Inferring phylogenies of evolving sequences without multiple sequence alignment
Identifieur interne : 001E54 ( Ncbi/Merge ); précédent : 001E53; suivant : 001E55Inferring phylogenies of evolving sequences without multiple sequence alignment
Auteurs : Cheong Xin Chan [Australie] ; Guillaume Bernard [Australie] ; Olivier Poirion [Australie, France] ; James M. Hogan [Australie] ; Mark A. Ragan [Australie]Source :
- Scientific Reports [ 2045-2322 ] ; 2014.
Abstract
Alignment-free methods, in which shared properties of sub-sequences (e.g. identity or match length) are extracted and used to compute a distance matrix, have recently been explored for phylogenetic inference. However, the scalability and robustness of these methods to key evolutionary processes remain to be investigated. Here, using simulated sequence sets of various sizes in both nucleotides and amino acids, we systematically assess the accuracy of phylogenetic inference using an alignment-free approach, based on
Url:
DOI: 10.1038/srep06504
PubMed: 25266120
PubMed Central: 4179140
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Inferring phylogenies of evolving sequences without multiple sequence alignment</title><author><name sortKey="Chan, Cheong Xin" sort="Chan, Cheong Xin" uniqKey="Chan C" first="Cheong Xin" last="Chan">Cheong Xin Chan</name><affiliation wicri:level="1"><nlm:aff id="a1"><institution>Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland</institution>, Brisbane, QLD 4072, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>, Brisbane, QLD 4072</wicri:regionArea><wicri:noRegion>QLD 4072</wicri:noRegion></affiliation></author><author><name sortKey="Bernard, Guillaume" sort="Bernard, Guillaume" uniqKey="Bernard G" first="Guillaume" last="Bernard">Guillaume Bernard</name><affiliation wicri:level="1"><nlm:aff id="a1"><institution>Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland</institution>, Brisbane, QLD 4072, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>, Brisbane, QLD 4072</wicri:regionArea><wicri:noRegion>QLD 4072</wicri:noRegion></affiliation></author><author><name sortKey="Poirion, Olivier" sort="Poirion, Olivier" uniqKey="Poirion O" first="Olivier" last="Poirion">Olivier Poirion</name><affiliation wicri:level="1"><nlm:aff id="a1"><institution>Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland</institution>, Brisbane, QLD 4072, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>, Brisbane, QLD 4072</wicri:regionArea><wicri:noRegion>QLD 4072</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="a3">Current address: Laboratoire Ampère, CNRS UMR 5005, École Centrale de Lyon, France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>Current address: Laboratoire Ampère, CNRS UMR 5005, École Centrale de Lyon</wicri:regionArea><wicri:noRegion>École Centrale de Lyon</wicri:noRegion><wicri:noRegion>École Centrale de Lyon</wicri:noRegion></affiliation></author><author><name sortKey="Hogan, James M" sort="Hogan, James M" uniqKey="Hogan J" first="James M." last="Hogan">James M. Hogan</name><affiliation wicri:level="1"><nlm:aff id="a2"><institution>School of Electrical Engineering and Computer Science, Queensland University of Technology</institution>, Brisbane, QLD 4000, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>, Brisbane, QLD 4000</wicri:regionArea><wicri:noRegion>QLD 4000</wicri:noRegion></affiliation></author><author><name sortKey="Ragan, Mark A" sort="Ragan, Mark A" uniqKey="Ragan M" first="Mark A." last="Ragan">Mark A. Ragan</name><affiliation wicri:level="1"><nlm:aff id="a1"><institution>Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland</institution>, Brisbane, QLD 4072, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>, Brisbane, QLD 4072</wicri:regionArea><wicri:noRegion>QLD 4072</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25266120</idno><idno type="pmc">4179140</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4179140</idno><idno type="RBID">PMC:4179140</idno><idno type="doi">10.1038/srep06504</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">000128</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000128</idno><idno type="wicri:Area/Pmc/Curation">000128</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000128</idno><idno type="wicri:Area/Pmc/Checkpoint">001515</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">001515</idno><idno type="wicri:Area/Ncbi/Merge">001E54</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Inferring phylogenies of evolving sequences without multiple sequence alignment</title><author><name sortKey="Chan, Cheong Xin" sort="Chan, Cheong