Ultrafast clustering algorithms for metagenomic sequence analysis
Identifieur interne : 000457 ( Main/Exploration ); précédent : 000456; suivant : 000458Ultrafast 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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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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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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