Ultrafast clustering algorithms for metagenomic sequence analysis
Identifieur interne : 000368 ( Pmc/Checkpoint ); précédent : 000367; suivant : 000369Ultrafast clustering algorithms for metagenomic sequence analysis
Auteurs : Weizhong Li ; Limin Fu ; Beifang Niu ; Sitao Wu ; John WooleySource :
- Briefings in Bioinformatics [ 1467-5463 ] ; 2012.
Abstract
The rapid advances of high-throughput sequencing technologies dramatically prompted metagenomic studies of microbial communities that exist at various environments. Fundamental questions in metagenomics include the identities, composition and dynamics of microbial populations and their functions and interactions. However, the massive quantity and the comprehensive complexity of these sequence data pose tremendous challenges in data analysis. These challenges include but are not limited to ever-increasing computational demand, biased sequence sampling, sequence errors, sequence artifacts and novel sequences. Sequence clustering methods can directly answer many of the fundamental questions by grouping similar sequences into families. In addition, clustering analysis also addresses the challenges in metagenomics. Thus, a large redundant data set can be represented with a small non-redundant set, where each cluster can be represented by a single entry or a consensus. Artifacts can be rapidly detected through clustering. Errors can be identified, filtered or corrected by using consensus from sequences within clusters.
Url:
DOI: 10.1093/bib/bbs035
PubMed: 22772836
PubMed Central: 3504929
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Ultrafast clustering algorithms for metagenomic sequence analysis</title><author><name sortKey="Li, Weizhong" sort="Li, Weizhong" uniqKey="Li W" first="Weizhong" last="Li">Weizhong Li</name></author><author><name sortKey="Fu, Limin" sort="Fu, Limin" uniqKey="Fu L" first="Limin" last="Fu">Limin Fu</name></author><author><name sortKey="Niu, Beifang" sort="Niu, Beifang" uniqKey="Niu B" first="Beifang" last="Niu">Beifang Niu</name></author><author><name sortKey="Wu, Sitao" sort="Wu, Sitao" uniqKey="Wu S" first="Sitao" last="Wu">Sitao Wu</name></author><author><name sortKey="Wooley, John" sort="Wooley, John" uniqKey="Wooley J" first="John" last="Wooley">John Wooley</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22772836</idno><idno type="pmc">3504929</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3504929</idno><idno type="RBID">PMC:3504929</idno><idno type="doi">10.1093/bib/bbs035</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000507</idno><idno type="wicri:Area/Pmc/Curation">000507</idno><idno type="wicri:Area/Pmc/Checkpoint">000368</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Ultrafast clustering algorithms for metagenomic sequence analysis</title><author><name sortKey="Li, Weizhong" sort="Li, Weizhong" uniqKey="Li W" first="Weizhong" last="Li">Weizhong Li</name></author><author><name sortKey="Fu, Limin" sort="Fu, Limin" uniqKey="Fu L" first="Limin" last="Fu">Limin Fu</name></author><author><name sortKey="Niu, Beifang" sort="Niu, Beifang" uniqKey="Niu B" first="Beifang" last="Niu">Beifang Niu</name></author><author><name sortKey="Wu, Sitao" sort="Wu, Sitao" uniqKey="Wu S" first="Sitao" last="Wu">Sitao Wu</name></author><author><name sortKey="Wooley, John" sort="Wooley, John" uniqKey="Wooley J" first="John" last="Wooley">John Wooley</name></author></analytic><series><title level="j">Briefings in Bioinformatics</title><idno type="ISSN">1467-5463</idno><idno type="eISSN">1477-4054</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The rapid advances of high-throughput sequencing technologies dramatically prompted metagenomic studies of microbial communities that exist at various environments. Fundamental questions in metagenomics include the identities, composition and dynamics of microbial populations and their functions and interactions. However, the massive quantity and the comprehensive complexity of these sequence data pose tremendous challenges in data analysis. These challenges include but are not limited to ever-increasing computational demand, biased sequence sampling, sequence errors, sequence artifacts and novel sequences. Sequence clustering methods can directly answer many of the fundamental questions by grouping similar sequences into families. In addition, clustering analysis also addresses the challenges in metagenomics. Thus, a large redundant data set can be represented with a small non-redundant set, where each cluster can be represented by a single entry or a consensus. Artifacts can be rapidly detected through clustering. Errors can be identified, filtered or corrected by using consensus from sequences within clusters.