Xin" uniqKey="Chan C" first="Cheong Xin" last="Chan">Cheong Xin Chan</name><affiliation wicri:level="1"><nlm:aff id="a1"><institution>Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland</institution>, Brisbane, QLD 4072, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>, Brisbane, QLD 4072</wicri:regionArea><wicri:noRegion>QLD 4072</wicri:noRegion></affiliation></author><author><name sortKey="Bernard, Guillaume" sort="Bernard, Guillaume" uniqKey="Bernard G" first="Guillaume" last="Bernard">Guillaume Bernard</name><affiliation wicri:level="1"><nlm:aff id="a1"><institution>Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland</institution>, Brisbane, QLD 4072, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>, Brisbane, QLD 4072</wicri:regionArea><wicri:noRegion>QLD 4072</wicri:noRegion></affiliation></author><author><name sortKey="Poirion, Olivier" sort="Poirion, Olivier" uniqKey="Poirion O" first="Olivier" last="Poirion">Olivier Poirion</name><affiliation wicri:level="1"><nlm:aff id="a1"><institution>Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland</institution>, Brisbane, QLD 4072, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>, Brisbane, QLD 4072</wicri:regionArea><wicri:noRegion>QLD 4072</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="a3">Current address: Laboratoire Ampère, CNRS UMR 5005, École Centrale de Lyon, France.</nlm:aff><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>Current address: Laboratoire Ampère, CNRS UMR 5005, École Centrale de Lyon</wicri:regionArea><wicri:noRegion>École Centrale de Lyon</wicri:noRegion><wicri:noRegion>École Centrale de Lyon</wicri:noRegion></affiliation></author><author><name sortKey="Hogan, James M" sort="Hogan, James M" uniqKey="Hogan J" first="James M." last="Hogan">James M. Hogan</name><affiliation wicri:level="1"><nlm:aff id="a2"><institution>School of Electrical Engineering and Computer Science, Queensland University of Technology</institution>, Brisbane, QLD 4000, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>, Brisbane, QLD 4000</wicri:regionArea><wicri:noRegion>QLD 4000</wicri:noRegion></affiliation></author><author><name sortKey="Ragan, Mark A" sort="Ragan, Mark A" uniqKey="Ragan M" first="Mark A." last="Ragan">Mark A. Ragan</name><affiliation wicri:level="1"><nlm:aff id="a1"><institution>Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland</institution>, Brisbane, QLD 4072, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>, Brisbane, QLD 4072</wicri:regionArea><wicri:noRegion>QLD 4072</wicri:noRegion></affiliation></author></analytic><series><title level="j">Scientific Reports</title><idno type="eISSN">2045-2322</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Alignment-free methods, in which shared properties of sub-sequences (e.g. identity or match length) are extracted and used to compute a distance matrix, have recently been explored for phylogenetic inference. However, the scalability and robustness of these methods to key evolutionary processes remain to be investigated. Here, using simulated sequence sets of various sizes in both nucleotides and amino acids, we systematically assess the accuracy of phylogenetic inference using an alignment-free approach, based on <italic>D<sub>2</sub></italic> statistics, under different evolutionary scenarios. We find that compared to a multiple sequence alignment approach, <italic>D<sub>2</sub></italic> methods are more robust against among-site rate heterogeneity, compositional biases, genetic rearrangements and insertions/deletions, but are more sensitive to recent sequence divergence and sequence truncation. Across diverse empirical datasets, the alignment-free methods perform well for sequences sharing low divergence, at greater computation speed. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Sci Rep</journal-id><journal-id journal-id-type="iso-abbrev">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type="epub">2045-2322</issn><publisher><publisher-name>Nature Publishing Group</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25266120</article-id><article-id pub-id-type="pmc">4179140</article-id><article-id pub-id-type="pii">srep06504</article-id><article-id pub-id-type="doi">10.1038/srep06504</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Inferring phylogenies of evolving sequences without multiple sequence alignment</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Chan</surname><given-names>Cheong Xin</given-names></name><xref ref-type="aff" rid="a1">1</xref></contrib><contrib contrib-type="author"><name><surname>Bernard</surname><given-names>Guillaume</given-names></name><xref ref-type="aff" rid="a1">1</xref></contrib><contrib contrib-type="author"><name><surname>Poirion</surname><given-names>Olivier</given-names></name><xref ref-type="aff" rid="a1">1</xref><xref ref-type="aff" rid="a3">3</xref></contrib><contrib contrib-type="author"><name><surname>Hogan</surname><given-names>James M.