Serveur d'exploration MERS

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Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes

Identifieur interne : 000142 ( Pmc/Curation ); précédent : 000141; suivant : 000143

Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes

Auteurs : John A. Lees [Royaume-Uni] ; Minna Vehkala [Finlande] ; Niko V Lim Ki [Finlande] ; Simon R. Harris [Royaume-Uni] ; Claire Chewapreecha [Royaume-Uni] ; Nicholas J. Croucher [Royaume-Uni] ; Pekka Marttinen [Finlande] ; Mark R. Davies [Australie] ; Andrew C. Steer [Australie] ; Steven Y. C. Tong [Australie] ; Antti Honkela [Finlande] ; Julian Parkhill [Royaume-Uni] ; Stephen D. Bentley [Royaume-Uni] ; Jukka Corander [Royaume-Uni, Finlande, Norvège]

Source :

RBID : PMC:5028413

Abstract

Bacterial genomes vary extensively in terms of both gene content and gene sequence. This plasticity hampers the use of traditional SNP-based methods for identifying all genetic associations with phenotypic variation. Here we introduce a computationally scalable and widely applicable statistical method (SEER) for the identification of sequence elements that are significantly enriched in a phenotype of interest. SEER is applicable to tens of thousands of genomes by counting variable-length k-mers using a distributed string-mining algorithm. Robust options are provided for association analysis that also correct for the clonal population structure of bacteria. Using large collections of genomes of the major human pathogens Streptococcus pneumoniae and Streptococcus pyogenes, SEER identifies relevant previously characterized resistance determinants for several antibiotics and discovers potential novel factors related to the invasiveness of S. pyogenes. We thus demonstrate that our method can answer important biologically and medically relevant questions.


