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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Assessment of Microbial Communities by Graph Partitioning in a Study of Soil Fungi in Two Alpine Meadows<xref ref-type="fn" rid="fn2">▿</xref> <xref ref-type="fn" rid="fn1">†</xref></title><author><name sortKey="Zinger, L" sort="Zinger, L" uniqKey="Zinger L" first="L." last="Zinger">L. Zinger</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Coissac, E" sort="Coissac, E" uniqKey="Coissac E" first="E." last="Coissac">E. Coissac</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Choler, P" sort="Choler, P" uniqKey="Choler P" first="P." last="Choler">P. Choler</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Geremia, R A" sort="Geremia, R A" uniqKey="Geremia R" first="R. A." last="Geremia">R. A. Geremia</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">19617385</idno><idno type="pmc">2747849</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2747849</idno><idno type="RBID">PMC:2747849</idno><idno type="doi">10.1128/AEM.00748-09</idno><date when="2009">2009</date><idno type="wicri:Area/Pmc/Corpus">000F57</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000F57</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Assessment of Microbial Communities by Graph Partitioning in a Study of Soil Fungi in Two Alpine Meadows<xref ref-type="fn" rid="fn2">▿</xref> <xref ref-type="fn" rid="fn1">†</xref></title><author><name sortKey="Zinger, L" sort="Zinger, L" uniqKey="Zinger L" first="L." last="Zinger">L. Zinger</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Coissac, E" sort="Coissac, E" uniqKey="Coissac E" first="E." last="Coissac">E. Coissac</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Choler, P" sort="Choler, P" uniqKey="Choler P" first="P." last="Choler">P. Choler</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Geremia, R A" sort="Geremia, R A" uniqKey="Geremia R" first="R. A." last="Geremia">R. A. Geremia</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">Applied and Environmental Microbiology</title><idno type="ISSN">0099-2240</idno><idno type="eISSN">1098-5336</idno><imprint><date when="2009">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Understanding how microbial community structure and diversity respond to environmental conditions is one of the main challenges in environmental microbiology. However, there is often confusion between determining the phylogenetic structure of microbial communities and assessing the distribution and diversity of molecular operational taxonomic units (MOTUs) in these communities. This has led to the use of sequence analysis tools such as multiple alignments and hierarchical clustering that are not adapted to the analysis of large and diverse data sets and not always justified for characterization of MOTUs. Here, we developed an approach combining a pairwise alignment algorithm and graph partitioning by using MCL (Markov clustering) in order to generate discrete groups for nuclear large-subunit rRNA gene and internal transcript spacer 1 sequence data sets obtained from a yearly monitoring study of two spatially close but ecologically contrasting alpine soils (namely, early and late snowmelt locations). We compared MCL with a classical single-linkage method (Ccomps) and showed that MCL reduced bias such as the chaining effect. Using MCL, we characterized fungal communities in early and late snowmelt locations. We found contrasting distributions of MOTUs in the two soils, suggesting that there is a high level of habitat filtering in the assembly of alpine soil fungal communities. However, few MOTUs were specific to one location.