MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage
Identifieur interne : 000286 ( Pmc/Curation ); précédent : 000285; suivant : 000287MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage
Auteurs : Jia Qian ; Matteo CominSource :
- BMC Bioinformatics [ 1471-2105 ] ; 2019.
Abstract
Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Because assembly typically produces only genome fragments, also known as contigs, it is crucial to group them into putative species for further taxonomic profiling and down-streaming functional analysis. Taxonomic analysis of microbial communities requires contig clustering, a process referred to as binning, that is still one of the most challenging tasks when analyzing metagenomic data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, sequencing errors, and the limitations due to binning contig of different lengths.
In this context we present MetaCon a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage. MetaCon uses a signature based on k-mers statistics that accounts for the different probability of appearance of a k-mer in different species, also contigs of different length are clustered in two separate phases. The effectiveness of MetaCon is demonstrated in both simulated and real datasets in comparison with state-of-art binning approaches such as CONCOCT, MaxBin and MetaBAT.
The online version of this article (10.1186/s12859-019-2904-4) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12859-019-2904-4
PubMed: 31757198
PubMed Central: 6873667
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage</title><author><name sortKey="Qian, Jia" sort="Qian, Jia" uniqKey="Qian J" first="Jia" last="Qian">Jia Qian</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Comin, Matteo" sort="Comin, Matteo" uniqKey="Comin M" first="Matteo" last="Comin">Matteo Comin</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">31757198</idno><idno type="pmc">6873667</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873667</idno><idno type="RBID">PMC:6873667</idno><idno type="doi">10.1186/s12859-019-2904-4</idno><date when="2019">2019</date><idno type="wicri:Area/Pmc/Corpus">000286</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000286</idno><idno type="wicri:Area/Pmc/Curation">000286</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000286</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage</title><author><name sortKey="Qian, Jia" sort="Qian, Jia" uniqKey="Qian J" first="Jia" last="Qian">Jia Qian</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Comin, Matteo" sort="Comin, Matteo" uniqKey="Comin M" first="Matteo" last="Comin">Matteo Comin</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">BMC Bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Motivation</title><p>Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Because assembly typically produces only genome fragments, also known as contigs, it is crucial to group them into putative species for further taxonomic profiling and down-streaming functional analysis. Taxonomic analysis of microbial communities requires contig clustering, a process referred to as binning, that is still one of the most challenging tasks when analyzing metagenomic data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, sequencing errors, and the limitations due to binning contig of different lengths.</p></sec><sec><title>Results</title><p>In this context we present MetaCon a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage. MetaCon uses a signature based on k-mers statistics that accounts for the different probability of appearance of a k-mer in different species, also contigs of different length are clustered in two separate phases. The effectiveness of MetaCon is demonstrated in both simulated and real datasets in comparison with state-of-art binning approaches such as CONCOCT, MaxBin and MetaBAT.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (10.1186/s12859-019-2904-4) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Staley, Jt" uniqKey="Staley J">JT Staley</name></author><author><name sortKey="Konopka, A" uniqKey="Konopka A">A Konopka</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Handelsman, J" uniqKey="Handelsman J">J Handelsman</name></author><author><name sortKey="Rondon, Mr" uniqKey="Rondon M">MR Rondon</name></author><author><name sortKey="Brady, Sf" uniqKey="Brady S">SF Brady</name></author><author><name sortKey="Clardy, J" uniqKey="Clardy J">J Clardy</name></author><author><name sortKey="Goodman, Rm" uniqKey="Goodman R">RM Goodman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Felczykowska, A" uniqKey="Felczykowska A">A Felczykowska</name></author><author><name sortKey="Bloch, Sk" uniqKey="Bloch S">SK Bloch</name></author><author><name sortKey="Nejman Fale Czyk, B" uniqKey="Nejman Fale Czyk B">B Nejman-Faleńczyk</name></author><author><name sortKey="Bara Ska, S" uniqKey="Bara Ska S">S Barańska</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mande, Ss" uniqKey="Mande S">SS Mande</name></author><author><name sortKey="Mohammed, Mh" uniqKey="Mohammed M">MH Mohammed</name></author><author><name sortKey="Ghosh, Ts" uniqKey="Ghosh T">TS Ghosh</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Alneberg, J" uniqKey="Alneberg J">J Alneberg</name></author><author><name sortKey="Brynjar Smari, B" uniqKey="Brynjar Smari B">B Brynjar Smári</name></author><author><name sortKey="Ino, Db" uniqKey="Ino D">DB Ino</name></author><author><name sortKey="Melanie, S" uniqKey="Melanie