Functional assignment of metagenomic data: challenges and applications
Identifieur interne : 000332 ( Ncbi/Merge ); précédent : 000331; suivant : 000333Functional assignment of metagenomic data: challenges and applications
Auteurs : Tulika Prakash ; Todd D. TaylorSource :
- Briefings in Bioinformatics [ 1467-5463 ] ; 2012.
Abstract
Metagenomic sequencing provides a unique opportunity to explore earth’s limitless environments harboring scores of yet unknown and mostly unculturable microbes and other organisms. Functional analysis of the metagenomic data plays a central role in projects aiming to explore the most essential questions in microbiology, namely ‘In a given environment, among the microbes present, what are they doing, and how are they doing it?’ Toward this goal, several large-scale metagenomic projects have recently been conducted or are currently underway. Functional analysis of metagenomic data mainly suffers from the vast amount of data generated in these projects. The shear amount of data requires much computational time and storage space. These problems are compounded by other factors potentially affecting the functional analysis, including, sample preparation, sequencing method and average genome size of the metagenomic samples. In addition, the read-lengths generated during sequencing influence sequence assembly, gene prediction and subsequently the functional analysis. The level of confidence for functional predictions increases with increasing read-length. Usually, the most reliable functional annotations for metagenomic sequences are achieved using homology-based approaches against publicly available reference sequence databases. Here, we present an overview of the current state of functional analysis of metagenomic sequence data, bottlenecks frequently encountered and possible solutions in light of currently available resources and tools. Finally, we provide some examples of applications from recent metagenomic studies which have been successfully conducted in spite of the known difficulties.
Url:
DOI: 10.1093/bib/bbs033
PubMed: 22772835
PubMed Central: 3504928
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Functional assignment of metagenomic data: challenges and applications</title><author><name sortKey="Prakash, Tulika" sort="Prakash, Tulika" uniqKey="Prakash T" first="Tulika" last="Prakash">Tulika Prakash</name></author><author><name sortKey="Taylor, Todd D" sort="Taylor, Todd D" uniqKey="Taylor T" first="Todd D." last="Taylor">Todd D. Taylor</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22772835</idno><idno type="pmc">3504928</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3504928</idno><idno type="RBID">PMC:3504928</idno><idno type="doi">10.1093/bib/bbs033</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000506</idno><idno type="wicri:Area/Pmc/Curation">000506</idno><idno type="wicri:Area/Pmc/Checkpoint">000416</idno><idno type="wicri:Area/Ncbi/Merge">000332</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Functional assignment of metagenomic data: challenges and applications</title><author><name sortKey="Prakash, Tulika" sort="Prakash, Tulika" uniqKey="Prakash T" first="Tulika" last="Prakash">Tulika Prakash</name></author><author><name sortKey="Taylor, Todd D" sort="Taylor, Todd D" uniqKey="Taylor T" first="Todd D." last="Taylor">Todd D. Taylor</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>Metagenomic sequencing provides a unique opportunity to explore earth’s limitless environments harboring scores of yet unknown and mostly unculturable microbes and other organisms. Functional analysis of the metagenomic data plays a central role in projects aiming to explore the most essential questions in microbiology, namely ‘In a given environment, among the microbes present, what are they doing, and how are they doing it?’ Toward this goal, several large-scale metagenomic projects have recently been conducted or are currently underway. Functional analysis of metagenomic data mainly suffers from the vast amount of data generated in these projects. The shear amount of data requires much computational time and storage space. These problems are compounded by other factors potentially affecting the functional analysis, including, sample preparation, sequencing method and average genome size of the metagenomic samples. In addition, the read-lengths generated during sequencing influence sequence assembly, gene prediction and subsequently the functional analysis. The level of confidence for functional predictions increases with increasing read-length. Usually, the most reliable functional annotations for metagenomic sequences are achieved using homology-based approaches against publicly available reference sequence databases. Here, we present an overview of the current state of functional analysis of metagenomic sequence data, bottlenecks frequently encountered and possible solutions in light of currently available resources and tools. Finally, we provide some examples of applications from recent metagenomic studies which have been successfully conducted in spite of the known difficulties.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Pace, Nr" uniqKey="Pace N">NR Pace</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tringe, Sg" uniqKey="Tringe S">SG Tringe</name></author><author><name sortKey="Rubin, Em" uniqKey="Rubin E">EM Rubin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kunin, V" uniqKey="Kunin V">V Kunin</name></author><author><name sortKey="Copeland, A" uniqKey="Copeland A">A Copeland</name></author><author><name sortKey="Lapidus, A" uniqKey="Lapidus A">A Lapidus</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 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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">22772835</article-id><article-id pub-id-type="pmc">3504928</article-id><article-id pub-id-type="doi">10.1093/bib/bbs033</article-id><article-id pub-id-type="publisher-id">bbs033</article-id><article-categories><subj-group subj-group-type="heading"><subject>Papers</subject></subj-group></article-categories><title-group><article-title>Functional assignment of metagenomic data: challenges and applications</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Prakash</surname><given-names>Tulika</given-names></name><xref ref-type="bio" rid="d34e36">*</xref></contrib><contrib contrib-type="author" corresp="yes"><name><surname>Taylor</surname><given-names>Todd D.</given-names></name><xref ref-type="bio" rid="d34e47">*</xref></contrib></contrib-group><author-notes><corresp>Corresponding author. Todd D. Taylor, Laboratory for MetaSystems Research, Quantitative Biology Center, Riken, Yokohama, Kanagawa 230-0045, Japan. Tel.: <phone>+81-45-503-9285</phone>; Fax: <fax>+81-45-503-9176</fax>; E-mail: <email>taylor@riken.jp</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>711</fpage><lpage>727</lpage><history><date date-type="received"><day>5</day><month>3</month><year>2012</year></date><date date-type="accepted"><day>26</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>Metagenomic sequencing provides a unique opportunity to explore earth’s limitless environments harboring scores of yet unknown and mostly unculturable microbes and other organisms. Functional analysis of the metagenomic data plays a central role in projects aiming to explore the most essential questions in microbiology, namely ‘In a given environment, among the microbes present, what are they doing, and how are they doing it?’ Toward this goal, several large-scale metagenomic projects have recently been conducted or are currently underway. Functional analysis of metagenomic data mainly suffers from the vast amount of data generated in these projects. The shear amount of data requires much computational time and storage space. These problems are compounded by other factors potentially affecting the functional analysis, including, sample preparation, sequencing method and average genome size of the metagenomic samples. In addition, the read-lengths generated during sequencing influence sequence assembly, gene prediction and subsequently the functional analysis. The level of confidence for functional predictions increases with increasing read-length. Usually, the most reliable functional annotations for metagenomic sequences are achieved using homology-based approaches against publicly available reference sequence databases. Here, we present an overview of the current state of functional analysis of metagenomic sequence data, bottlenecks frequently encountered and possible solutions in light of currently available resources and tools. Finally, we provide some examples of applications from recent metagenomic studies which have been successfully conducted in spite of the known difficulties.</p></abstract><kwd-group><kwd>functional annotation</kwd><kwd>metagenomics</kwd><kwd>bioinformatics</kwd><kwd>next-generation sequencing</kwd><kwd>pathway-mapping</kwd><kwd>comparative analysis</kwd></kwd-group><counts><page-count count="17"></page-count></counts></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Prakash, Tulika" sort="Prakash, Tulika" uniqKey="Prakash T" first="Tulika" last="Prakash">Tulika Prakash</name><name sortKey="Taylor, Todd D" sort="Taylor, Todd D" uniqKey="Taylor T" first="Todd D." last="Taylor">Todd D. Taylor</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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