</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">Appl Environ Microbiol</journal-id><journal-id journal-id-type="publisher-id">aem</journal-id><journal-title>Applied and Environmental Microbiology</journal-title><issn pub-type="ppub">0099-2240</issn><issn pub-type="epub">1098-5336</issn><publisher><publisher-name>American Society for Microbiology (ASM)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">19617385</article-id><article-id pub-id-type="pmc">2747849</article-id><article-id pub-id-type="publisher-id">0748-09</article-id><article-id pub-id-type="doi">10.1128/AEM.00748-09</article-id><article-categories><subj-group subj-group-type="heading"><subject>Environmental Microbiology</subject></subj-group></article-categories><title-group><article-title>Assessment of Microbial Communities by Graph Partitioning in a Study of Soil Fungi in Two Alpine Meadows<xref ref-type="fn" rid="fn2">▿</xref> <xref ref-type="fn" rid="fn1">†</xref></article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Zinger</surname><given-names>L.</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="corresp" rid="cor1">*</xref></contrib><contrib contrib-type="author"><name><surname>Coissac</surname><given-names>E.</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Choler</surname><given-names>P.</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff1">2</xref><xref ref-type="aff" rid="aff1">3</xref></contrib><contrib contrib-type="author"><name><surname>Geremia</surname><given-names>R. A.</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib></contrib-group><aff id="aff1">Laboratoire d'Ecologie Alpine, CNRS UMR 5553, Université de Grenoble, BP 53, F-38041 Grenoble Cedex 09, France,<label>1</label> Station Alpine J. Fourier CNRS UMS 2925, Université de Grenoble, F-38041 Grenoble, France,<label>2</label> CSIRO Marine and Atmospheric Research, Canberra, Australia<label>3</label></aff><author-notes><fn id="cor1"><label>*</label><p>Corresponding author. Mailing address: Laboratoire d'Ecologie Alpine UJF/CNRS, Université de Grenoble, 2233, rue de la Piscine, BP 53 Bat D Biologie, Grenoble F-38041, France. Phone: 33-4-76-51-44-59. Fax: 33-4-76-51-42-79. E-mail: <email>lucie@zinger.fr</email></p></fn></author-notes><pub-date pub-type="ppub"><month>9</month><year>2009</year></pub-date><pub-date pub-type="epub"><day>17</day><month>7</month><year>2009</year></pub-date><volume>75</volume><issue>18</issue><fpage>5863</fpage><lpage>5870</lpage><history><date date-type="received"><day>2</day><month>4</month><year>2009</year></date><date date-type="accepted"><day>10</day><month>7</month><year>2009</year></date></history><permissions><copyright-statement>Copyright © 2009, American Society for Microbiology</copyright-statement></permissions><self-uri xlink:title="pdf" xlink:href="zam01809005863.pdf"></self-uri><abstract><p>Understanding how microbial community structure and diversity respond to environmental conditions is one of the main challenges in environmental microbiology. However, there is often confusion between determining the phylogenetic structure of microbial communities and assessing the distribution and diversity of molecular operational taxonomic units (MOTUs) in these communities. This has led to the use of sequence analysis tools such as multiple alignments and hierarchical clustering that are not adapted to the analysis of large and diverse data sets and not always justified for characterization of MOTUs. Here, we developed an approach combining a pairwise alignment algorithm and graph partitioning by using MCL (Markov clustering) in order to generate discrete groups for nuclear large-subunit rRNA gene and internal transcript spacer 1 sequence data sets obtained from a yearly monitoring study of two spatially close but ecologically contrasting alpine soils (namely, early and late snowmelt locations). We compared MCL with a classical single-linkage method (Ccomps) and showed that MCL reduced bias such as the chaining effect. Using MCL, we characterized fungal communities in early and late snowmelt locations. We found contrasting distributions of MOTUs in the two soils, suggesting that there is a high level of habitat filtering in the assembly of alpine soil fungal communities. However, few MOTUs were specific to one location.</p></abstract></article-meta></front><floats-wrap><fig position="float" id="f1"><label>FIG. 1.