S">S Melanie</name></author><author><name sortKey="Joshua, Q" uniqKey="Joshua Q">Q Joshua</name></author><author><name sortKey="Umer Z, I" uniqKey="Umer Z I">I Umer Z</name></author><author><name sortKey="Leo, L" uniqKey="Leo L">L Leo</name></author><author><name sortKey="Nicholas J, L" uniqKey="Nicholas J L">L Nicholas J</name></author><author><name sortKey="Anders F, A" uniqKey="Anders F A">A Anders F</name></author><author><name sortKey="Christopher, Q" uniqKey="Christopher Q">Q Christopher</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bowers, Rm" uniqKey="Bowers R">RM Bowers</name></author><author><name sortKey="Clum, A" uniqKey="Clum A">A Clum</name></author><author><name sortKey="Tice, H" uniqKey="Tice H">H Tice</name></author><author><name sortKey="Lim, J" uniqKey="Lim J">J Lim</name></author><author><name sortKey="Singh, K" uniqKey="Singh K">K Singh</name></author><author><name sortKey="Ciobanu, D" uniqKey="Ciobanu D">D Ciobanu</name></author><author><name sortKey="Ngan, Cy" uniqKey="Ngan C">CY Ngan</name></author><author><name sortKey="Cheng, J F" uniqKey="Cheng J">J-F Cheng</name></author><author><name sortKey="Tringe, Sg" uniqKey="Tringe S">SG Tringe</name></author><author><name sortKey="Woyke, T" uniqKey="Woyke T">T Woyke</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sczyrba, A" uniqKey="Sczyrba A">A Sczyrba</name></author><author><name sortKey="Hofmann, P" uniqKey="Hofmann P">P Hofmann</name></author><author><name sortKey="Mchardy, Ac" uniqKey="Mchardy A">AC McHardy</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Huson, D H" uniqKey="Huson D">D. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Bioinformatics</journal-id><journal-id journal-id-type="iso-abbrev">BMC Bioinformatics</journal-id><journal-title-group><journal-title>BMC Bioinformatics</journal-title></journal-title-group><issn pub-type="epub">1471-2105</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">31757198</article-id><article-id pub-id-type="pmc">6873667</article-id><article-id pub-id-type="publisher-id">2904</article-id><article-id pub-id-type="doi">10.1186/s12859-019-2904-4</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research</subject></subj-group></article-categories><title-group><article-title>MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Qian</surname><given-names>Jia</given-names></name><xref ref-type="aff" rid="Aff1"></xref></contrib><contrib contrib-type="author" corresp="yes"><name><surname>Comin</surname><given-names>Matteo</given-names></name><address><email>comin@dei.unipd.it</email></address><xref ref-type="aff" rid="Aff1"></xref></contrib><aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1757 3470</institution-id><institution-id institution-id-type="GRID">grid.5608.b</institution-id><institution>Department of Information Engineering, University of Padova,</institution></institution-wrap>Via Giovanni Gradenigo 6, Padova, Italy</aff></contrib-group><pub-date pub-type="epub"><day>22</day><month>11</month><year>2019</year></pub-date><pub-date pub-type="pmc-release"><day>22</day><month>11</month><year>2019</year></pub-date><pub-date pub-type="collection"><year>2019</year></pub-date><volume>20</volume><issue>Suppl 9</issue><issue-sponsor>Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.</issue-sponsor><elocation-id>367</elocation-id><history><date date-type="received"><day>30</day><month>4</month><year>2019</year></date><date date-type="accepted"><day>15</day><month>5</month><year>2019</year></date></history><permissions><copyright-statement>© The Author(s) 2019</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold> This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><abstract id="Abs1"><sec><title>Motivation</title><p>Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Because assembly typically produces only genome fragments, also known as contigs, it is crucial to group them into putative species for further taxonomic profiling and down-streaming functional analysis. Taxonomic analysis of microbial communities requires contig clustering, a process referred to as binning, that is still one of the most challenging tasks when analyzing metagenomic data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, sequencing errors, and the limitations due to binning contig of different lengths.</p></sec><sec><title>Results</title><p>In this context we present MetaCon a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage. MetaCon uses a signature based on k-mers statistics that accounts for the different probability of appearance of a k-mer in different species, also contigs of different length are clustered in two separate phases. The effectiveness of MetaCon is demonstrated in both simulated and real datasets in comparison with state-of-art binning approaches such as CONCOCT, MaxBin and MetaBAT.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (10.1186/s12859-019-2904-4) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Metagenomics</kwd><kwd>Unsupervised clustering</kwd><kwd>K-mers statistics</kwd></kwd-group><conference xlink:href="http://bioinformatics.it/"><conf-name>Annual Meeting of the Bioinformatics Italian Society (BITS 2018)</conf-name><conf-acronym>BITS 2018</conf-acronym><conf-loc>Turin, Italy</conf-loc><conf-date>27 - 29 June 2018</conf-date></conference><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2019</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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