including binding at weak sites is critical for accurate prediction of gene expression. We also find that incorporating histone modification and chromatin accessibility data results in additional accuracy. Surprisingly, we find that models that use <italic>no</italic> TF binding data at all, but only histone modification and chromatin accessibility data, can be as (or more) accurate than those based on <italic>in vivo</italic> TF binding data.</p><p><bold>Availability and implementation:</bold> All scripts, motifs and data presented in this article are available online at <ext-link ext-link-type="uri" xlink:href="http://research.imb.uq.edu.au/t.bailey/supplementary_data/McLeay2011a">http://research.imb.uq.edu.au/t.bailey/supplementary_data/McLeay2011a</ext-link>.</p><p><bold>Contact:</bold><email>t.bailey@imb.uq.edu.au</email></p><p><bold>Supplementary information:</bold><ext-link ext-link-type="uri" xlink:href="http://bioinformatics.oxfordjournals.org/cgi/content/full/bts529/DC1">Supplementary data</ext-link> are available at <italic>Bioinformatics</italic> online.</p></abstract><counts><page-count count="8"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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