</label><caption><p>Weighted nondirected graph showing the clustering results for a data subset subjected to (A) Ccomps and (B) MCL clustering. Each node represents a clone, and each edge represents a similarity value of at least 98% for two clones. The different shades of gray of labels illustrate the subgroups formed by Ccomps and MCL. The graphic was drawn with fdp (<ext-link ext-link-type="uri" xlink:href="www.graphviz.org">www.graphviz.org</ext-link>).</p></caption><graphic xlink:href="zam0180902660001"></graphic></fig><fig position="float" id="f2"><label>FIG. 2.</label><caption><p>Comparison of MOTU abundance according to the DNA markers and the locations studied. (A) Abundance of MOTUs belonging to common fungal orders for the LSU and ITS1 markers. (B) Abundance of MOTUs for the LSM and ESM locations. The axes are log<sub>10</sub> scales. The nonparametric Kendall tau rank correlation coefficient revealed significant correspondence of the abundances of fungal orders for the DNA marker used and negative correspondence of MOTU abundances for the two locations studied.</p></caption><graphic xlink:href="zam0180902660002"></graphic></fig><fig position="float" id="f3"><label>FIG. 3.</label><caption><p>Spatial and temporal distribution of the main MOTUs. (a) LSU data set; (b) ITS data set. The columns indicate the 30 most abundant MOTUs. The lines indicate samples. The size of a square indicates the percentage of clones in the sample. A, <italic>Ascomycota</italic>; B, <italic>Basidiomycota</italic>; Z, <italic>Zygomycota</italic>; U, unclassified fungi.</p></caption><graphic xlink:href="zam0180902660003"></graphic></fig><table-wrap position="float" id="t1"><label>TABLE 1.</label><caption><p>Comparison of LSU and ITS1 graphs partitioned by Ccomps and MCL using different similarity thresholds</p></caption><table frame="hsides" rules="groups"><thead><tr><th colspan="1" rowspan="2" align="center" valign="middle">DNA region</th><th colspan="1" rowspan="2" align="center" valign="middle">Clustering method</th><th colspan="1" rowspan="2" align="center" valign="middle">Similarity threshold (%)</th><th colspan="3" rowspan="1" align="center" valign="bottom">Clustering performance
<hr></hr></th></tr><tr><th colspan="1" rowspan="1" align="center" valign="bottom">No. of groups<xref ref-type="table-fn" rid="t1fn1"><italic>a</italic></xref></th><th colspan="1" rowspan="1" align="center" valign="bottom">No. of singletons<xref ref-type="table-fn" rid="t1fn1"><italic>a</italic></xref></th><th colspan="1" rowspan="1" align="center" valign="bottom">Cohesion index (10<sup>3</sup>, mean ± SD)<xref ref-type="table-fn" rid="t1fn2"><italic>b</italic></xref></th></tr></thead><tbody><tr><td colspan="1" rowspan="1" align="left" valign="top">LSU</td><td colspan="1" rowspan="1" align="center" valign="top">Ccomps</td><td colspan="1" rowspan="1" align="char" char="." valign="top">95</td><td colspan="1" rowspan="1" align="char" char="." valign="top">11</td><td colspan="1" rowspan="1" align="char" char="." valign="top">23</td><td colspan="1" rowspan="1" align="center" valign="top">17 ± 33 (5)</td></tr><tr><td colspan="1" rowspan="1" align="left" valign="top"></td><td colspan="1" rowspan="1" align="center" valign="top"></td><td colspan="1" rowspan="1" align="char" char="." valign="top">97</td><td colspan="1" rowspan="1" align="char" char="." valign="top">34</td><td colspan="1" rowspan="1" align="char" char="." valign="top">63</td><td colspan="1" rowspan="1" align="center" valign="top">3 ± 7 (27)</td></tr><tr><td colspan="1" rowspan="1" align="left" valign="top"></td><td colspan="1" rowspan="1" align="center" valign="top"></td><td colspan="1" rowspan="1" align="char" char="." valign="top">98</td><td colspan="1" rowspan="1" align="char" char="." valign="top">56</td><td colspan="1" rowspan="1" align="char" char="." valign="top">115</td><td colspan="1" rowspan="1" align="center" valign="top">0.09 ± 0.16 (36)</td></tr><tr><td colspan="1" rowspan="1" align="left" valign="top"></td><td