Url:
DOI: 10.1038/ncomms12797
PubMed: 27633831
PubMed Central: 5028413

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PMC:5028413

Le document en format XML

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<name sortKey="V Lim Ki, Niko" sort="V Lim Ki, Niko" uniqKey="V Lim Ki N" first="Niko" last="V Lim Ki">Niko V Lim Ki</name>
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<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<name sortKey="Croucher, Nicholas J" sort="Croucher, Nicholas J" uniqKey="Croucher N" first="Nicholas J." last="Croucher">Nicholas J. Croucher</name>
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<institution>Department of Infectious Disease Epidemiology, Imperial College</institution>
, London W2 1NY,
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</nlm:aff>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<name sortKey="Marttinen, Pekka" sort="Marttinen, Pekka" uniqKey="Marttinen P" first="Pekka" last="Marttinen">Pekka Marttinen</name>
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<nlm:aff id="a6">
<institution>Department of Computer Science, Aalto University</institution>
, Espoo FI-00076,
<country>Finland</country>
</nlm:aff>
<country xml:lang="fr">Finlande</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<institution>Helsinki Institute of Information Technology HIIT, Department of Computer Science, Aalto University</institution>
, Espoo FI-00076,
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</nlm:aff>
<country xml:lang="fr">Finlande</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<name sortKey="Davies, Mark R" sort="Davies, Mark R" uniqKey="Davies M" first="Mark R." last="Davies">Mark R. Davies</name>
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<nlm:aff id="a8">
<institution>Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne</institution>
, Melbourne, Victoria 3010,
<country>Australia</country>
</nlm:aff>
<country xml:lang="fr">Australie</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<name sortKey="Steer, Andrew C" sort="Steer, Andrew C" uniqKey="Steer A" first="Andrew C." last="Steer">Andrew C. Steer</name>
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<institution>Centre for International Child Health, Department of Paediatrics, University of Melbourne</institution>
, Melbourne, Victoria 3052,
<country>Australia</country>
</nlm:aff>
<country xml:lang="fr">Australie</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1">
<nlm:aff id="a10">
<institution>Group A Streptococcal Research Group, Murdoch Children's Research Institute</institution>
, Parkville, Victoria 3052,
<country>Australia</country>
</nlm:aff>
<country xml:lang="fr">Australie</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<name sortKey="Tong, Steven Y C" sort="Tong, Steven Y C" uniqKey="Tong S" first="Steven Y. C." last="Tong">Steven Y. C. Tong</name>
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<nlm:aff id="a11">
<institution>Menzies School of Health Research</institution>
, Darwin, Northern Territory 0811,
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</nlm:aff>
<country xml:lang="fr">Australie</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
</affiliation>
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<name sortKey="Honkela, Antti" sort="Honkela, Antti" uniqKey="Honkela A" first="Antti" last="Honkela">Antti Honkela</name>
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<institution>Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki</institution>
, Helsinki FI-00014,
<country>Finland</country>
</nlm:aff>
<country xml:lang="fr">Finlande</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<name sortKey="Parkhill, Julian" sort="Parkhill, Julian" uniqKey="Parkhill J" first="Julian" last="Parkhill">Julian Parkhill</name>
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<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<name sortKey="Corander, Jukka" sort="Corander, Jukka" uniqKey="Corander J" first="Jukka" last="Corander">Jukka Corander</name>
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</nlm:aff>
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<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<nlm:aff id="a2">
<institution>Department of Mathematics and Statistics, University of Helsinki</institution>
, Helsinki FI-00014,
<country>Finland</country>
</nlm:aff>
<country xml:lang="fr">Finlande</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1">
<nlm:aff id="a13">
<institution>Department of Biostatistics, University of Oslo</institution>
, 0317 Oslo,
<country>Norway</country>
</nlm:aff>
<country xml:lang="fr">Norvège</country>
<wicri:regionArea># see nlm:aff country strict</wicri:regionArea>
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<title level="j">Nature Communications</title>
<idno type="eISSN">2041-1723</idno>
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<p>Bacterial genomes vary extensively in terms of both gene content and gene sequence. This plasticity hampers the use of traditional SNP-based methods for identifying all genetic associations with phenotypic variation. Here we introduce a computationally scalable and widely applicable statistical method (SEER) for the identification of sequence elements that are significantly enriched in a phenotype of interest. SEER is applicable to tens of thousands of genomes by counting variable-length k-mers using a distributed string-mining algorithm. Robust options are provided for association analysis that also correct for the clonal population structure of bacteria. Using large collections of genomes of the major human pathogens