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Handelsman, J" uniqKey="Handelsman J">J Handelsman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wooley, Jc" uniqKey="Wooley J">JC Wooley</name></author><author><name sortKey="Godzik, A" uniqKey="Godzik A">A Godzik</name></author><author><name sortKey="Friedberg, I" uniqKey="Friedberg I">I Friedberg</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Venter, Jc" uniqKey="Venter J">JC Venter</name></author><author><name sortKey="Remington, K" uniqKey="Remington K">K Remington</name></author><author><name sortKey="Heidelberg, Jf" uniqKey="Heidelberg J">JF Heidelberg</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gill, Sr" uniqKey="Gill S">SR Gill</name></author><author><name 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Brief Bioinform</journal-id><journal-id journal-id-type="iso-abbrev">Brief. Bioinformatics</journal-id><journal-id journal-id-type="publisher-id">bib</journal-id><journal-id journal-id-type="hwp">bib</journal-id><journal-title-group><journal-title>Briefings in Bioinformatics</journal-title></journal-title-group><issn pub-type="ppub">1467-5463</issn><issn pub-type="epub">1477-4054</issn><publisher><publisher-name>Oxford University Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22772836</article-id><article-id pub-id-type="pmc">3504929</article-id><article-id pub-id-type="doi">10.1093/bib/bbs035</article-id><article-id pub-id-type="publisher-id">bbs035</article-id><article-categories><subj-group subj-group-type="heading"><subject>Papers</subject></subj-group></article-categories><title-group><article-title>Ultrafast clustering algorithms for metagenomic sequence analysis</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Li</surname><given-names>Weizhong</given-names></name><xref ref-type="bio" rid="d34e36">*</xref></contrib><contrib contrib-type="author"><name><surname>Fu</surname><given-names>Limin</given-names></name><xref ref-type="bio" rid="d34e47">*</xref></contrib><contrib contrib-type="author"><name><surname>Niu</surname><given-names>Beifang</given-names></name><xref ref-type="bio" rid="d34e58">*</xref></contrib><contrib contrib-type="author"><name><surname>Wu</surname><given-names>Sitao</given-names></name><xref ref-type="bio" rid="d34e69">*</xref></contrib><contrib contrib-type="author"><name><surname>Wooley</surname><given-names>John</given-names></name><xref ref-type="bio" rid="d34e80">*</xref></contrib></contrib-group><author-notes><corresp>Corresponding author. Weizhong Li. Center for Research in Biological Systems, University of California San Diego, La Jolla, CA 92093, USA. Tel: <phone>858-534-4143</phone>; Fax: <fax>858-246-0644</fax>; E-mail: <email>liwz@sdsc.edu</email></corresp></author-notes><pub-date pub-type="ppub"><month>11</month><year>2012</year></pub-date><pub-date pub-type="epub"><day>6</day><month>7</month><year>2012</year></pub-date><pub-date pub-type="pmc-release"><day>6</day><month>7</month><year>2012</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<volume>13</volume><issue>6</issue><issue-title>Special Issue: Bioinformatics approaches and tools for metagenomic analysis</issue-title><fpage>656</fpage><lpage>668</lpage><history><date date-type="received"><day>7</day><month>3</month><year>2012</year></date><date date-type="accepted"><day>30</day><month>5</month><year>2012</year></date></history><permissions><copyright-statement>© The Author(s) 2012. Published by Oxford University Press.</copyright-statement><copyright-year>2012</copyright-year><license license-type="creative-commons" xlink:href="http://creativecommons.org/licenses/by-nc/3.0"><license-p><pmc-comment>CREATIVE COMMONS</pmc-comment>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/3.0">http://creativecommons.org/licenses/by-nc/3.0</ext-link>), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract><p>The rapid advances of high-throughput sequencing technologies dramatically prompted metagenomic studies of microbial communities that exist at various environments. Fundamental questions in metagenomics include the identities, composition and dynamics of microbial populations and their functions and interactions. However, the massive quantity and the comprehensive complexity of these sequence data pose tremendous challenges in data analysis. These challenges include but are not limited to ever-increasing computational demand, biased sequence sampling, sequence errors, sequence artifacts and novel sequences. Sequence clustering methods can directly answer many of the fundamental questions by grouping similar sequences into families. In addition, clustering analysis also addresses the challenges in metagenomics. Thus, a large redundant data set can be represented with a small non-redundant set, where each cluster can be represented by a single entry or a consensus. Artifacts can be rapidly detected through clustering. Errors can be identified, filtered or corrected by using consensus from sequences within clusters.</p></abstract><kwd-group><kwd>clustering</kwd><kwd>metagenomics</kwd><kwd>next-generation sequencing</kwd><kwd>protein families</kwd><kwd>artificial duplicates</kwd><kwd>OTU</kwd></kwd-group><counts><page-count count="13"></page-count></counts></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Fu, Limin" sort="Fu, Limin" uniqKey="Fu L" first="Limin" last="Fu">Limin Fu</name><name sortKey="Li, Weizhong" sort="Li, Weizhong" uniqKey="Li W" first="Weizhong" last="Li">Weizhong Li</name><name sortKey="Niu, Beifang" sort="Niu, Beifang" uniqKey="Niu B" first="Beifang" last="Niu">Beifang Niu</name><name sortKey="Wooley, John" sort="Wooley, John" uniqKey="Wooley J" first="John" last="Wooley">John Wooley</name><name sortKey="Wu, Sitao" sort="Wu, Sitao" uniqKey="Wu S" first="Sitao" last="Wu">Sitao Wu</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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