</given-names></name><xref ref-type="aff" rid="a2">2</xref></contrib><contrib contrib-type="author"><name><surname>Ragan</surname><given-names>Mark A.</given-names></name><xref ref-type="corresp" rid="c1">a</xref><xref ref-type="aff" rid="a1">1</xref></contrib><aff id="a1"><label>1</label><institution>Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland</institution>, Brisbane, QLD 4072, Australia</aff><aff id="a2"><label>2</label><institution>School of Electrical Engineering and Computer Science, Queensland University of Technology</institution>, Brisbane, QLD 4000, Australia</aff><aff id="a3"><label>3</label>Current address: Laboratoire Ampère, CNRS UMR 5005, École Centrale de Lyon, France.</aff></contrib-group><author-notes><corresp id="c1"><label>a</label><email>m.ragan@uq.edu.au</email></corresp></author-notes><pub-date pub-type="epub"><day>30</day><month>09</month><year>2014</year></pub-date><pub-date pub-type="collection"><year>2014</year></pub-date><volume>4</volume><elocation-id>6504</elocation-id><history><date date-type="received"><day>06</day><month>03</month><year>2014</year></date><date date-type="accepted"><day>10</day><month>09</month><year>2014</year></date></history><permissions><copyright-statement>Copyright © 2014, Macmillan Publishers Limited. All rights reserved</copyright-statement><copyright-year>2014</copyright-year><copyright-holder>Macmillan Publishers Limited. All rights reserved</copyright-holder><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><pmc-comment>author-paid</pmc-comment>
<license-p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 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 in order 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-nc-sa/4.0/">http://creativecommons.org/licenses/by-nc-sa/4.0/</ext-link></license-p></license></permissions><abstract><p>Alignment-free methods, in which shared properties of sub-sequences (e.g. identity or match length) are extracted and used to compute a distance matrix, have recently been explored for phylogenetic inference. However, the scalability and robustness of these methods to key evolutionary processes remain to be investigated. Here, using simulated sequence sets of various sizes in both nucleotides and amino acids, we systematically assess the accuracy of phylogenetic inference using an alignment-free approach, based on <italic>D<sub>2</sub></italic> statistics, under different evolutionary scenarios. We find that compared to a multiple sequence alignment approach, <italic>D<sub>2</sub></italic> methods are more robust against among-site rate heterogeneity, compositional biases, genetic rearrangements and insertions/deletions, but are more sensitive to recent sequence divergence and sequence truncation. Across diverse empirical datasets, the alignment-free methods perform well for sequences sharing low divergence, at greater computation speed. Our findings provide strong evidence for the scalability and the potential use of alignment-free methods in large-scale phylogenomics.</p></abstract></article-meta></front><floats-group><fig id="f1"><label>Figure 1</label><caption><title>Trees for simulation of sequence data.</title><p>Six situations showing distinct combinations of internal (<italic>x</italic>) and terminal (<italic>y</italic>) branches, labelled as T1 through T6, with <italic>y</italic> specified differently between the first (<italic>p1</italic>) and second (<italic>p2</italic>) half of the branches on a tree. The unit of branch lengths is number of substitutions per site. The length of each edge is either 0.01 or 0.05 substitutions per site.</p></caption><graphic xlink:href="srep06504-f1"></graphic></fig><fig id="f2"><label>Figure 2</label><caption><title>The accuracy of <italic>D<sub>2</sub></italic> methods based on sequence divergence of the nucleotide sequence sets.</title><p>For each size <italic>N</italic> at (i) 8, (ii) 32 and (iii) 128, mean <italic>RF<sub>D2n1</sub></italic> are shown in (a) across different <italic>k</italic>-mer lengths (shown for <italic>k</italic> = 8, 12, 16, 20, 24), for cases simulated under each of the six trees (T1 through T6 on the <italic>x</italic>-axis). The corresponding <italic>Q<sub>D2n1</sub></italic> for each case is shown in (b). Error bars indicate standard deviation from the mean. See <xref ref-type="supplementary-material" rid="s1">Supplementary Figures S1 through S4</xref> for complete results for all <italic>D</italic><sub>2</sub> methods for both nucleotide and protein sequence sets.