<italic>Streptococcus pneumoniae</italic>
and
<italic>Streptococcus pyogenes</italic>
, SEER identifies relevant previously characterized resistance determinants for several antibiotics and discovers potential novel factors related to the invasiveness of
<italic>S. pyogenes</italic>
. We thus demonstrate that our method can answer important biologically and medically relevant questions.</p>
</div>
</front>
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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">Nat Commun</journal-id>
<journal-id journal-id-type="iso-abbrev">Nat Commun</journal-id>
<journal-title-group>
<journal-title>Nature Communications</journal-title>
</journal-title-group>
<issn pub-type="epub">2041-1723</issn>
<publisher>
<publisher-name>Nature Publishing Group</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">27633831</article-id>
<article-id pub-id-type="pmc">5028413</article-id>
<article-id pub-id-type="pii">ncomms12797</article-id>
<article-id pub-id-type="doi">10.1038/ncomms12797</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Lees</surname>
<given-names>John A.</given-names>
</name>
<xref ref-type="aff" rid="a1">1</xref>
<xref ref-type="author-notes" rid="n1">*</xref>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0001-5360-1254</contrib-id>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Vehkala</surname>
<given-names>Minna</given-names>
</name>
<xref ref-type="aff" rid="a2">2</xref>
<xref ref-type="author-notes" rid="n1">*</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Välimäki</surname>
<given-names>Niko</given-names>
</name>
<xref ref-type="aff" rid="a3">3</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Harris</surname>
<given-names>Simon R.</given-names>
</name>
<xref ref-type="aff" rid="a1">1</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Chewapreecha</surname>
<given-names>Claire</given-names>
</name>
<xref ref-type="aff" rid="a4">4</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Croucher</surname>
<given-names>Nicholas J.</given-names>
</name>
<xref ref-type="aff" rid="a5">5</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Marttinen</surname>
<given-names>Pekka</given-names>
</name>
<xref ref-type="aff" rid="a6">6</xref>
<xref ref-type="aff" rid="a7">7</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Davies</surname>
<given-names>Mark R.</given-names>
</name>
<xref ref-type="aff" rid="a8">8</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Steer</surname>
<given-names>Andrew C.</given-names>
</name>
<xref ref-type="aff" rid="a9">9</xref>
<xref ref-type="aff" rid="a10">10</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Tong</surname>
<given-names>Steven Y. C.</given-names>
</name>
<xref ref-type="aff" rid="a11">11</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Honkela</surname>
<given-names>Antti</given-names>
</name>
<xref ref-type="aff" rid="a12">12</xref>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0001-9193-8093</contrib-id>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Parkhill</surname>
<given-names>Julian</given-names>
</name>
<xref ref-type="aff" rid="a1">1</xref>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0002-7069-5958</contrib-id>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Bentley</surname>
<given-names>Stephen D.</given-names>
</name>
<xref ref-type="aff" rid="a1">1</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Corander</surname>
<given-names>Jukka</given-names>
</name>
<xref ref-type="corresp" rid="c1">a</xref>
<xref ref-type="aff" rid="a1">1</xref>
<xref ref-type="aff" rid="a2">2</xref>
<xref ref-type="aff" rid="a13">13</xref>
</contrib>
<aff id="a1">
<label>1</label>
<institution>Pathogen Genomics, Wellcome Trust Sanger Institute</institution>
, Cambridge CB10 1SA,
<country>UK</country>
</aff>
<aff id="a2">
<label>2</label>
<institution>Department of Mathematics and Statistics, University of Helsinki</institution>
, Helsinki FI-00014,
<country>Finland</country>
</aff>
<aff id="a3">
<label>3</label>
<institution>Department of Medical and Clinical Genetics, Genome-Scale Biology Research Program, University of Helsinki</institution>
, Helsinki FI-00014,
<country>Finland</country>
</aff>
<aff id="a4">
<label>4</label>
<institution>Department of Medicine, University of Cambridge</institution>
, Cambridge CB2 0SP,
<country>UK</country>
</aff>
<aff id="a5">
<label>5</label>
<institution>Department of Infectious Disease Epidemiology, Imperial College</institution>
, London W2 1NY,
<country>UK</country>
</aff>
<aff id="a6">
<label>6</label>
<institution>Department of Computer Science, Aalto University</institution>
, Espoo FI-00076,
<country>Finland</country>
</aff>
<aff id="a7">
<label>7</label>
<institution>Helsinki Institute of Information Technology HIIT, Department of Computer Science, Aalto University</institution>
, Espoo FI-00076,
<country>Finland</country>
