Current opportunities and challenges in microbial metagenome analysis—a bioinformatic perspective
Identifieur interne : 000517 ( Main/Merge ); précédent : 000516; suivant : 000518Current opportunities and challenges in microbial metagenome analysis—a bioinformatic perspective
Auteurs : Hanno Teeling ; Frank Oliver GlöcknerSource :
- Briefings in Bioinformatics [ 1467-5463 ] ; 2012.
Abstract
Metagenomics has become an indispensable tool for studying the diversity and metabolic potential of environmental microbes, whose bulk is as yet non-cultivable. Continual progress in next-generation sequencing allows for generating increasingly large metagenomes and studying multiple metagenomes over time or space. Recently, a new type of holistic ecosystem study has emerged that seeks to combine metagenomics with biodiversity, meta-expression and contextual data. Such ‘ecosystems biology’ approaches bear the potential to not only advance our understanding of environmental microbes to a new level but also impose challenges due to increasing data complexities, in particular with respect to bioinformatic post-processing. This mini review aims to address selected opportunities and challenges of modern metagenomics from a bioinformatics perspective and hopefully will serve as a useful resource for microbial ecologists and bioinformaticians alike.
Url:
DOI: 10.1093/bib/bbs039
PubMed: 22966151
PubMed Central: 3504927
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Current opportunities and challenges in microbial metagenome analysis—a bioinformatic perspective</title><author><name sortKey="Teeling, Hanno" sort="Teeling, Hanno" uniqKey="Teeling H" first="Hanno" last="Teeling">Hanno Teeling</name></author><author><name sortKey="Glockner, Frank Oliver" sort="Glockner, Frank Oliver" uniqKey="Glockner F" first="Frank Oliver" last="Glöckner">Frank Oliver Glöckner</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22966151</idno><idno type="pmc">3504927</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3504927</idno><idno type="RBID">PMC:3504927</idno><idno type="doi">10.1093/bib/bbs039</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000509</idno><idno type="wicri:Area/Pmc/Curation">000509</idno><idno type="wicri:Area/Pmc/Checkpoint">000428</idno><idno type="wicri:Area/Ncbi/Merge">000351</idno><idno type="wicri:Area/Ncbi/Curation">000351</idno><idno type="wicri:Area/Ncbi/Checkpoint">000351</idno><idno type="wicri:doubleKey">1467-5463:2012:Teeling H:current:opportunities:and</idno><idno type="wicri:Area/Main/Merge">000517</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Current opportunities and challenges in microbial metagenome analysis—a bioinformatic perspective</title><author><name sortKey="Teeling, Hanno" sort="Teeling, Hanno" uniqKey="Teeling H" first="Hanno" last="Teeling">Hanno Teeling</name></author><author><name sortKey="Glockner, Frank Oliver" sort="Glockner, Frank Oliver" uniqKey="Glockner F" first="Frank Oliver" last="Glöckner">Frank Oliver Glöckner</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>Metagenomics has become an indispensable tool for studying the diversity and metabolic potential of environmental microbes, whose bulk is as yet non-cultivable. Continual progress in next-generation sequencing allows for generating increasingly large metagenomes and studying multiple metagenomes over time or space. Recently, a new type of holistic ecosystem study has emerged that seeks to combine metagenomics with biodiversity, meta-expression and contextual data. Such ‘ecosystems biology’ approaches bear the potential to not only advance our understanding of environmental microbes to a new level but also impose challenges due to increasing data complexities, in particular with respect to bioinformatic post-processing. 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