</p></caption><graphic xlink:href="srep06504-f2"></graphic></fig><fig id="f3"><label>Figure 3</label><caption><title>The accuracy of <italic>D<sub>2</sub></italic> methods based on genetic rearrangement.</title><p><italic>RF<sub>D2n1</sub></italic> are shown in (a) across different <italic>k</italic>-mer lengths (<italic>k</italic> ≥ 8), as well as that of the standard approach (<italic>RF<sub>MSA</sub></italic>), across different <italic>R</italic> at 10%, 25% and 50%. The corresponding <italic>Q<sub>D2n1</sub></italic> values are shown in (b). Error bars indicate standard deviation from the mean.</p></caption><graphic xlink:href="srep06504-f3"></graphic></fig><fig id="f4"><label>Figure 4</label><caption><title>The accuracy of phylogenetic approaches based on insertions/deletions.</title><p><italic>RF</italic> values are shown in (a) for <inline-formula id="m45"><inline-graphic id="d33e2408" xlink:href="srep06504-m45.jpg"></inline-graphic></inline-formula>, MUSCLE + MrBayes and MUSCLE + RAxML across different indel rates <italic>r</italic>. The corresponding <italic>Q</italic> values for MUSCLE + MrBayes and MUSCLE + RAxML are shown in (b). Error bars indicate standard deviation from the mean.</p></caption><graphic xlink:href="srep06504-f4"></graphic></fig><fig id="f5"><label>Figure 5</label><caption><title>The accuracy of phylogenetic approaches based on coalescent evolution of gene families.</title><p><italic>RF</italic> values are shown in (a) for <inline-formula id="m46"><inline-graphic id="d33e2425" xlink:href="srep06504-m46.jpg"></inline-graphic></inline-formula>, MUSCLE + MrBayes and MUSCLE + RAxML across different effective population size <italic>N<sub>e</sub></italic>. The corresponding <italic>Q</italic> values for MUSCLE + MrBayes and MUSCLE + RAxML are shown in (b). Error bars indicate standard deviation from the mean.</p></caption><graphic xlink:href="srep06504-f5"></graphic></fig><fig id="f6"><label>Figure 6</label><caption><title>The accuracy of <italic>D<sub>2</sub></italic> methods based on TreeBASE data.</title><p>The probability density of <italic>RF<sub>D2n1</sub></italic> at <italic>k</italic> = 8 as categorised based on (a) total number of sequences within a set, <italic>N</italic> (mean and median in <xref ref-type="supplementary-material" rid="s1">Supplementary Table S3</xref>), and (b) within-set sequence similarity, <italic>ID</italic> (mean and median in <xref ref-type="supplementary-material" rid="s1">Supplementary Table S4</xref>).</p></caption><graphic xlink:href="srep06504-f6"></graphic></fig><fig id="f7"><label>Figure 7</label><caption><title>Computation time of <italic>D<sub>2</sub></italic> methods.</title><p>The computation time in seconds is shown for (a) <italic>D</italic><sub>2</sub> method at <italic>k</italic> = 8 across subset of GreenGenes data across datasets of <italic>N</italic> = 1000, 2000, 3000, 4000 and 5000, and for (b) <inline-formula id="m47"><inline-graphic id="d33e2490" xlink:href="srep06504-m47.jpg"></inline-graphic></inline-formula> analysis across neighbourhood size <italic>n</italic> = 1 through 5, for nucleotide sequence sets of <italic>N</italic> = 8. Error bars indicate standard deviation from the mean.</p></caption><graphic xlink:href="srep06504-f7"></graphic></fig></floats-group></pmc><affiliations><list><country><li>Australie</li><li>France</li></country></list><tree><country name="Australie"><noRegion><name sortKey="Chan, Cheong Xin" sort="Chan, Cheong Xin" uniqKey="Chan C" first="Cheong Xin" last="Chan">Cheong Xin Chan</name></noRegion><name sortKey="Bernard, Guillaume" sort="Bernard, Guillaume" uniqKey="Bernard G" first="Guillaume" last="Bernard">Guillaume Bernard</name><name sortKey="Hogan, James M" sort="Hogan, James M" uniqKey="Hogan J" first="James M." last="Hogan">James M. Hogan</name><name sortKey="Poirion, Olivier" sort="Poirion, Olivier" uniqKey="Poirion O" first="Olivier" last="Poirion">Olivier Poirion</name><name sortKey="Ragan, Mark A" sort="Ragan, Mark A" uniqKey="Ragan M" first="Mark A." last="Ragan">Mark A. Ragan</name></country><country name="France"><noRegion><name sortKey="Poirion, Olivier" sort="Poirion, Olivier" uniqKey="Poirion O" first="Olivier" last="Poirion">Olivier Poirion</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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