</aff>
<aff id="a8">
<label>8</label>
<institution>Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne</institution>
, Melbourne, Victoria 3010,
<country>Australia</country>
</aff>
<aff id="a9">
<label>9</label>
<institution>Centre for International Child Health, Department of Paediatrics, University of Melbourne</institution>
, Melbourne, Victoria 3052,
<country>Australia</country>
</aff>
<aff id="a10">
<label>10</label>
<institution>Group A Streptococcal Research Group, Murdoch Children's Research Institute</institution>
, Parkville, Victoria 3052,
<country>Australia</country>
</aff>
<aff id="a11">
<label>11</label>
<institution>Menzies School of Health Research</institution>
, Darwin, Northern Territory 0811,
<country>Australia</country>
</aff>
<aff id="a12">
<label>12</label>
<institution>Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki</institution>
, Helsinki FI-00014,
<country>Finland</country>
</aff>
<aff id="a13">
<label>13</label>
<institution>Department of Biostatistics, University of Oslo</institution>
, 0317 Oslo,
<country>Norway</country>
</aff>
</contrib-group>
<author-notes>
<corresp id="c1">
<label>a</label>
<email>jukka.corander@helsinki.fi</email>
</corresp>
<fn id="n1">
<label>*</label>
<p>These authors contributed equally to this work.</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>16</day>
<month>09</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="collection">
<year>2016</year>
</pub-date>
<volume>7</volume>
<elocation-id>12797</elocation-id>
<history>
<date date-type="received">
<day>05</day>
<month>01</month>
<year>2016</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>07</month>
<year>2016</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright © 2016, The Author(s)</copyright-statement>
<copyright-year>2016</copyright-year>
<copyright-holder>The Author(s)</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0/">
<pmc-comment>author-paid</pmc-comment>
<license-p>This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
</license-p>
</license>
</permissions>
<abstract>
<p>Bacterial genomes vary extensively in terms of both gene content and gene sequence. This plasticity hampers the use of traditional SNP-based methods for identifying all genetic associations with phenotypic variation. Here we introduce a computationally scalable and widely applicable statistical method (SEER) for the identification of sequence elements that are significantly enriched in a phenotype of interest. SEER is applicable to tens of thousands of genomes by counting variable-length k-mers using a distributed string-mining algorithm. Robust options are provided for association analysis that also correct for the clonal population structure of bacteria. Using large collections of genomes of the major human pathogens
<italic>Streptococcus pneumoniae</italic>
and
<italic>Streptococcus pyogenes</italic>
, SEER identifies relevant previously characterized resistance determinants for several antibiotics and discovers potential novel factors related to the invasiveness of
<italic>S. pyogenes</italic>
. We thus demonstrate that our method can answer important biologically and medically relevant questions.</p>
</abstract>
<abstract abstract-type="web-summary">
<p>
<inline-graphic id="i1" xlink:href="ncomms12797-i1.jpg"></inline-graphic>
Plasticity and clonal population structure in bacterial genomes can hinder traditional SNP-based genetic association studies. Here, Corander and colleagues present a method to identify variable-length sequence elements enriched in a phenotype of interest, and demonstrate its use in human pathogens.</p>
</abstract>
</article-meta>
</front>
<floats-group>
<fig id="f1">
<label>Figure 1</label>
<caption>
<title>Power to find associations versus number of samples.</title>
<p>Using simulations and subsamples of the population as described in the methods, power for (
<bold>a</bold>
) detecting gene presence/absence at different odds ratios (
<bold>b</bold>
) using all informative k-mers versus a single length (
<bold>c</bold>
) detecting k-mers near, in the correct gene, or containing the causal variant for trimethoprim resistance. All curves are logistic fits to the mean power over 100 subsamples.</p>
</caption>
<graphic xlink:href="ncomms12797-f1"></graphic>
</fig>
<fig id="f2">
<label>Figure 2</label>
<caption>
<title>Fine mapping trimethoprim resistance.</title>
<p>The locus pictured contains 72 significant k-mers, the most of any gene cluster. Coverage over the locus is pictured at the bottom of the figure. Shown above the genes are high-quality missense SNPs, plotted using their
<italic>P</italic>
value for affecting protein function as predicted by SIFT. Scale bar is 200 base pairs.</p>
</caption>
<graphic xlink:href="ncomms12797-f2"></graphic>
</fig>
<table-wrap position="float" id="t1">
<label>Table 1</label>
<caption>
<title>K-mers associated with antibiotic resistance.</title>
</caption>
<table frame="hsides" rules="groups" border="1">
<colgroup>
<col align="left"></col>
<col align="center"></col>
<col align="center"></col>
<col align="center"></col>
<col align="left"></col>
<col align="left"></col>
</colgroup>
<thead valign="bottom">
<tr>
<th rowspan="2" align="left" valign="top" charoff="50">
<bold>Antibiotic</bold>