colspan="1" rowspan="1" align="center" valign="top">MCL</td><td colspan="1" rowspan="1" align="char" char="." valign="top">95</td><td colspan="1" rowspan="1" align="char" char="." valign="top">56</td><td colspan="1" rowspan="1" align="char" char="." valign="top">124</td><td colspan="1" rowspan="1" align="center" valign="top">245 ± 97 (25)</td></tr><tr><td colspan="1" rowspan="1" align="left" valign="top"></td><td colspan="1" rowspan="1" align="center" valign="top"></td><td colspan="1" rowspan="1" align="char" char="." valign="top">97</td><td colspan="1" rowspan="1" align="char" char="." valign="top">111</td><td colspan="1" rowspan="1" align="char" char="." valign="top">133</td><td colspan="1" rowspan="1" align="center" valign="top">215 ± 116 (48)</td></tr><tr><td colspan="1" rowspan="1" align="left" valign="top"></td><td colspan="1" rowspan="1" align="center" valign="top"></td><td colspan="1" rowspan="1" align="char" char="." valign="top">98</td><td colspan="1" rowspan="1" align="char" char="." valign="top">125</td><td colspan="1" rowspan="1" align="char" char="." valign="top">133</td><td colspan="1" rowspan="1" align="center" valign="top">248 ± 131 (62)</td></tr><tr><td colspan="1" rowspan="1" align="left" valign="top">ITS1</td><td colspan="1" rowspan="1" align="center" valign="top">Ccomps</td><td colspan="1" rowspan="1" align="char" char="." valign="top">95</td><td colspan="1" rowspan="1" align="char" char="." valign="top">153</td><td colspan="1" rowspan="1" align="char" char="." valign="top">185</td><td colspan="1" rowspan="1" align="center" valign="top">99 ± 16 (78)</td></tr><tr><td colspan="1" rowspan="1" align="left" valign="top"></td><td colspan="1" rowspan="1" align="center" valign="top"></td><td colspan="1" rowspan="1" align="char" char="." valign="top">97</td><td colspan="1" rowspan="1" align="char" char="." valign="top">191</td><td colspan="1" rowspan="1" align="char" char="." valign="top">227</td><td colspan="1" rowspan="1" align="center" valign="top">92 ± 16 (89)</td></tr><tr><td colspan="1" rowspan="1" align="left" valign="top"></td><td colspan="1" rowspan="1" align="center" valign="top"></td><td colspan="1" rowspan="1" align="char" char="." valign="top">98</td><td colspan="1" rowspan="1" align="char" char="." valign="top">203</td><td colspan="1" rowspan="1" align="char" char="." valign="top">274</td><td colspan="1" rowspan="1" align="center" valign="top">99 ± 16 (96)</td></tr><tr><td colspan="1" rowspan="1" align="left" valign="top"></td><td colspan="1" rowspan="1" align="center" valign="top">MCL</td><td colspan="1" rowspan="1" align="char" char="." valign="top">95</td><td colspan="1" rowspan="1" align="char" char="." valign="top">182</td><td colspan="1" rowspan="1" align="char" char="." valign="top">185</td><td colspan="1" rowspan="1" align="center" valign="top">143 ± 65 (93)</td></tr><tr><td colspan="1" rowspan="1" align="left" valign="top"></td><td colspan="1" rowspan="1" align="center" valign="top"></td><td colspan="1" rowspan="1" align="char" char="." valign="top">97</td><td colspan="1" rowspan="1" align="char" char="." valign="top">207</td><td colspan="1" rowspan="1" align="char" char="." valign="top">227</td><td colspan="1" rowspan="1" align="center" valign="top">118 ± 54 (97)</td></tr><tr><td colspan="1" rowspan="1" align="left" valign="top"></td><td colspan="1" rowspan="1" align="center" valign="top"></td><td colspan="1" rowspan="1" align="char" char="." valign="top">98</td><td colspan="1" rowspan="1" align="char" char="." valign="top">214</td><td colspan="1" rowspan="1" align="char" char="." valign="top">277</td><td colspan="1" rowspan="1" align="center" valign="top">1,278 ± 131 (100)</td></tr></tbody></table><table-wrap-foot><fn id="t1fn1"><label>a</label><p>The total numbers of sequences were 2,661 for LSU and 2,956 for ITS.</p></fn><fn id="t1fn2"><label>b</label><p>The values are based on data for MOTUs containing more than four sequences. 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