Low-dimensional clustering detects incipient dominant influenza strain clusters
Identifieur interne : 000925 ( Pmc/Checkpoint ); précédent : 000924; suivant : 000926Low-dimensional clustering detects incipient dominant influenza strain clusters
Auteurs : Jiankui He ; Michael W. Deem [États-Unis]Source :
- Protein Engineering, Design and Selection [ 1741-0126 ] ; 2010.
Abstract
Influenza has been circulating in the human population and has caused three pandemics in the last century (1918 H1N1, 1957 H2N2 and 1968 H3N2). The 2009 A(H1N1) was classified by World Health Organization as the fourth pandemic. Influenza has a high evolution rate, which makes vaccine design challenging. We here consider an approach for early detection of new dominant strains. By clustering the 2009 A(H1N1) sequence data, we found two main clusters. We then define a metric to detect the emergence of dominant strains. We show on historical H3N2 data that this method is able to identify a cluster around an incipient dominant strain before it becomes dominant. For example, for H3N2 as of 30 March 2009, the method detects the cluster for the new A/British Columbia/RV1222/2009 strain. This strain detection tool would appear to be useful for annual influenza vaccine selection.
Url:
DOI: 10.1093/protein/gzq078
PubMed: 21036781
PubMed Central: 2978544
Affiliations:
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Deem</name><affiliation><nlm:aff id="af1"><addr-line>Department of Physics & Astronomy</addr-line>,<institution>Rice University</institution></nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af2"><addr-line>Department of Bioengineering</addr-line>,<institution>Rice University</institution>,<addr-line>Houston, TX</addr-line>,<country>USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">21036781</idno><idno type="pmc">2978544</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2978544</idno><idno type="RBID">PMC:2978544</idno><idno type="doi">10.1093/protein/gzq078</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">000C25</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000C25</idno><idno type="wicri:Area/Pmc/Curation">000C25</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000C25</idno><idno type="wicri:Area/Pmc/Checkpoint">000925</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000925</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Low-dimensional clustering detects incipient dominant influenza strain clusters</title><author><name sortKey="He, Jiankui" sort="He, Jiankui" uniqKey="He J" first="Jiankui" last="He">Jiankui He</name><affiliation><nlm:aff id="af1"><addr-line>Department of Physics & Astronomy</addr-line>,<institution>Rice University</institution></nlm:aff></affiliation></author><author><name sortKey="Deem, Michael W" sort="Deem, Michael W" uniqKey="Deem M" first="Michael W." last="Deem">Michael W. Deem</name><affiliation><nlm:aff id="af1"><addr-line>Department of Physics & Astronomy</addr-line>,<institution>Rice University</institution></nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af2"><addr-line>Department of Bioengineering</addr-line>,<institution>Rice University</institution>,<addr-line>Houston, TX</addr-line>,<country>USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author></analytic><series><title level="j">Protein Engineering, Design and Selection</title><idno type="ISSN">1741-0126</idno><idno type="eISSN">1741-0134</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Influenza has been circulating in the human population and has caused three pandemics in the last century (1918 H1N1, 1957 H2N2 and 1968 H3N2). The 2009 A(H1N1) was classified by World Health Organization as the fourth pandemic. Influenza has a high evolution rate, which makes vaccine design challenging. We here consider an approach for early detection of new dominant strains. By clustering the 2009 A(H1N1) sequence data, we found two main clusters. We then define a metric to detect the emergence of dominant strains. We show on historical H3N2 data that this method is able to identify a cluster around an incipient dominant strain before it becomes dominant. For example, for H3N2 as of 30 March 2009, the method detects the cluster for the new A/British Columbia/RV1222/2009 strain. This strain detection tool would appear to be useful for annual influenza vaccine selection.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Bao, Y" uniqKey="Bao Y">Y. Bao</name></author><author><name sortKey="Bolotov, P" uniqKey="Bolotov P">P. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Protein Eng Des Sel</journal-id><journal-id journal-id-type="publisher-id">proeng</journal-id><journal-id journal-id-type="hwp">proeng</journal-id><journal-title-group><journal-title>Protein Engineering, Design and Selection</journal-title></journal-title-group><issn pub-type="ppub">1741-0126</issn><issn pub-type="epub">1741-0134</issn><publisher><publisher-name>Oxford University Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">21036781</article-id><article-id pub-id-type="pmc">2978544</article-id><article-id pub-id-type="doi">10.1093/protein/gzq078</article-id><article-id pub-id-type="publisher-id">gzq078</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>Low-dimensional clustering detects incipient dominant influenza strain clusters</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>He</surname><given-names>Jiankui</given-names></name><xref ref-type="aff" rid="af1">1</xref></contrib><contrib contrib-type="author"><name><surname>Deem</surname><given-names>Michael W.</given-names></name><xref ref-type="aff" rid="af1">1</xref><xref ref-type="aff" rid="af2">2</xref><xref ref-type="corresp" rid="cor3">3</xref></contrib></contrib-group><aff id="af1"><label>1</label><addr-line>Department of Physics & Astronomy</addr-line>,<institution>Rice University</institution></aff><aff id="af2"><label>2</label><addr-line>Department of Bioengineering</addr-line>,<institution>Rice University</institution>,<addr-line>Houston, TX</addr-line>,<country>USA</country></aff><author-notes><corresp id="cor3"><label>3</label>To whom correspondence should be addressed. E-mail: <email>mwdeem@rice.edu</email></corresp><fn fn-type="con"><p>Edited by Devarajan Thirumalai</p></fn></author-notes><pub-date pub-type="ppub"><month>12</month><year>2010</year></pub-date><pub-date pub-type="epub"><day>29</day><month>10</month><year>2010</year></pub-date><pub-date pub-type="pmc-release"><day>29</day><month>10</month><year>2010</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<volume>23</volume><issue>12</issue><fpage>935</fpage><lpage>946</lpage><history><date date-type="received"><day>1</day><month>9</month><year>2010</year></date><date date-type="rev-recd"><day>1</day><month>9</month><year>2010</year></date><date date-type="accepted"><day>22</day><month>9</month><year>2010</year></date></history><permissions><copyright-statement>© The Author 2010. Published by Oxford University Press.</copyright-statement><copyright-year>2010</copyright-year><license license-type="creative-commons" xlink:href="http://creativecommons.org/licenses/by-nc/2.5/"><license-p><pmc-comment>CREATIVE COMMONS</pmc-comment>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/2.5">http://creativecommons.org/licenses/by-nc/2.5</ext-link>), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type="pdf" xlink:type="simple" xlink:href="gzq078.pdf"></self-uri><abstract><p>Influenza has been circulating in the human population and has caused three pandemics in the last century (1918 H1N1, 1957 H2N2 and 1968 H3N2). The 2009 A(H1N1) was classified by World Health Organization as the fourth pandemic. Influenza has a high evolution rate, which makes vaccine design challenging. We here consider an approach for early detection of new dominant strains. By clustering the 2009 A(H1N1) sequence data, we found two main clusters. We then define a metric to detect the emergence of dominant strains. We show on historical H3N2 data that this method is able to identify a cluster around an incipient dominant strain before it becomes dominant. For example, for H3N2 as of 30 March 2009, the method detects the cluster for the new A/British Columbia/RV1222/2009 strain. This strain detection tool would appear to be useful for annual influenza vaccine selection.</p></abstract><kwd-group><kwd>clustering</kwd><kwd>H1N1</kwd><kwd>H3N2</kwd><kwd>influenza</kwd></kwd-group></article-meta></front></pmc><affiliations><list><country><li>États-Unis</li></country></list><tree><noCountry><name sortKey="He, Jiankui" sort="He, Jiankui" uniqKey="He J" first="Jiankui" last="He">Jiankui He</name></noCountry><country name="États-Unis"><noRegion><name sortKey="Deem, Michael W" sort="Deem, Michael W" uniqKey="Deem M" first="Michael W." last="Deem">Michael W. Deem</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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