</th>
<th rowspan="2" align="center" valign="top" charoff="50">
<bold>Resistant samples</bold>
</th>
<th colspan="4" align="center" valign="top" charoff="50">
<bold>Number of significant k-mers</bold>
<hr></hr>
</th>
</tr>
<tr>
<th align="center" valign="top" charoff="50">
<bold>Total</bold>
</th>
<th align="center" valign="top" charoff="50">
<bold>Mapped to reference</bold>
</th>
<th align="left" valign="top" charoff="50">
<bold>Highest coverage annotation</bold>
</th>
<th align="left" valign="top" charoff="50">
<bold>Causal element</bold>
</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left" valign="top" charoff="50">Chloramphenicol</td>
<td align="center" valign="top" charoff="50">204 (7%)</td>
<td align="center" valign="top" charoff="50">1,526</td>
<td align="center" valign="top" charoff="50">1,526</td>
<td align="left" valign="top" charoff="50">1,508—ICE</td>
<td align="left" valign="top" charoff="50">166—
<italic>cat</italic>
</td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">288—ORF (UniParc B8ZK82)</td>
<td align="left" valign="top" charoff="50"> </td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">206—
<italic>rep</italic>
</td>
<td align="left" valign="top" charoff="50"> </td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">
<bold>166—
<italic>cat</italic>
</bold>
</td>
<td align="left" valign="top" charoff="50"> </td>
</tr>
<tr>
<td align="left" valign="top" charoff="50">Erythromycin</td>
<td align="center" valign="top" charoff="50">803 (26%)</td>
<td align="center" valign="top" charoff="50">1,154</td>
<td align="center" valign="top" charoff="50">112</td>
<td align="left" valign="top" charoff="50">10—permease (UniParc B8ZKV5)</td>
<td align="left" valign="top" charoff="50">4—mega element</td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">8—
<italic>prfC</italic>
</td>
<td align="left" valign="top" charoff="50">2—
<italic>mef</italic>
</td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">6—
<italic>gatA</italic>
</td>
<td align="left" valign="top" charoff="50">2—omega element</td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">4—ICE</td>
<td align="left" valign="top" charoff="50"> </td>
</tr>
<tr>
<td align="left" valign="top" charoff="50">β−lactams</td>
<td align="center" valign="top" charoff="50">1,563 (51%)</td>
<td align="center" valign="top" charoff="50">23,876</td>
<td align="center" valign="top" charoff="50">17,453</td>
<td align="left" valign="top" charoff="50">381—ICE</td>
<td align="left" valign="top" charoff="50">47—
<italic>pbp2x</italic>
</td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">145—prophage MM1</td>
<td align="left" valign="top" charoff="50">20—
<italic>pbp2b</italic>
</td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">50—SPN23F15110 (UniParc B8ZLE7)</td>
<td align="left" valign="top" charoff="50">8—
<italic>pbp1a</italic>
</td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">49—ICE
<italic>orf16</italic>
</td>
<td align="left" valign="top" charoff="50"> </td>
</tr>
<tr>
<td align="left" valign="top" charoff="50">Tetracycline</td>
<td align="center" valign="top" charoff="50">1,958 (64%)</td>
<td align="center" valign="top" charoff="50">962</td>
<td align="center" valign="top" charoff="50">962</td>
<td align="left" valign="top" charoff="50">962—ICE</td>
<td align="left" valign="top" charoff="50">96—
<italic>tetM</italic>
</td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">136—ICE
<italic>orf16</italic>
</td>
<td align="left" valign="top" charoff="50"> </td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">121—ICE
<italic>orf15</italic>
</td>
<td align="left" valign="top" charoff="50"> </td>
</tr>
<tr>
<td align="left" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="center" valign="top" charoff="50"> </td>
<td align="left" valign="top" charoff="50">
<bold>96—
<italic>tetM</italic>
</bold>
</td>
<td align="left" valign="top" charoff="50"> </td>
</tr>
<tr>
<td align="left" valign="top" charoff="50">Trimethoprim</td>
<td align="center" valign="top" charoff="50">2,553 (83%)</td>
<td align="center" valign="top" charoff="50">2,639</td>
<td align="center" valign="top" charoff="50">210</td>
<td align="left" valign="top" charoff="50">
<bold>21—
<italic>dyr</italic>
</bold>
</td>
<td align="left" valign="top" charoff="50">21—
<italic>dyr</italic>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="t1-fn1">
<p>ICE, integrative conjugative element</p>
</fn>
<fn id="t1-fn2">
<p>Results from SEER for antibiotic resistance binary outcome on a population of 3069
<italic>S. pneumoniae</italic>
. Significant k-mers are first interpreted by mapping to the ATCC 700669 reference genome. Up to the first four highest covered annotations are shown, and if the known mechanism is amongst these it is highlighted in bold. The ICE is the top hit in three analyses, as it carries multiple drug-resistance elements and is commonly found in multi-drug resistant strains
<xref ref-type="bibr" rid="b16">16</xref>
. The distribution of phenotype across the phylogeny is shown in
<xref ref-type="supplementary-material" rid="S1">Supplementary Fig. 4</xref>
.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</floats-group>
</pmc>
</record>

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