PHYRN: A Robust Method for Phylogenetic Analysis of Highly Divergent Sequences
Identifieur interne : 000603 ( Pmc/Curation ); précédent : 000602; suivant : 000604PHYRN: A Robust Method for Phylogenetic Analysis of Highly Divergent Sequences
Auteurs : Gaurav Bhardwaj [États-Unis] ; Kyung Dae Ko [États-Unis] ; Yoojin Hong [États-Unis] ; Zhenhai Zhang [États-Unis] ; Ngai Lam Ho [États-Unis] ; Sree V. Chintapalli [États-Unis] ; Lindsay A. Kline [États-Unis] ; Matthew Gotlin [États-Unis] ; David Nicholas Hartranft [États-Unis] ; Morgen E. Patterson [États-Unis] ; Foram Dave [États-Unis] ; Evan J. Smith [États-Unis] ; Edward C. Holmes [États-Unis] ; Randen L. Patterson [États-Unis] ; Damian B. Van Rossum [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2012.
Abstract
Both multiple sequence alignment and phylogenetic analysis are problematic in the “twilight zone” of sequence similarity (≤25% amino acid identity). Herein we explore the accuracy of phylogenetic inference at extreme sequence divergence using a variety of simulated data sets. We evaluate four leading multiple sequence alignment (MSA) methods (MAFFT, T-COFFEE, CLUSTAL, and MUSCLE) and six commonly used programs of tree estimation (Distance-based: Neighbor-Joining; Character-based: PhyML, RAxML, GARLI, Maximum Parsimony, and Bayesian) against a novel MSA-independent method (PHYRN) described here. Strikingly, at “midnight zone” genetic distances (∼7% pairwise identity and 4.0 gaps per position), PHYRN returns high-resolution phylogenies that outperform traditional approaches. We reason this is due to PHRYN's capability to amplify informative positions, even at the most extreme levels of sequence divergence. We also assess the applicability of the PHYRN algorithm for inferring deep evolutionary relationships in the divergent DANGER protein superfamily, for which PHYRN infers a more robust tree compared to MSA-based approaches. Taken together, these results demonstrate that PHYRN represents a powerful mechanism for mapping uncharted frontiers in highly divergent protein sequence data sets.
Url:
DOI: 10.1371/journal.pone.0034261
PubMed: 22514627
PubMed Central: 3325999
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<author><name sortKey="Ko, Kyung Dae" sort="Ko, Kyung Dae" uniqKey="Ko K" first="Kyung Dae" last="Ko">Kyung Dae Ko</name>
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<author><name sortKey="Hong, Yoojin" sort="Hong, Yoojin" uniqKey="Hong Y" first="Yoojin" last="Hong">Yoojin Hong</name>
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<author><name sortKey="Zhang, Zhenhai" sort="Zhang, Zhenhai" uniqKey="Zhang Z" first="Zhenhai" last="Zhang">Zhenhai Zhang</name>
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<author><name sortKey="Ho, Ngai Lam" sort="Ho, Ngai Lam" uniqKey="Ho N" first="Ngai Lam" last="Ho">Ngai Lam Ho</name>
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<author><name sortKey="Chintapalli, Sree V" sort="Chintapalli, Sree V" uniqKey="Chintapalli S" first="Sree V." last="Chintapalli">Sree V. Chintapalli</name>
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</nlm:aff>
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<wicri:regionArea>Department of Physiology and Membrane Biology, School of Medicine, University of California Davis, Davis, California</wicri:regionArea>
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<author><name sortKey="Kline, Lindsay A" sort="Kline, Lindsay A" uniqKey="Kline L" first="Lindsay A." last="Kline">Lindsay A. Kline</name>
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<author><name sortKey="Gotlin, Matthew" sort="Gotlin, Matthew" uniqKey="Gotlin M" first="Matthew" last="Gotlin">Matthew Gotlin</name>
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</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Biology, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Hartranft, David Nicholas" sort="Hartranft, David Nicholas" uniqKey="Hartranft D" first="David Nicholas" last="Hartranft">David Nicholas Hartranft</name>
<affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Biology, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Patterson, Morgen E" sort="Patterson, Morgen E" uniqKey="Patterson M" first="Morgen E." last="Patterson">Morgen E. Patterson</name>
<affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Dave, Foram" sort="Dave, Foram" uniqKey="Dave F" first="Foram" last="Dave">Foram Dave</name>
<affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Smith, Evan J" sort="Smith, Evan J" uniqKey="Smith E" first="Evan J." last="Smith">Evan J. Smith</name>
<affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Biology, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Holmes, Edward C" sort="Holmes, Edward C" uniqKey="Holmes E" first="Edward C." last="Holmes">Edward C. Holmes</name>
<affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Biology, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff5"><addr-line>Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Fogarty International Center, National Institutes of Health, Bethesda, Maryland</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Patterson, Randen L" sort="Patterson, Randen L" uniqKey="Patterson R" first="Randen L." last="Patterson">Randen L. Patterson</name>
<affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff6"><addr-line>Department of Biochemistry and Molecular Medicine, School of Medicine, University of California Davis, Davis, California, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Biochemistry and Molecular Medicine, School of Medicine, University of California Davis, Davis, California</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff7"><addr-line>Department of Physiology and Membrane Biology, School of Medicine, University of California Davis, Davis, California, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Physiology and Membrane Biology, School of Medicine, University of California Davis, Davis, California</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff8"><addr-line>Center for Translational Bioscience and Computing, University of California Davis, Davis, California, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Translational Bioscience and Computing, University of California Davis, Davis, California</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Van Rossum, Damian B" sort="Van Rossum, Damian B" uniqKey="Van Rossum D" first="Damian B." last="Van Rossum">Damian B. Van Rossum</name>
<affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Biology, The Pennsylvania State University, University Park, Pennsylvania</wicri:regionArea>
</affiliation>
<affiliation wicri:level="1"><nlm:aff id="aff8"><addr-line>Center for Translational Bioscience and Computing, University of California Davis, Davis, California, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Translational Bioscience and Computing, University of California Davis, Davis, California</wicri:regionArea>
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</author>
</analytic>
<series><title level="j">PLoS ONE</title>
<idno type="eISSN">1932-6203</idno>
<imprint><date when="2012">2012</date>
</imprint>
</series>
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<front><div type="abstract" xml:lang="en"><p>Both multiple sequence alignment and phylogenetic analysis are problematic in the “twilight zone” of sequence similarity (≤25% amino acid identity). Herein we explore the accuracy of phylogenetic inference at extreme sequence divergence using a variety of simulated data sets. We evaluate four leading multiple sequence alignment (MSA) methods (MAFFT, T-COFFEE, CLUSTAL, and MUSCLE) and six commonly used programs of tree estimation (Distance-based: Neighbor-Joining; Character-based: PhyML, RAxML, GARLI, Maximum Parsimony, and Bayesian) against a novel MSA-independent method (PHYRN) described here. Strikingly, at “midnight zone” genetic distances (∼7% pairwise identity and 4.0 gaps per position), PHYRN returns high-resolution phylogenies that outperform traditional approaches. We reason this is due to PHRYN's capability to amplify informative positions, even at the most extreme levels of sequence divergence. We also assess the applicability of the PHYRN algorithm for inferring deep evolutionary relationships in the divergent DANGER protein superfamily, for which PHYRN infers a more robust tree compared to MSA-based approaches. Taken together, these results demonstrate that PHYRN represents a powerful mechanism for mapping uncharted frontiers in highly divergent protein sequence data sets.</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">PLoS One</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">plosone</journal-id>
<journal-title-group><journal-title>PLoS ONE</journal-title>
</journal-title-group>
<issn pub-type="epub">1932-6203</issn>
<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">22514627</article-id>
<article-id pub-id-type="pmc">3325999</article-id>
<article-id pub-id-type="publisher-id">PONE-D-11-24752</article-id>
<article-id pub-id-type="doi">10.1371/journal.pone.0034261</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="Discipline-v2"><subject>Biology</subject>
<subj-group><subject>Computational Biology</subject>
<subj-group><subject>Biological Data Management</subject>
<subject>Evolutionary Modeling</subject>
<subject>Sequence Analysis</subject>
</subj-group>
</subj-group>
<subj-group><subject>Evolutionary Biology</subject>
<subj-group><subject>Evolutionary Systematics</subject>
<subj-group><subject>Phylogenetics</subject>
</subj-group>
</subj-group>
<subj-group><subject>Evolutionary Theory</subject>
</subj-group>
</subj-group>
<subj-group><subject>Genetics</subject>
<subj-group><subject>Molecular Genetics</subject>
</subj-group>
</subj-group>
<subj-group><subject>Proteomics</subject>
<subj-group><subject>Sequence Analysis</subject>
</subj-group>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>PHYRN: A Robust Method for Phylogenetic Analysis of Highly Divergent Sequences</article-title>
<alt-title alt-title-type="running-head">Phylogenetic Accuracy in Divergent Data Sets</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Bhardwaj</surname>
<given-names>Gaurav</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff6"><sup>6</sup>
</xref>
<xref ref-type="aff" rid="aff8"><sup>8</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ko</surname>
<given-names>Kyung Dae</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Hong</surname>
<given-names>Yoojin</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zhang</surname>
<given-names>Zhenhai</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ho</surname>
<given-names>Ngai Lam</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chintapalli</surname>
<given-names>Sree V.</given-names>
</name>
<xref ref-type="aff" rid="aff7"><sup>7</sup>
</xref>
<xref ref-type="aff" rid="aff8"><sup>8</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kline</surname>
<given-names>Lindsay A.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Gotlin</surname>
<given-names>Matthew</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Hartranft</surname>
<given-names>David Nicholas</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Patterson</surname>
<given-names>Morgen E.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Dave</surname>
<given-names>Foram</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Smith</surname>
<given-names>Evan J.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Holmes</surname>
<given-names>Edward C.</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff5"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Patterson</surname>
<given-names>Randen L.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff6"><sup>6</sup>
</xref>
<xref ref-type="aff" rid="aff7"><sup>7</sup>
</xref>
<xref ref-type="aff" rid="aff8"><sup>8</sup>
</xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>van Rossum</surname>
<given-names>Damian B.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff8"><sup>8</sup>
</xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
<addr-line>Center for Computational Proteomics, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</aff>
<aff id="aff2"><label>2</label>
<addr-line>Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</aff>
<aff id="aff3"><label>3</label>
<addr-line>Department of Computer Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</aff>
<aff id="aff4"><label>4</label>
<addr-line>Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America</addr-line>
</aff>
<aff id="aff5"><label>5</label>
<addr-line>Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America</addr-line>
</aff>
<aff id="aff6"><label>6</label>
<addr-line>Department of Biochemistry and Molecular Medicine, School of Medicine, University of California Davis, Davis, California, United States of America</addr-line>
</aff>
<aff id="aff7"><label>7</label>
<addr-line>Department of Physiology and Membrane Biology, School of Medicine, University of California Davis, Davis, California, United States of America</addr-line>
</aff>
<aff id="aff8"><label>8</label>
<addr-line>Center for Translational Bioscience and Computing, University of California Davis, Davis, California, United States of America</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Tuller</surname>
<given-names>Tamir</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1">Tel Aviv University, Israel</aff>
<author-notes><corresp id="cor1">* E-mail: <email>randen100@gmail.com</email>
(RLP); <email>dbv10@psu.edu</email>
(DBV)</corresp>
<fn fn-type="con"><p>Conceived and designed the experiments: GB ECH RLP DBVR. Performed the experiments: GB KDK YH SVC ZZ NLH LAK MG DNH MEP FD EJS. Analyzed the data: GB ECH RLP DBVR. Wrote the paper: GB ECH RLP DBVR.</p>
</fn>
</author-notes>
<pub-date pub-type="collection"><year>2012</year>
</pub-date>
<pub-date pub-type="epub"><day>13</day>
<month>4</month>
<year>2012</year>
</pub-date>
<volume>7</volume>
<issue>4</issue>
<elocation-id>e34261</elocation-id>
<history><date date-type="received"><day>30</day>
<month>11</month>
<year>2011</year>
</date>
<date date-type="accepted"><day>24</day>
<month>2</month>
<year>2012</year>
</date>
</history>
<permissions><copyright-statement>This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.</copyright-statement>
<copyright-year>2012</copyright-year>
</permissions>
<abstract><p>Both multiple sequence alignment and phylogenetic analysis are problematic in the “twilight zone” of sequence similarity (≤25% amino acid identity). Herein we explore the accuracy of phylogenetic inference at extreme sequence divergence using a variety of simulated data sets. We evaluate four leading multiple sequence alignment (MSA) methods (MAFFT, T-COFFEE, CLUSTAL, and MUSCLE) and six commonly used programs of tree estimation (Distance-based: Neighbor-Joining; Character-based: PhyML, RAxML, GARLI, Maximum Parsimony, and Bayesian) against a novel MSA-independent method (PHYRN) described here. Strikingly, at “midnight zone” genetic distances (∼7% pairwise identity and 4.0 gaps per position), PHYRN returns high-resolution phylogenies that outperform traditional approaches. We reason this is due to PHRYN's capability to amplify informative positions, even at the most extreme levels of sequence divergence. We also assess the applicability of the PHYRN algorithm for inferring deep evolutionary relationships in the divergent DANGER protein superfamily, for which PHYRN infers a more robust tree compared to MSA-based approaches. Taken together, these results demonstrate that PHYRN represents a powerful mechanism for mapping uncharted frontiers in highly divergent protein sequence data sets.</p>
</abstract>
<counts><page-count count="13"></page-count>
</counts>
</article-meta>
</front>
</pmc>
</record>
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